/Momentum-Based-Strategy-Optimization-with-Grid-Search-on-Backtrader

This project backtests an SMA crossover strategy in Python, using Backtrader and yfinance, with optimization through grid search to find the best parameters.

Primary LanguageJupyter Notebook

SMA Crossover Strategy Project Guideline

Project Overview

This project implements and backtests a Simple Moving Average (SMA) Crossover trading strategy using Python and the Backtrader library. The strategy is optimized using grid search to find the best parameters.

Table of Contents

  1. Install the necessary libraries & Ignore warnings
  2. SMA crossover strategy class
  3. Define Backtest Engine class
  4. Backtest the strategy (find the best parameters by GridSearch)

Detailed Sections

1. Install the necessary libraries & Ignore warnings

  • Import required modules (warnings, backtrader, yfinance, pandas, numpy, matplotlib, seaborn, tabulate)
  • Configure warnings to be ignored
  • Install necessary libraries using pip (if needed)

2. SMA crossover strategy class

  • Define the SMACrossStrategy class, which inherits from bt.Strategy
  • Implement strategy logic:
    • Set up parameters for short/long windows and trade size
    • Calculate short and long SMAs
    • Generate buy/sell signals based on SMA crossovers
    • Manage position sizing and trade execution
    • Track trades, equity, and drawdown
  • Implement methods for:
    • Calculating trade size
    • Handling trade notifications

3. Define Backtest Engine class

  • Create the BacktestingEngine class
  • Implement methods for:
    • Downloading and processing historical data
    • Running backtests
    • Analyzing results (including performance metrics like Sharpe ratio, returns, volatility)
    • Plotting results
    • Optimizing strategy parameters
    • Displaying optimization results (including heatmaps and 3D surface plots)

4. Backtest the strategy (find the best parameters by GridSearch)

  • Set up the main execution block
  • Create an instance of the BacktestingEngine
  • Define parameter ranges for optimization:
    • Short MA range
    • Long MA range
    • Trade size range
  • Run the optimization process using grid search
  • Display the best parameters and corresponding performance metrics
  • Plot the results using the optimal parameters
  • Visualize the optimization results using heatmaps and 3D surface plots

Next Steps

  • Test the strategy on different assets and timeframes
  • Implement additional technical indicators or entry/exit conditions
  • Explore more sophisticated optimization techniques (e.g., genetic algorithms)
  • Incorporate advanced risk management features
  • Consider real-world factors like slippage and more realistic commission structures
  • Implement walk-forward optimization or out-of-sample testing

By following this guideline, you'll create a comprehensive SMA Crossover Strategy project that covers strategy development, backtesting, optimization, and analysis, aligned with the structure of your existing code.