trading_bot - raghavkhandelwal12/Artificial-Intelligence GitHub Wiki

Trading Bot Project Plan (Detailed Explanation)

MVP Features (Minimum Viable Product):

• Basic Trade Recommendations: Get buy/sell signals based on historical and real-time data.
• Real-Time Data Fetch & Execution: Fetch live market data and execute trades automatically.
• Simple Profit/Loss Dashboard: Track trades, profit/loss, and visualize performance.
• User Login & Authentication: Secure access with registration and login functionality.

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Day 1: Project Setup & Environment Configuration

• Install Libraries:

  • pip install llama-index langchain fastapi yfinance scikit-learn matplotlib pandas numpy httpx
  • For API testing: pip install httpx

• Learning Resource: [FastAPI Docs](https://fastapi.tiangolo.com/)

• Folder Structure:

  • /trading-bot
    • /data
    • /models
    • /api
    • main.py
    • requirements.txt

• Key Functions:

  • load_env(): Load API keys
  • create_folder_structure(): Organize project files

Day 2: Understanding LLaMA and LangChain

• LLaMA Model: Use LLaMA-7B (free on [Hugging Face](https://huggingface.co))

• LangChain components:

  • LLMChain, PromptTemplate, VectorStores

• Learning Resource: [LangChain Docs](https://python.langchain.com/)

• Class to Create: ModelManager

  • Handles model loading, prompt generation, and embedding management

Day 3: Data Collection (Historical Stock Data)

• Library: yfinance

• Methods: download(), Ticker, history()

• Class: StockDataCollector

  • Fetches historical and real-time stock data

• Learning Resource: [Yahoo Finance API](https://pypi.org/project/yfinance/)

Day 4: Data Preprocessing

• Libraries: pandas, numpy, scikit-learn

• Methods: fillna(), normalize(), train_test_split()

• Class: DataPreprocessor

• Learning Resource: [Pandas Docs](https://pandas.pydata.org/docs/)

Day 5: Training Dataset Preparation

• Prepare features and labels:

  • Moving Averages, RSI, Bollinger Bands
  • Price movements as labels

• Function: generate_training_data()

• Learning Resource: [Scikit-learn User Guide](https://scikit-learn.org/stable/user_guide.html)

Day 6: LLaMA Model Setup

• Download and load LLaMA model:

  • Use transformers and AutoModel

• Class: LLamaTrainer

  • load_model(), tokenize()

• Learning Resource: [Transformers Docs](https://huggingface.co/docs/transformers)

Day 7: Fine-Tuning LLaMA on Market Data

• Library: transformers

• Methods: Trainer, TrainingArguments

• Function: fine_tune_llama()

• Learning Resource: [Hugging Face Course](https://huggingface.co/course)

Day 8: Testing Model Predictions

• Split data for testing
• Class: PredictionEvaluator
  • evaluate_predictions()

Day 9: LangChain Integration

• LangChain components: LLMChain, Memory
• Class: TradeAdvisor
  • generate_trade_recommendation()

Day 10: Real-Time Market Data Fetching

• API: Alpaca, Binance, yfinance

• Class: MarketDataFetcher

  • get_realtime_data()

• Learning Resource: [Alpaca API](https://alpaca.markets/docs/api-references/)

Day 11: Trade Execution Logic

• Library: ccxt

• Class: TradeExecutor

  • buy(), sell(), check_balance()

• Learning Resource: [CCXT Docs](https://docs.ccxt.com/en/latest/)

Day 12: User Authentication & Registration

• Library: FastAPI, bcrypt
• Class: UserManager
  • register_user(), login_user()

Day 13: Building the API (FastAPI)

• Endpoints: /register, /login, /predict, /trade
• Class: TradingAPI
  • run_server()

Day 14: Backtesting with Historical Data

• Library: backtrader

• Class: Backtester

  • run_backtest()

• Learning Resource: [Backtrader Docs](https://www.backtrader.com/docu/)

Day 15: Error Handling & Logging

• Library: logging
• Methods: log_error(), log_trade()
• Class: Logger

Day 16: MVP Features & Final Testing

• MVP Features:

  • Basic trade recommendations
  • Real-time data fetch and execution
  • Simple profit/loss dashboard
  • User login & authentication

• Deploy with: Docker, Railway.app

• Method: containerize_project()

• Test all functionalities together

Let me know if you want me to refine anything or add more resources! 🚀