Course Overview
Quant Trading Masterclass (60h) হলো একটি Advanced, Professional & Practical Course, যেখানে quantitative methods, mathematical models, statistics এবং programming ব্যবহার করে systematic trading strategies তৈরি, test ও manage করা শেখানো হবে।
এই masterclass-এ Financial Mathematics, Market Data Analysis, Alpha Research, Factor Models, Algorithmic Trading, Backtesting, Portfolio Optimization, Risk Management এবং Machine Learning for Trading-এর fundamentals শেখানো হবে।
Practical Projects ও Quant Research Workflow-এর মাধ্যমে learners একটি complete quantitative trading framework গড়ে তোলার দক্ষতা অর্জন করবে।
Market Data → Research → Signal Generation → Strategy Development → Backtesting → Optimization → Portfolio Construction → Risk Management → Execution → Monitoring
What You Will Learn
✔ Quantitative Trading Fundamentals
✔ Quant Research Workflow
✔ Financial Mathematics
✔ Statistics for Trading
✔ Probability Concepts
✔ Market Data Analysis
✔ Return Analysis
✔ Risk Metrics
✔ Alpha Generation Concepts
✔ Factor Investing Fundamentals
✔ Quantitative Signals
✔ Trading Strategy Development
✔ Algorithmic Trading Systems
✔ Backtesting Framework
✔ Portfolio Construction
✔ Portfolio Optimization
✔ Risk Management
✔ Position Sizing
✔ Performance Evaluation
✔ Sharpe Ratio
✔ Information Ratio
✔ Maximum Drawdown
✔ Statistical Arbitrage Fundamentals
✔ Pairs Trading
✔ Momentum Strategies
✔ Mean Reversion Strategies
✔ Trend Following
✔ Machine Learning for Trading Fundamentals
✔ Feature Engineering
✔ Model Evaluation
✔ Trading Execution
✔ Market Microstructure
✔ Trading APIs
✔ Strategy Deployment Concepts
✔ Professional Quant Workflow
Course Curriculum
Module 1 — Introduction to Quant Trading
- What is Quantitative Trading?
- Quant Trading vs Manual Trading
- Algorithmic Trading vs Quant Trading
- Role of Mathematics & Statistics
- Quant Research Process
- Systematic Trading
- Data-Driven Decision Making
- Quant Trading Career Overview
- Professional Quant Workflow
Module 2 — Financial Mathematics for Quant Trading
- Mathematical Foundations
- Percentages & Growth Rates
- Returns Calculation
- Simple Returns
- Log Returns
- Compounding
- Annualized Returns
- Volatility Calculation
- Risk Measurement
- Time Series Fundamentals
Module 3 — Statistics for Trading
- Descriptive Statistics
- Mean
- Median
- Variance
- Standard Deviation
- Distribution Concepts
- Normal Distribution
- Correlation
- Covariance
- Regression Fundamentals
- Statistical Testing Basics
- Hypothesis Testing Fundamentals
Module 4 — Probability for Quant Finance
- Probability Fundamentals
- Random Variables
- Expected Value
- Probability Distribution
- Conditional Probability
- Bayesian Concept Fundamentals
- Risk Probability
- Trading Decision Under Uncertainty
- Probability-Based Thinking
Module 5 — Python for Quant Trading
- Python Environment
- NumPy
- Pandas
- Data Processing
- Financial Calculations
- Time Series Handling
- Data Visualization
- Statistical Analysis
- Writing Quant Research Code
- Building Reusable Functions
Module 6 — Financial Market Data
- Market Data Types
- OHLCV Data
- Tick Data Fundamentals
- Order Book Concept
- Intraday Data
- Data Cleaning
- Missing Data Handling
- Data Alignment
- Data Quality Analysis
- Data Preparation Pipeline
Module 7 — Quantitative Research Process
- Research Question
- Market Hypothesis
- Data Collection
- Data Analysis
- Signal Discovery
- Strategy Development
- Backtesting
- Validation
- Performance Evaluation
- Research Documentation
Module 8 — Alpha Generation Fundamentals
- What is Alpha?
