From 36984e163c70e01f8bfc20a6afd8f5daebc976e0 Mon Sep 17 00:00:00 2001 From: Amar723 <140028404+Amar723@users.noreply.github.com> Date: Fri, 4 Apr 2025 12:27:14 +1100 Subject: [PATCH 01/10] reset --- .DS_Store | Bin 0 -> 6148 bytes src/.DS_Store | Bin 0 -> 6148 bytes src/dataEngineering.ipynb | 86 -------------------------------------- 3 files changed, 86 deletions(-) create mode 100644 .DS_Store create mode 100644 src/.DS_Store delete mode 100644 src/dataEngineering.ipynb diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5172429f264de2441865cb4700216d4256da9242 GIT binary patch literal 6148 zcmeH~J!%6%427R!7lt%jx}3%b$PET#pTHLgIFQEJ;E>dF^gR7ES*H$5cmnB-G%I%Z zD|S`@Z2$T80!#olbXV*=%*>dt@PRwdU#I)^a=X5>;#J@&VrHyNnC;iLL0pQvfVyTmjO&;ssLc!1UOG})p;=82 zR;?Ceh}WZ?+UmMqI#RP8R>OzYoz15hnq@nzF`-!xQ4j$Um=RcIKKc27r2jVm&svm< zfC&6E0=7P!4tu^-ovjbA=k?dB`g+i*aXG_}p8zI)6mRKa+;6_1_R^8c3Qa!(fk8n8 H{*=HsM+*^= literal 0 HcmV?d00001 diff --git a/src/.DS_Store b/src/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01malpaca_trade_api\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtradeapi\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'alpaca_trade_api'" - ] - } - ], - "source": [ - "from alpaca.data.historical import StockHistoricalDataClient\n", - "from alpaca.data.requests import StockBarsRequest\n", - "from alpaca.data.timeframe import TimeFrame\n", - "import alpaca_trade_api as tradeapi\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'apiKey' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 8\u001b[0m\n\u001b[1;32m 1\u001b[0m request_params \u001b[38;5;241m=\u001b[39m StockBarsRequest(\n\u001b[1;32m 2\u001b[0m symbol_or_symbols\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAAPL\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m start\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m2014-01-01\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 4\u001b[0m end \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m2024-12-12\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5\u001b[0m timeframe\u001b[38;5;241m=\u001b[39mTimeFrame\u001b[38;5;241m.\u001b[39mMinute\n\u001b[1;32m 6\u001b[0m )\n\u001b[0;32m----> 8\u001b[0m client \u001b[38;5;241m=\u001b[39m StockHistoricalDataClient(api_key\u001b[38;5;241m=\u001b[39m\u001b[43mapiKey\u001b[49m, secret_key\u001b[38;5;241m=\u001b[39msecret, paper\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 10\u001b[0m bars \u001b[38;5;241m=\u001b[39m client\u001b[38;5;241m.\u001b[39mget_stock_bars(request_params)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28mprint\u001b[39m(bars)\n", - "\u001b[0;31mNameError\u001b[0m: name 'apiKey' is not defined" - ] - } - ], - "source": [ - "apiKey = 'AKREF539HEBKQQ6OID3U'\n", - "secret = 'q1ysWDHYs1oZVmxDqtIjmV9E4pMS8aEhobw7ihl0'\n", - "\n", - "# API credentials\n", - "API_KEY = \"your_api_key\"\n", - "API_SECRET = \"your_api_secret\"\n", - "BASE_URL = \"https://paper-api.alpaca.markets\" # Use this for paper trading\n", - "\n", - "# Initialize API\n", - "api = tradeapi.REST(API_KEY, API_SECRET, BASE_URL, api_version='v2')\n", - "\n", - "# Get historical data\n", - "barset = api.get_bars(\"AAPL\", \"day\", limit=5) # 'minute', 'hour', or 'day'\n", - "df = pd.DataFrame([bar.__dict__['_raw'] for bar in barset])\n", - "\n", - "print(df)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rnn_development", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From b117d3145480cdbca0e3071a5c35fa46da648429 Mon Sep 17 00:00:00 2001 From: Amar723 <140028404+Amar723@users.noreply.github.com> Date: Fri, 11 Apr 2025 09:26:14 +1000 Subject: [PATCH 02/10] Adding significant data features, need to add new feature to write data to csv to access since it is all historic --- src/dataEngineering.ipynb | 493 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 493 insertions(+) create mode 100644 src/dataEngineering.ipynb diff --git a/src/dataEngineering.ipynb b/src/dataEngineering.ipynb new file mode 100644 index 0000000..c780338 --- /dev/null +++ b/src/dataEngineering.ipynb @@ -0,0 +1,493 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "import yfinance as yf\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from tqdm import tqdm\n", + "import ta\n", + "import requests\n", + "\n", + "from matplotlib import rcParams\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.widgets import RadioButtons\n", + "from matplotlib.animation import FuncAnimation\n", + "from matplotlib.dates import DateFormatter\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tickers = [\n", + " 'AAPL', 'MSFT', 'GOOGL', 'AMZN', 'NVDA', 'META', 'TSLA', 'AVGO', 'ADBE', 'CRM',\n", + " 'CSCO', 'INTC', 'AMD', 'QCOM', 'ORCL', 'TXN', 'INTU', 'AMAT', 'MU', 'NOW',\n", + " 'SHOP', 'PANW', 'SNOW', 'ZM', 'PLTR', 'UBER', 'LYFT', 'DOCU', 'FSLY', 'TWLO'\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "API_KEY = \"B6WMMM9S4ZYE4KRT\"" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/2 [00:00 91\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_all_alpha_stocks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtickers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_csv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstock_data.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;66;03m# This will save the combined dataframe to 'stock_data.csv' locally in the working directory\u001b[39;00m\n", + "Cell \u001b[0;32mIn[32], line 77\u001b[0m, in \u001b[0;36mfetch_all_alpha_stocks\u001b[0;34m(tickers, output_csv)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;66;03m# Avoid hitting the API rate limit\u001b[39;00m\n\u001b[1;32m 75\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m12\u001b[39m) \u001b[38;5;66;03m# Sleep for 12 seconds between requests to avoid rate limits\u001b[39;00m\n\u001b[0;32m---> 77\u001b[0m combined_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 78\u001b[0m combined_df\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mnames \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDatetime\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 79\u001b[0m combined_df \u001b[38;5;241m=\u001b[39m combined_df\u001b[38;5;241m.\u001b[39msort_index()\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/pandas/core/reshape/concat.py:382\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 380\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 382\u001b[0m op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 383\u001b[0m \u001b[43m \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 384\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 385\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 386\u001b[0m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 387\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 391\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 393\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/pandas/core/reshape/concat.py:445\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverify_integrity \u001b[38;5;241m=\u001b[39m verify_integrity\n\u001b[1;32m 443\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy \u001b[38;5;241m=\u001b[39m copy\n\u001b[0;32m--> 445\u001b[0m objs, keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_clean_keys_and_objs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;66;03m# figure out what our result ndim is going to be\u001b[39;00m\n\u001b[1;32m 448\u001b[0m ndims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_ndims(objs)\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/pandas/core/reshape/concat.py:507\u001b[0m, in \u001b[0;36m_Concatenator._clean_keys_and_objs\u001b[0;34m(self, objs, keys)\u001b[0m\n\u001b[1;32m 504\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[1;32m 506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs_list) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 507\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 510\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs_list))\n", + "\u001b[0;31mValueError\u001b[0m: No objects to concatenate" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import requests\n", + "import time\n", + "from tqdm import tqdm\n", + "import ta\n", + "\n", + "# Assuming API_KEY is defined\n", + "API_KEY = 'your_api_key'\n", + "\n", + "def fetch_alpha_vantage_stock(ticker, interval='1min', outputsize='full'):\n", + " url = f'https://www.alphavantage.co/query'\n", + " params = {\n", + " 'function': 'TIME_SERIES_INTRADAY',\n", + " 'symbol': ticker,\n", + " 'interval': interval,\n", + " 'outputsize': outputsize,\n", + " 'apikey': API_KEY\n", + " }\n", + "\n", + " r = requests.get(url, params=params)\n", + " data = r.json()\n", + "\n", + " # Check for error messages or empty data\n", + " if \"Error Message\" in data or \"Note\" in data:\n", + " print(f\"Error for {ticker}: {data.get('Error Message', data.get('Note'))}\")\n", + " return None\n", + " \n", + " key = f'Time Series ({interval})'\n", + " if key not in data:\n", + " print(f\"No data for {ticker}\")\n", + " print(f\"Full response: {data}\")\n", + " return None\n", + "\n", + " df = pd.DataFrame(data[key]).T\n", + " df.columns = ['Open', 'High', 'Low', 'Close', 'Volume']\n", + " df.index = pd.to_datetime(df.index)\n", + " df = df.sort_index()\n", + " df = df.astype(float)\n", + " df['Ticker'] = ticker\n", + "\n", + " # Compute additional columns\n", + " df['Returns'] = df['Close'].pct_change()\n", + " df['Log_Returns'] = np.log(df['Close'] / df['Close'].shift(1))\n", + " df['SMA_5'] = df['Close'].rolling(window=5).mean()\n", + " df['EMA_2'] = df['Close'].ewm(span=2, adjust=False).mean()\n", + "\n", + " rsi = ta.momentum.RSIIndicator(close=df['Close'])\n", + " df['RSI'] = rsi.rsi()\n", + "\n", + " macd = ta.trend.MACD(close=df['Close'])\n", + " df['MACD'] = macd.macd()\n", + " df['MACD_Signal'] = macd.macd_signal()\n", + " df['MACD_Diff'] = macd.macd_diff()\n", + "\n", + " bb = ta.volatility.BollingerBands(close=df['Close'])\n", + " df['BB_High'] = bb.bollinger_hband()\n", + " df['BB_Low'] = bb.bollinger_lband()\n", + "\n", + " df['Volume_EMA'] = df['Volume'].ewm(span=20).mean()\n", + " df['Volatility'] = df['Log_Returns'].rolling(window=60).std()\n", + "\n", + " df.dropna(inplace=True)\n", + " return df\n", + "\n", + "\n", + "def fetch_all_alpha_stocks(tickers, output_csv=None):\n", + " all_data = []\n", + "\n", + " for ticker in tqdm(tickers):\n", + " df = fetch_alpha_vantage_stock(ticker)\n", + " if df is not None:\n", + " all_data.append(df)\n", + " \n", + " # Avoid hitting the API rate limit\n", + " time.sleep(12) # Sleep for 12 seconds between requests to avoid rate limits\n", + "\n", + " combined_df = pd.concat(all_data)\n", + " combined_df.index.names = ['Datetime']\n", + " combined_df = combined_df.sort_index()\n", + " \n", + " # If an output CSV file name is provided, save the data to a CSV\n", + " if output_csv:\n", + " combined_df.to_csv(output_csv)\n", + " print(f\"Data saved to {output_csv}\")\n", + "\n", + " return combined_df\n", + "\n", + "\n", + "# Example usage:\n", + "tickers = ['AAPL', 'MSFT']\n", + "df = fetch_all_alpha_stocks(tickers, output_csv='stock_data.csv')\n", + "\n", + "# This will save the combined dataframe to 'stock_data.csv' locally in the working directory\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the data to aurafarm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Dark Mode Setup\n", + "rcParams['figure.facecolor'] = '#1a1a1a'\n", + "rcParams['axes.facecolor'] = '#2a2a2a'\n", + "rcParams['axes.edgecolor'] = 'white'\n", + "rcParams['text.color'] = 'white'\n", + "rcParams['xtick.color'] = 'white'\n", + "rcParams['ytick.color'] = 'white'\n", + "\n", + "# -- CORRELATION HEATMAP --\n", + "def plot_correlation_heatmap(df):\n", + " pivot = df.pivot_table(index='Datetime', columns='Ticker', values='Close')\n", + " corr = pivot.corr()\n", + "\n", + " #fig, ax = plt.subplots(figsize=(12, 8))\n", + " sns.heatmap(corr, annot=True, cmap='GnBu', vmin=-1, vmax=1, center=0)\n", + " # ax.set_title('Ticker Correlation Heatmap', fontsize=16, pad=20)\n", + " # plt.tight_layout()\n", + " # plt.show()\n", + " return corr\n", + "\n", + "# -- ANIMATED BOLLINGER BANDS --\n", + "def animate_bollinger(ticker, df):\n", + " ticker_df = df[df['Ticker'] == ticker].copy()\n", + " ticker_df = ticker_df.sort_index()\n", + " ticker_df = ticker_df.last('30D') # Requires datetime index\n", + "\n", + " if ticker_df.empty:\n", + " print(f\"No data for {ticker} in the last 30 days.\")\n", + " return\n", + "\n", + " fig, ax = plt.subplots(figsize=(14, 7))\n", + " ax.xaxis.set_major_formatter(DateFormatter('%m-%d %H:%M'))\n", + "\n", + " def update(frame):\n", + " ax.clear()\n", + " current_data = ticker_df.iloc[:frame]\n", + "\n", + " ax.plot(current_data.index, current_data['Close'], label='Price', color='royalblue')\n", + " ax.plot(current_data.index, current_data['SMA_5'], label='SMA(5)', color='orange')\n", + " ax.plot(current_data.index, current_data['BB_High'], label='Upper Band', linestyle='--', color='red')\n", + " ax.plot(current_data.index, current_data['BB_Low'], label='Lower Band', linestyle='--', color='green')\n", + "\n", + " ax.fill_between(current_data.index,\n", + " current_data['BB_Low'],\n", + " current_data['BB_High'],\n", + " color='gray', alpha=0.2)\n", + "\n", + " ax.set_title(f'{ticker} Bollinger Bands Evolution', fontsize=16)\n", + " ax.legend()\n", + " plt.xticks(rotation=45)\n", + "\n", + " ani = FuncAnimation(fig, update, frames=len(ticker_df), interval=100)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# -- 3D VOLATILITY PLOT --\n", + "def plot_3d_volatility(df):\n", + " fig = plt.figure(figsize=(16, 10))\n", + " ax = fig.add_subplot(111, projection='3d')\n", + "\n", + " for ticker in df['Ticker'].unique():\n", + " ticker_df = df[df['Ticker'] == ticker].copy()\n", + " ticker_df = ticker_df.sort_index()\n", + " if len(ticker_df) < 2:\n", + " continue # skip if not enough data\n", + "\n", + " dates = pd.to_datetime(ticker_df.index).astype(np.int64) // 10**9 # numeric for 3D plot\n", + " ax.plot(dates,\n", + " ticker_df['Volatility'],\n", + " ticker_df['RSI'],\n", + " label=ticker,\n", + " linewidth=2)\n", + "\n", + " ax.set_xlabel('DateTime (numeric)', fontsize=12)\n", + " ax.set_ylabel('Volatility', fontsize=12)\n", + " ax.set_zlabel('RSI', fontsize=12)\n", + " ax.set_title('3D Volatility-RSI Timeline', fontsize=16)\n", + " ax.legend()\n", + " ax.view_init(elev=25, azim=45)\n", + " plt.tight_layout()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 25%|██▌ | 1/4 [00:00<00:02, 1.34it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No data for AAPL\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 50%|█████ | 2/4 [00:01<00:00, 2.08it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No data for MSFT\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 75%|███████▌ | 3/4 [00:01<00:00, 1.69it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No data for AMZN\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:02<00:00, 1.59it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No data for GOOG\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "ename": "ValueError", + "evalue": "No objects to concatenate", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[112], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_all_alpha_stocks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtickers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m plot_correlation_heatmap(df)\n", + "Cell \u001b[0;32mIn[109], line 58\u001b[0m, in \u001b[0;36mfetch_all_alpha_stocks\u001b[0;34m(tickers)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m df \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 56\u001b[0m all_data\u001b[38;5;241m.\u001b[39mappend(df)\n\u001b[0;32m---> 58\u001b[0m combined_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m combined_df\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mnames \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDatetime\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 60\u001b[0m combined_df \u001b[38;5;241m=\u001b[39m combined_df\u001b[38;5;241m.\u001b[39msort_index()\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/pandas/core/reshape/concat.py:382\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 380\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 382\u001b[0m op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 383\u001b[0m \u001b[43m \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 384\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 385\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 386\u001b[0m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 387\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 391\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 393\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/pandas/core/reshape/concat.py:445\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverify_integrity \u001b[38;5;241m=\u001b[39m verify_integrity\n\u001b[1;32m 443\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy \u001b[38;5;241m=\u001b[39m copy\n\u001b[0;32m--> 445\u001b[0m objs, keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_clean_keys_and_objs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;66;03m# figure out what our result ndim is going to be\u001b[39;00m\n\u001b[1;32m 448\u001b[0m ndims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_ndims(objs)\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/pandas/core/reshape/concat.py:507\u001b[0m, in \u001b[0;36m_Concatenator._clean_keys_and_objs\u001b[0;34m(self, objs, keys)\u001b[0m\n\u001b[1;32m 504\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[1;32m 506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs_list) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 507\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 510\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs_list))\n", + "\u001b[0;31mValueError\u001b[0m: No objects to concatenate" + ] + } + ], + "source": [ + "df = fetch_all_alpha_stocks(tickers)\n", + "\n", + "plot_correlation_heatmap(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = fetch_all_alpha_stocks(tickers)\n", + "\n", + "for ticker in df:\n", + " animate_bollinger(ticker, df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_3d_volatility(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "features = ['Close', 'Returns', 'Log_Returns', 'SMA_5', 'EMA_2', 'RSI', 'MACD', 'MACD_Signal', 'MACD_Diff', 'BB_High', 'BB_Low', 'Volume_EMA', 'Volatility']\n", + "df = fetch_all_alpha_stocks(tickers)\n", + "\n", + "def scale_features(df, feature_cols):\n", + " scalers = {}\n", + " \n", + " for ticker in df['Ticker'].unique():\n", + " \n", + " scaler = MinMaxScaler()\n", + " \n", + " idx = df['Ticker'] == ticker\n", + " \n", + " df.loc[idx, feature_cols] = scaler.fit_transform(df.loc[idx, feature_cols])\n", + " \n", + " scalers[ticker] = scaler\n", + " \n", + " return df, scalers\n", + "\n", + "\n", + "\n", + "def create_sequences(df, feature_cols, sequence_length=60):\n", + " X = []\n", + " y = []\n", + "\n", + "\n", + " for ticker in df['Ticker'].unique():\n", + " \n", + " df_ticker = df[df['Ticker'] == ticker]\n", + " \n", + " data = df_ticker[feature_cols].values\n", + " target = df_ticker['Close'].values # or next-day close/return etc.\n", + " \n", + " \n", + " for i in range(sequence_length, len(df_ticker)):\n", + " X.append(data[i-sequence_length:i]) # past 60 days\n", + " y.append(target[i]) # predict next close price\n", + "\n", + " return np.array(X), np.array(y)\n", + "\n", + "# Normalising the data\n", + "combined_df, scalers = scale_features(df, features)\n", + "\n", + "# Creating sequences for the LSTM and such\n", + "X, y = create_sequences(combined_df, features, sequence_length=60) # 60 candlesticks = 1 Hour\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rnn_development", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 08593de19fb55b6227eaf3d71b4f7faf66c44da9 Mon Sep 17 00:00:00 2001 From: Amar723 <140028404+Amar723@users.noreply.github.com> Date: Fri, 11 Apr 2025 14:38:48 +1000 Subject: [PATCH 03/10] comitting DataEngineering file --- .DS_Store | Bin 6148 -> 6148 bytes src/dataEngineering.ipynb | 383 +++++++++++++++++++++++++------------- 2 files changed, 253 insertions(+), 130 deletions(-) diff --git a/.DS_Store b/.DS_Store index 5172429f264de2441865cb4700216d4256da9242..075c13bd5864e6495340ca1fa0d3b3c8be1c1924 100644 GIT binary patch literal 6148 zcmeHKL2uJA6n<_xmNFsr0216Rajl>gT_waNlyca0LE`|ZYnG~wmc~_+u7|2p&Kv)L z+kU|`X$SrQXATv=XYbiAOV_a@Lg+o|`Lo~iXD44QI|cyJo`wy8Dge0Xgry1=cNpc# zH>_kM4-o~85f2*vks6IRdUD`jRe-)-8D78uBFKv0&$%4#6muih=>)L~FougE|2A0u zc)Okt@6C^gH*3SW`iWFzf(!pi<16C6QZz>T|R$|l2FasYMO+p=K3b#R@_Rr z`fNUL?lx<3_w}MC=Z#m*R!uhQt=3{uai2VY`DVX+9FG!pM$e0@9GAJaM;2G`6)X02 ziXy`D^!z4sOTxXOkJTSAjHF;Q5Tk%57MnW31Wh#|QwxF|0tKQA390up6lU;>j69#BMpVe@%r s3zp3vn9G?rvvcrs0FBuEkoi0FWPTAvPLP%akcP=7Jj$D6L{=~Z0P>d;nE(I) diff --git a/src/dataEngineering.ipynb b/src/dataEngineering.ipynb index c780338..799272c 100644 --- a/src/dataEngineering.ipynb +++ b/src/dataEngineering.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 29, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -23,12 +23,13 @@ "from matplotlib.dates import DateFormatter\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import pandas as pd\n", - "import numpy as np" + "import numpy as np\n", + "import plotly.graph_objects as go" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -41,23 +42,14 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "API_KEY = \"B6WMMM9S4ZYE4KRT\"" - ] - }, - { - "cell_type": "code", - "execution_count": 32, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/2 [00:00 91\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_all_alpha_stocks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtickers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_csv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstock_data.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;66;03m# This will save the combined dataframe to 'stock_data.csv' locally in the working directory\u001b[39;00m\n", - "Cell \u001b[0;32mIn[32], line 77\u001b[0m, in \u001b[0;36mfetch_all_alpha_stocks\u001b[0;34m(tickers, output_csv)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;66;03m# Avoid hitting the API rate limit\u001b[39;00m\n\u001b[1;32m 75\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m12\u001b[39m) \u001b[38;5;66;03m# Sleep for 12 seconds between requests to avoid rate limits\u001b[39;00m\n\u001b[0;32m---> 77\u001b[0m combined_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 78\u001b[0m combined_df\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mnames \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDatetime\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 79\u001b[0m combined_df \u001b[38;5;241m=\u001b[39m combined_df\u001b[38;5;241m.\u001b[39msort_index()\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/pandas/core/reshape/concat.py:382\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 380\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 382\u001b[0m op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 383\u001b[0m \u001b[43m \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 384\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 385\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 386\u001b[0m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 387\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 391\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 393\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/pandas/core/reshape/concat.py:445\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverify_integrity \u001b[38;5;241m=\u001b[39m verify_integrity\n\u001b[1;32m 443\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy \u001b[38;5;241m=\u001b[39m copy\n\u001b[0;32m--> 445\u001b[0m objs, keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_clean_keys_and_objs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;66;03m# figure out what our result ndim is going to be\u001b[39;00m\n\u001b[1;32m 448\u001b[0m ndims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_ndims(objs)\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/pandas/core/reshape/concat.py:507\u001b[0m, in \u001b[0;36m_Concatenator._clean_keys_and_objs\u001b[0;34m(self, objs, keys)\u001b[0m\n\u001b[1;32m 504\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[1;32m 506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs_list) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 507\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 510\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs_list))\n", - "\u001b[0;31mValueError\u001b[0m: No objects to concatenate" + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[24], line 89\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m combined_df\n\u001b[1;32m 87\u001b[0m \u001b[38;5;66;03m# Example usage:\u001b[39;00m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;66;03m#tickers = ['AAPL', 'MSFT']\u001b[39;00m\n\u001b[0;32m---> 89\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_all_alpha_stocks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtickers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_csv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mallStock_data.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;66;03m# This will save the combined dataframe to 'stock_data.csv' locally in the working directory\u001b[39;00m\n", + "Cell \u001b[0;32mIn[24], line 70\u001b[0m, in \u001b[0;36mfetch_all_alpha_stocks\u001b[0;34m(tickers, output_csv)\u001b[0m\n\u001b[1;32m 67\u001b[0m all_data \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ticker \u001b[38;5;129;01min\u001b[39;00m tqdm(tickers):\n\u001b[0;32m---> 70\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_alpha_vantage_stock\u001b[49m\u001b[43m(\u001b[49m\u001b[43mticker\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m df \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 72\u001b[0m all_data\u001b[38;5;241m.