What are EPAT Benefits?
Expert Faculty
An acclaimed team of subject matter experts
Dedicated Support
A Support Manager for each EPATian
Career Services
Lifetime placement and career assistance
EPAT features
Project work opportunity
Scholarships and Financial Aid
Lifetime access to latest course content
Verified Certification
Exclusive EPAT Community benefits
Credit Points for continuous professional development
120+
Hours Live Lectures
20+
World Class Faculty
300+
Placement Partners
90+
Participant Countries
EPAT Faculty

Dr. Gaurav Raizada
Founder, iRage
Visiting Professor, IIM Ahmedabad

Dr. Ankur Sinha
Professor, IIM Ahmedabad & Chairperson, 2-Year MBA (PGP)

Dr. Thomas Starke
CEO, AAAQuants
Ex - VivCourt Trading, Rolls Royce

Rajib Ranjan Borah
Co-Founder & CEO, iRage
Visiting Faculty, IIM Ahmedabad, IIT Bombay
Ex - Optiver, PwC, Bloomberg

Dr. Yves J. Hilpisch
CEO, The Python Quants

Dr. Hui Liu
Founder & CEO, Creator, Running River Investment LLC & IBridgePy

Anil Yadav
Systematic Trading Strategies, iRage
Ex - Lehman Brothers

Ashutosh Dave
Team Manager- Quantitative Research, Futures First
Ex - OSTC Ltd.

Brian Christopher
Founder and Researcher, Blackarbs LLC
Ex - Thomson Reuters

Ishan Shah
Lead, Research and Content, Quantra
Ex - Barclays, Bank of America Merrill Lynch

Jay Parmar
Quantitative Researcher, iRage
Ex - OSTC Ltd

Nitesh Khandelwal
Chief Executive Officer and Director, QuantInsti
Co-Founder, iRage
Ex - ICICI Bank

Nitin Aggarwal
Co-founder, Director, Alphom Advisory
Ex - Aditya Birla Group, BCG

Prodipta Ghosh
Vice President, QuantInsti
Ex - Deutsche Bank, Standard Chartered Bank, DRDO, Cognizant

Radha Krishna Pendyala
Data Scientist, Refinitiv

Dr. Robert Kissell
President, Kissell Research Group
Ex - UBS, JP Morgan

Varun Pothula
Quantitative Analyst, QuantInsti
Ex - Futures First, Accenture

Vivek Krishnamoorthy
Head - Content & Research, QuantInsti
Ex- ICICI Bank, Oracle, Infosys
300+ PLACEMENT PARTNERS












