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Finance & Data Science / Algorithmic Trading
Global Enrollment Open
Mastering Quantitative Trading: Python, AI & Algorithmic Strategies
Launch Your Career in High-Frequency Finance
In the rapidly evolving financial landscape, quantitative trading has emerged as the most sophisticated approach to global markets. Combining mathematics, computer science, and financial theory, algorithmic trading now accounts for over 70% of global equity volumes.
Edu Global Institute’s Quantitative Trading Course is a rigorous, comprehensive program designed to take you from foundational mathematics to deploying advanced, AI-driven algorithmic strategies. Whether you are in India, the USA, the UK, or the UAE, this course equips you with the exact tools hedge funds and proprietary trading firms demand.
Why Choose Edu Global Institute?
- Learn from the Best: Taught by industry experts with deep experience in market microstructure and algorithmic execution.
- Tech-Driven Curriculum: Heavy focus on Python, Machine Learning, and Artificial Intelligence (AI) in trading.
- Global Certification: Earn a globally recognized Algorithmic Trading Certification.
- Dedicated Career Placement: Resume optimization, mock interviews, and direct placement assistance with top trading firms.
The Comprehensive Syllabus
Module 1: The Absolute Foundations (Mathematics & Statistics)
- Probability Theory & Statistical Inference
- Linear Algebra & Principal Component Analysis (PCA) for factor modeling
- Calculus and Convex Optimization for portfolio management
Module 2: Programming & Data Architecture (Python Mastery)
- High-performance data manipulation with
NumPyandPandas - Managing tick-level data and working with time-series databases
- Hands-on Project: Building a high-speed financial data ingestion pipeline
Module 3: Financial Markets & Microstructure
- Understanding Equities, Fixed Income, Futures, and Derivatives (Greeks)
- Deep dive into Market Microstructure: The Limit Order Book (LOB) and order types
- Liquidity, bid-ask spreads, and maker-taker models
Module 4: Time Series Analysis for Trading
- Stationarity testing (ADF, Hurst Exponent) and fractional differencing
- Linear models (ARIMA) and Volatility modeling (GARCH)
- Cointegration and the mathematics of Statistical Arbitrage (Pairs Trading)
Module 5: Algorithmic Strategy Design & Backtesting
- Strategy Types: Mean Reversion, Trend Following, and Market Making
- The Science of Backtesting: Avoiding look-ahead bias and overfitting
- Hands-on Project: Developing and backtesting a Pairs Trading strategy from scratch
Module 6: Portfolio Management & Risk
- Position Sizing: The Kelly Criterion
- Modern Portfolio Theory and the Efficient Frontier
- Advanced Risk Management: Value at Risk (VaR) and Conditional VaR
Module 7: Advanced Machine Learning & AI in Finance
- Why standard ML fails in finance (and how to fix it with Purged Cross-Validation)
- Feature Engineering: Dollar bars, Meta-labeling
- Implementing Gradient Boosting (XGBoost) and Deep Learning for signal generation
Apply for Admission
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