\u001b[39mappend(df)\n", + "Cell \u001b[0;32mIn[24], line 20\u001b[0m, in \u001b[0;36mfetch_alpha_vantage_stock\u001b[0;34m(ticker, interval, outputsize)\u001b[0m\n\u001b[1;32m 11\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://www.alphavantage.co/query\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 12\u001b[0m params \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 13\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfunction\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTIME_SERIES_INTRADAY\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 14\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msymbol\u001b[39m\u001b[38;5;124m'\u001b[39m: ticker,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapikey\u001b[39m\u001b[38;5;124m'\u001b[39m: API_KEY\n\u001b[1;32m 18\u001b[0m }\n\u001b[0;32m---> 20\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mrequests\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m data \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mjson()\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Check for error messages or empty data\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/requests/api.py:73\u001b[0m, in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(url, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 63\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \n\u001b[1;32m 65\u001b[0m \u001b[38;5;124;03m :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;124;03m :rtype: requests.Response\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mget\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/requests/api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[0;32m---> 59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 585\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m 586\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m 587\u001b[0m }\n\u001b[1;32m 588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m 702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m 706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 664\u001b[0m timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m 666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 668\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 669\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 670\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 671\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 672\u001b[0m \u001b[43m \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 673\u001b[0m \u001b[43m \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 674\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 675\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 676\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 677\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 678\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 679\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 682\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/urllib3/connectionpool.py:787\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 784\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 786\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 787\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 788\u001b[0m \u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 789\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 790\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 791\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 792\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 793\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 794\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 795\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 796\u001b[0m \u001b[43m \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 797\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 798\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 799\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 800\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 802\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n\u001b[1;32m 803\u001b[0m clean_exit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/urllib3/connectionpool.py:534\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m 532\u001b[0m \u001b[38;5;66;03m# Receive the response from the server\u001b[39;00m\n\u001b[1;32m 533\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 534\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 535\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 536\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/urllib3/connection.py:516\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 513\u001b[0m _shutdown \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshutdown\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 515\u001b[0m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[0;32m--> 516\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 518\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 519\u001b[0m assert_header_parsing(httplib_response\u001b[38;5;241m.\u001b[39mmsg)\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/http/client.py:1378\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1376\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1377\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1378\u001b[0m \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1379\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[1;32m 1380\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/http/client.py:318\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 318\u001b[0m version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[1;32m 320\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/http/client.py:279\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 279\u001b[0m line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mreadline(_MAXLINE \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 280\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/socket.py:706\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 706\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 707\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m 708\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/ssl.py:1311\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m 1307\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 1308\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1309\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m 1310\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1313\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/ssl.py:1167\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m 1165\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1168\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1169\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -114,7 +223,7 @@ "import ta\n", "\n", "# Assuming API_KEY is defined\n", - "API_KEY = 'your_api_key'\n", + "API_KEY = \"25SKFAOGSF41JCEY\"\n", "\n", "def fetch_alpha_vantage_stock(ticker, interval='1min', outputsize='full'):\n", " url = f'https://www.alphavantage.co/query'\n", @@ -180,8 +289,6 @@ " if df is not None:\n", " all_data.append(df)\n", " \n", - " # Avoid hitting the API rate limit\n", - " time.sleep(12) # Sleep for 12 seconds between requests to avoid rate limits\n", "\n", " combined_df = pd.concat(all_data)\n", " combined_df.index.names = ['Datetime']\n", @@ -196,8 +303,8 @@ "\n", "\n", "# Example usage:\n", - "tickers = ['AAPL', 'MSFT']\n", - "df = fetch_all_alpha_stocks(tickers, output_csv='stock_data.csv')\n", + "#tickers = ['AAPL', 'MSFT']\n", + "df = fetch_all_alpha_stocks(tickers, output_csv='allStock_data.csv')\n", "\n", "# This will save the combined dataframe to 'stock_data.csv' locally in the working directory\n" ] @@ -211,64 +318,105 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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VPextbeRZp5K2Hs3dciQ7W9p6NLrAyYT7TsXJs467aVJoncpl1b/VfVpfyDbm5co4SJKSLl2+g7kvHextbeRZt5K2HokyHcvOlrYeiVKHhoXEoG6lG2LgaozBobMW09sYDBrWqa5cHO20n+X+LbK3s5Fnw6raeijCdCw7W9p66Iw6NLW8arCDva0yr1w1O5Zx5aq6NL/P7FiDGhUU9u0LOrb4eS15eaBqVna94/kvDeztbORZv4q2BuUOMmZnS1uDItWhcVWL5zjY2Srzivn2wRlXrqpLATHDzREH68uNQe49PTtb2hp8rsAY7DsRI8/6VUyTQut4lFP/trW1PsC4/YWdjY3sbG3yxSmTOBXIVC8E5m7vlZ0tbQ2MUIemNSye42Bvp8ysm9cLN/6Ox/s01zcbgu5cxksRYxvJvYA2Uv5VBCRpX0issY2UMym0ThVX9W9dU+sPmbdTP3qmi9YHntW2Gz4bltnb2cizQVVtPZS7nY6pjdTkNtpIl6+qS3PzsvPRC3213i9M2w6xXdvt+jt1RYFt16bV7mpeSwt7W4M8a7lp64nclUqys6WtJ+LUod6tvQw5pksd/eR/Tul56uPujdx15r2HFfTaA/rfSE9VdHG4o3kvTWirFk+muATnuScFnVWHxpbvMQ52tvnbTZevqksz4nIrGMOwPtpIxQNlATCyt7ORZ6Nq2nowwnQsO1vaejBcHZoVNIZRwD2pRWFjGC30zYbgO5bv0oZ7UvFEW7Xo0U6yPjuDQQ3cyuhQfJrpWLakQ/EX1aSi5dVc83K0s5GtjUEX8/Sn/+LmaKf2HuW08QxbnBfEWBY8tPWQhTGMJgXcf+wt3H9uMoZRLmccKekiE6fzYhwJAEoWq62J/fHHH2v16tVasGCBPvvsM4WGGt8CaNCggSZOnKiePXvK29v7pp/j4OAgBwfzBzxpdja6cvX6Xcn37XJ3dZSdrY3iUszfOoxLzVDj6uUtnrN8b7gquTppy6sPyiCD7O1stGDLCc37/bDF9AaDNG90B+05Gatj55Lv9CWUeIXHwM3iOcv3hBljMPvh3BhsPq55q80HaJrXrKDtrw2Uk72t0jKzNOLDLTpxPvkuXUnJ5l7O2RiHpHSz43FJl9T4Pstv1W0OCJfvY+21+8g5hUUnyat1bXl3aSRbW4MpzYGTUXr+g3UKOZeoqhVdNOOJrto8b5TaTlyitAzerLuRe7kyxhgk54lBcnrBMQiMlK+3p3YfPa+wmGR5tawl7871ZWtjsJgeN0ccrM/dtYzleiE5XY1ruFk8Z/muEFUq56Qtbw2RwSDZ29lqwfrDmvdzgCQpLTNL+05E65Xh7XXyXJJiU9I1vHtDdWxUVaExbAVjSaH1Qk3Lk4E2+4fJ97EO2h181lgveNaRd9fGBZaFQV0aya2sk77daLkNda9zd3WyXBZSCmmnmtpIA83bSL/lTsQd1rmeWteppG6vrr6r+S8tTPVC3rKQnK7GNQuoFw6Gy/fRdtp95KzCopNz2kgNzdpIw3o0UesGHuo2edldzX9p9Xfqis2BkfId1Fq7c1aU9mpZU96d6snWxmrvZpYo7mVz+m2p5gPucamZauxx8we07WpXUIsa5TVxWYDZ8U3HYvXboShFJFxSvcpl9bp3c/3m01U939um69l39BJKBdqqxZPpnpQ3LinpanxfBYvnbD4UKd9Bf8Ul557UuT73pFvEGIb10UYqHigLgJF7+b/KwiWz44WPYYTLd0gH7T4cqbConDGMboWNYTTOGcNgkmhBuCcVT7RVix7tJOsr52grWxuDkjPNJxsmX76qmmWdbukznmlWVYmZWWYTTW/Up2YFZVy9xlbzhSh0DKNGAfefwDPy9W6r3UdyxjBa1ZJ35wYF1s8GgzTvuV7ac+y8jkVeuOPXUNIxjgQAJYvVJokePXpU48eP1wcffKABAwaY/SwpKUkTJkzQkSNHbvo5Pj4+mjp1qtmxN385pLdWHbqT2S1S3ZtU1fRHWmry1/t0IDRe9T3K6f3RHRTt3VLv/pZ/gOCjMZ3U/L4K6jNnrRVyWzp1b1pV0we11OQle3Nj8GRHRQ9O17u/5k6CCIlKUcf//qryZRz0aMc6Wjihux54cx0TRe+QaV9u0We+AxS0YKyyJYVFJ2vppsNmW8ds9M9dge5IRLwOnIzWyW8maEj3xvqGSUH/2LRFO/TZi30U9OmTxhjEpGjplmMa08fyFgG4O4iD9XVvXkPTh7TV5AU7dCAkVvWrldf7Y7sretglvfuTvyTp2f9t0pc+fRS2+BldvXZdh8LitWL3KXnWt7xqNW7ftM836bMpDynoq+eNZSEqSUs3Bhe4Pf2YAa204UCoohMtD7Th9nVvWlXTvVtp8uI9uW2kpzop+tHWenfVId1X0UXznuqkgW+v0+Usy2/B45+b9sVWfTa5v4K+vKGNtPmIxvQzbqN0n7ur5o3vrYEzfiIORWjaVzv12Qu9FfTxE7n19dbjGtPb8vZ6uLPGdK2jw+dS5J9nhY2f/HNXOj4alarD51N0fM4A9WhUWdtPsgvEnUBbtXiatminPnuxt4I+uTEuxwvc8hP/HGMY1kcbqXigLABG0z7bpM+mPKigr8bf2hjGg620wS9U0RcYw7iTuCcVT7RVix7tpOJlWMPK6nGfm/6zO0xZBbzB2q92BW0/l1zgz/H3TFu4XZ/59FXQZ2NuKAtHC9ye/qMJvdW8ViX1+c+Kos1oKcY4EmCO6dEoSlabJCpJmzdvVseOHdWrVy/VqVNHBoNBYWFh2rFjhzIzb2257k8++UQLFiwwOxbTZcbdyO7fknDxsq5eu64q5cuYHa9SroxikjMsnjN7qKd++DNUX+84JUk6ei5Zzo52+vTZLpq7OljZN7QFP3yqox5qXVN931qn83neGINRoTFIsfw3mz20jX7YHaqvt4dIko6eTTLGYGxXzf0tyBSDrGvXFRZ7UZIUGHFBbetV1ov9m2nS4j1374JKqITUdGMcKphvs1Clgoti8ryBbTonJUPD56ySo72tKpUro6gLaXrz2Z4KL2RVvpRLl3X6fKLqV7f8hti9LCE1wxgDtzwxcHMuOAapGRr+zh/GGLg6KSrxkt58qqvCY3lz8e8iDtaXcDHDcr3g5qyY5ALqhVEd9cOOk/p68zFJ0tHIC3J2stOnE700d6W/srOl8JhUPTBzlZwd7VTO2UExSelaNrW/wmNS7/o1lUSF1gsFTOpMSMnQ8Nd+Nq8XxvZSeHRyvrS1qpRTb886evyNX+5G9kuFhIuZlstC+ULaqcPa6ofdp/O3kZ7rprm/HpJnPXd5lC+jvW8PNp1jZ2ujbk2qasIDzVT+qa91PZuBzRuZ6oW8ZcHNWTGJhdQLc341LwvP9DC1kTwbesijgov2fvyU6Rw7Wxt1a1FTEx5po/Le/6frDDAX6u/UFQmpmRr+7lrz+vrJztTXtyghLaffVs58xY0q5ZwUk1r4+ICzg62GtaupOb8fu+nviUi4pPiLl1W/clkmiVpAW7V4Mt2T8salvLNiChgLMsZlTZ64dCEut4gxDOujjVQ8UBYAo4SUv8qCi9nxwstCev4xjOe8Ch/DeP3nu5H9UoN7UvFEW7Xo0U6yvtTL13TterbcnMynWrg52inpclah5z7WwF1DG1XRjD/DFFHAeEfzSs6q6eqkuQciLf4cRoWOYRQ4fpeh4W//bn7/GdPN4v3nw/FeeqhdPfX97wqd5yUOixhHAoCSxerr9mdmZmr9+vX64osv9Pnnn2vDhg3KzMzUgw8+qE2bNt30/CtXrigtLc3sq7hsNS8ZJxEGRlyQV7NqpmMGg+TVvJr8Tlt+IFXGwTbfw/O/Gt6GG+aRf/hURw1qW0sD3lmvMwUsRY+cGIRfkFfz6qZjBoPk1aK6/E4VEANHu1uKQV42BsnR3vYO5Lr0ybp6XYGnYuTVurbpmMEgebWuLb/jUYWeeznrmqIupMnO1kaDuzbSH3tPFZjWxcledau5FTjB6F6WdfW6AkPj5NWypumYwSB5tawpv5MxhZ57OeuaohIvGWPQpYH+2B92t7NbahEH6yswBvffV2AMyjja5RsEu34tp14wmNcL6ZevKiYpXW4ujurrWUt/+IUL+eXWC3VMx3LrhfOFnmtWL3RrYrFeeLJ/S8Ulp2vd/tN3OuulhrGNlCCv5nnbqdXldyrO4jnGsmB+7MY20rYjUWr78i/q+Mqvpq+A0Hj9+GeoOr7yKxNELci6el2Bp2Pk1cpCG+nEbbaR9hn/vW87dEZtJy5RR59vTF8BIdH6cfsxdfT5hkH9W/B36oq/mNXXnetTD9yirGvZCoxMllfj3BW4DQbJq3Fl+YUVvp3XY23uk6OdjX7wu/nDkxpuZVTJxeGmE0/vVbRVi6fC4xJd6Lnm96QG+sOPuNwKxjCsjzZS8UBZAIyyrl5XYEi0vDzrmI4ZDJKXZx35HbudMYzG+mNvSL40T/ZvxRjGLeCeVDzRVi16tJOs72p2tk4nZ6h15bKmYwZJrSuX1YnEghd1GtKgsh5v7KFZe8J1uoCX9CXpgdoVdSopXeGMXRTKWBZi5dXKwv3nxG3cf7o01B/7Q81+/uF4Lw3q1EADZq7UmVgWAikI40gAULJYdSXR0aNHq0ePHrp8+bIWL16swMBAde3aVbNmzVK9evW0cuVKa2bvjpm/7qgWPt9dAeEJ8g9LkE//ZnJ2tNPSncZO6KLx3RSVlK5ZKw5KktYGnpPvg80UdCZRfqHxqu/hqllDPbU28KzpwfpHYzppROd6GvbRFqVlXpVHzgo3KelXlMk2APnMX3dEC8fnxCA0Xj4DmhtjsMM4ILNoQg9FJV3SrOUBkqS1B8/K96HmCoq4kBODcpo1tI3WBkaaYvDGiLbaEHROZxMuybWMvUZ0qaceTavpkbkbrHadxd38Vf5aOPUhBZyKkf/JaPkMbidnR3st3WTcsmXR1IcUdSFNs77eKUlq37iaqlcqq6CwONWo5KoZo7vKxmDQ/630M33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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[21], line 55\u001b[0m\n\u001b[1;32m 39\u001b[0m fig\u001b[38;5;241m.\u001b[39mupdate_layout(\n\u001b[1;32m 40\u001b[0m title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3D Volatility-RSI Timeline\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 41\u001b[0m scene\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 50\u001b[0m width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1200\u001b[39m\n\u001b[1;32m 51\u001b[0m )\n\u001b[1;32m 53\u001b[0m fig\u001b[38;5;241m.\u001b[39mshow()\n\u001b[0;32m---> 55\u001b[0m \u001b[43mplot_3d_volatility_interactive\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[21], line 53\u001b[0m, in \u001b[0;36mplot_3d_volatility_interactive\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m 29\u001b[0m fig\u001b[38;5;241m.\u001b[39madd_trace(go\u001b[38;5;241m.\u001b[39mScatter3d(\n\u001b[1;32m 30\u001b[0m x\u001b[38;5;241m=\u001b[39mdates,\n\u001b[1;32m 31\u001b[0m y\u001b[38;5;241m=\u001b[39mticker_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVolatility\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 36\u001b[0m marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m 37\u001b[0m ))\n\u001b[1;32m 39\u001b[0m fig\u001b[38;5;241m.\u001b[39mupdate_layout(\n\u001b[1;32m 40\u001b[0m title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3D Volatility-RSI Timeline\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 41\u001b[0m scene\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 50\u001b[0m width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1200\u001b[39m\n\u001b[1;32m 51\u001b[0m )\n\u001b[0;32m---> 53\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/plotly/basedatatypes.py:3410\u001b[0m, in \u001b[0;36mBaseFigure.show\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3377\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 3378\u001b[0m \u001b[38;5;124;03mShow a figure using either the default renderer(s) or the renderer(s)\u001b[39;00m\n\u001b[1;32m 3379\u001b[0m \u001b[38;5;124;03mspecified by the renderer argument\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3406\u001b[0m \u001b[38;5;124;03mNone\u001b[39;00m\n\u001b[1;32m 3407\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 3408\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpio\u001b[39;00m\n\u001b[0;32m-> 3410\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/plotly/io/_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 390\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 391\u001b[0m )\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 394\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 395\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 396\u001b[0m )\n\u001b[1;32m 398\u001b[0m ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n", + "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed" + ] + } + ], "source": [ - "# Dark Mode Setup\n", - "rcParams['figure.facecolor'] = '#1a1a1a'\n", - "rcParams['axes.facecolor'] = '#2a2a2a'\n", - "rcParams['axes.edgecolor'] = 'white'\n", - "rcParams['text.color'] = 'white'\n", - "rcParams['xtick.color'] = 'white'\n", - "rcParams['ytick.color'] = 'white'\n", + "df = pd.read_csv('allStock_data.csv', parse_dates=['Datetime'])\n", + "\n", "\n", "# -- CORRELATION HEATMAP --\n", "def plot_correlation_heatmap(df):\n", " pivot = df.pivot_table(index='Datetime', columns='Ticker', values='Close')\n", " corr = pivot.corr()\n", "\n", - " #fig, ax = plt.subplots(figsize=(12, 8))\n", + " fig, ax = plt.subplots(figsize=(30, 20))\n", " sns.heatmap(corr, annot=True, cmap='GnBu', vmin=-1, vmax=1, center=0)\n", - " # ax.set_title('Ticker Correlation Heatmap', fontsize=16, pad=20)\n", - " # plt.tight_layout()\n", - " # plt.show()\n", - " return corr\n", - "\n", - "# -- ANIMATED BOLLINGER BANDS --\n", - "def animate_bollinger(ticker, df):\n", - " ticker_df = df[df['Ticker'] == ticker].copy()\n", - " ticker_df = ticker_df.sort_index()\n", - " ticker_df = ticker_df.last('30D') # Requires datetime index\n", - "\n", - " if ticker_df.empty:\n", - " print(f\"No data for {ticker} in the last 30 days.\")\n", - " return\n", - "\n", - " fig, ax = plt.subplots(figsize=(14, 7))\n", - " ax.xaxis.set_major_formatter(DateFormatter('%m-%d %H:%M'))\n", - "\n", - " def update(frame):\n", - " ax.clear()\n", - " current_data = ticker_df.iloc[:frame]\n", - "\n", - " ax.plot(current_data.index, current_data['Close'], label='Price', color='royalblue')\n", - " ax.plot(current_data.index, current_data['SMA_5'], label='SMA(5)', color='orange')\n", - " ax.plot(current_data.index, current_data['BB_High'], label='Upper Band', linestyle='--', color='red')\n", - " ax.plot(current_data.index, current_data['BB_Low'], label='Lower Band', linestyle='--', color='green')\n", - "\n", - " ax.fill_between(current_data.index,\n", - " current_data['BB_Low'],\n", - " current_data['BB_High'],\n", - " color='gray', alpha=0.2)\n", - "\n", - " ax.set_title(f'{ticker} Bollinger Bands Evolution', fontsize=16)\n", - " ax.legend()\n", - " plt.xticks(rotation=45)\n", - "\n", - " ani = FuncAnimation(fig, update, frames=len(ticker_df), interval=100)\n", + " ax.set_title('Ticker Correlation Heatmap', fontsize=16, pad=20)\n", " plt.tight_layout()\n", " plt.show()\n", + " #return corr\n", + "\n", + "plot_correlation_heatmap(df)\n", + "\n", + "\n", + "def plot_3d_volatility_interactive(df):\n", + " fig = go.Figure()\n", + "\n", + " for ticker in df['Ticker'].unique():\n", + " ticker_df = df[df['Ticker'] == ticker].copy()\n", + " ticker_df = ticker_df.sort_index()\n", + " if len(ticker_df) < 2:\n", + " continue # skip if not enough data\n", + "\n", + " dates = pd.to_datetime(ticker_df.index).astype(np.int64) // 10**9 # convert datetime to numeric\n", + " fig.add_trace(go.Scatter3d(\n", + " x=dates,\n", + " y=ticker_df['Volatility'],\n", + " z=ticker_df['RSI'],\n", + " mode='lines+markers',\n", + " name=ticker,\n", + " line=dict(width=4),\n", + " marker=dict(size=3)\n", + " ))\n", + "\n", + " fig.update_layout(\n", + " title='3D Volatility-RSI Timeline',\n", + " scene=dict(\n", + " xaxis_title='DateTime (numeric)',\n", + " yaxis_title='Volatility',\n", + " zaxis_title='RSI',\n", + " camera_eye=dict(x=1.2, y=1.2, z=0.8),\n", + " aspectratio=dict(x=1.5, y=1, z=0.7)\n", + " ),\n", + " legend=dict(x=0.85, y=0.95),\n", + " height=800,\n", + " width=1200\n", + " )\n", + "\n", + " fig.show()\n", + " \n", + "plot_3d_volatility_interactive(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Dark Mode Setup\n", + "rcParams['figure.facecolor'] = '#1a1a1a'\n", + "rcParams['axes.facecolor'] = '#2a2a2a'\n", + "rcParams['axes.edgecolor'] = 'white'\n", + "rcParams['text.color'] = 'white'\n", + "rcParams['xtick.color'] = 'white'\n", + "rcParams['ytick.color'] = 'white'\n", "\n", "# -- 3D VOLATILITY PLOT --\n", "def plot_3d_volatility(df):\n", @@ -300,93 +448,60 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 25%|██▌ | 1/4 [00:00<00:02, 1.34it/s]" + " 0%| | 0/30 [00:00 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_all_alpha_stocks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtickers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m plot_correlation_heatmap(df)\n", - "Cell \u001b[0;32mIn[109], line 58\u001b[0m, in \u001b[0;36mfetch_all_alpha_stocks\u001b[0;34m(tickers)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m df \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 56\u001b[0m all_data\u001b[38;5;241m.\u001b[39mappend(df)\n\u001b[0;32m---> 58\u001b[0m combined_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m combined_df\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mnames \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDatetime\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 60\u001b[0m combined_df \u001b[38;5;241m=\u001b[39m combined_df\u001b[38;5;241m.\u001b[39msort_index()\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/pandas/core/reshape/concat.py:382\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 380\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 382\u001b[0m op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 383\u001b[0m \u001b[43m \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 384\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 385\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 386\u001b[0m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 387\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 391\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 393\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/pandas/core/reshape/concat.py:445\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverify_integrity \u001b[38;5;241m=\u001b[39m verify_integrity\n\u001b[1;32m 443\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy \u001b[38;5;241m=\u001b[39m copy\n\u001b[0;32m--> 445\u001b[0m objs, keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_clean_keys_and_objs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;66;03m# figure out what our result ndim is going to be\u001b[39;00m\n\u001b[1;32m 448\u001b[0m ndims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_ndims(objs)\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/pandas/core/reshape/concat.py:507\u001b[0m, in \u001b[0;36m_Concatenator._clean_keys_and_objs\u001b[0;34m(self, objs, keys)\u001b[0m\n\u001b[1;32m 504\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[1;32m 506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs_list) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 507\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 510\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs_list))\n", - "\u001b[0;31mValueError\u001b[0m: No objects to concatenate" + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_all_alpha_stocks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtickers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m plot_correlation_heatmap(df)\n", + "Cell \u001b[0;32mIn[9], line 75\u001b[0m, in \u001b[0;36mfetch_all_alpha_stocks\u001b[0;34m(tickers, output_csv)\u001b[0m\n\u001b[1;32m 72\u001b[0m all_data\u001b[38;5;241m.\u001b[39mappend(df)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;66;03m# Avoid hitting the API rate limit\u001b[39;00m\n\u001b[0;32m---> 75\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m12\u001b[39m) \u001b[38;5;66;03m# Sleep for 12 seconds between requests to avoid rate limits\u001b[39;00m\n\u001b[1;32m 77\u001b[0m combined_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat(all_data)\n\u001b[1;32m 78\u001b[0m combined_df\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mnames \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDatetime\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], - "source": [ - "df = fetch_all_alpha_stocks(tickers)\n", - "\n", - "plot_correlation_heatmap(df)" - ] + "source": [] }, { "cell_type": "code", @@ -463,10 +578,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.datasets import imdb\n", + "from tensorflow.keras.layers import Embedding, Dense, LSTM\n", + "from tensorflow.keras.losses import BinaryCrossentropy\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.optimizers.legacy import Adam\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences" + ] } ], "metadata": { @@ -485,7 +608,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.11.5" } }, "nbformat": 4, From c27fbf340c0df3f87bd4a3a8e8b8cbf03dcbf361 Mon Sep 17 00:00:00 2001 From: Amar723 <140028404+Amar723@users.noreply.github.com> Date: Fri, 2 May 2025 13:01:11 +1000 Subject: [PATCH 04/10] data features / graphs --- src/dataEngineering.ipynb | 512 ++++++++++++++------------------------ 1 file changed, 185 insertions(+), 327 deletions(-) diff --git a/src/dataEngineering.ipynb b/src/dataEngineering.ipynb index 799272c..3714132 100644 --- a/src/dataEngineering.ipynb +++ b/src/dataEngineering.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -10,7 +10,7 @@ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from sklearn.preprocessing import MinMaxScaler\n", + "#from sklearn.preprocessing import MinMaxScaler\n", "from tqdm import tqdm\n", "import ta\n", "import requests\n", @@ -29,192 +29,24 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "tickers = [\n", - " 'AAPL', 'MSFT', 'GOOGL', 'AMZN', 'NVDA', 'META', 'TSLA', 'AVGO', 'ADBE', 'CRM',\n", - " 'CSCO', 'INTC', 'AMD', 'QCOM', 'ORCL', 'TXN', 'INTU', 'AMAT', 'MU', 'NOW',\n", - " 'SHOP', 'PANW', 'SNOW', 'ZM', 'PLTR', 'UBER', 'LYFT', 'DOCU', 'FSLY', 'TWLO'\n", - "]\n" + "# tickers = [\n", + "# 'AAPL', 'MSFT', 'GOOGL', 'AMZN', 'NVDA', 'META', 'TSLA', 'AVGO', 'ADBE', 'CRM',\n", + "# 'CSCO', 'INTC', 'AMD', 'QCOM', 'ORCL', 'TXN', 'INTU', 'AMAT', 'MU', 'NOW',\n", + "# 'SHOP', 'PANW', 'SNOW', 'ZM', 'PLTR', 'UBER', 'LYFT', 'DOCU', 'FSLY', 'TWLO'\n", + "# ]\n", + "\n", + "tickers = ['AAPL', 'MSFT']\n" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 3%|▎ | 1/30 [00:00<00:21, 1.34it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No data for AAPL\n", - "Full response: {'Information': 'We have detected your API key as 25SKFAOGSF41JCEY and our standard API rate limit is 25 requests per day. Please subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly remove all daily rate limits.'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 7%|▋ | 2/30 [00:01<00:19, 1.41it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No data for MSFT\n", - "Full response: {'Information': 'We have detected your API key as 25SKFAOGSF41JCEY and our standard API rate limit is 25 requests per day. Please subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly remove all daily rate limits.'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 10%|█ | 3/30 [00:02<00:19, 1.38it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No data for GOOGL\n", - "Full response: {'Information': 'We have detected your API key as 25SKFAOGSF41JCEY and our standard API rate limit is 25 requests per day. Please subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly remove all daily rate limits.'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 13%|█▎ | 4/30 [00:02<00:14, 1.84it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No data for AMZN\n", - "Full response: {'Information': 'We have detected your API key as 25SKFAOGSF41JCEY and our standard API rate limit is 25 requests per day. Please subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly remove all daily rate limits.'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 17%|█▋ | 5/30 [00:03<00:15, 1.60it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No data for NVDA\n", - "Full response: {'Information': 'We have detected your API key as 25SKFAOGSF41JCEY and our standard API rate limit is 25 requests per day. Please subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly remove all daily rate limits.'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 20%|██ | 6/30 [00:04<00:16, 1.44it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No data for META\n", - "Full response: {'Information': 'We have detected your API key as 25SKFAOGSF41JCEY and our standard API rate limit is 25 requests per day. Please subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly remove all daily rate limits.'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 23%|██▎ | 7/30 [00:04<00:13, 1.74it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No data for TSLA\n", - "Full response: {'Information': 'We have detected your API key as 25SKFAOGSF41JCEY and our standard API rate limit is 25 requests per day. Please subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly remove all daily rate limits.'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 27%|██▋ | 8/30 [00:05<00:16, 1.35it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No data for AVGO\n", - "Full response: {'Information': 'We have detected your API key as 25SKFAOGSF41JCEY and our standard API rate limit is 25 requests per day. Please subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly remove all daily rate limits.'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 30%|███ | 9/30 [00:05<00:12, 1.65it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No data for ADBE\n", - "Full response: {'Information': 'We have detected your API key as 25SKFAOGSF41JCEY and our standard API rate limit is 25 requests per day. Please subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly remove all daily rate limits.'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 30%|███ | 9/30 [00:06<00:14, 1.50it/s]\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[24], line 89\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m combined_df\n\u001b[1;32m 87\u001b[0m \u001b[38;5;66;03m# Example usage:\u001b[39;00m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;66;03m#tickers = ['AAPL', 'MSFT']\u001b[39;00m\n\u001b[0;32m---> 89\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_all_alpha_stocks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtickers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_csv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mallStock_data.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;66;03m# This will save the combined dataframe to 'stock_data.csv' locally in the working directory\u001b[39;00m\n", - "Cell \u001b[0;32mIn[24], line 70\u001b[0m, in \u001b[0;36mfetch_all_alpha_stocks\u001b[0;34m(tickers, output_csv)\u001b[0m\n\u001b[1;32m 67\u001b[0m all_data \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ticker \u001b[38;5;129;01min\u001b[39;00m tqdm(tickers):\n\u001b[0;32m---> 70\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_alpha_vantage_stock\u001b[49m\u001b[43m(\u001b[49m\u001b[43mticker\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m df \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 72\u001b[0m all_data\u001b[38;5;241m.