The Certificate in Sentiment Analysis and Alternative Data for Finance (CSAF) programme is an instructor-led course designed for finance professionals by leading Algorithmic Traders, Sentiment Pundits, Quantitative Modelling experts and HFT thought leaders.
CSAF is comprehensive and offers unparalleled insights into the world of Algorithms and the latest thinking in financial technology. Perfect to develop your career in modern methods in finance, it covers various aspects of trading, investment decisions & application using News Analytics, Sentiment Analysis and Alternative Data.
June 2022 5 months
June 2022
5 months
programme benefits
World-Class Faculty
Learn from the best in the industry
Dedicated Support
Get answers to all your queries super quick
Career Services
Avail lifetime placement and career assistance
Jad Mawlawi
United Kingdom 
EPAT has been a great experience for me. It is definitely the best programme out there to learn quantitative finance and algorithmic trading. The… See More
EPAT has been a great experience for me. It is definitely the best programme out there to learn quantitative finance and algorithmic trading. The team was and still is very helpful and caring. The course itself is a combination of different disciplines including programming, finance, and statistics taught by very knowledgeable and experienced faculty.
Billy Davila
United States
I recently completed the EPAT programme from QuantInsti, and it was a rich experience. I learned more here than I did on my university curriculum… See More
I recently completed the EPAT programme from QuantInsti, and it was a rich experience. I learned more here than I did on my university curriculum. This is mainly since the EPAT course is very practical and I was able to learn a lot in such a short time. It provided me with a lot of theoretical and practical knowledge in the algorithmic trading domain. Besides their excellent curriculum, the support team is friendly, dedicated, and always there to support you during your EPAT journey. They also have a placement team that keeps you updated with career opportunities. However, keep in mind that your background will influence how well you fit into those career opportunities. They also have a self-paced learning portal named Quantra which I really enjoy. Overall, they are excellent at what they do.
Marcus Coleman
United States
QuantInsti is the best place to learn professional algorithmic and quantitative trading. The EPAT programme is a highly structured and hands-on l… See More
QuantInsti is the best place to learn professional algorithmic and quantitative trading. The EPAT programme is a highly structured and hands-on learning experience and it's being updated frequently. The faculty and staff are extremely competent and available to address any concerns you may have. Upon completion of the EPAT programme you will have the necessary tools to begin a career in algorithmic/quantitative trading.
Jim Ike
Singapore
From basic knowledge of quantitative finance to practical hands-on python session of back testing trading strategies, EPAT course covers a large … See More
From basic knowledge of quantitative finance to practical hands-on python session of back testing trading strategies, EPAT course covers a large portion of knowledge needed to join algorithmic trading industry. Good introduction to dive in.
Ronnie Varghese
United Arab Emirates
The Executive Programme in Algorithmic Trading (EPAT) is a well structured, intensive course which takes approx. 6 months to complete. The core f… See More
The Executive Programme in Algorithmic Trading (EPAT) is a well structured, intensive course which takes approx. 6 months to complete. The core focus areas of the course are stock market theories and quantitative principles, statistical analysis and programming. With the current trend of businesses moving towards implementing Artificial Intelligence (AI) or data-centric approaches to solving difficult problems, the skills gained from this course can be used to solve any AI-related problem (i.e. these skills can be used for any domain other than algorithmic trading). Having these skills in your repertoire will likely increase the probability of finding employment. Further, the Institute actively works towards the placement of the students enrolled (or alumni) in the course. The faculty are experts in their respective fields. In order to successfully complete this course, the student must be committed to completing the assignments and projects to cement their understanding of the course material. An added advantage is that there is lifetime access to the course materials, which will enable any alumni of the EPAT course to stay updated on the developments in this field. Overall, in my opinion, EPAT provides value for your money.
Avi Nandwani
United States
I found the EPAT course to be exactly what I was looking for – the right mix of statistics, financial markets and coding. The faculties were exce… See More
I found the EPAT course to be exactly what I was looking for – the right mix of statistics, financial markets and coding. The faculties were excellent, and most importantly, the support team was exceptional with their efforts towards my learning.
Rajeev Chahar
India
Your one-stop solution for the niche and opaque domain of Algorithmic Trading. Be it faculty, student support service, training content & resourc… See More
Your one-stop solution for the niche and opaque domain of Algorithmic Trading. Be it faculty, student support service, training content & resources or communication, they match the standards of international repute!
