A Python-based end-to-end trading strategy toolkit combining machine learning and technical indicators like RSI, MACD, Stochastic Oscillator, ARIMA time-series forecasting, and lagged returns. Focused on signal generation, performance visualization, and backtesting.

• 📈 Technical Indicators:

  • Relative Strength Index (RSI)
  • Moving Average Convergence Divergence (MACD)
  • Stochastic Oscillator

• 🧠 Machine Learning:

  • XGBoost Classifier
  • Random Forest on lagged returns

• 🔁 Time Series Forecasting:

  • ARIMA model for price prediction

• 📊 Backtesting:

  • RSI + MACD combined strategy
  • Stochastic Oscillator strategy
  • Visualized vs. market returns

• Combined MACD + RSI crossover logic with signal-driven cumulative return visualization
• Separate strategy backtesting for stochastic oscillator
• Compare with actual market return performance

• main.py: Runs all strategy pipelines and backtests
• src/preprocess.py: Computes all technical indicators
• src/data_fetcher.py: Fetches and stores stock data via yfinance
• src/train.py: Contains models and training utilities

git clone https://github.com/your-username/quant72.git
cd quant72

Tools Used: 
yfinance
pandas
numpy
scikit-learn
matplotlib
xgboost
statsmodels
tabulate