\u001b[39mappend(df)\n", - "Cell \u001b[0;32mIn[24], line 20\u001b[0m, in \u001b[0;36mfetch_alpha_vantage_stock\u001b[0;34m(ticker, interval, outputsize)\u001b[0m\n\u001b[1;32m 11\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://www.alphavantage.co/query\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 12\u001b[0m params \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 13\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfunction\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTIME_SERIES_INTRADAY\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 14\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msymbol\u001b[39m\u001b[38;5;124m'\u001b[39m: ticker,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapikey\u001b[39m\u001b[38;5;124m'\u001b[39m: API_KEY\n\u001b[1;32m 18\u001b[0m }\n\u001b[0;32m---> 20\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mrequests\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m data \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mjson()\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Check for error messages or empty data\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/requests/api.py:73\u001b[0m, in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(url, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 63\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \n\u001b[1;32m 65\u001b[0m \u001b[38;5;124;03m :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;124;03m :rtype: requests.Response\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mget\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/requests/api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[0;32m---> 59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 585\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m 586\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m 587\u001b[0m }\n\u001b[1;32m 588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m 702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m 706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 664\u001b[0m timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m 666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 668\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 669\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 670\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 671\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 672\u001b[0m \u001b[43m \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 673\u001b[0m \u001b[43m \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 674\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 675\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 676\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 677\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 678\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 679\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 682\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/urllib3/connectionpool.py:787\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 784\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 786\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 787\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 788\u001b[0m \u001b[43m \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 789\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 790\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 791\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 792\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 793\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 794\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 795\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 796\u001b[0m \u001b[43m \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 797\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 798\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 799\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 800\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 802\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n\u001b[1;32m 803\u001b[0m clean_exit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/urllib3/connectionpool.py:534\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m 532\u001b[0m \u001b[38;5;66;03m# Receive the response from the server\u001b[39;00m\n\u001b[1;32m 533\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 534\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 535\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 536\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/urllib3/connection.py:516\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 513\u001b[0m _shutdown \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshutdown\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 515\u001b[0m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[0;32m--> 516\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 518\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 519\u001b[0m assert_header_parsing(httplib_response\u001b[38;5;241m.\u001b[39mmsg)\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/http/client.py:1378\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1376\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1377\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1378\u001b[0m \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1379\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[1;32m 1380\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/http/client.py:318\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 318\u001b[0m version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[1;32m 320\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/http/client.py:279\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 279\u001b[0m line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mreadline(_MAXLINE \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 280\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/socket.py:706\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 706\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 707\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m 708\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/ssl.py:1311\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m 1307\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 1308\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1309\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m 1310\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1313\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/ssl.py:1167\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m 1165\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1168\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1169\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import requests\n", @@ -318,34 +150,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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+3PZ098x51d+y5pLmDZfnyytM86rlS5fUjn8/qd1BERo620dxKTfVsEZF5pLy0K9xZc3q3lDzd5zTieirGudWU18Ma63B3x1RYurdPM+rXrakXunWQAGXk3P9bFb3Bmpfq4Le8D2jyKu31Kl2Bc3u1VhxN25rVyjjNnO492x5zGEAQPFhse3mDQaDbt++rVdffVXz58/XggUL9Pbbb8tgMPzxycXMtIGttWz7Ga3YFayzl5M09as9Sr2TpvG9mppN37FJVR0MjpbPvguKiLum7ccva9X+C/JomPWGlPirtxSTnGr66t+2jkKiU7T3NDe5zJk2oKWW7TirFbvP6+yVZE39ep8xBj0bm03fsXEVHTwXI58DIYqIv67tJ65o1YFQeTSonC1d2r2M/2fvvsOjKvY/jn82nYQklEDovffQuwYUEFSKdFFsiAqEq4JXRUGvXayoqIAiRZEiKCjSe0+B0FsqJb2HJKT+/tiwYZMN+LsX2CS8X8+TR3MyZzMnw5wp53tmFJWUbvqKs/DgEgV8hnTQwo3HtWTLCZ2+EK8p32xR+tVsjb+/lcX0Y72b6+MVB7XRL0ShUUma//dRbfQL0dShHU1pBs9araVbT+pUeJyOhcTq2c83qk5VN3k18rxTl1Wq+Axur4WbjmvJ1pPGMpibXwb3FVMG9zbXxysPaaN/aEEZ+Ido6pAOpjSD31qjpdtO6tSFOB0LjdWzX26iDG6RTYfO6+0ft2vtHlYau9V8BrTQwh3ntGT3eZ2+nKQpP+1X+tUcjb+nkcX0XRtV1f5z0Vq+P0ThsVe09fhlrTgQoo4NPExpBn+yRUv3BOnUpUQdu5CgZ+fvUR2P8vKqX/lOXVap4/NIZy38+4iWbDyq0+GxmvLl38Z7Uv+2FtOPva+VPl62TxsPBSk0MlHz/wzQxkNBmjq8iylNbFKaohKumL4Gdm2koEvx2n2UVWosoZ9aMvgM66SFGwK1ZNMxnQ6P05Q5G5V+NUvj+7e2mH5s35b6+Nf92ugbrNDIJM3/84g2+gZr6iOdTWleHtlVF2OTNfHT9fI7E6GwqCRtDQhVSETiHbqq0sfn/qZauDtYS/aF6HREsqYs9TPWhx71LaZPSMtUVHKG6atv82pKy8wxC1C8/udRyRl6qF0N7TwTrdDYK3fqskoVn/ubaeHuIC3Ze60MfI1l0LOBxfQJVwqVQYtrZVBwz+/aqLKW7gvV7jPRCo+7oh93BenoxUR1pH0uls8jXbRw/XXt8xfrje3zgOLa59b6+Je9xvY5IlHz1xVtn7u2rKU/953VhoPnFR6VpDW7T2urf4g6Nqtxpy6rVLkdY+f1h4K10S9EQZcTdf5yot5aslepGVnq3LT6nbqsUsdnUJv8uaSzxrmkBbvz55KaWkzftUk141zS3iCFx6Rq69FLWrEvSB0bFvSTXn64nS7GpWridzvlFxSjsJgUbT16SSH/YLWhu5HP0I5a+PdRLdl83NhH+iq/j9SvmD5Sn5b6ePmBgj7SX/l9pGGdzNJl5+SajRnikos+uETxfB5up4WbT2jJtlPGMcR32433qL7NLaYfe28zffybvzYGhCk0KlnzNx7XxoAwTR3sdYdzXnoZ5/NOFMznfbstfz6vpcX0Y72b6eNVvvnzecmav+GYNvqHauqQgl2xNgWE6e2f92vtAVZX/6eYVy0ZfIZ21MIN+W3DhThN+XpTfttQTDn0aVnQT4pM0vz1R4z9pEJtg4uTvRa+MkgvzNmkRALiboj2uWQwzqteN5f05Yb8edU2FtOb5lV9r82rHs4ftxWaS4pJ0cRP/zLOJUUmaat/CHNJxXi8fW39djxCf5yMVHB8mt7Zelbp2bka0rL4MZaNQfpgQHPNPRCiixZePGtX3V1rT0bK72KiLidn6LfjETobk6pW+aspoiiePVsfcxgAUHpYLUj0eosWLdKYMWM0ePBgLVu2TO7u7tbO0i1jb2cjrwZVtO1owUoxeXnStqMX1bmJ5Y7EgTOR8mpQxfSwvV5VV/X3qqMNAZaDG+ztbDS6V2Mt2nb61l9AGWBvayOv+h7adrwgMCEvT9p2/JI6Ny6mDM5Gy6u+hykotF5VV/VvV1sbjlwwS9eompuC547RyS9GauGke1W7ssvtu5BSzt7ORl6NPLXtSMF2Onl50rYjYerczHKHzsHeVhmZOWbH0jOz1b1F8Q8S3VwcJYk3Gy0oKIOCe0lenrQtMPzGZZBl/lZcema2uje/URk4SJISLKzGCJQE9rY28qpXWdtOFGoXTl5W50ZVLJ5z4Hy0vOpVNgWF1qtSXv3b1tSGwEvF/h63cvl1IZUXCCyxt7ORV5Pq2nbdFiJ5edK2gBB1blHT4jnGdqHQPelqtrq3qlXs7xjdt5UWbTx6y/JdltBPLRns7Wzk1biatgUU6iMdDr1BXbAr2ke6mq3uLQvqwqCujRRwNlI/zxissOWTtf+bJ/TkA5YDvJDfNtStqG2nCraKzMuTtp2KUueGHjc4s8D4nvW10jdcaYXK5pqqro4a0LqGFu1hVV1LTGVw0kIZNPhnAZ3jezbQykPmZYmDL/EAAQAASURBVHDgfJwebFtDNSqUkyT1blpVjT1dteUEW+VZUtA+F2z9WNA+W25vHRwstc9Z6t6qtun7AycuyturnhrVrCRJat2gqrq1qqVNhwhMKexOjJ1tbAwa0bupXJzsdPA0L3FYYppLOlaon3TsUvH9pLORReeSvGprw3Vj8EEd6iogOFY//+s+hX3/mPZ/MExPFvNyzt3O1Ec6Emo6ZqoLxcxJWBwvZJr3kSSpUc2KCl76gk7++KwWvvKgaldxveX5L6vs7Wzk1bCqtgUWzJGaxhBNq1k8p9hyac4D3n+i4G9uYT6vuL+5XTHtwg3m83BjzKuWDMZyqFZMP+n/0TZczVb3lubj7S9euE8bDgVr+3WfjaJon0sGUzkcLjRuOxyqzs1vMJdk6Z50/VxSt8YKOBehn98YorAVPto/90nmkophZ2NQ86quOnChYPvxPEkHwxPUtnrxAZ3Pdamn+LRMrSlmTuJIRJLubVBZVfPbg061KqhuxXLaHx5/S/NfVvDs2fqYwwCA0sVq280XdvDgQQ0cOFA//PCD/vrrL7322mv/6DwHBwc5ODiYHUu1s1FmdsnYhsDD1Ul2tjaKLrSUdnRSuprWrGDxnOV7zquyq5O2vjNYBkn2draat+mEZq85bDH9w53qq4KLo5buYKU5SzzciiuDDDWtUcHiOcv3BRnL4K0HZZBB9nY2mrf5lGb/EWhK43s+Ws9+t0tnI5JUrUI5zXikvbbMelAdXlmt1IzitxG4W3m4lTOWQ6FtLqIT09S0ViWL52wJCJPPkPbac+KigiMS5d22jgZ3ayRbW8srDhsM0uwJ92rfiUs6GcZS84XdsAxqVrR4zpbDYfIZ3EF7jl9ScOR1ZWBzgzJ45l7tO3lJJ1nuHyWUh6ujsS4UelM3OilDTatbflFl+f4QVS7vqK1vDChoF7ae0ex1xyymNxik2eM6ad/ZKJ28lHirL6FM8HB3NpZDgvlqetEJV9S0tuVgoC1+IfJ5pLP2HAtX8OUEeXvV0+CeTYu9Jz3cvakqlHfS0k0EiVpCP7Vk8HDLrwuJhetCWvF1wT9EPo900p5jFxQckV8XejQxqwv1q1fQhAe9NGe1rz7+db86NKmuT5/vq8ysHP285fhtvabSyKO8g+W2ITlDTavdfLWGjvUqqVWtCnp+kW+xacZ1r6+Uq1n6na3mLfqfy6D+tTI4ZHb8pWX++ubxTgr6ZLCysnOVm5enFxb7au+5mFua/7Liv2ufg+UzvMt17XN9De7ZzOye9Mmv++Tm4qjAhc8pJzdXtjY2mrVwh37dduK2Xk9pdDvHzi3remjHJ6Pl5GCn1PRMjXpvnU5f4GGjJcXPJd2gn7Q3fy7p7Yevm0s6qdm/HzGlqV/VVRPua64564/p498Pq0PDKvr0ie7KzM7Rz7vO3cYrKn1MfaSEQnUh4UrxdcE/RD7DOmnP8YvGPlK7uhrcvYlZXfA9c1nPfvq3zl6MV7VKLprxaA9tmT1WHZ5fqNT0zNt6TWWBh2s5y3UjMa3YurHlcLh8Hm6nPScvKzgySd5tamtw1waytSkR61eUeP9Vu3A4XD6DvbTnRP58Xps6GtytYbFjZ9wc86olg6kcCrcNiWlqWru4flKIfIZ21J7jF4z9pHZ1Nbh7Y7O2YUTvZmrXyFM9py65rfkvC2ifS4YblsONxm3DOmvP0evnkppamEtqrzm/HdLHy/arQ9Pq+vSF+5WZnaufN1ueB79bVSxnLzsbg+LSzP99xqVlqn4lZ4vneNVw19CW1TXiZ79iP/eDHec0q29TbZnQXVk5ucrLk97eekb+l5Juaf7LCp49Wx9zGABQupSYIFFJunz5soYMGaJPPvlEixYt+kfnTJ48WS+//LLZsXdX+Om9lcV3sEq6Xi1qaPqw9po6f7d8z0erYTU3ffJkD0U80l4f/hZQJP34Ps208XC4IgoNBvDf69W8uqYPaaupP+4zloGnmz4Z300RQ9vpwzVHJEmbAgse7h4Pl3zPx+jMV6P1SNf6WrTjrJVyXrZMm7ddc6fcr8Bvn1CepOCIRC3ecqLY5em/eL6vWtatrL6vLL+zGS3Dps3fobmT71Pg3PHmZVDMNkpfPNdHLetUVt9XV9zZjAK3Wa9mnpr+UBtNXXRQvkExxnZhXCdFJLbRh38UDUD84vGualmzovq++7cVclt2TZu7WXNffECBP0w03pMuJ2jxpqPFbqM0/oG22ngoSBFxqXc2o2UY/dSSYdq3WzT3XwMUuOCZ6+rCMbPt6W0MBgWci9SshbskSYFB0WpZz0MTBrUjSPQ2GN+zgY5dTJRfaPETlY/3qK/lB8N1tYS80FjWmMogxLwMXujTWJ0bVNYjX+1SeNwV9WxcVV882kERienaft3KsfjvTftms+a+NFCBPz5XcE/aGGi2Pf3we1podJ9WeuL933UyLEZtGnpq9gv3KyI2hYeNt8A/HTufvRSvLj5L5e7soKE9m2j+i/3V79UVPGS5RXq1qK7pQ7w09Yc9+f0kd30yvrsihnnpw9XGF2psbAwKCI7RrF+NLxUEhsapZa1KmnBfC4JEb4Fp32/VXJ8BCpz3dEFd2HzMbPvbTX4FK24dD42R75kInVn0nB7p1VSLNnE/uh2m/bBLc1/oo8CvHjWWS2SSFm87pfF9Wlg7a2XWtAU7NXdSXwV+81jB33zrSY3va3l7etwezKuWDNO+26a5U/sr8Pvr2oYtx039pFoerpo9sY8enLFSV7Ms7wqB/w3tc8kw7dvNmvviQAX+8Gyx86o2BoMCzkZo1sKdkqTAoCi1rFdFEwZ5MW77Hznb2+r9/s309tYzSrzBQkNj29ZSm2pumvLHMV1OyVCHmhX0undjRadm6uB1q5biv8ezZ+tjDgMwZ+BdPtxBVgsSPXDggLKyinaCrl69qilTpujEiRN6/PHHb/o5X3/9tebNm2d2LLL9y8WkvvNiUzKUnZOrqu7lzI5XdS+nyETLD8tnje6kZbvO6qf8bTlPhMfL2dFe30zsrY9WBygvryBtHY/y6tOmpkbP3nTbrqG0i00urgycFJmYbvGcWSM7aNnu8/ppu3HVqxMXEuTsZK9vnumpj34/YlYG1ySlZep8RJIa/oNVbu5GscnpxnKoYP4GXdUKzoostErN9eeMfG+tHO1tVdmtnC7HperdJ3opJDKxSNrPn+ujgZ0a6L5Xl+sSwUAW3bAMirkfxSana+T764xl4Oqky/FX9O74ngqJKvrW4ucTvTWwYwPd9/oKygAlWmzKVWNdcHMyO17V3UmRScW0C494adm+IP200/jg9sTFRDk72umbJ7vpo7VHzdqFzx/rooHtaum+9zboEoFxxYpNSjOWQ0UXs+NVK7oU3y4kpWnkW7+ZtwvPeCskIrFI2jpV3dTHq55Gv/3b7ch+mUA/tWSITc6vCxUK14Ub9JGS0jXy7TXmdeHpexQSWdA+R8an6lRYrNl5py/EaUjPprf+IsqA2NRMy22Dm5Mik2+8lZSzg61GdKqtd9YWH3zbo7GHmlZ302Pz9t+S/JZFNyyDYtrna4xlUEfv/GFeBk72tnp7WBuN+maPNhyLkCQdv5ikNnUq6F/9mxEkasF/3T7PWpV/T3LW5biUIu3z+8/21Se/7tPKHSclSSdCYlTH013Tx3TnYWMht3PsnJWdq+D8cjkcFK0OjT016eH2mvLNlttxKaVa8XNJN+gnjeyoZbvPmc8lOdrpmwm99dGaw8rLkyIT0nTqYqLZeacvJ2hIl/q35TpKM1MfqWKhunDD+1G6Rr5TqI/0lHkfqbCkK1d1/lK8GtawvBIgzMWmpFuuGzecW8rQyA/Xm88tPdbN4twSivqv24UP/jT/mz/eg7/5/4B51ZLBVA6F24YKzoqMv0F9eOd387bhyd6mtsGrsac8K7po/1cFzyPtbG3Us1VtPfdQe7kP/ky5uRYeCN2laJ9LhhuWQ7zle0hsUnrRedWn7zUbt0XGp+pUeKG5pPBY5pIsSEjPUnZunio7m+92WtnZQbFXiq5+W7tCOdV0L6c5D1//grfxvwE+9+jhRQcVk5opnx719a91x7U7/yXkc7FX1KxKeT3RoTZBohbw7Nn6mMMAgNLFanu6jBgxQsnJycX+/LvvvlP37t1v+jmZmZlKTU01+yopW81LxsbrcHCMvFvXNB0zGCTv1jV16KzlB1LlHO2KDDqvfW8oFEb+mHczRSel6++AsFuc87IjKydXh0Ni5d2qhumYwSB5t6ypQ+eKKQMHO+XmFS4D478rgyyH8rs42qm+p6siE278APNulZWdq8Pno+Tdto7pmMEgebeto0OnI2547tWsHF2OS5WdrY2GdG+sPw8Gmf388+f66OFujTRgxkqFRRV/X7nbFZRBbdMxg0HyblP7n5VB/JXiy2Citx7u2kgD3lhFGaDEy8rJ1eHQOHm3rG46ZjBI3i2q69B5y1vPlnO4Qdt8Xbvw+WNd9HCHOhrw4UaFxTJpcCNZ2bk6fDZC3l71TMcMBsnbq54Onbx0w3PN2oWeTfXn/qIreD/Wv62iE9P098HztzrrZQb91JIhKztXh89FyturrumYwSB5t/tv6kLBCmT7T15Sk0Lb7TWuWUnh0bTTlmTl5OpwWIK8m3uajhkMkndzTx0Kir3BmdKwjrXlaG+rZQeK/7c+vmcD+YfG61ihwCAUKLYMmnnqUPCNt/Ma1rFOfhmEmh23tzXIwc5WhZ/p5uTmyYZXtC0ytc/t65mOFbTPF4s/UdfuSSnGe1KvZvpzX0H7XM6p6Bg7JzdPNmx7W8TtHDsXZmMwyNHe9pbku6wpmEsq1E9qVaP4fpLFuSTzMcP+s1FqUsPdLE3j6hUUHptyK7NfJpj6SO0K95Hq6tCpyzc816wu9Ghi1kcqzMXJXvWrVyg2oALmsrJzdTgoWt5tCs0tta6lQ2cib3iu2dxSt4b681DIDdPDqNi/eZva/7+/efdG+vNg8O3ObpnFvGrJYCyHSHm3tdA2nP5/tg0HjPNF24+EqcPzC9Vl8iLTl//ZCP2646S6TF5EgGghtM8lQ0E51DMdKyiH/89cUjPzuaQTF9Wklvl29Y1rVVI4LxkUkZ2bp1PRKepSu4LpmEFSl9oVFRhR9F4eEp+mYUt8NfJnP9PXjuA4+V5I1Mif/RSZclV2tgbZ29qo8F0nJy+PVeaKwbNn62MOAwBKF6utJFqjRo2bJ5JxC/rSbs6fRzV/krf8g2Lkdz5akwe1kbOjvRbnryywYLK3Lsdf0cxfDkmS1vuFyefBNgoMidWh/O2pZo7upPX+YWYDUoNBety7qX7eeVY5DFRvaM5fxzX/+d7yD46V3/kYTX6gpZwd7bQ4f0W4Bc/fo8sJVzTzVz9J0vqAcPkMbKXA0LiCMhjRQesDwk0T/h882ll/BYQrPCZVNSo6640RHZSTm6cV+27cgbmbzfndX/NfHCD/c1HyOxupyYPby9nJXou3nJAkLXhpgC7HpWrmoj2SpE5NqqlG5fIKDI5RTY/ymjG2m2xspM9+8zN95hfP99Goe5ppxLtrlZqWKc/8N5WS0jKVkZl95y+yhJvzR4Dm/6u//M9HG8vgYS9jGWzNL4N/9dfl+FTNXLxXUqEyqFxeM8Z0lY3BoM9WX1cGz/XRqN5NNeK9tUpNv74Mriojky16/hcuTvZqWLMgwKde9Qpq09BTCSnpukCQz/9kzoaTmj+hp/xD4uQXHKvJ/Zob24VdxgniBc/21OWENM1cadw+e/2RC/IZ0EKBYfE6FBSrhp6umvlIO60/csHULnwxvotGdW2gEV9sU2pGljzdjSuhJaVlKYPtqiya89shzX/lIfmfjZDfmcuaPLSz8Z608agkacErD+lybIpm/rhDktSpWQ3V8HBV4Pko1fRw1YzHe8nGxqDPlh8w+1yDQXq8fxv9vPkofaSboJ9aMsxZ7av50wbJ/2yk/M5EaPLQjsa6kL+l2oLpg4x1IX/r+E5NqxvrQlB+XRjXw9g+rzho+syvVvtq++fjNH10V/2267Q6Na2upwa21eQvNlrlGkuDOZvPaP5TXeQfGi+/kDhNvq+pnB3stHivMYBhwVNdjG3DGvNVD5/o2UDrDl9SvIWVIiTJ1clOwzrU1qsrj9zuSyj15mw+rflPdZV/WLz8QuI1+b4mxvZ5rzGgYcFTXXQ5MV0zVx81O89YBheLlEFKRrZ2nYnW+yPaKj0rR+FxV9SrSVU92q2e/r3iyJ26rFJnzm8HNf+Vh+V/Jr99HpbfPm/Ib5//nd8+/7BD0nXtc1CUala+vn0uWDl3/f5z+vfYHroQnayToTFq16iafB7prMUbAq1xiSXe7Rg7/2d8T230C9GFmBS5lnPQqHubqXfr2npoJquuF2fOX0c1//l75R8cY5xLGtja2E/aaQyAXvDCvcZ+Uv7W8ca5pNYKDLk2l+SmmSM7an1AmGnM8NVfx7T9P4M1fUg7/bY/WJ0aVdFTfZpp8vzdVrvOkmzOGj/Nf3mg/M/l95GGdDSWQf4KxAteHmisCz9d10eqXF6BwdHG+9G1PtKqQ6bP/OCZe/XXwSCFRyWpRuXyemNcT+N83s5TVrnG0mjO2iOa73Of/IOi5XcuSpMfbCtnJzst3mr8Gy7wuc9YN5Ya24FOjT1Vo7KLAkNiVbOSi2aM7mwslzUB1ryMUmXOHwGaP7WfcT7vXKQmP5Q/n7fFuEL3gn/1M9aFJfskSZ2aeKpGpfIKDMmfzxudP5+3pqBdcHGyV8PqBUHr9Tzd1aa+hxJSruoCgesWMa9aMsxZ46f5L+W3DWcjNHnwtbbBuKuAsW1I0cyfjG2redtQXjMeNW8bUtOzdLLQLhxXMrIUn5xe5DiMaJ9Lhjm/HdL86Q8ay+H0ZU0e1sl8XnX6g8a68KNx6/hOzWoYyyEo2jhmeKyXccywomBe9avVvtr+xWOaPrrbdXNJ7TT5iw1WucaSbnHABb3br7lORqXoWGSKxrWvpXL2Nvr9pDEw7r1+zRR15arm7A1RZk6uzseZr6qYctX4DPPa8ezMHPleTNRLPRsqIztXEckZ6lCrgh5q7qlPdvHsuTg8e7Y+5jAAoPSw6nbz11xbdSgvL8/sWF5enurUqVPk3NJm1b4gebg5aeaoTvKs4KyjobEa/N5fis7fMq+2h6vZyiYf/uavvLw8zRrTWTUquSg2OV1/+YXprWWHzD63T+taqlPFVYvyt/tE8VYdCDaWwfD2xjIIi9PgDzdcVwblzVZ7+HDNYeUpT7NGdsgvgwz9FRCut5YXdE5qVnLR4ineqlTeSbHJGdp3JlL3vLlWsSk33o7ybrZq91l5uDtr5rju8qzorKPBMRo8c7Wi87fkqV3F1SzAxNHBTrMe66H61dyVmp6ljf4hevrTv5V05aopzcRB7SRJmz8cafa7Jny+QUu3nrz9F1XKrNpzVh7u5TRzbLeCMnhrjXkZXFc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nvzTfd36oj6Hv/NEmRSZmaXjXZlrx0mgNfvF7RUQbFjT279hYn/4aobCTqbK3s9HrD/fVL6+Pk/+zX+lc0YWbfYtVAu0k65swsK3mPzVEgf/eaGgj3dtNa9+aJL8nFis9x1wbqb/uH9JBz3zwe3kbae69Gjzza0VEGcaRatV00pb/e0jbDsZr7KsrlZ59Tr6NatNGuoIJ/Vpp/uP9FfjJn4Yy6Z4uWjtvrPyeWWa+THqwt+4f1FbPfLzZMJ7n30wrXrpbg1/8QRExhjLJ1dlBh2IztGzzUa146e6bfUtVDmlwa6B+vjVM6N9a858cqMCPNxvmGMZ01dq/j5PftKXm0+Hhvrp/cNkcQ0KmIR1eGa3Bs78zpsPpM/ma89XOsjkG6aGhHbTy1THq9dzXzDGYQV64NdyIdpIkHYlN16i/rTD+fKGE/rMlw309NatfS7299ZQOp+bpAb+G+uiejhr3bZiyCootvq+Bm5P+0reFwpNyzP5+V1ymXt9ywvjz+ZLqv5bif0E7yfom9PXV/Mf6KfDTreVpMHe0/GZ8YzYN7uvfWm883FvTP9qiPceTDXPPQcMMc89LDQ91f/LsELVvWkePf/iHkjPP6v6BbfTrvDHqGvStkjLP3uQ7BIDqw6rHzUvSBx98oMWLF1/xVdUFjeqspVuOa/m2Ezp+OluBS3ao4PwFTRnUxmz4Xq3ra8+JVK3YFaX49HxtPnhaP+yOUjcfL5NwF0ouKjWnwPg6Y2bSDAZBd3fW0s3HtHxrpI4nZilw0XZDGgxpazZ8rzb1tScyRSt2nlJ8ep42H0zUD7tOqZtveRpk5BYqNbvA+LoroJmiUnK046j5nQAhBd0boKW/H9LyP47oeEKmAj/apILCYk0Z0cls+AcGt9e7PwRrQ2iMYlNytHh9hDaExui5ceU79a0PjtaG0BhFJWXrVFKW5i3bpfzC8+rRtsHNuq0qJeieLlr6xxEt//O4IS98tlUFRRc0ZWg7s+F7ta2vPcdTtGLnSUNeiEjQDztPmuSFXm3q65eQGP0eHqf49Dyt2RulzREJJmFgKmh8Ty1df0DLNxzU8fgMBX6w3pAOd/iZDf/AsE5699td2hAcpdjkbC1eF64NwVF6bkJPY5heHRrrl90n9Pu+U4pPzdGaHce1OSxG3do2vFm3VaVQN98agsb30NLfLssLH/5myAsjLeWFjnr3u92GvJCSrcW/VM4LGTnnlJp11vi6q5evok5nasfB+Jt1W1VK0N2dyttIp7MVuLisjTTYUhvJW3siU7Vi16myvHCpjVS+k9/zY7oo8Uy+pn2yVaFR6Yora0vFpOberNuqcgx5IULLNx7S8fgzCvzw97K80NlseGNeCLmUF/aX5YUexjDPT+qlxPQ8TfvnrwqNTFZcSo42h8UoJjn7Jt1V1RM0rLWW7orR8j1xOp6cp8Bvw1VQXKIpfZqbDZ91rlipuUXG19B2Xjp3vkSrw0wXKJ6/UGISLvuc5YmC2x1pULVsDD6l17/4U2t3snvo9RY0pquWbjys5ZuPGvrOC/8w1AvDOpoN/8Cgdnp3ZbA2hMUqNjVHi387qA1hMXpubHnfecy8Nfp6y1EdSzijQ7EZeurDjWrq5S5/X2+znwnaSbeCoHHdtfT3y9pICzaooKhYU0ZaGEca2kHvfr9HG0KiDeNIvxzQhpBoPTe+QhspI1fT/rne0EZKzdHm8FjaSFdgKJOOlJdJn2wpK5M6mA3/wOC2endVSFmZlKvFvx/ShrBYPTe2qzHMxvA4vf7NHq3dG3WzbqNKIw1uDdTPt4agsQFauuFw+RzDx2XpMNxCOgxup3d/2GeYY7iUDqExeu7ebsYwpnMM2Zq3fJfyC4vVow1zDOaQF24NN6KdJJWNb182tnrGzOYiMHioSyOtOZKidcdTFZN1Tm9vPaXCCxc1pp3l762tjfTm8Db6LDhOp3PMP6RUXHJRZ84VG195LJS+ItpJ1hc0uouWbjqi5VuOGeaeP/3zGuaek7Vix4nyuecdJ9StlWG+zdnRTmN7++iVZbu162iSolNy9NaKYEWl5GjqHebrGgDAtbH6ItH8/Hzl5eVd8VWVOdjZyr+Fp7YcKp+kKi2Vthw6rR6tzTcS955IkX8LT3XzMQwiN/dy00j/Jvr9gOniBt/6Hope+KCOfjhZS2cMVpO6rjfuRqowB3tb+bespy0HK6TBwUTLaRCZIv+W9YyL3Axp0FS/h5tfYOJgb6vJ/Vvpqy3Hr/8NVBMO9rby9/XWlsu+x6Wl0pYD8RYXdDo62Kmw2LTzU3D+gvq0b2Q2vK2tjSYOaCNXZwftO8Zi3Yoc7G3l72MpL9Q3+569x1Pk73NZXvB218iupnlhb2SKBndqLN8GHpKkTs3qqnfbBtq4nwVZ5jjY28q/dQNtCS8/xqW0VNoSHqMe7RubfY+jo50Kz1fIC0XF6tOxifHnvUcSNdi/uXwb1ZEkdWrppd4dG2tjMJ3Yiqibbw3leSHWeK08L5gv5x0dzOWFC+rT0XzecbC31eShHfXVBnY1NsfBrqyNdOi08ZohL1ypjZQq/5YV80JT/b4/wRhmVLfmCo9O1zczhylu8SPaM3+8HhtqfjEFyvJCq/rasr9CvbA/Vj3aWcoL9ubbSB3K88Ko3q0UfjJZ37w6VnE/BGnPwsf02J3mF2BDcrCzkX/TWtpyrHz3jNJSacuxVPVoWfeaPmNK3xZaGZqgc+dLTK73b11Pce/erYh5I/Xh/f6q4+p4XeNeXZAGgIHFvnPEf9F3bmf5gTH3snyQlcfuiebQTrI+YxvpsqNojW0ki/0FexVWqAMKiiq0kXr5KvxEir55ZYziVszQno8fpY10BYaxJC9tiTBTJrUxP5bkaG9XOR2uUibBMtLg1kD9fGsoT4cKdcOBuCung7n80N58OpTPMdhr33HmGCoiL9wablQ7SZJ8G9VW9LfP6OiX07T0xbvVpJ7b9b+BasDe1kZt67kpODHbeK1UUnBitjrVd7f4vqndmyqroFg/HzN/FLokBTSqpU2P9dSPDwTopYG+8nCy+sGwtyzaSdZXngbl/V7j3LOFNDDMPXsZF4U293bXyIBm+j3MUKbZ29rK3s62UjoVkk4A8D+zeqtizpw5OnPmvz+uwdHRUY6OphM8+fa2On+LHB/n6e4seztbpVXYSjstp0BtGtUy+54Vu6JU181Zm18fLRvZyMHeVos2HdV7Px0whgk5laanPtmqE8k5ql/LRa9M6Ko/5o1WwOxVyi9kV5TLebr9F2mw85QhDd4YIxtJDvZ2WrTxiN5bs99s+NHdW6iWq5O+3soOKpZ4utcwpEO26Rbwadnn1KZJHbPv+SM8VkFjA7TzcKKik7M12K+ZxvRuJTs7G5NwHZp5aus/75ezo73yC87rvjfX6nhC5g27l6rKmBcqHFublnNObRrVNvueFTtPqq57DW1+c5xsbMrywobDem91mDHM+2vC5O7ioIgFD6rk4kXZ2drqtW/36vsdJ8x+5u3O08PFkA5ZFfJC1lm1aWJ+AcQfodEKmtBTOw/FKzopS4P9W2hMv7YmR2q///1uubs6KWLp9PJ0WLpV3285ckPvpyqibr41/Hd5IUZB43tclheaa0y/NiZ54XKj+7RRrZrO+noji0TNMeaF7Ap5IbtAbRrWMvueFbtOqa77ldtILbzcNHV4ey349ZDeXbNfAT5e+udjfXX+wkV9s426oSJP90t5oUL9fLV6YVwP7TyYoOjksrzQ1zQvtGhQS1Pv7qoFPwbr3e/2KKBNA/3zmeGGdNh06IbeU1XkWdPJkA4Vjg9OyytSmysM7l/SrXltdWzkoaeXh5pc33QkRT/vP63YjLNqWa+mXh/bUT8H9tPA+Vt0kdPCTJAGgEF537lCvZBtud/2x/44BY0J0M7DpxWdkq3Bfk01prevxTaSjY303pODtPvoaR3lCFWzaCdZn7GNVHEcKeuc5TZSWIyCxnfXzkOXt5Fam2kj+WvB6hC9+/0eBbRuoH8+PVTni0v0zR+Hb+g9VUVXLJMaWxjP2x+voDH+2nmkrEzq3FRjevtYLJNwZaTBrYH6+dbwX+WH8DgFje2qnUcuzTGUpYO5OYb3J5fPMby1jjkGM8gLt4Yb1U4KOZ6sp95frxOJmapfp6Zeeaiv/vjngwqY9oXyC87f0Huqamo5O8je1kZnzpn+v5w5d17Na9cw+54uDdw1pl19PbAi3OLn7o7P0pboDCXlFqqxRw0926u5FtzTUY/9eIAxDDNoJ1mfp1sN8/Nt2ecsz7ftOGHoO781vnzu+fdDeu9Hw9xzfmGx9h5P1kuTuisyMUupOec0qX8r9WxdX1EpOTf6lgCgWrPqItHS0v+9NTNjxgw9//zzJtfeXBWmt1aFWXjHra9/+waaPdZfz32+UyGn0uRT30PvT+mj5HH+eme1YWB544HypzEOx2cq5FSaIj96QON7t9RXf7JQ8X/Vv31DzR7XVc8t3lGWBu56/7G+Sh7fVe/8WLnxPmVIW23YH6/kCpP7+N+88NmfWhg0QhGfPqZSSdHJ2Vr2xxFNGW56RMCJ05nqGbhcHq6Ourdvay2edYdGvLiCQZzroH+Hhpo9LkDPLd6mkJOphvLo8f5KntBN76wyTMBP6OOryf1b69EPNupoQqY6t/DUe4/1V3LWWX3Dwunr4oWPN2nhrLsU8cV0Q15IytKyDREmx9NPGNhek4d01KNv/6Sjcenq7OOt954ZruSMPBYDXQfUzbeGFxZu0sKZdyri82nleWHjQYtHck+5008bgqOUfCb/5ka0GuvfvoFm3+uv55bsVMjJS22kPiZtJFtbG4VHpeu174IlSRGxZ9ShaW1NHd6exQ/XyQufbNLCmXcp4vOnLOYFWxsbhZ9I1mtLt0mSIqJS1aF5PU0d5U+9cANM6dNChxKzFRqbZXJ9ZWj5rtVHknJ16HSOjr15pwa09tLWyLSKH4P/AWmA29kLi7dq4Yxhilg4xbTvbOHIzw+mD1GHpnU19G8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uDvW9zWnWHjyvd5bs1/IQdny4WYH9mmvO5lOat/W0KR92KuNKjkb1LCQfGlYz5sPOq/lw/rp8GPjxev24LVTHziXr0JkkPT9rm2p7e8ivXpWSuq0yJ/DhDprz+0HNW3dYx6MSNO4/a5RxJUuj7mtlNf2IXi304cJdWhMUpojoFM1atV9rgsL0yuCOFumyc3IVk3TJ/JVgZQAMo8AHW2vOxuOa9+dJHT+XrHGztyojM1uj7mliNb1/4+raeTJGC7eHKiouTRsOntOiHaHq0KCaRbrsnFzFpGSYvxJS6aQXpTjy4bUBbXU2IU1jvv5TwaFxioxL1YaD5xQek1pSt1WmBD7UWnM2HNO8zSd0/GySxn2zxZgHvZpaTe/fpLp2nojWwm2nFRWXqg0Hz2rR9tPq0DA/D+IvXlZMcob564H2dRQanaKtR8+X1G2VOYEPt9ecPw5p3vojOn4mUeO+WKeMy0W0CwHN9eGiPVoTHG5sF1Yf0JrgcL0yOH8349V7wrQmOFyh55N1+nyS3p67XWmXM9WpaY2Suq0yJfDBlqb66JSxPvp2m6k+amw1vX/jasb6aEeoouLTtOHQOS3aEaYODapapMvOyaNduAXUSaVD4MB2mrP2iOZtOGqsk/67URlXsjWqTwur6UcENNWHi4O0JiRCETEXNeuPQ1oTEqFXBuXvpLt2b6Temb9Ty3eFltRtlGkv3dNQc3dG6qc9UToRk6oJvxxQemaORnauYzX9yM51VMnNSY9/u0e7wxN1JilDO0ITdPi88XQHF0c7DWhdQ2+vOKodYQkKj7+kGWtOKCz+kp7pWrcE76xsCXyoVX6ddC5Z42aZ6qSAwuokH+08EaOF20+b+qpX66T8tuG1gaa+6n83X9NXPavwGE7isIa5pLJl7Z7Teue7TVq+jd1Db7fAAW01Z90Rzdt4zNhH+nqTsW3u3cxqev+m1bXz+AUt3HrS2Ec6cEaLtp5Uh0bGPpKLk70GdWmgSXN3aPvR8wqLTtG0hXsUGp2i0f1aluStlSnGPtLh/D7SV+tNfSTrv7MR9zTTh7/sMfWRUjTr94NaExKuVwblj9sGvr1MP248qmNnEnQoIl7Pf75Wtat5ya+hT0ndVpnCnGrpEDioveasOZw/h/GlqSzcW0hZCGimDxftNs5hXC0LweF65eH8HeEs5zCS9fa87Uq7nKVOTZjDKAzlwfYCH2ipOZtOXDOXtN1YFnoWNpfko50nY7VwR1iBuSRvi3Tuzg6a8/I9emn2NiXzYbIbCuzfRnPWH9W8TcdNc0l/GvPhhnNJp/L7SdtOqcM1ba9/k+paGRShP/ZGKiouVct2hWnDgTMWaZDvzLrf5dsjQL7desrdt6aajHxadk7OOr/9T6vpveo1UMOhI+TTqYvsHBytpqnSqo0aPDxUVdt1tPrvuB7zebbHM08AKDtK9SJRJycnZWVl3ThhKeVobye/ulW08cgF87W8PGnjkQvq1LCq1dfsOhUnv7pV1KG+caFh3aoe6tumpv44cLZEYi6P8vMh/4F4Xp608ej5wvPhdKwpH4yD1Px8OFfoz/FydZIkJaWxCMIaRwc7+TWqro37I8zX8vKkjfsj1amZr9XXODna63KBXVkzMrPVtcVdFtca1qyksB9f0tHvntecfzykWlWvP1oVprJQz1sbD+XXJ3l50sZD59SpsfVB/q6T0fKr521e/FO3mqf6+tXSH/ujLNI1rF5BYV89rqOfD9ecsQGqVcW9+G6kjCuufHiwfR3tDYvX/Ff7KPJ/T2jnvwbr6UImhO50jg528qtfVRsPFsiDg2cLz4MT0fKrX9W8AMuYB7X1x94oq+kdHew0vEcj/bDx+O2/gXLC0cFOfg19tPGav2NjuxBV6IJOJ0d7Xc6y0i40r2k1vZ2dQUPvbiJ3F0ftPsbCuILM9dHhAn2kw+fUqVFh9VHs9fVR21r6Y7/lgpOG1b0U9tVjOvrZMM15+R7ahSJQJ5UOjg528mtQTRsPFKiTDkSpU5PqVl/j5GCvy5mWR+NlZGarayF9WxTN0d6gtndV0OaT+UdR5eVJf56KU8c6lay+5v4W1RUUkaiPhrTWiXf7asc/AjShTyNd3UTXwc5ODvZ2ulzgCMPLWTnyr8+H+6xxtDfVSYfyx77GvmpRdVKM/OoX7KvW1h/78tuGBzvU1d6wOM0f30eRs57UzhmP6One9FWtYS4JMMpvm/PrEnMfqZC2edfxaPk1qGZeFFrXx0t929fRHyHGoyPN7UKB9vsy7XehCh23HfgL47Yifsde7qZ5VSu7Jd/pmFMtHfLLQv5RtOZ8KKosWBsvNLeeb/lzGA7afZw5DGsoD7ZX+FzSeXVqVM3qa3adjJFfvSrmRaH5c0mWfdXPnu6qP/ad0abD/P3fSKFzSYeK6CddN5fkpb7t6uiPa47Y3nUiWgGtaqphDeMumK3qVFGXpjW09po0MMrNzlZqZLgqN8tfiGiws1PlZi10MfS0DSO7szCfZ3s88wSAssWmx83fyGOPPaYjR44UmcbJyUlOTk4W19Ic7JSZbfujILw9neVgb6fYAluZx6ZkqEkNL6uvWbgrXFU8nbVhUj8ZZJCjg52+2XhCH608XBIhl0vmfCiwnXlsymU1qXH9dv+StHBnuKp4OGvDW9fkw4YT+miF9eMUDAbpo5EdteNkjI6eS77dt1AueHu5GfMhyfLIl9ikS2pyV2Wrr1kfEq7AwR217fBZhV1IUkDbOhrYtbHs7fOPTg06cV7Pf/K7Tp5NVPXK7pr0eDet/2iE2r84R2kZfNr0Wt5eLoXXSTUrWn3Nwu2hquLpog3vDMgvC+uO6qNf95vTBJ2O1fP/3ayTF1JUvaKbJg1pp/VvD1D7iYuVdrnsLvQvLsWVD/WqeWp0n2aaufqQPvx1n9o3qKpPnuqqzOwczd9yqhjvqOzx9vwLebDttDEP3hsogyRHB3t9s/aIPlq2z2r6AR3rqaK7s37czG5ChfH2cjXmQ7LlkY6xyelqUquQdmFvhAIHtTe1C8kKaFNHA7s0smgXJKlFHW9t/uQxuTg5KC0jU4++v1zHzyQW272UVYXXR5fVxLei1dcs3GGqj95+6Jr66Jg++u2AOU3Q6Vg9//UWU7vgqkmPtNP6qQ+p/T+W0i5YQZ1UOuTXSQX6qsnphfdV90UpcKCfth05p7DoZAW0rq2BXRrI3s5gNT2KVsXdOG6LK7DzcFzqFTWqZv0BbZ0qburRyFu/hJzVsG92qb63uz4e0kYO9nb6cM0JpV3J1p7wRE28r4lOxqQpNvWyhrS7Sx3rVlZYPEcKW2NuG5IL1EnJGYW3DdtPq4pX0XVSvWqeGn1vc81cdUgfLtun9g2q6ZOnuykzO1fz/zxZjHdU9jCXBBh5e7paLwvJ6YX3kbaeNNZH0x6RwWCqj/44pI+WhEiS0i5nadfxC/rnsI46cTZJMSnpGtajkTo3rq7Q6JTivqUyqcg+Uk3rH+JYvy9SgQPba9thUx+pTW0N7NKw0D6SwSB99Nw92nH0nI5yvPZ1mFMtHf7SeGFvpAIHtdO2I1fnMExlwdocxsfD8+cwpq1gDqMQlAfbK3IOw7eQ5207woxzGFOvmUtabzmXNLRLfbWtW0XdJy8v1vjLi/x8KFgnZRTaPi/cdso0bns4v5+05rA+WrrXnObjZXvl5eqkA5+PUE5uruzt7DR1wW79vJXnCwVlpaUqLzf3umPlnbwqKD36QiGvwu3GfJ7t8cwTAMoWmy4SnTp1qtXrnp6eatWqlerXr6/BgwcX+R5jx47Va6+9ZnHt/WUHNO3XA4W8onTr0dRHEx9qpVfm7lZQaLwa+Hjq48c76cKAdH2w3PoCRdx+PZr6aGL/1nrlh90KCo1TAx8vfTyyoy4kt9YHvx28Lv1nT/qrRc1K6v3+7zaItvx6/X8b9FVgPx345lnlSQq7kKy56w5ZHB2zNjjc/P+HI+IUdOKCTvzwgh7p0UQ/rKXM/F09mtfQxEF+euXbbQo6HasG1Svo41FddWGwnz5Yanzou/aa3eMORyUq6HSsTnwxQo90qa8fNrEY5Xa4mXywszNob1icpv4cJEk6EJGgFndV1ug+zRkw3QY9mvtq4uB2emXWVlMeeOnjp7vpwiPt9MGSvdelH9Wrqdbsi9KFApPW+Hte/98mfRV4nw58/XR+u7D+iEbda3l0zMlzieo8bp4quDvp4W6NNWtCP933xkIestwGPZrV0MRBbfTKdzuMZcHHSx+P6qILD7fVB8v2S5LWXrNr2eEoKeh0nE78Z7ge8a+nHzazEOh2oE4qHV6f/ae+erm3Dnz5hLFOik7R3A1HNaq39eOscPvZGQyKT7uiVxftV26edOBsimpUcNW4Xg314RpjP3TM/BB9MdxPx97pq+ycXB04m6Ile8+qTa2Ktg2+HOnRvIYmPuynV2ZvU9Cpq3VSV4s6yc7OoL2hcZq6YI8kU1+1diWNvrc5i0RvA+aSAKMeLWpq4iPt9co3fyroZIwa1Kigj5/toQtDL+mDX4IlSc98vk7/G9tbYd89reycXO0Pi9Oibafk18D6Tr24da/P2qyvxvbRga9GWY7bCjme/rMXeqlF7Srq/eaikg20HGNOtXR4/ZtN+mrcvTrw36cKzGFYloWT5xLVOfBHVXBz0sPdG2vW+L66781FzGHcJpQH2+vRrLomDjTNJV193vakv3ku6a7K7vroSX89NP13XSlwCgRunx4tfDXx4fZ6ZfYWBZ2KMT5jeLq7Lgxprw8WGz9QM6RrQw3v0VhPfb5OR88kqnVdb330dHddSLyk+X/yrAflA/N5tsczT8CSwZBn6xBwB7HpItGWLa1PDKWmpmrLli167rnndObMGatprvriiy/0zTffWFyL7j75tsX4d8SnXlF2Tq6qVXC1uF6tgquiU6wfnTN1cFst2BGm7/80bkV/5Gyy3Jwd9OVTXTRjxSHlUT/cMnM+eLlYXK9WwUXRBT7VctXUR/y0YEeovv/T2Mkw58PTXTRj+UGLfPj3E531QNu71GfaHzrHg/dCxV9MN+ZDJTeL69UquSs6yfouPvEpGRr23jI5O9qriperziek6f1neiq8iN0dUi5d0elziWrga/3Tkney+IuXC6+Tkq3/7U4d1kELtp7S96bFnkfOJBnLwui7NWPZPqt1Ukp6pk5fSFYDH+u73NzpiisfopPSdexsssXrjp9P0qDO9YrlPsqy+NS/kAfDO2rBlpP63nRU85GoRLk5O+rLMXdrxtK9FmWhtreHerWuqeEfrS22eygP4i9mGPOhouUx5NUquhXeLlzM0LD3f7NsF57ucV27kJWdq7ALyZKkfadj1b5xdb08sJ3GfbG+WO6lrCq8PnJRdHIhfaRh7bVg62nL+sjFUV8+110zft1fRLuQogbVaResoU4qHfLrpAJ91RvVSf9aaayTPF10PvGS3n+ym8Jj2Insr0i4ZBy3VfV0trhe1dP5ulMhroq5eFlZuXnKveZv/mRMqqp7ucjR3qCsnDxFJKTroS+3y83JXp4uDoq5eEXfPtlBkQnsJGqNuW2oWKBOquhaeNvwaEct2HIqv046k2hsG57vYa6TjH3VJIvXHT+brEGd6xfPjZRhzCUBRvGpGdbLQkW3wvtIIzprwZ8n9P36o5KkI1EJcnNx0JcvBmjG4mDl5Unh0Rd131vL5ObsIC83J0UnpWvea30VHn2x2O+pLCqyj1RIPsRfzNCw6Sss+0ijulvtI/17TIAe6FBfff5vkc4lpBXLPZR1zKmWDn95vDBtuWU+PNVD4dHJFuks5jBCY9W+kY9eHtBO475kDqMgyoPtFT2HUch4YWh7Ldh2Wt+bPjhsnts2zSX51feWTwVX7Zw+yPwaB3s7dW9aXS/c11wVnvxeuXRoLeTnQ8E6qai5pE5asOWEvt9wTNI1c0kv9NSMJSHKy5OmP9FVH/+6V79sP21OU