This project develops and tests trading alphas for the Indian and US equity markets, focusing on medium to high-frequency trading strategies. The objective is to create, backtest, and optimize these alphas to enhance trading performance.

• Develop trading strategies based on given alphas
• Backtest strategies using historical market data
• Optimize trading thresholds to maximize profit and loss (PnL)
• Visualize the performance of the trading strategies

• Python 3.x
• Pandas
• Numpy
• Matplotlib

1. Clone the repository:

   git clone https://github.com/saurabh4269/alpha.git
   cd alpha

2. Install the required packages:

   pip install pandas numpy matplotlib

3. Download the dataset from the provided link and place it in the project directory:

   • Dataset
   • Rename the downloaded file to asset_1.csv and place it in the project directory.

1. Run the script:

   python backtest.py

2. The script will:

   • Load the dataset
   • Optimize the trading thresholds
   • Apply the optimal strategy
   • Backtest the strategy
   • Save the results to backtest_results.csv
   • Visualize the performance

• Initialization: Accepts build and liquidate thresholds.
• Method: apply_strategy(data) applies the trading strategy to the dataset.

• Initialization: Accepts a strategy and dataset.
• Method: run_backtest() applies the strategy, calculates returns and PnL, and generates a cumulative PnL.

• Optimizes the build and liquidate thresholds to maximize PnL.

• Visualizes the asset price, position over time, and cumulative PnL.

• The script first optimizes the thresholds.
• It then applies the optimal strategy and runs the backtest.
• Finally, it visualizes the results and saves them to a CSV file.

Below are example plots generated by the script: