Unlock the power of Artificial Intelligence in the world of trading.

In this hands-on course, you’ll learn how to build, train, and backtest AI-driven algorithmic trading strategies using Python, machine learning, and deep learning tools. Whether you're from finance or tech, this course will help you turn market data into actionable trading signals using LSTM models, sentiment analysis, and advanced evaluation metrics.

You’ll begin with the basics of algorithmic trading, explore the role of AI, and dive deep into tools like Random Forest, Gradient Boosting, CNNs, LSTM, Reinforcement Learning, Genetic Algorithms, and Ensemble Methods. From there, you’ll move into real-world implementation — loading historical stock data, creating predictive features, labeling outcomes, handling class imbalance with focal loss, and evaluating your trading strategy through backtesting and risk metrics like Sharpe Ratio and Drawdown.

This course includes:

• Real Apple stock data for hands-on practice

• Feature engineering using technical indicators

• Custom loss functions like Focal Loss

• Building an LSTM model from scratch

• Visualizing trading signals and performance

• Backtesting with capital growth simulations

Real Apple stock data for hands-on practice

Feature engineering using technical indicators

Custom loss functions like Focal Loss

Building an LSTM model from scratch

Visualizing trading signals and performance

Backtesting with capital growth simulations

By the end, you’ll walk away with a fully functional trading strategy powered by AI — plus the knowledge to apply these techniques across any stock, ETF, or crypto asset.

What You'll Learn

• Understand how AI is transforming algorithmic trading

• Create predictive trading features from stock data

• Train LSTM models to predict buy, sell, or hold signals

• Handle imbalanced financial data using oversampling and focal loss

• Evaluate trading performance using accuracy, precision, recall, and confusion matrix

• Visualize predicted trading signals on real stock charts

• Backtest trading strategies using portfolio simulation

• Calculate Sharpe Ratio, Drawdown, and Returns for risk analysis

Understand how AI is transforming algorithmic trading

Create predictive trading features from stock data

Train LSTM models to predict buy, sell, or hold signals

Handle imbalanced financial data using oversampling and focal loss

Evaluate trading performance using accuracy, precision, recall, and confusion matrix

Visualize predicted trading signals on real stock charts

Backtest trading strategies using portfolio simulation

Calculate Sharpe Ratio, Drawdown, and Returns for risk analysis