Algorithmic Trading and Financial Data Analysis with Python
| Preference | Dates | Timing | Location | Registration Fees |
|---|---|---|---|---|
| Instructor-Led Live Online (Zoom) + LMS Access | February 21, 22, 28 March 1, 7, 8, 2026 | Saturdays & Sundays: 6:00 PM - 8:30 PM (Dubai Time, GMT+4) | Live Online (Innosoft Gulf - Dubai Knowledge Park) | USD 1195 |
Course Description
Immerse yourself in the world of financial data analysis and algorithmic trading with our interactive, hands-on Python course. It’s the ideal way to equip yourself with the tools and techniques to build data-driven strategies—from analysis and backtesting to automation. Whether you’re a beginner or experienced, you’ll learn through hands-on labs and real datasets, with guidance on building and evaluating strategies step by step.
This course is a comprehensive journey through the landscape of modern finance, seen through the lens of Python – a versatile and powerful language used by professionals worldwide. You will learn to scrape historical crypto price data, backtest your strategies, deploy your trading systems, and analyze their performance, all using Python.
Note: Education only — not financial advice.
Instructor
Ahmed El Koutbia — Founder & Lead Instructor at Innosoft Gulf; a seasoned data scientist and algorithmic trading developer specializing in trading workflows and risk management for financial markets. He began his career at the Chicago Board Options Exchange (CBOE), contributing to screen-based trading systems for futures and options. He has built and deployed an in-house automated crypto futures trading system and has designed and delivered advanced training programs in AI, big data, blockchain, and algorithmic trading.
Module 1: Environment Setup
- Introduction to algorithmic trading platforms and workflows
- Setting up your development environment
- Overview of core languages, libraries, and tools
Module 2: Data Scraping and Collection
- Understanding financial data sources
- Techniques for collecting/scraping market data
- Storing and managing data effectively
Module 3: Data Analysis
- Exploratory data analysis (EDA) for trading
- Statistical analysis and pattern discovery
- Intro to ML techniques used in trading
Module 4: Broker / Exchange API Connection
- Understanding broker/exchange APIs
- Integration for real-time data
- Order execution through APIs (paper/live workflow overview)
Module 5: Strategy Definition
- Formulating a trading hypothesis
- Strategy development frameworks
- Risk management and parameter optimization
Module 6: Strategy Backtesting
- Backtesting fundamentals
- Building a backtesting engine/workflow
- Interpreting results and diagnosing failure modes
- Realistic backtests: fees & slippage, avoiding look-ahead bias, simple walk-forward / out-of-sample checks
Module 7: Cloud Environment Setup
- Choosing the right cloud platform for trading systems
- Cloud environment configuration
- Security basics (API key handling, access control, data protection)
Module 8: Trading System Deployment
- Deployment approaches for trading systems
- Monitoring, logging, and maintenance
- Scaling and reliability basics
Module 9: Machine Learning for Financial Markets
- Introduction to ML in finance
- Key models for time-series forecasting
- Neural networks and deep learning for trading strategies
- Building and training deep learning models
- Implementing predictive models for financial data
Module 10: Order Book Analysis
- Understanding the order book and its components
- Measuring imbalances for trading insights
- Streaming and real-time processing of order book data
- Building indicators from order book features
- Advanced strategies for order book analysis and prediction
- Aspiring data analysts/data scientists who want to apply Python to financial market data
- Finance professionals looking to strengthen data analysis and understand algo trading workflows
- Software engineers/developers interested in finance and quantitative systems
- Traders/investors who want to build and test systematic strategies with Python
- Python basics: variables, loops, functions (pandas/NumPy helpful)
- Math basics: statistics & probability (practical level)
- Finance basics: familiarity with markets/trading terminology is helpful
- Setup: a laptop with Python + Jupyter (or PyCharm) installed
- Mindset: comfortable experimenting, debugging, and iterating on ideas
New to Python or finance? No problem — we’ll share pre-course resources to help you get up to speed.
- Build a clean Python workflow for financial data (collection, cleaning, feature engineering, and visualization)
- Scrape and work with historical crypto market data for research and strategy development
- Design and test systematic trading strategies using a structured backtesting process
- Run more realistic evaluations by accounting for common pitfalls (e.g., basic data leakage checks, fees/slippage assumptions)
- Connect to broker/exchange APIs to stream data and place orders safely (including paper/live workflow overview)
- Deploy a trading system in a cloud environment with basic security, logging, and monitoring practices
- Apply machine learning techniques for market prediction/analysis (at an applied, practical level)
- Analyze order book data to create indicators and explore advanced microstructure-based insights
Note: Education only — not financial advice.
- Course Completion Certificate issued by Innosoft Gulf upon successful completion.
- Optional KHDA attestation (Dubai) may be available upon request, subject to KHDA requirements and applicable fees.
Reviews
Ali Jaffari — Jun 14, 2023
“I recently completed the Python for Financial Data Analysis and Algorithmic Trading course and found it highly insightful. Ahmed is an excellent instructor—knowledgeable, engaging, and always willing to answer questions. The practical examples made the content easy to apply. Highly recommended.”
Lakshay Ramchandani — Feb 17, 2023
“The Algorithmic Trading and AI & Big Data specialization with Ahmed was one of the best courses I’ve taken. He explains the process step by step, starting from the basics and building a strong foundation. I highly recommend it.”
Iman Alibeigi — Aug 1, 2024
“Algorithmic Trading & Financial Data Analysis with Python was a very informative course. Thank you to Ahmed El Koutbia for a great learning experience and for sharing such valuable skills.”
Eline Hendrikse — Feb 2025
“I took the Algorithmic Trading with Python course with Ahmed in February 2025. We learned how to analyze trading data and build an automated trading system. I highly recommend it to anyone interested in trading, Python, and systematic strategies.”