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Certificate Program in AI for Trading (CPAIT)
Applied AI for Finance
Live Online Instructor-led Weekend Program
- English
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Indian Institute of Quantitative Finance
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Quick Facts
- Program Duration
- Program Schedule
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CPAIT Program | Certificate Program in AI for Trading Course Highlights
- Trade Execution Algorithms: Order-Size Driven Execution
- Strategy Implementation Algorithms: Real-Time Market Signal Based Execution
- Stealth/Gaming Algorithms: Large trades or other algorithmic strategies cause price movements that are exploited by stealth or gaming algorithms.
- Arbitrage Opportunities: Price Arbitrage Based Strategy Execution. This strategy involves selling higher-priced stocks and buying lower-priced ones.
- High-frequency Trading
Certificate Program in AI for Trading Course Outline
Quantitative TRADING & Modelling Domain Deep Dive
3 Weeks
Module 1
Imparts domain-specific know-how on relevant application use cases
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Classical Trading Strategies
- Fundamental & Technical Analysis
- Buy & Hold Trading Strategy
- Momentum Based Trading Strategy
- Market Arbitrage Trading Strategy
- Mean Reversion Trading Strategy
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Market Microstructure Based Trading Strategy
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Market-Neutral Trading Strategy
- Volume, Volatility & Large Order Based Strategy & Execution
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Automated Trading Strategies
- Identifying Local Minima Fitted Models
- Preventing Un-Calibrated Risk Exposure
- Generating Scenarios and Probabilities
Advanced Execution Strategies
- High-Frequency Trading (HFT): Order Book Analysis, Latency Optimization
- Risk Management: Portfolio Optimization, Position Sizing, Stop-Loss Strategies
Statistical & AIML Models Application Use Cases
6 Weeks
Module 2
Imparts practical application of Statistical & AIML modelling methods or techniques for the specific application use-cases at hand.
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Exploratory Data Analytics
- Market Data Features Engineering Price, Volume, Volatility, Time
- Methods for Sentiment Indicator Analysis
- Methods for Longitudinal Data Analysis
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Algorithmic Design & Development
- Deep Learning Based Neural Networks (NN) for Trading: Recurrent NN (RNNs), Convolutional NN (CNNs) for time series forecasting.
- Anomaly Detection: Identifying unusual market behaviors & patterns
- Model Optimization: Evaluating trading strategies on empirical data
- Model Backtesting: Parameter fine tuning
- Model Autonomous & Dynamic Ability:
- Auto-updates to trading strategies with lower performance
- Developing a real-time trading bot on a simulated trading platform.
AI & ML Adoption criteria, Considerations & Challenges
3 Weeks
Module 3
Imparts operational, strategic & technical aspects of building AI & ML in-house capability
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AI & ML Adoption Strategy
- AI & ML Driven Small Time Frame Methods
- AI & ML Driven Model Risk Forecasting
- AI & ML Driven Scenario Generation
- Identifying Predatory Algorithms Preventing Information Leakage
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AI & ML Acceptance Strategy
- Explainable AI: AI & ML Explainability & Interpretability
- Ethical & Responsible AI - Data Privacy & Security and Model Fairness
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AI & ML Go-To-Market Strategy
- Market Ask & Expectations
- Market Acceptance Criteria
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AI & ML Technology Strategy
- Cloud Computing for Trading: Scalable infrastructure for data processing and model deployment
CPAIT Prerequisites – Need prior AI & ML technique know-how
and intermediate-level programming proficiency in Python.
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More About Application of AIML in Algo Trading
AI- ML Data Science Applications in Finance Podcast Series
AI-ML in Algo Trading
Faculty
SANJAY BHATIA
Background
- Director in UBS - Risk Modelling & Analytics, Model Risk Management & Control, Chief Risk Office (CRO) Function
- MBA-Finance & MSc in Machine Learning & Artificial Intelligence from Liverpool John Moores University (LJMU)
- Post-Graduate Diploma in Machine Learning & Artificial Intelligence from IIIT-Bangalore
- Domain SME on Credit Risk , Derivatives Counterparty Credit Risk, Derivative Pricing, Stochastic Modelling, Stress Testing
ML Expertise (Teaching ML for Quantitative Finance & Risk Management)
- Financial Prediction (Regression & Classification ) - Lasso/Ridge Regression, CART Decision Trees, Ensemble Learning (Bagging & Boosting) & Support Vector Machines (SVM)
- Financial Time Series Forecasting - (Recurrent) Neural Networks, RNN-LSTM, RNN-GRU, Hybrid-RNN-LSTM-GRU
- Financial Instrument Pricing - Non-Linear & High Dimensional Derivative Pricing using Neural Networks
- ML Model Optimization – Hyperparameters Tuning K-Fold Cross-Validation, Stochastic Gradient Descent, Convergence etc.