Raymond Philips
South Africa
I had a great experience through QuantInsti Learning. If you are passionate about Algorithmic/Quantitative Trading, or you want to start your jou… See More
I had a great experience through QuantInsti Learning. If you are passionate about Algorithmic/Quantitative Trading, or you want to start your journey in this amazing discipline, this is a great place to begin and grow your knowledge and interest. The administration and faculty were outstanding. Lectures are well-delivered and informative and there is always additional help should you require it. There is a wide array of learning material both through coursework and through the community as a whole.
Eriz Zárate
Spain
Only great words to say about QuantInsti and my learning path during the EPAT programme. Always curious, always listening and improving. All the … See More
Only great words to say about QuantInsti and my learning path during the EPAT programme. Always curious, always listening and improving. All the staff, starting from the CEO down to the support people were very nice 120% of the time (the 20% excess goes to all the help that they have given me after concluding the course, every time with a consistent will to help others).
Regarding the EPAT programme content, the key thing I would like to say is that is a wide covering approach. During six months, industry experts (i.e. real practitioners, not Gurus) dive into a variety of topics from scratch, so that, after that, you can choose in which field are you going to focus.
My final thoughts for new EPATians are: it is a must-do course if you are beginning in the field of algorithmic trading and quantitative finance. Although the real value is in the people that drive the institution. Be sure that you will have to take more courses after EPAT to succeed in this field, but you won't find the life-long learning support that they will give you anywhere else.
Nicolò Pirozzi
Italy
The course is very organized, both theoretical and practical, the staff is very competent and helpful, I found myself at ease during the whole co… See More
The course is very organized, both theoretical and practical, the staff is very competent and helpful, I found myself at ease during the whole course of study, I learned the basics to start a career in algorithmic trading and finance in general. What I appreciated the most were the lessons held with prominent personalities from the world of finance and trading, who shared their knowledge and experiences with the students. I would definitely recommend the course to anyone wishing to pursue a career in trading and finance.
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Curriculum
1 Primer
- 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)
2 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
3 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
4 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
5 Module 4: Other Sources: Alternative Data (I)
- 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
- Taxonomy of models
6 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
7 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)
8 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
9 Module 8: AI, Machine Learning & Quantitative Models to predict market direction
- Quant models and AI & ML models- overview
- Interaction of Quant Models and AI & ML models to predict market direction
- Supervised and Unsupervised learning models
- Models for predicting market direction: K-Nearest Neighbor, Decision Tree Models, ANN, LSTM, SVM
- Trading Strategies using Quantitative Models and Machine Learning
10 Module 9: Role of Alternative Data in Financial Trading: Alternative Data (II)
- Rapid growth of Alternative Data in recent decades
- Improvement of technical ability to process data
- Categorization of Alternative Data and Application in Finance
- Use of Alternative Data to obtain insight into the Investment process
- Capture the predictive power of Alternative Data in Financial Trading
11 CSAF Exam
- CSAF requires you to successfully clear the Examination
- The exam is conducted in a proctored environment both at the Prometric centres in 80+ countries and remotely
Case studies
1 Grasping Behavioural Finance by Anthony Luciani
- MarketPsych has built a sentiment analytics suite on investor-relevant media from thousands of online sources with hundreds of over-arching themes and topics covering all major asset classes.
- In these sessions, we explore how the stationary processes of psychology interact with the nonstationary processes of financial markets.
- Witness two major themes of investor over- and under-reaction to the news.
- See cycles of fear and their interactions with crude oil prices.
- Find solutions to common questions within the field of sentiment analysis for markets.
- Observe how new themes become more predictive over time.
2 Classifying Earnings Calls & News by Dan Joldzic
- Alexandria Technology develops natural language processing (NLP) software to convert text into data.
- Alexandria uses machine learning to identify key phrases in financial documents such as news reports, press releases, earnings calls, and filings.
- There are official sources of information such as newswires (Dow, Reuters, Bloomberg), company filings (10-Qs, 10-Ks), earnings calls, research reports.
- News classification and its impact on asset returns.
- Two types of news: company-specific news and economic news.
- News works better on short time horizons like 1 week or lesser. For greater time horizons, alpha decays.
- Unstructured news can be converted to structured data showing information on Ticker, topic and sentiment score.
- We look at the ratio of positive news reports to negative news reports of a company on a day to create a sentiment score. These companies belong to the US all cap.
