60-trading-bot-simulator

60 - Algorithmic Trading Bot Simulator

What It Does

An algorithmic trading simulator that allows users to backtest a Moving Average Crossover strategy on real historical stock data. Built with Streamlit, it fetches live data from Yahoo Finance, applies the trading algorithm, and visualizes the hypothetical portfolio performance over time against a simple buy-and-hold strategy.

Project Structure

60-trading-bot-simulator/
  app.py               # Streamlit application with backtesting logic
  requirements.txt     # Python dependencies
  README.md            # This file

Setup and Run

1. Install dependencies

pip install -r requirements.txt

2. Run the dashboard

streamlit run app.py

Core Concepts

• Financial Data Fetching using the yfinance library
• Time-Series Analysis with pandas (rolling windows, shifts, cumulative returns)
• Moving Average Crossover Strategy: A classic quantitative trading algorithm where a short-term moving average crosses a long-term moving average to generate buy/sell signals.
• Vectorized Backtesting: Calculating portfolio returns across the entire dataset instantly using pandas array operations instead of slow for-loops.
• Data Visualization with streamlit line charts.

What You Will Learn

You will learn how quantitative analysts (quants) use Python to test trading ideas before risking real money. You'll master advanced pandas operations for financial time-series data, calculate standard financial metrics (like cumulative returns), and build interactive financial dashboards.

Example Output

Streamlit dashboard at http://localhost:8501

Ticker Selected: AAPL
Date Range: 2020-01-01 to 2024-01-01
Short MA: 20 days | Long MA: 50 days

Results:
- Total Buy/Sell Trades Executed: 14
- Buy & Hold Return: +124.5%
- Strategy Return: +142.1%

[Interactive Chart showing Stock Price, Moving Averages, and Buy/Sell Markers]
[Interactive Chart showing Portfolio Value Growth]