Week 11: Algorithmic Trading

Learn how to develop, implement, and backtest algorithmic trading strategies using Python.

Learning Objectives:

1. Algorithmic Trading Fundamentals

Start with these essential resources:

import pandas as pd
import numpy as np
import yfinance as yf

# Download stock data
symbol = 'AAPL'
data = yf.download(symbol, start='2020-01-01')

# Calculate moving averages
data['SMA20'] = data['Close'].rolling(window=20).mean()
data['SMA50'] = data['Close'].rolling(window=50).mean()

# Generate signals
data['Signal'] = np.where(data['SMA20'] > data['SMA50'], 1, -1)

2. Strategy Implementation

Learn from practical examples:

# Calculate strategy returns
data['Returns'] = data['Close'].pct_change()
data['Strategy'] = data['Signal'].shift(1) * data['Returns']

# Calculate cumulative returns
data['Cumulative_Market'] = (1 + data['Returns']).cumprod()
data['Cumulative_Strategy'] = (1 + data['Strategy']).cumprod()

# Calculate performance metrics
sharpe_ratio = np.sqrt(252) * data['Strategy'].mean() / data['Strategy'].std()
max_drawdown = (data['Cumulative_Strategy'].cummax() - data['Cumulative_Strategy']).max()

3. Popular Backtesting Frameworks

Explore these Python backtesting libraries:

📚 Learning Tip: Compare different frameworks by:

  1. Reading their documentation
  2. Trying their example code
  3. Checking their GitHub stars and issues
  4. Testing with your own simple strategy

💡 ChatGPT Learning Tips

Use these prompts to enhance your understanding of algorithmic trading:

  1. "Explain the components of a basic algorithmic trading system"
  2. "Show me how to implement a simple moving average crossover strategy"
  3. "What are the key performance metrics for evaluating trading strategies?"
  4. "Compare different Python backtesting frameworks: Backtesting.py vs Backtrader vs Zipline"
  5. "What are common pitfalls in backtesting and how to avoid them?"

📚 Self-Learning Resources:

  • Use Perplexity.ai to search for:
    • "Best Python backtesting frameworks comparison"
    • "Algorithmic trading strategy implementation Python"
    • "Common backtesting mistakes to avoid"
  • Search on GitHub for:
    • "algorithmic trading python"
    • "trading strategy backtest"
    • "quantitative finance python"
  • Follow these YouTube channels:
    • Part Time Larry
    • QuantPy
    • Algovibes

Weekly Project: Trading Strategy Development

Create a complete algorithmic trading system that includes:

  1. Data collection and preprocessing
  2. Strategy implementation (e.g., moving average crossover)
  3. Backtesting framework
  4. Performance analysis and visualization
  5. Optional: Risk management rules

Use the provided code samples and video tutorial as references for your implementation.

Additional Resources

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