3 NLP And ML Techniques in Finance: Some Examples by Dr. Matteo Campellone
- Datasets based on proprietary algorithms as Alternative Data.
- Introduction to the Brain Sentiment Indicator.
- Introduction to the Brain Language Metrics on Company Filings dataset.
- A workflow that uses ML and NLP for thematic selection.
4 Asset Allocation Enhanced by Sentiment Data by Dr. Zryan Sadik and Prof. Christina Erlwein-Sayer
- Introduction and Background.
- Market Data and News Data.
- Asset Allocation Strategy.
- Construction of Filters.
- Empirical Investigation.
- Discussions and Conclusion.
5 Beating Benchmark by Ravi Kashyap
- The Nature of Uncertainty.
- Objectively Subjective.
- The Circle of Investment.
- The Bounce Basket.
- The Miracle of Mathematics.
- Sharpening the Sharpe Ratio.
- From Symbols to Number.
6 Global Economic Recovery: Alternative Data Indicators by Boris Spiwak
- As the pandemic drove economic and social activity online, new data trails were left behind.
- This talk describes how innovative investors leverage web data to track their portfolio companies in the post-pandemic world - from using hiring data as a valuable leading indicator of future corporate performance, to running sentiment analysis on social media posts and online employee ratings, and more.
7 Using traditional structured data for long-term analysis of publicly-traded equity and using alternative, unstructured data for short-term analysis of public and private equity
- Quantitative investors have long used traditional data sources, such as income statements and balance sheets of public firms, to drive stock selection models.
- Factors such as value, growth, earnings quality, and earnings surprise can be effective at predicting the long-term performance of publicly-traded stocks.
- With the explosion in the amount and diversity of data in the last five years, alternative data sources are quickly revolutionizing quantitative investing.
- Alternative data sources can include natural language processing of news and social media content, review of credit card transactions and consumer emails, and geolocation data using cell phone signals and satellite images.
- Alternative data is more complex to process, moves in a different time frame than traditional data and may provide a new window into information availability for private companies.
8 News Sentiment Analysis in Em Sovereign Debt research, Investment and Risk Management by Jacob Gelfand and Kamilla Kasymova
- We present the internally developed framework for ex-ante analysis of the foreign exchange and sovereign bond markets based on the news sentiment in global and local media, the Global Economy and Markets Sentiment (GEMS) model.
- The predictive analytics from the GEMS model are used to enhance the fundamental analysis to better assess risks and opportunities in the Emerging Markets sovereign debt market.
- We introduce the long-term and short-term trading strategies based on the produced GEMS analytics and spearhead the discussion about the predictive qualities of the produced analytics.
9 The Art of (Alternative) Data Science by Ganesh Mani
- The dynamic world produces data that is constantly changing. Financial markets can be particularly mercurial, triggered by geopolitical events, regulation changes, industry news and the earnings outlook of companies.
- Exploiting data science to explain or predict the ebb and flow of security prices can be a bit of an art. Knowing which data – from the plethora of traditional and alternative datasets – to focus on, what techniques to use (e.g., traditional statistical, historical-data-intensive deep learning, reinforcement learning, forward-looking simulations or a combination); and, what aspects to the model are nuanced decisions that will significantly affect portfolio risk and return.
- Human-machine teaming is also a focus area and I hope to address some of the above themes in my brief presentation. A subsequent panel will elicit multiple opinions in this milieu.
10 Using alternative data: from research to production
- The use of alternative data is a necessary part of many investment processes running today.
- There is, however, a difference between running a one-off analysis and having a regular process running in production.
- In this lecture, we will provide some advice and suggestions for all stages of this process.
11 The Future of Impact Finance by Aisha Williams
- Big data and AI also have been advancing efforts in accurately measuring positive social impact, resulting in a more transparent and clear look at impact investment.
- Blockchain can then plot and record an accurate footprint of impact efforts, transforming impact investment analysis and identification.
12 ESG Data and Investment Returns by Richard Peterson
- Learn How Esg Perceptions And Controversies Are Detected In News And Social Media Using AI.
- Identify Which Esg Factors Have Been Leading Shares Higher, And Which Are Irrelevant (Or Even Damaging) To Shareholder Value.
- See How Specific ESGH Controversies Affect Corporate Share Valuations Over Time.
