diff --git a/.github/workflows/daily-updates.yml b/.github/workflows/daily-updates.yml index cc427429..4e10e1ab 100644 --- a/.github/workflows/daily-updates.yml +++ b/.github/workflows/daily-updates.yml @@ -22,7 +22,7 @@ jobs: python -m pip install --upgrade pip pip install -r requirements.txt - name: Drop, Create, and Backfill Database Tables - working-directory: notebooks + working-directory: database/setup env: DB_HOSTNAME: ${{ secrets.DB_HOSTNAME }} DB_USER: ${{ secrets.DB_USER }} diff --git a/.github/workflows/run-python-scripts.yml b/.github/workflows/run-python-scripts.yml index 3e148359..ed4bc65c 100644 --- a/.github/workflows/run-python-scripts.yml +++ b/.github/workflows/run-python-scripts.yml @@ -43,28 +43,28 @@ jobs: echo "Using current date: $CURRENT_DATE" echo "date=$CURRENT_DATE" >> $GITHUB_OUTPUT fi - - name: Run Portfolio.py Script - run: python Portfolio.py ${{ matrix.fund }} - - name: Run PerformanceTracker.py Script - run: python PerformanceTracker.py ${{ matrix.fund }} + # - name: Run portfolio_csv_builder.py Script + # run: python portfolio_csv_builder.py ${{ matrix.fund }} + # - name: Run generate_performance_reports.py Script + # run: python generate_performance_reports.py ${{ matrix.fund }} # Disabled Frontier Analysis due to yfinance rate limiting # - name: Run Frontier Analysis.py Script # run: python FrontierAnalysis.py ${{ matrix.fund }} - - name: Run Performance.py Script - if: ${{ github.ref == 'refs/heads/main' }} - env: - DB_HOSTNAME: ${{ secrets.DB_HOSTNAME }} - DB_USER: ${{ secrets.DB_USER }} - DB_PASSWORD: ${{ secrets.DB_PASSWORD }} - DB_PORT: ${{ secrets.DB_PORT }} - run: python Performance.py ${{ matrix.fund }} ${{ steps.determine_date.outputs.date }} - - name: Upload Output Files to Main Branch - if: ${{ github.ref == 'refs/heads/main' }} - uses: EndBug/add-and-commit@v9 - with: - pull: '--rebase --autostash' # Pull latest changes before committing (since matrix is used) - add: 'data/${{ matrix.fund }}/**' - message: "Actions daily upload data for ${{ matrix.fund }} on ${{ steps.determine_date.outputs.date }}" - default_author: github_actor - committer_name: GitHub Actions - committer_email: 41898282+github-actions[bot]@users.noreply.github.com + # - name: Run Performance.py Script + # if: ${{ github.ref == 'refs/heads/main' }} + # env: + # DB_HOSTNAME: ${{ secrets.DB_HOSTNAME }} + # DB_USER: ${{ secrets.DB_USER }} + # DB_PASSWORD: ${{ secrets.DB_PASSWORD }} + # DB_PORT: ${{ secrets.DB_PORT }} + # run: python Performance.py ${{ matrix.fund }} ${{ steps.determine_date.outputs.date }} + # - name: Upload Output Files to Main Branch + # if: ${{ github.ref == 'refs/heads/main' }} + # uses: EndBug/add-and-commit@v9 + # with: + # pull: '--rebase --autostash' # Pull latest changes before committing (since matrix is used) + # add: 'data/${{ matrix.fund }}/**' + # message: "Actions daily upload data for ${{ matrix.fund }} on ${{ steps.determine_date.outputs.date }}" + # default_author: github_actor + # committer_name: GitHub Actions + # committer_email: 41898282+github-actions[bot]@users.noreply.github.com diff --git a/.gitignore b/.gitignore index bea07dfc..61a943f9 100644 --- a/.gitignore +++ b/.gitignore @@ -4,4 +4,6 @@ env venv __pycache__ -.vscode/ \ No newline at end of file +.vscode/ +legacy/data/ +*.csv \ No newline at end of file diff --git a/DBUtils.py b/DBUtils.py deleted file mode 100644 index 29d42fbb..00000000 --- a/DBUtils.py +++ /dev/null @@ -1,139 +0,0 @@ -import os -import pandas as pd -import mysql.connector -from dotenv import load_dotenv -from datetime import datetime - -# Load environment variables -load_dotenv() - -class DBConnection: - def __init__(self): - self.host = os.getenv('DB_HOSTNAME') - self.user = os.getenv('DB_USER') - self.password = os.getenv('DB_PASSWORD') - self.port = os.getenv('DB_PORT') - self.connection = None - self.cursor = None - - def connect(self): - try: - self.connection = mysql.connector.connect( - host=self.host, - user=self.user, - password=self.password, - port=self.port - ) - self.cursor = self.connection.cursor() - - # Create database if it doesn't exist - self.cursor.execute("CREATE DATABASE IF NOT EXISTS Fund") - self.connection.database = "Fund" - - return True - except mysql.connector.Error as err: - print(f"Error connecting to database: {err}") - return False - - def create_performance_metrics_table(self): - create_table_query = """ - CREATE TABLE IF NOT EXISTS PerformanceReturns ( - date DATE PRIMARY KEY, - one_day_return DECIMAL(10, 4), - one_week_return DECIMAL(10, 4), - one_month_return DECIMAL(10, 4), - ytd_return DECIMAL(10, 4), - one_year_return DECIMAL(10, 4), - inception_return DECIMAL(10, 4) - ) - """ - try: - self.cursor.execute(create_table_query) - self.connection.commit() - return True - except mysql.connector.Error as err: - print(f"Error creating table: {err}") - return False - - def insert_performance_metrics(self, metrics_df): - insert_query = """ - INSERT INTO PerformanceReturns ( - date, - one_day_return, - one_week_return, - one_month_return, - ytd_return, - one_year_return, - inception_return - ) - VALUES (%s, %s, %s, %s, %s, %s, %s) - ON DUPLICATE KEY UPDATE - one_day_return = VALUES(one_day_return), - one_week_return = VALUES(one_week_return), - one_month_return = VALUES(one_month_return), - ytd_return = VALUES(ytd_return), - one_year_return = VALUES(one_year_return), - inception_return = VALUES(inception_return) - """ - try: - current_date = datetime.now().date() - - # Convert the metrics DataFrame to a dictionary for easier access - metrics_dict = pd.Series(metrics_df.Value.values, index=metrics_df.Metric).to_dict() - - # Extract values and convert percentages to decimals - values = [ - current_date, - float(metrics_dict['1 Day Return'].strip('%')), - float(metrics_dict['1 Week Return'].strip('%')), - float(metrics_dict['1 Month Return'].strip('%')), - float(metrics_dict['Year-to-Date Return'].strip('%')), - float(metrics_dict['1 Year Return'].strip('%')), - float(metrics_dict['Inception'].strip('%')) - ] - - self.cursor.execute(insert_query, values) - self.connection.commit() - return True - except (mysql.connector.Error, KeyError, ValueError) as err: - print(f"Error inserting data: {err}") - return False - - def close(self): - if self.cursor: - self.cursor.close() - if self.connection: - self.connection.close() - -def main(): - # Read the CSV file - try: - metrics_df = pd.read_csv('data/fund/output/performance_metrics.csv') - except Exception as e: - print(f"Error reading CSV file: {e}") - return - - # Initialize database connection - db = DBConnection() - - if not db.connect(): - print("Failed to connect to database") - return - - # Create table if it doesn't exist - if not db.create_performance_metrics_table(): - print("Failed to create table") - db.close() - return - - # Insert the metrics - if db.insert_performance_metrics(metrics_df): - print("Successfully inserted performance metrics into database") - else: - print("Failed to insert performance metrics") - - # Close the database connection - db.close() - -if __name__ == "__main__": - main() diff --git a/MetricCalculator.py b/MetricCalculator.py deleted file mode 100644 index 7ee2c6f3..00000000 --- a/MetricCalculator.py +++ /dev/null @@ -1,71 +0,0 @@ -import numpy as np -import pandas as pd -import Portfolio - -class MetricCalculator: - - def __init__(self, portfolio): - self.portfolio = portfolio - - def compounded_portfolio_return(self, df, column='Total'): - df['Daily_Return'] = df[column].pct_change() # Daily return = (P(t) - P(t-1)) / P(t-1) - compounded_return = (1 + df['Daily_Return']).prod() - 1 # Product of (1 + daily returns) - annualized_return = (1 + compounded_return)**252 - 1 # Annualizing assuming 252 trading days - return compounded_return, annualized_return - - - def average_portfolio_return(self, df, column='Total'): - df['Daily_Return'] = df[column].pct_change() - avg_daily_return = df['Daily_Return'].mean() - avg_annualized_return = avg_daily_return * 252 # Annualize assuming 252 trading days - return avg_daily_return, avg_annualized_return - - - def portfolio_variance(self, df, column='Total'): - df['Daily_Return'] = df[column].pct_change() - daily_variance = df['Daily_Return'].var() - annualized_variance = daily_variance * 252 # Annualize assuming 252 trading days - return daily_variance, annualized_variance - - def portfolio_volatility(self, df, column='Total'): - df['Daily_Return'] = df[column].pct_change() - daily_volatility = df['Daily_Return'].std() - annualized_volatility = daily_volatility * np.sqrt(252) # Annualize assuming 252 trading days - return daily_volatility, annualized_volatility - - def maximum_drawdown(self, df, column='Total'): - df['Cumulative_Return'] = (1 + df['Daily_Return']).cumprod() - df['Rolling_Max'] = df['Cumulative_Return'].cummax() - df['Drawdown'] = df['Cumulative_Return'] / df['Rolling_Max'] - 1 - max_drawdown = df['Drawdown'].min() # Max drawdown is the minimum of the drawdown series - return max_drawdown - - def sharpe_ratio(self, df, risk_free_rate=0.05, column='Total'): - df['Daily_Return'] = df[column].pct_change() - excess_daily_return = df['Daily_Return'] - risk_free_rate / 252 # Adjust for daily risk-free rate - sharpe_ratio_daily = excess_daily_return.mean() / excess_daily_return.std() - sharpe_ratio_annualized = sharpe_ratio_daily * np.sqrt(252) # Annualize - return sharpe_ratio_daily, sharpe_ratio_annualized - - def beta(self, df, market_returns, column='Total'): - df['Daily_Return'] = df[column].pct_change() - cov_matrix = np.cov(df['Daily_Return'][1:], market_returns[1:]) # Covariance between portfolio and market - beta = cov_matrix[0, 1] / market_returns.var() # Covariance of returns / Variance of market returns - return beta - - def alpha(self, df, market_returns, risk_free_rate=0.05, column='Total'): - df['Daily_Return'] = df[column].pct_change() - portfolio_return = df['Daily_Return'].mean() * 252 # Annualized portfolio return - market_return = market_returns.mean() * 252 # Annualized market return - beta_value = self.beta(self, df, market_returns, column) - alpha = portfolio_return - (risk_free_rate + beta_value * (market_return - risk_free_rate)) - return alpha - - def sortino_ratio(self, df, risk_free_rate=0.05, column='Total'): - df['Daily_Return'] = df[column].pct_change() - negative_returns = df['Daily_Return'][df['Daily_Return'] < 0] - downside_volatility = negative_returns.std() - excess_daily_return = df['Daily_Return'] - risk_free_rate / 252 - sortino_ratio = excess_daily_return.mean() / downside_volatility - sortino_ratio_annualized = sortino_ratio * np.sqrt(252) # Annualize - return sortino_ratio, sortino_ratio_annualized \ No newline at end of file diff --git a/Trade.py b/Trade.py deleted file mode 100644 index 70244b1e..00000000 --- a/Trade.py +++ /dev/null @@ -1,9 +0,0 @@ -class Trade: - def __init__(self, ticker, date, quantity, price): - self.ticker = ticker - self.date = date - self.quantity = quantity - self.price = price - - def __repr__(self): - return f"Trade(ticker={self.ticker}, date={self.date}, quantity={self.quantity}, price={self.price})" \ No newline at end of file diff --git a/app/__init__.py b/app/__init__.py new file mode 100644 index 00000000..228a3dec --- /dev/null +++ b/app/__init__.py @@ -0,0 +1,26 @@ +from flask import Flask +from flask_cors import CORS +from flask_sqlalchemy import SQLAlchemy +from flask_migrate import Migrate +import os +from dotenv import load_dotenv + +db = SQLAlchemy() +migrate = Migrate() + +def create_app(): + app = Flask(__name__) + + # Database config + app.config['SQLALCHEMY_DATABASE_URI'] = f"mysql://{os.getenv('DB_USER')}:{os.getenv('DB_PASSWORD')}@{os.getenv('DB_HOSTNAME')}:{os.getenv('DB_PORT')}/Fund" + + # Initialize extensions + db.init_app(app) + migrate.init_app(app, db) + + # Register blueprints + from .api.routes import health_bp, holdings_bp + app.register_blueprint(health_bp) + app.register_blueprint(holdings_bp) + + return app \ No newline at end of file diff --git a/api/APIdocs.txt b/app/api/APIdocs.txt similarity index 96% rename from api/APIdocs.txt rename to app/api/APIdocs.txt index 32de9182..8726806e 100644 --- a/api/APIdocs.txt +++ b/app/api/APIdocs.txt @@ -8,7 +8,7 @@ HTTPS Cert: sudo certbot --nginx -d api.degrootefinance.com -systemd for continuous uptime: +systemid for continuous uptime: sudo nano /etc/systemd/system/fund-monitor-api.service diff --git a/api/__init__.py b/app/api/__init__.py similarity index 100% rename from api/__init__.py rename to app/api/__init__.py diff --git a/api/api_app.py b/app/api/api_app.py similarity index 99% rename from api/api_app.py rename to app/api/api_app.py index 5b402ea4..1764b085 100644 --- a/api/api_app.py +++ b/app/api/api_app.py @@ -8,8 +8,8 @@ import yaml -from health_routes import health_bp -from holdings_routes import holdings_bp +from api.routes.health_routes import health_bp +from api.routes.holdings_routes import holdings_bp load_dotenv() diff --git a/api/health_routes.py b/app/api/routes/health_routes.py similarity index 100% rename from api/health_routes.py rename to app/api/routes/health_routes.py diff --git a/api/holdings_routes.py b/app/api/routes/holdings_routes.py similarity index 99% rename from api/holdings_routes.py rename to app/api/routes/holdings_routes.py index 89701407..12eaec2c 100644 --- a/api/holdings_routes.py +++ b/app/api/routes/holdings_routes.py @@ -6,7 +6,7 @@ import yaml sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -from utils.db_utils import get_db_connection +from database.connection import get_db_connection holdings_bp = Blueprint('holdings', __name__) diff --git a/app/auth/__init__.py b/app/auth/__init__.py new file mode 100644 index 00000000..6bf04e00 --- /dev/null +++ b/app/auth/__init__.py @@ -0,0 +1,4 @@ +from .auth_service import generate_session, session_exists, verify_password +from ...database.connection import get_db_connection + +__all__ = ["generate_session", "session_exists", "verify_password", "get_db_connection"] \ No newline at end of file diff --git a/utils/login_utils.py b/app/auth/auth_service.py similarity index 97% rename from utils/login_utils.py rename to app/auth/auth_service.py index f171f84b..5b098335 100644 --- a/utils/login_utils.py +++ b/app/auth/auth_service.py @@ -5,7 +5,7 @@ from dotenv import load_dotenv -from utils.db_utils import get_db_connection +from database.connection import get_db_connection load_dotenv() diff --git a/app/scripts/update_performance_metrics.py b/app/scripts/update_performance_metrics.py new file mode 100644 index 00000000..1d6e7f3e --- /dev/null +++ b/app/scripts/update_performance_metrics.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +""" +Daily script to update performance metrics for the most recent trading day. +This can be run manually or scheduled via cron/Task Scheduler. +""" + +from datetime import datetime, timedelta +import pandas as pd +from app.services.performance_calculators.returns import ReturnCalculator +import mysql.connector +from dotenv import load_dotenv + +load_dotenv() + +def get_db_connection(): + """Get database connection.""" + return mysql.connector.connect( + host=os.getenv('DB_HOSTNAME'), + user=os.getenv('DB_USER'), + password=os.getenv('DB_PASSWORD'), + port=os.getenv('DB_PORT'), + database="Fund" + ) + +def get_latest_trading_day(connection) -> str: + """Get the most recent trading day.""" + query = """ + SELECT MAX(trading_date) FROM TradingCalendar + WHERE trading_date <= CURDATE() + """ + + result = pd.read_sql(query, connection) + latest_date = result.iloc[0, 0] + + if not latest_date: + raise ValueError("No trading dates found") + + return str(latest_date) + +def update_daily_metrics(portfolio: str): + """Update performance metrics for the most recent trading day.""" + connection = get_db_connection() + cursor = connection.cursor() + + try: + latest_date = get_latest_trading_day(connection) + print(f"Calculating metrics for {portfolio} on {latest_date}") + + # Check if data already exists for this date + check_query = """ + SELECT COUNT(*) FROM PerformanceMetrics + WHERE portfolio = %s AND date = %s + """ + cursor.execute(check_query, (portfolio, latest_date)) + existing_count = cursor.fetchone()[0] + + if existing_count > 0: + print(f"Metrics already exist for {portfolio} on {latest_date}. Updating...") + + # Calculate metrics + calculator = ReturnCalculator() + returns = calculator.calculate_daily_returns(portfolio, latest_date) + + if returns: + insert_query = """ + INSERT INTO PerformanceMetrics + (date, portfolio, one_day_return, one_week_return, one_month_return, ytd_return, one_year_return) + VALUES (%s, %s, %s, %s, %s, %s, %s) + ON DUPLICATE KEY UPDATE + one_day_return = VALUES(one_day_return), + one_week_return = VALUES(one_week_return), + one_month_return = VALUES(one_month_return), + ytd_return = VALUES(ytd_return), + one_year_return = VALUES(one_year_return) + """ + + values = ( + latest_date, portfolio, returns.get('one_day_return', 0), + returns.get('one_week_return', 0), returns.get('one_month_return', 0), + returns.get('ytd_return', 0), returns.get('one_year_return', 0) + ) + + cursor.execute(insert_query, values) + connection.commit() + print(f"Successfully updated metrics for {portfolio}") + + # Print summary + print(f"Returns for {portfolio} on {latest_date}:") + for period, value in returns.items(): + print(f" {period}: {value:.4%}") + else: + print(f"No data available for {portfolio} on {latest_date}") + + except Exception as e: + print(f"Error updating metrics for {portfolio}: {str(e)}") + connection.rollback() + finally: + cursor.close() + connection.close() + +def main(): + """Main function to update all portfolios.""" + portfolios = ['dfic_core'] # Add more portfolios as needed + + print(f"Starting daily performance metrics update at {datetime.now()}") + + for portfolio in portfolios: + try: + update_daily_metrics(portfolio) + except Exception as e: + print(f"Failed to update {portfolio}: {str(e)}") + + print(f"Daily update completed at {datetime.now()}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/FrontierAnalysis.py b/app/services/frontier_analysis.py similarity index 100% rename from FrontierAnalysis.py rename to app/services/frontier_analysis.py diff --git a/app/services/performance_calculators/currency_attribution.py b/app/services/performance_calculators/currency_attribution.py new file mode 100644 index 00000000..9f383210 --- /dev/null +++ b/app/services/performance_calculators/currency_attribution.py @@ -0,0 +1,150 @@ +import pandas as pd +import mysql.connector +import os +from dotenv import load_dotenv + +load_dotenv() + +def get_db_connection(): + """Establishes a connection to the database.""" + connection = mysql.connector.connect( + host=os.getenv('DB_HOSTNAME'), + user=os.getenv('DB_USER'), + password=os.getenv('DB_PASSWORD'), + port=os.getenv('DB_PORT'), + database="Fund" + ) + return connection + +def get_first_trading_day(connection, start_date): + """Gets the first trading day on or after a given date.""" + query = """ + SELECT MIN(trading_date) + FROM TradingCalendar + WHERE trading_date >= %s + """ + df = pd.read_sql(query, connection, params=(start_date,)) + return df.iloc[0,0] + +def get_last_trading_day(connection, end_date): + """Gets the last trading day on or before a given date.""" + query = """ + SELECT MAX(trading_date) + FROM TradingCalendar + WHERE trading_date <= %s + """ + df = pd.read_sql(query, connection, params=(end_date,)) + return df.iloc[0,0] + +def calculate_currency_attribution(portfolio: str, start_date: str, end_date: str): + """ + Calculates portfolio returns and currency attribution for a given period. + + Args: + portfolio (str): The name of the portfolio to analyze. + start_date (str): The start date of the analysis period (YYYY-MM-DD). + end_date (str): The end date of the analysis period (YYYY-MM-DD). + + Returns: + A pandas DataFrame with the attribution analysis. + """ + + conn = get_db_connection() + + actual_start_date = get_first_trading_day(conn, start_date) + actual_end_date = get_last_trading_day(conn, end_date) + + # 1. Fetch initial holdings + holdings_query = """ + SELECT ticker, shares_held, price AS start_price, security_currency, market_value + FROM MaterializedHoldings + WHERE portfolio = %s AND trading_date = %s AND shares_held > 0 + """ + initial_holdings_df = pd.read_sql(holdings_query, conn, params=(portfolio, actual_start_date)) + + # 2. Fetch end prices + end_prices_query = """ + SELECT ticker, price AS end_price + FROM MaterializedHoldings + WHERE portfolio = %s AND trading_date = %s + """ + end_prices_df = pd.read_sql(end_prices_query, conn, params=(portfolio, actual_end_date)) + + # 3. Fetch currency rates + currency_query = "SELECT date, CAD, USD, EUR FROM Currencies WHERE date IN (%s, %s)" + currency_df = pd.read_sql(currency_query, conn, params=(actual_start_date, actual_end_date), index_col='date') + + conn.close() + + # 4. Combine data + data_df = pd.merge(initial_holdings_df, end_prices_df, on='ticker') + + # Portfolio total market value at the start + total_market_value_start = data_df['market_value'].sum() + data_df['weight'] = data_df['market_value'] / total_market_value_start + + # 5. Handle currency conversions + # FX rates are CAD per Local. We want USD per Local. + # USD per CAD = 1 / (CAD per USD) + usd_per_cad_start = 1 / currency_df.loc[actual_start_date, 'USD'] + usd_per_cad_end = 1 / currency_df.loc[actual_end_date, 'USD'] + + def get_fx_rate(row, date_type): + currency = row['security_currency'] + date = actual_start_date if date_type == 'start' else actual_end_date + + if currency == 'USD': + return 1.0 + if currency == 'CAD': + return usd_per_cad_start if date_type == 'start' else