
After this lecture, students will be able to explain the concept of algorithmic trading and distinguish it from traditional manual trading.
Students will be able to describe how AI enhances trading algorithms and enables adaptive strategy development.
Students will be able to summarize the current and projected market size of algorithmic trading and identify growth factors.
Students will be able to list major global companies involved in algorithmic trading and their areas of specialization.
Students will be able to identify key financial markets using algorithmic trading, including stocks, forex, bonds, and crypto. Students will be able to outline the core components of an algorithmic trading system including data, strategy, risk, and execution.
Students will be able to describe various trading strategies such as market making, arbitrage, trend following, and pattern recognition.
Students will be able to explain the benefits of algorithmic trading, such as speed, accuracy, and reduced emotional bias. Students will be able to evaluate both positive and negative impacts of algorithmic trading on market liquidity and volatility.
Students will be able to describe key ML models like Random Forest, Gradient Boosting, SVM, and KNN in a trading context.
Students will be able to explain how LSTM and CNNs are used to analyze time-series and visual trading data.
Students will be able to summarize the concept of reinforcement learning and how it helps build adaptive trading agents.
Students will be able to describe how genetic algorithms simulate evolution to optimize trading strategies.
Students will be able to use NLP concepts to extract sentiment signals from financial text sources like news or tweets.
Students will be able to explain how combining multiple models improves prediction accuracy in algorithmic trading.
Students will understand the structure of an LSTM-based trading pipeline and the goals of the practical demo.
Students will be able to load, clean, and visualize historical stock data for modeling.
Students will be able to engineer technical indicators such as moving averages, momentum, and volatility.
Students will be able to create buy/sell/hold labels based on future price movements and scale features appropriately.
Students will be able to implement fixed random seeds to make model training results consistent and repeatable.
Students will be able to convert time-series data into sequences suitable for LSTM training.
Students will be able to identify class imbalance and apply oversampling techniques to balance their training data.
Students will be able to implement focal loss to help models learn from difficult or minority class examples.
Students will be able to build, compile, and train an LSTM model using Keras with custom loss functions.
Students will be able to assess model performance using accuracy, precision, recall, F1-score, and confusion matrix.
Students will be able to simulate trades using model predictions and evaluate portfolio performance over time.
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
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