🚀 Want to build your first algorithmic trading bot in Python before 2027? This is Part 1 of the Algo Quant Trading Bot Roadmap — a beginner-friendly series where we’ll learn how to build, backtest, and improve trading algorithms step by step.

In this tutorial, we’ll build a Python trading bot in Google Colab using real historical SPY market data from Yahoo Finance.

You’ll learn how to:

✅ Download financial market data using Python and yfinance
✅ Build a simple 20/50 moving-average crossover strategy
✅ Generate automatic BUY and SELL signals
✅ Avoid a common lookahead-bias mistake using next-day execution
✅ Backtest with $10,000 starting capital
✅ Add spreads, slippage, and commissions
✅ Compare backtest results with and without trading costs

📊 **Our historical backtest results:**

Starting capital: $10,000
Without trading costs: +27.13%
With trading costs: +26.81%
Maximum drawdown with costs: −12.55%
Historical period: January 2018 – September 2026

These results come from one educational strategy and one historical dataset. They do not guarantee live profitability.

💻 **Tools used:** Python, Google Colab, Yahoo Finance (yfinance), pandas, and historical backtesting.

📚 **This is just Part 1!**

In upcoming episodes, we’ll explore strategy evaluation, risk management, backtesting mistakes, optimization, and the tools needed to build more reliable algorithmic trading systems.

🔔 Subscribe to Algo Quant and follow the roadmap to build your first trading algorithm before 2027.

💬 **YOUR CHALLENGE:** Try changing the 20/50 moving averages. Which settings did you test, and what happened to your results? Share your experiment in the comments!

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