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https://github.com/shelton-beep/trading-algorithm

A simple trading algorithm for SPY ETF using a moving average crossover strategy. This project analyzes SPY weekly price data, implements a buy/sell algorithm, and tracks performance metrics to evaluate profitability and risk. Ideal for learning algorithmic trading basics and financial data analysis.
https://github.com/shelton-beep/trading-algorithm

data-analysis financial-analysis investment-strategy jupyter-notebook pandas python quantitative-finance technical-analysis time-series-analysis trading-strategies

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A simple trading algorithm for SPY ETF using a moving average crossover strategy. This project analyzes SPY weekly price data, implements a buy/sell algorithm, and tracks performance metrics to evaluate profitability and risk. Ideal for learning algorithmic trading basics and financial data analysis.

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README 

        
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:



```bash

pip install pandas plotly numpy

```



Getting Started



1. Clone the Repository:



   ```bash

   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:



   ```bash

   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.