package_203_project 0.3.0

Package created for the Python course project

package_203_project

A package for doing great things!

Installation

$ pip install package_203_project

Usage

• Rebalance Classes

Three classes define different rebalancing strategies:

1. EndOfMonth: Rebalances the portfolio on the last business day of each month.
2. EndOfWeek: Rebalances the portfolio every Friday (last business day of the week).
3. EndOfDay: Rebalances the portfolio every day.

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• Portfolio Construction Strategies

Two classes define different portfolio construction methods:

1. SharpeRatioMaximization:
   This class optimizes portfolio weights to maximize the Sharpe ratio.

   • Objective: Maximize the Sharpe ratio by calculating optimal weights based on expected returns and the covariance matrix of asset prices.

2. EqualWeightPortfolio:
   This class assigns equal weights to all assets in the portfolio, providing a simple and robust strategy.

   • Objective: Create a portfolio where each asset has equal weight 1/n, where n is the number of assets.

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Backtest Class

This is one of the core class of the script, designed to conduct a backtest for a portfolio strategy, modified from the original pybacktestchain script.

Key Attributes

• initial_date & final_date: The start and end dates for the backtest.
• universe: A predefined list of stocks to be included in the portfolio.
• rebalance_flag: Defines how frequently the portfolio will be rebalanced (daily, weekly, or monthly).
• risk_model: Implements stop-loss functionality to manage portfolio risk.
• broker: Handles execution of trades and portfolio transactions.
• name_blockchain: The blockchain ledger used for storing transaction logs.
• information: The backtest is run based on a choice on three different portfolio strategies: equal weight pf, max share ratio, or first two moments from pybacktestchain

run_backtest:

• Retrieves historical stock data for the given time period.
• Simulates portfolio rebalancing based on the defined frequency.
• Calculates key portfolio statistics:
  • Returns, volatility, skewness, kurtosis.
  • Value-at-Risk (VaR) either as the normally distributed returns OR an adjusted methods based on skew/kurtosis.
• Stores results in CSV files for portfolio values and transaction logs.
• Saves cumulative returns and transaction details in a blockchain for secure storage.
• Generates a graph for cumulative returns over time.
• Visualizes portfolio weight allocation over time.
• Uses plotly for creating interactive, stacked-area graphs of weight allocation = better than matlib for weight allocation vizualisation through time
• Saves the resulting graph to a file for analysis.

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Generated Outputs

1. CSV Files:
   • Portfolio values: Saved in backtests_portfolio_values/.
   • Transaction logs: Saved in backtests/.
2. Graphs:
   • Cumulative returns: Saved in backtests_portfolio_graphs/.
   • Portfolio weight allocation: Saved in plot_portfolio_weight_graphs/.

• Stock Analysis and Benchmarking

Three classes to handle ndle stock analysis, data management, and benchmark data:

1. StockAnalysis:
   Provides key analysis functionalities:

   • Rank stocks by volume: Ranks stocks based on their average trading volume.
   • Calculate betas: Computes the sensitivity (beta) of each stock to a benchmark and calculates the average beta using OLS regression

2. StockDataHandler:

   • Draws historical stock data.
   • Computes daily log returns for each stock and organizes them into a pivot table.

3. BenchmarkHandler:

   • Draws benchmark historical data.
   • Calculates daily log returns for the chosen benchmark like S&P 500, CAC 40 etc.

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Interactive User Analysis

The script allows user interaction to analyze stocks:

• Users input stock tickers, date range, and benchmark choice.
• Outputs include:
  • Ranked stocks by volume.
  • Beta values for each stock and the average beta.
  • Visualizations:
    1. Cumulative Returns: Comparison of stock and benchmark performance.
    2. Rolling Volatility: Examines stock and benchmark volatility over time.
    3. Sharpe Ratios: Compares risk-adjusted returns for stocks and the benchmark.

Generated graphs are saved in the stocks_function_graphs/ folder.

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

package_203_project was created by Maelys Malichecq. It is licensed under the terms of the MIT license.

Credits

package_203_project was created with cookiecutter and the py-pkgs-cookiecutter template.