Trading Strategy Optimization with Moving Averages and RSI

This project explores optimizing a trading strategy using technical indicators, specifically Moving Averages and Relative Strength Index (RSI), and compares it to a simple buy-and-hold strategy. The goal is to demonstrate the potential of using technical analysis to outperform the market return.

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Project Overview

The core idea of this project is to implement a technical trading strategy and backtest it against a buy-and-hold strategy for a selected stock. The strategy uses moving averages and RSI to generate buy/sell signals, aiming to optimize returns.

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Strategy Overview

Buy-and-Hold Strategy

The baseline strategy is the standard buy-and-hold, where the asset is purchased at the start of the time period and held throughout, generating a return based on the price difference from the start to the end.

Optimized Strategy

The optimized strategy involves:

• Moving Averages (MA5 and MA20): We generate buy signals when the short-term moving average (MA5) crosses above the long-term moving average (MA20) and sell signals when MA5 crosses below MA20.
• Relative Strength Index (RSI): An oscillator that measures overbought (sell) and oversold (buy) conditions. We buy when the RSI is below 30 and sell when it is above 70.

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How I Implemented It

2. Buy and Sell Signals:

   • Buy signal: When MA5 crosses above MA20 and RSI is below 30 (oversold).
   • Sell signal: When MA5 crosses below MA20 or RSI is above 70 (overbought).

3. Backtesting: I backtested the strategy by calculating the strategy returns and comparing them to the market returns. Cumulative returns were plotted for both strategies.

4. Results: I visualized and compared the performance of both strategies to evaluate how the technical strategy performed against a simple buy-and-hold approach.

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Code Structure

All the code is contained in a single Jupyter Notebook file, which includes:

1. Data Loading and Preprocessing:

   • Loading stock price data.
   • Calculating the 5-period and 20-period moving averages.
   • Computing RSI using TA-lib.
   • Generating buy/sell signals based on MA and RSI conditions.

2. Strategy Backtesting:

   • Calculating returns for both the buy-and-hold strategy and the optimized strategy.
   • Comparing cumulative returns for both strategies.

3. Results Visualization:

   • A plot that compares the performance of the two strategies (market vs. optimized).

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How to Run the Code

Prerequisites:

• Install the required libraries:

  pip install pandas numpy matplotlib talib