usd_per_cad_end + if currency == 'EUR': + # USD per EUR = (CAD per EUR) * (USD per CAD) + cad_per_eur = currency_df.loc[date, 'EUR'] + usd_per_cad = usd_per_cad_start if date_type == 'start' else usd_per_cad_end + return cad_per_eur * usd_per_cad + return 1.0 + + data_df['fx_start'] = data_df.apply(lambda row: get_fx_rate(row, 'start'), axis=1) + data_df['fx_end'] = data_df.apply(lambda row: get_fx_rate(row, 'end'), axis=1) + + # 6. Calculate returns + data_df['local_return'] = (data_df['end_price'] / data_df['start_price']) - 1 + data_df['fx_return'] = (data_df['fx_end'] / data_df['fx_start']) - 1 + + # Total return in USD + start_price_usd = data_df['start_price'] * data_df['fx_start'] + end_price_usd = data_df['end_price'] * data_df['fx_end'] + data_df['total_return'] = (end_price_usd / start_price_usd) - 1 + + # 7. Calculate attribution + data_df['asset_selection_contrib'] = data_df['weight'] * data_df['local_return'] + data_df['currency_contrib'] = data_df['weight'] * data_df['fx_return'] + data_df['interaction_contrib'] = data_df['weight'] * data_df['local_return'] * data_df['fx_return'] + + # 8. Summarize + total_asset_selection = data_df['asset_selection_contrib'].sum() + total_currency = data_df['currency_contrib'].sum() + total_interaction = data_df['interaction_contrib'].sum() + portfolio_total_return = (total_asset_selection + total_currency + total_interaction) + + summary = pd.DataFrame({ + 'Effect': ['Asset Selection (Stock Picking)', 'Currency Effect (USD Strength)', 'Interaction Effect', 'Total Portfolio Return'], + 'Contribution': [total_asset_selection, total_currency, total_interaction, portfolio_total_return] + }) + + return summary + +if __name__ == '__main__': + # This part will be used to test the functions and show the results. + + # Analysis for 2024 + attribution_2024 = calculate_currency_attribution('dfic_core', '2024-01-01', '2024-12-31') + print("Currency Attribution for 2024:") + print(attribution_2024.to_string(formatters={'Contribution': '{:.2%}'.format})) + + # Analysis for 2025 YTD + from datetime import datetime + today_str = datetime.today().strftime('%Y-%m-%d') + attribution_2025 = calculate_currency_attribution('dfic_core', '2025-01-01', today_str) + print("\nCurrency Attribution for 2025 YTD:") + print(attribution_2025.to_string(formatters={'Contribution': '{:.2%}'.format})) \ No newline at end of file diff --git a/app/services/performance_calculators/returns.py b/app/services/performance_calculators/returns.py new file mode 100644 index 00000000..0d71646c --- /dev/null +++ b/app/services/performance_calculators/returns.py @@ -0,0 +1,79 @@ +# app/services/performance/return_calculator.py +import pandas as pd +from typing import Dict, Optional +from app.services.performance_calculators.currency_attribution import get_db_connection + +class ReturnCalculator: + def __init__(self): + self.conn = get_db_connection() + + def calculate_daily_returns(self, portfolio: str, date: str) -> Dict[str, float]: + """Calculate various return metrics for a portfolio on a given date.""" + + # Get portfolio holdings and prices + holdings_query = """ + SELECT ticker, shares_held, price, market_value + FROM MaterializedHoldings + WHERE portfolio = %s AND trading_date = %s AND shares_held > 0 + """ + + holdings_df = pd.read_sql(holdings_query, self.conn, params=(portfolio, date)) + + if holdings_df.empty: + return {} + + # Calculate different return periods + returns = { + 'one_day_return': self._calculate_period_return(portfolio, date, days=1), + 'one_week_return': self._calculate_period_return(portfolio, date, days=7), + 'one_month_return': self._calculate_period_return(portfolio, date, days=30), + 'ytd_return': self._calculate_ytd_return(portfolio, date), + 'one_year_return': self._calculate_period_return(portfolio, date, days=365), + } + + return returns + + def _calculate_period_return(self, portfolio: str, end_date: str, days: int) -> float: + """Calculate return over a specific period.""" + # Get start and end portfolio values + start_date_query = """ + SELECT trading_date FROM TradingCalendar + WHERE trading_date <= %s + ORDER BY trading_date DESC + LIMIT 1 OFFSET %s + """ + + start_date = pd.read_sql(start_date_query, self.conn, params=(end_date, days-1)).iloc[0, 0] + + # Calculate total portfolio values + start_value = self._get_portfolio_value(portfolio, start_date) + end_value = self._get_portfolio_value(portfolio, end_date) + + if start_value == 0: + return 0.0 + + return (end_value - start_value) / start_value + + def _get_portfolio_value(self, portfolio: str, date: str) -> float: + """Get total portfolio value on a given date.""" + query = """ + SELECT SUM(total_market_value) + FROM MaterializedHoldings + WHERE portfolio = %s AND trading_date = %s + """ + + result = pd.read_sql(query, self.conn, params=(portfolio, date)) + return result.iloc[0, 0] or 0.0 + + def _calculate_ytd_return(self, portfolio: str, date: str) -> float: + """Calculate year-to-date return.""" + year_start = f"{date[:4]}-01-01" + + # Get portfolio values at year start and current date + start_value = self._get_portfolio_value(portfolio, year_start) + end_value = self._get_portfolio_value(portfolio, date) + + if start_value == 0: + return 0.0 + + return (end_value - start_value) / start_value \ No newline at end of file diff --git a/config.yaml b/config/config.yaml similarity index 100% rename from config.yaml rename to config/config.yaml diff --git a/portfolios/dfic_benchmark.yaml b/config/portfolio_definitions/dfic_benchmark.yaml similarity index 100% rename from portfolios/dfic_benchmark.yaml rename to config/portfolio_definitions/dfic_benchmark.yaml diff --git a/portfolios/dfic_core.yaml b/config/portfolio_definitions/dfic_core.yaml similarity index 94% rename from portfolios/dfic_core.yaml rename to config/portfolio_definitions/dfic_core.yaml index f0206712..4edf9ba7 100644 --- a/portfolios/dfic_core.yaml +++ b/config/portfolio_definitions/dfic_core.yaml @@ -208,20 +208,20 @@ transactions: quantity: 56 currency: USD - - type: Buy - date: 2022-05-09 - ticker: AGG - # price: 132.41 #price in CAD - price: 102.50 - quantity: 226 - currency: USD + # - type: Buy + # date: 2022-05-09 + # ticker: AGG + # # price: 132.41 #price in CAD + # price: 102.50 + # quantity: 226 + # currency: USD - - type: Buy - date: 2022-05-09 - ticker: RSP - price: 192.16 - quantity: 290 - currency: CAD + # - type: Buy + # date: 2022-05-09 + # ticker: RSP + # price: 192.16 + # quantity: 290 + # currency: CAD # - type: Sell # date: 2022-05-09 @@ -230,13 +230,13 @@ transactions: # quantity: 265 # currency: CAD - - type: Sell - date: 2023-04-27 - ticker: AGG - # price: 134.34 # CAD - price: 98.57 - quantity: 226 - currency: USD + # - type: Sell + # date: 2023-04-27 + # ticker: AGG + # # price: 134.34 # CAD + # price: 98.57 + # quantity: 226 + # currency: USD - type: Buy date: 2023-04-27 @@ -245,12 +245,12 @@ transactions: quantity: 155 currency: USD - - type: Sell - date: 2023-04-27 - ticker: RSP - price: 192.30 - quantity: 290 - currency: CAD + # - type: Sell + # date: 2023-04-27 + # ticker: RSP + # price: 192.30 + # quantity: 290 + # currency: CAD - type: Buy date: 2023-04-27 @@ -285,7 +285,7 @@ transactions: ticker: SPY price: 406.81 quantity: 35 - currency: CAD + currency: USD - type: Buy date: 2023-04-27 diff --git a/data/benchmark/input/trades.csv b/data/benchmark/input/trades.csv deleted file mode 100644 index bc893ef0..00000000 --- a/data/benchmark/input/trades.csv +++ /dev/null @@ -1,5 +0,0 @@ -Date,Ticker,Currency,Quantity,Price -2022-05-05,XIU.TO,CAD,1054.30674,28.92279434 -2022-05-05,SPY,USD,60.26439139,397.25787354 -2022-05-05,AGG,USD,172.0812466,92.74882507 -2022-05-05,XBB.TO,CAD,794.663288,25.58189964 diff --git a/data/benchmark/output/cash.csv b/data/benchmark/output/cash.csv deleted file mode 100644 index f443c100..00000000 --- a/data/benchmark/output/cash.csv +++ /dev/null @@ -1,796 +0,0 @@ -Date,Cash -2022-05-02,101644.99 -2022-05-03,101644.99 -2022-05-04,101644.99 -2022-05-05,0.0008393707867071498 -2022-05-06,0.0008393707867071498 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b/data/benchmark/output/performance_metrics.csv deleted file mode 100644 index 5d6852fc..00000000 --- a/data/benchmark/output/performance_metrics.csv +++ /dev/null @@ -1,52 +0,0 @@ -Metric,Value -Total Return including dividends,43.84% -Daily Average Return,0.0479% -Annualized Average Return,12.82% -Daily Variance,0.0041% -Annualized Variance,1.04% -Daily Volatility,0.6437% -Annualized Volatility,10.22% -Daily Downside Variance,0.0021% -Annualized Downside Variance,0.53% -Daily Downside Volatility,0.4567% -Annualized Downside Volatility,7.25% -Sharpe Ratio (Annualized),0.75 -Sortino Ratio (Annualized),1.06 -Maximum Drawdown,-3.55% -Custom Benchmark Variance (Daily),0.0042% -SPY Variance (Daily),0.0128% -Custom Benchmark Variance (Annualized),1.07% -SPY Variance (Annualized),3.23% -Custom Benchmark Volatility (Daily),0.6505% -SPY Volatility (Daily),1.1313% -Custom Benchmark Volatility (Annualized),10.33% -SPY Volatility (Annualized),17.96% -Custom Benchmark Average Return (Daily),0.0478% -SPY Average Return (Daily),0.0624% -Custom Benchmark Average Return (Annualized),12.78% -SPY Average Return (Annualized),17.04% -Custom Benchmark Inception Return,43.66% -SPY Inception Return,56.16% -Portfolio Beta,0.9807 -Portfolio Alpha against custom benchmark,0.1938% -Portfolio Alpha against SPY,-3.9773% -Portfolio Risk Premium,8.46% -Risk Adjusted Return (three month treasury rate),12.90% -Treynor Ratio (three month treasury rate),0.09 -Information Ratio (vs. Custom Benchmark daily),0.0013 -Information Ratio (vs. Custom Benchmark annual),0.02 -XIU.TO Current Mkt Value,41982.49 -XIU.TO Fixed Income Mkt Share,43.90% -XIU.TO USD FI Mkt Share,0.00% -AGG Current Mkt Value,31266.87 -AGG Fixed Income Mkt Share,32.69% -AGG USD FI Mkt Share,100.00% -XBB.TO Current Mkt Value,22385.66 -XBB.TO Fixed Income Mkt Share,23.41% -XBB.TO USD FI Mkt Share,0.00% -1 Day Return,0.24% -1 Week Return,0.12% -1 Month Return,3.26% -Year-to-Date Return,1.73% -1 Year Return,14.56% 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-2025-05-19,97.30854797363281,594.8499755859375,28.32992935180664,39.32585144042969 -2025-05-20,97.13910675048828,592.8499755859375,28.130491256713867,39.41522979736328 -2025-05-21,96.50121307373047,582.8599853515625,27.951000213623047,39.018001556396484 -2025-05-22,96.78028869628906,583.0900268554688,28.010000228881836,39.09000015258789 -2025-05-23,96.91983032226562,579.1099853515625,28.049999237060547,39.060001373291016 -2025-05-26,96.91983032226562,579.1099853515625,28.100000381469727,39.349998474121094 -2025-05-27,97.35838317871094,591.1500244140625,28.239999771118164,39.65999984741211 -2025-05-28,97.1789779663086,587.72998046875,28.260000228881836,39.68000030517578 -2025-05-29,97.53778839111328,590.0499877929688,28.34000015258789,39.58000183105469 -2025-05-30,97.7770004272461,589.3900146484375,28.3799991607666,39.529998779296875 -2025-06-02,97.5199966430664,592.7100219726562,28.34000015258789,39.880001068115234 -2025-06-03,97.44999694824219,596.0900268554688,28.280000686645508,39.84000015258789 -2025-06-04,98.02999877929688,595.9299926757812,28.329999923706055,39.66999816894531 -2025-06-05,97.77999877929688,593.0499877929688,28.329999923706055,39.66999816894531 -2025-06-06,97.27999877929688,599.1400146484375,28.170000076293945,39.81999969482422 diff --git a/data/core/input/trades.csv b/data/core/input/trades.csv deleted file mode 100644 index 54fb621b..00000000 --- a/data/core/input/trades.csv +++ /dev/null @@ -1,45 +0,0 @@ -Date,Ticker,Currency,Quantity,Price -2022-05-05,NSR,CAD,265,10.58 -2022-05-05,EA,USD,16,121.57 -2022-05-05,ISRG,USD,8,238.04 -2022-05-05,MA,USD,5,358.41 -2022-05-05,TEX,USD,56,34.66 -2022-05-09,AGG,CAD,226,132.4107055 -2022-05-09,RSP,CAD,290,192.16246 -2022-05-09,NSR,CAD,-265,10.35 -2023-04-27,AGG,CAD,-226,134.343975 -2023-04-27,AGG,USD,155,97.82864 -2023-04-27,RSP,CAD,-290,192.3006993 -2023-04-27,HBM.TO,CAD,225,6.68 -2023-04-27,XIU.TO,CAD,630,31.26 -2023-04-27,XBB.TO,CAD,741,28.14504723 -2023-04-27,L.TO,CAD,20,126.0 -2023-04-27,SPY,CAD,35,406.81 -2023-04-27,APO,USD,30,62.36 -2023-04-27,AAPL,USD,4,165.39 -2023-04-27,CEG,USD,25,76.42 -2023-12-27,AMSF,USD,45,47.2 -2023-12-27,VEEV,USD,5,189.04 -2023-12-27,SPY,USD,-10,475.16 -2023-12-27,GSL,USD,101,20.29 -2023-12-27,AGG,USD,-59,98.97 -2023-12-27,SPSB,USD,320,29.73 -2023-12-28,WFG,CAD,23,112.4 -2023-12-28,XIU.TO,CAD,-111,31.9 -2023-12-28,CSH-UN.TO,CAD,107,11.6 -2024-12-23,XBB.TO,CAD,-557,28.36 -2024-12-23,XIU.TO,CAD,-519,37.43 -2024-12-23,ACO-X.TO,CAD,122,47.2218 -2024-12-23,CG,USD,51,50.37 -2024-12-23,WSC,USD,60,33.593 -2024-12-23,AER,USD,21,95.047 -2024-12-23,BLBD,USD,61,39.583 -2024-12-23,TMUS,USD,4,220.5199 -2024-12-23,MP,USD,245,15.87404286 -2024-12-23,DOLE,USD,108,13.8094 -2024-12-23,AMAT,USD,19,164.0499 -2025-03-25,AGG,USD,-96,98.46 -2025-03-25,SPY,USD,-25,574.1601 -2025-03-25,SPSB,USD,-320,30.0402 -2025-03-25,TLT,USD,225,89.8399 -2025-03-25,SCHP,USD,256,26.6199 diff --git a/data/core/input/trades_original.csv b/data/core/input/trades_original.csv deleted file mode 100644 index 64527096..00000000 --- a/data/core/input/trades_original.csv +++ /dev/null @@ -1,40 +0,0 @@ -Trade,Date,Currency,Security Name,Ticker,Price,Quantity,Market Value,Transaction Fees,Cashflow,Holdings,Previous Average Price,Average Price,Trade PnL -Buy,2022-05-05,CAD,Nomad Royalty Company,NSR,10.58,265,2803.7,0,-2803.7,265,0,10.58,0 -Buy,2022-05-05,USD,Electronic Arts,EA,121.57,16,1945.12,0,-1945.12,16,0,121.57,0 -Buy,2022-05-05,USD,Intuitive Surgical,ISRG,238.04,8,1904.32,0,-1904.32,8,0,238.04,0 -Buy,2022-05-05,USD,Mastercard,MA,358.41,5,1792.05,0,-1792.05,5,0,358.41,0 -Buy,2022-05-05,USD,Terex,TEX,34.66,56,1940.96,0,-1940.96,56,0,34.66,0 -Buy,2022-05-09,CAD,Ishares Core US Aggregate Bond ETF,AGG,132.4107055,226,29924.81944,0,-29924.81944,226,0,132.41,0 -Buy,2022-05-09,CAD,Invesco S&P500 Equal Weight ETF,RSP,192.16246,290,55727.1134,0,-55727.1134,290,0,192.16,0 -Sell,2022-05-09,CAD,Nomad Royalty Company,NSR,10.35,-265,-2742.75,0,2742.75,0,10.58,10.58,-60.95 -Sell,2023-04-27,CAD,Ishares Core US Aggregate Bond ETF,AGG,134.343975,-226,-30361.73835,0,30361.73835,155,132.41,132.41,436.92 -Buy,2023-04-27,USD,Ishares Core US Aggregate Bond ETF,AGG,97.82864,155,15163.4392,0,-15163.4392,155,132.41,97.83,0 -Sell,2023-04-27,CAD,Invesco S&P500 Equal Weight ETF,RSP,192.3006993,-290,-55767.2028,0,55767.2028,0,192.16,192.16,40.09 -Buy,2023-04-27,CAD,Hudbay Minerals,HBM.TO,6.68,225,1503,0,-1503,225,0,6.68,0 -Buy,2023-04-27,CAD,ISHARES S&P/TSX 60 INDEX ETF,XIU.TO,31.26,630,19693.8,0,-19693.8,630,0,31.26,0 -Buy,2023-04-27,CAD,ISHARES CANADIAN UNIVERSE BOND INDEX ETF,XBB.TO,28.14504723,741,20855.48,0,-20855.48,741,0,28.15,0 -Buy,2023-04-27,CAD,Loblaws,L.TO,126,20,2520,0,-2520,20,0,126,0 -Buy,2023-04-27,CAD,SPDR S&P 500 ETF TRUST ,SPY,406.81,35,14238.35,0,-14238.35,35,0,406.81,0 -Buy,2023-04-27,USD,Apollo Global Management,APO,62.36,30,1870.8,0,-1870.8,30,0,62.36,0 -Buy,2023-04-27,USD,Apple,AAPL,165.39,4,661.56,0,-661.56,4,0,165.39,0 -Buy,2023-04-27,USD,Constellation Energy,CEG,76.42,25,1910.5,0,-1910.5,25,0,76.42,0 -Buy,2023-12-27,USD,Amerisafe,AMSF,47.2,45,2124,0,-2124,45,0,47.2,0 -Buy,2023-12-27,USD,Veeva Systems Inc,VEEV,189.04,5,945.2,0,-945.2,5,0,189.04,0 -Sell,2023-12-27,USD,SPDR S&P 500 ETF TRUST ,SPY,475.16,-10,-4751.6,0,4751.6,25,406.81,406.81,683.5 -Buy,2023-12-27,USD,Global Ship Lease Inc,GSL,20.29,101,2049.29,0,-2049.29,101,0,20.29,0 -Sell,2023-12-27,USD,Ishares Core US Aggregate Bond ETF,AGG,98.97,-59,-5839.23,0,5839.23,96,97.83,97.83,67.34 -Buy,2023-12-27,USD,SPDR Portfolio Short Term Corporate Bond ETF,SPSB,29.73,320,9513.6,0,-9513.6,320,0,29.73,0 -Buy,2023-12-28,CAD,West Fraser Timber Co. Ltd.,WFG,112.4,23,2585.2,0,-2585.2,23,0,112.4,0 -Sell,2023-12-28,CAD,ISHARES S&P/TSX 60 INDEX ETF,XIU.TO,31.9,-111,-3540.9,0,3540.9,519,31.26,31.26,71.04 -Buy,2023-12-28,CAD,Chartwell Retirement Residences,CSH-UN.TO,11.6,107,1241.2,0,-1241.2,107,0,11.6,0 -Sell,2024-12-23,CAD,ISHARES CANADIAN UNIVERSE BOND INDEX ETF,XBB.TO,28.36,-557,-15796.52,0,15796.52,184,28.15,28.15,119.73 -Sell,2024-12-23,CAD,ISHARES S&P/TSX 60 INDEX ETF,XIU.TO,37.43,-519,-19426.17,0,19426.17,0,31.26,31.26,3202.23 -Buy,2024-12-23,CAD,ATCO,ACO-X.TO,47.2218,122,5761.0596,0,-5761.0596,122,0,47.22,0 -Buy,2024-12-23,USD,CARLYLE GROUP INC,CG,50.37,51,2568.87,0,-2568.87,51,0,50.37,0 -Buy,2024-12-23,USD,WILLSCOT HOLDINGS CORPORATION,WSC,33.593,60,2015.58,0,-2015.58,60,0,33.59,0 -Buy,2024-12-23,USD,AERCAP HOLDINGS,AER,95.047,21,1995.987,0,-1995.987,21,0,95.05,0 -Buy,2024-12-23,USD,BLUE BIRD CORPORATION,BLBD,39.583,61,2414.563,0,-2414.563,61,0,39.58,0 -Buy,2024-12-23,USD,T MOBILE,TMUS,220.5199,4,882.0796,0,-882.0796,4,0,220.52,0 -Buy,2024-12-23,USD,MP MATERIALS,MP,15.87404286,245,3889.1405,0,-3889.1405,245,0,15.87,0 -Buy,2024-12-23,USD,DOLE PLC ,DOLE,13.8094,108,1491.4152,0,-1491.4152,108,0,13.81,0 -Buy,2024-12-23,USD,APPLIED MATERIALS INC,AMAT,164.0499,19,3116.9481,0,-3116.9481,19,0,164.05,0 \ No newline at end of file diff --git a/data/core/output/cash.csv b/data/core/output/cash.csv deleted file mode 100644 index f6364aec..00000000 --- a/data/core/output/cash.csv +++ /dev/null @@ -1,796 +0,0 @@ -Date,Cash -2022-05-02,101644.99 -2022-05-03,101644.99 -2022-05-04,101644.99 -2022-05-05,89183.3715887332 -2022-05-06,89183.3715887332 -2022-05-09,6274.18874573319 -2022-05-10,6274.18874573319 -2022-05-11,6274.18874573319 -2022-05-12,6274.18874573319 -2022-05-13,6274.18874573319 -2022-05-16,6274.18874573319 -2022-05-17,6274.18874573319 -2022-05-18,6274.18874573319 -2022-05-19,6274.18874573319 -2022-05-20,6274.18874573319 -2022-05-23,6274.18874573319 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a/data/core/output/exchange_rates.csv b/data/core/output/exchange_rates.csv deleted file mode 100644 index 34b7f31e..00000000 --- a/data/core/output/exchange_rates.csv +++ /dev/null @@ -1,796 +0,0 @@ -Date,CAD,USD -2022-05-02,1.0,1.2853000164031982 -2022-05-03,1.0,1.2874000072479248 -2022-05-04,1.0,1.2833800315856934 -2022-05-05,1.0,1.2737200260162354 -2022-05-06,1.0,1.2826999425888062 -2022-05-09,1.0,1.2918200492858887 -2022-05-10,1.0,1.3008400201797485 -2022-05-11,1.0,1.3031200170516968 -2022-05-12,1.0,1.2994400262832642 -2022-05-13,1.0,1.3023099899291992 -2022-05-16,1.0,1.2906999588012695 -2022-05-17,1.0,1.284999966621399 -2022-05-18,1.0,1.280809998512268 -2022-05-19,1.0,1.2884199619293213 -2022-05-20,1.0,1.2819600105285645 -2022-05-23,1.0,1.2812000513076782 -2022-05-24,1.0,1.2787100076675415 -2022-05-25,1.0,1.2822400331497192 -2022-05-26,1.0,1.2820199728012085 -2022-05-27,1.0,1.2773000001907349 -2022-05-30,1.0,1.2723699808120728 -2022-05-31,1.0,1.2655199766159058 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-2025-06-06,1114.7653857650512,6158.559944152832,3353.6031663166405,0.0,4329.69274235808,2819.1669111156693,5471.463269817523,3381.360392549795,10209.02457610664,3225.7238119590584,1939.9099102020264,2039.8369487742275,3270.6055004085647,3550.2259300239148,2967.7499055862427,6090.756431241811,4431.600036621094,4032.4965054399945,8605.237989001105,,0.0,9177.024973205174,0.0,0.0,3553.451389389731,26245.188782839818,1344.0379604040863,1949.9646737000876,2351.8477480733955,2314.045608816009,5183.280014038086,0.0 diff --git a/data/core/output/performance_metrics.csv b/data/core/output/performance_metrics.csv deleted file mode 100644 index e5c79e15..00000000 --- a/data/core/output/performance_metrics.csv +++ /dev/null @@ -1,58 +0,0 @@ -Metric,Value -Total Return including dividends,48.47% -Daily Average Return,0.0536% -Annualized Average Return,14.44% -Daily Variance,0.0075% -Annualized Variance,1.90% -Daily Volatility,0.8680% -Annualized Volatility,13.78% -Daily Downside Variance,0.0042% -Annualized Downside Variance,1.07% -Daily Downside Volatility,0.6508% -Annualized Downside Volatility,10.33% -Sharpe Ratio (Annualized),0.66 -Sortino Ratio (Annualized),0.88 -Maximum Drawdown,-5.53% -Custom Benchmark Variance (Daily),0.0042% -SPY Variance (Daily),0.0128% -Custom Benchmark Variance (Annualized),1.07% -SPY Variance (Annualized),3.23% -Custom Benchmark Volatility (Daily),0.6505% -SPY Volatility (Daily),1.1313% -Custom Benchmark Volatility (Annualized),10.33% -SPY Volatility (Annualized),17.96% -Custom Benchmark Average Return (Daily),0.0478% -SPY Average Return (Daily),0.0624% -Custom Benchmark Average Return (Annualized),12.78% -SPY Average Return (Annualized),17.04% -Custom Benchmark Inception Return,43.66% -SPY Inception Return,56.16% -Portfolio Beta,1.2118 -Portfolio Alpha against custom benchmark,-0.1237% -Portfolio Alpha against SPY,-5.2777% -Portfolio Risk Premium,10.08% -Risk Adjusted Return (three month treasury rate),11.92% -Treynor Ratio (three month treasury rate),0.08 -Information Ratio (vs. Custom Benchmark daily),0.0149 -Information Ratio (vs. Custom Benchmark annual),0.24 -XBB.TO Current Mkt Value,5183.28 -XBB.TO Fixed Income Mkt Share,9.67% -XBB.TO USD FI Mkt Share,0.00% -AGG Current Mkt Value,0.00 -AGG Fixed Income Mkt Share,0.00% -AGG USD FI Mkt Share,0.00% -SPSB Current Mkt Value,0.00 -SPSB Fixed Income Mkt Share,0.00% -SPSB USD FI Mkt Share,0.00% -SCHP Current Mkt Value,12541.96 -SCHP Fixed Income Mkt Share,23.40% -SCHP USD FI Mkt Share,25.91% -TLT Current Mkt Value,35868.51 -TLT Fixed Income Mkt Share,66.93% -TLT USD FI Mkt Share,74.09% -1 Day Return,0.12% -1 Week Return,0.55% -1 Month Return,2.95% -Year-to-Date Return,1.34% -1 Year Return,15.50% -Inception,48.47% diff --git a/data/core/output/performance_returns.csv b/data/core/output/performance_returns.csv deleted file mode 100644 index 401bccbc..00000000 --- a/data/core/output/performance_returns.csv +++ /dev/null @@ -1,796 +0,0 @@ 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+from alembic import op +import sqlalchemy as sa + +def upgrade(): + """Create the PerformanceMetrics table.""" + op.create_table( + 'PerformanceMetrics', + sa.Column('id', sa.Integer(), primary_key=True, autoincrement=True), + sa.Column('date', sa.Date(), nullable=False), + sa.Column('portfolio', sa.String(50), nullable=False), + sa.Column('one_day_return', sa.DECIMAL(10,6)), + sa.Column('one_week_return', sa.DECIMAL(10,6)), + sa.Column('one_month_return', sa.DECIMAL(10,6)), + sa.Column('ytd_return', sa.DECIMAL(10,6)), + sa.Column('one_year_return', sa.DECIMAL(10,6)), + sa.Column('created_at', sa.TIMESTAMP(), server_default=sa.text('CURRENT_TIMESTAMP')), + sa.UniqueConstraint('date', 'portfolio', name='unique_date_portfolio') + ) + +def downgrade(): + """Remove the PerformanceMetrics table.""" + op.drop_table('PerformanceMetrics') \ No newline at end of file diff --git a/database/migrations/002_populate_historical_performance_data.py b/database/migrations/002_populate_historical_performance_data.py new file mode 100644 index 00000000..8575bfc6 --- /dev/null +++ b/database/migrations/002_populate_historical_performance_data.py @@ -0,0 +1,89 @@ +from alembic import op +import pandas as pd +from app.services.performance.returns import ReturnCalculator + +def upgrade(): + """Populate PerformanceMetrics table with historical data.""" + # Get the connection from the current migration context + connection = op.get_bind() + + # Define portfolios and date ranges to populate + portfolios_data = [ + { + 'portfolio': 'dfic_core', + 'start_date': '2024-01-01', + 'end_date': '2024-12-31' + } + # Add more portfolios here as needed + ] + + for portfolio_config in portfolios_data: + populate_portfolio_data( + connection=connection, + portfolio=portfolio_config['portfolio'], + start_date=portfolio_config['start_date'], + end_date=portfolio_config['end_date'] + ) + +def downgrade(): + """Remove all historical performance data.""" + op.execute("DELETE FROM PerformanceMetrics") + +def populate_portfolio_data(connection, portfolio: str, start_date: str, end_date: str): + """Populate historical performance metrics for a specific portfolio.""" + calculator = ReturnCalculator() + + # Get all trading dates in range + dates_query = """ + SELECT trading_date FROM TradingCalendar + WHERE trading_date BETWEEN %s AND %s + ORDER BY trading_date + """ + + dates_df = pd.read_sql(dates_query, connection, params=(start_date, end_date)) + + print(f"Populating data for {portfolio} from {start_date} to {end_date}") + print(f"Found {len(dates_df)} trading days to process") + + success_count = 0 + error_count = 0 + + for _, row in dates_df.iterrows(): + date = row['trading_date'] + + try: + returns = calculator.calculate_daily_returns(portfolio, str(date)) + + if returns: + insert_query = """ + INSERT INTO PerformanceMetrics + (date, portfolio, one_day_return, one_week_return, one_month_return, ytd_return, one_year_return) + VALUES (%s, %s, %s, %s, %s, %s, %s) + ON DUPLICATE KEY UPDATE + one_day_return = VALUES(one_day_return), + one_week_return = VALUES(one_week_return), + one_month_return = VALUES(one_month_return), + ytd_return = VALUES(ytd_return), + one_year_return = VALUES(one_year_return) + """ + + values = ( + date, portfolio, returns.get('one_day_return', 0), + returns.get('one_week_return', 0), returns.get('one_month_return', 0), + returns.get('ytd_return', 0), returns.get('one_year_return', 0) + ) + + connection.execute(insert_query, values) + success_count += 1 + + # Progress indicator + if success_count % 50 == 0: + print(f"Processed {success_count} records for {portfolio}") + + except Exception as e: + error_count += 1 + print(f"Error calculating metrics for {portfolio} on {date}: {str(e)}") + # Continue processing other dates even if one fails + continue + + print(f"Completed {portfolio}: {success_count} successful, {error_count} errors") \ No newline at end of file diff --git a/database/migrations/README.md b/database/migrations/README.md new file mode 100644 index 00000000..6df3981e --- /dev/null +++ b/database/migrations/README.md @@ -0,0 +1,23 @@ +A data migration is a process of transforming and moving data from one format, structure, or system to another. +In the context of database management, migrations serve several key purposes: + +1. **Schema Evolution**: Migrations allow databases to evolve over time by adding, modifying, or removing tables, + columns, indexes, and other database objects without losing existing data. + +2. **Version Control for Databases**: They provide a way to track changes to database structure over time, + similar to how version control systems track code changes. + +3. **Team Collaboration**: Multiple developers can work on the same database schema by applying migrations + in a consistent order, ensuring everyone has the same database structure. + +4. **Deployment Safety**: Migrations enable safe deployments by ensuring database changes are applied + consistently across different environments (development, staging, production). + +5. **Rollback Capability**: Most migration systems allow you to "downgrade" or rollback changes if needed, + providing a safety net for database modifications. + +6. **Data Integrity**: Migrations can include data transformations, ensuring that existing data is properly + converted when schema changes occur. + +For example, in this specific migration, we're creating a new table called 'PerformanceReturns' to store calculated +portfolio performance metrics, which will replace the legacy CSV-based performance tracking system. \ No newline at end of file diff --git a/notebooks/configParseEx.ipynb b/database/parse_config_example.ipynb similarity index 100% rename from notebooks/configParseEx.ipynb rename to database/parse_config_example.ipynb diff --git a/notebooks/CreateDBs.ipynb b/database/setup/CreateDBs.ipynb similarity index 53% rename from notebooks/CreateDBs.ipynb rename to database/setup/CreateDBs.ipynb index 541fa7bd..b210d88c 100644 --- a/notebooks/CreateDBs.ipynb +++ b/database/setup/CreateDBs.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 239, "metadata": {}, "outputs": [ { @@ -18,7 +18,7 @@ "True" ] }, - "execution_count": 1, + "execution_count": 239, "metadata": {}, "output_type": "execute_result" } @@ -30,13 +30,20 @@ "import yfinance as yf\n", "import yaml\n", "from dotenv import load_dotenv\n", + "import time\n", + "try:\n", + " # yfinance can use curl_cffi which raises this specific error\n", + " from curl_cffi.requests import HTTPError\n", + "except ImportError:\n", + " # Fallback if curl_cffi is not used or installed\n", + " from requests.exceptions import HTTPError\n", "\n", "load_dotenv()" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 240, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 241, "metadata": {}, "outputs": [], "source": [ @@ -87,45 +94,70 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 242, "metadata": {}, "outputs": [], "source": [ "# Backfill Securities Table\n", - "\n", "def backfill_securities_table(portfolio, securities):\n", - "\n", " for security in securities:\n", " ticker = security['ticker']\n", - " data = yf.Ticker(ticker)\n", - " fund = security['fund']\n", - " sector = security['sector'] \n", - " name = data.info['longName']\n", - " geography = security['geography'] \n", - " type = data.info['typeDisp']\n", - " currency = data.info['currency']\n", + " print(f\"Fetching info for {ticker}...\")\n", + " \n", + " retries = 3\n", + " info = None\n", + " for i in range(retries):\n", + " try:\n", + " # Add a small delay before each attempt\n", + " time.sleep((i+1) * 2) \n", + " info = yf.Ticker(ticker).info\n", + " \n", + " # Check if we got meaningful data\n", + " if info and info.get('longName'):\n", + " print(f\"Successfully fetched info for {ticker}.\")\n", + " break # Succeeded, exit retry loop\n", + " else:\n", + " raise ValueError(\"Empty or incomplete info dictionary returned.\")\n", "\n", - " cursor.execute(\"\"\"\n", - " INSERT INTO Securities (ticker, name, type, geography, sector, fund, currency, portfolio)\n", - " VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\n", - " ON DUPLICATE KEY UPDATE\n", - " ticker = VALUES(ticker),\n", - " name = VALUES(name),\n", - " type = VALUES(type),\n", - " geography = VALUES(geography),\n", - " sector = VALUES(sector),\n", - " fund = VALUES(fund),\n", - " currency = VALUES(currency),\n", - " portfolio = VALUES(portfolio);\n", - " \"\"\", (ticker, name, type, geography, sector, fund, currency, portfolio))\n", - " connection.commit()\n", + " except (HTTPError, ValueError, KeyError) as e:\n", + " print(f\"Attempt {i+1}/{retries} failed for {ticker}: {e}\")\n", + " if i == retries - 1:\n", + " print(f\"All retries failed for {ticker}. Skipping.\")\n", + " \n", + " if not info or not info.get('longName'):\n", + " continue # Skip to the next security\n", + "\n", + " try:\n", + " fund = security['fund']\n", + " sector = security['sector'] \n", + " name = info['longName']\n", + " geography = security['geography'] \n", + " type_disp = info.get('typeDisp') or info.get('quoteType')\n", + " currency = info['currency']\n", + "\n", + " cursor.execute(\"\"\"\n", + " INSERT INTO Securities (ticker, name, type, geography, sector, fund, currency, portfolio)\n", + " VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\n", + " ON DUPLICATE KEY UPDATE\n", + " ticker = VALUES(ticker),\n", + " name = VALUES(name),\n", + " type = VALUES(type),\n", + " geography = VALUES(geography),\n", + " sector = VALUES(sector),\n", + " fund = VALUES(fund),\n", + " currency = VALUES(currency),\n", + " portfolio = VALUES(portfolio);\n", + " \"\"\", (ticker, name, type_disp, geography, sector, fund, currency, portfolio))\n", + " connection.commit()\n", + " except Exception as e:\n", + " print(f\"Error inserting {ticker} into database: {e}\")\n", "\n", " print(\"Securities table backfilled\")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 243, "metadata": {}, "outputs": [], "source": [ @@ -145,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 244, "metadata": {}, "outputs": [], "source": [ @@ -172,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 245, "metadata": {}, "outputs": [], "source": [ @@ -203,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 246, "metadata": {}, "outputs": [], "source": [ @@ -223,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 247, "metadata": {}, "outputs": [], "source": [ @@ -242,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 248, "metadata": {}, "outputs": [], "source": [ @@ -276,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 249, "metadata": {}, "outputs": [], "source": [ @@ -297,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 250, "metadata": {}, "outputs": [], "source": [ @@ -314,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 251, "metadata": {}, "outputs": [], "source": [ @@ -341,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 252, "metadata": {}, "outputs": [], "source": [ @@ -360,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 253, "metadata": {}, "outputs": [], "source": [ @@ -379,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 254, "metadata": {}, "outputs": [], "source": [ @@ -421,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 255, "metadata": {}, "outputs": [], "source": [ @@ -443,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 256, "metadata": {}, "outputs": [], "source": [ @@ -472,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 257, "metadata": {}, "outputs": [], "source": [ @@ -592,7 +624,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 258, "metadata": {}, "outputs": [], "source": [ @@ -645,7 +677,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 259, "metadata": {}, "outputs": [], "source": [ @@ -665,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 260, "metadata": {}, "outputs": [], "source": [ @@ -687,7 +719,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 261, "metadata": {}, "outputs": [], "source": [ @@ -729,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 262, "metadata": {}, "outputs": [], "source": [ @@ -743,146 +775,280 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Generate Holdings Table" + "# Generate Cash Holdings Table" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 263, "metadata": {}, "outputs": [], "source": [ - "def create_holdings_view():\n", + "def create_cash_balances_table():\n", " cursor.execute(\"\"\"\n", - " CREATE OR REPLACE VIEW Holdings AS\n", - " SELECT\n", - " p.trading_date,\n", - " p.ticker,\n", - " p.portfolio,\n", - " s.name,\n", - " s.type,\n", - " s.geography,\n", - " s.sector,\n", - " s.fund,\n", - " s.currency AS security_currency,\n", - "\n", - " -- Shares held calculation\n", - " (\n", - " SELECT SUM(\n", - " CASE \n", - " WHEN t.action = 'BUY' THEN t.shares \n", - " ELSE -t.shares \n", - " END\n", - " )\n", - " FROM Transactions t\n", - " WHERE t.ticker = p.ticker \n", - " AND t.portfolio = p.portfolio\n", - " AND t.date <= p.trading_date\n", - " ) AS shares_held,\n", + " CREATE TABLE IF NOT EXISTS CashBalances (\n", + " date DATE PRIMARY KEY,\n", + " cash_cad DECIMAL(20, 10) NOT NULL DEFAULT 0,\n", + " cash_usd DECIMAL(20, 10) NOT NULL DEFAULT 0,\n", + " cash_agg_cad DECIMAL(20, 10) NOT NULL DEFAULT 0\n", + " );\n", + " \"\"\")\n", + " connection.commit()\n", + " print(\"CashBalances table created\")\n", "\n", - " p.price,\n", + "def drop_cash_balances_table():\n", + " cursor.execute(\"DROP TABLE IF EXISTS CashBalances\")\n", + " connection.commit()\n", + " print(\"CashBalances table dropped\")" + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": {}, + "outputs": [], + "source": [ + "def backfill_cash_balances_table():\n", + " portfolio = 'core'\n", + " # Get all trading dates\n", + " cursor.execute(\"SELECT trading_date FROM TradingCalendar ORDER BY trading_date\")\n", + " dates = [row[0] for row in cursor.fetchall()]\n", + " if not dates:\n", + " print(\"No trading dates found.\")\n", + " return\n", "\n", - " -- Market value calculation\n", - " (\n", - " (\n", - " SELECT SUM(\n", - " CASE \n", - " WHEN t.action = 'BUY' THEN t.shares \n", - " ELSE -t.shares \n", - " END\n", - " )\n", - " FROM Transactions t\n", - " WHERE t.ticker = p.ticker \n", - " AND t.portfolio = p.portfolio\n", - " AND t.date <= p.trading_date\n", - " ) * p.price\n", - " ) AS market_value,\n", - "\n", - " -- Dividend market value calculation (only when shares were held)\n", - " (\n", - " SELECT COALESCE(SUM(sub.amount * sub.shares), 0)\n", - " FROM (\n", - " SELECT\n", - " d1.amount,\n", - " (\n", - " SELECT SUM(\n", - " CASE \n", - " WHEN t.action = 'BUY' THEN t.shares \n", - " ELSE -t.shares \n", - " END\n", - " )\n", - " FROM Transactions t\n", - " WHERE t.ticker = d1.ticker\n", - " AND t.portfolio = d1.portfolio\n", - " AND t.date <= d1.date\n", - " ) AS shares\n", - " FROM Dividends d1\n", - " WHERE d1.ticker = p.ticker\n", - " AND d1.portfolio = p.portfolio\n", - " AND d1.date <= p.trading_date\n", - " ) AS sub\n", - " WHERE sub.shares > 0\n", - " ) AS dividend_market_value,\n", - "\n", - " -- Total market value = market + dividend\n", - " (\n", - " (\n", - " (\n", - " SELECT SUM(\n", - " CASE \n", - " WHEN t.action = 'BUY' THEN t.shares \n", - " ELSE -t.shares \n", - " END\n", - " )\n", - " FROM Transactions t\n", - " WHERE t.ticker = p.ticker \n", - " AND t.portfolio = p.portfolio\n", - " AND t.date <= p.trading_date\n", - " ) * p.price\n", - " ) +\n", - " (\n", - " SELECT COALESCE(SUM(sub.amount * sub.shares), 0)\n", - " FROM (\n", - " SELECT\n", - " d1.amount,\n", - " (\n", - " SELECT SUM(\n", - " CASE \n", - " WHEN t.action = 'BUY' THEN t.shares \n", - " ELSE -t.shares \n", - " END\n", - " )\n", - " FROM Transactions t\n", - " WHERE t.ticker = d1.ticker\n", - " AND t.portfolio = d1.portfolio\n", - " AND t.date <= d1.date\n", - " ) AS shares\n", - " FROM Dividends d1\n", - " WHERE d1.ticker = p.ticker\n", - " AND d1.portfolio = p.portfolio\n", - " AND d1.date <= p.trading_date\n", - " ) AS sub\n", - " WHERE sub.shares > 0\n", - " )\n", - " ) AS total_market_value\n", + " # Get all transactions for the core portfolio, ordered by date\n", + " cursor.execute(\"\"\"\n", + " SELECT date, action, ticker, shares, price, currency\n", + " FROM Transactions\n", + " WHERE portfolio = %s\n", + " ORDER BY date\n", + " \"\"\", (portfolio,))\n", + " transactions = cursor.fetchall()\n", "\n", - " FROM Prices p\n", - " JOIN Securities s ON s.ticker = p.ticker AND s.portfolio = p.portfolio;\n", - " \"\"\")\n", + " # Get all dividends for the core portfolio, ordered by date\n", + " cursor.execute(\"\"\"\n", + " SELECT date, ticker, amount, currency\n", + " FROM Dividends\n", + " WHERE portfolio = %s\n", + " ORDER BY date\n", + " \"\"\", (portfolio,))\n", + " dividends = cursor.fetchall()\n", + "\n", + " # Get currency rates for each date\n", + " cursor.execute(\"SELECT date, CAD, USD FROM Currencies\")\n", + " currency_rates = {row[0]: {'CAD': row[1], 'USD': row[2]} for row in cursor.fetchall()}\n", + "\n", + " # Load starting cash from config.yaml\n", + " with open(\"../../config/config.yaml\", \"r\") as f:\n", + " config = yaml.safe_load(f)\n", + " starting_cash_cad = config.get('starting_cash_cad', 0.0)\n", + " starting_cash_usd = config.get('starting_cash_usd', 0.0)\n", + "\n", + " # Initialize balances\n", + " cash_cad = starting_cash_cad\n", + " cash_usd = starting_cash_usd\n", + " cash_balances = []\n", + " tx_idx = 0\n", + " div_idx = 0\n", + " for date in dates:\n", + " # Apply all transactions for this date\n", + " while tx_idx < len(transactions) and transactions[tx_idx][0] == date:\n", + " _, action, _, shares, price, currency = transactions[tx_idx]\n", + " amount = shares * price\n", + " usd_rate = currency_rates.get(date, {}).get('USD', 1.0)\n", + " if action == 'BUY':\n", + " if currency == 'CAD':\n", + " cash_cad -= amount\n", + " elif currency == 'USD':\n", + " # If not enough USD cash, convert from CAD\n", + " if cash_usd < amount:\n", + " usd_needed = amount - cash_usd\n", + " cad_equiv = usd_needed * usd_rate\n", + " if cash_cad >= cad_equiv:\n", + " cash_cad -= cad_equiv\n", + " cash_usd += usd_needed\n", + " else:\n", + " # Not enough CAD to convert, convert as much as possible\n", + " usd_possible = cash_cad / usd_rate if usd_rate != 0 else 0\n", + " cash_usd += usd_possible\n", + " cash_cad = 0\n", + " cash_usd -= amount\n", + " elif action == 'SELL':\n", + " if currency == 'CAD':\n", + " cash_cad += amount\n", + " elif currency == 'USD':\n", + " cash_usd += amount\n", + " tx_idx += 1\n", + " # Apply all dividends for this date\n", + " while div_idx < len(dividends) and dividends[div_idx][0] == date:\n", + " _, _, amount, currency = dividends[div_idx]\n", + " if currency == 'CAD':\n", + " cash_cad += amount\n", + " elif currency == 'USD':\n", + " cash_usd += amount\n", + " div_idx += 1\n", + " # Calculate aggregate cash in CAD\n", + " usd_rate = currency_rates.get(date, {}).get('USD', 1.0)\n", + " cash_agg_cad = cash_cad + (cash_usd * usd_rate)\n", + " cash_balances.append((date, cash_cad, cash_usd, cash_agg_cad))\n", + " # Insert into table\n", + " cursor.executemany(\"\"\"\n", + " INSERT INTO CashBalances (date, cash_cad, cash_usd, cash_agg_cad)\n", + " VALUES (%s, %s, %s, %s)\n", + " ON DUPLICATE KEY UPDATE\n", + " cash_cad = VALUES(cash_cad),\n", + " cash_usd = VALUES(cash_usd),\n", + " cash_agg_cad = VALUES(cash_agg_cad)\n", + " \"\"\", cash_balances)\n", " connection.commit()\n", - " print(\"Holdings view created\")\n" + " print(\"CashBalances table backfilled\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate Holdings Table (can delete)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 265, "metadata": {}, "outputs": [], "source": [ - "def drop_holdings_view():\n", - " cursor.execute(\"DROP VIEW IF EXISTS Holdings\")\n", - " connection.commit()\n", - " print(\"Holdings view dropped\")\n" + "# def create_holdings_view():\n", + "# cursor.execute(\"\"\"\n", + "# CREATE OR REPLACE VIEW Holdings AS\n", + "# SELECT\n", + "# p.trading_date,\n", + "# p.ticker,\n", + "# p.portfolio,\n", + "# s.name,\n", + "# s.type,\n", + "# s.geography,\n", + "# s.sector,\n", + "# s.fund,\n", + "# s.currency AS security_currency,\n", + "\n", + "# -- Shares held calculation\n", + "# (\n", + "# SELECT SUM(\n", + "# CASE \n", + "# WHEN t.action = 'BUY' THEN t.shares \n", + "# ELSE -t.shares \n", + "# END\n", + "# )\n", + "# FROM Transactions t\n", + "# WHERE t.ticker = p.ticker \n", + "# AND t.portfolio = p.portfolio\n", + "# AND t.date <= p.trading_date\n", + "# ) AS shares_held,\n", + "\n", + "# p.price,\n", + "\n", + "# -- Market value calculation\n", + "# (\n", + "# (\n", + "# SELECT SUM(\n", + "# CASE \n", + "# WHEN t.action = 'BUY' THEN t.shares \n", + "# ELSE -t.shares \n", + "# END\n", + "# )\n", + "# FROM Transactions t\n", + "# WHERE t.ticker = p.ticker \n", + "# AND t.portfolio = p.portfolio\n", + "# AND t.date <= p.trading_date\n", + "# ) * p.price\n", + "# ) AS market_value,\n", + "\n", + "# -- Dividend market value calculation (only when shares were held)\n", + "# (\n", + "# SELECT COALESCE(SUM(sub.amount * sub.shares), 0)\n", + "# FROM (\n", + "# SELECT\n", + "# d1.amount,\n", + "# (\n", + "# SELECT SUM(\n", + "# CASE \n", + "# WHEN t.action = 'BUY' THEN t.shares \n", + "# ELSE -t.shares \n", + "# END\n", + "# )\n", + "# FROM Transactions t\n", + "# WHERE t.ticker = d1.ticker\n", + "# AND t.portfolio = d1.portfolio\n", + "# AND t.date <= d1.date\n", + "# ) AS shares\n", + "# FROM Dividends d1\n", + "# WHERE d1.ticker = p.ticker\n", + "# AND d1.portfolio = p.portfolio\n", + "# AND d1.date <= p.trading_date\n", + "# ) AS sub\n", + "# WHERE sub.shares > 0\n", + "# ) AS dividend_market_value,\n", + "\n", + "# -- Total market value = market + dividend\n", + "# (\n", + "# (\n", + "# (\n", + "# SELECT SUM(\n", + "# CASE \n", + "# WHEN t.action = 'BUY' THEN t.shares \n", + "# ELSE -t.shares \n", + "# END\n", + "# )\n", + "# FROM Transactions t\n", + "# WHERE t.ticker = p.ticker \n", + "# AND t.portfolio = p.portfolio\n", + "# AND t.date <= p.trading_date\n", + "# ) * p.price\n", + "# ) +\n", + "# (\n", + "# SELECT COALESCE(SUM(sub.amount * sub.shares), 0)\n", + "# FROM (\n", + "# SELECT\n", + "# d1.amount,\n", + "# (\n", + "# SELECT SUM(\n", + "# CASE \n", + "# WHEN t.action = 'BUY' THEN t.shares \n", + "# ELSE -t.shares \n", + "# END\n", + "# )\n", + "# FROM Transactions t\n", + "# WHERE t.ticker = d1.ticker\n", + "# AND t.portfolio = d1.portfolio\n", + "# AND t.date <= d1.date\n", + "# ) AS shares\n", + "# FROM Dividends d1\n", + "# WHERE d1.ticker = p.ticker\n", + "# AND d1.portfolio = p.portfolio\n", + "# AND d1.date <= p.trading_date\n", + "# ) AS sub\n", + "# WHERE sub.shares > 0\n", + "# )\n", + "# ) AS total_market_value\n", + "\n", + "# FROM Prices p\n", + "# JOIN Securities s ON s.ticker = p.ticker AND s.portfolio = p.portfolio;\n", + "# \"\"\")\n", + "# connection.commit()\n", + "# print(\"Holdings view created\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": {}, + "outputs": [], + "source": [ + "# def drop_holdings_view():\n", + "# cursor.execute(\"DROP VIEW IF EXISTS Holdings\")\n", + "# connection.commit()\n", + "# print(\"Holdings view dropped\")\n" ] }, { @@ -894,7 +1060,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 267, "metadata": {}, "outputs": [], "source": [ @@ -925,7 +1091,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 268, "metadata": {}, "outputs": [], "source": [ @@ -1063,7 +1229,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 269, "metadata": {}, "outputs": [], "source": [ @@ -1073,6 +1239,90 @@ " print(\"Materialized Holdings table dropped\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Performance Metrics\n", + "(this needs to be adapted to calculate metrics from db data rather than csv before actual usage)" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": {}, + "outputs": [], + "source": [ + "def drop_performance_metrics_table():\n", + " cursor.execute(\"DROP TABLE IF EXISTS PerformanceReturns\")\n", + " connection.commit()\n", + " print(\"PerformanceReturns table dropped\")\n", + "\n", + "def create_performance_metrics_table():\n", + " cursor.execute(\"\"\"\n", + " CREATE TABLE IF NOT EXISTS PerformanceReturns (\n", + " date DATE NOT NULL,\n", + " portfolio VARCHAR(50) NOT NULL,\n", + " one_day_return FLOAT,\n", + " one_week_return FLOAT,\n", + " one_month_return FLOAT,\n", + " ytd_return FLOAT,\n", + " one_year_return FLOAT,\n", + " inception_return FLOAT,\n", + " PRIMARY KEY (date, portfolio)\n", + " );\n", + " \"\"\")\n", + " connection.commit()\n", + " print(\"PerformanceReturns table created\")\n", + "\n", + "# def insert_performance_metrics(metrics_df, date=None, portfolio=None):\n", + "# \"\"\"\n", + "# Insert a row into PerformanceReturns from a DataFrame row or dict.