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

data['ML_Signal'] = 0 data.loc[X_test.index, 'ML_Signal'] = y_pred # Only trade on predictions

Without clean data, your algorithm is garbage-in, garbage-out.

Using ta library:

import ta
data['rsi'] = ta.momentum.RSIIndicator(data['Close'], window=14).rsi()
data['macd'] = ta.trend.MACD(data['Close']).macd()
data['bb_high'] = ta.volatility.BollingerBands(data['Close']).bollinger_hband()
data['bb_low'] = ta.volatility.BollingerBands(data['Close']).bollinger_lband()
data['volume_ratio'] = data['Volume'] / data['Volume'].rolling(20).mean()

Add target variable (future return):

data['target'] = data['Close'].shift(-1) / data['Close'] - 1  # next day return

data = yf.download('AAPL', start='2020-01-01', end='2024-01-01') data['Returns'] = data['Close'].pct_change() print(data.head())

Alternatives: Alpha Vantage (free tier), Polygon.io (professional), Binance API (crypto).

Instead of predicting price, teach an agent to maximize equity curve. Using Stable-Baselines3:

import gym
from stable_baselines3 import PPO
class TradingEnv(gym.Env):
# Define state (portfolio, prices), actions (buy/sell/hold), rewards (PnL)
pass
env = TradingEnv(data)
model = PPO('MlpPolicy', env, verbose=1)
model.learn(total_timesteps=10000)

Moving from backtest to live trading requires an execution engine that connects to a broker via API (e.g., Alpaca, Interactive Brokers, Binance). Key components: