Quantitative Finance Projects 📈

This repository contains institutional-grade quantitative trading strategies and financial analysis tools developed in Python.

🛠 Project 1: Nifty 50 Trend Follower

• Concept: Moving Average Crossover (20-day vs 50-day).
• Objective: Identifying market momentum in the Indian Benchmark Index.
• Key Learnings: Data wrangling with yfinance, handling time-series data, and visualizing trend signals.

🛠 Project 2: RSI Mean Reversion (Reality-Adjusted)

• Status: Completed - Research Phase.
• Objective: Test if simple RSI signals survive Indian market friction (STT, GST, Fees).
• Key Realistic Parameters:
  • Execution Lag: 1-Day shift (Signal at Close, Trade at Next Open).
  • Transaction Costs: 0.15% (15bps) per trade (Indian STT + Exchange charges).
• Results:
  • Final Return: -5.46% (Post-cost).
  • Max Drawdown: -9.63%.
• Conclusion: Standalone RSI signals in the Nifty 50 are not strong enough to overcome transaction costs. This necessitates a pivot to multi-factor models and machine learning filters.
• Key Tech: pandas_ta for professional-grade indicators and vectorized backtesting logic.