Welcome to Investment-Strategy-Backtester, a robust open-source library designed to aid in the crafting and evaluation of investment strategies. Built, maintained and used by Alpha Rho Technologies LLC.

• Versatility: Backtest any investment strategy, whether traditional or unconventional.
• Flexibility: Test across a range of timeframes, from intraday to multi-year spans.
• Precision: Incorporate transaction costs for realistic and accurate simulation results.

1. Prerequisites:

   • Python 3.6 or higher.

   pip install pandas
   pip install datetime
   pip install numpy

2. Clone the Repository:

   git clone https://github.com/Alpha-Rho-Technologies/invbt
   

3. Navigate to the cloned directory:

   cd path_to_directory

• File Structure: Ensure your downloaded data is structured in a table with with dates utilized as the index column and respective asset prices presented in subsequent columns.
• Content: The table should encapsulate the historical price data for all portfolio assets, facilitating accurate and comprehensive backtesting.

Note: Ensure the data frequency (e.g., daily, monthly) aligns with your backtesting objectives.

• File Structure: Construct a CSV file wherein the first column enumerates the assets and subsequent columns represent rebalance dates.
• Content: Populate the table cells with the corresponding asset weight on each rebalance date.

Note: If needed, refer to the sample files located within the repository's files folder, for further guidance.

• Place the prepared files into the files folder within the repository, adhering to the following naming conventions:
  • Portfolio asset data: apd.csv
  • Portfolio weightings at rebalance dates: portfolios.csv

• Initiate the backtesting process by running example.ipynb using a Jupyter Notebook interface.

Note: Ensure your working directory is set to the repository location to avoid file path issues.

For any queries or issues, please raise an issue in this repository or contact [email protected].