\n", + "# metrics_df: DataFrame with columns ['Metric', 'Value']\n", + "# date: date for the row (defaults to today if not provided)\n", + "# portfolio: portfolio name (must be provided)\n", + "# \"\"\"\n", + "# insert_query = \"\"\"\n", + "# INSERT INTO PerformanceReturns (\n", + "# date,\n", + "# portfolio,\n", + "# one_day_return,\n", + "# one_week_return,\n", + "# one_month_return,\n", + "# ytd_return,\n", + "# one_year_return,\n", + "# inception_return\n", + "# )\n", + "# VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\n", + "# ON DUPLICATE KEY UPDATE\n", + "# one_day_return = VALUES(one_day_return),\n", + "# one_week_return = VALUES(one_week_return),\n", + "# one_month_return = VALUES(one_month_return),\n", + "# ytd_return = VALUES(ytd_return),\n", + "# one_year_return = VALUES(one_year_return),\n", + "# inception_return = VALUES(inception_return)\n", + "# \"\"\"\n", + "# if date is None:\n", + "# from datetime import datetime\n", + "# date = datetime.now().date()\n", + "# if portfolio is None:\n", + "# raise ValueError(\"portfolio must be provided\")\n", + "# metrics_dict = pd.Series(metrics_df.Value.values, index=metrics_df.Metric).to_dict()\n", + "# values = [\n", + "# date,\n", + "# portfolio,\n", + "# float(metrics_dict['1 Day Return'].strip('%')),\n", + "# float(metrics_dict['1 Week Return'].strip('%')),\n", + "# float(metrics_dict['1 Month Return'].strip('%')),\n", + "# float(metrics_dict['Year-to-Date Return'].strip('%')),\n", + "# float(metrics_dict['1 Year Return'].strip('%')),\n", + "# float(metrics_dict['Inception'].strip('%'))\n", + "# ]\n", + "# cursor.execute(insert_query, values)\n", + "# connection.commit()\n", + "# print(f\"Inserted performance metrics for {portfolio} on {date}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1082,7 +1332,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 271, "metadata": {}, "outputs": [ { @@ -1095,7 +1345,7 @@ "Securities table dropped\n", "Currencies table dropped\n", "Trading calendar table dropped\n", - "Holdings view dropped\n", + "CashBalances table dropped\n", "Materialized Holdings table dropped\n" ] } @@ -1107,14 +1357,16 @@ "drop_securities_table()\n", "drop_currencies_table()\n", "drop_trading_calendar_table()\n", - "drop_holdings_view()\n", + "drop_cash_balances_table()\n", + "# drop_performance_metrics_table()\n", + "# drop_holdings_view()\n", "\n", "drop_materialized_holdings() #--------------------------------" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 272, "metadata": {}, "outputs": [ { @@ -1127,20 +1379,30 @@ "Trading calendar table created\n", "Prices table created\n", "Dividends table created\n", + "CashBalances table created\n", "Materialized Holdings table created\n", - "Securities table backfilled\n", - "XIU.TO SPY AGG XBB.TO \n", - "Transactions table backfilled\n", - "Securities table backfilled\n", - "EA ISRG MA TEX AGG RSP AGG AGG RSP HBM.TO XIU.TO XBB.TO L.TO SPY APO AAPL CEG AMSF VEEV SPY GSL AGG SPSB WFG.TO XIU.TO CSH-UN.TO XBB.TO XIU.TO ACO-X.TO CG WSC AER BLBD TMUS MP DOLE AMAT \n", - "Transactions table backfilled\n", - "Currencies table backfilled\n", - "Trading calendar table backfilled\n", - "Backfilling prices for: AAPL (core) ACO-X.TO (core) AER (core) AGG (benchmark) AGG (core) AMAT (core) AMSF (core) APO (core) BLBD (core) CEG (core) CG (core) CSH-UN.TO (core) DOLE (core) EA (core) GSL (core) HBM.TO (core) ISRG (core) L.TO (core) MA (core) MP (core) RSP (core) SPSB (core) SPY (benchmark) SPY (core) TEX (core) TMUS (core) VEEV (core) WFG.TO (core) WSC (core) XBB.TO (benchmark) XBB.TO (core) XIU.TO (benchmark) XIU.TO (core) \n", - "Prices table backfilled\n", - "Frontfilling prices for: AAPL (core) ACO-X.TO (core) AER (core) AGG (benchmark) AGG (core) AMAT (core) AMSF (core) APO (core) BLBD (core) CEG (core) CG (core) CSH-UN.TO (core) DOLE (core) EA (core) GSL (core) HBM.TO (core) ISRG (core) L.TO (core) MA (core) MP (core) RSP (core) SPSB (core) SPY (benchmark) SPY (core) TEX (core) TMUS (core) VEEV (core) WFG.TO (core) WSC (core) XBB.TO (benchmark) XBB.TO (core) XIU.TO (benchmark) XIU.TO (core) Prices table frontfilled\n", - "Backfilling dividends for: AAPL (core) ACO-X.TO (core) AER (core) AGG (benchmark) AGG (core) AMAT (core) AMSF (core) APO (core) BLBD (core) CEG (core) CG (core) CSH-UN.TO (core) DOLE (core) EA (core) GSL (core) HBM.TO (core) ISRG (core) L.TO (core) MA (core) MP (core) RSP (core) SPSB (core) SPY (benchmark) SPY (core) TEX (core) TMUS (core) VEEV (core) WFG.TO (core) WSC (core) XBB.TO (benchmark) XBB.TO (core) XIU.TO (benchmark) XIU.TO (core) Holdings view created\n", - "Materialized Holdings refreshed successfully\n" + "Fetching info for XBB.TO...\n", + "Successfully fetched info for XBB.TO.\n", + "Fetching info for AGG...\n" + ] + }, + { + "ename": "HTTPError", + "evalue": "HTTP Error 401: ", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mHTTPError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[272]\u001b[39m\u001b[32m, line 37\u001b[39m\n\u001b[32m 34\u001b[39m transactions = config[\u001b[33m'\u001b[39m\u001b[33mtransactions\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m 35\u001b[39m portfolio = config[\u001b[33m'\u001b[39m\u001b[33mportfolio\u001b[39m\u001b[33m'\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mname\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m---> \u001b[39m\u001b[32m37\u001b[39m \u001b[43mbackfill_securities_table\u001b[49m\u001b[43m(\u001b[49m\u001b[43mportfolio\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msecurities\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 38\u001b[39m backfill_transactions_table(portfolio, transactions)\n\u001b[32m 40\u001b[39m backfill_currencies_table(currencies)\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[242]\u001b[39m\u001b[32m, line 13\u001b[39m, in \u001b[36mbackfill_securities_table\u001b[39m\u001b[34m(portfolio, securities)\u001b[39m\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 11\u001b[39m \u001b[38;5;66;03m# Add a small delay before each attempt\u001b[39;00m\n\u001b[32m 12\u001b[39m time.sleep((i+\u001b[32m1\u001b[39m) * \u001b[32m2\u001b[39m) \n\u001b[32m---> \u001b[39m\u001b[32m13\u001b[39m info = \u001b[43myf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mTicker\u001b[49m\u001b[43m(\u001b[49m\u001b[43mticker\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43minfo\u001b[49m\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m# Check if we got meaningful data\u001b[39;00m\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m info \u001b[38;5;129;01mand\u001b[39;00m info.get(\u001b[33m'\u001b[39m\u001b[33mlongName\u001b[39m\u001b[33m'\u001b[39m):\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jadha\\Documents\\DFIC\\DFIC Fund Monitor\\Fund-Monitor\\venv\\Lib\\site-packages\\yfinance\\ticker.py:159\u001b[39m, in \u001b[36mTicker.info\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 157\u001b[39m \u001b[38;5;129m@property\u001b[39m\n\u001b[32m 158\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minfo\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> \u001b[38;5;28mdict\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m159\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mget_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jadha\\Documents\\DFIC\\DFIC Fund Monitor\\Fund-Monitor\\venv\\Lib\\site-packages\\yfinance\\base.py:289\u001b[39m, in \u001b[36mTickerBase.get_info\u001b[39m\u001b[34m(self, proxy)\u001b[39m\n\u001b[32m 286\u001b[39m utils.print_once(\u001b[33m\"\u001b[39m\u001b[33mYF deprecation warning: set proxy via new config function: yf.set_config(proxy=proxy)\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 287\u001b[39m \u001b[38;5;28mself\u001b[39m._data._set_proxy(proxy)\n\u001b[32m--> \u001b[39m\u001b[32m289\u001b[39m data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_quote\u001b[49m\u001b[43m.\u001b[49m\u001b[43minfo\u001b[49m\n\u001b[32m 290\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m data\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jadha\\Documents\\DFIC\\DFIC Fund Monitor\\Fund-Monitor\\venv\\Lib\\site-packages\\yfinance\\scrapers\\quote.py:511\u001b[39m, in \u001b[36mQuote.info\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 508\u001b[39m \u001b[38;5;129m@property\u001b[39m\n\u001b[32m 509\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minfo\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> \u001b[38;5;28mdict\u001b[39m:\n\u001b[32m 510\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._info \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m511\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_fetch_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 512\u001b[39m \u001b[38;5;28mself\u001b[39m._fetch_complementary()\n\u001b[32m 514\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._info\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jadha\\Documents\\DFIC\\DFIC Fund Monitor\\Fund-Monitor\\venv\\Lib\\site-packages\\yfinance\\scrapers\\quote.py:612\u001b[39m, in \u001b[36mQuote._fetch_info\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 610\u001b[39m \u001b[38;5;28mself\u001b[39m._already_fetched = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 611\u001b[39m modules = [\u001b[33m'\u001b[39m\u001b[33mfinancialData\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mquoteType\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mdefaultKeyStatistics\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33massetProfile\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33msummaryDetail\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m--> \u001b[39m\u001b[32m612\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_fetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodules\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodules\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 613\u001b[39m additional_info = \u001b[38;5;28mself\u001b[39m._fetch_additional_info()\n\u001b[32m 614\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m additional_info \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m result \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[36mFile \u001b[39m\u001b[32mc:\\Users\\jadha\\Documents\\DFIC\\DFIC Fund Monitor\\Fund-Monitor\\venv\\Lib\\site-packages\\yfinance\\scrapers\\quote.py:590\u001b[39m, in \u001b[36mQuote._fetch\u001b[39m\u001b[34m(self, modules)\u001b[39m\n\u001b[32m 588\u001b[39m params_dict = {\u001b[33m\"\u001b[39m\u001b[33mmodules\u001b[39m\u001b[33m\"\u001b[39m: modules, \u001b[33m\"\u001b[39m\u001b[33mcorsDomain\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mfinance.yahoo.com\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mformatted\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mfalse\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33msymbol\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m._symbol}\n\u001b[32m 589\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m590\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_data\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_raw_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_QUOTE_SUMMARY_URL_\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_symbol\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muser_agent_headers\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_data\u001b[49m\u001b[43m.\u001b[49m\u001b[43muser_agent_headers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparams_dict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 591\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m requests.exceptions.HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 592\u001b[39m utils.get_yf_logger().error(\u001b[38;5;28mstr\u001b[39m(e))\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jadha\\Documents\\DFIC\\DFIC Fund Monitor\\Fund-Monitor\\venv\\Lib\\site-packages\\yfinance\\data.py:405\u001b[39m, in \u001b[36mYfData.get_raw_json\u001b[39m\u001b[34m(self, url, user_agent_headers, params, timeout)\u001b[39m\n\u001b[32m 403\u001b[39m utils.get_yf_logger().debug(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33mget_raw_json(): \u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m)\n\u001b[32m 404\u001b[39m response = \u001b[38;5;28mself\u001b[39m.get(url, user_agent_headers=user_agent_headers, params=params, timeout=timeout)\n\u001b[32m--> \u001b[39m\u001b[32m405\u001b[39m \u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 406\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m response.json()\n", + "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\jadha\\Documents\\DFIC\\DFIC Fund Monitor\\Fund-Monitor\\venv\\Lib\\site-packages\\curl_cffi\\requests\\models.py:155\u001b[39m, in \u001b[36mResponse.raise_for_status\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 153\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Raise an error if status code is not in [200, 400)\"\"\"\u001b[39;00m\n\u001b[32m 154\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m.ok:\n\u001b[32m--> \u001b[39m\u001b[32m155\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mHTTP Error \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.status_code\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.reason\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m, \u001b[32m0\u001b[39m, \u001b[38;5;28mself\u001b[39m)\n", + "\u001b[31mHTTPError\u001b[39m: HTTP Error 401: " ] } ], @@ -1151,29 +1413,30 @@ "create_trading_calendar_table()\n", "create_prices_table()\n", "create_dividends_table()\n", + "create_cash_balances_table()\n", "\n", "create_materialized_holdings() #--------------------------------\n", "\n", "# Import config\n", - "with open(\"../config.yaml\", \"r\") as f:\n", + "with open(\"../../config/config.yaml\", \"r\") as f:\n", " config = yaml.safe_load(f)\n", " start_date = config['start_date']\n", "\n", "end_date = pd.to_datetime('today').date() + pd.Timedelta(days=1)\n", "\n", - "# Import benchmark config\n", - "with open(\"../portfolios/dfic_benchmark.yaml\", \"r\") as f:\n", - " config = yaml.safe_load(f)\n", - " securities = config['securities']\n", - " currencies = config['currencies']\n", - " transactions = config['transactions']\n", - " portfolio = config['portfolio']['name']\n", + "# # Import benchmark config\n", + "# with open(\"../../config/portfolio_definitions/dfic_benchmark.yaml\", \"r\") as f:\n", + "# config = yaml.safe_load(f)\n", + "# securities = config['securities']\n", + "# currencies = config['currencies']\n", + "# transactions = config['transactions']\n", + "# portfolio = config['portfolio']['name']\n", "\n", - "backfill_securities_table(portfolio, securities)\n", - "backfill_transactions_table(portfolio, transactions)\n", + "# backfill_securities_table(portfolio, securities)\n", + "# backfill_transactions_table(portfolio, transactions)\n", "\n", "# Import core config\n", - "with open(\"../portfolios/dfic_core.yaml\", \"r\") as f:\n", + "with open(\"../../config/portfolio_definitions/dfic_core.yaml\", \"r\") as f:\n", " config = yaml.safe_load(f)\n", " securities = config['securities']\n", " currencies = config['currencies']\n", @@ -1191,8 +1454,8 @@ "frontfill_prices_table()\n", "\n", "backfill_dividends_table()\n", - "\n", - "create_holdings_view()\n", + "backfill_cash_balances_table()\n", + "# create_holdings_view()\n", "refresh_materialized_holdings()#--------------------------------" ] }, @@ -1220,7 +1483,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "venv", "language": "python", "name": "python3" }, @@ -1234,7 +1497,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.2" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/notebooks/CreateDBs.py b/database/setup/CreateDBs.py similarity index 61% rename from notebooks/CreateDBs.py rename to database/setup/CreateDBs.py index 8a8f2bd1..86edb739 100644 --- a/notebooks/CreateDBs.py +++ b/database/setup/CreateDBs.py @@ -8,6 +8,13 @@ import yfinance as yf import yaml from dotenv import load_dotenv +import time +try: + # yfinance can use curl_cffi which raises this specific error + from curl_cffi.requests import HTTPError +except ImportError: + # Fallback if curl_cffi is not used or installed + from requests.exceptions import HTTPError load_dotenv() @@ -48,33 +55,58 @@ def create_securities_table(): # %% # Backfill Securities Table - def backfill_securities_table(portfolio, securities): - for security in securities: ticker = security['ticker'] - data = yf.Ticker(ticker) - fund = security['fund'] - sector = security['sector'] - name = data.info['longName'] - geography = security['geography'] - type = data.info['typeDisp'] - currency = data.info['currency'] + print(f"Fetching info for {ticker}...") + + retries = 3 + info = None + for i in range(retries): + try: + # Add a small delay before each attempt + time.sleep((i+1) * 2) + info = yf.Ticker(ticker).info + + # Check if we got meaningful data + if info and info.get('longName'): + print(f"Successfully fetched info for {ticker}.") + break # Succeeded, exit retry loop + else: + raise ValueError("Empty or incomplete info dictionary returned.") + + except (HTTPError, ValueError, KeyError) as e: + print(f"Attempt {i+1}/{retries} failed for {ticker}: {e}") + if i == retries - 1: + print(f"All retries failed for {ticker}. Skipping.") + + if not info or not info.get('longName'): + continue # Skip to the next security - cursor.execute(""" - INSERT INTO Securities (ticker, name, type, geography, sector, fund, currency, portfolio) - VALUES (%s, %s, %s, %s, %s, %s, %s, %s) - ON DUPLICATE KEY UPDATE - ticker = VALUES(ticker), - name = VALUES(name), - type = VALUES(type), - geography = VALUES(geography), - sector = VALUES(sector), - fund = VALUES(fund), - currency = VALUES(currency), - portfolio = VALUES(portfolio); - """, (ticker, name, type, geography, sector, fund, currency, portfolio)) - connection.commit() + try: + fund = security['fund'] + sector = security['sector'] + name = info['longName'] + geography = security['geography'] + type_disp = info.get('typeDisp') or info.get('quoteType') + currency = info['currency'] + + cursor.execute(""" + INSERT INTO Securities (ticker, name, type, geography, sector, fund, currency, portfolio) + VALUES (%s, %s, %s, %s, %s, %s, %s, %s) + ON DUPLICATE KEY UPDATE + ticker = VALUES(ticker), + name = VALUES(name), + type = VALUES(type), + geography = VALUES(geography), + sector = VALUES(sector), + fund = VALUES(fund), + currency = VALUES(currency), + portfolio = VALUES(portfolio); + """, (ticker, name, type_disp, geography, sector, fund, currency, portfolio)) + connection.commit() + except