- Regulatory & Industry ML Adoption, Challenges & Use Cases – Model Explainibility, Performance Evaluation & Testing
Prof Rituparna Sen
Background
- - Associate Professor at the Applied Statistics Division, Indian Statistical Institute, Bangalore.
- - She worked as Assistant Professor at the University of California at Davis from 2004–2011
- - She has also taught courses in Chennai Mathematical Institute and Madras School of Economics.
- - An elected member of the International Statistical Institute
- - A council member of the International Society for Business and Industrial Statistics.
- - She has been awarded the Young Statistical Scientist Award by the International Indian Statistical Association, the Best - Student Paper Award by the American Statistical Association and the Women in Mathematical Sciences award by Technical University of Munich, Germany.
ML Expertise (Teaching ML Statistics for Computational Finance)
- - Authored A Book on Computational Finance with R
- - Authored 30+ Research Papers & Articles
- - Editor: Journal of Applied Stochastic Models in Business and Industry
- - Member of CAIML (Center for Artificial Intelligence and Machine Learning) at ISI
- - Associate Editor of several other journals
- - Guided several masters students on theses in the ML area
Dr. Arindam Chaudhuri
Background
- Worked as post doctoral fellow with Department of Computer Science, University of Copenhagen and Department of Computer Science, Technical University of Berlin.
- Worked as researcher with Siemens Research Labs Amsterdam and Samsung Research Labs at New Delhi & Bangalore.
- His current research interests include business analytics, artificial intelligence, machine learning, deep learning.
- He has published 4 research monographs and 60 articles in international journals and conference proceedings.
- He has served as reviewer for several international journals and conferences.
ML Expertise (Teaching ML Statistics for Computational Finance)
- Implemented Machine Learning Methods - Support Vector Machines, Artificial Neural Networks, Deep Learning Networks, Clustering, Genetic Algorithms and Evolutionary Computing with basic mathematical foundations of probability theory, fuzzy sets, rough sets, possibility theory and a variation of these for solution of various business problems.
- Integrated various artificial intelligence methods to form different soft computing frameworks such as neuro-fuzzy, fuzzy-genetic, neuro-genetic and rough-neuro-fuzzy-genetic.
- Successfully applied these methods for different categories of industrial problems such as decision theory, time series forecasting and prediction, image compression, sentiment analysis, recommendation systems, social networks analytics in order to achieve better results
Ritesh Chandra
Background
- - B. Tech. (IIT-Kanpur), PGDM (IIM-Calcutta), CFA
- - 15 years banking experience in risk management with domestic and MNC banks
- - Member, Board of Studies in the area of Finance at IMT-CDL, Ghaziabad
ML Expertise
- - Passionate about teaching. Has been conducting workshops / training programmes for the last 8 yrs in areas of Quantitative Finance, Financial Management, Risk Management and Machine Learning
- - Has contributed to a book on Applications of Blockchains in Financial Services industry
- - Has worked as visiting faculty with several institutions.
Rupal
Background
- B.Tech from IIT, Kanpur and Executive MBA from IIM Kozhikode.
- Currently working as Vice President, Fixed Income at one of the largest International Bank for their Corporate Investment Banking Division.
- Prior to this he was working as Assistant Vice President at Credit Suisse, Investment Banking Division.
- Regularly worked as an internal trainer in the organizations that he has worked in.
ML Expertise (Teaching ML Application for Financial Systems)
- Passionate about teaching, he has been conducting workshops and training programs on Machine Learning & Data Science.
- His areas of interest are fixed income pricing, financial analytics and statistical learning
Dr. Hari
Background
- M.Sc. in Mathematics from B.H.U. Varanasi.
- Awarded four gold medals because of his outstanding performance in B.H.U.
- He has completed his Ph.D. in Mathematics from Indian Institute of Science, Bangalore.
- He has also worked with Deep Value (Algorithm trading firm).
- Currently involved with one of the world's leading analytics product company.
ML Expertise (Teaching ML Application for Algorithmic Trading)
- Passionate about teaching, he has been conducting workshops and training programs on Machine Learning for Trading.
- ML applications for Algorithmic Trading, High Frequency Trading & Quantitative Analytics
Admission Process in AI Trading Course
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Send Your Application
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Get on a call with a counsellor
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Wait for Application Acceptance
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Pay the fee & join the upcoming batch
Finance your Study
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Student Aid
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