Curriculum
Statistics & Econometrics
Quantitative Trading Techniques
Financial Computing
Python (key focus), Excel, MATLAB
Backtesting & Market Data
Equity, FX, Options, Futures, ETFs
1 EPAT Primer
- Stock market basics: Learning about financial markets and a brief understanding of how they work.
- Excel primer: Spreadsheet basics, learning to format and visualize data, using built -in functions to summarize and manipulate data, working with examples to familiarize yourself with spreadsheets.
- Python primer: Learning to work with Python in multiple ways (Spyder IDE, Jupyter Notebook), variables, data structures, functions, key libraries used
- Options primer: Learning terminology used, types of options, volatility and its types, and simple options trading strategies.
- Statistics primer: Introduction to descriptive statistics, probability theory, probability distributions, standard statistical distributions, inferential statistics, Introduction to linear regression.
- Macroeconomics primer: Introduction to key macroeconomic ideas like inflation, exchange rates, GDP, interest rates, fiscal and monetary tools.
- Machine learning for trading primer: Basic machine learning concepts, types of ML, and their application to trading, supervised and unsupervised ML algorithms with their implementation in financial markets.
- We will conduct two preparatory sessions to answer queries and resolve doubts on the statistics primer and the Python primer
2 Statistics for Financial Markets
- Key ideas in statistics and probability, and animating them with financial market data
- Creating and analyzing quant trading strategies on spreadsheets, creating charts to interpret their performance
- Learning portfolio construction and optimizing them using modern portfolio theory
- Introduction to Monte Carlo simulations and their applications on market data
- Studying the random walk model to predict future stock prices using simulations and assessing outcomes
- Understanding the capital asset pricing model
3 Python: Basics & Its Quant Ecosystem
- Data types, variables, Python in-built data structures, inbuilt functions, logical operators, and control structures
- Introduction to the main libraries in the data science stack: NumPy, pandas, and matplotlib
- Learning to write functions in Python
- Studying pandas routine used to analyze and visualize OHLCV data
- Writing and backtesting trading strategies
- Two Python tutorials will be conducted to answer queries and resolve doubts on the material covered in the lectures
4 Market Microstructure for Trading
- Overview of Electronic and Algorithmic Trading.
- Understanding market terminology, order book concepts and order types
- Introduction to execution strategies and handling market impact cost
- Understanding various algorithms such as Execution, Quant, and HFT
- Understanding different investment styles and trading algorithms
- Learning trading analytics
- Case studies
5 Equity, FX, & Futures Strategies
- Strategy building in equities - moving crossover, and VWAP
- Profit and loss analysis of a trading system
- Different types of Momentum (Time series & Cross-sectional)
- Trend following strategies and Statistical Arbitrage Trading strategy modeling with Python
- Arbitrage, market making and asset allocation strategies using ETFs
- Introduction to position sizing and risk management
6 Data Analysis & Modeling in Python
- Learning to backtest and analyze various strategies on Python using historical data
- Understanding object-oriented programming (OOP) concepts and using OOP to backtest trading strategies
- Glimpse of the basic cloud infrastructure to host automated Python strategies
- Learning vectorized and event-driven backtesting along with trade parameter optimization
- Learning the work flow of a quant strategy
- Two tutorials will be conducted to answer queries and resolve doubts related to the material covered in the related lectures
7 Machine Learning for Trading
- Classical ML algorithms: Support Vector Machines (SVM), k-means clustering, logistic regression, decision trees, random forests
- Introduction to deep learning: Neural networks, gradient descent, and backpropagation algorithms
- Using Python to build and evaluate ML models for potential trading strategies (by creating features and selecting suitable ones)
- Learning principal component analysis (PCA) and using it to create statistical arbitrage strategies in multiple co-integrated assets
- Learning about alternate data: sources, data formats, storage and retrieval
- Introduction to deep reinforcement learning and implementing RL in a simple strategy using 'gamification'
- Learn how to backtest a strategy using machine learning
8 Trading Tech, Infra & Operations
- Learning the different trading paradigms - LFT, MFT, and HFT
- Understanding the system architecture of a traditional trading system
- Understanding the system architecture of an automated trading system
- Assessing the challenges in building a trading system
- Learning about the infrastructure (hardware, software, network, etc.) requirements to start an algo trading desk
- Understanding the business environment (including regulatory, financials, etc.) to start an algo trading desk
- Overview on business requirements to start an algo trading desk
- Examples of starting out in developing markets and developed markets
9 Advanced Statistics for Quant Strategies
- Learning time series-centric terminology like stationarity, ACF, PACF
- Learning common features of financial asset returns
- Introduction to the ARIMA family of models
- Learning to model volatility
- Introduction to ARCH and GARCH models
- Building and testing trading strategies using time series modeling techniques on Python
10 Trading & Back-testing Platforms
- Introduction to the Interactive Brokers (IB) platform and Blueshift
- Working with IB Trader WorkStation (TWS) and the IB TWS API architecture
- Learning the REST API (used by hundreds of brokers worldwide) and its components
- Exposure to multiple Python solutions for algo trading
- Learning to use IBridgePy (API used to backtest and live trade strategies with brokers like Interactive Brokers, Robinhood, TD Ameritrade)
- Managing multiple accounts as a fund manager
11 Portfolio Optimization & Risk Management
- Learning about different methods to evaluate portfolio and strategy performance
- Understanding risk management: sources of risk, risk limits, risk evaluation and mitigation, risk control systems
- Backtesting of portfolio management and risk analysis in Python using the Modern Portfolio Theory and Monte Carlo simulations
- Profitability analysis of individual strategies
- Building a portfolio with multiple stocks
- Profitability analysis of a portfolio
12 Options Trading & Strategies
- Introduction to options, payoff diagrams, common option structures
- General option trading principles, model-independent option features
- Option pricing variables and parameters
- Option pricing concepts (Black-Scholes-Merton)
- Options Greeks
- Options trading (early exercise and expiration trading)
- Risk management and trade evaluation
- Volatility premium
- Volatility trading with options, realized and implied volatility
- Hedging in practice
- Building an Options Backtesting model
13 Hands-on Project
- Self-study project work under the mentorship of a domain expert
- Project topic can come from different areas of financial market trading (be it asset class, techniques used, market selected) and can help crystallize your learnings from the EPAT into something concrete
14 EPAT Exam
- EPAT exam is conducted at proctored centers in 80+ countries
NEW AI For Trading - Supplementary Lectures
This lecture series demonstrates how generative AI, including large language models and agent-based systems, is used in trading and research. It focuses on implementation with illustrations showing how these tools fit into real workflows.
Lecture 1: Large Language Models in Trading
- Mental model of how LLMs work, including transformers and attention.
- Grounded understanding of where LLMs create value in trading workflows, using the FinBERT model.
- Framework for using LLMs in research tasks such as news analysis, earnings call parsing, and research summarization.
- How to design prompts for finance use cases that reduce noise and hallucinations.
- Awareness of model risk, latency, and production constraints in trading environments.
Lecture 2: Quantitative trading using AI agents
- What agentic AI is and why single LLM calls are usually not enough for trading workflows.
- A blueprint to build a multi-agent quant research pipeline using make.com.
- Orchestrating financial research tasks.
- Challenges in production systems.
- Framework to evaluate how to deploy agentic systems in your setup.
Certificate