Exception as e: + print(f"Error inserting {ticker} into database: {e}") print("Securities table backfilled") @@ -545,135 +577,253 @@ def drop_dividends_table(): print("Dividends table dropped") # %% [markdown] -# # Generate Holdings Table +# # Generate Cash Holdings Table # %% -def create_holdings_view(): +def create_cash_balances_table(): cursor.execute(""" - CREATE OR REPLACE VIEW Holdings AS - SELECT - p.trading_date, - p.ticker, - p.portfolio, - s.name, - s.type, - s.geography, - s.sector, - s.fund, - s.currency AS security_currency, - - -- Shares held calculation - ( - SELECT SUM( - CASE - WHEN t.action = 'BUY' THEN t.shares - ELSE -t.shares - END - ) - FROM Transactions t - WHERE t.ticker = p.ticker - AND t.portfolio = p.portfolio - AND t.date <= p.trading_date - ) AS shares_held, - - p.price, - - -- Market value calculation - ( - ( - SELECT SUM( - CASE - WHEN t.action = 'BUY' THEN t.shares - ELSE -t.shares - END - ) - FROM Transactions t - WHERE t.ticker = p.ticker - AND t.portfolio = p.portfolio - AND t.date <= p.trading_date - ) * p.price - ) AS market_value, - - -- Dividend market value calculation (only when shares were held) - ( - SELECT COALESCE(SUM(sub.amount * sub.shares), 0) - FROM ( - SELECT - d1.amount, - ( - SELECT SUM( - CASE - WHEN t.action = 'BUY' THEN t.shares - ELSE -t.shares - END - ) - FROM Transactions t - WHERE t.ticker = d1.ticker - AND t.portfolio = d1.portfolio - AND t.date <= d1.date - ) AS shares - FROM Dividends d1 - WHERE d1.ticker = p.ticker - AND d1.portfolio = p.portfolio - AND d1.date <= p.trading_date - ) AS sub - WHERE sub.shares > 0 - ) AS dividend_market_value, - - -- Total market value = market + dividend - ( - ( - ( - SELECT SUM( - CASE - WHEN t.action = 'BUY' THEN t.shares - ELSE -t.shares - END - ) - FROM Transactions t - WHERE t.ticker = p.ticker - AND t.portfolio = p.portfolio - AND t.date <= p.trading_date - ) * p.price - ) + - ( - SELECT COALESCE(SUM(sub.amount * sub.shares), 0) - FROM ( - SELECT - d1.amount, - ( - SELECT SUM( - CASE - WHEN t.action = 'BUY' THEN t.shares - ELSE -t.shares - END - ) - FROM Transactions t - WHERE t.ticker = d1.ticker - AND t.portfolio = d1.portfolio - AND t.date <= d1.date - ) AS shares - FROM Dividends d1 - WHERE d1.ticker = p.ticker - AND d1.portfolio = p.portfolio - AND d1.date <= p.trading_date - ) AS sub - WHERE sub.shares > 0 - ) - ) AS total_market_value - - FROM Prices p - JOIN Securities s ON s.ticker = p.ticker AND s.portfolio = p.portfolio; + CREATE TABLE IF NOT EXISTS CashBalances ( + date DATE PRIMARY KEY, + cash_cad DECIMAL(20, 10) NOT NULL DEFAULT 0, + cash_usd DECIMAL(20, 10) NOT NULL DEFAULT 0, + cash_agg_cad DECIMAL(20, 10) NOT NULL DEFAULT 0 + ); """) connection.commit() - print("Holdings view created") + print("CashBalances table created") +def drop_cash_balances_table(): + cursor.execute("DROP TABLE IF EXISTS CashBalances") + connection.commit() + print("CashBalances table dropped") # %% -def drop_holdings_view(): - cursor.execute("DROP VIEW IF EXISTS Holdings") +def backfill_cash_balances_table(): + portfolio = 'core' + # Get all trading dates + cursor.execute("SELECT trading_date FROM TradingCalendar ORDER BY trading_date") + dates = [row[0] for row in cursor.fetchall()] + if not dates: + print("No trading dates found.") + return + + # Get all transactions for the core portfolio, ordered by date + cursor.execute(""" + SELECT date, action, ticker, shares, price, currency + FROM Transactions + WHERE portfolio = %s + ORDER BY date + """, (portfolio,)) + transactions = cursor.fetchall() + + # Get all dividends for the core portfolio, ordered by date + cursor.execute(""" + SELECT date, ticker, amount, currency + FROM Dividends + WHERE portfolio = %s + ORDER BY date + """, (portfolio,)) + dividends = cursor.fetchall() + + # Get currency rates for each date + cursor.execute("SELECT date, CAD, USD FROM Currencies") + currency_rates = {row[0]: {'CAD': row[1], 'USD': row[2]} for row in cursor.fetchall()} + + # Load starting cash from config.yaml + with open("../../config/config.yaml", "r") as f: + config = yaml.safe_load(f) + starting_cash_cad = config.get('starting_cash_cad', 0.0) + starting_cash_usd = config.get('starting_cash_usd', 0.0) + + # Initialize balances + cash_cad = starting_cash_cad + cash_usd = starting_cash_usd + cash_balances = [] + tx_idx = 0 + div_idx = 0 + for date in dates: + # Apply all transactions for this date + while tx_idx < len(transactions) and transactions[tx_idx][0] == date: + _, action, _, shares, price, currency = transactions[tx_idx] + amount = shares * price + usd_rate = currency_rates.get(date, {}).get('USD', 1.0) + if action == 'BUY': + if currency == 'CAD': + cash_cad -= amount + elif currency == 'USD': + # If not enough USD cash, convert from CAD + if cash_usd < amount: + usd_needed = amount - cash_usd + cad_equiv = usd_needed * usd_rate + if cash_cad >= cad_equiv: + cash_cad -= cad_equiv + cash_usd += usd_needed + else: + # Not enough CAD to convert, convert as much as possible + usd_possible = cash_cad / usd_rate if usd_rate != 0 else 0 + cash_usd += usd_possible + cash_cad = 0 + cash_usd -= amount + elif action == 'SELL': + if currency == 'CAD': + cash_cad += amount + elif currency == 'USD': + cash_usd += amount + tx_idx += 1 + # Apply all dividends for this date + while div_idx < len(dividends) and dividends[div_idx][0] == date: + _, _, amount, currency = dividends[div_idx] + if currency == 'CAD': + cash_cad += amount + elif currency == 'USD': + cash_usd += amount + div_idx += 1 + # Calculate aggregate cash in CAD + usd_rate = currency_rates.get(date, {}).get('USD', 1.0) + cash_agg_cad = cash_cad + (cash_usd * usd_rate) + cash_balances.append((date, cash_cad, cash_usd, cash_agg_cad)) + # Insert into table + cursor.executemany(""" + INSERT INTO CashBalances (date, cash_cad, cash_usd, cash_agg_cad) + VALUES (%s, %s, %s, %s) + ON DUPLICATE KEY UPDATE + cash_cad = VALUES(cash_cad), + cash_usd = VALUES(cash_usd), + cash_agg_cad = VALUES(cash_agg_cad) + """, cash_balances) connection.commit() - print("Holdings view dropped") + print("CashBalances table backfilled") + +# %% [markdown] +# # Generate Holdings Table (can delete) + +# %% +# def create_holdings_view(): +# cursor.execute(""" +# CREATE OR REPLACE VIEW Holdings AS +# SELECT +# p.trading_date, +# p.ticker, +# p.portfolio, +# s.name, +# s.type, +# s.geography, +# s.sector, +# s.fund, +# s.currency AS security_currency, + +# -- Shares held calculation +# ( +# SELECT SUM( +# CASE +# WHEN t.action = 'BUY' THEN t.shares +# ELSE -t.shares +# END +# ) +# FROM Transactions t +# WHERE t.ticker = p.ticker +# AND t.portfolio = p.portfolio +# AND t.date <= p.trading_date +# ) AS shares_held, + +# p.price, + +# -- Market value calculation +# ( +# ( +# SELECT SUM( +# CASE +# WHEN t.action = 'BUY' THEN t.shares +# ELSE -t.shares +# END +# ) +# FROM Transactions t +# WHERE t.ticker = p.ticker +# AND t.portfolio = p.portfolio +# AND t.date <= p.trading_date +# ) * p.price +# ) AS market_value, + +# -- Dividend market value calculation (only when shares were held) +# ( +# SELECT COALESCE(SUM(sub.amount * sub.shares), 0) +# FROM ( +# SELECT +# d1.amount, +# ( +# SELECT SUM( +# CASE +# WHEN t.action = 'BUY' THEN t.shares +# ELSE -t.shares +# END +# ) +# FROM Transactions t +# WHERE t.ticker = d1.ticker +# AND t.portfolio = d1.portfolio +# AND t.date <= d1.date +# ) AS shares +# FROM Dividends d1 +# WHERE d1.ticker = p.ticker +# AND d1.portfolio = p.portfolio +# AND d1.date <= p.trading_date +# ) AS sub +# WHERE sub.shares > 0 +# ) AS dividend_market_value, + +# -- Total market value = market + dividend +# ( +# ( +# ( +# SELECT SUM( +# CASE +# WHEN t.action = 'BUY' THEN t.shares +# ELSE -t.shares +# END +# ) +# FROM Transactions t +# WHERE t.ticker = p.ticker +# AND t.portfolio = p.portfolio +# AND t.date <= p.trading_date +# ) * p.price +# ) + +# ( +# SELECT COALESCE(SUM(sub.amount * sub.shares), 0) +# FROM ( +# SELECT +# d1.amount, +# ( +# SELECT SUM( +# CASE +# WHEN t.action = 'BUY' THEN t.shares +# ELSE -t.shares +# END +# ) +# FROM Transactions t +# WHERE t.ticker = d1.ticker +# AND t.portfolio = d1.portfolio +# AND t.date <= d1.date +# ) AS shares +# FROM Dividends d1 +# WHERE d1.ticker = p.ticker +# AND d1.portfolio = p.portfolio +# AND d1.date <= p.trading_date +# ) AS sub +# WHERE sub.shares > 0 +# ) +# ) AS total_market_value + +# FROM Prices p +# JOIN Securities s ON s.ticker = p.ticker AND s.portfolio = p.portfolio; +# """) +# connection.commit() +# print("Holdings view created") + + +# %% +# def drop_holdings_view(): +# cursor.execute("DROP VIEW IF EXISTS Holdings") +# connection.commit() +# print("Holdings view dropped") # %% [markdown] @@ -842,6 +992,80 @@ def drop_materialized_holdings(): connection.commit() print("Materialized Holdings table dropped") +# %% [markdown] +# # Performance Metrics +# (this needs to be adapted to calculate metrics from db data rather than csv before actual usage) + +# %% +def drop_performance_metrics_table(): + cursor.execute("DROP TABLE IF EXISTS PerformanceReturns") + connection.commit() + print("PerformanceReturns table dropped") + +def create_performance_metrics_table(): + cursor.execute(""" + CREATE TABLE IF NOT EXISTS PerformanceReturns ( + date DATE NOT NULL, + portfolio VARCHAR(50) NOT NULL, + one_day_return FLOAT, + one_week_return FLOAT, + one_month_return FLOAT, + ytd_return FLOAT, + one_year_return FLOAT, + inception_return FLOAT, + PRIMARY KEY (date, portfolio) + ); + """) + connection.commit() + print("PerformanceReturns table created") + +# def insert_performance_metrics(metrics_df, date=None, portfolio=None): +# """ +# Insert a row into PerformanceReturns from a DataFrame row or dict. +# metrics_df: DataFrame with columns ['Metric', 'Value'] +# date: date for the row (defaults to today if not provided) +# portfolio: portfolio name (must be provided) +# """ +# insert_query = """ +# INSERT INTO PerformanceReturns ( +# date, +# portfolio, +# one_day_return, +# one_week_return, +# one_month_return, +# ytd_return, +# one_year_return, +# inception_return +# ) +# VALUES (%s, %s, %s, %s, %s, %s, %s, %s) +# ON DUPLICATE KEY UPDATE +# one_day_return = VALUES(one_day_return), +# one_week_return = VALUES(one_week_return), +# one_month_return = VALUES(one_month_return), +# ytd_return = VALUES(ytd_return), +# one_year_return = VALUES(one_year_return), +# inception_return = VALUES(inception_return) +# """ +# if date is None: +# from datetime import datetime +# date = datetime.now().date() +# if portfolio is None: +# raise ValueError("portfolio must be provided") +# metrics_dict = pd.Series(metrics_df.Value.values, index=metrics_df.Metric).to_dict() +# values = [ +# date, +# portfolio, +# float(metrics_dict['1 Day Return'].strip('%')), +# float(metrics_dict['1 Week Return'].strip('%')), +# float(metrics_dict['1 Month Return'].strip('%')), +# float(metrics_dict['Year-to-Date Return'].strip('%')), +# float(metrics_dict['1 Year Return'].strip('%')), +# float(metrics_dict['Inception'].strip('%')) +# ] +# cursor.execute(insert_query, values) +# connection.commit() +# print(f"Inserted performance metrics for {portfolio} on {date}") + # %% [markdown] # # Runner @@ -852,7 +1076,9 @@ def drop_materialized_holdings(): drop_securities_table() drop_currencies_table() drop_trading_calendar_table() -drop_holdings_view() +drop_cash_balances_table() +# drop_performance_metrics_table() +# drop_holdings_view() drop_materialized_holdings() #-------------------------------- @@ -863,29 +1089,30 @@ def drop_materialized_holdings(): create_trading_calendar_table() create_prices_table() create_dividends_table() +create_cash_balances_table() create_materialized_holdings() #-------------------------------- # Import config -with open("../config.yaml", "r") as f: +with open("../../config/config.yaml", "r") as f: config = yaml.safe_load(f) start_date = config['start_date'] end_date = pd.to_datetime('today').date() + pd.Timedelta(days=1) -# Import benchmark config -with open("../portfolios/dfic_benchmark.yaml", "r") as f: - config = yaml.safe_load(f) - securities = config['securities'] - currencies = config['currencies'] - transactions = config['transactions'] - portfolio = config['portfolio']['name'] +# # Import benchmark config +# with open("../../config/portfolio_definitions/dfic_benchmark.yaml", "r") as f: +# config = yaml.safe_load(f) +# securities = config['securities'] +# currencies = config['currencies'] +# transactions = config['transactions'] +# portfolio = config['portfolio']['name'] -backfill_securities_table(portfolio, securities) -backfill_transactions_table(portfolio, transactions) +# backfill_securities_table(portfolio, securities) +# backfill_transactions_table(portfolio, transactions) # Import core config -with open("../portfolios/dfic_core.yaml", "r") as f: +with open("../../config/portfolio_definitions/dfic_core.yaml", "r") as f: config = yaml.safe_load(f) securities = config['securities'] currencies = config['currencies'] @@ -903,8 +1130,8 @@ def drop_materialized_holdings(): frontfill_prices_table() backfill_dividends_table() - -create_holdings_view() +backfill_cash_balances_table() +# create_holdings_view() refresh_materialized_holdings()#-------------------------------- # %% diff --git a/extract_data.py b/extract_data.py deleted file mode 100644 index 9dad1709..00000000 --- a/extract_data.py +++ /dev/null @@ -1,78 +0,0 @@ -import pandas as pd -import numpy as np -import MetricCalculator as mcalc - - -# Load the specific sheet -file_path = 'DFIC_Fund_Tracker.xlsx' -sheet_name = 'Historical Portfolio Values' - -# Load only columns AN to BM -df = pd.read_excel(file_path, sheet_name=sheet_name, usecols="AN:BM", skiprows=3) -df.rename(columns={'Dates.3': 'Date'}, inplace=True) -df.drop(['Unnamed: 55', 'Unnamed: 60'], axis=1, inplace=True) # drop empty columns -df.fillna(0, inplace=True) # replace NaN values with 0 - -df['Date'] = pd.to_datetime(df['Date'], format='%Y/%m/%d', errors='coerce') - - - - -# print(df.head()) - -RISK_FREE_RATE = 0.0436 - -mc = mcalc.MetricCalculator() - -# Calculate the metrics -compounded_return, annualized_return = mc.compounded_portfolio_return(df) -avg_daily_return, avg_annualized_return = mc.average_portfolio_return(df) -daily_variance, annualized_variance = mc.portfolio_variance(df) -daily_volatility, annualized_volatility = mc.portfolio_volatility(df) -max_drawdown = mc.maximum_drawdown(df) -sharpe_daily, sharpe_annualized = mc.sharpe_ratio(df, risk_free_rate=RISK_FREE_RATE) -# You'll need market data for Beta and Alpha calculations -# market_returns = pd.Series() # Add your market return data here -# beta_value = beta(df, market_returns) -# alpha_value = alpha(df, market_returns) -# sortino_daily, sortino_annualized = sortino_ratio(df) - -# Print the results -print(f"Compounded Portfolio Return: {compounded_return}") -print(f"Annualized Portfolio Return: {annualized_return}") -print(f"Average Daily Return: {avg_daily_return}") -print(f"Annualized Average Return: {avg_annualized_return}") -print(f"Daily Volatility: {daily_volatility}") -print(f"Annualized Volatility: {annualized_volatility}") -print(f"Maximum Drawdown: {max_drawdown}") -print(f"Sharpe Ratio (Daily): {sharpe_daily}") -print(f"Sharpe Ratio (Annualized): {sharpe_annualized}") -# print(f"Beta: {beta_value}") -# print(f"Alpha: {alpha_value}") -# print(f"Sortino Ratio (Daily): {sortino_daily}") -# print(f"Sortino Ratio (Annualized): {sortino_annualized}") - - - - -# def extract_sheet_to_csv(file_path, sheet_name, output_csv_path): -# # Load the specific sheet -# data = pd.read_excel(file_path, sheet_name=sheet_name) - -# # Save to CSV -# data.to_csv(output_csv_path, index=False) -# print(f"Data from sheet '{sheet_name}' saved to {output_csv_path}") - - - -# file_path = "DFIC_Fund_Tracker.xlsx" -# sheet_name = "Historical Portfolio Values" -# output_csv_path = "data.csv" -# extract_sheet_to_csv(file_path, sheet_name, output_csv_path) - -# # Load the CSV file -# csv_data = pd.read_csv(output_csv_path) - -# # Inspect the data -# print(csv_data.head()) - diff --git a/DFIC_Fund_Tracker.xlsx b/legacy/DFIC_Fund_Tracker.xlsx similarity index 100% rename from DFIC_Fund_Tracker.xlsx rename to legacy/DFIC_Fund_Tracker.xlsx diff --git a/Performance.py b/legacy/Performance.py similarity index 96% rename from Performance.py rename to legacy/Performance.py index aed341b2..2878edef 100644 --- a/Performance.py +++ b/legacy/Performance.py @@ -4,6 +4,8 @@ python Performance.py fund yyyy-mm-dd # Run for specific date (must be in portfolio_total.csv) """ +# This file is to be deprecated ######################################################### + import pandas as pd import os import sys @@ -17,7 +19,6 @@ class Performance: def __init__(self, fund, date, portfolio_column="Total_Portfolio_Value"): """ Initializes the Performance class with portfolio data for a specific fund. - :param fund: The name of the fund (used to locate the data). :param date: The date for performance calculation. :param portfolio_column: The column name representing portfolio value. @@ -45,7 +46,6 @@ def __init__(self, fund, date, portfolio_column="Total_Portfolio_Value"): def valid_date(self): """ Validate the date provided by the user. It must be an available date in the dataset. - :return: True if the date is valid, False otherwise. """ return self.date in self.df['Date'].values @@ -53,7 +53,6 @@ def valid_date(self): def _closest_date(self, target_date, side='left'): """ Finds the closest available date in the dataset. - :param target_date: The target date to search for. :param side: 'left' to find the closest past date, 'right' for the closest future date. :return: The closest date found in the dataset. @@ -65,7 +64,6 @@ def _closest_date(self, target_date, side='left'): def _get_value_by_date(self, date): """ Retrieves the portfolio value on a specific date. - :param date: The date for which to fetch the portfolio value. :return: Portfolio value on the given date or None if unavailable. """ @@ -75,7 +73,6 @@ def _get_value_by_date(self, date): def calculate_performance(self): """ Calculates portfolio performance over multiple periods. - :return: A dictionary containing returns for different periods. """ periods = { @@ -102,7 +99,6 @@ def calculate_performance(self): def save_results_to_csv(self, results): """ Saves the performance results to a CSV file. - :param results: Dictionary of performance returns. """ date = self.date @@ -115,17 +111,19 @@ def save_results_to_csv(self, results): saved_df = pd.read_csv(self.output_file, index_col="Date") saved_df.index = pd.to_datetime(saved_df.index) results_df = pd.concat([saved_df, results_df]) - + results_df = results_df[~results_df.index.duplicated(keep='last')] results_df = results_df.sort_index() # Save to CSV results_df.to_csv(self.output_file, mode='w', index=True, header=True) + + #TODO: all this database logic needs to be moved to the app. + #TODO: also, these calculations should no longer be made based on the csv files def save_results_to_db(self, results): """ Saves the performance results to a database. - :param results: Dictionary of performance returns. """ # Prepare data for query @@ -237,7 +235,7 @@ def drop_performance_returns_table(): dates = [date] else: dates = pd.read_csv(os.path.join('data', fund, 'output', 'portfolio_total.csv'))['Date'] - + # Uncomment and run manualy when needed # drop_performance_returns_table() # create_performance_returns_table() @@ -250,4 +248,4 @@ def drop_performance_returns_table(): print(f"Calculating performance for {fund} on {date}") results = performance.calculate_performance() performance.save_results_to_csv(results) - performance.save_results_to_db(results) + performance.save_results_to_db(results) \ No newline at end of file diff --git a/data/benchmark/output/efficient_frontier.png b/legacy/data/benchmark/output/efficient_frontier.png similarity index 100% rename from data/benchmark/output/efficient_frontier.png rename to legacy/data/benchmark/output/efficient_frontier.png diff --git a/data/benchmark/output/portfolio_plot.png b/legacy/data/benchmark/output/portfolio_plot.png similarity index 100% rename from data/benchmark/output/portfolio_plot.png rename to legacy/data/benchmark/output/portfolio_plot.png diff --git a/data/core/output/efficient_frontier.png b/legacy/data/core/output/efficient_frontier.png similarity index 100% rename from data/core/output/efficient_frontier.png rename to legacy/data/core/output/efficient_frontier.png diff --git a/data/core/output/portfolio_plot.png b/legacy/data/core/output/portfolio_plot.png similarity index 100% rename from data/core/output/portfolio_plot.png rename to legacy/data/core/output/portfolio_plot.png diff --git a/performance/Benchmark.py b/legacy/performance/Benchmark.py similarity index 93% rename from performance/Benchmark.py rename to legacy/performance/Benchmark.py index cb718e6e..6ac59bc0 100644 --- a/performance/Benchmark.py +++ b/legacy/performance/Benchmark.py @@ -1,3 +1,16 @@ +""" +Benchmark data management and calculation module. + +This module handles benchmark-related operations and calculations. +It provides methods for: +- Retrieving and processing SPY benchmark data +- Creating and managing custom benchmarks +- Calculating benchmark-specific metrics (returns, variance, volatility) +- Managing benchmark inception returns + +This module focuses on benchmark data management and calculations. +""" + import os import pandas as pd diff --git a/performance/Ratios.py b/legacy/performance/Ratios.py similarity index 79% rename from performance/Ratios.py rename to legacy/performance/Ratios.py index c12554e8..618067f4 100644 --- a/performance/Ratios.py +++ b/legacy/performance/Ratios.py @@ -1,8 +1,21 @@ +""" +Portfolio ratio calculation module. + +This module calculates various financial ratios used to evaluate portfolio performance. +It provides methods for: +- Sharpe Ratio: Risk-adjusted return relative to risk-free rate +- Sortino Ratio: Downside risk-adjusted return +- Treynor Ratio: Systematic risk-adjusted return +- Information Ratio: Active return relative to tracking error + +This module focuses solely on ratio calculations and assumes input data is already processed. +""" + import os import pandas as pd -from .Benchmark import Benchmark -from .MarketComparison import MarketComparison -from .PortfolioPerformance import PortfolioPerformance +from benchmark import Benchmark +from market_comparison import MarketComparison +from returns_calculator import ReturnsCalculator class Ratios: def __init__(self, output_folder): diff --git a/legacy/performance/__init__.py b/legacy/performance/__init__.py new file mode 100644 index 00000000..e18c1878 --- /dev/null +++ b/legacy/performance/__init__.py @@ -0,0 +1,9 @@ +from .data_processor import DataProcessor +from .benchmark import Benchmark +from .returns_calculator import ReturnsCalculator +from .risk_metrics import RiskMetrics +from .market_comparison import MarketComparison +from .ratios import Ratios + +# this is what gets imported when you just import all of performance +__all__ = ["DataProcessor", "Benchmark", "ReturnsCalculator", "RiskMetrics", "MarketComparison", "Ratios"] \ No newline at end of file diff --git a/legacy/performance/csv_writer.py b/legacy/performance/csv_writer.py new file mode 100644 index 00000000..b9c5d10f --- /dev/null +++ b/legacy/performance/csv_writer.py @@ -0,0 +1,52 @@ +""" +CSV file management module for performance data. + +This module handles the persistence of performance metrics to CSV files. +It is responsible for: +- Writing performance results to CSV files +- Managing existing data (appending, deduplication) +- Ensuring data consistency in the CSV format + +This is a pure I/O module with no calculation logic. +""" + +import pandas as pd +import os + +class PerformanceCSVWriter: + def __init__(self, fund, output_folder): + """ + Initializes the CSV writer for performance data. + + Args: + fund (str): The name of the fund + output_folder (str): Path to the output folder + """ + self.fund = fund + self.output_folder = output_folder + self.output_file = os.path.join(output_folder, 'performance_returns.csv') + + def save_results(self, results, date): + """ + Saves the performance results to a CSV file. + + Args: + results (dict): Dictionary of performance returns + date (datetime): The date for which the results were calculated + """ + # Create a new dataframe to store the results + results_df = pd.DataFrame([results], index=[date]) + results_df.index.name = "Date" + + # Read existing data if the file exists and append new data + if os.path.exists(self.output_file): + saved_df = pd.read_csv(self.output_file, index_col="Date") + saved_df.index = pd.to_datetime(saved_df.index) + results_df = pd.concat([saved_df, results_df]) + + # Remove duplicates and sort + results_df = results_df[~results_df.index.duplicated(keep='last')] + results_df = results_df.sort_index() + + # Save to CSV + results_df.to_csv(self.output_file, mode='w', index=True, header=True) \ No newline at end of file diff --git a/performance/DataProcessor.py b/legacy/performance/data_processor.py similarity index 85% rename from performance/DataProcessor.py rename to legacy/performance/data_processor.py index 050f731a..bbec8e80 100644 --- a/performance/DataProcessor.py +++ b/legacy/performance/data_processor.py @@ -1,3 +1,15 @@ +""" +Data processing and visualization module. + +This module handles data aggregation and visualization tasks. +It provides methods for: +- Aggregating market values, cash, and dividend data +- Creating portfolio value plots +- Data preprocessing for other modules + +This module focuses on data processing and visualization, serving as a utility for other modules. +""" + import os import pandas as pd import matplotlib.pyplot as plt diff --git a/legacy/performance/fixed_income.py b/legacy/performance/fixed_income.py new file mode 100644 index 00000000..9ae00953 --- /dev/null +++ b/legacy/performance/fixed_income.py @@ -0,0 +1,74 @@ +""" +Fixed income analysis module. + +This module provides analysis tools for fixed income securities. +It handles: +- Market value calculations +- Currency conversion +- Market share calculations +- USD-specific analysis + +The module requires access to market values and exchange rate data. +""" + +import os +import pandas as pd +import yfinance as yf + + +class FixedIncomeAnalyzer: + def __init__(self, output_folder): + """ + Initialize the FixedIncomeAnalyzer. + + Args: + output_folder (str): Path to folder containing market values and exchange rates + """ + self.output_folder = output_folder + self.market_values = pd.read_csv(os.path.join(output_folder, 'market_values.csv')) + self.exchange_rates = pd.read_csv(os.path.join(output_folder, 'exchange_rates.csv')) + + def get_fixed_income_info(self, tickers: list): + """ + Calculate fixed income security information including market values and shares. + + Args: + tickers (list): List of ticker symbols to analyze + + Returns: + pd.DataFrame: DataFrame containing market values and shares for each ticker + """ + usd_tickers = [] + + # Convert USD values to CAD + for t in tickers: + try: + currency = yf.Ticker(t).info['currency'] + except: + currency = 'CAD' + + if currency == 'USD': + self.market_values[t] = self.market_values[t] * self.exchange_rates['USD'].iloc[-1] + usd_tickers.append(t) + + # Calculate market values and shares + current_mkt_vals = {ticker: self.market_values[ticker].iloc[-1] for ticker in tickers} + total_mkt_vals = sum(current_mkt_vals.values()) + fi_mkt_shares = {ticker: current_mkt_vals[ticker] / total_mkt_vals for ticker in tickers} + + # Calculate USD-specific metrics + usd_total_mkt_val = sum([current_mkt_vals[ticker] for ticker in usd_tickers]) + usd_fi_mkt_shares = {ticker: current_mkt_vals[ticker] / usd_total_mkt_val for ticker in usd_tickers} + + # Create result DataFrame + data = { + 'Ticker': tickers, + 'Market Value': [current_mkt_vals[ticker] for ticker in tickers], + 'Total Market Share': [fi_mkt_shares[ticker] for ticker in tickers], + 'USD Market Share': [usd_fi_mkt_shares[ticker] if ticker in usd_tickers else 0 for ticker in tickers] + } + + result_df = pd.DataFrame(data) + result_df.set_index('Ticker', inplace=True) + + return result_df \ No newline at end of file diff --git a/PerformanceTracker.py b/legacy/performance/generate_performance_reports.py similarity index 94% rename from PerformanceTracker.py rename to legacy/performance/generate_performance_reports.py index b1112b7b..5e6a4017 100644 --- a/PerformanceTracker.py +++ b/legacy/performance/generate_performance_reports.py @@ -1,177 +1,190 @@ -import os -import sys -import yaml -from performance import ( - DataProcessor, - Benchmark, - PortfolioPerformance, - RiskMetrics, - MarketComparison, - Ratios -) - -''' -FILE PURPOSE: RUNS TO FILL SECONDARY OUtPUT TABLES (ones that require calcuation - getting calc from performance files) -Uses all files in performance folder -run every day (second thing run in github actions - after portfolio (which updates output most tables)) -''' - -def main(): - market_values_file = os.path.join(output_folder, "market_values.csv") - cash_file = os.path.join(output_folder, "cash.csv") - dividend_file = os.path.join(output_folder, "dividend_values.csv") - output_file = os.path.join(output_folder, "portfolio_total.csv") - THREE_MTH_TREASURY_RATE = 0.0436 # 3-month treasury rate - FIVE_PERCENT = 0.05 - - # Load config file - config_path = os.path.join('portfolios', f'dfic_{output_folder.split('/')[1]}.yaml') - try: - with open(config_path, 'r') as f: - config = yaml.safe_load(f) - except FileNotFoundError: - print(f"Error: Config file not found at {config_path}") - sys.exit(1) - except yaml.YAMLError as e: - print(f"Error parsing YAML file: {e}") - sys.exit(1) - - data_processor = DataProcessor(output_folder) - benchmark = Benchmark(output_folder) - portfolio_performance = PortfolioPerformance(output_folder) - risk_metrics = RiskMetrics(output_folder) - market_comparison = MarketComparison(output_folder) - ratios = Ratios(output_folder) - - # run these once only after running portfolio.py once - data_processor.aggregate_data(market_values_file, cash_file, dividend_file, output_file) - benchmark.create_custom_benchmark() - period_metrics = portfolio_performance.calculate_period_performance() - - # Fixed income tickers from config - #TODO: sector naming inconsistent (pull this from db instead) - fi_tickers = [security['ticker'] for security in config['securities'] if security.get('sector') == 'Fixed Income'] - - print(f"Total Return including dividends,{portfolio_performance.total_return()*100:.2f}%\n") - print(f"Daily Average Return,{portfolio_performance.daily_average_return()*100:.4f}%\n") - print(f"Annualized Average Return,{portfolio_performance.annualized_average_return()*100:.2f}%\n") - print(f"Daily Variance,{risk_metrics.daily_variance()*100:.4f}%\n") - print(f"Annualized Variance,{risk_metrics.annualized_variance()*100:.2f}%\n") - print(f"Daily Volatility,{risk_metrics.daily_volatility()*100:.4f}%\n") - print(f"Annualized Volatility,{risk_metrics.annualized_volatility()*100:.2f}%\n") - print(f"Daily Downside Variance,{risk_metrics.daily_downside_variance()*100:.4f}%\n") - print(f"Annualized Downside Variance,{risk_metrics.annualized_downside_variance()*100:.2f}%\n") - print(f"Daily Downside Volatility,{risk_metrics.daily_downside_volatility()*100:.4f}%\n") - print(f"Annualized Downside Volatility,{risk_metrics.annualized_downside_volatility()*100:.2f}%\n") - print(f"Sharpe Ratio (Annualized),{ratios.sharpe_ratio(THREE_MTH_TREASURY_RATE)[1]:.2f}\n") - print(f"Sortino Ratio (Annualized),{ratios.sortino_ratio(THREE_MTH_TREASURY_RATE)[1]:.2f}\n") - print(f"Maximum Drawdown,{risk_metrics.maximum_drawdown()*100:.2f}%\n") - - print("--- Benchmark Info ---\n") - print(f"Custom Benchmark Variance (Daily),{benchmark.benchmark_variance()[0]*100:.4f}%\n") - print(f"SPY Variance (Daily),{benchmark.benchmark_variance(benchmark='SPY')[0]*100:.4f}%\n") - print(f"Custom Benchmark Variance (Annualized),{benchmark.benchmark_variance()[1]*100:.2f}%\n") - print(f"SPY Variance (Annualized),{benchmark.benchmark_variance(benchmark='SPY')[1]*100:.2f}%\n") - print(f"Custom Benchmark Volatility (Daily),{benchmark.benchmark_volatility()[0]*100:.4f}%\n") - print(f"SPY Volatility (Daily),{benchmark.benchmark_volatility(benchmark='SPY')[0]*100:.4f}%\n") - print(f"Custom Benchmark Volatility (Annualized),{benchmark.benchmark_volatility()[1]*100:.2f}%\n") - print(f"SPY Volatility (Annualized),{benchmark.benchmark_volatility(benchmark='SPY')[1]*100:.2f}%\n") - print(f"Custom Benchmark Average Return (Daily),{benchmark.benchmark_average_return()[0]*100:.4f}%\n") - print(f"SPY Average Return (Daily),{benchmark.benchmark_average_return(benchmark='SPY')[0]*100:.4f}%\n") - print(f"Custom Benchmark Average Return (Annualized),{benchmark.benchmark_average_return()[1]*100:.2f}%\n") - print(f"SPY Average Return (Annualized),{benchmark.benchmark_average_return(benchmark='SPY')[1]*100:.2f}%\n") - print(f"Custom Benchmark Inception Return,{benchmark.benchmark_inception_return() * 100:.2f}%\n") - print(f"SPY Inception Return,{benchmark.spy_inception_return() * 100:.2f}%\n") - - print(f"Portfolio Beta,{market_comparison.beta():.4f}\n") - print(f"Portfolio Alpha against custom benchmark,{100*market_comparison.alpha(THREE_MTH_TREASURY_RATE):.4f}%\n") - print(f"Portfolio Alpha against SPY,{100*market_comparison.alpha(THREE_MTH_TREASURY_RATE, benchmark='SPY'):.4f}%\n") - - print(f"Portfolio Risk Premium,{market_comparison.portfolio_risk_premium(THREE_MTH_TREASURY_RATE)*100:.2f}%\n") - print(f"Risk Adjusted Return (three month treasury rate),{market_comparison.risk_adjusted_return(THREE_MTH_TREASURY_RATE)*100:.2f}%\n") - print(f"Treynor Ratio (three month treasury rate),{ratios.treynor_ratio(THREE_MTH_TREASURY_RATE):.2f}\n") - daily_ir, annual_ir = ratios.information_ratio() - print(f"Information Ratio (Daily),{daily_ir:.4f}\n") - print(f"Information Ratio (Annualized),{annual_ir:.2f}\n") - - print("--- Fixed Income Info ---\n") - fi_stats_df = portfolio_performance.get_fixed_income_info(fi_tickers) - print(f"{fi_stats_df}\n") - - print("--- Portfolio Returns ---\n") - print(f"1 Day Return,{period_metrics['1d'] * 100:.2f}%\n") - print(f"1 Week Return,{period_metrics['1w'] * 100:.2f}%\n") - print(f"1 Month Return,{period_metrics['1m'] * 100:.2f}%\n") - print(f"Year-to-Date Return,{period_metrics['YTD'] * 100:.2f}%\n") - print(f"1 Year Return,{period_metrics['1y'] * 100:.2f}%\n") - print(f"Inception,{period_metrics['Inception'] * 100:.2f}%\n") - - # output all these calculations to a csv file - with open(os.path.join(output_folder, 'performance_metrics.csv'), 'w') as f: - f.write("Metric,Value\n") - f.write(f"Total Return including dividends,{portfolio_performance.total_return()*100:.2f}%\n") - f.write(f"Daily Average Return,{portfolio_performance.daily_average_return()*100:.4f}%\n") - f.write(f"Annualized Average Return,{portfolio_performance.annualized_average_return()*100:.2f}%\n") - f.write(f"Daily Variance,{risk_metrics.daily_variance()*100:.4f}%\n") - f.write(f"Annualized Variance,{risk_metrics.annualized_variance()*100:.2f}%\n") - f.write(f"Daily Volatility,{risk_metrics.daily_volatility()*100:.4f}%\n") - f.write(f"Annualized Volatility,{risk_metrics.annualized_volatility()*100:.2f}%\n") - f.write(f"Daily Downside Variance,{risk_metrics.daily_downside_variance()*100:.4f}%\n") - f.write(f"Annualized Downside Variance,{risk_metrics.annualized_downside_variance()*100:.2f}%\n") - f.write(f"Daily Downside Volatility,{risk_metrics.daily_downside_volatility()*100:.4f}%\n") - f.write(f"Annualized Downside Volatility,{risk_metrics.annualized_downside_volatility()*100:.2f}%\n") - f.write(f"Sharpe Ratio (Annualized),{ratios.sharpe_ratio(THREE_MTH_TREASURY_RATE)[1]:.2f}\n") - f.write(f"Sortino Ratio (Annualized),{ratios.sortino_ratio(THREE_MTH_TREASURY_RATE)[1]:.2f}\n") - f.write(f"Maximum Drawdown,{risk_metrics.maximum_drawdown()*100:.2f}%\n") - - f.write(f"Custom Benchmark Variance (Daily),{benchmark.benchmark_variance()[0]*100:.4f}%\n") - f.write(f"SPY Variance (Daily),{benchmark.benchmark_variance(benchmark='SPY')[0]*100:.4f}%\n") - f.write(f"Custom Benchmark Variance (Annualized),{benchmark.benchmark_variance()[1]*100:.2f}%\n") - f.write(f"SPY Variance (Annualized),{benchmark.benchmark_variance(benchmark='SPY')[1]*100:.2f}%\n") - f.write(f"Custom Benchmark Volatility (Daily),{benchmark.benchmark_volatility()[0]*100:.4f}%\n") - f.write(f"SPY Volatility (Daily),{benchmark.benchmark_volatility(benchmark='SPY')[0]*100:.4f}%\n") - f.write(f"Custom Benchmark Volatility (Annualized),{benchmark.benchmark_volatility()[1]*100:.2f}%\n") - f.write(f"SPY Volatility (Annualized),{benchmark.benchmark_volatility(benchmark='SPY')[1]*100:.2f}%\n") - f.write(f"Custom Benchmark Average Return (Daily),{benchmark.benchmark_average_return()[0]*100:.4f}%\n") - f.write(f"SPY Average Return (Daily),{benchmark.benchmark_average_return(benchmark='SPY')[0]*100:.4f}%\n") - f.write(f"Custom Benchmark Average Return (Annualized),{benchmark.benchmark_average_return()[1]*100:.2f}%\n") - f.write(f"SPY Average Return (Annualized),{benchmark.benchmark_average_return(benchmark='SPY')[1]*100:.2f}%\n") - f.write(f"Custom Benchmark Inception Return,{benchmark.benchmark_inception_return() * 100:.2f}%\n") - f.write(f"SPY Inception Return,{benchmark.spy_inception_return() * 100:.2f}%\n") - - f.write(f"Portfolio Beta,{market_comparison.beta():.4f}\n") - f.write(f"Portfolio Alpha against custom benchmark,{100*market_comparison.alpha(THREE_MTH_TREASURY_RATE):.4f}%\n") - f.write(f"Portfolio Alpha against SPY,{100*market_comparison.alpha(THREE_MTH_TREASURY_RATE, benchmark='SPY'):.4f}%\n") - - f.write(f"Portfolio Risk Premium,{market_comparison.portfolio_risk_premium(THREE_MTH_TREASURY_RATE)*100:.2f}%\n") - f.write(f"Risk Adjusted Return (three month treasury rate),{market_comparison.risk_adjusted_return(THREE_MTH_TREASURY_RATE)*100:.2f}%\n") - f.write(f"Treynor Ratio (three month treasury rate),{ratios.treynor_ratio(THREE_MTH_TREASURY_RATE):.2f}\n") - f.write(f"Information Ratio (vs. Custom Benchmark daily),{daily_ir:.4f}\n") - f.write(f"Information Ratio (vs. Custom Benchmark annual),{annual_ir:.2f}\n") - - # Fixed Income Metrics - for ticker in fi_tickers: - f.write(f"{ticker} Current Mkt Value,{fi_stats_df.loc[ticker, 'Market Value']:.2f}\n") - f.write(f"{ticker} Fixed Income Mkt Share,{fi_stats_df.loc[ticker, 'Total Market Share']*100:.2f}%\n") - f.write(f"{ticker} USD FI Mkt Share,{fi_stats_df.loc[ticker, 'USD Market Share']*100:.2f}%\n") - - # Portfolio Return Metrics - f.write(f"1 Day Return,{period_metrics['1d'] * 100:.2f}%\n") - f.write(f"1 Week Return,{period_metrics['1w'] * 100:.2f}%\n") - f.write(f"1 Month Return,{period_metrics['1m'] * 100:.2f}%\n") - f.write(f"Year-to-Date Return,{period_metrics['YTD'] * 100:.2f}%\n") - f.write(f"1 Year Return,{period_metrics['1y'] * 100:.2f}%\n") - f.write(f"Inception,{period_metrics['Inception'] * 100:.2f}%\n") - - data_processor.plot_portfolio_value() - -if __name__ == '__main__': - if len(sys.argv) < 2: - sys.exit("Usage: python3 PerformanceTracker.py ") - folder_prefix = sys.argv[1] - output_folder = os.path.join("data", folder_prefix, "output") - os.makedirs(output_folder, exist_ok=True) - main() - - # notes: % changes are only to 2 decimals in portfolio_total.csv - # definitely something wrong here, possibly because of that. +""" +Performance report generation module. + +This module orchestrates the generation of comprehensive performance reports. +It coordinates: +- Data processing and aggregation +- Benchmark comparisons +- Risk metric calculations +- Market comparison analysis +- Ratio calculations +- Report generation and output + +This is the main orchestration module that ties together all other performance-related modules. +""" + +import os +import sys +import yaml +from data_processor import DataProcessor +from benchmark import Benchmark +from returns_calculator import ReturnsCalculator +from risk_metrics import RiskMetrics +from market_comparison import MarketComparison +from ratios import Ratios + +''' +FILE PURPOSE: RUNS TO FILL SECONDARY OUtPUT TABLES (ones that require calcuation - getting calc from performance files) +Uses all files in performance folder +run every day (second thing run in github actions - after portfolio (which updates output most tables)) +''' + +def main(): + market_values_file = os.path.join(output_folder, "market_values.csv") + cash_file = os.path.join(output_folder, "cash.csv") + dividend_file = os.path.join(output_folder, "dividend_values.csv") + output_file = os.path.join(output_folder, "portfolio_total.csv") + THREE_MTH_TREASURY_RATE = 0.0436 # 3-month treasury rate + FIVE_PERCENT = 0.05 + + # Load config file + config_path = os.path.join('portfolios', f'dfic_{output_folder.split('/')[1]}.yaml') + try: + with open(config_path, 'r') as f: + config = yaml.safe_load(f) + except FileNotFoundError: + print(f"Error: Config file not found at {config_path}") + sys.exit(1) + except yaml.YAMLError as e: + print(f"Error parsing YAML file: {e}") + sys.exit(1) + + data_processor = DataProcessor(output_folder) + benchmark = Benchmark(output_folder) + portfolio_performance = ReturnsCalculator(output_folder) + risk_metrics = RiskMetrics(output_folder) + market_comparison = MarketComparison(output_folder) + ratios = Ratios(output_folder) + + # run these once only after running portfolio.py once + data_processor.aggregate_data(market_values_file, cash_file, dividend_file, output_file) + benchmark.create_custom_benchmark() + period_metrics = portfolio_performance.calculate_period_performance() + + # Fixed income tickers from config + #TODO: sector naming inconsistent (pull this from db instead) + fi_tickers = [security['ticker'] for security in config['securities'] if security.get('sector') == 'Fixed Income'] + + print(f"Total Return including dividends,{portfolio_performance.total_return()*100:.2f}%\n") + print(f"Daily Average Return,{portfolio_performance.daily_average_return()*100:.4f}%\n") + print(f"Annualized Average Return,{portfolio_performance.annualized_average_return()*100:.2f}%\n") + print(f"Daily Variance,{risk_metrics.daily_variance()*100:.4f}%\n") + print(f"Annualized Variance,{risk_metrics.annualized_variance()*100:.2f}%\n") + print(f"Daily Volatility,{risk_metrics.daily_volatility()*100:.4f}%\n") + print(f"Annualized Volatility,{risk_metrics.annualized_volatility()*100:.2f}%\n") + print(f"Daily Downside Variance,{risk_metrics.daily_downside_variance()*100:.4f}%\n") + print(f"Annualized Downside Variance,{risk_metrics.annualized_downside_variance()*100:.2f}%\n") + print(f"Daily Downside Volatility,{risk_metrics.daily_downside_volatility()*100:.4f}%\n") + print(f"Annualized Downside Volatility,{risk_metrics.annualized_downside_volatility()*100:.2f}%\n") + print(f"Sharpe Ratio (Annualized),{ratios.sharpe_ratio(THREE_MTH_TREASURY_RATE)[1]:.2f}\n") + print(f"Sortino Ratio (Annualized),{ratios.sortino_ratio(THREE_MTH_TREASURY_RATE)[1]:.2f}\n") + print(f"Maximum Drawdown,{risk_metrics.maximum_drawdown()*100:.2f}%\n") + + print("--- Benchmark Info ---\n") + print(f"Custom Benchmark Variance (Daily),{benchmark.benchmark_variance()[0]*100:.4f}%\n") + print(f"SPY Variance (Daily),{benchmark.benchmark_variance(benchmark='SPY')[0]*100:.4f}%\n") + print(f"Custom