Algorithmic Trading A-z With Python- Machine Le... May 2026

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

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You cannot trade what you cannot test. Use Python to fetch data:

import yfinance as yf
import pandas as pd
import time
from alpaca.trading.client import TradingClient
API_KEY = "your_key"
SECRET_KEY = "your_secret"
trading_client = TradingClient(API_KEY, SECRET_KEY) Algorithmic Trading A-Z with Python- Machine Le...
def live_run():
while True:
# 1. Fetch latest 5-minute bars
latest_data = fetch_recent_bars()
    # 2. Engineer features (same as training)
    features = engineer_features(latest_data)
# 3. Predict direction
    prob = model.predict_proba(features)[0, 1]
# 4. Risk Management
    if prob > 0.65 and get_current_position() == 0:
        submit_order(symbol="AAPL", qty=10, side="buy")
# 5. Wait for next iteration
    time.sleep(60)  # Run every minute


account = trading_client.get_account()
order = trading_client.submit_order(
symbol='AAPL',
qty=10,
side='buy',
type='market',
time_in_force='gtc'
)

Participants in this course typically engage with the following technology stack: Using ta library: import ta data['rsi'] = ta

APIs: yfinance, IEX Cloud, Interactive Brokers API.

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).

test_data = data.iloc[split:].copy()
test_data['prediction'] = preds
test_data['signal'] = 0  # 1 = buy, -1 = sell
test_data.loc[test_data['prediction'] == 1, 'signal'] = 1
test_data.loc[test_data['prediction'] == 0, 'signal'] = -1

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