AI in Financial Markets
NSE · BSE · Equity · Futures · Options
From Basics to Intelligent Trading Systems. A comprehensive 6-month program to understand financial markets the way AI understands them, and build safe, platform-agnostic, AI-assisted trading systems. Learn markets, technical analysis, fundamental analysis, ML/DL/RL, LLMs, and build your own trading system.
6 Months
Duration
24 Weeks
Curriculum
Hands-on
Trading Systems
₹6,999
Early Bird
Early Bird Price
🔥 Only 10 early bird spots remaining
Course Philosophy
"Learn how financial markets work the way AI understands them, and build safe, platform-agnostic, AI-assisted trading systems."
Trading is NOT prediction — it's risk management
AI is an assistant, not an oracle
FA gives direction, TA gives timing
Always verify AI outputs
Platform-agnostic, API-first design
24-Week Curriculum
A structured 6-month journey from market fundamentals to building your own AI trading system.
Markets + AI Foundations
Market Literacy + AI Intuition
What Are Markets & How AI Sees Them
Understand market structure and AI data representation
Core Topics
- NSE & BSE overview (India's primary exchanges)
- What is a stock, why prices change (supply, demand, news)
- Price, Volume, Time as core data types for AI
- Model types: Rule-based → ML → DL → LLM
Equity, Futures & Options + Market Regimes
Understand instruments and market conditions
Core Topics
- Equity vs Futures vs Options explained
- Lot size, expiry, margin basics
- Trending vs ranging markets, volatility regimes
- Market regime as classification problem
Technical Analysis + Rule-Based Algo
Convert Chart Logic into Code
Technical Analysis as Code
Transform visual patterns into algorithms
Core Topics
- Market structure (HH/HL, LH/LL patterns)
- Moving Averages (SMA, EMA), RSI, ATR
- TA as conditional if-else logic
- Feature engineering principles
Strategy Design + API Trading
Build complete trading pipeline
Core Topics
- Entry/exit rules, stop loss, take profit
- ML as confidence filter (not signal generator)
- REST API fundamentals for trading
- Broker abstraction pattern
Fundamental Analysis + AI
Understand WHY Markets Move
Fundamental Analysis & NLP
Convert fundamentals to features
Core Topics
- Financial statements (Income, Balance Sheet, Cash Flow)
- Key ratios (PE, ROE, margins)
- Sentiment analysis basics with FinBERT
- Correlating sentiment with price moves
FA + TA + AI Integration & Risk
Complete stock selection & timing system
Core Topics
- FA for stock selection (what to buy)
- TA for timing (when to buy)
- Position sizing, drawdown management
- Volatility-scaled position sizing
Deeper AI for Markets
Let AI Learn Patterns Humans Can't Code
ML Validation & Deep Learning
Proper ML usage in trading
Core Topics
- Walk-forward validation, purged CV
- Overfitting, non-stationarity pitfalls
- LSTM for time series (long-term memory)
- CNN for local pattern detection
Reinforcement Learning & Regime Detection
Adaptive trading systems
Core Topics
- Trading as MDP (states, actions, rewards)
- Reward design (Sharpe, drawdown penalties)
- Regime detection (HMM, clustering)
- Strategy switching & ensemble approaches
LLMs, RAG & AI Agents
Use AI Professionally in Finance
LLMs & Financial Prompting
Master LLM capabilities for finance
Core Topics
- Transformers explained (global context)
- What LLMs CAN vs CANNOT do in trading
- Prompt Engineering: Role + Context + Task
- Chain-of-thought, verification mindset
RAG & Multi-Agent Trading Systems
Build autonomous research systems
Core Topics
- RAG: retrieve → augment → generate
- Vector databases & embeddings
- Multi-Agent: Research, Analysis, Risk, Execution
- Human-in-the-loop safety controls
Capstone Project
Build Your Complete AI Trading System
Capstone Planning & Build
Design and implement your system
Core Topics
- Project options: Trading Bot, Research Assistant, Risk Engine
- Architecture design & data flow
- Modular code, Git, documentation
- Integration: Strategy + Risk + Execution
Testing, Evaluation & Demo Day
Validate and present your work
Core Topics
- Backtesting with realistic assumptions
- Metrics: CAGR, Sharpe, drawdown, win rate
- Failure analysis & edge cases
- Final presentation & Q&A
Technology Stack
Assessment Structure
Weekly Assignments
40%18 weekly coding submissions
Mid-Course Project
20%Month 3 integrated FA+TA+ML strategy
Capstone Project
30%Complete working trading system
Participation
10%Engagement + peer collaboration
Pass: 60% | Distinction: 80%+
Career Outcomes
Think Like
Build Systems For
Learn from Experts
Industry practitioners with real-world experience in AI and financial markets.
Mohit Mandawat
AI Engineer & Financial Markets Expert
5+ years of experience in financial markets. Specialized in AI/ML deployment and quantitative trading systems.
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