PGDip in Quantitative Trading, Algorithmic Strategies & AI

About Course

Duration: 12 Months
Fees: N350,000
Format: Hybrid

The Postgraduate Diploma (PGDip) in Quantitative Trading, Algorithmic
Strategies & AI is designed to equip professionals with the advanced
mathematical, programming, and machine learning skills necessary to develop
and deploy cutting-edge trading systems in modern financial markets.
This industry-focused PGDip is an intensive program blending the foundations
of quantitative finance with advanced computational techniques
(Algorithmic Trading) and Artificial Intelligence (AI). It caters to individuals
seeking careers as Quantitative Analysts, Algorithmic Traders, Financial Data
Scientists, or Portfolio Managers in hedge funds, investment banks, asset
management firms, and proprietary trading desks. The curriculum emphasizes
a hands-on, project-based approach, ensuring graduates can immediately
apply complex concepts from high-frequency trading microstructure to deep
reinforcement learning in a live trading environment.

What Will You Learn?

Quantitative finance
Python & C++ for trading
High-frequency trading systems
AI trading strategies
Market microstructure
Risk metrics (VaR, CVaR)
Portfolio optimization
ML-based signal generation
Backtesting frameworks
Algorithm deployment systems

Course Curriculum

Computational Finance & Market Foundations
Python for Quants (NumPy, Pandas, SciPy); Financial Time Series Analysis (ARIMA, GARCH); Introduction to Financial Markets (Equities, Futures, Options); Market Microstructure & OrderBooks; Low-Latency Programming (C++ basics).

: Algorithmic Strategy Design & Backtesting
Classical Strategies (Mean-Reversion, Trend-Following, Statistical Arbitrage/Pairs Trading); Strategy Development Lifecycle; Robust Backtesting Frameworks (Vectorized vs. Event-Driven); Transaction Costs & Slippage Modeling; Portfolio Construction (Markowitz, Black-Litterman).

Machine Learning & AI for Alpha
Supervised/Unsupervised Learning in Finance (Classification/Clustering of Market Regimes); Deep Learning(CNNs, LSTMs for time series); Reinforcement Learning (RL) for optimal trade execution; Generative AI for news sentiment analysis and strategy idea generation.

Trading System Implementation & Ris
Trading Infrastructure (FIX Protocol, DMA); Cloud Computing for Scalability; Regulatory Compliance (Reg NMS, MiFID II); Advanced Risk Metrics (Value-at-Risk, Conditional VaR); Model Risk Management (Overfitting, Data Snooping).

Your Instructors

Ratings & Reviews

No Review Yet

Hello There!

Information

Follow Us

PGDip in Quantitative Trading, Algorithmic Strategies & AI

About Course

What Will You Learn?

Course Curriculum

Computational Finance & Market Foundations
Python for Quants (NumPy, Pandas, SciPy); Financial Time Series Analysis (ARIMA, GARCH); Introduction to Financial Markets (Equities, Futures, Options); Market Microstructure & OrderBooks; Low-Latency Programming (C++ basics).

Machine Learning & AI for Alpha
Supervised/Unsupervised Learning in Finance (Classification/Clustering of Market Regimes); Deep Learning(CNNs, LSTMs for time series); Reinforcement Learning (RL) for optimal trade execution; Generative AI for news sentiment analysis and strategy idea generation.

Trading System Implementation & Ris
Trading Infrastructure (FIX Protocol, DMA); Cloud Computing for Scalability; Regulatory Compliance (Reg NMS, MiFID II); Advanced Risk Metrics (Value-at-Risk, Conditional VaR); Model Risk Management (Overfitting, Data Snooping).

Your Instructors

Hello There!

Information

Follow Us

About Course

What Will You Learn?

Course Curriculum

Computational Finance & Market Foundations Python for Quants (NumPy, Pandas, SciPy); Financial Time Series Analysis (ARIMA, GARCH); Introduction to Financial Markets (Equities, Futures, Options); Market Microstructure & OrderBooks; Low-Latency Programming (C++ basics).

Machine Learning & AI for Alpha Supervised/Unsupervised Learning in Finance (Classification/Clustering of Market Regimes); Deep Learning(CNNs, LSTMs for time series); Reinforcement Learning (RL) for optimal trade execution; Generative AI for news sentiment analysis and strategy idea generation.

Trading System Implementation & Ris Trading Infrastructure (FIX Protocol, DMA); Cloud Computing for Scalability; Regulatory Compliance (Reg NMS, MiFID II); Advanced Risk Metrics (Value-at-Risk, Conditional VaR); Model Risk Management (Overfitting, Data Snooping).

Your Instructors

Ratings & Reviews

Computational Finance & Market Foundations
Python for Quants (NumPy, Pandas, SciPy); Financial Time Series Analysis (ARIMA, GARCH); Introduction to Financial Markets (Equities, Futures, Options); Market Microstructure & OrderBooks; Low-Latency Programming (C++ basics).

Machine Learning & AI for Alpha
Supervised/Unsupervised Learning in Finance (Classification/Clustering of Market Regimes); Deep Learning(CNNs, LSTMs for time series); Reinforcement Learning (RL) for optimal trade execution; Generative AI for news sentiment analysis and strategy idea generation.

Trading System Implementation & Ris
Trading Infrastructure (FIX Protocol, DMA); Cloud Computing for Scalability; Regulatory Compliance (Reg NMS, MiFID II); Advanced Risk Metrics (Value-at-Risk, Conditional VaR); Model Risk Management (Overfitting, Data Snooping).