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Merge pull request #474 from DanielGoldfarb/actions
Implement Github Actions (to replace Travis CI tests)
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name: mplfinance Checks
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on: [ push, pull_request ]
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jobs:
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Regression_Tests:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: [3.6, 3.7, 3.8, 3.9]
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steps:
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- name: Preliminary Information
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run: |
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echo "The job was automatically triggered by a ${{ github.event_name }} event."
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echo "This job is now running on a ${{ runner.os }} server hosted by GitHub!"
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echo "The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
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echo " "
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echo "github.ref = ${{ github.ref }}"
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echo "github.sha = ${{ github.sha }}"
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echo "github.event.pull_request.head.ref = ${{ github.event.pull_request.head.ref }}"
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echo "github.event.pull_request.head.sha = ${{ github.event.pull_request.head.sha }}"
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echo "github.event.pull_request.base.ref = ${{ github.event.pull_request.base.ref }}"
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echo "github.event.pull_request.base.sha = ${{ github.event.pull_request.base.sha }}"
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echo " "
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- name: Check out repository code
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uses: actions/checkout@v2
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- run: echo "The ${{ github.repository }} repository has been cloned to the runner."
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install pytest
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if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
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- name: Install My Package
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run: pip install .
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- name: Run Pytest
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run: python -m pytest
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- run: echo "This job's status is ${{ job.status }}."
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Pull_Request_Updates_Version:
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runs-on: ubuntu-latest
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if: github.event_name == 'pull_request'
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steps:
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- name: Check out repository code
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uses: actions/checkout@v2
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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with:
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python-version: '3.10'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install packaging
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- name: Fetch base and head on PR
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if: ${{ github.event.pull_request.base.sha }}
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run: |
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git fetch origin master ${{ github.event.pull_request.base.sha }}
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git fetch origin master ${{ github.event.pull_request.head.sha }}
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- name: Check that Pull Request includes updating the Version
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run: |
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git show ${{ github.event.pull_request.base.sha }}:src/mplfinance/_version.py > scripts/tv0.py
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git show ${{ github.sha }}:src/mplfinance/_version.py > scripts/tv1.py
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python scripts/version_update_check.py tv0 tv1
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tox.ini renamed to archive.tox.ini

File renamed without changes.

examples/mpf_demo.py

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import pandas as pd
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import mplfinance as mpf
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infile = 'data/yahoofinance-SPY-20200901-20210113.csv'
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df = pd.read_csv(infile, index_col=0, parse_dates=True).iloc[0:60]
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# mpf.plot(df,figscale=1.5,type='candle',mav=(10,20))
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#mpf.plot(df,type='candle',figscale=1.5)
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#df = pd.read_csv(infile, index_col=0, parse_dates=True).iloc[0:180]
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#mpf.plot(df,type='renko',figscale=1.5)
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#mpf.plot(df,type='pnf',figscale=1.5)
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#mpf.plot(df,type='candle',figscale=1.5,mav=10)
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mpf.plot(df,type='candle',volume=True,mav=(10,20),figscale=1.5)

examples/mpf_rsi_demo.py

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import pandas as pd
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import mplfinance as mpf
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infile = 'data/yahoofinance-SPY-20200901-20210113.csv'
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df = pd.read_csv(infile, index_col=0, parse_dates=True).iloc[0:60]
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import rsi
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df['rsi'] = rsi.relative_strength(df['Close'],n=7)
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print(df.head())
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print(df.tail())
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apd = mpf.make_addplot(df['rsi'],panel=2,color='lime',ylim=(10,90),secondary_y=True)
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mpf.plot(df,type='candle',volume=True,mav=(10,20),figscale=1.5,addplot=apd,panel_ratios=(1,0.6))
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mpf.plot(df,type='candle',style='charles',volume=True,mav=(10,20),figscale=1.5,addplot=apd,panel_ratios=(1,0.6))
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mpf.plot(df,type='candle',style='mike',volume=True,mav=(10,20),figscale=1.5,addplot=apd,panel_ratios=(1,0.6))
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mpf.plot(df,type='candle',style='checkers',volume=True,mav=(10,20),figscale=1.5,addplot=apd,panel_ratios=(1,0.6))
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mpf.plot(df,type='candle',style='yahoo',volume=True,mav=(10,20),figscale=1.5,addplot=apd,panel_ratios=(1,0.6))