13 A Quantitative Metric for Corporate Sustainability
- In recent years the concept of “sustainability” companies has been at the forefront of concerns for many investors. As most consideration of sustainability has focused on the broad ideas of ESG concerns (Environmental, Social, Governance) the field has lacked a straightforward metric by which investors can assess “How many years into the future is a given company likely to survive without bankruptcy?”.
- While somewhat similar to a credit rating, such a measure must also consider the situation of firms with no current debt, and those that are pathologically conservative so as to survive until eventually becoming obsolete.
- Such a metric was introduced by Dan diBartolomeo (Journal of Investing, 2010) based on an extension of the Merton contingent-claims model (Journal of Finance, 1974). In this study, we illustrate refinements of the methodology and present empirical analysis of the relationship between the sustainability metric and investor returns from 1992 through 2021 for all equities traded on US exchanges (inclusive of non-US firms traded in ADR form).
- The results show statistically significant relationships that may be exploited for superior returns in both equity and corporate bond markets.
14 Discussion on Project Results
- Objectives: To introduce to the participants a guideline for preparing technical reports of empirical investigations; how to develop an experimental project; and simultaneously prepare for report writing.
- Learning Outcomes: Develop a generic approach to preparing Technical Reports and develop reports collaboratively as a team.
Certificate
CSAF is provided by QuantInsti, UNICOM and OptiRisk.
QuantInsti is one of the world's biggest algorithmic & quantitative trading institutes, and today, it has learners from 200+ countries and territories.
Established in 1984, UNICOM is an events and training company specialising in the areas of Quantitative Finance and many aspects of IT.
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Faculty Members
Aisha Williams
Aisha is the Founder and CEO of ImpactVest. Aisha has the honour of holding the designation of United States Boren Scholar. She is a University of Zurich Center for Sustainable Finance Adviser Alumna, Frankfurt School of Management Certified Expert in Sustainable Finance, a Blockchain council Blockchain and Finance Professional, and an Associate Member of the Chartered Institute for Securities and Investment.
Anthony Luciani
Anthony Luciani is a Senior Quantitative Analyst at MarketPsych. He is working on simplified sentiment and “Superforecasters” models. He developed sentiment-based financial models, previously for Optirisk. He has a Master’s Degree in Financial Mathematics from the University of Leicester.
Dr. Arkaja Chakraverty
Dr. Arkaja received her PhD from the Indian School of Business in Financial Economics in 2017; and is affiliated with Higher School of Economics, Moscow. Currently, she is based out of Melbourne, Australia and is working on a series of research papers. As of April 2021, Arkaja has joined the OptiRisk team as a Senior Research Associate (Consultant).
Boris Spiwak
Boris Spiwak is Director of Marketing at Thinknum and KgBase. Before joining Thinknum, Boris worked as a Financial Manager at Procter & Gamble’s EMEA HQ in Geneva, Switzerland, and as VP of Finance in one of Colombia’s largest hotel chains. He is from Venezuela and holds degrees in Political Economy and Finance from Princeton, the London School of Economics, and HEC Paris.
Prof. Christina Erlwein-Sayer
Prof. Christina is Professor of Statistics and Financial Mathematics at Hochschule für Technik und Wirtschaft (HTW) Berlin and has worked at OptiRisk Systems as a quantitative analyst and senior researcher. She completed her PhD in Mathematics at Brunel University, London in 2008. She then worked as a researcher and consultant in the Financial Mathematics Department at Fraunhofer ITWM, Kaiserslautern, Germany.
Dr. Cristiano Arbex Valle
Dr. Valle has a bachelor’s degree in Computer Science and an MSc in Operations Research from Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil. In 2011 he joined OptiRisk as a software engineer and a researcher and obtained his PhD in the Department of Mathematical Sciences from Brunel University (UK) in 2014.
Dan diBartolomeo
Dan diBartolomeo is President and founder of Northfield Information Services, Inc. Based in Boston since 1986. Dan serves on the Board of Directors of the Chicago Quantitative Alliance and is an active member of the Financial Management Association, (“QWAFAFEW”), the Society of Quantitative Analysts. Dan is a Director of the American Computer Foundation as well.
Dan Joldzic
Dan Joldzic, CFA, FRM is CEO of Alexandria Technology, Inc, which develops artificial intelligence to analyse financial news. Prior to joining Alexandria, Dan served dual roles as an equity portfolio manager and quantitative research analyst at Alliance Bernstein where he performed factor research to enhance the performance of equity portfolios.
