🐢 Turtle Trading – Classic Breakout and Trend-Following Method (with Python)

 “Let the trend be your friend.”

That’s the philosophy behind Turtle Trading – one of the most iconic and successful trend-following strategies in trading history.

In this blog, we’ll explore what Turtle Trading is, how it works, and implement a simple version in Python using historical stock price data. 📈🐍

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📖 The Story of Turtle Trading

In the 1980s, two famous traders – Richard Dennis and William Eckhardt – had a debate:

• Dennis believed anyone could be taught to trade.

• Eckhardt thought good traders were born, not made.

To settle this, Dennis trained a group of people (his “turtles”) in just two weeks, gave them real money, and let them trade.
The result? The turtles made millions using a simple, rule-based system!

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🔍 Turtle Trading Strategy: The Basics

The strategy focuses on breakouts – when price moves outside its recent high/low range.

📌 Entry Rules:

1. Buy when price breaks above the 20-day high.

2. Sell when price breaks below the 20-day low.

🛑 Exit Rules:

1. Exit buy if price drops below the 10-day low.

2. Exit sell if price rises above the 10-day high.

✅ Risk Management:

• Risk a small percentage of capital per trade (e.g. 1%).

• Use a volatility-based stop-loss called ATR (Average True Range).

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🧪 Let’s Code It in Python!

We’ll use:

• yfinance to download stock data

• pandas for calculations

• matplotlib for visualization

🔧 Step 1: Install Required Libraries

pip install yfinance matplotlib pandas

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💻 Step 2: Python Code

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

# Download stock data
stock = "AAPL"
data = yf.download(stock, start="2022-01-01", end="2024-01-01")
data['High20'] = data['High'].rolling(window=20).max()
data['Low20'] = data['Low'].rolling(window=20).min()
data['Low10'] = data['Low'].rolling(window=10).min()
data['High10'] = data['High'].rolling(window=10).max()

# Entry & Exit signals
data['Position'] = 0
data['Position'][20:] = [1 if data['Close'].iloc[i] > data['High20'].iloc[i-1] 
                         else -1 if data['Close'].iloc[i] < data['Low20'].iloc[i-1] 
                         else 0 
                         for i in range(20, len(data))]

# Carry forward positions
data['Position'] = data['Position'].replace(to_replace=0, method='ffill')

# Plotting
plt.figure(figsize=(14, 6))
plt.plot(data['Close'], label='Close Price', alpha=0.7)
plt.plot(data['High20'], label='20-Day High', linestyle='--')
plt.plot(data['Low20'], label='20-Day Low', linestyle='--')
plt.title(f"{stock} Turtle Trading Strategy")
plt.legend()
plt.show()

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📊 Sample Output

This code will download Apple stock (AAPL), calculate breakout levels, and plot them.
You’ll visually see when the price crosses breakout points—where trades could be entered or exited.

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⚠️ Things to Keep in Mind

• Turtle Trading works best in trending markets – not in sideways ones.

• Use proper position sizing and stop losses.

• Backtest the strategy with historical data before using it live.

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💡 What You Learned

• History of Turtle Trading

• Breakout-based trend-following logic

• How to implement a basic system in Python

• Visualization of entry/exit zones

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🐍 Ready to Explore More?

Try enhancing the strategy:

• Add ATR-based position sizing

• Apply to other assets (like crypto or ETFs)

• Add a trailing stop-loss or pyramiding rules (as the real turtles did)

“A good system is one that is robust, simple, and repeatable. That’s exactly what Turtle Trading is.”

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