examples/rsi.py

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import numpy as np
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def relative_strength(prices, n=14):
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"""
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compute the n period relative strength indicator
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http://stockcharts.com/school/doku.php?id=chart_school:glossary_r#relativestrengthindex
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http://www.investopedia.com/terms/r/rsi.asp
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"""
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deltas = np.diff(prices)
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seed = deltas[:n + 1]
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up = seed[seed >= 0].sum() / n
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down = -seed[seed < 0].sum() / n
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rs = up / down
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rsi = np.zeros_like(prices)
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rsi[:n] = 100. - 100. / (1. + rs)
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for i in range(n, len(prices)):
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delta = deltas[i - 1] # cause the diff is 1 shorter
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if delta > 0:
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upval = delta
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downval = 0.
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else:
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upval = 0.
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downval = -delta
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up = (up * (n - 1) + upval) / n
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down = (down * (n - 1) + downval) / n
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rs = up / down
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rsi[i] = 100. - 100. / (1. + rs)
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return rsi
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from select import select
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from datetime import datetime
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import time
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import pandas as pd
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import mplfinance as mpf
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import sys
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coin = 'BTC'
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bot_status = 'trading'
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def limit():
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timeout = 2.5
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print(end='')
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rlist, _, _ = select([sys.stdin], [], [], timeout)
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print('rlist=',rlist)
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if rlist:
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s = sys.stdin.readline().strip()
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print('s=',s)
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if s == 'g':
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print('\033[1;34m show chart')
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chart()
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def dataframe():
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# response = **_api exchange_**.candles(coin + '-EUR', '1m', {})
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# ohlcv = []
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# for s in range(len(response)):
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# timestamp = (int(response[s][0]))/1000
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# open = float(response[s][1])
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# high = float(response[s][2])
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# low = float(response[s][3])
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# close = float(response[s][4])
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# volume = float(response[s][5])
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# candles = {'timestamp': (datetime.utcfromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S')), 'open': open, 'high': high, 'low': low, 'close': close, 'volume': volume}
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# ohlcv.append(candles)
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# dataframe = pd.DataFrame(data=ohlcv, dtype=float)
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dataframe = pd.read_csv('../../data/SP500_NOV2019_Hist.csv',index_col=0, parse_dates=True)
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return dataframe
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def chart():
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df = dataframe()
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df.index = pd.DatetimeIndex(df['timestamp'])
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df = df.iloc[::-1]
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s = mpf.make_mpf_style(base_mpf_style='charles', gridcolor='#555555', gridstyle="--", rc={'axes.edgecolor': 'white', 'font.size': 5})
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fig, axlist = mpf.plot(df, type='candle', style=s, title= coin, ylabel = 'Price (€)', volume=True, warn_too_much_data=9999999, returnfig=True)
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mpf.show(block=True)
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# trade loop
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while bot_status == 'trading':
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limit()
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print('test')
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import mplfinance as mpf
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import pandas as pd
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#daily = pd.read_csv('/Users/jsb/Desktop/mplfinance-master/examples/data/SP500_NOV2019_Hist.csv',
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# index_col=0, parse_dates=True)
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daily = pd.read_csv('../../data/SP500_NOV2019_Hist.csv',
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index_col=0, parse_dates=True)
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daily.index.name = 'Date'
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# print(daily.shape)
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print(daily)
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# daily = daily.loc[:, ['Open', 'High', 'Low', 'Close', 'Volume']]
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mpf.figure(figsize=(20, 8), dpi=100)
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mpf.plot(daily, type='candle', tight_layout=True)
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### !pip install yfinance
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### !pip install mplfinance
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import yfinance as yf
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import mplfinance as mpf
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import numpy as np
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import pandas as pd
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# get the data from yfiance
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df=yf.download('BTC-USD',start='2008-01-04',end='2021-06-3',interval='1d')
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#code snippet 5.1
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# Fit linear regression on close
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# Return the t-statistic for a given parameter estimate.
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def tValLinR(close):
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#tValue from a linear trend
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x = np.ones((close.shape[0],2))
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x[:,1] = np.arange(close.shape[0])
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ols = sm1.OLS(close, x).fit()
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return ols.tvalues[1]
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#code snippet 5.2
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'''
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#search for the maximum absolutet-value. To identify the trend
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# - molecule - index of observations we wish to labels.
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# - close - which is the time series of x_t
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# - span - is the set of values of L (look forward period) that the algorithm will #try (window_size)
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# The L that maximizes |tHat_B_1| (t-value) is choosen - which is the look-forward #period
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# with the most significant trend. (optimization)
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'''
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def getBinsFromTrend(molecule, close, span):
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#Derive labels from the sign of t-value of trend line
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#output includes:
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# - t1: End time for the identified trend
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# - tVal: t-value associated with the estimated trend coefficient
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#- bin: Sign of the trend (1,0,-1)
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#The t-statistics for each tick has a different look-back window.
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#- idx start time in look-forward window
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#- dt1 stop time in look-forward window
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#- df1 is the look-forward window (window-size)
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#- iloc ?
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out = pd.DataFrame(index=molecule, columns=['t1', 'tVal', 'bin', 'windowSize'])
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hrzns = range(*span)
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windowSize = span[1] - span[0]
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maxWindow = span[1]-1
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minWindow = span[0]
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for idx in close.index:
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idx += (maxWindow*pd.Timedelta('1 day'))
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if idx >= close.index[-1]:
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break
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df_tval = pd.Series(dtype='float64')
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iloc0 = close.index.get_loc(idx)
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if iloc0+max(hrzns) > close.shape[0]:
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continue
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for hrzn in hrzns:
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dt1 = close.index[iloc0-hrzn+1]
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df1 = close.loc[dt1:idx]
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df_tval.loc[dt1] = tValLinR(df1.values) #calculates t-statistics on period
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dt1 = df_tval.replace([-np.inf, np.inf, np.nan], 0).abs().idxmax() #get largest t-statistics calculated over span period
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print(df_tval.index[-1])
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print(dt1)
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print(abs(df_tval.values).argmax() + minWindow)
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out.loc[idx, ['t1', 'tVal', 'bin', 'windowSize']] = df_tval.index[-1], df_tval[dt1], np.sign(df_tval[dt1]), abs(df_tval.values).argmax() + minWindow #prevent leakage
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out['t1'] = pd.to_datetime(out['t1'])
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out['bin'] = pd.to_numeric(out['bin'], downcast='signed')
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#deal with massive t-Value outliers - they dont provide more confidence and they ruin the scatter plot
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tValueVariance = out['tVal'].values.var()
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tMax = 20
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if tValueVariance < tMax:
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tMax = tValueVariance
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out.loc[out['tVal'] > tMax, 'tVal'] = tMax #cutoff tValues > 20
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out.loc[out['tVal'] < (-1)*tMax, 'tVal'] = (-1)*tMax #cutoff tValues < -20
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return out.dropna(subset=['bin'])
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if __name__ == '__main__':
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#snippet 5.3
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idx_range_from = 3
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idx_range_to = 10
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df1 = getBinsFromTrend(df.index, df['Close'], [idx_range_from,idx_range_to,1]) #[3,10,1] = range(3,10) This is the issue
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tValues = df1['tVal'].values #tVal
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doNormalize = False
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#normalise t-values to -1, 1
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if doNormalize:
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np.min(tValues)
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minusArgs = [i for i in range(0, len(tValues)) if tValues[i] < 0]
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tValues[minusArgs] = tValues[minusArgs] / (np.min(tValues)*(-1.0))
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plus_one = [i for i in range(0, len(tValues)) if tValues[i] > 0]
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tValues[plus_one] = tValues[plus_one] / np.max(tValues)
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#+(idx_range_to-idx_range_from+1)
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plt.scatter(df1.index, df0.loc[df1.index].values, c=tValues, cmap='viridis') #df1['tVal'].values, cmap='viridis')
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plt.plot(df0.index, df0.values, color='gray')
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plt.colorbar()
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plt.show()
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plt.savefig('fig5.2.png')
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plt.clf()
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plt.df['Close']()
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plt.scatter(df1.index, df0.loc[df1.index].values, c=df1['bin'].values, cmap='vipridis')
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#Test methods
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ols_tvalue = tValLinR( np.array([3.0, 3.5, 4.0]) )