This programme has been accredited by The Institute of Banking and Finance (IBF, Singapore) under the IBF Standards. IBF-STS provides upto 50% funding for direct training costs subject to a cap of S$ 3,000 per candidate per programme subject to all eligibility criteria being met. This is applicable to Singapore Citizens or Singapore Permanent Residents, physically based in Singapore. Find out more on www.ibf.org.sg

EPAT is accredited by CPD, UK (Continuing Professional Development, UK)

QuantInsti has registered this program with GARP for Continuing Professional Development (CPD) credits. Attending this program qualifies for 30 GARP CPD credit hours. If you are a Certified Financial Risk Manager (FRM®), or Energy Risk Professional (ERP®), please record this activity in your Credit Tracker.
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ADMISSION PROCESS
Send your
Application
Get on a call
with a Counsellor
Application
acceptance
Pay the fee
and get started
Send your
Application
Get on a call
with a Counsellor
Application
acceptance
Pay the fee
and get started
Before admission, we will facilitate a one-on-one counselling session that will focus on understanding the strengths and weaknesses of the participant. These sessions do not necessarily decide the participants' eligibility but help counsellors assist them with informed guidance prior to enrollment.
Batches & Fee Structure
Super Early Bird Discount
Early Bird Discount
Standard Fees
Start Date
11th Jul 2026
Super Early Bird Enrollment Fees
6,999 (valid till 30th Apr 2026) 7,799 800 OFF | Limited Period
Early Enrollment Fees
8,599 (valid till 5th Jun 2026)
Standard Enrollment Fees
9,499 (valid till 3rd Jul 2026)
Start Date
10th Oct 2026
Super Early Bird Enrollment Fees
7,799 (valid till 31st Jul 2026)
Early Enrollment Fees
8,599 (valid till 7th Sep 2026)
Standard Enrollment Fees
9,499 (valid till 3rd Oct 2026)
Start Learning Now!
Batch 71
717/mo*
for 12 mo. at 0% APR
batch 71
start date
11th Jul 2026
Early Bird Discount
8,599 Valid till 5th Jun 2026
9,499
Valid till 5th Jun 2026
Participants have the opportunity to receive additional financial aid on top of existing discounted fees. Connect with us to check eligibility.




