📈 StrategiesBacktestedonIndianStocks

Welcome to Indian Stock Market Analysis & Strategy Backtester, a comprehensive collection of Jupyter notebooks for analyzing and backtesting stock trading strategies on Indian equities using historical data.

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🛠️ Tools & Libraries Used

This project leverages the following Python libraries and tools:

• 📦 NumPy – Efficient numerical computations
• 📈 Pandas – Data manipulation and analysis
• 📊 Matplotlib – Visualization and plotting
• 💹 yfinance – Fetching historical stock data from Yahoo Finance
• ☁️ Google Colab – Cloud-based notebook environment for execution and collaboration

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🧠 Features

• ✅ Analyze Indian stock market data from the past 10 years
• 📊 Implement technical indicators (EMA, SMA, RSI, MACD, etc.)
• 🔁 Backtest trading strategies using historical price data
• 🧪 Ready-to-use strategies: EMA crossover, SMA bias, long bias
• 📈 Visualize signals, trades, and portfolio performance
• 🗃 Modular notebooks for easy experimentation

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📂 Repository Structure

📁 indian_stock_analysis/
│
├── 📘 [ADVANCED]indian_stock_analysis_past10years.ipynb    # Deep analysis of Indian stocks
├── 📘 [BACKTESTER]Financial_Functions.ipynb                # Custom financial & helper functions
├── 📘 Basic_analysis.ipynb                                 # Entry-level analysis on price/returns
├── 📘 emacrossoverstategy.ipynb                            # EMA crossover strategy backtesting
├── 📘 SMA_and_LONGBIAS_Strategy.ipynb                      # SMA-based long-only trading logic
├── 📘 Technical_Indicators.ipynb                           # Technical indicator implementations

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📸 Screenshots

✅ 1. SMA Crossover Strategy vs Buy & Hold

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📊 2. Price with SMA Short & Long Overlay

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🔥 3. Stock Correlation Heatmap

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📈 4. Return vs Risk Scatter Plot

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📉 5. Normalized Growth of Multiple Stocks

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🧮 6. Backtest Function Code Snippet