David Jessop
David Jessop has a MA in Mathematics from Trinity College, Cambridge. Currently, at Columbia Threadneedle Investments, David was previously the Global Head of Quantitative Research at UBS for over 17 years, Head of Quantitative Marketing at Citigroup & started his career at Morgan Grenfell, initially as a derivative analyst and then as a quantitative portfolio manager.
Prof. Enza Messina
Prof. Enza Messina is a Professor in Operations Research at the Department of Informatics Systems and Communications, University of Milano-Bicocca, Italy, and holds a PhD in Computational Mathematics and Operations Research from the University of Milano. She is a co-founder of Sharper Analytics, a spin-off from the University of Milano Bicocca.
Ganesh Mani
Ganesh Mani is a Global executive and thought leader with deep experience in Pro-Active Intelligence (PAINT), NLP (METAPHOR), Multi-media analytics (ALADDIN); multiple patents. He has pioneered multiple innovations, leading teams using multi-modal (incl. alternative) data and augmented intelligence. He is a Mentor/advisor at many entities (e.g., TiE.org, Sabudh.org, FDPinstitute.org; IvyCap Ventures).
Prof. Gautam Mitra
Prof. Mitra is an internationally renowned research scientist in the field of Operational Research in general and computational optimization and modelling in particular. He is the founder and chairman of OptiRisk Systems and UNICOM seminars. He has published five books and over a hundred and fifty research articles and was awarded the title of ‘distinguished professor’ by Brunel University in 2004.
Jacob Gelfand
Jacob Gelfand, CFA is the Director of Quantitative Strategy & Research, Investment Risk Management. Jacob is an adjunct faculty in the Lubar School of Business, University of Wisconsin, Milwaukee. An MS in Computer Science from MSUCE, Russia, an MBA in Finance & Strategy from the University of Chicago Booth School of Business, he has experience working at Capgemini, Northwestern Mutual, & Mason Street Advisors (MSA).
Dr. Kamilla Kasymova
Dr. Kamilla Kasymova is a Quantitative Research and Analytics Director in the Investment Risk Management of Northwestern Mutual Life Insurance Company since 2014. She has taught undergraduate mathematics and economics classes since her graduation. Sr. Kasymova has a BSc in Economics from Moscow State University, MS in Finance from the University of Ulm, MS in Mathematics and a PhD in Economics both from the University of Wisconsin-Milwaukee.
Dr. Katharina Schwaiger
Dr. Katharina Schwaiger is the Director of Factor Based Strategies Group (FBSG) and Co-Head of Sustainable Investing at BlackRock. She was a member of the ETF and Index Investments Product Innovation group, has worked as a lecturer at the London School of Economics. She has a BSc in Financial Mathematics & a PhD in Mathematics/Operational Research from Brunel University. She is a committee member of Quantess London.
Dr. Keith Black
Dr. Keith Black is the managing director and program director of the FDP Institute. Previously, he served as the managing director of content strategy at the CAIA Association, where he was a co-author of the CAIA curriculum. Dr. Black earned a BA from Whittier College, an MBA from Carnegie Mellon University, and a PhD from the Illinois Institute of Technology. He is a CFA, CAIA, and FDP charterholder.
Dr. Ravi Kashyap
Dr. Kashyap is experienced working as a Product Manager and a Quantitative Strategist working for financial services companies, namely, Goldman Sachs, Morgan Stanley, Merrill Lynch, Citigroup and IHS Markit. He holds a PhD from the City University of Hong Kong. He was a finance professor at SolBridge International School of Business, South Korea and subsequently with SP Jain School of Global Management, Singapore.
Dr. Richard Peterson
Dr. Peterson is CEO of MarketPsych Data which produces psychological and macroeconomic data derived from text analytics of news and social media. He is an award-winning financial writer, an associate editor of the Journal of Behavioral Finance, has published widely in academia, and performed postdoctoral neuroeconomics research at Stanford University.
Dr. Zryan Sadik
Dr. Sadik holds a Bachelors in Mathematics from Salahaddin University, Iraq, a Masters in Computational Mathematics with Modelling and a PhD in Applied Mathematics from Brunel University, London (2018). His research interests include news sentiment analysis, filtering in linear and nonlinear time series applying Kalman filters, volatility forecasting, optimization, risk assessment, and the role of news sentiment in financial markets.
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.
