📈 Stock Market Backtesting Engine (NumPy)

📌 Overview

This project implements a stock market backtesting engine using only NumPy. It simulates a moving average crossover trading strategy on historical price data and evaluates performance based on portfolio returns.

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🚀 Features

• Data cleaning and preprocessing from raw CSV
• Moving Average (MA) calculation using NumPy
• Signal generation (Buy/Sell) using MA crossover
• Backtesting engine for trade simulation
• Portfolio value tracking over time
• Final performance evaluation (profit & returns)

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🧠 Strategy Used

Moving Average Crossover

• Short-term MA (10 days)
• Long-term MA (50 days)

Signal Logic:

• MA_short > MA_long → Buy (1)
• MA_short < MA_long → Sell (-1)

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⚙️ How It Works

1. Load and clean stock price data
2. Convert data into NumPy arrays
3. Compute moving averages using convolution
4. Generate trading signals
5. Simulate trades using a backtesting loop
6. Track portfolio value over time

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📊 Output

• Final Portfolio Value
• Total Profit / Loss
• Return Percentage
• Number of Trades (optional)

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🛠️ Tech Stack

• Python
• NumPy

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📂 Project Structure

moving_average_backtest.ipynb
data.csv
README.md

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💡 Key Learnings

• Vectorized computations using NumPy
• Time-series data handling
• Financial strategy simulation
• Building logic without high-level libraries

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🔥 Future Improvements

• Add multiple strategies
• Include transaction costs
• Support multi-stock backtesting
• Visualize results using plots

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📄 Resume Description

Built a stock market backtesting engine using NumPy, implementing a moving average crossover strategy with vectorized signal generation and portfolio simulation. Evaluated trading performance using return-based metrics.

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