7uqpyYObsci0QIcPTxlsLNT5kXLuiTzYsp1u4ui+DCfZ3s88wSAssXOlj986NChVr+eeeYZTZs27YYLRCUpMzNTaWlpFl+l4ah5ScrKydW+iAQFNK9hvmYwSAHNq2vP6Tirr3F1drhusJNreuJlENuc/xXmfGhRMB9qFJ4PTg7m3/tV1vLh30901oD2tdXvgzWKjGcisyhZ2bnadypaAW3rmK8ZDFJA2zrac+x8ka+9kpWj8wlpcrC306BujbVyZ+GfEHJ3cVS9GhUVnUh+FJSVk6t94fEKaFnTfM1gkAJa+mrPyRirr3F1uvU6yd3ZQfV8vArt/N/piisfdp6MUeMCR/o0qlFRUfGptzP8ciErO1f7wuIU0KpAHrSqWXgeOBfRLhgsy8ITAU0Vm5Kh3/dG3ubIy5es7FztOx2jgLa1zdeM7UJt7Tle9JE8Fu1C10ZauSu0yPR2BoOcHe1vS9zlSX595Gu+ZjBIAS1qas+pW6mPjH3votsFT0UnWX9YcKejTiodsrJztS80VgGta5mvGQxSQOta2nMiusjXXsnK0fnES6Y6qaFW7g4r7nDLpaycPO0/m6KejfN3cjMYpLsbVVVQZJLV1+wOT1R9b3dd+2ffoJqHLqRcVlaOZRlJz8xRzMUrquDqqN5Nq2n14aLz9U6VlWOqk67rq96gTrpB27DzRLQaFziuvpFvBUXF0VctiLkkwKjQtrnVXYW2zVb7SDnW+0jpV7IVnZSuiu7O6uNXWyv3hAvXM4/b2ljpI93MuM3cR2qklbstx23/HhOgAf4N1e+txYqMYZFuYZhTLR3yy0KBOYw2f2EOYzdzGH8V5cH2zHNJBZ+3tfDVnlOxVl9jbJ8tr13bV910+Lza/2OpOv/zV/NXSGicft4eqs7//JUFolYUPpdURD+pqDk9Uz/JWl8qJzdPdgbGFAXZOTjIs049JR07Yr6Wl5urpGNH5NWgoQ0ju7Mwn2d7PPMEgLLFpjuJfvLJJzeVruBx8mXJzD+OadbobgoJj1dwWILG9m0mN2cHzd1q/BTW7Oe76XxSuqb8Ytw6e/W+swrs10wHIhO1x3RE2JTBbbV6/1lzY+nu7KAGPp7mn1G3qoda166kpLRMnUlkNxRrZv5xVLNGd1dIeIKCw+I19j5TPmy5mg/dTflgPApv9f4zCuzX3DIfHmmr1fvPmPPhs1Gd9ah/fQ39bKPSLmfJp4Jxp9KU9Cxd5kgMq2YuC9as1x5QyKloBZ+4oLGDOsjN2VFz1xmPbJn92gM6n5CmKd9vkSR1bFJDvlU8dCAsVjWreGrSyG6yMxj06eI95vf813P3aNXuUEXFpMi3iofeGtldObl5WvTnMZvcY2k3c9VBzXrxHoWExSn4dJzGPtDKmAemIx5nv3SPzide0hTT9v2r90Yp8IFWOhCeoD2m42ynDOug1XsjzWXhXyM7a1VIlKLiU+VbyV1vDWlvzIPtRU943smKIx/+s+qQNr07UBMHtdWSnWHq2LCqnunVVGNnbbXZfZZmM1ce1KyXAxQSGqfg07Ea+2BrYx6YPrk4e2yAMQ9+MtY3q4MjFfhQax0IjzflQQVNGd5Rq0MiLSbODAbpyYAmmv/nSeXkMoF5IzOXhWjWhH7GduFktMYObCc3F0fNXXdYkjR7Qj9ju/DDNklSxybVTe1CnGpW8dCkEV1kZ2fQp0uCzO/57qjuWhMcrjNxqfJ0ddKj9zTV3a1qqf/kJTa5x9Ju5qrDmvXi3QoJizfWR/e3MPaRTLupz36xp84nXdKUn43Hcxrro5Y6EJGQXxaGttfqvVH57cLjnbRqb5Si4tLkW8lNbw01tQs7aBcKQ51UOsz8ba9mvXKfQk7HKvhUtMb29zPWSabdyGa/ep+xTpq3Q5LUsbGPfCt76EC4qU4a7m/sqy4LNr+nu4ujGtTIn9Cs61NBret5Kyn1is4wqXmdrzaf1lcj2mnfmWTtjUzSiz0byN3JXvN3R0mS/juinS6kZOjdVca+/nc7wvVcj3r64OFW+mZrmBpU9dCEPo30zZb8hT69mlSVwWDQqdg01fd217sDWuhkTKr5PXG9mSsPadbLV/uqsfl91c2mOullU51kOjp+dUikAh801Umm4+anPNpRq0OiLPuq7w3UxIf9tGRHqDo2rKZnejfT2G+22Ow+SzPmksoWdxdHNahZ2fx93RoV1bqBj5JSM3QmloVvf8fM5fs1K7CPQkJjFXwqRmMfaiM3FwfNNe2ANTuwj7E++nGnJGl1UIQCB7Q11kcno9WgRkVNGdFZq4MizH2kPm1ry2CQTp5LUoMaFTV9VFedPJukuRuZRyrMzN/2atarfY19pJPRGjvA1EfaYFwUMfvVvjqfmKYpc7dLkjo2LjBue8zUR1qa30f67IVeevTuJho6bbnSMjLlY9r9KSX9ii5nMq9aEHOqpcPMX0M0a3w/hZyKsZzDWG8qCwXnMK4tC95X5zCkT5fkl4VC5zCmMIdRGMqD7c1cfVizXjDNJYXGaez9LY3t89W57Rfv1vnEdE1ZeM1c0v0tdSDSNJfk42Uxl5R2OUtHC5w6cOlKthLTLl93HflmrjigWWN7FZhLctDcTcYTHmaP663zCZc05addkkzjtofamMZtMaa5pM5aHZw/l7Q6OEJvPNJeZ+LTdPRMotrW81bgQ200dxNlwZpa996vY9/9T55168mrXgOdWf+HcjKvyLdbT0nS0W+/lnOlSmow+FFJUm52ti6dP2f+/ytJiUqNipS9i7PcqlWXJGVfvqyM2PyFdRnxcUqNipSju7tcqniX8B2WDczn2R7PPAGg7LDpItFhw4bp7NmzOnz48HWf5i4vFu+JkLeXs6YMbiufCq46GJWogR9vMB+XV6uyu8WD3A+WH1Se8jT1kbbyreSm+NQrWrXvjN5ess+cpl29Klr7z77m7z8c0VGSNG/raT0/e0cJ3VnZsnh3hLw9XSzz4aP1+flQxd3iEysf/GY8Un7qED9TPlzWqn1n9fbiveY0Y3o3lSStm9TP4meN/mabftzGIghrFm85Lu8Krpoysrt8KrvrYGisBk7+RbGmHSdrVfOyyAdnJwdNHdVD9apXVFpGptYEhenZj1Yp5dIVc5qa3p6a+0Z/VfZyUXxKhnYcOaue439UfAq7lVmzeGeYvL1cNWVoB/lUdNPByAQN/GC1Yk2/r1reHpZlYele5eXlaeqjHeRb2V3xFy9rVUik3l6YvyCrZmUPzR3XS5U9XRR/MUM7TsSo5+RfFZ9q/ShEFE8+hITF6dFP1+rd4Z30f4PbKSIuVRPn7tTPpqNhYGnxjlB5e7loyqMdjXkQEa+B01ZdkweeFkfXfrAkxJgHj3Uy5UGGVgVH6u0Feyzet1eru1S7qqd+MB23iqIt3nrC1C50k08lNx0Mi9PAKUvy24WqBdoFRwdNfaK76lWvoLSMLK0JDtOzn/xu0S5Ureimb1+7X9UruyvlUqYOR8Sp/+Ql2rifXRStWbwrzFgWhrS7pj76o/D6aNk+Y191WPv8+mhvlN5emD+JVrOyu+aOC1BlDxfFX7ysHSei1XPyctqFIlAnlQ6Lt50yts8j/I11Uni8Br7zq2JTTHWSt6fF2M3Z0UFTR3ZRPZ8KSrucpTUhEXr2szVKuZRpTtOuYTWtnTbE/P2Hz94tSZq34aien7muhO6s7Fi2/7y8PZz1f/2aqpqXsw6du6gh/9uluDRjPX9XJVeLOulc8mUN+Xqnpg1qqW0TA3Qh5bL+tyVMn23I3xXIy9VRUx5sLt+KLkpKz9KKA+f1/upjymbhdKEW7zTVScM65NdJ04voqy4xHik/dXjH/DopJMqiTgoJjdOjH6/VuyM66f8eaaeI2FRN/GGHft5GX9Ua5pLKlnZNfLX2s1Hm7z982fh7nvfHfj0/Y7mtwioXFm8/bWybh3eSTyV3HQyP08B3V+TXR1U9LeujX4KMfaQRneVb2cPURwrX2z/uMqep4Oakd5/ooppVPJSYelm/7QrV1Pm7lJ1TOk6mKo0WbztpHLeN6JI/bnt72TXjNs8C4zZ7TX28q3HcdjlLa4L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dNu877zwdr3a1K1g8p3/TqvKPTNK73i10ZEY/7fhXD03u1UA2ecZ43vVuoc0n47TzNCuu30xu2zTSdCw7W9oaFFlw29TOVpl5xiuMbVPu/XeKKS7BefoMQWfVobHlPoCDna0ysyzUFc2Iy60w1s8e2nrIQlloUsDf3N7C3/wmZaGci4MkKekiD+DzMvXbDuZpIwVGFDKOZJf/fnTZvN/2cKcGOhgSo+9meOvMch/t/fRpPfOg5ZeXgeKAMYzigXtS8fB3xlH3nYiRZ/0qpkmhdTzKqX/b2lofYIylnY2N7Gxt8sUqk/asRfa2NvKsV1lbD583HcvOlrYePqcOjQroO5+MlWe9PH1nz1paH5gbx4fb1dHBsHh9N6Wvzix8SnvnDtEzfSxPOgVjScWBncGgeuXKKDghzXQsW9LhhItq7OZ8S5/hYGsjW4NBaRa21/6Ls52trmdn6xI7NFnEc0/rIwYAULJYdbv5ChUqKDo6usCfR0dHy83NrdDPcHBwkIODg9mxDDsbXblaPLZGqeTiKDtbG8Wlmq8WE3cxU408yt70/La1K6h59fJ64fvAAtM42tlozqDm+ingnC5msjVVXu5lHYwxyLNiT1xqphpXvfnAV7s6FdWihpsmLs3duryKq5Ncnew1bUATvf7bEc38JVgPNK+qHyd0Vf//267dp9i6LS/38s7GOCSZb88Sl3RJjWtanoiy2T9cvkM6aPfhSIVFJcnLs468uzWW7Q1Pfd//cY/KOTsoaPF4Xbt+XbY2Npq9ZLt+3Hr0rl5PSeTu6mSMQZ5td+JSMtS4enmL5yzfE6ZKrk7aMnugDDLI3s5GCzYf17zfgkxphnWup9Z1Kqnbq6vvav5LK3fXMpbjkpyuxjXcLJ6zOTBSvoNaa3fO2+1eLWvKu1M92dpY9d2PEsO93F/3I/Ntd+KSLqlxAW/5bg4Il+9j7bX7yDmFRSfJq3VteXdpJFvb3PvRgZNRev6DdQo5l6iqFV0044mu2jxvlNpOXKK0DN5yzMu9XBnLcUhOV+OaBcThYLh8H22n3UfOKiw6OScODc3iMKxHE7Vu4KFuk5fd1fyXBrSRigfKQvHgXq6gdlKmGld3s3jO8j2hxnbSaze0kzaZt5MOnI7T81/sVEh0iqq6ldGMIW20efZAtX35F6VZ2BL9XlbR2XhPik8zvyfFp11Rw8qW+861KzqrW4VK+uXQeY362k91KznrXe/7ZWdr0AdbTkmSBresrpY1yqv/J7vv+jWUBqZ7UrKFe1JB7aTASPl6e2r30fMKi0mWV8ta8u5c36zfhn/G1GfIG5eUdDW+z/Ik6s2HIuU76K+45PQZOtenz3CLCi0LNQr4mweeka93W+0+klMWWtWSd+cGBZYFg0Ga91wv7Tl2Xsci2TovL1O/LTnvOFJ6weNIAeHyHdJeuw+fNfbbPOvIu2sjsxjUreamcQM9Nf+XA3rvx71q26iaPpjYR1eyrum7zUfu6jUBfwdjGMUD96Ti4e+Moy7fFaJK5Zy05a0hMhgkeztbLVh/WPN+DpAkpWVmad+JaL0yvL1OnktSbEq6hndvqI6Nqio0JuVuX1KJY+o7J+eNQUbBfec/TxtjMMdbBuXEYONRzVuV+9yzbhVXjevXTPPXHNZ7qwLVtn4VffBMV125el3f7Qi5i1dUMjGWZH2uDraytTEoJc/E2+QrV1WjrNMtfcboxlWVdDlLwRfSLP7c3sag0Y2r6s/oZGUUk3kPxQ3PPa2PGABAyWLVSaIODg7Kyir4wdjVq1fzTQDNy8fHR1OnTjU79vba43p73Yk7kkdrG9Opto6cT1HAmSSLP7ezMWjpsx1kMBj0rxWHijZz94gxXevq8Llk+Uckmo79NY7zR9B5fbzF2EENPpesjvUraVyP+kyAuEOmfbZJn015UEFfjVe2pLCoJC3dGKwx/XNXGB7as5ke791CT7/zm45FxKtlAw/Nm9hX0RfS9N2mw9bLfCnRvWlVTfdupcmL9+hAaLzqe5TT+091UvSjrfXuqkO6r6KL5j3VSQPfXqfLhbztiDtr2lc79dkLvRX08RPGshGToqVbj2tMb8tbTeKfm/blFn3mO0BBC8Ya/+bRyVq66bDZ1m4b/cNN/38kIl4HTkbr5DcTNKR7Y32zkfvRnTDti636bHJ/BX15Qxw2H9GYfsZtlO5zd9W88b01cMZP3JOKAG0k66EsFA/dm1bT9ME57aTTccZ20pjOpnaSJG0Myn0D/kikdOB0vE5+/LiGdKqrb7bzoOufsjFICZeuaOovwbqeLQWfT1HVck56sUd9fbDllKqXd9KbjzTX8K/26TIPVO6aaYt26LMX+yjo0ydz26ZbjmlMH8vb06NoTFu0U5+92FtBn9wYl+MFbk+Pf27awu36zKevgj4bc0P9fLTA7ek/mtBbzWtVUp//rCjajJZi0z7frM/+NUBBi567YRzpsNlW0DYGgw6eitHsJTslSUGhcWpex13jHm7NhCyUGoxhFA/ck4qH7s1raPqQtpq8YIcOhMSqfrXyen9sd0UPu6R3fzK+9Prs/zbpS58+Clv8jK5eu65DYfFasfuUPOtXvsmn41Z0b1ZN0x/11ORFu3XgVJzqVy2n95/poughbfTuzwclSTY2Bh0MjdfsH/wkSUERF9S8VgWN69eMSaJ3CGNJxcvgepXVtZqbXvMLU9b17Hw/tzVIL7WuLYMMWnD0vIVPwN/Fc0/rIwYAYD1WnSQqSS+//LIyMjIs/qxMmTI3Pf+TTz7RggULzI7FP/TeHcnbnXDh0mVdvXZdVco5mh2v4uqk2Dyri+bl7GCrIW3v01trjlv8uZ2NQcue7aBaFZ318PzdrCJagIS0K8YYuOaJQTknxaQUvp2Xs4OthrWvqTmrzVelTEi7oqxr13U8OtXs+MmYi+qSszQ6zCWkpBvjUMF8e80qFVwUk2d10RvPGf7az3K0t1WlcmUUdSFNbz7npfDoZFOat8f11vvL9+qn7cckSUcj4lWrSnlNf7wLk0TzSLiYaYxBefN7a5XyZRSTbPk+PHtYW/2w+7S+zpnEcPRskpwd7fTpc90099dD8qznLo/yZbT37cGmc+xsbdStSVVNeKCZyj/1ta5n5+/gIlfCxQzLcXFzVkyeVWtM56Rmavi7a41lw9VJUYmX9OaTnRUey9vttyIh9a/7kfm2L4XfjzI0fM4q8/vRsz0VXsiKAimXLuv0+UTVr255paF7XUJqhuU4uDkrJrGAOKRmaPicX83j8EwPUxw8G3rIo4KL9n78lOkcO1sbdWtRUxMeaaPy3v+n6xYG3e5VtJGKB8pC8ZCQWlA7yangdtLwtvph12l9ve2kpJx2kpO9qZ1kqQmUkn5Fp6NTVL9quTt+DSVdYrrxnlS5rPk9qXJZB8WlWe47x1409rdv/Od8Ki5NHuWcZG9rUKsa5VXZ1VGbJnU3/dzO1kad61TUs53rqObMtaIomDPdk9ws3JMKaielZmj4O3+Yt02f6krb9A4y9RnyxqW8s2KSCuozZGj4O2vyxKULcblFhZaFAvtpGRr+9u/mf/Mx3Sz+zT8c76WH2tVT3/+u0PkCVg6615n6bW55x5EKuR+lZGj463n6bWPN+20xiWk6fibB7LwTZy9ocLfGd/4igDuAMYzigXtS8fB3xlFnj+qoH3ac1Nebc54fRF6Qs5OdPp3opbkr/ZWdLYXHpOqBmavk7Gincs4OiklK17Kp/RUek2rxM+9lpr6zW94YFPKMYUR7/bDzlL7ealzc5+jZRGPf+fnumvvLQWVnSzFJ6Tp+znyxnBPnkjW4Y727cyElHGNJ1nfxyjVdu56t8g7mUy3cHOyUfLnwnWMG1XXXo/Wq6A2/MJ25mH8M9q8JopXL2Os1vzBWES0Ezz2tjxgAQMli1T2m9u3bp/r166tFixYWv+rXr6/9+/cX+hlXrlxRWlqa2Vdx2WpekrKuZSvwbLJ6Ncp949BgkHo1qiy/G1ZdsuRRzxpytLPRjwfO5vvZXxNE61cuq0c+2a3EdLaAKUjWtesKjEySV1MP0zGDQfJqUkV+YYVv5/VY25pytLPVD/vP5PvMgIhENfIw34q1YZWyiiygA3avy7p6XYEh0fLyrGM6ZjBIXp515Hes8LfgLmddU9SFNNnZ2mhwt8b6Y2/um6NlnOzydUyvXb8udtDLL+vadQWGJ8ireTXTMYNB8mpeXX6n4iyeU8bRTtfz3FL/+nsbZNC2I1Fq+/Iv6vjKr6avgNB4/fhnqDq+8iuN9FuQdfW6AkPj5NWypumYwSB53X+f/E7GFHru5axrikq8ZCwbnevrD7/wQtPDKOvqdQWeipFX69qmYwaD5NW6tvyORxV6rtn9qGsj/bH3VIFpXZzsVbeam2ISeehrSdbV6wo8HSOvVhbicOI247DvtCRp26EzajtxiTr6fGP6CgiJ1o/bj6mjzzcMZOZBG6l4oCwUD6Z2UovqpmPGdlIN+Z2KtXhOGQe7fG2d6zkNJ4Msby3s4minuh6uikmyPEh6L8u6lq3g8ynq3iB3QrnBIHVv4C7/AnbWOHAmUXXcXWS44c9dv3JZxaRmKutatnaeTlDPD3eoz/xdpq/As8n6+dB59Zm/iwmiFhTYNm1Z8/bapl0a6I/9YXc7u/eMwuMSXei55n2GBvrDj7jcCmP9HCuvVhb+5idu42/epaH+2B9q9vMPx3tpUKcGGjBzpc7EMvmkIKZ+m2feNtLfGUfK7bftPXZejfJsw9qwRkVFxhELFE+MYRQP3JOKh78zjmoc387Tb7uWM75tMO+3pV++qpikdLm5OKqvZy3GWi3IunZdgWHx8mpRw3TMYJC8WtSQX0gBfWfHm/ed956MUaM829U3rF5ekfEX72DuSw/Gkqzvana2wlIzdH+lsqZjBkn3u5fVyQImrUuSd93KGlLfQ2/6hys0Nf/Y0F8TRKu5OOqNA2FKYxXFQvHc0/qIAQCULFZdSXTYsGHW/PVF5pNtp/Xl6LY6GJmsgDNJerF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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[21], line 55\u001b[0m\n\u001b[1;32m 39\u001b[0m fig\u001b[38;5;241m.\u001b[39mupdate_layout(\n\u001b[1;32m 40\u001b[0m title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3D Volatility-RSI Timeline\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 41\u001b[0m scene\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 50\u001b[0m width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1200\u001b[39m\n\u001b[1;32m 51\u001b[0m )\n\u001b[1;32m 53\u001b[0m fig\u001b[38;5;241m.\u001b[39mshow()\n\u001b[0;32m---> 55\u001b[0m \u001b[43mplot_3d_volatility_interactive\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[21], line 53\u001b[0m, in \u001b[0;36mplot_3d_volatility_interactive\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m 29\u001b[0m fig\u001b[38;5;241m.\u001b[39madd_trace(go\u001b[38;5;241m.\u001b[39mScatter3d(\n\u001b[1;32m 30\u001b[0m x\u001b[38;5;241m=\u001b[39mdates,\n\u001b[1;32m 31\u001b[0m y\u001b[38;5;241m=\u001b[39mticker_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVolatility\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 36\u001b[0m marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m 37\u001b[0m ))\n\u001b[1;32m 39\u001b[0m fig\u001b[38;5;241m.\u001b[39mupdate_layout(\n\u001b[1;32m 40\u001b[0m title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3D Volatility-RSI Timeline\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 41\u001b[0m scene\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 50\u001b[0m width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1200\u001b[39m\n\u001b[1;32m 51\u001b[0m )\n\u001b[0;32m---> 53\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/plotly/basedatatypes.py:3410\u001b[0m, in \u001b[0;36mBaseFigure.show\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3377\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 3378\u001b[0m \u001b[38;5;124;03mShow a figure using either the default renderer(s) or the renderer(s)\u001b[39;00m\n\u001b[1;32m 3379\u001b[0m \u001b[38;5;124;03mspecified by the renderer argument\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3406\u001b[0m \u001b[38;5;124;03mNone\u001b[39;00m\n\u001b[1;32m 3407\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 3408\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpio\u001b[39;00m\n\u001b[0;32m-> 3410\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/plotly/io/_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 390\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 391\u001b[0m )\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 394\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 395\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 396\u001b[0m )\n\u001b[1;32m 398\u001b[0m ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n", - "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed" - ] - } - ], + "outputs": [], "source": [ "df = pd.read_csv('allStock_data.csv', parse_dates=['Datetime'])\n", "\n", @@ -366,162 +173,213 @@ "\n", "\n", "def plot_3d_volatility_interactive(df):\n", + " # Set the renderer for Plotly to open in the browser\n", + " pio.renderers.default = \"browser\"\n", + "\n", + " # Calculate the 30-day rolling volatility for each stock\n", + " df['Volatility'] = df.groupby('Ticker')['Close'].pct_change().rolling(window=30).std()\n", + "\n", + " # Calculate the mean volatility per ticker and sort by it\n", + " volatility_by_ticker = df.groupby('Ticker')['Volatility'].mean().sort_values(ascending=False)\n", + "\n", + " # Get the 5 most volatile tickers\n", + " top_5_tickers = volatility_by_ticker.head(5).index\n", + "\n", + " # Initialize the figure\n", " fig = go.Figure()\n", "\n", - " for ticker in df['Ticker'].unique():\n", + " # Loop through the top 5 most volatile tickers and create a trace\n", + " for ticker in top_5_tickers:\n", " ticker_df = df[df['Ticker'] == ticker].copy()\n", - " ticker_df = ticker_df.sort_index()\n", + " ticker_df = ticker_df.sort_values('Datetime')\n", " if len(ticker_df) < 2:\n", - " continue # skip if not enough data\n", + " continue\n", "\n", - " dates = pd.to_datetime(ticker_df.index).astype(np.int64) // 10**9 # convert datetime to numeric\n", + " # Convert Datetime to Unix timestamp for plotting on the x-axis\n", + " base_dates = pd.to_datetime(ticker_df['Datetime']).astype(np.int64) // 10**9\n", + "\n", + " # Create custom data to include Ticker and DateTime in hover info\n", + " custom_data = np.stack([\n", + " ticker_df['Datetime'].astype(str),\n", + " np.full(len(ticker_df), ticker)\n", + " ], axis=-1)\n", + "\n", + " # Add the 3D scatter trace for this ticker\n", " fig.add_trace(go.Scatter3d(\n", - " x=dates,\n", - " y=ticker_df['Volatility'],\n", - " z=ticker_df['RSI'],\n", + " x=base_dates, # Time on the x-axis\n", + " y=ticker_df['Volatility'], # Volatility on the y-axis\n", + " z=ticker_df['RSI'], # RSI on the z-axis\n", " mode='lines+markers',\n", " name=ticker,\n", + " customdata=custom_data, # Include Ticker and DateTime in hover data\n", + " hovertemplate=(\n", + " \"Ticker: %{customdata[1]}
\" +\n", + " \"Datetime: %{customdata[0]}
\" +\n", + " \"Volatility: %{y:.3f}
\" +\n", + " \"RSI: %{z}
\" +\n", + " \"\"\n", + " ),\n", " line=dict(width=4),\n", " marker=dict(size=3)\n", " ))\n", "\n", + " # Update the layout of the plot\n", " fig.update_layout(\n", - " title='3D Volatility-RSI Timeline',\n", + " title='3D Volatility-RSI Timeline for 5 Most Volatile Stocks',\n", " scene=dict(\n", - " xaxis_title='DateTime (numeric)',\n", - " yaxis_title='Volatility',\n", + " xaxis_title='Time',\n", + " yaxis_title='Volatility (30-day rolling)',\n", " zaxis_title='RSI',\n", - " camera_eye=dict(x=1.2, y=1.2, z=0.8),\n", - " aspectratio=dict(x=1.5, y=1, z=0.7)\n", + " camera_eye=dict(x=1.5, y=1.5, z=1),\n", + " aspectratio=dict(x=2, y=1, z=0.7)\n", " ),\n", " legend=dict(x=0.85, y=0.95),\n", " height=800,\n", " width=1200\n", " )\n", "\n", + " # Show the plot\n", " fig.show()\n", - " \n", - "plot_3d_volatility_interactive(df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Dark Mode Setup\n", - "rcParams['figure.facecolor'] = '#1a1a1a'\n", - "rcParams['axes.facecolor'] = '#2a2a2a'\n", - "rcParams['axes.edgecolor'] = 'white'\n", - "rcParams['text.color'] = 'white'\n", - "rcParams['xtick.color'] = 'white'\n", - "rcParams['ytick.color'] = 'white'\n", - "\n", - "# -- 3D VOLATILITY PLOT --\n", - "def plot_3d_volatility(df):\n", - " fig = plt.figure(figsize=(16, 10))\n", - " ax = fig.add_subplot(111, projection='3d')\n", "\n", - " for ticker in df['Ticker'].unique():\n", - " ticker_df = df[df['Ticker'] == ticker].copy()\n", - " ticker_df = ticker_df.sort_index()\n", - " if len(ticker_df) < 2:\n", - " continue # skip if not enough data\n", - "\n", - " dates = pd.to_datetime(ticker_df.index).astype(np.int64) // 10**9 # numeric for 3D plot\n", - " ax.plot(dates,\n", - " ticker_df['Volatility'],\n", - " ticker_df['RSI'],\n", - " label=ticker,\n", - " linewidth=2)\n", - "\n", - " ax.set_xlabel('DateTime (numeric)', fontsize=12)\n", - " ax.set_ylabel('Volatility', fontsize=12)\n", - " ax.set_zlabel('RSI', fontsize=12)\n", - " ax.set_title('3D Volatility-RSI Timeline', fontsize=16)\n", - " ax.legend()\n", - " ax.view_init(elev=25, azim=45)\n", - " plt.tight_layout()\n", - " plt.show()\n" + "# Call the function with your DataFrame\n", + "plot_3d_volatility_interactive(df)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/30 [00:00 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mfetch_all_alpha_stocks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtickers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m plot_correlation_heatmap(df)\n", - "Cell \u001b[0;32mIn[9], line 75\u001b[0m, in \u001b[0;36mfetch_all_alpha_stocks\u001b[0;34m(tickers, output_csv)\u001b[0m\n\u001b[1;32m 72\u001b[0m all_data\u001b[38;5;241m.\u001b[39mappend(df)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;66;03m# Avoid hitting the API rate limit\u001b[39;00m\n\u001b[0;32m---> 75\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m12\u001b[39m) \u001b[38;5;66;03m# Sleep for 12 seconds between requests to avoid rate limits\u001b[39;00m\n\u001b[1;32m 77\u001b[0m combined_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat(all_data)\n\u001b[1;32m 78\u001b[0m combined_df\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mnames \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDatetime\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "/var/folders/t_/1j24y4gj22q4lrhhw9bl5n600000gn/T/ipykernel_97772/3021605551.py:20: MatplotlibDeprecationWarning:\n", + "\n", + "The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", + "\n" ] } ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ - "df = fetch_all_alpha_stocks(tickers)\n", + "import plotly.graph_objects as go\n", + "import pandas as pd\n", + "import numpy as np\n", + "import plotly.io as pio\n", + "import ta # Assuming 'ta' is the technical analysis library\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.cm as cm\n", "\n", - "for ticker in df:\n", - " animate_bollinger(ticker, df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_3d_volatility(df)" + "def plot_3d_bollinger_and_stock_price(df):\n", + " # Set the renderer for Plotly to open in the browser\n", + " pio.renderers.default = \"browser\"\n", + "\n", + " # Calculate Bollinger Bands\n", + " bb = ta.volatility.BollingerBands(close=df['Close'])\n", + " df['BB_High'] = bb.bollinger_hband()\n", + " df['BB_Low'] = bb.bollinger_lband()\n", + "\n", + " # Generate a color map for unique colors for each stock\n", + " ticker_list = df['Ticker'].unique()\n", + " color_map = cm.get_cmap('tab10', len(ticker_list)) # Use 'tab10' colormap for up to 10 colors\n", + " ticker_colors = {ticker: color_map(i) for i, ticker in enumerate(ticker_list)}\n", + "\n", + " # Initialize the figure\n", + " fig = go.Figure()\n", + "\n", + " # Loop through each ticker and create a trace\n", + " for ticker in ticker_list:\n", + " ticker_df = df[df['Ticker'] == ticker].copy()\n", + " ticker_df = ticker_df.sort_values('Datetime')\n", + " if len(ticker_df) < 2:\n", + " continue\n", + "\n", + " # Convert Datetime to Unix timestamp for plotting on the x-axis\n", + " base_dates = pd.to_datetime(ticker_df['Datetime']).astype(np.int64) // 10**9\n", + "\n", + " # Get the color assigned to this ticker\n", + " color = ticker_colors[ticker]\n", + " hex_color = f'rgb({int(color[0]*255)}, {int(color[1]*255)}, {int(color[2]*255)})' # Convert to rgb for Plotly\n", + "\n", + " # Create custom data to include Ticker and DateTime in hover info\n", + " custom_data = np.stack([\n", + " ticker_df['Datetime'].astype(str),\n", + " np.full(len(ticker_df), ticker)\n", + " ], axis=-1)\n", + "\n", + " # Add the 3D scatter trace for Upper Bollinger Band\n", + " fig.add_trace(go.Scatter3d(\n", + " x=base_dates, # Time on the x-axis\n", + " y=ticker_df['BB_High'], # Upper Bollinger Band on the y-axis\n", + " z=ticker_df['Close'], # Stock Price on the z-axis\n", + " mode='lines',\n", + " name=f'{ticker} - BB High',\n", + " line=dict(color=hex_color, width=4), # Use unique color for the upper Bollinger Band\n", + " customdata=custom_data, # Include Ticker and DateTime in hover data\n", + " hovertemplate=(\n", + " \"Ticker: %{customdata[1]}
\" +\n", + " \"Datetime: %{customdata[0]}
\" +\n", + " \"BB High: %{y:.3f}
\" +\n", + " \"Stock Price: %{z:.3f}
\" +\n", + " \"\"\n", + " )\n", + " ))\n", + "\n", + " # Add the 3D scatter trace for Lower Bollinger Band (same color as Upper)\n", + " fig.add_trace(go.Scatter3d(\n", + " x=base_dates, # Time on the x-axis\n", + " y=ticker_df['BB_Low'], # Lower Bollinger Band on the y-axis\n", + " z=ticker_df['Close'], # Stock Price on the z-axis\n", + " mode='lines',\n", + " name=f'{ticker} - BB Low',\n", + " line=dict(color=hex_color, width=4), # Same color as the upper Bollinger Band\n", + " customdata=custom_data, # Include Ticker and DateTime in hover data\n", + " hovertemplate=(\n", + " \"Ticker: %{customdata[1]}
\" +\n", + " \"Datetime: %{customdata[0]}
\" +\n", + " \"BB Low: %{y:.3f}
\" +\n", + " \"Stock Price: %{z:.3f}
\" +\n", + " \"\"\n", + " )\n", + " ))\n", + "\n", + " # Add the 3D scatter trace for Stock Price\n", + " fig.add_trace(go.Scatter3d(\n", + " x=base_dates, # Time on the x-axis\n", + " y=ticker_df['Close'], # Stock Price on the y-axis\n", + " z=ticker_df['Close'], # Stock Price on the z-axis\n", + " mode='lines',\n", + " name=f'{ticker} - Stock Price',\n", + " line=dict(color=hex_color, width=4), # Use the same color for stock price\n", + " customdata=custom_data, # Include Ticker and DateTime in hover data\n", + " hovertemplate=(\n", + " \"Ticker: %{customdata[1]}
\" +\n", + " \"Datetime: %{customdata[0]}
\" +\n", + " \"Stock Price: %{y:.3f}
\" +\n", + " \"\"\n", + " )\n", + " ))\n", + "\n", + " # Update the layout of the plot\n", + " fig.update_layout(\n", + " title='3D Stock Price vs Bollinger Bands (Upper and Lower)',\n", + " scene=dict(\n", + " xaxis_title='Time',\n", + " yaxis_title='Price / Bollinger Bands',\n", + " zaxis_title='Stock Price',\n", + " camera_eye=dict(x=1.5, y=1.5, z=1),\n", + " aspectratio=dict(x=2, y=1, z=0.7)\n", + " ),\n", + " legend=dict(x=0.85, y=0.95),\n", + " height=800,\n", + " width=1200\n", + " )\n", + "\n", + " # Show the plot\n", + " fig.show()\n", + "\n", + "# Call the function with your DataFrame\n", + "plot_3d_bollinger_and_stock_price(df)\n" ] }, { @@ -578,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +466,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.0" } }, "nbformat": 4, From d039e14d948e9ae042d05d95bb7f35b73e0e75a0 Mon Sep 17 00:00:00 2001 From: Amar723 <140028404+Amar723@users.noreply.github.com> Date: Fri, 9 May 2025 15:03:11 +1000 Subject: [PATCH 05/10] commiting LSTM model 1 - Amar --- src/LSTM_Model.ipynb | 500 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 500 insertions(+) create mode 100644 src/LSTM_Model.ipynb diff --git a/src/LSTM_Model.ipynb b/src/LSTM_Model.ipynb new file mode 100644 index 0000000..bc5bfa5 --- /dev/null +++ b/src/LSTM_Model.ipynb @@ -0,0 +1,500 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6467be29", + "metadata": {}, + "source": [ + "Import Statements" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2acc5f90", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from sklearn.preprocessing import RobustScaler\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from torch.utils.data import DataLoader, TensorDataset\n", + "from tqdm import tqdm\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "7e7db2d5", + "metadata": {}, + "source": [ + "Small Data Manipulation" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "edbaaf13", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Open High Low Close Volume Ticker \\\n", + "Datetime \n", + "2025-03-17 17:00:00 604.3100 609.5460 562.5385 604.2600 524850.0 META \n", + "2025-03-17 17:00:00 60.4476 60.6660 60.4217 60.4217 446.0 CSCO \n", + "2025-03-17 17:00:00 237.2000 391.0340 195.4941 237.1300 284585.0 TSLA \n", + "2025-03-17 17:00:00 104.8350 111.3073 97.5796 104.9650 44201.0 AMD \n", + "2025-03-17 17:00:00 398.6400 416.0327 397.5400 399.0000 1110.0 ADBE \n", + "... ... ... ... ... ... ... \n", + "2025-04-10 19:00:00 382.0200 383.4800 377.4500 380.0000 72151.0 MSFT \n", + "2025-04-10 19:00:00 88.8200 88.9000 85.6700 88.0337 62420.0 AMD \n", + "2025-04-10 19:00:00 191.0000 191.2000 189.0000 189.2000 153517.0 AAPL \n", + "2025-04-10 19:00:00 255.1200 258.2400 248.9500 253.0300 1235916.0 CRM \n", + "2025-04-10 19:00:00 133.3500 134.4200 131.2000 131.3561 1541190.0 ORCL \n", + "\n", + " Returns Log_Returns SMA_5 EMA_2 ... \\\n", + "Datetime ... \n", + "2025-03-17 17:00:00 0.001243 0.001242 219.96176 604.130827 ... \n", + "2025-03-17 17:00:00 -0.001085 -0.001085 201.31134 60.444087 ... \n", + "2025-03-17 17:00:00 -0.000379 -0.000379 243.57334 237.251496 ... \n", + "2025-03-17 17:00:00 0.001097 0.001096 240.60534 104.928920 ... \n", + "2025-03-17 17:00:00 0.003774 0.003766 281.15534 398.612097 ... \n", + "... ... ... ... ... ... \n", + "2025-04-10 19:00:00 -0.008713 -0.008751 171.25600 380.922543 ... \n", + "2025-04-10 19:00:00 -0.009856 -0.009905 177.54674 88.275050 ... \n", + "2025-04-10 19:00:00 -0.009839 -0.009888 165.84274 189.755541 ... \n", + "2025-04-10 19:00:00 -0.010171 -0.010223 185.99674 253.889851 ... \n", + "2025-04-10 19:00:00 -0.019731 -0.019928 208.32396 132.169405 ... \n", + "\n", + " MACD_Diff BB_High BB_Low Volume_EMA \\\n", + "Datetime \n", + "2025-03-17 17:00:00 -0.060178 618.808237 589.068633 1.297782e+06 \n", + "2025-03-17 17:00:00 0.048045 60.642216 59.550324 1.673223e+06 \n", + "2025-03-17 17:00:00 -0.883215 255.584140 231.656020 8.687349e+06 \n", + "2025-03-17 17:00:00 0.141079 106.750061 98.960129 3.235962e+06 \n", + "2025-03-17 17:00:00 1.158275 399.582292 388.309708 4.443507e+05 \n", + "... ... ... ... ... \n", + "2025-04-10 19:00:00 -0.782430 392.129908 372.410332 3.295464e+06 \n", + "2025-04-10 19:00:00 -0.625805 97.879752 84.601608 5.193740e+06 \n", + "2025-04-10 19:00:00 -0.577644 198.508194 185.702446 9.705006e+06 \n", + "2025-04-10 19:00:00 -0.824333 266.644745 249.270005 1.126969e+06 \n", + "2025-04-10 19:00:00 -0.580931 140.167977 130.058173 1.535120e+06 \n", + "\n", + " Volatility Return SMA_20 Momentum RSI_14 \\\n", + "Datetime \n", + "2025-03-17 17:00:00 2.746628 2.079032 219.832770 498.1949 60.903191 \n", + "2025-03-17 17:00:00 1.414078 -0.900007 212.154355 -176.7983 39.114092 \n", + "2025-03-17 17:00:00 1.605892 2.924583 214.335315 -42.7736 50.907117 \n", + "2025-03-17 17:00:00 1.655972 -0.557353 199.708565 -283.9950 50.002495 \n", + "2025-03-17 17:00:00 1.719390 2.801267 211.449565 245.3461 57.407143 \n", + "... ... ... ... ... ... \n", + "2025-04-10 19:00:00 5.860938 16.023317 189.345750 209.8900 54.202133 \n", + "2025-04-10 19:00:00 5.934532 -0.768332 186.042935 -261.7663 49.153586 \n", + "2025-04-10 19:00:00 5.862943 1.149177 190.113935 82.9500 50.958863 \n", + "2025-04-10 19:00:00 5.861230 0.337368 201.770435 -287.9600 49.952877 \n", + "2025-04-10 19:00:00 5.847489 -0.480867 189.171240 -0.8239 49.306697 \n", + "\n", + " Momentum_5 \n", + "Datetime \n", + "2025-03-17 17:00:00 484.6100 \n", + "2025-03-17 17:00:00 -93.2521 \n", + "2025-03-17 17:00:00 211.3100 \n", + "2025-03-17 17:00:00 -14.8400 \n", + "2025-03-17 17:00:00 202.7500 \n", + "... ... \n", + "2025-04-10 19:00:00 200.6050 \n", + "2025-04-10 19:00:00 31.4537 \n", + "2025-04-10 19:00:00 -58.5200 \n", + "2025-04-10 19:00:00 100.7700 \n", + "2025-04-10 19:00:00 111.6361 \n", + "\n", + "[4361 rows x 23 columns]\n" + ] + } + ], + "source": [ + "def compute_rsi(series, period=14):\n", + " delta = series.diff()\n", + " gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()\n", + " loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()\n", + " rs = gain / loss\n", + " return 100 - (100 / (1 + rs))\n", + "\n", + "def compute_macd(series, fast=12, slow=26, signal=9):\n", + " ema_fast = series.ewm(span=fast, adjust=False).mean()\n", + " ema_slow = series.ewm(span=slow, adjust=False).mean()\n", + " macd_line = ema_fast - ema_slow\n", + " signal_line = macd_line.ewm(span=signal, adjust=False).mean()\n", + " macd_hist = macd_line - signal_line\n", + " return macd_hist.fillna(0) # Return MACD histogram\n", + "\n", + "# === 1. LOAD & ENHANCE DATA ===\n", + "def load_your_data(path):\n", + " df = pd.read_csv(path, parse_dates=['Datetime'], index_col='Datetime')\n", + " df['Return'] = df['Close'].pct_change()\n", + " df['SMA_5'] = df['Close'].rolling(5).mean()\n", + " df['SMA_20'] = df['Close'].rolling(20).mean()\n", + " df['Volatility'] = df['Return'].rolling(10).std()\n", + " df['Momentum'] = df['Close'] - df['Close'].shift(10)\n", + " df['RSI_14'] = compute_rsi(df['Close'], period=14)\n", + " df['Momentum_5'] = df['Close'] - df['Close'].shift(5)\n", + " df = df.dropna()\n", + " return df\n", + "\n", + "# === 2. PREPROCESS DATA ===\n", + "def preprocess_data(df, sequence_length=30):\n", + " features = ['Close', 'SMA_5', 'SMA_20', 'Volatility', 'Momentum']\n", + " scaler = MinMaxScaler()\n", + " data = scaler.fit_transform(df[features])\n", + " X, y = [], []\n", + " for i in range(sequence_length, len(data)):\n", + " X.append(data[i-sequence_length:i])\n", + " # Simple label: 0 = sell, 1 = hold, 2 = buy\n", + " future_return = df['Close'].iloc[i+1] - df['Close'].iloc[i] if i+1 < len(df) else 0\n", + " if future_return > 0.5:\n", + " y.append(2) # Buy\n", + " elif future_return < -0.5:\n", + " y.append(0) # Sell\n", + " else:\n", + " y.append(1) # Hold\n", + " return np.array(X), np.array(y)\n", + "\n", + "# Removing NaN values\n", + "def clean_data(df):\n", + " df = df.replace([np.inf, -np.inf], np.nan)\n", + " df = df.ffill().bfill()\n", + "\n", + " for col in df.select_dtypes(include=np.number):\n", + " q1 = df[col].quantile(0.01)\n", + " q99 = df[col].quantile(0.99)\n", + " df[col] = df[col].clip(q1, q99)\n", + " assert not df.isnull().any().any(), \"NaN values still present after cleaning\"\n", + " return df\n", + "\n", + "\n", + "def create_sequences(data, labels, seq_length):\n", + " X_seqs, y_seqs = [], []\n", + " for i in range(seq_length, len(data)):\n", + " X_seqs.append(data[i - seq_length:i])\n", + " y_seqs.append(labels[i])\n", + " return torch.FloatTensor(X_seqs), torch.LongTensor(y_seqs)\n", + "\n", + "\n", + "df = load_your_data('allStock_data.csv')\n", + "df = clean_data(df)\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "id": "12c9fc5e", + "metadata": {}, + "source": [ + "Model" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "53d833af", + "metadata": {}, + "outputs": [], + "source": [ + "class LSTMModel(nn.Module):\n", + " def __init__(self, input_size, hidden_size=64, num_layers=2, num_classes=3):\n", + " super(LSTMModel, self).