This programme aims to serve the participants who are equipped with high intellectual curiosity, possess a strong interest in finance and have analytical skills. This includes participants who come from various quantitative disciplines such as mathematics, statistics, physical sciences, engineering, operational research, computer science, finance or economics.
Course Duration
60+ hours including live sessions on case studies and project work
Lecture Duration
3 hours every weekend over Saturday and Sunday
Standard Programme Fees
| Tier | Applicable till | Global Participants | Indian Residents* |
|---|---|---|---|
| Super Early Bird Enrollment Fees | 6th April 2022 | 2,999 | 1,49,900 |
| Early Enrollment Fees | 3rd June 2022 | 3,349 | 1,69,900 |
| Standard Enrollment Fees | 24th June 2022 | 3,699 | 1,89,900 |
| Tier | Super Early Bird Enrollment Fees | Early Enrollment Fees | Standard Enrollment Fees |
| Applicable till | 6th April 2022 | 3rd June 2022 | 24th June 2022 |
| Global Participants | 2,999 | 3,349 | 3,699 |
| Indian Residents* | 1,49,900 | 1,69,900 | 1,89,900 |
| Tier | Applicable till | Global Participants | Indian Residents* |
|---|---|---|---|
| Super Early Bird Enrollment Fees | 27th August 2022 | 2,999 | 1,49,900 |
| Early Enrollment Fees | 30th September 2022 | 3,349 | 1,69,900 |
| Standard Enrollment Fees | 4th November 2022 | 3,699 | 1,89,900 |
| Tier | Super Early Bird Enrollment Fees | Early Enrollment Fees | Standard Enrollment Fees |
| Applicable till | 27th August 2022 | 30th September 2022 | 4th November 2022 |
| Global Participants | 2,999 | 3,349 | 3,699 |
| Indian Residents* | 1,49,900 | 1,69,900 | 1,89,900 |
Financial Aid
CSAF® programme comes with Financial Assistance for the eligible participants
Scholarship by QuantInsti
Participants (Other than full time student) can Earn the Merit-based Scholarship. Go get it here!
Flexible Payment Plans
Flexible Payment Plans are available at 0% interest for full-time Indian residents.
Student Aid
QuantInsti encourages full-time students to enter this domain. Claim your benefits, if you are still pursuing full-time education.
Emerging Market Fee
Emerging market participants (Participants residing full time in countries with per capita GDP (nominal) less than USD 20,000 as per World Bank data), please share your details below to check for Emerging market fee eligibility.
FAQ
What are the fees for CSAF?
The complete fee details can be found in the admission section.
Can I register for EPAT and CSAF at the same time?
Yes, you may register for both at the same time, provided you are confident that you can keep up with the additional training hours.
Would I get a refund if I change my mind after enrolling on the programme?
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.
What financial assistance is available?
Financial assistance provided to qualified applicants include but are not limited to the following:
- Student discounts - QuantInsti believes in investing in the future of tomorrow, the students of today. We have a discounted fee for full-time students.
- Participants from Emerging Markets - Special consideration is given to participants from Emerging Markets so that the programme is more affordable.
- Flexible Payment Plans - QuantInsti is partnered with financial institutes that provide education-related flexible payment plans to Indian resident participants.
Get in touch with our programme counsellors here.
What is the admission process for CSAF?
It is a simple three step process:
- Submit your application form here.
- Wait for your application to get accepted.
- Pay the fees.
Soon after the successful receipt of your EPAT fees & acceptance, you are given access to the learning management system (LMS portal) and your EPAT journey begins!
Before admission, we offer to 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 enrolment.
What are the Flexible Payment Plans offered?
QuantInsti has partnered with government approved NBFCs to facilitate 0% financial assistance with minimal documentation for the Indian resident participants. To apply for the assistance, you would be required to share following documents with QuantInsti:
- First and Last Name
- Scanned Copy of PAN Card & Aadhaar Card - This will help to generate your CIBIL score
- Last three months pay slip (in case of a salaried employee) or last three year filed ITR (in case of self-employed)
- Last 4 months bank statement
Once you share the mentioned documents, your programme manager will connect you with the respective NBFC associates. Respective NBFC will share the sanction letter (loan approval letter) on loan approval, and you can proceed with the downpayment as per the plan agreed with the NBFC associate.
On receipt of a defined downpayment and the payment from partnered NBFC, you'll get access to the Learning Management System (Primer Modules) to kick off your Algo and Quant learning journey.
