📈 Live Trading Simulation – Algo Trading with Backtesting, Paper Trading & Live Data

📘 Overview

This project implements a full algorithmic trading simulation system with support for:

• Historical backtesting
• Real-time paper trading
• Automated strategy execution
• Portfolio state tracking
• Interactive analytics dashboard
• REST + WebSocket market data services

It uses FastAPI, Streamlit, PostgreSQL, and yFinance to recreate a realistic end-to-end trading workflow for research, experimentation, and educational purposes.

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🎯 Objectives

🤖 Automated Trading

• Implements two algorithmic trading strategies:
  • SMA Crossover
  • RSI Momentum
• Generates deterministic buy/sell signals from historical or live data.

💹 Backtesting & Paper Trading

• Simulates virtual trading with cash, positions, and PnL tracking.
• Logs trades and equity snapshots.
• Provides complete historical performance curves.

🛢 Backend Data & APIs

• REST APIs for trades, equity, portfolio state, and strategy execution.
• WebSockets for streaming live ticks and portfolio updates.
• PostgreSQL storage for persistent analytics.

📊 Dashboard Visualization

• Real-time equity & price charts
• Trade markers
• Portfolio metrics
• Backtest trigger button
• Live simulation controls

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⚙️ Tech Stack

• FastAPI
• Streamlit
• PostgreSQL
• SQLAlchemy
• yFinance
• Plotly
• TA indicators (ta)

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🗂 Project Structure

/trading
├── /backend
│ ├── main.py
│ ├── api.py
│ ├── backtest.py
│ ├── database.py
│ ├── models.py
│ ├── strategies.py
│ ├── paper_trading.py
│
├── dashboard.py
├── connect.py
├── db.py
├── requirements.txt
└── /portfolio_state

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🚀 Setup Instructions

1. Clone

git clone https://github.com/shuvivyas/Live-Trading-Simulation.git
cd Live-Trading-Simulation

2. Virtual Environment

python -m venv env
env\Scripts\activate (Windows)
source env/bin/activate (Linux/Mac)

3. Install Dependencies

pip install -r requirements.txt

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🛢 Database Setup

1. Create PostgreSQL DB: trading
2. Update credentials if needed
3. Initialize tables: python trading/db.py

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🖥️ Run Backend (FastAPI)

uvicorn trading.backend.api:app --reload --port 8000

API Docs → http://127.0.0.1:8000/docs

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📊 Run Dashboard

streamlit run trading/dashboard.py

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📡 API Overview

GET /api/trades
GET /api/equity
GET /api/portfolio
GET /api/run_strategy

WS /ws/market/{symbol}
WS /ws/snapshots

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📘 Example Output

{ "symbol": "AAPL", "strategy": "sma_crossover", "portfolio": { "cash": 0, "position": 44.42, "equity": 11347.88 } }