__init__()\n", + " self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n", + " self.fc = nn.Linear(hidden_size, num_classes)\n", + "\n", + " def forward(self, x):\n", + " out, _ = self.lstm(x)\n", + " out = self.fc(out[:, -1, :]) # Last time step\n", + " return out\n" + ] + }, + { + "cell_type": "markdown", + "id": "d2912ecd", + "metadata": {}, + "source": [ + "Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "92f78a47", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30 - Test Accuracy: 0.4305\n", + "Epoch 2/30 - Test Accuracy: 0.4294\n", + "Epoch 3/30 - Test Accuracy: 0.4237\n", + "Epoch 4/30 - Test Accuracy: 0.4294\n", + "Epoch 5/30 - Test Accuracy: 0.4282\n", + "Epoch 6/30 - Test Accuracy: 0.4351\n", + "Epoch 7/30 - Test Accuracy: 0.4363\n", + "Epoch 8/30 - Test Accuracy: 0.4363\n", + "Epoch 9/30 - Test Accuracy: 0.4351\n", + "Epoch 10/30 - Test Accuracy: 0.4397\n", + "Epoch 11/30 - Test Accuracy: 0.4374\n", + "Epoch 12/30 - Test Accuracy: 0.4524\n", + "Epoch 13/30 - Test Accuracy: 0.4581\n", + "Epoch 14/30 - Test Accuracy: 0.4546\n", + "Epoch 15/30 - Test Accuracy: 0.4719\n", + "Epoch 16/30 - Test Accuracy: 0.4501\n", + "Epoch 17/30 - Test Accuracy: 0.4638\n", + "Epoch 18/30 - Test Accuracy: 0.4569\n", + "Epoch 19/30 - Test Accuracy: 0.4558\n", + "Epoch 20/30 - Test Accuracy: 0.4524\n", + "Epoch 21/30 - Test Accuracy: 0.4719\n", + "Epoch 22/30 - Test Accuracy: 0.4638\n", + "Epoch 23/30 - Test Accuracy: 0.4661\n", + "Epoch 24/30 - Test Accuracy: 0.4856\n", + "Epoch 25/30 - Test Accuracy: 0.4627\n", + "Epoch 26/30 - Test Accuracy: 0.4569\n", + "Epoch 27/30 - Test Accuracy: 0.4765\n", + "Epoch 28/30 - Test Accuracy: 0.4604\n", + "Epoch 29/30 - Test Accuracy: 0.4799\n", + "Epoch 30/30 - Test Accuracy: 0.4845\n" + ] + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", + "from sklearn.model_selection import train_test_split\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "def train_lstm_model(df, feature_columns, target_column='Returns', seq_length=10, epochs=30, batch_size=64, learning_rate=1e-3, device='cuda' if torch.cuda.is_available() else 'cpu'):\n", + " \"\"\"\n", + " Train an LSTM model on financial time series data (single-ticker version).\n", + " \"\"\"\n", + " \n", + " trainingLoss = []\n", + " valueAccuracy = []\n", + " \n", + " # Sort by datetime only\n", + " df = df.sort_index()\n", + "\n", + " # Normalize features\n", + " scaler = StandardScaler()\n", + " X_scaled = scaler.fit_transform(df[feature_columns])\n", + " y = df[target_column].values\n", + "\n", + " # Convert regression target into 3-class classification\n", + " y_class = np.digitize(y, bins=[-0.001, 0.001]) # 0: down, 1: neutral, 2: up\n", + "\n", + " X_all = []\n", + " y_all = []\n", + " for i in range(seq_length, len(X_scaled)):\n", + " X_all.append(X_scaled[i - seq_length:i])\n", + " y_all.append(y_class[i])\n", + "\n", + " X_all = np.array(X_all)\n", + " y_all = np.array(y_all)\n", + "\n", + " # Split into train/test\n", + " X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=0.2, random_state=42, stratify=y_all)\n", + "\n", + " # Convert to PyTorch tensors\n", + " X_train = torch.tensor(X_train, dtype=torch.float32).to(device)\n", + " y_train = torch.tensor(y_train, dtype=torch.long).to(device)\n", + " X_test = torch.tensor(X_test, dtype=torch.float32).to(device)\n", + " y_test = torch.tensor(y_test, dtype=torch.long).to(device)\n", + "\n", + " # Define model, loss, optimizer\n", + " input_size = X_train.shape[2]\n", + " model = LSTMModel(input_size=input_size).to(device)\n", + " criterion = nn.CrossEntropyLoss()\n", + " optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n", + "\n", + " # Training loop\n", + " for epoch in range(epochs):\n", + " model.train()\n", + " epochLoss = 0\n", + " permutation = torch.randperm(X_train.size(0))\n", + " \n", + " \n", + " for i in range(0, X_train.size(0), batch_size):\n", + " indices = permutation[i:i+batch_size]\n", + " batch_x, batch_y = X_train[indices], y_train[indices]\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(batch_x)\n", + " loss = criterion(outputs, batch_y)\n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " epochLoss += loss.item()\n", + " \n", + " trainingLoss.append(epochLoss / (X_train.size(0) // batch_size))\n", + "\n", + " # Evaluation\n", + " model.eval()\n", + " with torch.no_grad():\n", + " test_outputs = model(X_test)\n", + " predictions = torch.argmax(test_outputs, dim=1)\n", + " acc = (predictions == y_test).float().mean().item()\n", + " valueAccuracy.append(acc)\n", + " print(f\"Epoch {epoch+1}/{epochs} - Test Accuracy: {acc:.4f}\")\n", + "\n", + "\n", + " return model, X_test, y_test, test_dates, trainingLoss, valueAccuracy # ensure all 6 are returned\n", + "\n", + "\n", + "\n", + "\n", + "def load_your_data(path):\n", + " df = pd.read_csv(path, parse_dates=['Datetime'], index_col='Datetime')\n", + " df['Return'] = df['Close'].pct_change()\n", + " df['SMA_5'] = df['Close'].rolling(5).mean()\n", + " df['SMA_20'] = df['Close'].rolling(20).mean()\n", + " df['Volatility'] = df['Return'].rolling(10).std()\n", + " df['Momentum'] = df['Close'] - df['Close'].shift(10)\n", + " df['RSI_14'] = compute_rsi(df['Close'], period=14)\n", + " df['Momentum_5'] = df['Close'] - df['Close'].shift(5)\n", + " df = df.dropna()\n", + " return df\n", + "\n", + "df = load_your_data('allStock_data.csv')\n", + "df = df.dropna() # or custom cleaning\n", + "features = ['Open', 'High', 'Low', 'Close', 'Volume', 'Volatility', 'Momentum_5', 'RSI_14', 'MACD']\n", + "\n", + "\n", + "model, X_test, y_test, test_dates, train_losses, val_accuracies = train_lstm_model(df, features)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "36588908", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "epochs_range = range(1, len(train_losses) + 1)\n", + "\n", + "plt.figure(figsize=(12, 5))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(epochs_range, train_losses, label='Training Loss')\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.title(\"Training Loss over Epochs\")\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(epochs_range, val_accuracies, label='Validation Accuracy')\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.title(\"Validation Accuracy over Epochs\")\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "cb3128a6", + "metadata": {}, + "source": [ + "Backtesting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "073f98f0", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import numpy as np\n", + "import pandas as pd\n", + "import vectorbt as vbt\n", + "\n", + "# 1. Predict with trained model\n", + "model.eval()\n", + "with torch.no_grad():\n", + " outputs = model(X_test)\n", + " predictions = torch.argmax(outputs, dim=1).cpu().numpy()\n", + "\n", + "# 2. Map predictions to trading signals\n", + "# 2 = up → 1 (buy), 1 = neutral/down → 0 (hold)\n", + "signals = np.where(predictions == 2, 1, 0)\n", + "\n", + "# 3. Align with dates and prices\n", + "# Ensure test_dates is a pandas DatetimeIndex\n", + "if not isinstance(test_dates, pd.DatetimeIndex):\n", + " test_dates = pd.to_datetime(test_dates)\n", + "\n", + "# Get close prices for the same period\n", + "close_prices = df.loc[test_dates, 'Close']\n", + "\n", + "# Ensure the signal length matches\n", + "signals_series = pd.Series(signals, index=test_dates).reindex(close_prices.index).fillna(0)\n", + "\n", + "# 4. Run vectorbt backtest\n", + "portfolio = vbt.Portfolio.from_signals(\n", + " close=close_prices,\n", + " entries=signals_series == 1,\n", + " exits=signals_series.shift(1) == 1, # Exit after 1 bar\n", + " direction='longonly',\n", + " freq='1D' # Adjust if intraday\n", + ")\n", + "\n", + "# 5. Show performance\n", + "print(portfolio.stats())\n", + "portfolio.plot().show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rnn_development", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 311f9fc99c0b87b85dc89b5df14311959a3ec0d9 Mon Sep 17 00:00:00 2001 From: Amar723 <140028404+Amar723@users.noreply.github.com> Date: Fri, 9 May 2025 15:21:10 +1000 Subject: [PATCH 06/10] commiting DataEngineering File --- src/dataEngineering.ipynb | 86 ++++++++++++++++++++------------------- 1 file changed, 44 insertions(+), 42 deletions(-) diff --git a/src/dataEngineering.ipynb b/src/dataEngineering.ipynb index 3714132..a0034b6 100644 --- a/src/dataEngineering.ipynb +++ b/src/dataEngineering.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,19 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'tickers' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 89\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m combined_df\n\u001b[1;32m 87\u001b[0m \u001b[38;5;66;03m# Example usage:\u001b[39;00m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;66;03m#tickers = ['AAPL', 'MSFT']\u001b[39;00m\n\u001b[0;32m---> 89\u001b[0m df \u001b[38;5;241m=\u001b[39m fetch_all_alpha_stocks(\u001b[43mtickers\u001b[49m, output_csv\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mallStock_data.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 91\u001b[0m \u001b[38;5;66;03m# This will save the combined dataframe to 'stock_data.csv' locally in the working directory\u001b[39;00m\n", + "\u001b[0;31mNameError\u001b[0m: name 'tickers' is not defined" + ] + } + ], "source": [ "import pandas as pd\n", "import requests\n", @@ -138,7 +150,7 @@ "#tickers = ['AAPL', 'MSFT']\n", "df = fetch_all_alpha_stocks(tickers, output_csv='allStock_data.csv')\n", "\n", - "# This will save the combined dataframe to 'stock_data.csv' locally in the working directory\n" + "# This will save the combined dataframe to 'stock_data.csv' locally in the working directory" ] }, { @@ -150,12 +162,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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QsR/k9Bueyu29983c27+d8X88JbM/WpTz+w0uLzPm/dnp9cdncuzvBuSb1z+ZhYuX5smfH5b2bap+qEfNYIxKdVpb7W3MxJnp9buHcuzP7803f/NAFi5amif/cGrab9x87VaIddrKOd/VGKMOfzff6vnp9tY6p+2/Q4U539223STf6rlDvnvdY2u3AqxXtDcKyTNU1qZm9euktFbJKqt8zlywKBtW8gr5JGnXtF6+v/sW+fHjb6aqt8e/O2t+fv7E6Jz30Mhc+tgbWbh0Wf5y7I5p11T7A2qOP/zhDznjjDNy5plnZptttsk111yTdu3a5eabb660/B//+Mdsuummueaaa7LNNtvkzDPPzLe//e387ne/Ky9zzTXXZP/9988ll1ySrbfeOpdcckm+/vWv55prrllr9Sj9MoUeeOCBVbYdeeSRefrpp3PSSSelpKSkvMzhhx/+pb74xhtvzK9//euce+65FbbXqVMnvXv3zpIlS3LDDTfktNNO+9zzLFy4MAsXLqywbfnSxSmpXedLxVHMPnsdLykpe5XLFzl1j/aZPX9RHhhecSWEH/Yflpu+uWte+fWhWb48eXvah7lt0Ns5bY8t12DUFItV2l+qbn9X/uOFtG7WIE9fdXxKSkrywez5+duTo3LB0Ttl6bIvbrPUPJ9tSyUlJamqe7vy70PTunnDPP37k8va16yP8reBI3PBsbtk6bJl5eXmLViUXb9/exrVr5N9umyaq878Wt6ZMjvPvjap8hNTY1Ta3qooe+Udg8va27XfWNneBpS9Wm3pihF2rZKSTJs9P9+79tEsW7Y8I8ZNTdsNG+W8Y3fJlXcMruLM1BSV379VXnbrrzTP78/cK1f+48UMGDEhbZo3zK+/uXuuP3vvnHPjk0mSH/Z9Jjd9d9+8cv0pWZ7k7SlzctuTb+S0fSt/BSo1y2fb1uf3b4NW9G+nrezfHn81F5ywW6X3a988sEtef+eDDBs9uZKzUROt2r+VfMnx6ZZl49MRFe/J9uzUKhcdul1+8LdhefHt6WnfqnF+d1L3TDn04/zm36+vwcgBPl/l/VvlZbfepFl+/63dcuV9IzLg1ffSpln9/PqUXXP9GV/NOX8qe0XVC+Om5YVxK1/zPXjM1Az59VH5bs/tcsGtQ9ZSLVifGKNSndZ0e3vhzffzwpsrV34fPHJShtz4rXz3iG654OaBa6sarCcqHaNW9Uyh/5Cy9vaHU1a2tydezwXH7Zqly5anUf06+csPD853r3ssM+YuqPQc1GzaG4XkGSpr12fu31bZUqZWSfLrntvkj8+Pz4TZVfddr02dl9emrnwN/cvvz82dJ3bLiV02ydWf+VE3wPqislzDunXrpm7duquUXbRoUV566aVcfPHFFbYfcMABGTy48nmTIUOG5IADDqiwrWfPnunbt28WL16cOnXqZMiQIenTp88qZQqeJHrkkUdWue8vf/lL/vKXvyQpu4FeunRplWU/beTIkTniiCM+9zt/8pOffOF5rrzyylx++eUVttXe8ajU6bp6K5sWk+nzFmbJ0mVp/Zlfb7RqXG+V1Vsq8809t0z/Ie9k8dJlFbZPn7cwx9/wTOqW1sqGjerm/dkL8stjd8y70z9co/Gzfps+b0FZ+/vMqmetmjbIB1XcYH68aGnOvmFgzr35qbRu1iCTZ32UMw7YPnPnL8p0A2o+ZfrcFe3rM7/oLWtfH1V6zMeLluTsax/LuTcMWNm+DuycufMXVmhfy5cnb0+enSR59e1p6dRuw1x43K6SRGuw6XPnV93eZn1Oe/vDIzn32sfSunnDTJ75Yc44uEvmfrQw0+eW/Tp4yswPs3jpsiz71ATOmxNmpO2GjVKntFYWL1lW6bkpbp97/Zwzv9JjLjyme4a8OTn/d/+IJMnr42dk/sLFGfjrY3L5nUMzZdb8TJ/7cY7/zcOpW6d2NmxcL+/P/Ci//MZueXfq3LVeJ9Zd5f1bi8/0b82+qH97OOde++in+rcdy/q3z7TR+nVLc9ze2+QXtz671urA+qN8fPqZ1S1aNfmS49Ovtk//Ie+uMj792ZGd03/IO+n3bNlk9Mj35qRB3dLceNouueqh16tM0AJYU6bP/XjF/FvFVUPL+rfK5zIuPKJLhoyemv/7d9krSF+fkMxfOCgDf35YLv/HsEypZN5k+fLkpbenWUkUY1Sq1dpqb5+1fHny0pgpab9JizVeB9YfVc75Nmuwymp7n/h40ZKcfc2jOff6xz8159tlxZzv/OywRats3qZZ7v3Z0eXH1CopSZLMe/CCdO7VN+9Mmb3W6sS6S3ujkDxDZW2avWBxlixbvsqqoc3rb5CZ8xetUr5BndrZrnXjdNqoUX70tQ5JyhJHa5WU5IXv7Znv/eu1vDhp9irHLU8y8oN52bSZt83CmvBfvf6b/1lluYY/+9nP8vOf/3yVstOnT8/SpUvTunXrCttbt26dKVOmVHr+KVOmVFp+yZIlmT59etq2bVtlmarOuSZ8qfa2bNmyL/X5sgmiSVK7du0sWrTqxegTixcvTu3atb/wPJdccknmzJlT4VPa+cutZlqsFi9dlhHjZ2bfbdtW2L7vdm0zdNz0zz12z06t0qF1k/IHbZVZuGRZ3p+9IKW1S3Jk903z7xESqFhp8ZJlGfHWB9l3x00rbN93x00z9M3PX0lqydJleW/Gh1m2bHmO+2rHPDLsHQ93qWDxkmUZMW5q9u26WYXt+3bdLEPfeL+Ko8pUaF97dcojL7z9ue2rpCSpW+eLr0MUr8VLlmXE2CnZt9vmFbbv223zDB313uceu2Tpsrw3fV5Ze/vaNnnkhbfK29uQUe+lfdvmWTFPmCTZ6ivNM3nGPA/farDy62eXdhW279ulXYa+WflgoEHdOhUe5CYp//V4SUoqbF+4eGnen/lRSmvXypG7tc+/X3hnDUbP+mZl/7ZFhe37dtsiQ0d9/r19hf5t723zyPPjVrmeHrPXNqlbpzT9B45c06GzHiofn27XpsL2fbdt8yXHp43T77lVx6f1NyjNZxfMWLZseUpKVu0DAdaGxUuXZcQ707Nv500qbN93h00ydMzUSo9pUEnfVX7/VlJ139Vlsw0zpYqkBWoOY1Sq09pqb5XpsmWrTJlpIYqarGzOd0oVc75for19Muf7ta3L29voiTPS/Zy/Ztdzby3/PPT8uDz96oTseu6tmTTdj2drKu2NQvIMlbVpybLleeODedm1XfMK23ts2iyvTF61H/po0dIcd8ewnNT/pfLPPa9Nzjuz5uek/i/ltSlV912dWjbK9I+qzvUBWNdVlmt4ySWXfO4xn527W758+efO51VW/rPbV/ec/6svtZLo2tC9e/fccccd+cUvflHp/ttvvz3dunX7wvNUttyrV80n1z32Zvr22i3D352R59+anjO+1iHtWjTILf8ZmyS54pgds3Hz+jnzloqvqfrWnh3ywlvTM+q9Oaucc+ctN8zGzRrklYmzskmz+vnxEZ1Tq1byh0dGVUudWH9c968R6XveARk+7oM8P3pyzjhg+7Rr2Si3PFa2UsYVp+6ejTdsmDOvHZAk6bBxs+y0Veu8OGZKmjeql96Hd822m7bImdc9XshqsI667p8vpe8FB2X42Kl5/s33c8aBndNuo8a55eFXkiRXfPOr2XjDRjnzD48mSTps3Dw7dWqTF0dPLmtfR3bPtpu1LN+fJD88bpcMHzs1b0+ZnQ1Ka+fAnbbIKftum943es1VTXfdfS+m74WHZviYKXn+jfdzxsFd0q5Vk9zy0MtJkitO3ysbt2ycM69+KEnSYZPm2alT27z45uQ0b1wvvY/eOdtuvlHO/N1D5ef8879H5JzDu+X35+yXm/71Ujps0jwXnrhbbvrXS4WoIuuQ6x54OX1/sH+Gv/VBnh89JWfsv92K62fZa5OvOHW3bNyiYc687okkyUMvvpObvrtPevXcPgNenpC2zRvk6m/vmRfHTMnkFSu77LxV62y8YcO88s70bNKiYX584i6pVVKSP/xzeMHqybrhuntfSN+LDsvwMZPz/Kj3csYhO5b1b/8uW5n2im9/LRtv2DhnXv3vJEmHTVpkp63b5sU33i/r347Zpax/W7H/0751YJc8OHhMZs6zmgFlrnv8zfQ9c7cMf3dm2fh0rxXj06dXjE+P7pKNmzfImX0/Oz5tX+X49OFX3kvvA7bOKxNm5YUVr5v/6ZGd89DL72WZpyR8SQ3r1amwctnmbZulc/vWmTVvQSZ+4EEuX+y6h15P3+99LcPfnp7nx3yQM77eqez+7Yk3kyRXnLhTNm7eMGfe/HSS5KHhE3JTrz3Ta79tMuDVSWnbrEGuPq1HXhz3QSbPKksCvfSYrnlh7AcZN2VumtSvk+8euF06b7Zhzvur135jjEr1Whvt7dJT9sgLb76fce/NTJMGdfPdI7unc/tWOe/GAYWoIuuQ6/45LH0vOCTDx05ZMefbJe02arJyzvdbe5aNUX//cJIV7a1j27w4+v2yOd+jdiqb812xf+HipRk1vuKP0mZ/WPY6yc9up+bR3igkz1BZm+54+b38Yv9OeeODeXl1ytwcvV3btGlUL/e+XpaEfO5um6dVo7r56YDRWZ7krZkVf4w4a8HiLFqyrML2s3bZNK9NmZcJsxek4Qa1c1KXTdKxZcP85ulx1Vk1gDWqqlfLV6Zly5apXbv2Kit8fvDBB6usBPqJNm3aVFq+tLQ0G2644eeWqeqca8JqJ4n27t07HTp0SO/evStsv+GGGzJu3Lhcc801X+o8F1xwQY488sgsXLgwF1xwQXklp0yZkt///ve55ppr8s9//nN1w2OFe14cnxaNNsilh++QNk3rZ+R7s3PkNf/JhBllCQNtmtZLu8+8XrJJ/To5snu7/LD/sErPWbe0dn52dJdssVGjfPjx4jz22vs545bBmbNg8VqvD+uXewaNTYsm9XLpCbukTfOGGTlhRo78xQOZMG1ekqRNiwZpt1Hj8vK1a5XkB0d0S8dNmmXxkmV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Bk+9vpU2Hzup4Uqa6Nr9D/drWkaMDScv2zMfNWU4ODkq/dNXkeNqlq6rmXsnsOakXc/TSykM6mJSlSo4OerBZoCIeC9UT3+zUjhuSQD1cHLVxYmdVcnRQfkGBXlsToy3x525lcwAAt4BVk0Rr1qypzz77rOjfgYGB+uor0wlFg8FQbpJoTk6OcnJyTI4V5OXK4Oh8cytrAwoKTP9tMBhUoAKzZRsFe2n2420087/7tXb/WQV6V9abj4Tqgyfba/zn0RaoLSqaghIBZpChjOiSGt3ho9kjO2vmkh1au/eUAr3d9OawTvpg7D0a/9GvkqTOTYP13MDWmjJ/o7YfTVbdQC+9M+JuJWW01r+X7rzFrcHtrlS8GUofu27md1sV4OOmDW8PlsFgUEpGthZGHtK0h9ooL7+sKAWKmb1+lhVvi6IU4OOuDf953Bhv5y9p4boDmvZwe+XlF8ijsrM+f7aPJsxZrfQLbPmN0sxeT8voqhpV99bsJztp5ve7tHbfaeP19PH2+mB0Z43/ZKMkaVtcirbFpRSdsyUmSVEzB2pCz2aatmDLLWsHKobS19Oy423m4mhj/zZ7SHH/FnlQ0wa1U15+/g392xr6N5j1V+KtUbCXZg9tp5k/7dPaA2cU6OWmNx9trQ+e7KDxn0dZoLao6MzGWxllZ369xdi/vf9Ecf+21rjdfF6e8axtR85q25Hi1SK3HDytqLnDNeHBUE37KPJWNQMVBP0bLK30GLWcZyKLohTg664N7w41O0aVpG1HErXtSPEOVFsOnVbUB09qwgOhmvYxfRwAy+GZLyyJeIMlmY23Mso2usNHs0cVzqHuSVCgT+Ec6lP3aPw84xzqs5//rnnju2rv+0NUIOl4Uqa+XH9Ew8Ia3dqGoEIoNV5Q2fF24ly2TpwrTnTfczZTgVVcNapdLZMk0Us5eer/+Va5VXJUx9q+ej6svk5lXNa2BF4qA/5XBoPB2lWAHbFqkmh8fPz//BszZ87Ua6+9ZnLMseUAObca+D//tq1Iy8rRtbx8BXhVNjnu7+laarWD66b3a6qouFS9u/KQJOnA6Qxl52xT5D966rXv9yopk4lfGKVlXTHGV4k33vy9Kpd6E/O66QNCFRWTqHf/u0eSdOBkurI/26DINwbqtUVblZSRrVcebadFG2MUEXlYknQw4ZzcXJw1d9w9euv7nWVOuMC2pV24bIy3Em/0lhdvV67madwH6zRp3noFeLsp8fwljerRTBeyc5RGEgvKUWa8ebuVE2/XNO69VZr0wZrieOvVsjDestW8jr9qB3rr+1eK71McCm/+s5ZPU4sx4TqRlHHL2oTbV9qFsq6nrkrJLON62j9EUbHJevfnfZKkAwnnlJ2Tq8jXHtRr325XUkbpPq6gQNp5LFV1g6rc/Eagwij7eupWavXk665cvaZx76/WpA/X3tC/tSi6njavU021A730/SsDis4p6t/+O1Utxn6uE0mZt65RuG0VjUe9S4xHq7iW2u3iuun9minqaIreXXlQknTgVIayF1xT5Eu99Nr3exiPokxpF7LL7t/Ol9O//WelJr2/WgE+7ko8d1Gj+rTUhUvG+zdzCgqknbFJqlvd96a3ARUH/RssraiP8zV3D1fOGPXdVZo0Z40CfNyUeO6SRvUuHqOaU9THBZe/zSQA3Cw884UlEW+wpP/XHOrA1oo6kqh3fzLurHjgZLqyr2xU5L8G6rVvjHOoaReuaPBbK+Xi7Kiqnq46e+6S3hjaUfEpWbe8Tbh9nc/O1bX8fPl5mK4aWtW9ktJKrC5anr1nMvVA0yCTYwWSEgrnG46kXFTdqu4a26E2SaIAUMFU+DXHX3jhBWVmZpp8nJrfb+1q3VZy8/K1O/6cwpoGmhwPaxqo6LhUs+e4VXJSfom3366/DUciO26Uey1fu4+nKqyF6dbyYS3uUHRMstlz3FyclJ9veqxkfFWu5KT8Epmg+fn5MsjA2xR2LPdavnYfS1FYy5omx8Na1VT0DatemHMtL19n0i8qP79AD3duoJXb40k2Rrlyr+Vrd1ySwkJqmRwPC6ml6MNnyj3XJN7uaaSV246poECKOZWu1uO/UPtJC4o+K7bGacO+BLWftECn09j+217l5uVr94k0hTWvbnI8rPkdio4t43pq5lpZfD0t+1rZsnZVJZ3nAbY9M/ZvyWX0b2fLOMvIpH/r0lArtx0v7N/OqfWECLX/25dFnxVbjxn7t799qdNpPKS2V8bxaLrCmppuvRzWNKj88WiJ+7S8wgEEQwGUJ/davnYfTVJYaG2T42GhtRV96E/cv6VlFd6/NS66fytLyzv9lXTu4k2oNSoq+jdYWlEfF1Lb5Pif7+MuFvdxW/+gj6vrr6Tz9HEALINnvrAk4g2WZIy3VIW1LDmHWkPRMUlmz3FzMffM1/yYISc3T2fPXZKTo4P6d6irn7eduHmVR4WTm1+gg0lZuqu26QutnWr7aveZjD/9O40DPJV6KafcMgZJlZwqfKoRANgdq64kunXrVp07d069e/cuOvbll1/qlVde0aVLl9S/f3998MEHcnFxKfM3XFxcSn3PVvOlzVl9WOFjO2lX/DltjUvVqHvrq0ZVd83/9agkacagVgr2cdPoz4zbjq7Yc1rzhnfQmK71tfZAooK8KmvWkDbafixNiWZWpYJ9m7N8r8L/dp92HU/R1phkjereRDX8PDV/zQFJ0owhHRRc1V2jPzBuUbViR7zmjbtXY3o01do9pxTk46ZZI+7W9qPJSjxvfHPul50nNblfS+09kaZthdvNv/xoe63YEV8qgRn2Zc5PuxT+dE/tikvW1phEjerZ3Bhvq/ZLkmY80UnBVT00+r01kqR6wd5qUz9A22OT5ePhoskPhqhJzaoa/f6aot90dnJQ4xrGQVMlZwcFV/VQizp+ung5V8dZ9cyuzflxh8Kn9dWuo0naeuSsRvVqqRrVqmj+L3slSTOGd1ZwVU+Nnv2LJKledR+1aRCk7TFn5ePhqskD2qhJLb+i73Ny83ToZJrJ38i4aBxslzwO+zNnxT6FT+yqXcfTtDU2WaO6NVYNPw/NX2dcVXvGo20V7Ouu0fN+kySt2HVS88Z00ZjujbV272kFebtp1pMdtT0upeh6+uJDodp2NEVxSZmqUrmSJvRqpha1/PT055ut1UzcJub8uFPh03pr19Hkwv6thWpU8yzu356823g9/c8qSVK9YB+1aRio7TGJxv6tf2tj/1b4vbF/Szf5GxmXjLsWlDwO+zNn1WGFP3WXdp1IN45HuxaOR9fHSpJmPBxiHI9+auybVuw5rXkjOmpMWAOt3X9WQd6VNWtIW20/llo0HnV2dFDj6l6SjA+lg33c1KKmjy5euabjrJxh1+b8sF3h0/tpV2ySth4+q1F9WqqGfxXNX7FHkjRjRBcF+3lq9KwVkgrv3xoGafuRRPl4umrywLZqUruaRr+zoug3X3z8Lm07clZxZ86pipuLJvRvrRZ1/fX03LXWaCJuI/RvsLQ5P+5Q+LOFY9TDZzSqd6vCMeoeSdKM4V2M93ClxqiJxmciA9sa7+Fu7OOGdDL2cWfPG/u4B0PV4k76OPx57q7OJqtr1w7yVou6ATqfdVmnUngZFn8Oz3xhScQbLGnO8j0Kn9xNu46lamtM0g1zqMbdBWY83sH4zLfkHGrPwjlUbzfNGnm3tscWz6G2rR+gYF937Y1PU3Vfd/1jcDs5OEj/WbbLau3E7eGLbQl6+/6mOpCUpd1nMvRIqzsUVMVVi3cbXyp75p66CvB01d9/Nsbfk21q6HTmFcWlXZSzo4MeaBqoXo0CNOmHvUW/ObZDbR1IuqCE85dVydGgLnX99GCzIL26+ohV2ggA+P+zapLoq6++qnvvvbcoSXT//v0aNWqUhg8frsaNG2vWrFkKDg7Wq6++as1q2oSl207K18NFLz7YXIFelXXwTIb6/+dXJaQbt1sL9K6sGlWLt1ZYuOm4PF2dNa5bQ/370dbKzL6q3w4n66XvuLlEaUu3xMnX00UvDmqjQB93HUxIV/83f1ZCmnHFgUAfN9Xw8ygqv/C3GHlWrqRxvZvr3092Uualq/rtwBm9tDCqqMy/l+5QQUGBXnm0vYJ93ZV24bJW7IzXq99stXj7cHtZuumofD0r68VH2ivQ100HT6ar/4yflJBqnCwL9HFXDT/PovKODgZN6R+qBtV9lHstXxv3n1bX579Twg2Ta0G+7tr63uNF/546oLWmDmitjftPq+dL31uucbjtLN0YY4y3IZ0U6Ouug/Fp6v/K90oonOQI9PFQjWol4m1gGzWo7qvcvHxt3JegrtO+LioPlGdp1HH5erjqxYdCFejtpoOnzqn/v1eWfT3dEGu8X+vRVP8e2lGZl3L028GzeumGa6W3u4vmjumsAG83ZWZf1d74NHV/7b/accz86lawH0t/j5FvFVe9+FgHY/92Ml39X/mh+Hrq664a1aoUlXd0NGjKgDbG62levjbuO6Wuzy6if8OfsnRbfOF4tIUCva+PRyOLx6NelVXD98bx6LHC8Wgj/fvRNoXj0SS99N3OojJBPpW19fXiXUSm9mmqqX2aauPhJPX8d/FEHezP0g1HjPdvj99V2L+lqf9LS4rv33w9TPs3BwdNeaidGtxReP+296S6Tl2ohOTi/s3bw0Vzp/RUgI+7MrNztDcuRd2f/UY7YspfaQi2j/4NlrZ04xH5erqajlFfXnpDH+euGv439nEGTXmobfEYdW+Cuj5jOkb19nDV3Mk9FeDrrsxLOdp7LEXdpy/Sjljzq1sBJYU2DNaa954s+vfbE3tKkr5atUdj3/qvtaqFCoZnvrAk4g2WZJxDddWLD984h7r8hnhzM4m3hb8eMY4ZerfQv5+8yziHuv+0yRyqi7OjXnmsveoEVNHFK7laveukRs1Zq8zsP7+lOGzTyiPJ8qnsrAl31ZG/u4ti0y5q7JI9OnvB+DK/NYaZAADcEUlEQVR/NQ8XBVVxLSrv7Oigv4fVV4CHi65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38GGp3aBhpW32HXBFdj3imDTuuGcatu+QzhcOSMo2ZOGYkTuyK1QD5/XcNX8fMzt/Hz07kxetzH+/MCmzl63Omfu12+553zi8Ux4fPzdvzd76A47XZizOM5PmZ/KilZm+ZFX++NaMTJi/PL3aNd1R3aCa+Gz/PfLQG9Pz4BvTM3H+8vzXk+Mya8mqnNfn/3b/Vpatx1w4v9eueXjUrDw8alYmLVyZHz/3Tvn1bf/22z3vm0fuk8fGzsmb2/kAd/6KtRV+4BN77ZKnpi3MU9MWZPry1bl79MzMW7U2x+7eqtL2d4+emX9MmpuJi1dm1oo1eWDcrMxcvib92mweKycuXpl7x87MizMXZ+2WFz5qvB0xRz2r165pVr9OPvfnN/LqtEWZsXhVhk9dlDGzl237DakRPnPg7vnLiBl56M0ZeWf+ivxgcPka3Kc+aA1uxbbz7eUpCzNo3Ny8M39Fpi5amd8Pn5qxc5al727Nd2xnqHZO77xLHp+0II9NXpCpS1fnVyOmZ+6KtfnE3lt/iTZJJi5amWemLsq7S1Zl9oo1eWrKwrw6e2m6t25UxZFTHZzRtU0ee2d+Br4zP1OWrMovX5+WOSvW5uROu1TafsKilXl6ysLy/Fq+JoPfXZDhM5em+y6NN7UZMWdZXpi+OFOWrMrMZWvyt3HlRaXvb0PN9Oleu+bvo2fn4dGzM3nhytz6/DuZvXR1zuy+/TnqN47YJ4+Nm5s3Z1W+42hZzFH5v3ni5Qm54bdP5+/PjSl0KABUEUWiRahO7ZLs36FpnptY8XFAQybOS9+Ozbd77qOfOySvXHtE7r/wgBy8xaNw69auldXr1lc4tmrthvTbvcWHEjfVU2mtkuzbpkmGTqn4Te+X3l2Ynu23XRBwSre26di8fn41bHKlr78+Y3G6tWmS7m3Li0J3bVo/h+7ZMs9N9q3fmqxOrZLs165JXtji29/PT5qf3h+wmPzwZ/vn+S8elns+1Sf9K7luXXnoR7JgxZo89OaMDzNkqrE6tUrSvX2TPPdOxXx7buL89PmAfPvnpQfnpS99LPd9um8O2mP74+TZvXfNP0fOysq167fbjuK2Yd26LJ0yKS277V/heItu+2fJxPEf2t9Zv2Z1ytavT51GPiSpyUprlaTrLo0zbIvdPYdNXZQe2ynoPLlLm+zWrH5+M3zKv/V3Dti1WfZo3iCv/RuPz6J41alVkv3aN8nzW96/vbPgA8fTvw84KC9cfXjuOa9P+lcynjasWzvPXHlonvviYfn12b3Sra0vlNV0781PX9riyQBDp2x/fvrJfdtmt2YN8uuX391mmwZ1audfFx2YgZ/pn599Yr90UXBQ45WWlOQjTRtkxLyKhcVvzluWLi0q/wLPlkqSNCitlWVrzAX4YDtqjnp0513y+vRFueGEffPyNR/LwM8dki98dK/UKvmwe0B18t4a3PNbrMW+MHl+eu/afLvnPvyZ/nnuysNy97mVr8G930F7tMheLRvlFU8e4H1KS0qyT/OGeW2LLye+Nntp9m31792D7d28QfZt1ShvzVXwTkWltUrSuUXDDN+i8O7VWUvS7d+8x+/UvEH2a90ob87Zdn71btskuzWtt902FL/yNbit56gvTV24/TW4rm2zW9P6+fUr25+j/vPCA/LoRQfmpyd1M0cFqK5KSvwU4qeGKtjj5pcs2f4HhUuX2nr/f6tFw7oprVVrq11U5i1fk10a16v0nDlLV+dr/3g7b89ckrq1a+X0nh1y/0UH5Jy7X87L75bfuA6ZOC8DDt4zw95dmHcXrshH92qVY7u2Sa0a/H8gkhYN6qS0VkkWbPENtfkr16Z1o60f0ZckuzdvkKs++pF85sE3sq0nHzw+bm5aNKiT353VK0lSp3at/PnN6fnd8KkfZvhUMy0a1qn0+jZ/+Zpt5tvcZavzrUdH5e1ZS1O3tFZO6d4u95zXJ5/+w6sZvnEBus+uzXJmjw455bfDdnQXqEY2j6f/v707j6uq2v8//j7Mo4CgguIMirOmZlo5lKZWas5YlqapN820NC2brJtWmtmc3d/XqbK0Uksb1XLINM0BNJznCRwQEBBRYP3+QE4eDyAmgh5ez/s4j2LvdfZeu/t57LXXOp+9lu2SL/H5tacp6Xru+xhre9q1fnl9/nAT9fl0g/66bCBIkuqXL6WaZX01djGzOpZ0F1KSpawsuZbys9nuWspP588kFdp59i+YKzf/0gqoVbfQjombj79HzvPbZe1p2nkFevnn+p2Kfh4adlsVDf52S57Pb1L2cvQ/PnKr3JwsyjTSm7/v1fojiYVXedx0rO1pyuX903QF+eQ+097JlHQ9/8M2/R2X3Z4+UC9Enz7UWH0/22BNINh36qzGLo7RrhMp8nF3Ub+mlTS3X1N1+n9/6mDC2et9WbhB+V/sn14+g8rptPMK9Mo9SaWin4eGt6iqgfOj87y/HUg4q/HLdmp3fKp83JzVp0EFzejRUJFfbtThpLyXd4Zj83VzlrOTRYnptktyJ6ZfkL9bwZLWO1UtI3dnJ62JS7wONYSjuV591IoBXmru76HvtsZqwJebVCXQW690iJCzk0Xv/77vul8Xbkw5Y3Dxuf3GkN8Y3E/bFBOXLDfn7DG4WX1u0cNf/DMGJ0k+7s5aNexOuTk7KcsYvbJkp9YcYKl5/KOUe3Ybm5Bu+0yXkH5BpT3yb2M/u7e2/Nxd5Oxk0ZxtcfqZ2MJl/Nyy4yPhnO0zXHZ85b8Syped62bHl8WiT2Ni9dNlL254uzppbud6cr14f3tvw2G7ZGeULDljcPFpl4/BXVBgLkvISxf7qM2r6LEFeY/B7U9I0/hfd2pP/Fl5uzmrT/0KmtGtgSLnbaKPCgAA8lRsSaL+/v6y5JNcaIzJd3+O9PR0pafbDoyZjPOyuOQ+UFGSmMseHC2SjHJ/mtwXn6p98anWvzcdSVRIKQ8NblHVmiQ6/qfteqNzXf32xJ0yMjp4Ok1fbz6inldYXgYlQ67xlku4OVmk1ztE6OM/D+hQPkt6N6ngp8eaVtbE5bu1NS5ZFf09NKZVmAalntf/W1+w2avguOxCK5/mYv/ps9p/+p9EgaijSQrx9dDAZpW14XCivN2cNblzXb3w03YlpLEcB+zZ3cssud/fJGl//Fntj/8n3jYfTVJIKQ8Nal451yTRXg0raOeJZG05xix7yHHZDS2vYPsXDv28WCfWr1WD0S/IyZVnZdi3p/k9v73Wtqb+99chHbrCQPPZ85l66KvN8nJ1VtNQfz3VoqqOnjmnTccKL9kZNye7eLNY8m5Pc3t+K+WhgbdV0V+Ho7K3HUtS1CVxtfFwor577DY90rSi/rtkZyHXHjcf2+Cy2G3J5mSRJravpWnrDubbP916PFlbL/lxN+rYGX0ReYsiG1TQ5FV7C6nOcBQWiyXP8bdL3R7ir55h5TRp0wGdYSZRXIXC7qM6WbITTcf9sE1ZRvo7LlnlfNw1qHkVkkSRx28MubN7hjuWpOBSHhp4a2WbJNHU9Ew9MGOdvNyc1bxKaT17V7gOJ6ZpfS7jJijh7OIv7z5EjtEr9sjTxUkRgV4aULe8jqWkawUz1SIX9s2p5YpPcE/9ukueLk6qFeitxxpU0LHkdC2/5N519kKWhvyyQ54uTmpUzlf/aVRBsanpimY20RIvt/Y0N04WaUK7CH2y/qAOJeXdR/37eLL+vqSPGh17RnN6N1Jk/fKazPMbAADIQ7EliS5fvrxQjvP666/rlVdesdlWqtVD8m/dt1COfzNKOHteGVlZKuNj++N/oLeb3ewt+dl8JFFd65e3/n367AUNnrtZ7i5O8vd01fHkdD3btoYOM0tLiZaQdkEZWUaB3rZvvJX2dFX8Wft483Z1Vp1ypVSzjK+ebR0uKbvT42SxaMPwlnp84Rb9dSRRQ5tX1Q87jmthTJwkaU98qjxdnPXi3TX0f+sPFeDnFjiihLMXsu9vl81YEOjlZje7aH6ijiWpS50QSdkz21b099S0ng2s+3NmSN429i61/2StDufzgzEc1z/tqe2MLNnxlp7Ht+xtPpqoB+qF2G33cHFSpzrBmrqSxAJIrj6+kpOTLpxJtNl+IfmM3C6bXfTfOLzkBx36aZEaPPWcfEIrXfPxcHNLPHfx+c3Ltj0t7emm07m8MOHl6qzaZX1VI8hHz9xZXdI/z29rh9yu4d//rQ1Hs5P1jKQjZ7ITSXfFp6pKgKf6NwolSbQEy7N/6uVmNzNVfqKOJqlL3eA89xtJW44lqXLpgi3xDMeUmJb7/S3A081u9mQp+/5Wp5yvapbx0dhWYZL+ub+tH3anhn23VX/lMhuykRRzIlmV/D2vx2XgJpF8PlOZWUb+7rbDq35uLko6n5HHt7K1CPbT4/VC9fbmg9oaT9IACuZ69VFPpKQrI9Mo65LBtj2nUlXW112uThZdyGIUriTKGYMLyu03hqt4hos+mqTOdWzHRIxkfTljx4kUVQ/01uDbqpAkCqsz6dltbICH7W8O/u4uSkjPv409fvGZ78CZcwpwd1Xf2sEkicJG0vkMZWYZlfawfYbzd3dRwrn8J5GIu3j/2590TgEernqkbohNkqiRdCwlu03em5imSqU81KdWsKJP7Cnci8BNI2cMLshuDM7VbgUMybaPOqalbR913eN36IlFW/XXUfsxNiNp2/FkVfSjjwoAAPJWbEmirVq1KpTjPPfcc3r66adtttWdtKJQjn2zupBptPXYGd1ZPUi/7Dhh3X5n9SAtueTvK6kTUkonUuwHGNMzsnQ8OV0uThZ1rF1O319M4kPJlJFltP1EsppXCtDyvf8srdGsUoBWXLbUhiSlnM9U98//stnWu355NQ0N0OgfY3T04uxUHi7Zy3FcKssYWSySJZ8ZEuDYLmQZxcQlq0XV0lq666R1++1VS+vXS/6+ktrlfK33t73xZ3Xf/1trs/+pVtXl7eai15buVNwZluYoqS5kGf0dm6w7qgVqyc5/2s87qgVq2a6raE+DS+lEsv0PKPf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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "df = pd.read_csv('allStock_data.csv', parse_dates=['Datetime'])\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import plotly.graph_objects as go\n", + "import plotly.io as pio\n", "\n", + "# Load data\n", + "df = pd.read_csv('allStock_data.csv', parse_dates=['Datetime'])\n", "\n", "# -- CORRELATION HEATMAP --\n", "def plot_correlation_heatmap(df):\n", @@ -167,51 +197,37 @@ " ax.set_title('Ticker Correlation Heatmap', fontsize=16, pad=20)\n", " plt.tight_layout()\n", " plt.show()\n", - " #return corr\n", "\n", "plot_correlation_heatmap(df)\n", "\n", - "\n", + "# -- 3D VOLATILITY PLOT --\n", "def plot_3d_volatility_interactive(df):\n", - " # Set the renderer for Plotly to open in the browser\n", " pio.renderers.default = \"browser\"\n", - "\n", - " # Calculate the 30-day rolling volatility for each stock\n", " df['Volatility'] = df.groupby('Ticker')['Close'].pct_change().rolling(window=30).std()\n", - "\n", - " # Calculate the mean volatility per ticker and sort by it\n", " volatility_by_ticker = df.groupby('Ticker')['Volatility'].mean().sort_values(ascending=False)\n", - "\n", - " # Get the 5 most volatile tickers\n", " top_5_tickers = volatility_by_ticker.head(5).index\n", "\n", - " # Initialize the figure\n", " fig = go.Figure()\n", "\n", - " # Loop through the top 5 most volatile tickers and create a trace\n", " for ticker in top_5_tickers:\n", " ticker_df = df[df['Ticker'] == ticker].copy()\n", " ticker_df = ticker_df.sort_values('Datetime')\n", " if len(ticker_df) < 2:\n", " continue\n", "\n", - " # Convert Datetime to Unix timestamp for plotting on the x-axis\n", " base_dates = pd.to_datetime(ticker_df['Datetime']).astype(np.int64) // 10**9\n", - "\n", - " # Create custom data to include Ticker and DateTime in hover info\n", " custom_data = np.stack([\n", " ticker_df['Datetime'].astype(str),\n", " np.full(len(ticker_df), ticker)\n", " ], axis=-1)\n", "\n", - " # Add the 3D scatter trace for this ticker\n", " fig.add_trace(go.Scatter3d(\n", - " x=base_dates, # Time on the x-axis\n", - " y=ticker_df['Volatility'], # Volatility on the y-axis\n", - " z=ticker_df['RSI'], # RSI on the z-axis\n", + " x=base_dates,\n", + " y=ticker_df['Volatility'],\n", + " z=ticker_df['RSI'],\n", " mode='lines+markers',\n", " name=ticker,\n", - " customdata=custom_data, # Include Ticker and DateTime in hover data\n", + " customdata=custom_data,\n", " hovertemplate=(\n", " \"Ticker: %{customdata[1]}
