Post Graduate Program in Algorithmic Trading - Indian Institute of Quantitative Finance
The PGPAT course or Post graduate program in Algorithmic trading online conducted by the IIQF®️ is taught by highly qualified and experienced market practitioners and is a job-oriented Masters in Algorithm Trading online course that aims to produce industry-ready Algo-Traders, who can join trading desks of various financial institutions or setup their own independent algorithmic prop trading desks. We do offer Certificate Program in Algorithmic Trading (CPAT) online with world class faculties.
The Financial Markets the world over have seen a major paradigm shift in how trading is done. Algorithmic Trading (abv. Algo Trading) also known as Program Trading or Automated Trading, essentially implies that the trading is done by computer programs. Currently a vast majority of the trades in some of the markets are algorithmic in nature.
These algorithms depend on quantitative finance techniques for formulating trading strategies, detection of profitable trade opportunities, generating trade signals, generating the trades and trade order execution. At each stage there is extensive use of technologies.
Algorithm Trading, both High-Frequency as well as Low Frequency, using Quantitative Methods is now a very lucrative career. A breed of traders known as the Algo-Traders or Quant-Traders has emerged who have certain skill-sets that are much sought after in the industry.
PGPAT Program - Algorithmic Trading Course Highlights
- Highly qualified industry practitioner faculty
- Advanced Curriculum
- Thoroughly hands-on training in programming algorithmic trading strategies in Python
- Training on industry leading algorithmic trading platforms
Course duration - 8 months.
Course Structure
Part 1
Module 301 – Introduction to Algorithmic and Quantitative Trading
- What is “Algorithmic” Trading?
- Market Structures
- Evolution: Algorithmic Trading trends and their impact on the markets
- Types of Algorithmic Trading Strategies
- Lifecycle of Algorithmic Trading
- Market Microstructure and Concepts
- Latency
Module 302 – Technical Trading Strategies
- Overview of indicators in Technical Analysis
- Trend following Strategies
- Momentum based Strategies
Module 303 – Strategy Development and Back-testing
- Ideation and Strategy Creation
- Architecture of a back-testing System
- Common Pitfalls (Look-ahead bias, survivorship bias etc.)
- Implementing a back-tester
- Performance Measurement Statistics
- Parameter Optimization
Module 304 – Money Management and Risk Management
- Optimal Capital Allocation
- Risk Management
Module 305 – Algorithm Trading Infrastructure Setup
- Algorithm Trading Mechanics
- Architectural design
- Basic platform design and architectural setup
- Operational considerations and pitfalls
Module 306 – Algorithmic System Design and Implementation
- Implementing Strategies
- Order Management
- Risk Management
- Error Handling
- API Integration
Part 2
Module 307 – Options Trading Strategies
- Options Pricing
- Options Greeks
- Options Trading Strategies
- Market Neutral Strategies
- Bullish Strategies
- Bearish Strategies
- Arbitrage Strategies
Module 308 – Machine Learning for Quantitative Trading Using Python
- Introduction to Machine Learning
- Regression Models
- Simple Linear Regression
- Multiple Linear Regression
- Logistic Regression
- Decision Tree Regression
- Random Forest Regression
- Classification Models
- Decision Tree Classification
- Random Forest Classification
Module 309 – Optimization Methods
- Analytical vs Numerical Optimization
- Cost Functions for Regression
- Cost Functions for Classification
- Gradient Descent
- Stochastic Gradient Descent
- Adam Gradient Descent
Module 310 – Time Series Analysis Using Python
- Auto Regressive Models (AR)
- Moving Average Models (MA)
- MA as basic model for stock data predictions
- Auto Regressive Moving Average Models (ARMA)
- Auto Regressive Integrated Moving Average Models (ARIMA)
- Exponentially Weighted Moving Average Models (EWMA)
- Generalized Auto Regressive Conditional Heteroskedasticity Models (GARCH)
- Stock data examples
Module 311 – Deep Learning for Quantitative Trading Using Python
- Introduction to Deep Learning – Artificial Neural Networks (ANN)
- Traditional Machine Learning Vs Deep Learning
- Universal Approximation Theorem
- Perceptron
- Activation Functions
- Cost Functions
- Back Propagation
- Feed Forward Neural Network (FFN)
- Recurrent Neural Network (RNN)
- Long Short Term Memory (LSTM) Network
Module 312 – Quantitative Trading Strategies
- Introduction to Quantitative Trading
- Quantitative Directional Strategies
- Statistical Arbitrage Strategies
- Arbitrage Strategies
- Gamma Scalping
- Volatility Trading
- Electronic Market Making Strategies
Module 313 – Algorithmic Execution Strategies
- Execution Algorithms
- Percentage of Volume (POV)
- Volume Weighted Average Price (VWAP)
- Time Weighted Average Price (TWAP)
Get Post Graduate Program in Algorithmic Trading or the other courses from the same one of these categories: Indian Institute of Quantitative Finance, mean reversion, Algorithmic, Strategies, Arbitrage, Trading, Finance, Returns, Course, Stocks, Trend, Quant, Risk for free on Download Courses.
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