Algorithmic Trading Simulator

This project is a basic algorithmic trading simulator that allows you to backtest different trading strategies using historical market data. It is designed with a focus on object-oriented programming principles and a modular structure to ensure scalability and maintainability.

Sources

It currently uses yfinance

Project Structure

The project consists of the following modules:

• data_handler.py: Fetches, stores, and provides historical market data.
• strategies/basic/: Contains different trading strategies.
  • moving_average.py: Implements a moving average crossover strategy.
  • rsi.py: Implements an RSI based strategy.
  • bollinger_bands.py: Implements a Bollinger bands strategy.
  • stochastic_oscillator.py: Implements a stochastic oscillator strategy.
  • macd.py: Implements a MACD based strategy.
  • ichimoku_cloud.py: Implements an Ichimoku cloud based strategy.
  • adx.py: Implements an ADX based strategy.
• strategies/hybrid/: Contains classes that makes it easy to combine many different strategies
• strategy.py: Defines the abstract base class for all trading strategies.
• broker.py: Simulates the execution of trades and manages the portfolio.
• backtester.py: Simulates the backtesting process.
• main.py: The entry point of the application.

How to Use

1. Install Dependencies: Make sure you have all the required packages installed, like yfinance, numpy, etc (run pip3 install -r requirements.txt). Also make sure you have a venv.
2. Run main.py: Execute the main.py script using python python3 main.py (after activating your virtual environment).
3. Modify Parameters: You can change the parameters of the strategies in main.py, by creating new instances of the strategy classes. You can also configure the data that will be backtested.
4. Analyze Results: After running the simulation, the output will display the backtest results, including profit, number of trades, and a transaction history.

Available Strategies

• Moving Average Crossover: Uses short-term and long-term moving averages to generate buy/sell signals.
• RSI: Uses the Relative Strength Index to identify overbought and oversold conditions.
• Bollinger Bands: Uses Bollinger Bands to identify when a price might be overbought or oversold based on volatility.
• Stochastic Oscillator: Uses the stochastic oscillator to generate signals, based on momentum.
• MACD: Uses the Moving Average Convergence Divergence indicator to generate trading signals, based on the relationship between two moving averages.
• Ichimoku Cloud: Uses the Ichimoku Cloud indicator to generate trading signals, based on the relationship between the price and the cloud.
• ADX: Uses the Average Directional Index to generate trading signals, based on the strength of the trend.

Further Development

• Implement more advanced trading strategies.
• Add more data sources.
• Include more risk management features.
• Add visualizations of backtesting results.