Skip to content

Latest commit

 

History

History
65 lines (41 loc) · 2.62 KB

README.md

File metadata and controls

65 lines (41 loc) · 2.62 KB

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.

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.

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.

How I Implemented It

  1. 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).

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

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

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).

How to Run the Code

Prerequisites:

  • Install the required libraries: 
    pip install pandas numpy matplotlib talib