import pandas as pd import numpy as np import matplotlib.pyplot as plt # Generate some mock price data np.random.seed(42) dates = pd.date_range(start='2023-01-01', periods=100, freq='D') close = pd.Series(100 + np.cumsum(np.random.randn(100)), index=dates) # Create empty DataFrames for signals short_entries = pd.DataFrame(False, index=close.index, columns=['Short Entry']) short_exits = pd.DataFrame(False, index=close.index, columns=['Short Exit']) long_entries = pd.DataFrame(False, index=close.index, columns=['Long Entry']) long_exits = pd.DataFrame(False, index=close.index, columns=['Long Exit']) # Short on the first day of each month short_entry_mask = ~close.index.to_period("M").duplicated() short_entries.loc[short_entry_mask, 'Short Entry'] = True # Short exits 5 days after short entry short_exit_mask = short_entries.shift(5, fill_value=False) short_exits.loc[short_exit_mask.index, 'Short Exit'] = short_exit_mask.squeeze() # Long entries 7 days before short entry long_entry_mask = short_entries.shift(-7, fill_value=False) long_entries.loc[long_entry_mask.index, 'Long Entry'] = long_entry_mask.squeeze() # Long exits 1 day before short entry long_exit_mask = short_entries.shift(-1, fill_value=False) long_exits.loc[long_exit_mask.index, 'Long Exit'] = long_exit_mask.squeeze() # Plot the price data with signals plt.figure(figsize=(12, 6)) plt.plot(close, label='Asset Price', color='blue', alpha=0.6) plt.scatter(close.index[short_entries['Short Entry']], close[short_entries['Short Entry']], marker='v', color='red', label='Short Entry', alpha=1) plt.scatter(close.index[short_exits['Short Exit']], close[short_exits['Short Exit']], marker='x', color='darkred', label='Short Exit', alpha=1) plt.scatter(close.index[long_entries['Long Entry']], close[long_entries['Long Entry']], marker='^', color='green', label='Long Entry', alpha=1) plt.scatter(close.index[long_exits['Long Exit']], close[long_exits['Long Exit']], marker='o', color='darkgreen', label='Long Exit', alpha=1) plt.title('Trading Signals on Asset Price') plt.xlabel('Date') plt.ylabel('Price') plt.legend() plt.grid() plt.show()

How a Hunch and Python Code Turned into a 472% Return Strategy

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