SPY Weekly Prices Analysis

This repository contains Jupyter notebooks that analyze weekly price data for the SPY ETF (S&P 500 ETF Trust). The analysis focuses on calculating key technical indicators, generating visualizations, and exploring potential trading strategies based on these indicators.

Files in this Repository

1. SPY_Weekly_Prices (1).ipynb
   • Purpose: This notebook loads weekly SPY price data, calculates key moving averages, and provides an exploratory data analysis (EDA) of SPY's historical price movements.
   • Features:
     • Loads SPY data and formats the date for readability.
     • Calculates important technical indicators, such as the Exponential Moving Average (EMA) and Simple Moving Average (SMA).
     • Visualizes price trends with candlestick charts and overlays of calculated indicators.
     • Provides foundational data and insights for understanding SPY's weekly trends and historical performance.
2. weekly_spy_data2 (1).ipynb
   • Purpose: This notebook builds on the exploratory data by implementing a basic trading strategy based on moving average crossovers.
   • Features:
     • Implements a trading algorithm where:
       • A buy signal is generated when the 50-day SMA crosses above the 21-day EMA.
       • A sell signal is triggered when the 21-day EMA crosses above the 50-day SMA.
     • Records trade entry and exit points, including:
       • Entry and exit dates.
       • Entry and exit prices.
       • Calculated profit or loss (PnL) for each trade.
     • Provides a summary of trades based on the crossover strategy, including cumulative returns.

Requirements

• Python 3.8+
• Jupyter Notebook
• Libraries:
  • pandas for data manipulation.
  • plotly for data visualization, especially candlestick charts.
  • numpy for numerical operations.

To install the required packages, you can use:

pip install pandas plotly numpy

Getting Started

1. Clone the Repository:

   git clone https://github.com/Shelton-beep/trading-algorithm.git
   cd trading-algorithm

2. Open the Jupyter Notebooks: Launch Jupyter Notebook in the project directory to access the notebooks:

   jupyter notebook

   Open SPY_Weekly_Prices (1).ipynb for data exploration and visualization, or weekly_spy_data2 (1).ipynb for the trading strategy implementation.

3. Run the Notebooks: Follow the code cells in each notebook sequentially to load data, process indicators, and generate visualizations or trading signals.

Project Goals

This project aims to:

• Provide an overview of SPY’s historical weekly price movements.
• Develop a basic trading algorithm based on moving average crossovers.
• Demonstrate the impact of technical indicators on trading strategies.

Future Work

Planned improvements include:

• Backtesting additional technical indicators.
• Optimizing the strategy with different moving average windows.
• Incorporating risk management techniques.