This module provides hands-on training in using pandas for financial data analysis, covering data structures, transformations, and practical applications like volatility measurement and data resampling. Learners will gain skills in manipulating financial datasets and applying statistical techniques to derive insights.

This module covers the fundamentals of visualizing financial market data using Python libraries such as Matplotlib, Seaborn, and Plotly Dash. Learners will gain hands-on experience in creating dynamic visualizations, including yield curve animations, volatility surfaces, and interactive dashboards for trading analytics.

This module covers techniques for storing financial market data on local systems using formats like CSV, SQLite, and HDF5. Learners will gain practical skills in data storage strategies, file handling, and working with command-line tools to execute scripts efficiently.

This module covers the process of identifying, building, and analyzing alpha factors for stock portfolios, including techniques like linear regression, PCA, and the Fama-French model. Learners will gain hands-on experience with tools like Zipline Pipelines and understand how to assess market inefficiencies through volatility and factor sensitivity.

This module focuses on advanced backtesting techniques using VectorBT, including strategy evaluation, walk-forward optimization, and SuperTrend strategy refinement. Learners will gain practical skills in optimizing trading strategies for real-world market conditions and improving analytical efficiency.

This module introduces event-based backtesting using Zipline Reloaded, focusing on implementing and analyzing momentum and mean reversion strategies. Learners will gain hands-on experience with the Pipeline API for portfolio construction and explore the advantages of event-driven frameworks over traditional methods.

This module explores how to evaluate factor risk and performance using Alphalens Reloaded, focusing on key metrics such as the Information Coefficient, factor returns, and turnover. Learners will gain a deeper understanding of quantitative portfolio construction and how to make data-driven investment decisions. Practical insights into factor-based strategies and their real-world implications are also covered.

This module covers how to assess backtest risk and performance metrics using Pyfolio Reloaded. Learners will gain practical skills in analyzing strategy performance, evaluating risk factors, and interpreting trade-level data to make more informed trading decisions.

This module guides learners through integrating the Interactive Brokers Python API to build algorithmic trading systems. It covers creating and managing Contract and Order objects, as well as streaming and storing market data. Learners will gain hands-on experience in handling real-time and historical data using the IB API.

This module provides hands-on guidance on managing orders, positions, and portfolios using the IB API. Learners will gain skills in real-time PnL analysis, order execution, and portfolio risk assessment through Python-based implementation.

This module focuses on implementing real-time risk metrics, designing sophisticated order strategies, and deploying algorithmic trading strategies into live or paper trading environments. Learners will gain hands-on experience with tools like Empyrical Reloaded and learn how to manage portfolio adjustments and options-based strategies.

This module teaches learners how to handle real-time market data using ThetaData, store and manage large-scale tick data with ArcticDB, and implement automated risk alerts and end-of-day reporting systems. It provides practical skills for building efficient data pipelines and notification systems in algorithmic trading environments.