This repository implements trading algorithms based on technical indicators and sentiment analysis. It evaluates and executes strategies using historical stock data from Yahoo Finance, and real-time sentiment insights from news sources.

• final.ipynb: Contains the main code for strategies, backtesting, and performance visualizations.
• constituents.csv: Lists S&P500 stock symbols for retrieving data.

• MACD (Moving Average Convergence Divergence): Measures momentum through divergences in moving averages (Learn more).
• RSI (Relative Strength Index): Identifies overbought/oversold conditions (Learn more).
• Bollinger Bands: Highlights price volatility using bands (Learn more).
• Breakout Strategy: A momentum-based strategy that buys on price increases beyond a volatility threshold (Learn more).
• MACD-Breakout: Combines MACD signals with breakout patterns for trading decisions.

• Simulates historical performance of strategies using stock data.
• Visualizes profit/loss results and calculates:
  • Win rate: Proportion of profitable trades.
  • Maximum Drawdown (MDD): Largest observed loss from a peak.

• The bullish_bearish function classifies market conditions.
• Strategies are compared during bullish and bearish periods, with findings indicating outperformance in bearish markets compared to a simple buy-and-hold strategy.

• Retrieves news headlines using GoogleNews (Library).
• Analyzes sentiment polarity using NLP (Natural Language Processing) with NLTK.
• Implements a sentiment strategy: Buys stocks with positive sentiment and sells on negative sentiment.
• Categorizes sentiment into five levels: extremely negative, negative, neutral, positive, and extremely positive.

• Strategy performance is visualized using line and bar graphs.
• p-values are calculated to determine statistical significance between strategy returns.
• Comparison of strategy performance across different sentiment categories and market conditions helps assess overall effectiveness.