\" +\n", " \"Datetime: %{customdata[0]}
\" +\n", @@ -223,7 +239,6 @@ " marker=dict(size=3)\n", " ))\n", "\n", - " # Update the layout of the plot\n", " fig.update_layout(\n", " title='3D Volatility-RSI Timeline for 5 Most Volatile Stocks',\n", " scene=dict(\n", @@ -238,29 +253,16 @@ " width=1200\n", " )\n", "\n", - " # Show the plot\n", " fig.show()\n", "\n", - "# Call the function with your DataFrame\n", "plot_3d_volatility_interactive(df)\n" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/t_/1j24y4gj22q4lrhhw9bl5n600000gn/T/ipykernel_97772/3021605551.py:20: MatplotlibDeprecationWarning:\n", - "\n", - "The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import plotly.graph_objects as go\n", "import pandas as pd\n", From a18d01d39dca51c1ef0cbfc090d93d0f728580ea Mon Sep 17 00:00:00 2001 From: Amar723 <140028404+Amar723@users.noreply.github.com> Date: Sat, 17 May 2025 16:02:31 +1000 Subject: [PATCH 07/10] Comitting LSTM model with working back test, need to fine tune this model and ensure backtesting works properly --- src/LSTMMODELAMAR.ipynb | 63476 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 63476 insertions(+) create mode 100644 src/LSTMMODELAMAR.ipynb diff --git a/src/LSTMMODELAMAR.ipynb b/src/LSTMMODELAMAR.ipynb new file mode 100644 index 0000000..9ca4c38 --- /dev/null +++ b/src/LSTMMODELAMAR.ipynb @@ -0,0 +1,63476 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 60, + "id": "8d34c501", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import classification_report, confusion_matrix" + ] + }, + { + "cell_type": "markdown", + "id": "be942f7a", + "metadata": {}, + "source": [ + "Define LSTM Architecture" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "9b5d91ad", + "metadata": {}, + "outputs": [], + "source": [ + "class LSTM(nn.Module):\n", + " def __init__(self, input_size, hidden_size=128, num_layers=2, dropout=0.2, num_classes=3):\n", + " super(LSTM, self).__init__()\n", + " self.lstm = nn.LSTM(\n", + " input_size=input_size,\n", + " hidden_size=hidden_size,\n", + " num_layers=num_layers,\n", + " batch_first=True,\n", + " dropout=dropout\n", + " )\n", + " self.attention_network = nn.Sequential(\n", + " nn.Linear(hidden_size, 64),\n", + " nn.Tanh(),\n", + " nn.Linear(64, 1)\n", + " )\n", + " self.fc1 = nn.Linear(hidden_size, 64)\n", + " self.dropout = nn.Dropout(dropout)\n", + " self.fc2 = nn.Linear(64, num_classes)\n", + " \n", + " def forward(self, x):\n", + " lstm_out, _ = self.lstm(x) # (batch, seq, hidden)\n", + "\n", + " # Apply attention correctly\n", + " attn_scores = self.attention_network(lstm_out).squeeze(-1) # (batch, seq)\n", + " attn_weights = torch.softmax(attn_scores, dim=1).unsqueeze(-1) # (batch, seq, 1)\n", + " context = torch.sum(attn_weights * lstm_out, dim=1) # (batch, hidden)\n", + "\n", + " out = self.fc1(context)\n", + " out = torch.relu(out)\n", + " out = self.dropout(out)\n", + " return self.fc2(out)\n" + ] + }, + { + "cell_type": "markdown", + "id": "af876517", + "metadata": {}, + "source": [ + "More Indicators - Eventually move to dataEngineering file" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "d34a61cc", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_technical_indicators(df):\n", + " \"\"\"Compute technical indicators with proper handling of NaN values\"\"\"\n", + " # Price and volume features\n", + " df['Returns'] = df['Close'].pct_change()\n", + " df['Log_Returns'] = np.log(df['Close'] / df['Close'].shift(1))\n", + " df['Volume_Change'] = df['Volume'].pct_change()\n", + " \n", + " # Moving averages\n", + " for window in [5, 10, 20, 50]:\n", + " df[f'SMA_{window}'] = df['Close'].rolling(window=window).mean()\n", + " df[f'EMA_{window}'] = df['Close'].ewm(span=window, adjust=False).mean()\n", + " \n", + " # Price relative to moving averages\n", + " df['Price_to_SMA50'] = df['Close'] / df['SMA_50']\n", + " df['Price_to_SMA200'] = df['Close'] / df['Close'].rolling(window=200).mean()\n", + " \n", + " # Volatility indicators\n", + " df['Volatility_10'] = df['Returns'].rolling(window=10).std()\n", + " df['Volatility_30'] = df['Returns'].rolling(window=30).std()\n", + " \n", + " # RSI\n", + " delta = df['Close'].diff()\n", + " gain = delta.where(delta > 0, 0).rolling(window=14).mean()\n", + " loss = -delta.where(delta < 0, 0).rolling(window=14).mean()\n", + " rs = gain / loss\n", + " df['RSI_14'] = 100 - (100 / (1 + rs))\n", + " \n", + " # MACD\n", + " ema12 = df['Close'].ewm(span=12, adjust=False).mean()\n", + " ema26 = df['Close'].ewm(span=26, adjust=False).mean()\n", + " df['MACD'] = ema12 - ema26\n", + " df['MACD_Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()\n", + " df['MACD_Hist'] = df['MACD'] - df['MACD_Signal']\n", + " \n", + " # Bollinger Bands\n", + " df['BB_Middle'] = df['Close'].rolling(window=20).mean()\n", + " df['BB_Std'] = df['Close'].rolling(window=20).std()\n", + " df['BB_Upper'] = df['BB_Middle'] + 2 * df['BB_Std']\n", + " df['BB_Lower'] = df['BB_Middle'] - 2 * df['BB_Std']\n", + " df['BB_Width'] = (df['BB_Upper'] - df['BB_Lower']) / df['BB_Middle']\n", + " \n", + " # Momentum indicators\n", + " for window in [5, 10, 20]:\n", + " df[f'Momentum_{window}'] = df['Close'] - df['Close'].shift(window)\n", + " df[f'ROC_{window}'] = df['Close'].pct_change(periods=window) * 100\n", + " \n", + " # Clean up NaN values\n", + " df = df.replace([np.inf, -np.inf], np.nan)\n", + " \n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "bc5c1822", + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_data(df, feature_columns, target_column='Returns', seq_length=20, prediction_horizon=5, threshold_pct=0.005):\n", + " df = df.sort_index()\n", + "\n", + " print(\"Initial shape:\", df.shape)\n", + "\n", + " # Compute future returns\n", + " df['Future_Return'] = df[target_column].shift(-prediction_horizon).rolling(window=prediction_horizon).sum()\n", + "\n", + " # Compute volatility\n", + " volatility = df[target_column].rolling(window=30).std()\n", + " upper_threshold = threshold_pct * volatility\n", + " lower_threshold = -threshold_pct * volatility\n", + "\n", + " # Classification labels\n", + " conditions = [\n", + " df['Future_Return'] > upper_threshold,\n", + " df['Future_Return'] < lower_threshold\n", + " ]\n", + " choices = [2, 0]\n", + " df['Target'] = np.select(conditions, choices, default=1)\n", + "\n", + " # Drop rows with NaNs now that all necessary columns are computed\n", + " df = df.dropna(subset=feature_columns + ['Future_Return', target_column])\n", + "\n", + " print(\"After dropna, shape:\", df.shape)\n", + "\n", + " # Normalize features\n", + " scaler = StandardScaler()\n", + " df_scaled = pd.DataFrame(\n", + " scaler.fit_transform(df[feature_columns]),\n", + " columns=feature_columns,\n", + " index=df.index\n", + " )\n", + "\n", + " # Create sequences\n", + " X, y, dates = [], [], []\n", + " for i in range(seq_length, len(df_scaled) - prediction_horizon):\n", + " X.append(df_scaled.iloc[i-seq_length:i].values)\n", + " y.append(df['Target'].iloc[i])\n", + " dates.append(df.index[i])\n", + "\n", + " X = np.array(X)\n", + " y = np.array(y)\n", + " dates = pd.DatetimeIndex(dates)\n", + "\n", + " print(f\"Final dataset: X={X.shape}, y={y.shape}, dates={dates.shape}\")\n", + "\n", + " # Time-based split\n", + " train_size = int(len(X) * 0.7)\n", + " val_size = int(len(X) * 0.15)\n", + "\n", + " X_train, y_train = X[:train_size], y[:train_size]\n", + " X_val, y_val = X[train_size:train_size+val_size], y[train_size:train_size+val_size]\n", + " X_test, y_test = X[train_size+val_size:], y[train_size+val_size:]\n", + " test_dates = dates[train_size+val_size:]\n", + " \n", + " test_start_idx = df.index.get_loc(test_dates[0])\n", + "\n", + " return X_train, y_train, X_val, y_val, X_test, y_test, test_dates, scaler, test_start_idx\n", + "\n", + "# Missing from your code - proper time series split\n", + "def time_series_split(df, test_size=0.2, val_size=0.2):\n", + " \"\"\"Split data chronologically instead of randomly\"\"\"\n", + " n = len(df)\n", + " test_idx = int(n * (1 - test_size))\n", + " val_idx = int(test_idx * (1 - val_size))\n", + " \n", + " train = df.iloc[:val_idx]\n", + " val = df.iloc[val_idx:test_idx]\n", + " test = df.iloc[test_idx:]\n", + " \n", + " return train, val, test\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "2c052cd0", + "metadata": {}, + "outputs": [], + "source": [ + "def train_enhanced_lstm(X_train, y_train, X_val, y_val, input_size, \n", + " hidden_size=128, num_layers=2, dropout=0.2, \n", + " epochs=50, batch_size=64, learning_rate=0.001, \n", + " patience=10, class_weights=None,\n", + " device='cuda' if torch.cuda.is_available() else 'cpu'):\n", + " \"\"\"\n", + " Train LSTM model with early stopping and learning rate scheduling\n", + " \"\"\"\n", + " # Convert to PyTorch tensors\n", + " X_train = torch.tensor(X_train, dtype=torch.float32).to(device)\n", + " y_train = torch.tensor(y_train, dtype=torch.long).to(device)\n", + " X_val = torch.tensor(X_val, dtype=torch.float32).to(device)\n", + " y_val = torch.tensor(y_val, dtype=torch.long).to(device)\n", + " \n", + " # Initialize model\n", + " model = LSTM(\n", + " input_size=input_size,\n", + " hidden_size=hidden_size,\n", + " num_layers=num_layers,\n", + " dropout=dropout\n", + " ).to(device)\n", + " \n", + " # Loss function with class weights if provided\n", + " if class_weights is not None:\n", + " weights = torch.tensor(class_weights, dtype=torch.float32).to(device)\n", + " criterion = nn.CrossEntropyLoss(weight=weights)\n", + " else:\n", + " criterion = nn.CrossEntropyLoss()\n", + " \n", + " # Optimizer with weight decay (L2 regularization)\n", + " optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-5)\n", + " \n", + " # Learning rate scheduler\n", + " scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n", + " optimizer, mode='min', factor=0.5, patience=5, verbose=True\n", + " )\n", + " \n", + " # Training loop with early stopping\n", + " best_val_loss = float('inf')\n", + " patience_counter = 0\n", + " train_losses = []\n", + " val_losses = []\n", + " val_accuracies = []\n", + " \n", + " for epoch in range(epochs):\n", + " # Training\n", + " model.train()\n", + " train_loss = 0\n", + " permutation = torch.randperm(X_train.size(0))\n", + " \n", + " for i in range(0, X_train.size(0), batch_size):\n", + " indices = permutation[i:i+batch_size]\n", + " batch_x, batch_y = X_train[indices], y_train[indices]\n", + " \n", + " optimizer.zero_grad()\n", + " outputs = model(batch_x)\n", + " loss = criterion(outputs, batch_y)\n", + " loss.backward()\n", + " \n", + " # Gradient clipping to prevent exploding gradients\n", + " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n", + " \n", + " optimizer.step()\n", + " train_loss += loss.item() * batch_x.size(0)\n", + " \n", + " train_loss /= X_train.size(0)\n", + " train_losses.append(train_loss)\n", + " \n", + " # Validation\n", + " model.eval()\n", + " val_loss = 0\n", + " correct = 0\n", + " with torch.no_grad():\n", + " for i in range(0, X_val.size(0), batch_size):\n", + " batch_x = X_val[i:i+batch_size]\n", + " batch_y = y_val[i:i+batch_size]\n", + " \n", + " outputs = model(batch_x)\n", + " loss = criterion(outputs, batch_y)\n", + " val_loss += loss.item() * batch_x.size(0)\n", + " \n", + " _, predicted = torch.max(outputs.data, 1)\n", + " correct += (predicted == batch_y).sum().item()\n", + " \n", + " val_loss /= X_val.size(0)\n", + " val_accuracy = correct / X_val.size(0)\n", + " val_losses.append(val_loss)\n", + " val_accuracies.append(val_accuracy)\n", + " \n", + " # Update learning rate\n", + " scheduler.step(val_loss)\n", + " \n", + " feature_columns = [\n", + " 'Open', 'High', 'Low', 'Close', 'Volume',\n", + " 'Returns', 'Log_Returns', 'SMA_5', 'EMA_2',\n", + " 'RSI', 'MACD', 'MACD_Signal', 'MACD_Diff',\n", + " 'BB_High', 'BB_Low', 'Volume_EMA', 'Volatility'\n", + " ]\n", + " \n", + " # Save best model\n", + " model_name = f\"lstm_input{len(feature_columns)}_hidden{hidden_size}_layers{num_layers}_drop{int(dropout*100)}.pth\"\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " patience_counter = 0\n", + " torch.save(model.state_dict(), model_name)\n", + " # else:\n", + " # patience_counter += 1\n", + " # if patience_counter >= patience:\n", + " # print(\"Early stopping triggered.\")\n", + " # break\n", + "\n", + "\n", + " \n", + " print(f\"Epoch {epoch+1}/{epochs} - Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_accuracy:.4f}\")\n", + " \n", + " # Load best model\n", + " model.load_state_dict(torch.load(model_name))\n", + " \n", + " return model, train_losses, val_losses, val_accuracies" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "c37002b6", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_model(model, X_test, y_test, test_start_idx, batch_size=64, device='cuda' if torch.cuda.is_available() else 'cpu'):\n", + " \"\"\"\n", + " Evaluate model on test set with detailed metrics\n", + " \"\"\"\n", + " X_test = torch.tensor(X_test, dtype=torch.float32).to(device)\n", + " y_test = torch.tensor(y_test, dtype=torch.long).to(device)\n", + " \n", + " model.eval()\n", + " all_preds = []\n", + " all_probs = []\n", + "\n", + " with torch.no_grad():\n", + " for i in range(0, X_test.size(0), batch_size):\n", + " batch_x = X_test[i:i+batch_size]\n", + " outputs = model(batch_x)\n", + " probs = torch.softmax(outputs, dim=1)\n", + " _, preds = torch.max(outputs, 1)\n", + " \n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_probs.extend(probs.cpu().numpy())\n", + "\n", + " all_preds = np.array(all_preds)\n", + " all_probs = np.array(all_probs)\n", + " y_test_np = y_test.cpu().numpy()\n", + "\n", + " print(\"\\nClassification Report:\")\n", + " print(classification_report(y_test_np, all_preds, target_names=['Sell', 'Hold', 'Buy']))\n", + " \n", + " # Assuming:\n", + " # all_preds contains integers: 0 = Sell, 1 = Hold, 2 = Buy\n", + "\n", + " mapping = {0: -1, 1: 0, 2: 1}\n", + " signals = np.vectorize(mapping.get)(all_preds)\n", + " \n", + " # signals_full = np.zeros(len(df)) # or np.full(len(df), np.nan)\n", + " # signals_full[test_start_idx:test_start_idx + len(signals)] = signals\n", + " \n", + "\n", + " # Confusion matrix\n", + " cm = confusion_matrix(y_test_np, all_preds)\n", + " print(\"\\nConfusion Matrix:\")\n", + " print(cm)\n", + " \n", + " # Overall accuracy\n", + " accuracy = (all_preds == y_test_np).mean()\n", + " print(f\"\\nTest Accuracy: {accuracy:.4f}\")\n", + " \n", + " return all_preds, all_probs, signals " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5cfa42d", + "metadata": {}, + "outputs": [], + "source": [ + "# import pandas as pd\n", + "# from io import StringIO\n", + "\n", + "# # Step 1: Remove starting/ending quotes and add trailing comma\n", + "# with open(\"All_Stock_Data.csv\", \"r\") as f:\n", + "# cleaned_lines = [\n", + "# (line[1:-2] if line.endswith('\\n') else line[1:-1]) + ',' # strip quotes, add comma\n", + "# for line in f\n", + "# ]\n", + "\n", + "# # Step 2: Write cleaned lines to a new CSV file\n", + "# with open(\"cleaned_file.csv\", \"w\") as f:\n", + "# for line in cleaned_lines:\n", + "# f.write(line + '\\n') # ensure newline at end of each line" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "bd4ee4ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame shape: (417079, 19)\n", + "\n", + "First few rows:\n", + " Open High Low Close Volume Ticker \\\n", + "Datetime \n", + "2025-03-12 05:00:00 220.6000 220.8100 220.6000 220.800 3941.0 AAPL \n", + "2025-03-12 05:00:00 110.3200 110.3800 110.3000 110.370 8421.0 NVDA \n", + "2025-03-12 05:00:00 191.8759 191.8759 191.8061 191.846 326.0 AVGO \n", + "2025-03-12 05:00:00 21.4100 21.4100 21.3700 21.370 10334.0 INTC \n", + "2025-03-12 05:00:00 383.1500 383.1500 383.1500 383.150 1.0 MSFT \n", + "\n", + " Returns Log_Returns SMA_5 EMA_2 RSI \\\n", + "Datetime \n", + "2025-03-12 05:00:00 0.000952 0.000952 220.61200 220.730627 73.503526 \n", + "2025-03-12 05:00:00 0.000272 0.000272 110.32800 110.360319 63.674896 \n", + "2025-03-12 05:00:00 -0.000156 -0.000156 191.69642 191.832628 62.185993 \n", + "2025-03-12 05:00:00 -0.001868 -0.001870 21.38800 21.381602 46.317425 \n", + "2025-03-12 05:00:00 -0.000104 -0.000104 383.10600 383.147354 67.718210 \n", + "\n", + " MACD MACD_Signal MACD_Diff BB_High BB_Low \\\n", + "Datetime \n", + "2025-03-12 05:00:00 0.165474 0.142982 0.022493 220.734069 220.111931 \n", + "2025-03-12 05:00:00 0.108562 0.115661 -0.007099 110.538588 109.972412 \n", + "2025-03-12 05:00:00 0.276184 0.277492 -0.001308 192.181188 190.781982 \n", + "2025-03-12 05:00:00 -0.004046 -0.004011 -0.000035 21.432227 21.340773 \n", + "2025-03-12 05:00:00 0.360097 0.352635 0.007462 383.371155 382.088845 \n", + "\n", + " Volume_EMA Volatility Unnamed: 19 \n", + "Datetime \n", + "2025-03-12 05:00:00 1102.586608 0.000506 NaN \n", + "2025-03-12 05:00:00 10743.023677 0.000699 NaN \n", + "2025-03-12 05:00:00 334.650245 0.001159 NaN \n", + "2025-03-12 05:00:00 6741.179620 0.002308 NaN \n", + "2025-03-12 05:00:00 145.648977 0.000463 NaN \n", + "\n", + "Columns available:\n", + "['Open', 'High', 'Low', 'Close', 'Volume', 'Ticker', 'Returns', 'Log_Returns', 'SMA_5', 'EMA_2', 'RSI', 'MACD', 'MACD_Signal', 'MACD_Diff', 'BB_High', 'BB_Low', 'Volume_EMA', 'Volatility', 'Unnamed: 19']\n" + ] + } + ], + "source": [ + "feature_columns = [\n", + " 'Open', 'High', 'Low', 'Close', 'Volume',\n", + " 'Returns', 'Log_Returns', 'SMA_5', 'EMA_2',\n", + " 'RSI', 'MACD', 'MACD_Signal', 'MACD_Diff',\n", + " 'BB_High', 'BB_Low', 'Volume_EMA', 'Volatility'\n", + "]\n", + "\n", + "df = pd.read_csv('cleaned_file.csv', parse_dates=['Datetime'], index_col='Datetime')\n", + "df = df.dropna(subset=feature_columns) # then drop only if required\n", + "# df = df.drop('Unnamed: 19', axis=1)\n", + "print(\"DataFrame shape:\", df.shape)\n", + "print(\"\\nFirst few rows:\")\n", + "print(df.head())\n", + "print(\"\\nColumns available:\")\n", + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "33868637", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial shape: (417063, 44)\n", + "After dropna, shape: (417054, 46)\n", + "Final dataset: X=(417019, 30, 17), y=(417019,), dates=(417019,)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/amarpritsingh/anaconda3/envs/rnn_development/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:60: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30 - Train Loss: 1.1151, Val Loss: 1.0070, Val Acc: 0.9179\n", + "Epoch 2/30 - Train Loss: 1.0869, Val Loss: 1.0129, Val Acc: 0.9179\n", + "Epoch 3/30 - Train Loss: 1.0728, Val Loss: 1.0145, Val Acc: 0.9179\n", + "Epoch 4/30 - Train Loss: 1.0643, Val Loss: 1.0326, Val Acc: 0.1387\n", + "Epoch 5/30 - Train Loss: 1.0623, Val Loss: 0.9857, Val Acc: 0.9179\n", + "Epoch 6/30 - Train Loss: 1.0569, Val Loss: 1.0608, Val Acc: 0.9179\n", + "Epoch 7/30 - Train Loss: 1.0764, Val Loss: 0.9924, Val Acc: 0.9179\n", + "Epoch 8/30 - Train Loss: 1.0756, Val Loss: 1.0236, Val Acc: 0.9179\n", + "Epoch 9/30 - Train Loss: 1.0652, Val Loss: 0.9794, Val Acc: 0.9179\n", + "Epoch 10/30 - Train Loss: 1.1018, Val Loss: 1.1439, Val Acc: 0.1949\n", + "Epoch 11/30 - Train Loss: 1.0766, Val Loss: 0.9772, Val Acc: 0.9179\n", + "Epoch 12/30 - Train Loss: 1.0603, Val Loss: 1.0345, Val Acc: 0.9179\n", + "Epoch 13/30 - Train Loss: 1.0586, Val Loss: 1.0058, Val Acc: 0.9179\n", + "Epoch 14/30 - Train Loss: 1.0474, Val Loss: 0.9738, Val Acc: 0.9179\n", + "Epoch 15/30 - Train Loss: 1.0464, Val Loss: 1.0298, Val Acc: 0.9179\n", + "Epoch 16/30 - Train Loss: 1.0524, Val Loss: 1.0373, Val Acc: 0.1179\n", + "Epoch 17/30 - Train Loss: 1.0598, Val Loss: 0.9857, Val Acc: 0.9179\n", + "Epoch 18/30 - Train Loss: 1.0541, Val Loss: 0.9800, Val Acc: 0.9179\n", + "Epoch 19/30 - Train Loss: 1.0612, Val Loss: 1.0308, Val Acc: 0.9179\n", + "Epoch 20/30 - Train Loss: 1.0562, Val Loss: 1.0025, Val Acc: 0.9179\n", + "Epoch 21/30 - Train Loss: 1.0495, Val Loss: 1.0499, Val Acc: 0.9179\n", + "Epoch 22/30 - Train Loss: 1.0526, Val Loss: 1.0002, Val Acc: 0.9179\n", + "Epoch 23/30 - Train Loss: 1.0500, Val Loss: 0.9908, Val Acc: 0.9179\n", + "Epoch 24/30 - Train Loss: 1.0405, Val Loss: 1.0384, Val Acc: 0.9179\n", + "Epoch 25/30 - Train Loss: 1.0401, Val Loss: 1.0110, Val Acc: 0.9176\n", + "Epoch 26/30 - Train Loss: 1.0334, Val Loss: 1.0186, Val Acc: 0.9176\n", + "Epoch 27/30 - Train Loss: 1.0404, Val Loss: 0.9982, Val Acc: 0.9177\n", + "Epoch 28/30 - Train Loss: 1.0347, Val Loss: 1.0034, Val Acc: 0.9175\n", + "Epoch 29/30 - Train Loss: 1.0327, Val Loss: 0.9897, Val Acc: 0.9173\n", + "Epoch 30/30 - Train Loss: 1.0300, Val Loss: 1.0060, Val Acc: 0.9174\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/t_/1j24y4gj22q4lrhhw9bl5n600000gn/T/ipykernel_31490/3918135168.py:117: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_name))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Sell 0.00 0.00 0.00 4719\n", + " Hold 0.00 0.00 0.00 231\n", + " Buy 0.92 1.00 0.96 57604\n", + "\n", + " accuracy 0.92 62554\n", + " macro avg 0.31 0.33 0.32 62554\n", + "weighted avg 0.85 0.92 0.88 62554\n", + "\n", + "\n", + "Confusion Matrix:\n", + "[[ 0 0 4719]\n", + " [ 0 0 231]\n", + " [ 0 0 57604]]\n", + "\n", + "Test Accuracy: 0.9209\n", + "Enhanced LSTM model for stock prediction with proper time series handling, attention mechanism, and realistic backtesting\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/amarpritsingh/anaconda3/envs/rnn_development/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/Users/amarpritsingh/anaconda3/envs/rnn_development/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/Users/amarpritsingh/anaconda3/envs/rnn_development/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" + ] + } + ], + "source": [ + "# Example usage (commented out as we don't have the actual data)\n", + "# Define features\n", + "feature_columns = [\n", + " 'Open', 'High', 'Low', 'Close', 'Volume',\n", + " 'Returns', 'Log_Returns', 'SMA_5', 'EMA_2',\n", + " 'RSI', 'MACD', 'MACD_Signal', 'MACD_Diff',\n", + " 'BB_High', 'BB_Low', 'Volume_EMA', 'Volatility'\n", + "]\n", + "\n", + "# Load and preprocess data\n", + "df = compute_technical_indicators(df) # apply indicators first\n", + "df = df.dropna(subset=feature_columns) # then drop only if required\n", + "# df = df.drop('Unnamed: 19', axis=1)\n", + "\n", + "# Prepare data\n", + "X_train, y_train, X_val, y_val, X_test, y_test, test_dates, scaler, test_start_idx = prepare_data(\n", + " df, feature_columns, seq_length=30, prediction_horizon=5\n", + ")\n", + "\n", + "# Calculate class weights to handle imbalance\n", + "class_counts = np.bincount(y_train)\n", + "class_weights = 1.0 / class_counts\n", + "class_weights = class_weights / np.sum(class_weights) * len(class_weights)\n", + "\n", + "# Train model\n", + "model, train_losses, val_losses, val_accuracies = train_enhanced_lstm(\n", + " X_train, y_train, X_val, y_val, \n", + " input_size=len(feature_columns),\n", + " hidden_size=128,\n", + " num_layers=2,\n", + " dropout=0.3,\n", + " epochs=30,\n", + " batch_size=64,\n", + " learning_rate=0.001,\n", + " patience=10,\n", + " class_weights=class_weights\n", + ")\n", + "\n", + "# Evaluate model\n", + "predictions, probabilities, signals = evaluate_model(\n", + " model, X_test, y_test, test_start_idx=test_start_idx\n", + ")\n", + "\n", + "# Backtest strategy\n", + "#backtest_results = backtest_strategy(predictions, test_dates, df)\n", + "\n", + "# This is a placeholder for demonstration - in a real scenario, you would run the above code with actual data\n", + "print(\"Enhanced LSTM model for stock prediction with proper time series handling, attention mechanism, and realistic backtesting\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "1ca8a7c4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "epochs = range(1, len(train_losses) + 1)\n", + "\n", + "plt.figure(figsize=(14, 5))\n", + "\n", + "# Plot Loss\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(epochs, train_losses, label='Train Loss')\n", + "plt.plot(epochs, val_losses, label='Validation Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.title('Training and Validation Loss')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "# Plot Validation Accuracy\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(epochs, val_accuracies, label='Validation Accuracy', color='green')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.title('Validation Accuracy Over Epochs')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "cda6e922", + "metadata": {}, + 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"Sharpe Ratio: 0.04\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "def backtest_trading_strategy(df, signals, initial_capital=10000, max_position_pct=0.1):\n", + " \"\"\"\n", + " Backtest a trading strategy using model-generated signals.\n", + " \n", + " Parameters:\n", + " - df: DataFrame with stock price data. Must contain columns ['Datetime', 'Ticker', 'Close'].\n", + " - signals: DataFrame or array aligned with df that contains predicted signals:\n", + " -1 = sell, 0 = hold, 1 = buy\n", + " - initial_capital: Starting cash.\n", + " - max_position_pct: Max fraction of portfolio to invest per trade (e.g., 0.1 = 10%)\n", + " \n", + " Returns:\n", + " - portfolio_value: Series with portfolio value over time.\n", + " - trades_log: DataFrame logging executed trades.