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20-Day Comprehensive Plan to Build a Stock Market AI Bot

MVP Features

  • AI-Driven Stock Analysis: Analyze NSE/BSE indices and individual stocks.
  • Investment Decision Support: Suggest if it's the right time to invest, based on historical and real-time data.
  • Personalized Recommendations: Tailored insights for different investment durations (3 months, 6 months, 1 year, etc.).
  • Audio & Text Inputs: Users can interact with the bot via voice or text.
  • Sentiment Analysis: Understand market sentiment from news and social media.
  • Risk Assessment & Forecasting: Predict future stock movements and assess potential risks.
  • Visual Insights: Interactive charts and graphs for better decision-making.

Day 1-3: Understanding and Setting Up LLaMA Locally (High Priority on Training for Finance Data)

Key Concepts:

  • Large Language Models (LLMs)
  • LLaMA (Meta’s Open-Source Model)
  • Fine-tuning and inference optimization
  • LangChain for building AI-powered apps

Tools & Libraries:

  • LLaMA 3 (7B) — Free, open-source, suitable for text-based financial analysis.
  • Hugging Face Transformers
  • LangChain (for building AI-driven pipelines)
  • PyTorch (for training and inference)
  • FastAPI (for API development)

Tasks:

  1. Set up a local environment:

    • Install Python, PyTorch, and Hugging Face libraries.
    • Download the LLaMA 3 model from Hugging Face or Meta’s repository.

  2. Configure GPU/CPU settings:

    • Use CUDA if available (or run on CPU with optimized settings).

  3. Prepare financial datasets:

    • Collect historical stock market data, financial reports, and news articles.
    • Clean and format data for training.

  4. Train LLaMA for financial analysis:

    • Fine-tune LLaMA on the financial dataset.
    • Train the model to understand stock market jargon and interpret patterns.

  5. Build a test pipeline:

    • Create a simple FastAPI server to run local queries.
    • Test the model’s response with real market data questions.

  6. Evaluate and refine:

    • Measure model accuracy with test prompts.
    • Refine training data and retrain for better results.

Day 4-6: Stock Market Fundamentals

Key Concepts:

  • Stock exchanges (NSE/BSE)
  • Market indices (Nifty 50, Sensex)
  • Sectors & industry classifications

Tasks:

  1. Understand stock market basics: How shares work, what affects stock prices.
  2. Study key financial metrics: PE ratio, EPS, market cap, volume.
  3. Research historical data sources: Yahoo Finance, Alpha Vantage (free APIs).
  4. Analyze market trends: Bullish vs bearish markets, volatility indicators.

Day 7-9: Data Collection & Preprocessing

Tools & Libraries:

  • yfinance (for stock data)
  • Alpha Vantage (API for real-time & historical data)
  • Pandas (data manipulation)
  • NumPy (numerical computations)

Tasks:

  1. Fetch stock data: Historical prices, trading volume, financial statements.
  2. Clean & preprocess the data: Handle missing values, normalize data.
  3. Create stock sector datasets: Group stocks by industry for sector analysis.
  4. Basic exploratory analysis: Calculate moving averages, RSI, Bollinger Bands.

Day 10-12: Building AI-Powered Analysis with Fine-Tuned LLaMA

Key Concepts:

  • Sentiment analysis (for news & social media)
  • Time series forecasting
  • Risk assessment

Tools & Libraries:

  • Fine-tuned LLaMA 3 (7B) for finance data
  • LangChain (for conversational AI)
  • Scikit-learn (basic ML models)
  • Statsmodels (for statistical analysis)

Tasks:

  1. Refine model responses:

    • Test and adjust prompt engineering.
    • Use LangChain to handle complex user queries.

  2. Implement sentiment analysis:

    • Scrape market news and social media.
    • Use LLaMA to classify sentiments.

  3. Build a simple forecast model:

    • Combine historical data with LLaMA-generated insights.
    • Create time series models for price predictions.

  4. User data management:

    • Save user interactions and queries in JSON format.
    • Automatically create a JSON entry for each new user, storing preferences and history.

Day 13-15: Visualization & Insights

Tools & Libraries:

  • Matplotlib (basic plots)
  • Plotly (interactive charts)
  • Seaborn (statistical visualization)

Tasks:

  1. Visualize stock trends: Candlestick charts, moving averages.
  2. Sector comparison: Show stock performance across industries.
  3. Risk-reward graphs: Visualize potential returns vs volatility.

Day 16-18: Risk Management & Decision-Making

Key Concepts:

  • Portfolio diversification
  • Value at Risk (VaR)
  • Monte Carlo simulations

Tasks:

  1. Analyze risk factors: Interest rates, inflation, global events.
  2. Simulate different scenarios: Monte Carlo for potential price paths.
  3. Suggest strategies: Conservative vs aggressive investments.

Day 19-20: Deployment & Testing

Tools & Libraries:

  • FastAPI (for API deployment)
  • Docker (containerization)
  • Streamlit (optional UI)

Tasks:

  1. Deploy the AI model: Create endpoints for predictions & analysis.
  2. Test with sample users: Handle text and audio queries.
  3. Optimize model performance: Use caching, optimize inference time.
  4. Prepare for real-world use: Plan for model updates & retraining.

Final Outcome:

An AI-powered trading assistant that can:

  • Analyze stock data (NSE/BSE) and market indices.
  • Forecast future stock performance based on historical trends.
  • Interpret financial news & sentiment to inform decisions.
  • Provide personalized recommendations for different investment horizons (months/years).
  • Visualize market insights to help users understand potential risks & rewards.
  • Manage user data in JSON format for personalization and history tracking.

With a fine-tuned LLaMA model running locally, you’ll have complete control over your AI’s performance and accuracy. Let me know if you want me to dig even deeper into any section! 🚀