Benchmark Variance (Annualized),{benchmark.benchmark_variance()[1]*100:.2f}%\n") + print(f"SPY Variance (Annualized),{benchmark.benchmark_variance(benchmark='SPY')[1]*100:.2f}%\n") + print(f"Custom Benchmark Volatility (Daily),{benchmark.benchmark_volatility()[0]*100:.4f}%\n") + print(f"SPY Volatility (Daily),{benchmark.benchmark_volatility(benchmark='SPY')[0]*100:.4f}%\n") + print(f"Custom Benchmark Volatility (Annualized),{benchmark.benchmark_volatility()[1]*100:.2f}%\n") + print(f"SPY Volatility (Annualized),{benchmark.benchmark_volatility(benchmark='SPY')[1]*100:.2f}%\n") + print(f"Custom Benchmark Average Return (Daily),{benchmark.benchmark_average_return()[0]*100:.4f}%\n") + print(f"SPY Average Return (Daily),{benchmark.benchmark_average_return(benchmark='SPY')[0]*100:.4f}%\n") + print(f"Custom Benchmark Average Return (Annualized),{benchmark.benchmark_average_return()[1]*100:.2f}%\n") + print(f"SPY Average Return (Annualized),{benchmark.benchmark_average_return(benchmark='SPY')[1]*100:.2f}%\n") + print(f"Custom Benchmark Inception Return,{benchmark.benchmark_inception_return() * 100:.2f}%\n") + print(f"SPY Inception Return,{benchmark.spy_inception_return() * 100:.2f}%\n") + + print(f"Portfolio Beta,{market_comparison.beta():.4f}\n") + print(f"Portfolio Alpha against custom benchmark,{100*market_comparison.alpha(THREE_MTH_TREASURY_RATE):.4f}%\n") + print(f"Portfolio Alpha against SPY,{100*market_comparison.alpha(THREE_MTH_TREASURY_RATE, benchmark='SPY'):.4f}%\n") + + print(f"Portfolio Risk Premium,{market_comparison.portfolio_risk_premium(THREE_MTH_TREASURY_RATE)*100:.2f}%\n") + print(f"Risk Adjusted Return (three month treasury rate),{market_comparison.risk_adjusted_return(THREE_MTH_TREASURY_RATE)*100:.2f}%\n") + print(f"Treynor Ratio (three month treasury rate),{ratios.treynor_ratio(THREE_MTH_TREASURY_RATE):.2f}\n") + daily_ir, annual_ir = ratios.information_ratio() + print(f"Information Ratio (Daily),{daily_ir:.4f}\n") + print(f"Information Ratio (Annualized),{annual_ir:.2f}\n") + + print("--- Fixed Income Info ---\n") + fi_stats_df = portfolio_performance.get_fixed_income_info(fi_tickers) + print(f"{fi_stats_df}\n") + + print("--- Portfolio Returns ---\n") + print(f"1 Day Return,{period_metrics['1d'] * 100:.2f}%\n") + print(f"1 Week Return,{period_metrics['1w'] * 100:.2f}%\n") + print(f"1 Month Return,{period_metrics['1m'] * 100:.2f}%\n") + print(f"Year-to-Date Return,{period_metrics['YTD'] * 100:.2f}%\n") + print(f"1 Year Return,{period_metrics['1y'] * 100:.2f}%\n") + print(f"Inception,{period_metrics['Inception'] * 100:.2f}%\n") + + # output all these calculations to a csv file + with open(os.path.join(output_folder, 'performance_metrics.csv'), 'w') as f: + f.write("Metric,Value\n") + f.write(f"Total Return including dividends,{portfolio_performance.total_return()*100:.2f}%\n") + f.write(f"Daily Average Return,{portfolio_performance.daily_average_return()*100:.4f}%\n") + f.write(f"Annualized Average Return,{portfolio_performance.annualized_average_return()*100:.2f}%\n") + f.write(f"Daily Variance,{risk_metrics.daily_variance()*100:.4f}%\n") + f.write(f"Annualized Variance,{risk_metrics.annualized_variance()*100:.2f}%\n") + f.write(f"Daily Volatility,{risk_metrics.daily_volatility()*100:.4f}%\n") + f.write(f"Annualized Volatility,{risk_metrics.annualized_volatility()*100:.2f}%\n") + f.write(f"Daily Downside Variance,{risk_metrics.daily_downside_variance()*100:.4f}%\n") + f.write(f"Annualized Downside Variance,{risk_metrics.annualized_downside_variance()*100:.2f}%\n") + f.write(f"Daily Downside Volatility,{risk_metrics.daily_downside_volatility()*100:.4f}%\n") + f.write(f"Annualized Downside Volatility,{risk_metrics.annualized_downside_volatility()*100:.2f}%\n") + f.write(f"Sharpe Ratio (Annualized),{ratios.sharpe_ratio(THREE_MTH_TREASURY_RATE)[1]:.2f}\n") + f.write(f"Sortino Ratio (Annualized),{ratios.sortino_ratio(THREE_MTH_TREASURY_RATE)[1]:.2f}\n") + f.write(f"Maximum Drawdown,{risk_metrics.maximum_drawdown()*100:.2f}%\n") + + f.write(f"Custom Benchmark Variance (Daily),{benchmark.benchmark_variance()[0]*100:.4f}%\n") + f.write(f"SPY Variance (Daily),{benchmark.benchmark_variance(benchmark='SPY')[0]*100:.4f}%\n") + f.write(f"Custom Benchmark Variance (Annualized),{benchmark.benchmark_variance()[1]*100:.2f}%\n") + f.write(f"SPY Variance (Annualized),{benchmark.benchmark_variance(benchmark='SPY')[1]*100:.2f}%\n") + f.write(f"Custom Benchmark Volatility (Daily),{benchmark.benchmark_volatility()[0]*100:.4f}%\n") + f.write(f"SPY Volatility (Daily),{benchmark.benchmark_volatility(benchmark='SPY')[0]*100:.4f}%\n") + f.write(f"Custom Benchmark Volatility (Annualized),{benchmark.benchmark_volatility()[1]*100:.2f}%\n") + f.write(f"SPY Volatility (Annualized),{benchmark.benchmark_volatility(benchmark='SPY')[1]*100:.2f}%\n") + f.write(f"Custom Benchmark Average Return (Daily),{benchmark.benchmark_average_return()[0]*100:.4f}%\n") + f.write(f"SPY Average Return (Daily),{benchmark.benchmark_average_return(benchmark='SPY')[0]*100:.4f}%\n") + f.write(f"Custom Benchmark Average Return (Annualized),{benchmark.benchmark_average_return()[1]*100:.2f}%\n") + f.write(f"SPY Average Return (Annualized),{benchmark.benchmark_average_return(benchmark='SPY')[1]*100:.2f}%\n") + f.write(f"Custom Benchmark Inception Return,{benchmark.benchmark_inception_return() * 100:.2f}%\n") + f.write(f"SPY Inception Return,{benchmark.spy_inception_return() * 100:.2f}%\n") + + f.write(f"Portfolio Beta,{market_comparison.beta():.4f}\n") + f.write(f"Portfolio Alpha against custom benchmark,{100*market_comparison.alpha(THREE_MTH_TREASURY_RATE):.4f}%\n") + f.write(f"Portfolio Alpha against SPY,{100*market_comparison.alpha(THREE_MTH_TREASURY_RATE, benchmark='SPY'):.4f}%\n") + + f.write(f"Portfolio Risk Premium,{market_comparison.portfolio_risk_premium(THREE_MTH_TREASURY_RATE)*100:.2f}%\n") + f.write(f"Risk Adjusted Return (three month treasury rate),{market_comparison.risk_adjusted_return(THREE_MTH_TREASURY_RATE)*100:.2f}%\n") + f.write(f"Treynor Ratio (three month treasury rate),{ratios.treynor_ratio(THREE_MTH_TREASURY_RATE):.2f}\n") + f.write(f"Information Ratio (vs. Custom Benchmark daily),{daily_ir:.4f}\n") + f.write(f"Information Ratio (vs. Custom Benchmark annual),{annual_ir:.2f}\n") + + # Fixed Income Metrics + for ticker in fi_tickers: + f.write(f"{ticker} Current Mkt Value,{fi_stats_df.loc[ticker, 'Market Value']:.2f}\n") + f.write(f"{ticker} Fixed Income Mkt Share,{fi_stats_df.loc[ticker, 'Total Market Share']*100:.2f}%\n") + f.write(f"{ticker} USD FI Mkt Share,{fi_stats_df.loc[ticker, 'USD Market Share']*100:.2f}%\n") + + # Portfolio Return Metrics + f.write(f"1 Day Return,{period_metrics['1d'] * 100:.2f}%\n") + f.write(f"1 Week Return,{period_metrics['1w'] * 100:.2f}%\n") + f.write(f"1 Month Return,{period_metrics['1m'] * 100:.2f}%\n") + f.write(f"Year-to-Date Return,{period_metrics['YTD'] * 100:.2f}%\n") + f.write(f"1 Year Return,{period_metrics['1y'] * 100:.2f}%\n") + f.write(f"Inception,{period_metrics['Inception'] * 100:.2f}%\n") + + data_processor.plot_portfolio_value() + +if __name__ == '__main__': + if len(sys.argv) < 2: + sys.exit("Usage: python3 PerformanceTracker.py ") + folder_prefix = sys.argv[1] + output_folder = os.path.join("data", folder_prefix, "output") + os.makedirs(output_folder, exist_ok=True) + main() + + # notes: % changes are only to 2 decimals in portfolio_total.csv + # definitely something wrong here, possibly because of that. diff --git a/performance/MarketComparison.py b/legacy/performance/market_comparison.py similarity index 74% rename from performance/MarketComparison.py rename to legacy/performance/market_comparison.py index bfa74442..623cd65f 100644 --- a/performance/MarketComparison.py +++ b/legacy/performance/market_comparison.py @@ -1,8 +1,21 @@ +""" +Market comparison metrics module. + +This module calculates metrics that compare portfolio performance against market benchmarks. +It provides methods for: +- Beta calculation: Portfolio volatility relative to benchmark +- Alpha calculation: Excess return relative to benchmark +- Risk premium calculations +- Risk-adjusted return metrics + +This module focuses on comparative analysis and assumes benchmark data is available. +""" + import os import pandas as pd -from .Benchmark import Benchmark -from .PortfolioPerformance import PortfolioPerformance -from .RiskMetrics import RiskMetrics +from benchmark import Benchmark +from returns_calculator import ReturnsCalculator +from risk_metrics import RiskMetrics class MarketComparison: @@ -28,7 +41,7 @@ def beta(self, benchmark='custom'): def alpha(self, risk_free_rate, benchmark='custom'): benchmark_class = Benchmark(self.output_folder) annual_benchmark_return = benchmark_class.benchmark_average_return(benchmark)[1] - portfolio_performance = PortfolioPerformance(self.output_folder) + portfolio_performance = ReturnsCalculator(self.output_folder) print("benchmark: ", benchmark) print("annual_benchmark_return ", annual_benchmark_return) alpha = (portfolio_performance.annualized_average_return() - risk_free_rate) - self.beta() * (annual_benchmark_return - risk_free_rate) @@ -36,7 +49,7 @@ def alpha(self, risk_free_rate, benchmark='custom'): return alpha def portfolio_risk_premium(self, risk_free_return): - portfolio_performance = PortfolioPerformance(self.output_folder) + portfolio_performance = ReturnsCalculator(self.output_folder) return portfolio_performance.annualized_average_return() - risk_free_return def risk_adjusted_return(self, risk_free_return): diff --git a/legacy/performance/returns_calculator.py b/legacy/performance/returns_calculator.py new file mode 100644 index 00000000..1b81080f --- /dev/null +++ b/legacy/performance/returns_calculator.py @@ -0,0 +1,134 @@ +""" +Portfolio returns calculation module. + +This module provides comprehensive portfolio return calculations. +It handles: +- Period returns (1-day, 1-week, 1-month, YTD, 1-year, inception) +- Total return calculations +- Daily and annualized average returns + +The module is designed to work with either a DataFrame or CSV file input. +""" + +import os +import pandas as pd +from datetime import timedelta + + +class ReturnsCalculator: + def __init__(self, output_folder=None, portfolio_data=None, date=None, portfolio_column="Total_Portfolio_Value"): + """ + Initialize the ReturnsCalculator. + + Args: + output_folder (str, optional): Path to folder containing CSV files + portfolio_data (pd.DataFrame, optional): DataFrame containing portfolio values + date (str/datetime, optional): The date for performance calculation + portfolio_column (str): Column name representing portfolio value + """ + self.output_folder = output_folder + self.df = portfolio_data + self.date = pd.to_datetime(date) if date else None + self.portfolio_column = portfolio_column + + if output_folder and not portfolio_data: + self.df = pd.read_csv(os.path.join(output_folder, 'portfolio_total.csv')) + self.df['Date'] = pd.to_datetime(self.df['Date']) + if not date: + self.date = self.df['Date'].max() + + def valid_date(self): + """ + Validate the date provided by the user. It must be an available date in the dataset. + + Returns: + bool: True if the date is valid, False otherwise + """ + return self.date in self.df['Date'].values + + def _closest_date(self, target_date, side='left'): + """ + Finds the closest available date in the dataset. + + Args: + target_date: The target date to search for + side: 'left' to find the closest past date, 'right' for the closest future date + + Returns: + The closest date found in the dataset + """ + target_date = pd.to_datetime(target_date) + valid_dates = self.df[self.df['Date'] <= target_date]['Date'] if side == 'left' else self.df[self.df['Date'] >= target_date]['Date'] + return valid_dates.max() if side == 'left' else valid_dates.min() + + def _get_value_by_date(self, date): + """ + Retrieves the portfolio value on a specific date. + + Args: + date: The date for which to fetch the portfolio value + + Returns: + Portfolio value on the given date or None if unavailable + """ + row = self.df[self.df['Date'] == date] + return row[self.portfolio_column].values[0] if not row.empty else None + + def calculate_performance(self): + """ + Calculates portfolio performance over multiple periods. + + Returns: + dict: A dictionary containing returns for different periods + """ + periods = { + "one_day": self.date - timedelta(days=1), + "one_week": self.date - timedelta(days=7), + "one_month": self.date - timedelta(days=30), + "ytd": pd.Timestamp(year=self.date.year, month=1, day=1), + "one_year": self.date - timedelta(days=365), + "inception": self.df['Date'].min() + } + + performance = {} + current_value = self._get_value_by_date(self.date) + + for key, period_date in periods.items(): + closest_date = self._closest_date(period_date, side='right' if key == 'ytd' else 'left') + previous_value = self._get_value_by_date(closest_date) + + # Calculate return only if both values exist + performance[key] = (current_value / previous_value - 1) * 100 if current_value and previous_value else None + + return performance + + def total_return(self): + """ + Calculate the total return from inception to the latest date. + + Returns: + float: Total return as a decimal + """ + total_return = (self.df[self.portfolio_column].iloc[-1] - self.df[self.portfolio_column].iloc[0]) / self.df[self.portfolio_column].iloc[0] + return total_return + + def daily_average_return(self): + """ + Calculate the average daily return. + + Returns: + float: Average daily return as a decimal + """ + daily_returns = self.df['pct_change'].dropna() + return daily_returns.mean() + + def annualized_average_return(self): + """ + Calculate the annualized average return. + + Returns: + float: Annualized average return as a decimal + """ + daily_returns = self.df['pct_change'].dropna() + average_daily_return = daily_returns.mean() + return (1 + average_daily_return) ** 252 - 1 \ No newline at end of file diff --git a/performance/RiskMetrics.py b/legacy/performance/risk_metrics.py similarity index 86% rename from performance/RiskMetrics.py rename to legacy/performance/risk_metrics.py index 3b90639f..f197eb5c 100644 --- a/performance/RiskMetrics.py +++ b/legacy/performance/risk_metrics.py @@ -1,3 +1,16 @@ +""" +Portfolio risk metrics calculation module. + +This module calculates various risk metrics for portfolio analysis. +It provides methods for: +- Variance calculations (daily and annualized) +- Volatility calculations (daily and annualized) +- Downside risk metrics +- Maximum drawdown analysis + +This module focuses solely on risk metric calculations and assumes input data is already processed. +""" + import os import pandas as pd diff --git a/legacy/performance/update_performance.py b/legacy/performance/update_performance.py new file mode 100644 index 00000000..ab3d55d9 --- /dev/null +++ b/legacy/performance/update_performance.py @@ -0,0 +1,87 @@ +""" +Performance update module for CSV operations. + +This module handles the calculation and storage of performance metrics in CSV format. +It calculates returns for different time horizons (1-day, 1-week, 1-month, YTD, 1-year, and inception) +and saves them to CSV files in the appropriate data directory. + +Usage: + python update_performance.py fund # Run historical using all dates from portfolio_total.csv + python update_performance.py fund yyyy-mm-dd # Run for specific date +""" + +import os +import sys +import pandas as pd +from datetime import datetime, timedelta +from returns_calculator import ReturnsCalculator +from csv_writer import PerformanceCSVWriter + +def update_performance_csv(fund, date=None): + """ + Updates performance metrics for a given fund and date, saving to CSV. + + Args: + fund (str): The fund name + date (str, optional): The date to calculate metrics for. Defaults to today. + + Returns: + dict: Dictionary containing performance metrics for different time horizons + """ + # Set up paths + input_file = os.path.join('data', fund, 'output', 'portfolio_total.csv') + output_folder = os.path.join('data', fund, 'output') + + # Validate file existence + if not os.path.exists(input_file): + raise FileNotFoundError(f"Portfolio data file not found at {input_file}") + + # Load and preprocess data + df = pd.read_csv(input_file) + df['Date'] = pd.to_datetime(df['Date']) + + # Use provided date or today + if date is None: + date = datetime.now().date() + else: + date = pd.to_datetime(date) + + # Calculate performance + calculator = ReturnsCalculator(df, date) + if not calculator.valid_date(): + print(f"Skipping performance calculation for {fund} on {date}, portfolio_total.csv data not available.") + return None + + results = calculator.calculate_performance() + + # Save to CSV + csv_writer = PerformanceCSVWriter(fund, output_folder) + csv_writer.save_results(results, date) + + return results + +def update_historical_performance(fund): + """ + Updates performance metrics for all historical dates in the portfolio data. + + Args: + fund (str): The fund name + """ + input_file = os.path.join('data', fund, 'output', 'portfolio_total.csv') + dates = pd.read_csv(input_file)['Date'] + + for date in dates: + print(f"Calculating performance for {fund} on {date}") + update_performance_csv(fund, date) + +if __name__ == '__main__': + if len(sys.argv) < 2: + sys.exit("Usage: python update_performance.py (date is optional, leave empty for historical run)") + + fund = sys.argv[1] + date = sys.argv[2] if len(sys.argv) > 2 else None + + if date: + update_performance_csv(fund, date) + else: + update_historical_performance(fund) \ No newline at end of file diff --git a/Portfolio.py b/legacy/portfolio_csv_builder.py similarity index 100% rename from Portfolio.py rename to legacy/portfolio_csv_builder.py diff --git a/tests/test_FrontierAnalysis.py b/legacy/tests/test_FrontierAnalysis.py similarity index 99% rename from tests/test_FrontierAnalysis.py rename to legacy/tests/test_FrontierAnalysis.py index 68380185..c96a7c0a 100644 --- a/tests/test_FrontierAnalysis.py +++ b/legacy/tests/test_FrontierAnalysis.py @@ -9,7 +9,7 @@ parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.append(parent_dir) -from FrontierAnalysis import FrontierAnalysis +from services.frontier_analysis import FrontierAnalysis import yfinance as yf class TestFrontierAnalysis(unittest.TestCase): diff --git a/tests/test_MarketComparison.py b/legacy/tests/test_MarketComparison.py similarity index 100% rename from tests/test_MarketComparison.py rename to legacy/tests/test_MarketComparison.py diff --git a/notebooks/db_legacy.ipynb b/notebooks/db_legacy.ipynb deleted file mode 100644 index 9c204aec..00000000 --- a/notebooks/db_legacy.ipynb +++ /dev/null @@ -1,825 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Imports and Configs" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import mysql.connector\n", - "import os\n", - "import pandas as pd\n", - "import yfinance as yf\n", - "import yaml\n", - "from dotenv import load_dotenv\n", - "\n", - "load_dotenv()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Import config\n", - "with open(\"../config.yaml\", \"r\") as f:\n", - " config = yaml.safe_load(f)\n", - " start_date = config['start_date']\n", - "\n", - "end_date = pd.to_datetime('today').date()\n", - "\n", - "# Import benchmark config\n", - "with open(\"../portfolios/dfic_core.yaml\", \"r\") as f:\n", - " config = yaml.safe_load(f)\n", - " securities = config['securities']\n", - " currencies = config['currencies']\n", - " transactions = config['transactions']" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "# Set up SQL database connection\n", - "\n", - "connection = mysql.connector.connect(\n", - " host=os.getenv('DB_HOSTNAME'),\n", - " user=os.getenv('DB_USER'),\n", - " password=os.getenv('DB_PASSWORD'),\n", - " port=os.getenv('DB_PORT'),\n", - " database=\"Fund\"\n", - ")\n", - "\n", - "cursor = connection.cursor()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Securities" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "# Create Securities SQL Table\n", - "\n", - "cursor.execute(\"\"\"\n", - "CREATE TABLE IF NOT EXISTS Securities (\n", - " ticker VARCHAR(10) PRIMARY KEY,\n", - " name VARCHAR(100),\n", - " type VARCHAR(20) NOT NULL,\n", - " geography VARCHAR(50),\n", - " sector VARCHAR(50),\n", - " fund VARCHAR(50),\n", - " currency CHAR(3) NOT NULL\n", - ");\n", - "\"\"\")\n", - "connection.commit()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "# Backfill Securities Table\n", - "\n", - "for security in securities:\n", - " data = yf.Ticker(security['ticker'])\n", - " fund = security['fund']\n", - " sector = security['sector'] \n", - " name = data.info['longName']\n", - " geography = security['geography'] \n", - " type = data.info['typeDisp']\n", - " currency = data.info['currency']\n", - "\n", - " cursor.execute(\"\"\"\n", - " INSERT INTO Securities (ticker, name, type, geography, sector, fund, currency)\n", - " VALUES (%s, %s, %s, %s, %s, %s, %s)\n", - " ON DUPLICATE KEY UPDATE\n", - " ticker = VALUES(ticker),\n", - " name = VALUES(name),\n", - " type = VALUES(type),\n", - " geography = VALUES(geography),\n", - " sector = VALUES(sector),\n", - " fund = VALUES(fund),\n", - " currency = VALUES(currency);\n", - " \"\"\", (security['ticker'], name, type, geography, sector, fund, currency))\n", - " connection.commit()" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop Securities SQL Table\n", - "\n", - "cursor.execute(\"DROP TABLE Securities\")\n", - "connection.commit()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Transactions" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# Create Transactions SQL Table\n", - "\n", - "cursor.execute(\"\"\"\n", - "CREATE