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

Report: Algorithmic Trading A-Z with Python & Machine Learning

Subject Course: Algorithmic Trading A-Z with Python- Machine Le... Report Type: Course Overview & Curriculum Analysis Date: October 26, 2023

2. Python Environment Setup

pip install pandas numpy matplotlib scikit-learn ta yfinance backtrader

Libraries:

I. Advanced: LSTM (Long Short-Term Memory)

For price sequences, LSTMs capture long-term dependencies. Using TensorFlow/Keras:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout

def create_lstm_dataset(data, lookback=60):
X, y = [], []
for i in range(lookback, len(data)):
X.append(data[i-lookback:i])
y.append(data[i])
return np.array(X), np.array(y) Algorithmic Trading A-Z with Python- Machine Le...


Part 2: Data Acquisition & Feature Engineering (D-G)


Generate Trading Signals


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





Phase II: Trading Strategies & Technical Analysis


    

  • Momentum & Trend Following: Implementing Moving Averages (SMA/EMA), MACD, and Bollinger Bands.
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  • Mean Reversion: Strategies based on the assumption that asset prices revert to their mean (e.g., Pairs Trading, RSI).
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  • Performance Metrics: Evaluating strategies using the Sharpe Ratio, Sortino Ratio, Maximum Drawdown, and CAGR.
    • 



Level 3: Machine Learning (Advanced)


We move from static rules to predictive models.


Problem: Predict whether the price will go up (1) or down (0) in the next 5 minutes. Report: Algorithmic Trading A-Z with Python & Machine


Feature Set (X):


    

  • Last 50 returns.
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  • Order book imbalance.
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  • Time of day.
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  • VIX level.
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Label (y):

1 if Close(t+5) > Close(t), else 0. Libraries:




Part B: Setting Up Your Infrastructure (The "Zero" Day)


Do not start by building a Neural Network. Start by building a pipeline.