- To understand high-frequency market microstructure and order book dynamics.
- To implement LSTM and Transformer models for financial forecasting.
- To apply reinforcement learning for execution strategy design.
- To conduct vectorized backtesting with risk-adjusted evaluation.
- To develop reproducible deep learning workflows for quantitative trading research.
Deep Learning for Financial Market Microstructure
Advanced AI Methods for High-Frequency Financial Research
About This Course
Advanced workshop on deep learning for high-frequency markets and algorithmic trading, Covers order book modeling, LSTM/Transformers, reinforcement learning, and risk-adjusted backtesting, For quantitative researchers proficient in Python and machine learning.
Aim
To equip quantitative researchers with advanced deep learning frameworks for modeling high-frequency market microstructure and developing robust, data-driven algorithmic trading strategies.
Workshop Objectives
Workshop Structure
📅 Day 1 — Hands-On Market Microstructure Data Engineering
- Focus: Practical processing of high-frequency tick and order book data
- Hands-On Activities:
- Importing and cleaning tick-by-tick NASDAQ / crypto order book data
- Constructing order flow imbalance and liquidity features
- Generating volatility signature plots
- Handling asynchronous multi-asset data streams
- Building efficient data pipelines for large-scale datasets
📅 Day 2 — Hands-On Deep Learning for Financial Time Series
- Focus: Building predictive and execution models
- Hands-On Activities:
- Developing LSTM-Attention models for price direction prediction
- Implementing Transformer architectures for volatility forecasting
- Training and evaluating deep learning models using PyTorch
- Designing reinforcement learning agents for optimal execution
- Performance tuning and model validation
📅 Day 3 — Hands-On Backtesting & Strategy Evaluation
- Focus: Strategy validation and research-ready outputs
- Hands-On Activities:
- Implementing vectorized backtesting with transaction cost modeling
- Calculating Sharpe ratios, drawdowns, and risk-adjusted metrics
- Generating performance heatmaps and regime analysis
- Exporting LaTeX-formatted performance tables
- Building a complete reproducible quant research pipeline
Who Should Enrol?
- PhD scholars and researchers in Finance or Financial Engineering
- Quantitative analysts and algorithmic trading professionals
- Financial data scientists working with time-series data
- Advanced postgraduate students with strong quantitative skills
Important Dates
Registration Ends
02/28/2026
IST 4 : 30 PM
Workshop Dates
02/28/2026 – 03/02/2026
IST 5: 30PM
Workshop Outcomes
- Engineer and analyze high-frequency order book data.
- Develop LSTM and Transformer models for financial forecasting.
- Design reinforcement learning-based execution strategies.
- Implement vectorized backtesting with transaction cost modeling.
- Evaluate trading performance using risk-adjusted financial metrics.
Fee Structure
Student
₹2499 | $75
Ph.D. Scholar / Researcher
₹3499 | $85
Academician / Faculty
₹4499 | $95
Industry Professional
₹6498 | $115
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
★★★★★
Cancer Drug Discovery: Creating Cancer Therapies
Mario Rigo • ★★★★★
Power BI and Advanced SQL Mastery Integration Workshop, CRISPR-Cas Genome Editing: Workflow, Tools and Techniques
Silvia Santopolo • ★★★★★
Artificial Intelligence for Cancer Drug Delivery
G Jyothi • ★★★★★
Artificial Intelligence for Cancer Drug Delivery
RAVIKANT SHEKHAR • View All Feedbacks →