Algorithmic Trading A-z With Python- Machine Le... -

import pandas as pd import yfinance as yf import numpy as np data = yf.download('AAPL', start='2019-01-01', end='2024-01-01') Calculate essential features data['Returns'] = data['Close'].pct_change() data['Log_Returns'] = np.log(1 + data['Returns']) data['Volatility'] = data['Returns'].rolling(20).std() * np.sqrt(252) Feature Engineering (The secret sauce) data['SMA_20'] = data['Close'].rolling(20).mean() data['BB_upper'] = data['SMA_20'] + (data['Close'].rolling(20).std() * 2) data['BB_lower'] = data['SMA_20'] - (data['Close'].rolling(20).std() * 2)

# Mark to market current_equity = capital + (position * current_price) equity_curve.append(current_equity) import matplotlib.pyplot as plt plt.plot(equity_curve) plt.title("ML Strategy Equity Curve") plt.show()

if prob > 0.6 and position == 0: # Buy position = capital / current_price capital = 0 elif prob < 0.4 and position > 0: # Sell capital = position * current_price position = 0 Algorithmic Trading A-Z with Python- Machine Le...

In the modern financial landscape, the days of screaming pit traders and hand-signed order slips are fading. Today, markets are dominated by silent, powerful computers executing millions of orders per second. This is the world of Algorithmic Trading .

A 51% accuracy is phenomenal in finance. If you see 99% accuracy, you have look-ahead bias (leaked future data into your training set). Part F: Backtesting the ML Strategy Accuracy doesn't pay bills. Profit does. You need to simulate trading based on the model's confidence. import pandas as pd import yfinance as yf

print(data[['Close', 'Volatility', 'BB_upper']].tail())

y_pred = model.predict(X_test) print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}") print(classification_report(y_test, y_pred)) A 51% accuracy is phenomenal in finance

trading_client = TradingClient(API_KEY, SECRET_KEY)