\n", + " \"\"\"\n", + " \n", + " # Sort data by time\n", + " df = df.copy()\n", + " df = df.sort_values(['Ticker']) # if you want to sort by time, use index\n", + " df = df.reset_index() # this moves 'Datetime' from index to a column\n", + " df = df.sort_values(['Datetime', 'Ticker']).reset_index(drop=True)\n", + " df = df.sort_values(['Datetime', 'Ticker']).reset_index(drop=True)\n", + " \n", + " # Align signals with df\n", + " df['Signal'] = signals\n", + " \n", + " # Initialize portfolio state\n", + " cash = initial_capital\n", + " positions = {} # key: ticker, value: shares held\n", + " portfolio_value = []\n", + " trade_log = []\n", + " \n", + " # Get unique timestamps\n", + " times = df['Datetime'].unique()\n", + " \n", + " for t in times:\n", + " df_t = df[df['Datetime'] == t]\n", + " \n", + " # Calculate total portfolio value at time t\n", + " current_value = cash\n", + " for ticker, shares in positions.items():\n", + " # Get last close price for this ticker\n", + " price = df_t[df_t['Ticker'] == ticker]['Close'].values\n", + " if len(price) > 0:\n", + " current_value += shares * price[0]\n", + " else:\n", + " # No price data at this timestamp, skip\n", + " current_value += shares * df[df['Ticker'] == ticker].iloc[-1]['Close']\n", + " \n", + " # Record portfolio value\n", + " portfolio_value.append({'Datetime': t, 'Portfolio Value': current_value})\n", + " \n", + " # Execute trades for this timestamp\n", + " for _, row in df_t.iterrows():\n", + " ticker = row['Ticker']\n", + " price = row['Close']\n", + " signal = row['Signal']\n", + " \n", + " position_size = max_position_pct * current_value\n", + " \n", + " shares_held = positions.get(ticker, 0)\n", + " \n", + " if signal == 1: # Buy\n", + " # Calculate max shares to buy\n", + " max_shares_to_buy = int(position_size // price)\n", + " if max_shares_to_buy > 0:\n", + " # Buy shares and update cash/position\n", + " cost = max_shares_to_buy * price\n", + " if cash >= cost:\n", + " cash -= cost\n", + " positions[ticker] = shares_held + max_shares_to_buy\n", + " trade_log.append({'Datetime': t, 'Ticker': ticker, 'Action': 'BUY',\n", + " 'Price': price, 'Shares': max_shares_to_buy, 'Cash': cash})\n", + " \n", + " elif signal == -1 and shares_held > 0: # Sell all shares\n", + " proceeds = shares_held * price\n", + " cash += proceeds\n", + " trade_log.append({'Datetime': t, 'Ticker': ticker, 'Action': 'SELL',\n", + " 'Price': price, 'Shares': shares_held, 'Cash': cash})\n", + " positions[ticker] = 0\n", + " \n", + " # If signal == 0: hold, do nothing\n", + " \n", + " # Clean zero-share positions\n", + " positions = {k: v for k, v in positions.items() if v > 0}\n", + " \n", + " portfolio_df = pd.DataFrame(portfolio_value).set_index('Datetime')\n", + " trades_df = pd.DataFrame(trade_log)\n", + " \n", + " return portfolio_df, trades_df\n", + "\n", + "import numpy as np\n", + "\n", + "def performance_metrics(portfolio_df):\n", + " # Portfolio value series\n", + " pv = portfolio_df['Portfolio Value']\n", + " \n", + " # Daily returns\n", + " returns = pv.pct_change().dropna()\n", + " \n", + " # Total return\n", + " total_return = (pv.iloc[-1] / pv.iloc[0]) - 1\n", + " \n", + " # Max drawdown\n", + " rolling_max = pv.cummax()\n", + " drawdown = (pv - rolling_max) / rolling_max\n", + " max_drawdown = drawdown.min()\n", + " \n", + " # Annualized return and volatility (assuming daily data, 252 trading days)\n", + " trading_days = 252\n", + " ann_return = (1 + total_return) ** (trading_days / len(pv)) - 1\n", + " ann_volatility = returns.std() * np.sqrt(trading_days)\n", + " \n", + " # Sharpe ratio (risk-free rate = 0)\n", + " sharpe_ratio = ann_return / ann_volatility if ann_volatility != 0 else np.nan\n", + " \n", + " print(f\"Total Return: {total_return:.2%}\")\n", + " print(f\"Max Drawdown: {max_drawdown:.2%}\")\n", + " print(f\"Annualized Return: {ann_return:.2%}\")\n", + " print(f\"Annualized Volatility: {ann_volatility:.2%}\")\n", + " print(f\"Sharpe Ratio: {sharpe_ratio:.2f}\")\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_equity_curve(portfolio_df):\n", + " plt.figure(figsize=(12,6))\n", + " plt.plot(portfolio_df.index, portfolio_df['Portfolio Value'], label='Portfolio Value')\n", + " plt.title(\"Equity Curve\")\n", + " plt.xlabel(\"Date\")\n", + " plt.ylabel(\"Portfolio Value ($)\")\n", + " plt.grid(True)\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "\n", + "\n", + "# Example usage:\n", + "# Assuming `df` is your price DataFrame and `signals` is a list/array aligned with df rows:\n", + "# portfolio, trades = backtest_trading_strategy(df, signals)\n", + "\n", + "# Align df to last N rows, where N = length of signals\n", + "test_df = df.iloc[-len(signals):].copy()\n", + "test_df['Signal'] = signals\n", + "\n", + "\n", + "portfolio_df, trades_df = backtest_trading_strategy(test_df, test_df['Signal'].values, initial_capital=10000, max_position_pct=0.1)\n", + "\n", + "# Calculate performance\n", + "performance_metrics(portfolio_df)\n", + "\n", + "# Plot equity curve\n", + "plot_equity_curve(portfolio_df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rnn_development", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c18854e9f1ff44b920e97e2ed9f5f53486db17d6 Mon Sep 17 00:00:00 2001 From: Amar723 <140028404+Amar723@users.noreply.github.com> Date: Thu, 17 Jul 2025 17:23:25 +1000 Subject: [PATCH 08/10] Working LSTM (No Backtest) --- src/DIALEDINLSTM.ipynb | 1095 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1095 insertions(+) create mode 100644 src/DIALEDINLSTM.ipynb diff --git a/src/DIALEDINLSTM.ipynb b/src/DIALEDINLSTM.ipynb new file mode 100644 index 0000000..a0c341d --- /dev/null +++ b/src/DIALEDINLSTM.ipynb @@ -0,0 +1,1095 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c79be69b", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import datetime as dt\n", + "from datetime import date\n", + "import matplotlib.pyplot as plt\n", + "import yfinance as yf\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1e607452", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/amarpritsingh/anaconda3/envs/rnn_development/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Looks like you're using an outdated `kagglehub` version, please consider updating (latest version: 0.3.12)\n", + "Path to dataset files: /Users/amarpritsingh/.cache/kagglehub/datasets/jacksoncrow/stock-market-dataset/versions/2\n" + ] + } + ], + "source": [ + "import kagglehub\n", + "\n", + "# Download latest version\n", + "path = kagglehub.dataset_download(\"jacksoncrow/stock-market-dataset\")\n", + "\n", + "print(\"Path to dataset files:\", path)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "04caf28d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Date Open High Low Close Adj Close Volume\n", + "0 1980-12-12 0.513393 0.515625 0.513393 0.513393 0.406782 117258400\n", + "1 1980-12-15 0.488839 0.488839 0.486607 0.486607 0.385558 43971200\n", + "2 1980-12-16 0.453125 0.453125 0.450893 0.450893 0.357260 26432000\n", + "3 1980-12-17 0.462054 0.464286 0.462054 0.462054 0.366103 21610400\n", + "4 1980-12-18 0.475446 0.477679 0.475446 0.475446 0.376715 18362400\n" + ] + } + ], + "source": [ + "df = pd.read_csv(\"/Users/amarpritsingh/Library/CloudStorage/OneDrive-MonashUniversity/Deepneuron-Amar's MacBook Pro/Parallel-RNN-Training/src/archive/stocks/AAPL.csv\")\n", + "data = pd.read_csv(\"/Users/amarpritsingh/Library/CloudStorage/OneDrive-MonashUniversity/Deepneuron-Amar's MacBook Pro/Parallel-RNN-Training/src/archive/stocks/AAPL.csv\")\n", + "print(df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "45dd49a2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Open High Low Close Volume\n", + "0 0.513393 0.515625 0.513393 0.513393 117258400\n", + "1 0.488839 0.488839 0.486607 0.486607 43971200\n", + "2 0.453125 0.453125 0.450893 0.450893 26432000\n", + "3 0.462054 0.464286 0.462054 0.462054 21610400\n", + "4 0.475446 0.477679 0.475446 0.475446 18362400" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.drop(['Date', 'Adj Close'], axis = 1)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b8469d47", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Close Price Visualization\")\n", + "plt.plot(df.Close)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2e4378f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 NaN\n", + "1 NaN\n", + "2 NaN\n", + "3 NaN\n", + "4 NaN\n", + " ... \n", + "9904 285.387599\n", + "9905 285.306799\n", + "9906 285.279899\n", + "9907 285.251499\n", + "9908 285.088199\n", + "Name: Close, Length: 9909, dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ma100 = df.Close.rolling(100).mean()\n", + "ma100" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "adad13b8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Graph Of Moving Averages Of 100 Days')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# MOVING AVERAGE #\n", + "\n", + "plt.figure(figsize = (12,6))\n", + "plt.plot(df.Close)\n", + "plt.plot(ma100, 'r')\n", + "plt.title('Graph Of Moving Averages Of 100 Days')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "930cd39b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 NaN\n", + "1 NaN\n", + "2 NaN\n", + "3 NaN\n", + "4 NaN\n", + " ... \n", + "9904 249.47610\n", + "9905 249.74385\n", + "9906 250.04715\n", + "9907 250.35490\n", + "9908 250.59000\n", + "Name: Close, Length: 9909, dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ma200 = df.Close.rolling(200).mean()\n", + "ma200" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ff578216", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, '100 Days vs 200 Days Moving Averages')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (12,6))\n", + "plt.plot(df.Close)\n", + "plt.plot(ma100, 'r')\n", + "plt.plot(ma200, 'g')\n", + "plt.title('100 Days vs 200 Days Moving Averages')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a8e1b5fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9909, 5)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "974d7f84", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(6936, 7)\n", + "(2973, 7)\n" + ] + } + ], + "source": [ + "# Splitting data into training and testing\n", + "\n", + "train = pd.DataFrame(data[0:int(len(data)*0.70)])\n", + "test = pd.DataFrame(data[int(len(data)*0.70): int(len(data))])\n", + "\n", + "print(train.shape)\n", + "print(test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "22ff5b4f", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "scaler = MinMaxScaler(feature_range=(0,1))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a337043e", + "metadata": {}, + "outputs": [], + "source": [ + "train_close = train.iloc[:, 4:5].values\n", + "test_close = test.iloc[:, 4:5].values" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "251800f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.01118012],\n", + " [0.01023532],\n", + " [0.00897559],\n", + " ...,\n", + " [0.92849769],\n", + " [0.90819082],\n", + " [0.92849769]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_training_array = scaler.fit_transform(train_close)\n", + "data_training_array" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f75a9dc5", + "metadata": {}, + "outputs": [], + "source": [ + "x_train = []\n", + "y_train = [] \n", + "\n", + "for i in range(100, data_training_array.shape[0]):\n", + " x_train.append(data_training_array[i-100: i])\n", + " y_train.append(data_training_array[i, 0])\n", + "\n", + "x_train, y_train = np.array(x_train), np.array(y_train) " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "679647ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6836, 100, 1)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train.shape" + ] + }, + { + "cell_type": "markdown", + "id": "85eb76c6", + "metadata": {}, + "source": [ + "ML MODEL" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "30d6e58f", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import Dense, Dropout, LSTM\n", + "from tensorflow.keras.models import Sequential" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "992f7878", + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(LSTM(units = 50, activation = 'relu', return_sequences=True\n", + " ,input_shape = (x_train.shape[1], 1)))\n", + "model.add(Dropout(0.2))\n", + "\n", + "\n", + "model.add(LSTM(units = 60, activation = 'relu', return_sequences=True))\n", + "model.add(Dropout(0.3))\n", + "\n", + "\n", + "model.add(LSTM(units = 80, activation = 'relu', return_sequences=True))\n", + "model.add(Dropout(0.4))\n", + "\n", + "\n", + "model.add(LSTM(units = 120, activation = 'relu'))\n", + "model.add(Dropout(0.5))\n", + "\n", + "model.add(Dense(units = 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4c99a301", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " lstm (LSTM) (None, 100, 50) 10400 \n", + " \n", + " dropout (Dropout) (None, 100, 50) 0 \n", + " \n", + " lstm_1 (LSTM) (None, 100, 60) 26640 \n", + " \n", + " dropout_1 (Dropout) (None, 100, 60) 0 \n", + " \n", + " lstm_2 (LSTM) (None, 100, 80) 45120 \n", + " \n", + " dropout_2 (Dropout) (None, 100, 80) 0 \n", + " \n", + " lstm_3 (LSTM) (None, 120) 96480 \n", + " \n", + " dropout_3 (Dropout) (None, 120) 0 \n", + " \n", + " dense (Dense) (None, 1) 121 \n", + " \n", + "=================================================================\n", + "Total params: 178,761\n", + "Trainable params: 178,761\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "2f630215", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "214/214 [==============================] - 30s 133ms/step - loss: 0.0047 - MAE: 0.0334 - val_loss: 12638.0479 - val_MAE: 90.8395\n", + "Epoch 2/50\n", + "214/214 [==============================] - 29s 134ms/step - loss: 0.0018 - MAE: 0.0225 - val_loss: 12637.4502 - val_MAE: 90.8265\n", + "Epoch 3/50\n", + "214/214 [==============================] - 28s 132ms/step - loss: 0.0018 - MAE: 0.0218 - val_loss: 12623.8438 - val_MAE: 90.7812\n", + "Epoch 4/50\n", + "214/214 [==============================] - 28s 132ms/step - loss: 0.0014 - MAE: 0.0201 - val_loss: 12637.5957 - val_MAE: 90.8300\n", + "Epoch 5/50\n", + "214/214 [==============================] - 28s 132ms/step - loss: 0.0016 - MAE: 0.0220 - val_loss: 12626.3291 - val_MAE: 90.7863\n", + "Epoch 6/50\n", + "214/214 [==============================] - 29s 135ms/step - loss: 0.0015 - MAE: 0.0207 - val_loss: 12636.6104 - val_MAE: 90.8324\n", + "Epoch 7/50\n", + "214/214 [==============================] - 29s 134ms/step - loss: 0.0015 - MAE: 0.0191 - val_loss: 12645.7500 - val_MAE: 90.8641\n", + "Epoch 8/50\n", + "214/214 [==============================] - 28s 132ms/step - loss: 0.0013 - MAE: 0.0198 - val_loss: 12634.7578 - val_MAE: 90.8226\n", + "Epoch 9/50\n", + "214/214 [==============================] - 28s 132ms/step - loss: 0.0013 - MAE: 0.0181 - val_loss: 12633.7178 - val_MAE: 90.8183\n", + "Epoch 10/50\n", + "214/214 [==============================] - 28s 129ms/step - loss: 0.0013 - MAE: 0.0188 - val_loss: 12638.8613 - val_MAE: 90.8365\n", + "Epoch 11/50\n", + "214/214 [==============================] - 28s 129ms/step - loss: 0.0013 - MAE: 0.0190 - val_loss: 12636.8164 - val_MAE: 90.8322\n", + "Epoch 12/50\n", + "214/214 [==============================] - 28s 129ms/step - loss: 0.0013 - MAE: 0.0189 - val_loss: 12646.3271 - val_MAE: 90.8557\n", + "Epoch 13/50\n", + "214/214 [==============================] - 28s 129ms/step - loss: 0.0013 - MAE: 0.0194 - val_loss: 12639.5840 - val_MAE: 90.8412\n", + "Epoch 14/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 0.0012 - MAE: 0.0173 - val_loss: 12642.8877 - val_MAE: 90.8530\n", + "Epoch 15/50\n", + "214/214 [==============================] - 28s 131ms/step - loss: 0.0012 - MAE: 0.0180 - val_loss: 12636.1045 - val_MAE: 90.8180\n", + "Epoch 16/50\n", + "214/214 [==============================] - 28s 129ms/step - loss: 0.0013 - MAE: 0.0183 - val_loss: 12634.7354 - val_MAE: 90.8159\n", + "Epoch 17/50\n", + "214/214 [==============================] - 28s 129ms/step - loss: 0.0013 - MAE: 0.0171 - val_loss: 12629.1865 - val_MAE: 90.7956\n", + "Epoch 18/50\n", + "214/214 [==============================] - 28s 131ms/step - loss: 0.0011 - MAE: 0.0163 - val_loss: 12627.5469 - val_MAE: 90.7921\n", + "Epoch 19/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 9.8552e-04 - MAE: 0.0157 - val_loss: 12634.8145 - val_MAE: 90.8291\n", + "Epoch 20/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 9.7744e-04 - MAE: 0.0150 - val_loss: 12629.9971 - val_MAE: 90.8067\n", + "Epoch 21/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 9.0777e-04 - MAE: 0.0149 - val_loss: 12631.7588 - val_MAE: 90.8110\n", + "Epoch 22/50\n", + "214/214 [==============================] - 28s 131ms/step - loss: 0.0011 - MAE: 0.0158 - val_loss: 12636.4746 - val_MAE: 90.8314\n", + "Epoch 23/50\n", + "214/214 [==============================] - 29s 135ms/step - loss: 0.0011 - MAE: 0.0156 - val_loss: 12631.3525 - val_MAE: 90.8132\n", + "Epoch 24/50\n", + "214/214 [==============================] - 29s 136ms/step - loss: 0.0011 - MAE: 0.0155 - val_loss: 12630.7402 - val_MAE: 90.8137\n", + "Epoch 25/50\n", + "214/214 [==============================] - 29s 135ms/step - loss: 9.0980e-04 - MAE: 0.0146 - val_loss: 12629.2490 - val_MAE: 90.8081\n", + "Epoch 26/50\n", + "214/214 [==============================] - 29s 136ms/step - loss: 8.8412e-04 - MAE: 0.0146 - val_loss: 12630.0615 - val_MAE: 90.8059\n", + "Epoch 27/50\n", + "214/214 [==============================] - 29s 134ms/step - loss: 9.9781e-04 - MAE: 0.0154 - val_loss: 12630.9375 - val_MAE: 90.8052\n", + "Epoch 28/50\n", + "214/214 [==============================] - 28s 132ms/step - loss: 0.0010 - MAE: 0.0155 - val_loss: 12634.7451 - val_MAE: 90.8233\n", + "Epoch 29/50\n", + "214/214 [==============================] - 29s 134ms/step - loss: 9.7029e-04 - MAE: 0.0150 - val_loss: 12631.9541 - val_MAE: 90.8033\n", + "Epoch 30/50\n", + "214/214 [==============================] - 28s 132ms/step - loss: 9.5473e-04 - MAE: 0.0151 - val_loss: 12633.7168 - val_MAE: 90.8228\n", + "Epoch 31/50\n", + "214/214 [==============================] - 28s 132ms/step - loss: 0.0010 - MAE: 0.0153 - val_loss: 12637.3652 - val_MAE: 90.8382\n", + "Epoch 32/50\n", + "214/214 [==============================] - 28s 133ms/step - loss: 8.7946e-04 - MAE: 0.0145 - val_loss: 12644.6230 - val_MAE: 90.8659\n", + "Epoch 33/50\n", + "214/214 [==============================] - 29s 133ms/step - loss: 7.9632e-04 - MAE: 0.0142 - val_loss: 12627.7266 - val_MAE: 90.7947\n", + "Epoch 34/50\n", + "214/214 [==============================] - 28s 131ms/step - loss: 8.8575e-04 - MAE: 0.0148 - val_loss: 12639.7227 - val_MAE: 90.8435\n", + "Epoch 35/50\n", + "214/214 [==============================] - 28s 131ms/step - loss: 8.8528e-04 - MAE: 0.0144 - val_loss: 12631.8789 - val_MAE: 90.8130\n", + "Epoch 36/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 9.1417e-04 - MAE: 0.0151 - val_loss: 12637.1602 - val_MAE: 90.8291\n", + "Epoch 37/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 8.8330e-04 - MAE: 0.0144 - val_loss: 12631.7939 - val_MAE: 90.8149\n", + "Epoch 38/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 8.3657e-04 - MAE: 0.0144 - val_loss: 12636.2285 - val_MAE: 90.8299\n", + "Epoch 39/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 9.1063e-04 - MAE: 0.0148 - val_loss: 12628.2910 - val_MAE: 90.7940\n", + "Epoch 40/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 8.1551e-04 - MAE: 0.0141 - val_loss: 12631.1191 - val_MAE: 90.8046\n", + "Epoch 41/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 23892594.0000 - MAE: 59.1345 - val_loss: 12614.5254 - val_MAE: 90.7558\n", + "Epoch 42/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 1535025741824.0000 - MAE: 14986.1719 - val_loss: 12631.1631 - val_MAE: 90.8100\n", + "Epoch 43/50\n", + "214/214 [==============================] - 28s 129ms/step - loss: 8.8831e-04 - MAE: 0.0168 - val_loss: 12635.5508 - val_MAE: 90.8226\n", + "Epoch 44/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 0.0011 - MAE: 0.0171 - val_loss: 12629.4922 - val_MAE: 90.7996\n", + "Epoch 45/50\n", + "214/214 [==============================] - 28s 130ms/step - loss: 8.8055e-04 - MAE: 0.0163 - val_loss: 12631.7305 - val_MAE: 90.8108\n", + "Epoch 46/50\n", + "214/214 [==============================] - 28s 131ms/step - loss: 9.4340e-04 - MAE: 0.0164 - val_loss: 12631.7578 - val_MAE: 90.8026\n", + "Epoch 47/50\n", + "214/214 [==============================] - 28s 131ms/step - loss: 9.0406e-04 - MAE: 0.0161 - val_loss: 12633.5645 - val_MAE: 90.8117\n", + "Epoch 48/50\n", + "214/214 [==============================] - 28s 133ms/step - loss: 8.7945e-04 - MAE: 0.0161 - val_loss: 12634.3057 - val_MAE: 90.8168\n", + "Epoch 49/50\n", + "214/214 [==============================] - 28s 132ms/step - loss: 8.8925e-04 - MAE: 0.0158 - val_loss: 12638.0596 - val_MAE: 90.8234\n", + "Epoch 50/50\n", + "214/214 [==============================] - 28s 132ms/step - loss: 8.6077e-04 - MAE: 0.0159 - val_loss: 12635.4053 - val_MAE: 90.8178\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['MAE'])\n", + "model.fit(x_train, y_train, validation_data = (x_test, y_test) ,epochs = 50)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "993fa759", + "metadata": {}, + "outputs": [], + "source": [ + "model.save('keras_model.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "9c71e62a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 25.82999992],\n", + " [ 24.7514286 ],\n", + " [ 24.62428665],\n", + " ...,\n", + " [254.80999756],\n", + " [254.28999329],\n", + " [240.91000366]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_close.shape\n", + "test_close" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "b58d606b", + "metadata": {}, + "outputs": [], + "source": [ + "past_100_days = pd.DataFrame(train_close[-100:])" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "ed953083", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "test_df = pd.DataFrame(test_close)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "82f5e755", + "metadata": {}, + "outputs": [], + "source": [ + "final_df = pd.concat([past_100_days, test_df], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "33eacc64", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"input_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "01d3cc85", + "metadata": {}, + "outputs": [], + "source": [ + "x_test = []\n", + "y_test = []\n", + "for i in range(100, input_data.shape[0]):\n", + " x_test.append(input_data[i-100: i])\n", + " y_test.append(input_data[i, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "2a03fe7b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2973, 100, 1)\n", + "(2973,)\n" + ] + } + ], + "source": [ + "x_test, y_test = np.array(x_test), np.array(y_test)\n", + "print(x_test.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "81272424", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "93/93 [==============================] - 5s 48ms/step\n" + ] + } + ], + "source": [ + "\n", + "# Making predictions\n", + "\n", + "y_pred = model.predict(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "692ff45a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2973, 1)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "y_pred.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "b5d9e73c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.04638369, 0.0429708 , 0.04256848, ..., 0.77093839, 0.76929296,\n", + " 0.72695505])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "24dedf7e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.06509618],\n", + " [0.06545883],\n", + " [0.06579964],\n", + " ...,\n", + " [0.7161539 ],\n", + " [0.7160022 ],\n", + " [0.7181316 ]], dtype=float32)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "7d8d245f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00316427])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scaler.scale_" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "bbf6f296", + "metadata": {}, + "outputs": [], + "source": [ + "scale_factor = 1/0.00316427\n", + "y_pred = y_pred * scale_factor\n", + "y_test = y_test * scale_factor" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "dfdaa5e5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (12,6))\n", + "plt.plot(y_test, 'b', label = \"Original Price\")\n", + "plt.plot(y_pred, 'r', label = \"Predicted Price\")\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Price')\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rnn_development", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From deb1a693d73324600c1b9cfa65f98277aca9504a Mon Sep 17 00:00:00 2001 From: Amar723 <140028404+Amar723@users.noreply.github.com> Date: Thu, 17 Jul 2025 17:26:31 +1000 Subject: [PATCH 09/10] LSTM Model (Backtest Attempt) --- src/LSTMMODELAMAR copy.ipynb | 796 +++++++++++++++++++++++++++++++++++ 1 file changed, 796 insertions(+) create mode 100644 src/LSTMMODELAMAR copy.ipynb diff --git a/src/LSTMMODELAMAR copy.ipynb b/src/LSTMMODELAMAR copy.ipynb new file mode 100644 index 0000000..f680d7e --- /dev/null +++ b/src/LSTMMODELAMAR copy.ipynb @@ -0,0 +1,796 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 60, + "id": "8d34c501", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import classification_report, confusion_matrix" + ] + }, + { + "cell_type": "markdown", + "id": "be942f7a", + "metadata": {}, + "source": [ + "Define LSTM Architecture" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "9b5d91ad", + "metadata": {}, + "outputs": [], + "source": [ + "class LSTM(nn.Module):\n", + " def __init__(self, input_size, hidden_size=128, num_layers=2, dropout=0.2, num_classes=3):\n", + " super(LSTM, self).