Tradeflow

An interactive FnO learning platform with paper trading, pre-market analysis, and AI-powered trade reports.

What It Does

• Pre-market Analysis — Scores global markets (GIFT NIFTY, NASDAQ, VIX, DXY, etc.) with weighted signals to generate a NIFTY bias score and trading playbook
• Paper Trading — Enter trades with stop loss, target, and thesis. Track P&L with virtual ₹5,00,000 balance
• AI Trade Reports — After closing a trade, an LLM generates a contextual report explaining what went right/wrong
• Gamification — XP for process (writing thesis, setting SL) not profit. Streaks, levels, badges

Tech Stack

Layer	Tool
Backend	FastAPI (Python)
Frontend	React + Vite
Database	SQLite
Market Data	yfinance + NSE API
LLM	Groq (free tier, LLaMA 3) + Ollama
Scheduling	APScheduler

Setup

1. Requirements

Ensure you have Python 3.10+ and Node.js 18+ installed.

2. Quick Start (Scripts) (Recommended)

You can easily start both the backend and frontend concurrently using the provided scripts:

Windows:

start.bat

Linux/macOS:

./start.sh

3. Manual Setup

If you prefer to start them manually:

Backend:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
cd backend
pip install -r requirements.txt
uvicorn main:app --reload

Runs at http://localhost:8000

Frontend:

cd frontend
npm install
npm run dev

4. Docker

If you have Docker installed, you can spin up the entire stack seamlessly:

docker-compose up --build

Frontend runs at http://localhost:5173 Backend runs at http://localhost:8000

Configuration & Data Sources

Important: Tradeflow does not hardcode URLs for market data scraping.

1. Once the application is running, open the Settings page in the UI.
2. Refer to the DATA_SOURCES.md file to find the required URLs.
3. Paste the URLs into the Data Sources section and save.

LLM integrations (like Groq and Ollama) are also configured directly within the Settings page.

Usage

1. Start backend, then frontend.
2. Go to Settings and configure your data sources (see above).
3. Go to Analysis tab → click Run Analysis to fetch global market data and generate the playbook.
4. Go to Trade tab → fetch option chain, click a strike, fill in your thesis, open a paper trade.
5. Close the trade when done → go to Reports → generate an AI report.
6. Check Portfolio for stats, XP, and trade history.

Releases and Building

Compiled desktop builds and versioned releases are published via GitHub Releases. To create a release, maintainers can use standard GitHub tag workflows. Check out the release page for standalone desktop installers (if available).

If you want to compile the desktop application locally, use the provided build scripts:

Windows:

build.bat

Linux/macOS:

chmod +x build.sh
./build.sh

This will build the frontend, package the python backend via PyInstaller, and bundle them into an Electron app located in desktop/dist/.

License

This project is open-source under the MIT License.