Trading Chatbot

A powerful web application that combines real-time stock analysis, technical strategy ranking, and an AI-powered chatbot to help traders make informed decisions.

Features

• Automated Strategy Ranking: Scans the market for Breakout, Swing, and Day trading opportunities based on daily OHLCV data.
• AI-Powered Analysis: Integrated Chatbot (LLM) to explain technical signals and answer queries like "Why is RELIANCE in the top 5?".
• Interactive Charts: Detailed daily candle charts with annotated patterns, entry, stop, and target levels.
• Real-time Updates: WebSocket integration for live ranking updates and price changes.
• Fundamental Insights: Integrates fundamental data to provide a holistic view of stocks.

Architecture

The system is built with a clean separation of concerns, featuring a FastAPI backend for data processing and a React frontend for the user interface.

graph TD
    subgraph "Frontend (React)"
        UI[User Interface]
        Chart[Interactive Charts]
        Chat[Chat Window]
    end

    subgraph "Backend (FastAPI)"
        API[REST API]
        WS[WebSocket Server]
        TA[TA Engine]
        Strategy[Strategy Engine]
        LLM[LLM Service]
    end

    subgraph "Data Layer"
        DB[("SQLite Cache")]
        ExtAPI["External APIs (Fyers/NSE)"]
        Scraper[Fundamental Scraper]
    end

    UI -->|HTTP Requests| API
    UI <-->|Real-time Updates| WS
    Chat -->|Queries| API
    API --> TA
    API --> Strategy
    API --> LLM
    TA --> DB
    Strategy --> DB
    TA --> ExtAPI
    Scraper --> DB

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User Flow

sequenceDiagram
    participant User
    participant UI as Frontend
    participant API as Backend
    participant LLM as AI Service

    User->>UI: Select Strategy (e.g., Breakout)
    UI->>API: Request Ranked Stocks
    API->>API: Run Strategy Engine
    API-->>UI: Return Ranked List
    User->>UI: Click on Stock (e.g., TATASTEEL)
    UI->>API: Get Details & Chart Data
    API-->>UI: Return OHLCV & Indicators
    User->>UI: Ask "Why is this a good buy?"
    UI->>API: Send Chat Message
    API->>LLM: Generate Explanation (Context: TA + Fundamentals)
    LLM-->>API: Return Explanation
    API-->>UI: Display AI Response

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Tech Stack

• Backend: FastAPI (Python), Pandas, Pandas-TA, NumPy
• Frontend: React.js, TailwindCSS (optional), Plotly/Recharts
• Database: SQLite (for caching and metadata)
• AI/LLM: Ollama - llama 3.1b (or OpenAI API) for natural language explanations
• Data Sources: nsepy (Historical), Fyers (Real-time/Optional), Screener.in (Fundamentals)

Setup Instructions

Follow these steps to get the project running on your local machine.

Prerequisites

• Python 3.9 or higher
• Node.js and npm
• Git

1. Clone the Repository

git clone https://github.com/Anshul-ydv/Trading_Chatbot.git
cd Trading_Chatbot

2. Backend Setup

# Create a virtual environment
python -m venv .venv

# Activate the virtual environment
# On macOS/Linux:
source .venv/bin/activate
# On Windows:
# .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

3. Frontend Setup

cd frontend
# Install Node dependencies
npm install

4. Environment Configuration

Create a .env file in the root directory and add your API keys:

OPENAI_API_KEY=your_openai_api_key_here # Or u can use a local LLM like ollama (we used the local one so it will easier to set up)
FYERS_APP_ID=your_fyers_app_id_here  # Optional
FYERS_ACCESS_TOKEN=your_fyers_token_here # Optional

5. Fyers

Use the authtoken.ipynb file to generate the fyers token for this project refer to this : https://myapi.fyers.in

Running the Application

Start the Backend

From the root directory:

# Make sure your virtual environment is activated
uvicorn src.main:app --reload
or TCHATBOT/.venv/bin/python trading-chatbot/run.py

The API will be available at http://localhost:8000.

Start the Frontend

Open a new terminal, navigate to the frontend folder:

cd frontend
npm start
or cd trading-chatbot/frontend && npm run dev

The application will open in your browser at http://localhost:3000.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.