The Curriculum

  • Knowledge of basic trading procedures and basics of algorithmic trading: know and understand the terminology
  • Understand statistical methods and statistical measurements including autocorrelation function, partial autocorrelation function, Maximum Likelihood Estimation (MLE), Akaike Information Criterion (AIC), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)
  • Basic knowledge of time series analysis, stationarity of time series, and forecasting using ARIMA
  • Fundamentals of Autoregressive and GARCH Models, and understanding volatility
  • Logistic regression to predict the conditional probability of the market direction
  • Different methodologies of evaluating portfolio and strategy performance (back-testing methodologies and statistical figures for evaluation including Sharpe ratio, Sortino ratio, Max drawdown)
  • Basic knowledge of Asset Allocation Models
  • Understand all the most practical indicators and oscillators (e.g., RSI, MA, EMA)
  • Distinguish between Macroeconomic and Microeconomic news
  • Basic knowledge of models for spot prices, futures prices
  • General knowledge of types of multifactor models and updating a traditional factor model
  • Knowledge on the basics of the financial market in general and the stock market in particular
  • A clear understanding of the type of instruments and the stock markets.
  • Understand the concept of the stock market index and its calculation
  • Basic knowledge of machine learning, pattern recognition as well as Natural Language Processing (NLP)
Module 1: Sentiment… What and Whose
  • Understanding investor sentiment and the pendulum of investors’ emotions
  • The role of “Noise Traders” in driving the asset prices in the financial markets
  • Media sentiment and how it affects asset prices
  • Market sentiment and its measurement
  • Determining crowd sentiment and its impact on financial markets
Module 2: Sentiment Data
  • Classical newswires and macroeconomic announcements
  • Various Sources of sentiment data such as news, social media, and search engines
  • The impact of Micro-blogging platforms on stock markets
  • Converting qualitative information to the sentiment score
  • Using bag-of-words, natural language processing and lexicon-based methods in sentiment analysis
Module 3: Structure and Coverage
  • News analytics (Meta) data structure
  • The exact polarity of sentiment in the news
  • News characteristics such as relevance, novelty, and sentiment scores
  • Leading data providers for sentiment data analysis in finance
  • Description of the data provided by major sentiment vendors
Module 4: Other Sources: Alternative Data
  • Scheduled (expected) and Unscheduled (unexpected) financial news
  • Macroeconomic news and their usage in automated trading
  • Relevance and use of alternative data in sentiment analysis
  • Major types of alternative data
  • Different categories of alternative data such as satellite data, geolocation data, etc.
  • Providers of alternative data
Module 5: Models to Exploit Sentiment Analysis (I)
  • Taxonomy of models
  • Descriptive, normative, prescriptive and decision models explained
  • Modelling and information architecture
  • Examples of modelling in the domain of finance
  • The key role of time and uncertainty in decision making
Module 6: Models to Exploit Sentiment Analysis (II)
  • Financial applications of sentiment data and their properties
  • Risk management through risk quantification: risk computed for exposures of varying time spans, namely, weekly, monthly, or annualized
  • Fund rebalancing on calendar dates: weekly, monthly, yearly
  • Automated trading daily or intraday
  • Retail application (creditworthiness, loan, and savings advice)
Module 7: Opinion and Biases
  • Various challenges in the area of sentiment analysis
  • Distinction between opinions and facts
  • Role of behavioural finance in investor decision making
  • Different types of biases that affect investor behaviour in financial markets
  • Revisiting the pendulum of fear and greed
EPAT (NSA) Specialization Exam
  • EPAT (NSA) Specialization certification requires you to successfully clear the Examination
  • The exam is conducted at proctored centers in 80+ countries

Case Studies

Asset Allocation Strategies: Enhanced by News
  • Trading Strategy and Sentiment Analysis
  • Market Data and News Meta Data Analysis
  • Asset Allocation Strategy
  • Construction of Filters and its applications
  • Empirical Investigation
Forecasting crude oil futures prices using global macroeconomic news sentiment
  • Impact of crude oil price variation
  • Forecasting arbitrage-free (futures) prices
  • Macroeconomic news analytic data
  • Models for spot prices and futures prices
  • Kalman filter and removal of noise
  • Analysis, estimation, and forecasting results
Asset Allocation Strategies: Enhanced by Micro-Blog
  • Trading Strategy & Sentiment Analysis
  • Market Data and Micro-blog Sentiment Data
  • Asset Allocation Strategy
  • Construction of Filters
  • Application of Filters
  • Empirical Investigation and Back-testing Results
Improved Volatility Prediction and Trading using StockTwits Sentiment Data
  • Volatility prediction
  • Market Data and Micro-blog Sentiment Data
  • Impact Scores from Sentiment
  • GARCH & ARCH Model
  • Metrics for Evaluation
  • Evaluation of model performance
Equity portfolio risk estimation using market information and sentiment
  • Understanding equity price uncertainty
  • Update a traditional factor model
  • Types of multifactor models
  • Updating model volatility using quantified news
  • Computational experiments
An Impact Measure for News: Its Use in (Daily) Trading Strategies
  • Impact of News & Sentiment
  • Designing equity trading strategies
  • Return, Volatility and Liquidity Measures
  • Sentiment Measure & Impact score
  • Autoregressive and GARCH Models
  • Experimental trading results