pytest.ini

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[pytest]
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python_files = tests/*

scripts/version_update_check.py

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import os
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import sys
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import importlib
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from packaging import version
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if len(sys.argv) < 3:
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raise RuntimeError('Got less than 2 Version Arguments!')
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debug = True if (len(sys.argv) > 3 and sys.argv[3] == 'debug') else False
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v0 = importlib.import_module(sys.argv[1])
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pv0 = version.parse(v0.__version__)
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v1 = importlib.import_module(sys.argv[2])
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pv1 = version.parse(v1.__version__)
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if debug:
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print('sys.argv=',sys.argv)
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print('v0=',v0)
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print('v1=',v1)
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print('pv0=',pv0)
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print('pv1=',pv1)
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# cmd='cat '+sys.argv[1]+'.py'
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# print('os.system("'+cmd+'")')
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# os.system(cmd)
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# cmd='cat '+sys.argv[2]+'.py'
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# print('os.system("'+cmd+'")')
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# os.system(cmd)
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print('v0.__version__=',v0.__version__)
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print('v1.__version__=',v1.__version__)
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if not pv1 > pv0:
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print('ERROR: Pull Request requires mplfinance version to be updated: (Version '+str(pv1)+' is NOT greater than '+str(pv0)+')')
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exit(1)
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else:
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print('Version was updated OK (from '+str(pv0)+' to '+str(pv1)+')')
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exit(0)

src/mplfinance/_version.py

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version_info = (0, 12, 7, 'alpha', 18)
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version_info = (0, 12, 8, 'beta', 0)
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_specifier_ = {'alpha': 'a','beta': 'b','candidate': 'rc','final': ''}
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src/mplfinance/plotting.py

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def _warn_no_xgaps_deprecated(value):
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warnings.warn('\n\n ================================================================= '+
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'\n\n WARNING: `no_xgaps` is deprecated:'+
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'\n\n WARNING: `no_xgaps` is /deprecated/:'+
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'\n Default value is now `no_xgaps=True`'+
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'\n However, to set `no_xgaps=False` and silence this warning,'+
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'\n use instead: `show_nontrading=True`.'+

tests/reference_images/addplot12.png

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