https://github.com/coderixc/banknifty_algo_strategy
Python Trading Strategy Analyzer: Backtesting and Metrics Framework
https://github.com/coderixc/banknifty_algo_strategy
algotrading backtesting-engine python python-script quant-dev quanttrading stock-market
Last synced: 2 months ago
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Python Trading Strategy Analyzer: Backtesting and Metrics Framework
- Host: GitHub
- URL: https://github.com/coderixc/banknifty_algo_strategy
- Owner: Coderixc
- Created: 2023-08-11T17:59:42.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-03-12T04:40:54.000Z (over 1 year ago)
- Last Synced: 2025-04-14T23:53:47.140Z (2 months ago)
- Topics: algotrading, backtesting-engine, python, python-script, quant-dev, quanttrading, stock-market
- Language: Python
- Homepage:
- Size: 2.82 MB
- Stars: 6
- Watchers: 1
- Forks: 3
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
## Python Trading Strategy Analyzer
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.
## Key Features
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.
# Step 2: Load Data
The project loads CSV data into memory, allowing users to access and analyze trading data.
# Step 3: Extract Future Data
It extracts future trading data based on predefined logic.
# Step 4: Extract Option Data
The project extracts option trading data, distinguishing it from other types of trades.
# Calculate Moving Averages
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
Exit strategies are implemented based on stop-loss, target points, and predefined timeframes.
## Learning Objectives
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.