What is the last date to apply for the upcoming batch?
The updated dates and fees related information is available here.
As an EPAT alumnus, what special benefits or discounts will I get for CSAF?
Special discounts are available for EPAT alumni. Please connect with your programme manager for more details.
What are the course requirements?
A personal machine with a good internet connection is all that is required to get started immediately. As soon as you enrol, you will be provided with learning material that will assist you through the entire duration of the programme. We recommend giving 15-20 hours per week to review and complete the course work within a period of 5 months before proceeding to the final exam.
Will I get a certificate for this programme?
Yes, you would be getting a certificate from QuantInsti, UNICOM and OptiRisk, post successful completion of the programme.
What are the modules covered in this programme?
9 modules are covered in total. You can check out the curriculum for complete details of these modules.
What is the duration of the programme?
The duration of the programme is about 5 months. The live sessions would be conducted over the weekends (Saturday and Sunday) for this duration.
Who are the faculty for this course?
You can check the complete details of the faculty here.
How many hours of commitment is required on a weekly basis to perform well in the programme?
Typically 15-20 hours including 3-4 hours of live lectures are required weekly to do well in the programme.
What are my options if I can’t complete programme requirements within the allotted period of time? Can I move to a later batch?
Your dedicated Support Manager will help you throughout the programme to ensure that you do not lag behind. Even if it happens, then depending on the completion date, you may choose the self-paced learning option available with a few months of extension. You can apply for a batch defer to a later one, before the starting date of your enrolled batch. If your application is complete along with all required documents and it fulfils the required criteria, you could be moved to a later batch.
What is the minimum passing requirement for passing the CSAF exam?
The minimum requirements to be eligible for ‘Certification of Excellence’ includes a certain percentage of:
- Score in Live Project/s
- Score in the final examination
What are the prerequisites to start with the programme?
You need to have:
- A personal computer with the minimum configuration as: Operating system such as Windows: (Windows 8, Windows 8.1, Windows 10) or Mac: Mac (v 10.10), Mac(v 10.11), Mac(v 10.12), Mac(v 10.13).
- Additionally, you will need some software/programming languages installed on your system in order to have hands-on experience as well as finishing the program. The list of required software and the installation manuals will be shared with you before the programme starts.
- Language skills: You should be able to understand spoken and written English well.
- Enough time and motivation: You should be able to devote 15-20 hours on a weekly basis. The more the better!
Would the recordings of the lectures be provided?
Yes. Recordings of all the lectures would be made available to you on the LMS, once they are Live.
How to attend the sessions?
You can attend the sessions online with the link shared by the Support team to attend the lecture.
How will the lectures be conducted?
The lectures would be completely online.
When will the live sessions be conducted?
The live sessions would be conducted in evening hours in IST (after 1100 GMT) over the weekend ie. Saturday and Sunday.
How will the exam be conducted?
The exams would be conducted online and at Prometric centres globally. Participants can opt for either a remotely proctored exam (given they meet the pre-requisites) or write the exam at Prometric centres globally.
Are there any case studies explained in this course?
Yes, there are 13 case studies covered. You can check this section for complete details.
Would I get support for learning?
Yes. You would get continuous support from the Support Team throughout the programme.
Can I attend classroom sessions?
CSAF is 100% online. But, yes, you can attend a few classroom sessions in Mumbai, India, when the concerned faculty is based in Mumbai. Since most of the faculty members are located outside Mumbai, even if you are present in the QuantInsti classroom, you will be attending the lecture online. The weekly announcements mention the sessions happening in Mumbai. You can opt for writing the exam at your nearby centre of our exam partner or remotely from home, given the pre-requisites are met.
How will I, as a CSAF participant, interact with faculty members?
You will interact with the faculty in many ways:
- During the lecture you get to interact with the faculty
- Post or before the lecture, you get to share your doubts and queries which will be resolved by the faculty
- You can also interact with faculty through your support manager anytime
What kind of help/assistance is available to a CSAF participant?
- A dedicated Support Manager will guide you for the entire period of 5 months. Your manager will resolve your doubts and keep you motivated and engaged in this new and demanding career path
- You will get advice and answers from the faculty members who are industry practitioners
- We will share additional links & content with you to further enhance your learning
- A dedicated alumni cell is available post-completion to help you grow in the algo domain and network with fellow alumnus
- Lifelong access to updated lecture notes and videos after you complete the CSAF course successfully