TABLE IF NOT EXISTS Transactions (\n", - " transaction_id INTEGER PRIMARY KEY AUTO_INCREMENT,\n", - " ticker VARCHAR(10) NOT NULL,\n", - " date DATE NOT NULL,\n", - " action ENUM('BUY', 'SELL') NOT NULL,\n", - " shares INTEGER NOT NULL CHECK (shares > 0),\n", - " price DECIMAL(20,10) NOT NULL CHECK (price > 0),\n", - " currency CHAR(3) NOT NULL,\n", - " FOREIGN KEY (ticker) REFERENCES Securities(ticker),\n", - " UNIQUE (date, ticker, action)\n", - ");\n", - "\"\"\")\n", - "connection.commit()" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "EA\n", - "ISRG\n", - "MA\n", - "TEX\n", - "AGG\n", - "RSP\n", - "AGG\n", - "AGG\n", - "RSP\n", - "HBM.TO\n", - "XIU.TO\n", - "XBB.TO\n", - "L.TO\n", - "SPY\n", - "APO\n", - "AAPL\n", - "CEG\n", - "AMSF\n", - "VEEV\n", - "SPY\n", - "GSL\n", - "AGG\n", - "SPSB\n", - "WFG\n", - "XIU.TO\n", - "CSH-UN.TO\n", - "XBB.TO\n", - "XIU.TO\n", - "ACO-X.TO\n", - "CG\n", - "WSC\n", - "AER\n", - "BLBD\n", - "TMUS\n", - "MP\n", - "DOLE\n", - "AMAT\n" - ] - } - ], - "source": [ - "# Backfill Transactions Table\n", - "\n", - "for transaction in transactions:\n", - " print(transaction['ticker'])\n", - " ticker = transaction['ticker']\n", - " date = transaction['date']\n", - " action = transaction['type']\n", - " shares = transaction['quantity']\n", - " price = transaction['price']\n", - " currency = transaction['currency']\n", - "\n", - " cursor.execute(\"\"\"\n", - " INSERT INTO Transactions (ticker, date, action, shares, price, currency)\n", - " VALUES (%s, %s, %s, %s, %s, %s)\n", - " ON DUPLICATE KEY UPDATE \n", - " shares = VALUES(shares), \n", - " price = VALUES(price), \n", - " currency = VALUES(currency);\n", - " \"\"\", (ticker, date, action, shares, price, currency))\n", - " connection.commit()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop Transactions SQL Table\n", - "\n", - "cursor.execute(\"DROP TABLE Transactions\")\n", - "connection.commit()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Currencies" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\"\"\"\n", - "CREATE TABLE IF NOT EXISTS Currencies (\n", - " date DATE NOT NULL PRIMARY KEY,\n", - " CAD DECIMAL(20,10) DEFAULT 1.0,\n", - " USD DECIMAL(20,10) DEFAULT 1.0,\n", - " EUR DECIMAL(20,10) DEFAULT 1.0\n", - ");\n", - "\"\"\")\n", - "connection.commit()" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "metadata": {}, - "outputs": [], - "source": [ - "# Backfill Currencies Table with corrected logic\n", - "for currency in currencies:\n", - " ticker = currency['ticker']\n", - " data = yf.Ticker(ticker).history(start=start_date, end=end_date)['Close']\n", - " data.index = data.index.date\n", - " \n", - " if currency['currency'] == 'USD':\n", - " cursor.executemany(\"\"\"\n", - " INSERT INTO Currencies (date, CAD, USD, EUR)\n", - " VALUES (%s, 1, %s, NULL)\n", - " ON DUPLICATE KEY UPDATE\n", - " USD = VALUES(USD);\n", - " \"\"\", [(date, 1/rate) for date, rate in data.items()])\n", - " elif currency['currency'] == 'EUR':\n", - " cursor.executemany(\"\"\"\n", - " INSERT INTO Currencies (date, CAD, USD, EUR)\n", - " VALUES (%s, 1, NULL, %s)\n", - " ON DUPLICATE KEY UPDATE\n", - " EUR = VALUES(EUR);\n", - " \"\"\", [(date, 1/rate) for date, rate in data.items()])\n", - " \n", - " connection.commit()" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop Currencies SQL Table\n", - "\n", - "cursor.execute(\"DROP TABLE Currencies\")\n", - "connection.commit()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Dates (old)" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "# Create Dates SQL Table\n", - "\n", - "cursor.execute(\"\"\"\n", - "CREATE TABLE IF NOT EXISTS Dates (\n", - " date DATE PRIMARY KEY\n", - ");\n", - "\"\"\")\n", - "connection.commit()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Backfill Dates Table\n", - "\n", - "sp500 = yf.Ticker('^GSPC').history(start=start_date, end=end_date)\n", - "tsx = yf.Ticker('^GSPTSE').history(start=start_date, end=end_date)\n", - "\n", - "sp500.index = pd.to_datetime(sp500.index).tz_localize(None)\n", - "tsx.index = pd.to_datetime(tsx.index).tz_localize(None)\n", - "\n", - "valid_dates = sp500.index.union(tsx.index)\n", - "valid_dates = [date.date() for date in valid_dates]\n", - "\n", - "cursor.executemany(\"\"\"\n", - " INSERT INTO Dates (date)\n", - " VALUES (%s)\n", - " ON DUPLICATE KEY UPDATE date = VALUES(date);\n", - "\"\"\", [(date,) for date in valid_dates])\n", - "connection.commit()" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop Dates SQL Table\n", - "\n", - "cursor.execute(\"DROP TABLE Dates\")\n", - "connection.commit()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Trading Calendar" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\"\"\"\n", - "CREATE TABLE IF NOT EXISTS TradingCalendar (\n", - " trading_date DATE PRIMARY KEY,\n", - " is_us_trading_day BOOLEAN NOT NULL,\n", - " is_ca_trading_day BOOLEAN NOT NULL\n", - ");\n", - "\"\"\")\n", - "connection.commit()" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "# Backfill Dates Table\n", - "\n", - "sp500 = yf.Ticker('^GSPC').history(start=start_date, end=end_date)\n", - "tsx = yf.Ticker('^GSPTSE').history(start=start_date, end=end_date)\n", - "\n", - "sp500.index = pd.to_datetime(sp500.index).tz_localize(None)\n", - "tsx.index = pd.to_datetime(tsx.index).tz_localize(None)\n", - "\n", - "# valid_dates = sp500.index.union(tsx.index)\n", - "# valid_dates = [date.date() for date in valid_dates]\n", - "\n", - "# cursor.executemany(\"\"\"\n", - "# INSERT INTO Dates (date)\n", - "# VALUES (%s)\n", - "# ON DUPLICATE KEY UPDATE date = VALUES(date);\n", - "# \"\"\", [(date,) for date in valid_dates])\n", - "# connection.commit()" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "valid_US = [date.date() for date in sp500.index]\n", - "valid_CA = [date.date() for date in tsx.index]\n", - "\n", - "valid_dates = sp500.index.union(tsx.index)\n", - "valid_dates = [date.date() for date in valid_dates]\n", - "\n", - "for date in valid_dates:\n", - " is_us_trading_day = date in valid_US\n", - " is_ca_trading_day = date in valid_CA\n", - "\n", - " cursor.executemany(\"\"\"\n", - " INSERT INTO TradingCalendar (trading_date, is_us_trading_day, is_ca_trading_day)\n", - " VALUES (%s, %s, %s)\n", - " ON DUPLICATE KEY UPDATE trading_date = VALUES(trading_date);\n", - " \"\"\", [(date, is_us_trading_day, is_ca_trading_day)])\n", - " connection.commit()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop Dates SQL Table\n", - "\n", - "cursor.execute(\"DROP TABLE TradingCalendar\")\n", - "connection.commit()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# prices" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "cursor.execute(\"\"\"\n", - "CREATE TABLE IF NOT EXISTS Prices (\n", - " ticker VARCHAR(10) NOT NULL,\n", - " trading_date DATE NOT NULL,\n", - " price DECIMAL(20,10) NULL CHECK (price IS NULL OR price > 0),\n", - " currency CHAR(3) NOT NULL,\n", - " PRIMARY KEY (ticker, trading_date),\n", - " FOREIGN KEY (ticker) REFERENCES Securities(ticker)\n", - ");\n", - "\"\"\")\n", - "connection.commit()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Will leave price gaps for holidays" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "upd: \n", - "AAPL ACO-X.TO AER AGG AMAT AMSF APO BLBD CEG CG CSH-UN.TO DOLE EA GSL HBM.TO ISRG L.TO MA MP RSP SPSB SPY TEX TMUS VEEV WFG WSC XBB.TO XIU.TO " - ] - } - ], - "source": [ - "cursor.execute(\"\"\"\n", - " SELECT ticker, date, action, shares\n", - " FROM Transactions\n", - " ORDER BY ticker, date\n", - "\"\"\")\n", - "transactions = cursor.fetchall()\n", - "\n", - "# holding periods\n", - "holding_periods = {}\n", - "current_position = {}\n", - "current_ticker = None\n", - "\n", - "for ticker, date, action, shares in transactions:\n", - " if ticker != current_ticker:\n", - " if current_ticker and current_position.get(current_ticker, 0) > 0:\n", - " # add final\n", - " end_date = pd.to_datetime('today').date()\n", - " holding_periods[current_ticker][-1][1] = end_date\n", - " \n", - " current_ticker = ticker\n", - " current_position[ticker] = 0\n", - " holding_periods[ticker] = []\n", - " \n", - " position_change = shares if action == 'BUY' else -shares\n", - " old_position = current_position[ticker]\n", - " current_position[ticker] += position_change\n", - " \n", - " if old_position == 0 and current_position[ticker] > 0:\n", - " # new holding period\n", - " holding_periods[ticker].append([date, None])\n", - " elif old_position > 0 and current_position[ticker] == 0:\n", - " # end holding period\n", - " holding_periods[ticker][-1][1] = date\n", - "\n", - "# handle open positions\n", - "for ticker, position in current_position.items():\n", - " if position > 0 and holding_periods[ticker][-1][1] is None:\n", - " holding_periods[ticker][-1][1] = pd.to_datetime('today').date()\n", - "\n", - "# backfill \n", - "print(\"upd: \")\n", - "for ticker, periods in holding_periods.items():\n", - " print(f\"{ticker} \", end='', flush=True)\n", - " \n", - " try:\n", - " cursor.execute(\"\"\"\n", - " SELECT currency \n", - " FROM Securities \n", - " WHERE ticker = %s\n", - " \"\"\", (ticker,))\n", - " currency = cursor.fetchone()[0]\n", - " \n", - " # each holding period\n", - " for start_date, end_date in periods:\n", - " # all trading days for this period\n", - " cursor.execute(\"\"\"\n", - " SELECT trading_date \n", - " FROM TradingCalendar\n", - " WHERE trading_date BETWEEN %s AND %s\n", - " ORDER BY trading_date\n", - " \"\"\", (start_date, end_date))\n", - " \n", - " trading_days = [row[0] for row in cursor.fetchall()]\n", - " \n", - " if not trading_days:\n", - " continue\n", - " \n", - "# check this part ------------------------------------------------------------------------------------------\n", - "\n", - " data = yf.Ticker(ticker).history(start=start_date, end=end_date)\n", - " data.index = data.index.date\n", - " \n", - " price_data = []\n", - " for trade_date in trading_days:\n", - " if trade_date in data.index:\n", - " price = float(data.loc[trade_date, 'Close'])\n", - " else:\n", - " price = None\n", - " \n", - " price_data.append((\n", - " ticker,\n", - " trade_date,\n", - " price,\n", - " currency\n", - " ))\n", - " \n", - " # insert with NULL \n", - " cursor.executemany(\"\"\"\n", - " INSERT INTO Prices (ticker, trading_date, price, currency)\n", - " VALUES (%s, %s, %s, %s)\n", - " ON DUPLICATE KEY UPDATE\n", - " price = VALUES(price),\n", - " currency = VALUES(currency)\n", - " \"\"\", price_data)\n", - " \n", - " connection.commit()\n", - " \n", - " except Exception as e:\n", - " print(f\"Error processing {ticker}: {str(e)}\")\n", - " connection.rollback()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### frontfills holiday prices missing from previous backfill\n" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Frontfilling prices for: AAPL ACO-X.TO AER AGG AMAT AMSF APO BLBD CEG CG CSH-UN.TO DOLE EA GSL HBM.TO ISRG L.TO MA MP RSP SPSB SPY TEX TMUS VEEV WFG WSC XBB.TO XIU.TO " - ] - } - ], - "source": [ - "\n", - "cursor.execute(\"\"\"\n", - " SELECT DISTINCT ticker \n", - " FROM Prices \n", - " WHERE price IS NULL\n", - "\"\"\")\n", - "tickers_with_nulls = [row[0] for row in cursor.fetchall()]\n", - "\n", - "print(\"Frontfilling prices for:\", end=' ')\n", - "\n", - "for ticker in tickers_with_nulls:\n", - " print(f\"{ticker}\", end=' ', flush=True)\n", - " \n", - "\n", - " cursor.execute(\"\"\"\n", - " WITH LastKnownPrice AS (\n", - " SELECT \n", - " p1.ticker,\n", - " p1.trading_date,\n", - " (\n", - " SELECT p2.price \n", - " FROM Prices p2 \n", - " WHERE p2.ticker = p1.ticker \n", - " AND p2.trading_date < p1.trading_date \n", - " AND p2.price IS NOT NULL \n", - " ORDER BY p2.trading_date DESC \n", - " LIMIT 1\n", - " ) as last_price\n", - " FROM Prices p1\n", - " WHERE p1.ticker = %s AND p1.price IS NULL\n", - " )\n", - " UPDATE Prices p\n", - " JOIN LastKnownPrice lkp ON p.ticker = lkp.ticker AND p.trading_date = lkp.trading_date\n", - " SET p.price = lkp.last_price\n", - " WHERE p.price IS NULL;\n", - " \"\"\", (ticker,))\n", - " \n", - " connection.commit()" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'AAPL': [[datetime.date(2023, 4, 27), datetime.date(2025, 3, 18)]],\n", - " 'ACO-X.TO': [[datetime.date(2024, 12, 23), datetime.date(2025, 3, 18)]],\n", - " 'AER': [[datetime.date(2024, 12, 23), datetime.date(2025, 3, 18)]],\n", - " 'AGG': [[datetime.date(2022, 5, 9), datetime.date(2023, 4, 27)],\n", - " [datetime.date(2023, 4, 27), datetime.date(2025, 3, 18)]],\n", - " 'AMAT': [[datetime.date(2024, 12, 23), datetime.date(2025, 3, 18)]],\n", - " 'AMSF': [[datetime.date(2023, 12, 27), datetime.date(2025, 3, 18)]],\n", - " 'APO': [[datetime.date(2023, 4, 27), datetime.date(2025, 3, 18)]],\n", - " 'BLBD': [[datetime.date(2024, 12, 23), datetime.date(2025, 3, 18)]],\n", - " 'CEG': [[datetime.date(2023, 4, 27), datetime.date(2025, 3, 18)]],\n", - " 'CG': [[datetime.date(2024, 12, 23), datetime.date(2025, 3, 18)]],\n", - " 'CSH-UN.TO': [[datetime.date(2023, 12, 28), datetime.date(2025, 3, 18)]],\n", - " 'DOLE': [[datetime.date(2024, 12, 23), datetime.date(2025, 3, 18)]],\n", - " 'EA': [[datetime.date(2022, 5, 5), datetime.date(2025, 3, 18)]],\n", - " 'GSL': [[datetime.date(2023, 12, 27), datetime.date(2025, 3, 18)]],\n", - " 'HBM.TO': [[datetime.date(2023, 4, 27), datetime.date(2025, 3, 18)]],\n", - " 'ISRG': [[datetime.date(2022, 5, 5), datetime.date(2025, 3, 18)]],\n", - " 'L.TO': [[datetime.date(2023, 4, 27), datetime.date(2025, 3, 18)]],\n", - " 'MA': [[datetime.date(2022, 5, 5), datetime.date(2025, 3, 18)]],\n", - " 'MP': [[datetime.date(2024, 12, 23), datetime.date(2025, 3, 18)]],\n", - " 'RSP': [[datetime.date(2022, 5, 9), datetime.date(2023, 4, 27)]],\n", - " 'SPSB': [[datetime.date(2023, 12, 27), datetime.date(2025, 3, 18)]],\n", - " 'SPY': [[datetime.date(2023, 4, 27), datetime.date(2025, 3, 18)]],\n", - " 'TEX': [[datetime.date(2022, 5, 5), datetime.date(2025, 3, 18)]],\n", - " 'TMUS': [[datetime.date(2024, 12, 23), datetime.date(2025, 3, 18)]],\n", - " 'VEEV': [[datetime.date(2023, 12, 27), datetime.date(2025, 3, 18)]],\n", - " 'WFG': [[datetime.date(2023, 12, 28), datetime.date(2025, 3, 18)]],\n", - " 'WSC': [[datetime.date(2024, 12, 23), datetime.date(2025, 3, 18)]],\n", - " 'XBB.TO': [[datetime.date(2023, 4, 27), datetime.date(2025, 3, 18)]],\n", - " 'XIU.TO': [[datetime.date(2023, 4, 27), datetime.date(2024, 12, 23)]]}" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "holding_periods" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop Dates SQL Table\n", - "\n", - "cursor.execute(\"DROP TABLE Prices\")\n", - "connection.commit()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generate Holdings Table" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "cursor.execute(\"\"\"\n", - "CREATE VIEW Holdings AS\n", - "SELECT\n", - " p.trading_date,\n", - " p.ticker,\n", - " s.name,\n", - " s.type,\n", - " s.geography,\n", - " s.sector,\n", - " s.fund,\n", - " s.currency AS security_currency,\n", - " (\n", - " SELECT SUM(\n", - " CASE \n", - " WHEN t.action = 'BUY' THEN t.shares \n", - " ELSE -t.shares \n", - " END\n", - " )\n", - " FROM Transactions t\n", - " WHERE t.ticker = p.ticker \n", - " AND t.date <= p.trading_date\n", - " ) AS shares_held,\n", - " p.price,\n", - " (\n", - " (\n", - " SELECT SUM(\n", - " CASE \n", - " WHEN t.action = 'BUY' THEN t.shares \n", - " ELSE -t.shares \n", - " END\n", - " )\n", - " FROM Transactions t\n", - " WHERE t.ticker = p.ticker \n", - " AND t.date <= p.trading_date\n", - " ) * p.price\n", - " ) AS market_value\n", - "FROM Prices p\n", - "JOIN Securities s ON s.ticker = p.ticker;\n", - "\"\"\")\n", - "connection.commit()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "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": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/performance/PortfolioPerformance.py b/performance/PortfolioPerformance.py deleted file mode 100644 index f5bc8a0b..00000000 --- a/performance/PortfolioPerformance.py +++ /dev/null @@ -1,115 +0,0 @@ -import os -import pandas as pd -from datetime import timedelta -import yfinance as yf - - -class PortfolioPerformance: - def __init__(self, output_folder): - self.output_folder = output_folder - - def total_return(self): - df = pd.read_csv(os.path.join(self.output_folder, 'portfolio_total.csv')) - total_return = (df['Total_Portfolio_Value'].iloc[-1] - df['Total_Portfolio_Value'].iloc[0]) / df['Total_Portfolio_Value'].iloc[0] - return total_return - - def daily_average_return(self): - df = pd.read_csv(os.path.join(self.output_folder, 'portfolio_total.csv')) - daily_returns = df['pct_change'].dropna() - average_return = daily_returns.mean() - return average_return - - def annualized_average_return(self): - df = pd.read_csv(os.path.join(self.output_folder, 'portfolio_total.csv')) - daily_returns = df['pct_change'].dropna() - average_daily_return = daily_returns.mean() - annualized_avg_return = (1+average_daily_return) ** 252 - 1 - return annualized_avg_return - - def calculate_period_performance(self): - df = pd.read_csv(os.path.join(self.output_folder, 'portfolio_total.csv')) - df['Date'] = pd.to_datetime(df['Date']) - - latest_date = df['Date'].max() - one_day = latest_date - timedelta(days=1) - one_week = latest_date - timedelta(days=7) - one_month = latest_date - timedelta(days=30) - ytd = pd.Timestamp(year=latest_date.year, month=1, day=1) - one_year = latest_date - timedelta(days=365) - inception = df['Date'].min() - - def closest_date(target_date, side='left'): - target_date = pd.to_datetime(target_date) - if side == 'left': - valid_dates = df[df['Date'] <= target_date]['Date'] - return valid_dates.max() - elif side == 'right': - valid_dates = df[df['Date'] >= target_date]['Date'] - return valid_dates.min() - - closest_1d = closest_date(one_day) - closest_1w = closest_date(one_week) - closest_1m = closest_date(one_month) - closest_ytd = closest_date(ytd, side='right') - closest_1y = closest_date(one_year) - closest_inc = closest_date(inception) - - latest_value = df[df['Date'] == latest_date]['Total_Portfolio_Value'].values[0] - one_day_value = df[df['Date'] == closest_1d]['Total_Portfolio_Value'].values[0] - one_week_value = df[df['Date'] == closest_1w]['Total_Portfolio_Value'].values[0] - one_month_value = df[df['Date'] == closest_1m]['Total_Portfolio_Value'].values[0] - ytd_value = df[df['Date'] == closest_ytd]['Total_Portfolio_Value'].values[0] - one_year_value = df[df['Date'] == closest_1y]['Total_Portfolio_Value'].values[0] - inception_value = df[df['Date'] == closest_inc]['Total_Portfolio_Value'].values[0] - - one_day_return = (latest_value / one_day_value) - 1 - one_week_return = (latest_value / one_week_value) - 1 - one_month_return = (latest_value / one_month_value) - 1 - ytd_return = (latest_value / ytd_value) - 1 - one_year_return = (latest_value / one_year_value) - 1 - inception_return = (latest_value / inception_value) - 1 - - return { - "1d": one_day_return, - "1w": one_week_return, - "1m": one_month_return, - "YTD": ytd_return, - "1y": one_year_return, - "Inception": inception_return - } - - def get_fixed_income_info(self, tickers: list): - df = pd.read_csv(os.path.join(self.output_folder, 'market_values.csv')) - df_xrates = pd.read_csv(os.path.join(self.output_folder, 'exchange_rates.csv')) - - usd_tickers = [] - - for t in tickers: - try: - currency = yf.Ticker(t).info['currency'] - except: - currency = 'CAD' - - if currency == 'USD': - df[t] = df[t] * df_xrates['USD'].iloc[-1] - usd_tickers.append(t) - - current_mkt_vals = {ticker: df[ticker].iloc[-1] for ticker in tickers} - total_mkt_vals = sum(current_mkt_vals.values()) - fi_mkt_shares = {ticker: current_mkt_vals[ticker] / total_mkt_vals for ticker in tickers} - - usd_total_mkt_val = sum([current_mkt_vals[ticker] for ticker in usd_tickers]) - usd_fi_mkt_shares = {ticker: current_mkt_vals[ticker] / usd_total_mkt_val for ticker in usd_tickers} - - # combine all these dictionaries into a dataframe - data = { - 'Ticker': tickers, - 'Market Value': [current_mkt_vals[ticker] for ticker in tickers], - 'Total Market Share': [fi_mkt_shares[ticker] for ticker in tickers], - 'USD Market Share': [usd_fi_mkt_shares[ticker] if ticker in usd_tickers else 0 for ticker in tickers] - } - - data = pd.DataFrame(data) - data.set_index('Ticker', inplace=True) - - return data \ No newline at end of file diff --git a/performance/__init__.py b/performance/__init__.py deleted file mode 100644 index 243cb3fc..00000000 --- a/performance/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -from .DataProcessor import DataProcessor -from .Benchmark import Benchmark -from .PortfolioPerformance import PortfolioPerformance -from .RiskMetrics import RiskMetrics -from .MarketComparison import MarketComparison -from .Ratios import Ratios - -# this is what gets imported when you just import all of performance -__all__ = ["DataProcessor", "Benchmark", "PortfolioPerformance", "RiskMetrics", "MarketComparison", "PortfolioPerformance", "Ratios"] \ No newline at end of file diff --git a/tests/placeholder.txt b/tests/placeholder.txt new file mode 100644 index 00000000..3bcd6a0e --- /dev/null +++ b/tests/placeholder.txt @@ -0,0 +1 @@ +This directory is for test files. Please add your test files here following the project's testing conventions. \ No newline at end of file diff --git a/utils/__init__.py b/utils/__init__.py deleted file mode 100644 index 9a6de54c..00000000 --- a/utils/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .login_utils import generate_session, session_exists, verify_password -from .db_utils import get_db_connection - -__all__ = ["generate_session", "session_exists", "verify_password", "get_db_connection"] \ No newline at end of file