January 21, 2025
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Python Code For False Breakout Strategy

The.Data.Scientist04 mins 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import yfinance as yf

# Function to identify support and resistance levels
def identify_levels(data, window=14):
    data['High_Rolling'] = data['High'].rolling(window=window).max()
    data['Low_Rolling'] = data['Low'].rolling(window=window).min()
    return data

# Function to identify breakouts
def identify_breakouts(data):
    data['Breakout_High'] = np.where(data['High'] > data['High_Rolling'].shift(1), 1, 0)
    data['Breakout_Low'] = np.where(data['Low'] < data['Low_Rolling'].shift(1), 1, 0)
    return data

# Function to identify false breakouts
def identify_false_breakouts(data, reversal_period=3):
    data['False_Breakout_High'] = np.where(
        (data['Breakout_High'] == 1) &
        (data['Close'] < data['High_Rolling'].shift(1)) &
        (data['Close'] > data['Close'].shift(1).rolling(window=reversal_period).min()),
        1, 0
    )

    data['False_Breakout_Low'] = np.where(
        (data['Breakout_Low'] == 1) &
        (data['Close'] > data['Low_Rolling'].shift(1)) &
        (data['Close'] < data['Close'].shift(1).rolling(window=reversal_period).max()),
        1, 0
    )
    return data

# Function to backtest the false breakout strategy
def backtest_strategy(data):
    initial_capital = 10000
    position = 0
    capital = initial_capital

    for i in range(len(data)):
        if data['False_Breakout_High'].iloc[i] == 1:
            position = capital / data['Close'].iloc[i]
            capital = 0
        elif data['False_Breakout_Low'].iloc[i] == 1 and position > 0:
            capital = position * data['Close'].iloc[i]
            position = 0
     final_capital = capital + (position * data['Close'].iloc[-1] if position > 0 else 0)
    return final_capital
# Load historical data
stock_data = yf.download('AAPL', start='2020-01-01', end='2023-01-01')
stock_data = identify_levels(stock_data)
stock_data = identify_breakouts(stock_data)
stock_data = identify_false_breakouts(stock_data)

# Backtest the strategy
final_capital = backtest_strategy(stock_data)
print(f"Final capital after backtesting: ${final_capital:.2f}")

# Plot the results
plt.figure(figsize=(14, 7))
plt.plot(stock_data['Close'], label='Close Price')
plt.scatter(stock_data.loc[stock_data['False_Breakout_High'] == 1].index, 
            stock_data.loc[stock_data['False_Breakout_High'] == 1].Close, color='red', 
            label='False Breakout High', marker='^', alpha=1)
plt.scatter(stock_data.loc[stock_data['False_Breakout_Low'] == 1].index, 
            stock_data.loc[stock_data['False_Breakout_Low'] == 1].Close, color='blue', 
            label='False Breakout Low', marker='v', alpha=1)
plt.title('False Breakout Strategy')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.show()

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