__init__()\n", + " self.lstm = nn.LSTM(\n", + " input_size=input_size,\n", + " hidden_size=hidden_size,\n", + " num_layers=num_layers,\n", + " batch_first=True,\n", + " dropout=dropout\n", + " )\n", + " self.attention_network = nn.Sequential(\n", + " nn.Linear(hidden_size, 64),\n", + " nn.Tanh(),\n", + " nn.Linear(64, 1)\n", + " )\n", + " self.fc1 = nn.Linear(hidden_size, 64)\n", + " self.dropout = nn.Dropout(dropout)\n", + " self.fc2 = nn.Linear(64, num_classes)\n", + " \n", + " def forward(self, x):\n", + " lstm_out, _ = self.lstm(x) # (batch, seq, hidden)\n", + "\n", + " # Apply attention correctly\n", + " attn_scores = self.attention_network(lstm_out).squeeze(-1) # (batch, seq)\n", + " attn_weights = torch.softmax(attn_scores, dim=1).unsqueeze(-1) # (batch, seq, 1)\n", + " context = torch.sum(attn_weights * lstm_out, dim=1) # (batch, hidden)\n", + "\n", + " out = self.fc1(context)\n", + " out = torch.relu(out)\n", + " out = self.dropout(out)\n", + " return self.fc2(out)\n" + ] + }, + { + "cell_type": "markdown", + "id": "af876517", + "metadata": {}, + "source": [ + "More Indicators - Eventually move to dataEngineering file" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "d34a61cc", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_technical_indicators(df):\n", + " \"\"\"Compute technical indicators with proper handling of NaN values\"\"\"\n", + " # Price and volume features\n", + " df['Returns'] = df['Close'].pct_change()\n", + " df['Log_Returns'] = np.log(df['Close'] / df['Close'].shift(1))\n", + " df['Volume_Change'] = df['Volume'].pct_change()\n", + " \n", + " # Moving averages\n", + " for window in [5, 10, 20, 50]:\n", + " df[f'SMA_{window}'] = df['Close'].rolling(window=window).mean()\n", + " df[f'EMA_{window}'] = df['Close'].ewm(span=window, adjust=False).mean()\n", + " \n", + " # Price relative to moving averages\n", + " df['Price_to_SMA50'] = df['Close'] / df['SMA_50']\n", + " df['Price_to_SMA200'] = df['Close'] / df['Close'].rolling(window=200).mean()\n", + " \n", + " # Volatility indicators\n", + " df['Volatility_10'] = df['Returns'].rolling(window=10).std()\n", + " df['Volatility_30'] = df['Returns'].rolling(window=30).std()\n", + " \n", + " # RSI\n", + " delta = df['Close'].diff()\n", + " gain = delta.where(delta > 0, 0).rolling(window=14).mean()\n", + " loss = -delta.where(delta < 0, 0).rolling(window=14).mean()\n", + " rs = gain / loss\n", + " df['RSI_14'] = 100 - (100 / (1 + rs))\n", + " \n", + " # MACD\n", + " ema12 = df['Close'].ewm(span=12, adjust=False).mean()\n", + " ema26 = df['Close'].ewm(span=26, adjust=False).mean()\n", + " df['MACD'] = ema12 - ema26\n", + " df['MACD_Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()\n", + " df['MACD_Hist'] = df['MACD'] - df['MACD_Signal']\n", + " \n", + " # Bollinger Bands\n", + " df['BB_Middle'] = df['Close'].rolling(window=20).mean()\n", + " df['BB_Std'] = df['Close'].rolling(window=20).std()\n", + " df['BB_Upper'] = df['BB_Middle'] + 2 * df['BB_Std']\n", + " df['BB_Lower'] = df['BB_Middle'] - 2 * df['BB_Std']\n", + " df['BB_Width'] = (df['BB_Upper'] - df['BB_Lower']) / df['BB_Middle']\n", + " \n", + " # Momentum indicators\n", + " for window in [5, 10, 20]:\n", + " df[f'Momentum_{window}'] = df['Close'] - df['Close'].shift(window)\n", + " df[f'ROC_{window}'] = df['Close'].pct_change(periods=window) * 100\n", + " \n", + " # Clean up NaN values\n", + " df = df.replace([np.inf, -np.inf], np.nan)\n", + " \n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "bc5c1822", + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_data(df, feature_columns, target_column='Returns', seq_length=20, prediction_horizon=5, threshold_pct=0.005):\n", + " df = df.sort_index()\n", + "\n", + " print(\"Initial shape:\", df.shape)\n", + "\n", + " # Compute future returns\n", + " df['Future_Return'] = df[target_column].shift(-prediction_horizon).rolling(window=prediction_horizon).sum()\n", + "\n", + " # Compute volatility\n", + " volatility = df[target_column].rolling(window=30).std()\n", + " upper_threshold = threshold_pct * volatility\n", + " lower_threshold = -threshold_pct * volatility\n", + "\n", + " # Classification labels\n", + " conditions = [\n", + " df['Future_Return'] > upper_threshold,\n", + " df['Future_Return'] < lower_threshold\n", + " ]\n", + " choices = [2, 0]\n", + " df['Target'] = np.select(conditions, choices, default=1)\n", + "\n", + " # Drop rows with NaNs now that all necessary columns are computed\n", + " df = df.dropna(subset=feature_columns + ['Future_Return', target_column])\n", + "\n", + " print(\"After dropna, shape:\", df.shape)\n", + "\n", + " # Normalize features\n", + " scaler = StandardScaler()\n", + " df_scaled = pd.DataFrame(\n", + " scaler.fit_transform(df[feature_columns]),\n", + " columns=feature_columns,\n", + " index=df.index\n", + " )\n", + "\n", + " # Create sequences\n", + " X, y, dates = [], [], []\n", + " for i in range(seq_length, len(df_scaled) - prediction_horizon):\n", + " X.append(df_scaled.iloc[i-seq_length:i].values)\n", + " y.append(df['Target'].iloc[i])\n", + " dates.append(df.index[i])\n", + "\n", + " X = np.array(X)\n", + " y = np.array(y)\n", + " dates = pd.DatetimeIndex(dates)\n", + "\n", + " print(f\"Final dataset: X={X.shape}, y={y.shape}, dates={dates.shape}\")\n", + "\n", + " # Time-based split\n", + " train_size = int(len(X) * 0.7)\n", + " val_size = int(len(X) * 0.15)\n", + "\n", + " X_train, y_train = X[:train_size], y[:train_size]\n", + " X_val, y_val = X[train_size:train_size+val_size], y[train_size:train_size+val_size]\n", + " X_test, y_test = X[train_size+val_size:], y[train_size+val_size:]\n", + " test_dates = dates[train_size+val_size:]\n", + " \n", + " test_start_idx = df.index.get_loc(test_dates[0])\n", + "\n", + " return X_train, y_train, X_val, y_val, X_test, y_test, test_dates, scaler, test_start_idx\n", + "\n", + "# Missing from your code - proper time series split\n", + "def time_series_split(df, test_size=0.2, val_size=0.2):\n", + " \"\"\"Split data chronologically instead of randomly\"\"\"\n", + " n = len(df)\n", + " test_idx = int(n * (1 - test_size))\n", + " val_idx = int(test_idx * (1 - val_size))\n", + " \n", + " train = df.iloc[:val_idx]\n", + " val = df.iloc[val_idx:test_idx]\n", + " test = df.iloc[test_idx:]\n", + " \n", + " return train, val, test\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "2c052cd0", + "metadata": {}, + "outputs": [], + "source": [ + "def train_enhanced_lstm(X_train, y_train, X_val, y_val, input_size, \n", + " hidden_size=128, num_layers=2, dropout=0.2, \n", + " epochs=50, batch_size=64, learning_rate=0.001, \n", + " patience=10, class_weights=None,\n", + " device='cuda' if torch.cuda.is_available() else 'cpu'):\n", + " \"\"\"\n", + " Train LSTM model with early stopping and learning rate scheduling\n", + " \"\"\"\n", + " # Convert to PyTorch tensors\n", + " X_train = torch.tensor(X_train, dtype=torch.float32).to(device)\n", + " y_train = torch.tensor(y_train, dtype=torch.long).to(device)\n", + " X_val = torch.tensor(X_val, dtype=torch.float32).to(device)\n", + " y_val = torch.tensor(y_val, dtype=torch.long).to(device)\n", + " \n", + " # Initialize model\n", + " model = LSTM(\n", + " input_size=input_size,\n", + " hidden_size=hidden_size,\n", + " num_layers=num_layers,\n", + " dropout=dropout\n", + " ).to(device)\n", + " \n", + " # Loss function with class weights if provided\n", + " if class_weights is not None:\n", + " weights = torch.tensor(class_weights, dtype=torch.float32).to(device)\n", + " criterion = nn.CrossEntropyLoss(weight=weights)\n", + " else:\n", + " criterion = nn.CrossEntropyLoss()\n", + " \n", + " # Optimizer with weight decay (L2 regularization)\n", + " optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-5)\n", + " \n", + " # Learning rate scheduler\n", + " scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n", + " optimizer, mode='min', factor=0.5, patience=5, verbose=True\n", + " )\n", + " \n", + " # Training loop with early stopping\n", + " best_val_loss = float('inf')\n", + " patience_counter = 0\n", + " train_losses = []\n", + " val_losses = []\n", + " val_accuracies = []\n", + " \n", + " for epoch in range(epochs):\n", + " # Training\n", + " model.train()\n", + " train_loss = 0\n", + " permutation = torch.randperm(X_train.size(0))\n", + " \n", + " for i in range(0, X_train.size(0), batch_size):\n", + " indices = permutation[i:i+batch_size]\n", + " batch_x, batch_y = X_train[indices], y_train[indices]\n", + " \n", + " optimizer.zero_grad()\n", + " outputs = model(batch_x)\n", + " loss = criterion(outputs, batch_y)\n", + " loss.backward()\n", + " \n", + " # Gradient clipping to prevent exploding gradients\n", + " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n", + " \n", + " optimizer.step()\n", + " train_loss += loss.item() * batch_x.size(0)\n", + " \n", + " train_loss /= X_train.size(0)\n", + " train_losses.append(train_loss)\n", + " \n", + " # Validation\n", + " model.eval()\n", + " val_loss = 0\n", + " correct = 0\n", + " with torch.no_grad():\n", + " for i in range(0, X_val.size(0), batch_size):\n", + " batch_x = X_val[i:i+batch_size]\n", + " batch_y = y_val[i:i+batch_size]\n", + " \n", + " outputs = model(batch_x)\n", + " loss = criterion(outputs, batch_y)\n", + " val_loss += loss.item() * batch_x.size(0)\n", + " \n", + " _, predicted = torch.max(outputs.data, 1)\n", + " correct += (predicted == batch_y).sum().item()\n", + " \n", + " val_loss /= X_val.size(0)\n", + " val_accuracy = correct / X_val.size(0)\n", + " val_losses.append(val_loss)\n", + " val_accuracies.append(val_accuracy)\n", + " \n", + " # Update learning rate\n", + " scheduler.step(val_loss)\n", + " \n", + " feature_columns = [\n", + " 'Open', 'High', 'Low', 'Close', 'Volume',\n", + " 'Returns', 'Log_Returns', 'SMA_5', 'EMA_2',\n", + " 'RSI', 'MACD', 'MACD_Signal', 'MACD_Diff',\n", + " 'BB_High', 'BB_Low', 'Volume_EMA', 'Volatility'\n", + " ]\n", + " \n", + " # Save best model\n", + " model_name = f\"lstm_input{len(feature_columns)}_hidden{hidden_size}_layers{num_layers}_drop{int(dropout*100)}.pth\"\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " patience_counter = 0\n", + " torch.save(model.state_dict(), model_name)\n", + " # else:\n", + " # patience_counter += 1\n", + " # if patience_counter >= patience:\n", + " # print(\"Early stopping triggered.\")\n", + " # break\n", + "\n", + "\n", + " \n", + " print(f\"Epoch {epoch+1}/{epochs} - Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_accuracy:.4f}\")\n", + " \n", + " # Load best model\n", + " model.load_state_dict(torch.load(model_name))\n", + " \n", + " return model, train_losses, val_losses, val_accuracies" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "c37002b6", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_model(model, X_test, y_test, test_start_idx, batch_size=64, device='cuda' if torch.cuda.is_available() else 'cpu'):\n", + " \"\"\"\n", + " Evaluate model on test set with detailed metrics\n", + " \"\"\"\n", + " X_test = torch.tensor(X_test, dtype=torch.float32).to(device)\n", + " y_test = torch.tensor(y_test, dtype=torch.long).to(device)\n", + " \n", + " model.eval()\n", + " all_preds = []\n", + " all_probs = []\n", + "\n", + " with torch.no_grad():\n", + " for i in range(0, X_test.size(0), batch_size):\n", + " batch_x = X_test[i:i+batch_size]\n", + " outputs = model(batch_x)\n", + " probs = torch.softmax(outputs, dim=1)\n", + " _, preds = torch.max(outputs, 1)\n", + " \n", + " all_preds.extend(preds.cpu().numpy())\n", + " all_probs.extend(probs.cpu().numpy())\n", + "\n", + " all_preds = np.array(all_preds)\n", + " all_probs = np.array(all_probs)\n", + " y_test_np = y_test.cpu().numpy()\n", + "\n", + " print(\"\\nClassification Report:\")\n", + " print(classification_report(y_test_np, all_preds, target_names=['Sell', 'Hold', 'Buy']))\n", + " \n", + " # Assuming:\n", + " # all_preds contains integers: 0 = Sell, 1 = Hold, 2 = Buy\n", + "\n", + " mapping = {0: -1, 1: 0, 2: 1}\n", + " signals = np.vectorize(mapping.get)(all_preds)\n", + " \n", + " # signals_full = np.zeros(len(df)) # or np.full(len(df), np.nan)\n", + " # signals_full[test_start_idx:test_start_idx + len(signals)] = signals\n", + " \n", + "\n", + " # Confusion matrix\n", + " cm = confusion_matrix(y_test_np, all_preds)\n", + " print(\"\\nConfusion Matrix:\")\n", + " print(cm)\n", + " \n", + " # Overall accuracy\n", + " accuracy = (all_preds == y_test_np).mean()\n", + " print(f\"\\nTest Accuracy: {accuracy:.4f}\")\n", + " \n", + " return all_preds, all_probs, signals " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5cfa42d", + "metadata": {}, + "outputs": [], + "source": [ + "# import pandas as pd\n", + "# from io import StringIO\n", + "\n", + "# # Step 1: Remove starting/ending quotes and add trailing comma\n", + "# with open(\"All_Stock_Data.csv\", \"r\") as f:\n", + "# cleaned_lines = [\n", + "# (line[1:-2] if line.endswith('\\n') else line[1:-1]) + ',' # strip quotes, add comma\n", + "# for line in f\n", + "# ]\n", + "\n", + "# # Step 2: Write cleaned lines to a new CSV file\n", + "# with open(\"cleaned_file.csv\", \"w\") as f:\n", + "# for line in cleaned_lines:\n", + "# f.write(line + '\\n') # ensure newline at end of each line" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "bd4ee4ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame shape: (417079, 19)\n", + "\n", + "First few rows:\n", + " Open High Low Close Volume Ticker \\\n", + "Datetime \n", + "2025-03-12 05:00:00 220.6000 220.8100 220.6000 220.800 3941.0 AAPL \n", + "2025-03-12 05:00:00 110.3200 110.3800 110.3000 110.370 8421.0 NVDA \n", + "2025-03-12 05:00:00 191.8759 191.8759 191.8061 191.846 326.0 AVGO \n", + "2025-03-12 05:00:00 21.4100 21.4100 21.3700 21.370 10334.0 INTC \n", + "2025-03-12 05:00:00 383.1500 383.1500 383.1500 383.150 1.0 MSFT \n", + "\n", + " Returns Log_Returns SMA_5 EMA_2 RSI \\\n", + "Datetime \n", + "2025-03-12 05:00:00 0.000952 0.000952 220.61200 220.730627 73.503526 \n", + "2025-03-12 05:00:00 0.000272 0.000272 110.32800 110.360319 63.674896 \n", + "2025-03-12 05:00:00 -0.000156 -0.000156 191.69642 191.832628 62.185993 \n", + "2025-03-12 05:00:00 -0.001868 -0.001870 21.38800 21.381602 46.317425 \n", + "2025-03-12 05:00:00 -0.000104 -0.000104 383.10600 383.147354 67.718210 \n", + "\n", + " MACD MACD_Signal MACD_Diff BB_High BB_Low \\\n", + "Datetime \n", + "2025-03-12 05:00:00 0.165474 0.142982 0.022493 220.734069 220.111931 \n", + "2025-03-12 05:00:00 0.108562 0.115661 -0.007099 110.538588 109.972412 \n", + "2025-03-12 05:00:00 0.276184 0.277492 -0.001308 192.181188 190.781982 \n", + "2025-03-12 05:00:00 -0.004046 -0.004011 -0.000035 21.432227 21.340773 \n", + "2025-03-12 05:00:00 0.360097 0.352635 0.007462 383.371155 382.088845 \n", + "\n", + " Volume_EMA Volatility Unnamed: 19 \n", + "Datetime \n", + "2025-03-12 05:00:00 1102.586608 0.000506 NaN \n", + "2025-03-12 05:00:00 10743.023677 0.000699 NaN \n", + "2025-03-12 05:00:00 334.650245 0.001159 NaN \n", + "2025-03-12 05:00:00 6741.179620 0.002308 NaN \n", + "2025-03-12 05:00:00 145.648977 0.000463 NaN \n", + "\n", + "Columns available:\n", + "['Open', 'High', 'Low', 'Close', 'Volume', 'Ticker', 'Returns', 'Log_Returns', 'SMA_5', 'EMA_2', 'RSI', 'MACD', 'MACD_Signal', 'MACD_Diff', 'BB_High', 'BB_Low', 'Volume_EMA', 'Volatility', 'Unnamed: 19']\n" + ] + } + ], + "source": [ + "feature_columns = [\n", + " 'Open', 'High', 'Low', 'Close', 'Volume',\n", + " 'Returns', 'Log_Returns', 'SMA_5', 'EMA_2',\n", + " 'RSI', 'MACD', 'MACD_Signal', 'MACD_Diff',\n", + " 'BB_High', 'BB_Low', 'Volume_EMA', 'Volatility'\n", + "]\n", + "\n", + "df = pd.read_csv('cleaned_file.csv', parse_dates=['Datetime'], index_col='Datetime')\n", + "df = df.dropna(subset=feature_columns) # then drop only if required\n", + "# df = df.drop('Unnamed: 19', axis=1)\n", + "print(\"DataFrame shape:\", df.shape)\n", + "print(\"\\nFirst few rows:\")\n", + "print(df.head())\n", + "print(\"\\nColumns available:\")\n", + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "33868637", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial shape: (417075, 44)\n", + "After dropna, shape: (417066, 46)\n", + "Final dataset: X=(417031, 30, 17), y=(417031,), dates=(417031,)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/amarpritsingh/anaconda3/envs/rnn_development/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:60: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n", + " warnings.warn(\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[67], line 26\u001b[0m\n\u001b[1;32m 23\u001b[0m class_weights \u001b[38;5;241m=\u001b[39m class_weights \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39msum(class_weights) \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mlen\u001b[39m(class_weights)\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# Train model\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m model, train_losses, val_losses, val_accuracies \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_enhanced_lstm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 27\u001b[0m \u001b[43m \u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_val\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_val\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfeature_columns\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 29\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m128\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 30\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_layers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 31\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.3\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 32\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 33\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m64\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 34\u001b[0m \u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.001\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 35\u001b[0m \u001b[43m \u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 36\u001b[0m \u001b[43m \u001b[49m\u001b[43mclass_weights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_weights\u001b[49m\n\u001b[1;32m 37\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# Evaluate model\u001b[39;00m\n\u001b[1;32m 40\u001b[0m predictions, probabilities, signals \u001b[38;5;241m=\u001b[39m evaluate_model(\n\u001b[1;32m 41\u001b[0m model, X_test, y_test, test_start_idx\u001b[38;5;241m=\u001b[39mtest_start_idx\n\u001b[1;32m 42\u001b[0m )\n", + "Cell \u001b[0;32mIn[64], line 56\u001b[0m, in \u001b[0;36mtrain_enhanced_lstm\u001b[0;34m(X_train, y_train, X_val, y_val, input_size, hidden_size, num_layers, dropout, epochs, batch_size, learning_rate, patience, class_weights, device)\u001b[0m\n\u001b[1;32m 53\u001b[0m batch_x, batch_y \u001b[38;5;241m=\u001b[39m X_train[indices], y_train[indices]\n\u001b[1;32m 55\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 56\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_x\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 57\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(outputs, batch_y)\n\u001b[1;32m 58\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "Cell \u001b[0;32mIn[61], line 21\u001b[0m, in \u001b[0;36mLSTM.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 21\u001b[0m lstm_out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# (batch, seq, hidden)\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Apply attention correctly\u001b[39;00m\n\u001b[1;32m 24\u001b[0m attn_scores \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattention_network(lstm_out)\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# (batch, seq)\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/rnn_development/lib/python3.11/site-packages/torch/nn/modules/rnn.py:917\u001b[0m, in \u001b[0;36mLSTM.forward\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m 914\u001b[0m hx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpermute_hidden(hx, sorted_indices)\n\u001b[1;32m 916\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_sizes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 917\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_weights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_layers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 918\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbidirectional\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_first\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 919\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 920\u001b[0m result \u001b[38;5;241m=\u001b[39m _VF\u001b[38;5;241m.\u001b[39mlstm(\u001b[38;5;28minput\u001b[39m, batch_sizes, hx, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_flat_weights, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias,\n\u001b[1;32m 921\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_layers, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbidirectional)\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# Example usage (commented out as we don't have the actual data)\n", + "# Define features\n", + "feature_columns = [\n", + " 'Open', 'High', 'Low', 'Close', 'Volume',\n", + " 'Returns', 'Log_Returns', 'SMA_5', 'EMA_2',\n", + " 'RSI', 'MACD', 'MACD_Signal', 'MACD_Diff',\n", + " 'BB_High', 'BB_Low', 'Volume_EMA', 'Volatility'\n", + "]\n", + "\n", + "# Load and preprocess data\n", + "df = compute_technical_indicators(df) # apply indicators first\n", + "df = df.dropna(subset=feature_columns) # then drop only if required\n", + "# df = df.drop('Unnamed: 19', axis=1)\n", + "\n", + "# Prepare data\n", + "X_train, y_train, X_val, y_val, X_test, y_test, test_dates, scaler, test_start_idx = prepare_data(\n", + " df, feature_columns, seq_length=30, prediction_horizon=5\n", + ")\n", + "\n", + "# Calculate class weights to handle imbalance\n", + "class_counts = np.bincount(y_train)\n", + "class_weights = 1.0 / class_counts\n", + "class_weights = class_weights / np.sum(class_weights) * len(class_weights)\n", + "\n", + "# Train model\n", + "model, train_losses, val_losses, val_accuracies = train_enhanced_lstm(\n", + " X_train, y_train, X_val, y_val, \n", + " input_size=len(feature_columns),\n", + " hidden_size=128,\n", + " num_layers=2,\n", + " dropout=0.3,\n", + " epochs=50,\n", + " batch_size=64,\n", + " learning_rate=0.001,\n", + " patience=10,\n", + " class_weights=class_weights\n", + ")\n", + "\n", + "# Evaluate model\n", + "predictions, probabilities, signals = evaluate_model(\n", + " model, X_test, y_test, test_start_idx=test_start_idx\n", + ")\n", + "\n", + "# Backtest strategy\n", + "#backtest_results = backtest_strategy(predictions, test_dates, df)\n", + "\n", + "# This is a placeholder for demonstration - in a real scenario, you would run the above code with actual data\n", + "print(\"Enhanced LSTM model for stock prediction with proper time series handling, attention mechanism, and realistic backtesting\")" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "f42955a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Return: 1.20%\n", + "Max Drawdown: -7.46%\n", + "Annualized Return: 7.08%\n", + "Annualized Volatility: 27.26%\n", + "Sharpe Ratio: 0.26\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "def backtest_trading_strategy(df, signals, initial_capital=10000, max_position_pct=0.1):\n", + " \"\"\"\n", + " Backtest a trading strategy using model-generated signals.\n", + " \n", + " Parameters:\n", + " - df: DataFrame with stock price data. Must contain columns ['Datetime', 'Ticker', 'Close'].\n", + " - signals: DataFrame or array aligned with df that contains predicted signals:\n", + " -1 = sell, 0 = hold, 1 = buy\n", + " - initial_capital: Starting cash.\n", + " - max_position_pct: Max fraction of portfolio to invest per trade (e.g., 0.1 = 10%)\n", + " \n", + " Returns:\n", + " - portfolio_value: Series with portfolio value over time.\n", + " - trades_log: DataFrame logging executed trades.\n", + " \"\"\"\n", + " \n", + " # Sort data by time\n", + " df = df.copy()\n", + " df = df.sort_values(['Ticker']) # if you want to sort by time, use index\n", + " df = df.reset_index() # this moves 'Datetime' from index to a column\n", + " df = df.sort_values(['Datetime', 'Ticker']).reset_index(drop=True)\n", + "\n", + " \n", + " \n", + " df = df.sort_values(['Datetime', 'Ticker']).reset_index(drop=True)\n", + " \n", + " # Align signals with df\n", + " df['Signal'] = signals\n", + " \n", + " # Initialize portfolio state\n", + " cash = initial_capital\n", + " positions = {} # key: ticker, value: shares held\n", + " portfolio_value = []\n", + " trade_log = []\n", + " \n", + " # Get unique timestamps\n", + " times = df['Datetime'].unique()\n", + " \n", + " for t in times:\n", + " df_t = df[df['Datetime'] == t]\n", + " \n", + " # Calculate total portfolio value at time t\n", + " current_value = cash\n", + " for ticker, shares in positions.items():\n", + " # Get last close price for this ticker\n", + " price = df_t[df_t['Ticker'] == ticker]['Close'].values\n", + " if len(price) > 0:\n", + " current_value += shares * price[0]\n", + " else:\n", + " # No price data at this timestamp, skip\n", + " current_value += shares * df[df['Ticker'] == ticker].iloc[-1]['Close']\n", + " \n", + " # Record portfolio value\n", + " portfolio_value.append({'Datetime': t, 'Portfolio Value': current_value})\n", + " \n", + " # Execute trades for this timestamp\n", + " for _, row in df_t.iterrows():\n", + " ticker = row['Ticker']\n", + " price = row['Close']\n", + " signal = row['Signal']\n", + " \n", + " position_size = max_position_pct * current_value\n", + " \n", + " shares_held = positions.get(ticker, 0)\n", + " \n", + " if signal == 1: # Buy\n", + " # Calculate max shares to buy\n", + " max_shares_to_buy = int(position_size // price)\n", + " if max_shares_to_buy > 0:\n", + " # Buy shares and update cash/position\n", + " cost = max_shares_to_buy * price\n", + " if cash >= cost:\n", + " cash -= cost\n", + " positions[ticker] = shares_held + max_shares_to_buy\n", + " trade_log.append({'Datetime': t, 'Ticker': ticker, 'Action': 'BUY',\n", + " 'Price': price, 'Shares': max_shares_to_buy, 'Cash': cash})\n", + " \n", + " elif signal == -1 and shares_held > 0: # Sell all shares\n", + " proceeds = shares_held * price\n", + " cash += proceeds\n", + " trade_log.append({'Datetime': t, 'Ticker': ticker, 'Action': 'SELL',\n", + " 'Price': price, 'Shares': shares_held, 'Cash': cash})\n", + " positions[ticker] = 0\n", + " \n", + " # If signal == 0: hold, do nothing\n", + " \n", + " # Clean zero-share positions\n", + " positions = {k: v for k, v in positions.items() if v > 0}\n", + " \n", + " portfolio_df = pd.DataFrame(portfolio_value).set_index('Datetime')\n", + " trades_df = pd.DataFrame(trade_log)\n", + " \n", + " return portfolio_df, trades_df\n", + "\n", + "import numpy as np\n", + "\n", + "def performance_metrics(portfolio_df):\n", + " # Portfolio value series\n", + " pv = portfolio_df['Portfolio Value']\n", + " \n", + " # Daily returns\n", + " returns = pv.pct_change().dropna()\n", + " \n", + " # Total return\n", + " total_return = (pv.iloc[-1] / pv.iloc[0]) - 1\n", + " \n", + " # Max drawdown\n", + " rolling_max = pv.cummax()\n", + " drawdown = (pv - rolling_max) / rolling_max\n", + " max_drawdown = drawdown.min()\n", + " \n", + " # Annualized return and volatility (assuming daily data, 252 trading days)\n", + " trading_days = 252\n", + " ann_return = (1 + total_return) ** (trading_days / len(pv)) - 1\n", + " ann_volatility = returns.std() * np.sqrt(trading_days)\n", + " \n", + " # Sharpe ratio (risk-free rate = 0)\n", + " sharpe_ratio = ann_return / ann_volatility if ann_volatility != 0 else np.nan\n", + " \n", + " print(f\"Total Return: {total_return:.2%}\")\n", + " print(f\"Max Drawdown: {max_drawdown:.2%}\")\n", + " print(f\"Annualized Return: {ann_return:.2%}\")\n", + " print(f\"Annualized Volatility: {ann_volatility:.2%}\")\n", + " print(f\"Sharpe Ratio: {sharpe_ratio:.2f}\")\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_equity_curve(portfolio_df):\n", + " plt.figure(figsize=(12,6))\n", + " plt.plot(portfolio_df.index, portfolio_df['Portfolio Value'], label='Portfolio Value')\n", + " plt.title(\"Equity Curve\")\n", + " plt.xlabel(\"Date\")\n", + " plt.ylabel(\"Portfolio Value ($)\")\n", + " plt.grid(True)\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "\n", + "\n", + "# Example usage:\n", + "# Assuming `df` is your price DataFrame and `signals` is a list/array aligned with df rows:\n", + "# portfolio, trades = backtest_trading_strategy(df, signals)\n", + "\n", + "# Align df to last N rows, where N = length of signals\n", + "test_df = df.iloc[-len(signals):].copy()\n", + "test_df['Signal'] = signals\n", + "\n", + "\n", + "portfolio_df, trades_df = backtest_trading_strategy(test_df, test_df['Signal'].values, initial_capital=10000, max_position_pct=0.1)\n", + "\n", + "# Calculate performance\n", + "performance_metrics(portfolio_df)\n", + "\n", + "# Plot equity curve\n", + "plot_equity_curve(portfolio_df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rnn_development", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From d8e62d160f15d08eb9709411dec0e9d835235cc6 Mon Sep 17 00:00:00 2001 From: Amar723 <140028404+Amar723@users.noreply.github.com> Date: Thu, 17 Jul 2025 17:40:51 +1000 Subject: [PATCH 10/10] Added some additional graphs / plotting for the predictions --- src/DIALEDINLSTM.ipynb | 131 +++++++++++++++++++++++++++++++++++++---- 1 file changed, 121 insertions(+), 10 deletions(-) diff --git a/src/DIALEDINLSTM.ipynb b/src/DIALEDINLSTM.ipynb index a0c341d..4b2577b 100644 --- a/src/DIALEDINLSTM.ipynb +++ b/src/DIALEDINLSTM.ipynb @@ -1045,15 +1045,49 @@ }, { "cell_type": "code", - "execution_count": 54, - "id": "dfdaa5e5", + "execution_count": 60, + "id": "06e31631", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 78.47\n", + "RMSE: 8.86\n", + "MAE: 6.90\n", + "R²: 0.9822\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", + "import numpy as np\n", + "\n", + "# Assuming y_pred and y_test are already scaled back\n", + "\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "rmse = np.sqrt(mse)\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "r2 = r2_score(y_test, y_pred)\n", + "\n", + "print(f\"MSE: {mse:.2f}\")\n", + "print(f\"RMSE: {rmse:.2f}\")\n", + "print(f\"MAE: {mae:.2f}\")\n", + "print(f\"R²: {r2:.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "e7be09ac", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] }, "metadata": {}, @@ -1061,13 +1095,90 @@ } ], "source": [ - "plt.figure(figsize = (12,6))\n", - "plt.plot(y_test, 'b', label = \"Original Price\")\n", - "plt.plot(y_pred, 'r', label = \"Predicted Price\")\n", - "plt.xlabel('Time')\n", - "plt.ylabel('Price')\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "rmse = np.sqrt(mse)\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "r2 = r2_score(y_test, y_pred)\n", + "\n", + "# Plot\n", + "plt.figure(figsize=(14,6))\n", + "plt.plot(y_test, label=\"Original Price\", color='blue')\n", + "plt.plot(y_pred, label=\"Predicted Price\", color='red')\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Price\")\n", + "plt.title(\"Price Prediction vs Actual\")\n", + "\n", + "# Add text box with metrics\n", + "textstr = '\\n'.join((\n", + " f'MSE: {mse:.2f}',\n", + " f'RMSE: {rmse:.2f}',\n", + " f'MAE: {mae:.2f}',\n", + " f'R²: {r2:.4f}',\n", + "))\n", + "plt.gcf().text(0.75, 0.75, textstr, fontsize=12, bbox=dict(facecolor='white', alpha=0.8))\n", + "\n", "plt.legend()\n", - "plt.show()" + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "755a41d8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "residuals = y_test - y_pred\n", + "\n", + "plt.figure(figsize=(12,4))\n", + "plt.plot(residuals, color='purple')\n", + "plt.axhline(0, color='black', linestyle='--')\n", + "plt.title(\"Residuals (Actual - Predicted)\")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Error\")\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "eaea86b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6,6))\n", + "plt.scatter(y_test, y_pred, alpha=0.5, color='teal')\n", + "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', linestyle='--')\n", + "plt.xlabel(\"Actual Price\")\n", + "plt.ylabel(\"Predicted Price\")\n", + "plt.title(\"Actual vs Predicted Prices\")\n", + "plt.grid(True)\n", + "plt.show()\n" ] } ],