EPAT faculty - Anthony Luciani
EPAT faculty - Prof. Christina Erlwein-Sayer
EPAT faculty - Dr. Cristiano Arbex Valle
EPAT faculty - Dan Joldzic
EPAT faculty - Prof. Enza Messina
EPAT faculty - Prof. Gautam Mitra
EPAT faculty - Dr. Matteo Campellone
EPAT faculty - Ravi Kashyap
EPAT faculty - Dr. Richard Peterson
EPAT faculty - Shradha Berry
EPAT faculty - Dr. Zryan Sadik


This is an exclusive programme available for participants of the Executive Programme in Algorithmic Trading (EPAT).

The EPAT participants are equipped with high intellectual curiosity, possess strong interest in finance and have analytical skills. EPAT participants come from various quantitative disciplines such as mathematics, statistics, physical sciences, engineering, operational research, computer science, finance or economics. They are adept at building trading strategies, creating their own algorithms and even establishing their own trading desk.

It is desirable that aspirants of the EPAT (NSA) Specialization are EPAT alumni. New batch participants of the EPAT programme of QuantInsti may simultaneously register for EPAT (NSA).

Course Duration

21 hours for foundation modules +18 hours for Case Studies

Lecture Duration

Foundation Modules: 1.5 hours each day on Saturday and Sunday

Case Studies: 3 hours any one day on the weekend

Standard Programme Fees
Batch 2, Start Date: 15th May, 2021
TierApplicable tillGlobal ParticipantsIndian Residents*
Early Bird Fee16th April, 20212,31995,200
Standard Fee15th May, 20212,8991,19,000
TierEarly Bird FeeStandard Fee
Applicable till16th April, 202115th May, 2021
Global Participants2,3192,899
Indian Residents*95,2001,19,000
* Additional 18% GST applicable for Resident Indian Participants
** Special discounts for Developing Market Participants and Full-Time Students


This specialization programme is open for participants of the Executive Programme in Algorithmic Trading (EPAT).
This specialization programme is not limited to any particular batch and is open to all participants of EPAT past and present.
Yes. You would benefit from the continued support through the EPAT Support Team.
Participants of the present and previous batches of the Executive Programme in Algorithmic Trading (EPAT) can join the programme.
The lectures would be completely online.
You can attend the sessions the same way as the EPAT lectures from across the globe.
Yes. Recordings of all the lectures would be made available to you on the LMS, once they are Live.
The fees are as follows:
  • Indian residents: INR 119,999 + GST
  • Developed Market participants: USD 2899
You would be receiving the following benefits of being an EPAT alumnus:
  • Financial assistance through zero interest EMI for Indian residents
  • Fee to be revised after first/second cohort
7 modules are covered in total. You can check out the complete details of these modules here.
Yes, there are 6 case studies covered. You can checkout the details here.
The duration is 3 months. It is as follows:
  • 21 hours for foundation modules
  • 18 hours for case studies
  • Foundation Modules: 1.5 hours each day on Saturday and Sunday
  • Case Studies: 3 hours any one day on the weekend
You can check the complete details of the faculty here.
Yes, you would be getting a certificate from QuantInsti and Unicom, post completion of the specialization.
We provide an opportunity to clear all your doubts about the programme prior to enrollment. Also, you get access to dedicated team support. Therefore, we follow a no refund policy.
The exams would be conducted similar to the EPAT Exams, online and at Prometric centres globally.



QuantInsti is one of the world's biggest algorithmic & quantitative trading institutes. From its early days, QuantInsti focused on bridging the industry knowledge gap in the field of high-frequency trading and has come a long way in the last decade. Today, it has learners from 200+ countries and territories.

It was founded by a group of technocrats and traders in 2010 with the goal of democratizing Algorithmic & Quantitative Trading for everyone through educational and technological solutions. QuantInsti is a venture by iRage, one of the leading HFT and Algorithmic trading firms in India.

QuantInsti has a well-regarded instructor-led online training product that is popular and highly beneficial for serious investors and traders across multiple asset classes and geographies: Executive Programme in Algorithmic Trading (EPAT®).


Established in 1984, UNICOM is an events and training company specialising in the areas of Quantitative Finance and many aspects of IT including development, management, testing and deployment. The company’s products include conferences, public and in-house training courses (including certified training) and networking events.

UNICOM and OptiRisk operate both from UK and India; they have a long association and have the same founder and shared ownership. In the domain of Quantitative Finance OptiRisk is an acknowledged leader. UNICOM draws upon the specialist knowledge of OptiRisk Systems in Financial Analytics. Qu@antinsti US 1