This Python project is designed to facilitate the backtesting of trading strategies and the analysis of their performance. The project is built entirely from scratch using Python as the primary programming language. The framework enables users to analyze various metrics to generate comprehensive reports on the performance of their trading strategies.

Step 1: Import Dependencies

The project imports necessary libraries including pandas for data manipulation, datetime for time-related operations, and plotly.graph_objects for visualization.

The project loads CSV data into memory, allowing users to access and analyze trading data.

It extracts future trading data based on predefined logic.

The project extracts option trading data, distinguishing it from other types of trades.

Various moving average calculations are performed to aid in strategy analysis. #Generate Trades

The framework generates trades based on predefined conditions and moving average crossovers.

Exit strategies are implemented based on stop-loss, target points, and predefined timeframes.

Gain hands-on experience in Python programming for algorithmic trading. Understand the importance of preprocessing and analyzing trading data. Learn how to implement common trading strategies and indicators. Explore techniques for managing trades and defining exit strategies. Develop skills in statistical analysis and performance evaluation of trading strategies.rate meaningful insights. The project assumes certain fixed values for stop-loss and target points, which users may need to adjust based on market conditions and individual preferences. Statistical Analysis The framework includes statistical analysis tools to evaluate the performance of trading strategies. Metrics such as profit trades, loss trades, profit points, loss points, and profit-to-loss ratio are calculated to assess strategy effectiveness.

If you are interested in collaborating or need assistance with algorithmic trading strategies, feel free to reach out.