This course provides a practical introduction to designing, testing, and deploying algorithmic trading strategies using Python. Learners explore data ingestion, technical indicators, time-series analysis, backtesting engines, and API-based execution. The course demonstrates how systematic trading models are built, evaluated, and optimized in real-world environments. Designed for traders, analysts, and technically minded learners, it equips students with the tools to develop automated trading systems confidently and professionally.
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Skills & learning objectives
There are 6 modules in this class
- Basic ConceptsUnlock this class to watch7m 28s
- Calculate Moving AverageUnlock this class to watch3m 42s
- What defines a trend in a time series?Unlock this class to watch7m 36s
- What is an API?Unlock this class to watch3m 17s
- Making API Requests in PythonUnlock this class to watch6m 29s
- Using API with QueryUnlock this class to watch3m 39s
- What is IBAPI?Unlock this class to watch22m 12s
- Import necessary libraries.Unlock this class to watch6m 10s
- What is Backtesting.py?Unlock this class to watch3m 46s
- Step-by-Step Practical ApplicationUnlock this class to watch12m 54s
- The Python back-testing frameworkUnlock this class to watch12m 19s
- IntroductionUnlock this class to watch9m 4s
- Step-by-Step InstructionsUnlock this class to watch17m 7s
- ClosingUnlock this class to watch1m 21s
This class is part of the following certifications
ClassFAQs
The course aims to teach learners how to design, implement, and validate algorithmic trading strategies using Python, including data analysis, machine learning, technical indicators, and API integration.
The course is designed for finance professionals, quantitative analysts, and learners with basic programming knowledge who want to build and optimize algorithmic trading systems.
The module covers retrieving real-time and historical market data, handling rate limits, processing JSON/XML responses, and using APIs to support algorithmic trading workflows.
The course explains how to retrieve data, generate trading signals, implement moving-average strategies, and back-test performance using Python functions and libraries.
Learners will develop skills in feature engineering, model training, performance evaluation, and using predictive analytics to improve the effectiveness of trading algorithms.
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