Quantitative Trading Strategy & Backtesting Framework

A modular Python framework for backtesting systematic quantitative trading signals, computing risk metrics, and evaluating portfolio performance across historical price series.

Overview

This repository contains tools developed to test quantitative hypotheses against financial market data, evaluating whether statistical anomalies generate exploitable alpha after accounting for drawdowns and realistic trading costs.

Key Features

• Signal Generation: Implements quantitative signals including mean reversion, momentum, and moving average crossovers.
• Risk & Performance Metrics: Automated calculation of:
  • Annualized Return & Volatility
  • Sharpe Ratio & Sortino Ratio
  • Maximum Drawdown (MDD)
  • Win Rate & Profit Factor
• Visualization: Performance equity curves and drawdown distribution plots using matplotlib.

Requirements & Setup

git clone https://github.com/yb-vicki/Python_trading.git
cd Python_trading
pip install numpy pandas matplotlib scipy

Structure

• *.ipynb / *.py — Strategy simulation notebooks and signal execution scripts.
• Data analysis routines for price normalization, rolling window statistics, and factor returns.