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Quantitative Trading Strategies (ML Optimization)

This project implements an algorithmic trading strategy using Backtrader and XGBoost to optimize trading decisions based on technical indicators. The strategy is backtested using historical data and optimized through machine learning to improve its predictive accuracy.

Project Overview

Key Components:

  • Backtrader: Used for backtesting and simulating the trading strategy on historical data.
  • XGBoost: Applied to optimize the model's performance by tuning hyperparameters and improving prediction accuracy.
  • Technical Indicators: Various technical indicators like SMA, RSI, Bollinger Bands, MACD, etc., are used to generate trading signals.
  • Data Sources: Historical price data (Open, High, Low, Close, Volume) is utilized for backtesting the strategy.
  • Performance Metrics: The project evaluates the strategy's performance based on metrics such as final portfolio value, accuracy of predictions, and other relevant financial indicators.

Strategy Logic:

  • The trading strategy is based on RSI (Relative Strength Index), SMA (Simple Moving Average), and XGBoost for predictive modeling.
  • Buy signals are triggered when certain thresholds of technical indicators (e.g., RSI < 30, price crosses above SMA) are met.
  • Sell signals are triggered when RSI > 70 or price crosses below the SMA.
  • The strategy is fine-tuned using XGBoost to optimize the parameters and improve the overall portfolio performance.

How it Works:

  1. Data Preprocessing: The raw market data is cleaned and transformed into a format compatible with Backtrader.
  2. Strategy Development: Define a trading strategy based on technical indicators.
  3. Backtesting: The strategy is run through Backtrader’s backtesting engine, and performance metrics are collected.
  4. Optimization: Hyperparameter tuning is performed using XGBoost to identify the optimal settings for the strategy.
  5. Evaluation: The strategy's performance is evaluated based on various metrics like final portfolio value, Sharpe ratio, etc.

Requirements

  • Python 3
  • Backtrader
  • XGBoost
  • Pandas
  • Matplotlib
  • Seaborn
  • Scikit-learn

Installation

pip install backtrader xgboost pandas matplotlib seaborn ta numpy scikit-learn