- Alpha vs Beta
- Market Inefficiencies
- Signal Discovery
- Predictive Features
- Trading Factors
- Quantitative Signals
- Signal Strength
- Alpha Evaluation
Module 9 — Factor Investing & Factor Models
- Factor Investing Concept
- Value Factor
- Momentum Factor
- Quality Factor
- Volatility Factor
- Size Factor
- Factor Ranking
- Factor Combination
- Factor Portfolio
- Factor Backtesting
Module 10 — Quantitative Trading Strategies
Trend Following
- Trend Identification
- Moving Average Models
- Breakout Systems
- Signal Generation
Momentum Strategy
- Price Momentum
- Relative Strength
- Ranking Models
- Portfolio Selection
Mean Reversion
- Statistical Mean
- Price Deviation
- Z-Score
- Reversion Signals
Statistical Arbitrage
- Pair Relationship
- Spread Analysis
- Cointegration Fundamentals
- Trading Signals
Module 11 — Algorithmic Trading System Development
- Strategy Architecture
- Data Layer
- Signal Layer
- Strategy Layer
- Risk Layer
- Execution Layer
- Portfolio Layer
- Monitoring Layer
- Automated Workflow
Module 12 — Advanced Backtesting
- Backtesting Framework
- Historical Simulation
- Event-Based Backtesting
- Portfolio Backtesting
- Transaction Costs
- Slippage
- Market Impact
- Execution Modeling
- Performance Analysis
Module 13 — Strategy Validation
- In-Sample Testing
- Out-of-Sample Testing
- Walk-Forward Analysis
- Robustness Testing
- Parameter Stability
- Stress Testing
- Monte Carlo Simulation Fundamentals
- Avoiding Overfitting
Module 14 — Portfolio Construction
- Portfolio Theory Fundamentals
- Asset Allocation
- Portfolio Weighting
- Diversification
- Correlation Analysis
- Portfolio Return
- Portfolio Risk
- Rebalancing
- Multi-Asset Strategies
Module 15 — Portfolio Optimization
- Optimization Concepts
- Risk-Return Tradeoff
- Mean-Variance Optimization
- Efficient Frontier Fundamentals
- Minimum Variance Portfolio
- Risk-Based Allocation
- Optimization Limitations
Module 16 — Risk Management
- Trading Risk
- Portfolio Risk
- Position Sizing
- Stop Loss
- Exposure Management
- Volatility Targeting
- Drawdown Control
- Risk Limits
- Risk Monitoring
Module 17 — Performance Measurement
- Return Analysis
- Sharpe Ratio
- Sortino Ratio
- Information Ratio
- Alpha
- Beta
- Maximum Drawdown
- Calmar Ratio
- Profit Factor
- Risk Adjusted Performance
Module 18 — Machine Learning for Quant Trading Fundamentals
- Machine Learning Overview
- Supervised Learning
- Features
- Labels
- Training Data
- Testing Data
- Classification
- Regression
- Model Evaluation
- Overfitting Prevention
Module 19 — Feature Engineering for Trading
- Price Features
- Volume Features
- Technical Features
- Statistical Features
- Rolling Features
- Lag Features
- Feature Selection
- Feature Importance
Module 20 — Trading Execution & Market Microstructure
- Market Structure
- Liquidity
- Bid-Ask Spread
- Order Types
- Execution Algorithms Fundamentals
- Slippage
- Market Impact
- Execution Quality
Module 21 — Trading API & Automation
- Trading API Concept
- Market Data API
- Order API
- Authentication
- Paper Trading
- Automated Execution Workflow
- Error Handling
- Monitoring
Module 22 — Professional Quant Trading Workflow
- Idea Generation
- Data Research
- Signal Development
- Strategy Coding
- Backtesting
- Validation
- Portfolio Construction
- Risk Analysis
- Execution
- Monitoring
- Performance Review
Practical Quant Trading Projects
Project 1 — Quant Research Pipeline
Complete workflow:
Market Data → Cleaning → Analysis → Signal Discovery → Strategy → Backtest → Report
Project 2 — Momentum Quant Strategy
Develop:
- Asset Ranking
- Signal Generation
- Portfolio Selection
- Backtesting
- Performance Analysis
Project 3 — Mean Reversion Model
Build:
- Statistical Model
- Entry Signal
- Exit Signal
- Risk Rules
- Backtesting
Project 4 — Statistical Arbitrage Strategy
Develop:
- Pair Selection
- Spread Calculation
- Z-Score Analysis
- Trading Signal
- Performance Evaluation
Project 5 — Factor-Based Portfolio
Create:
- Factor Scores
- Asset Ranking
- Portfolio Construction
- Rebalancing System
Project 6 — Machine Learning Trading Model
Build fundamentals:
- Feature Creation
- Dataset Preparation
- Model Training
- Prediction Analysis
- Evaluation
Project 7 — Complete Quant Trading System
Final project:
Data → Research → Alpha Signal → Strategy → Backtest → Optimization → Portfolio → Risk → Report
Course Features
✅ 60 Hours Professional Masterclass
✅ বাংলা + English Explanation
✅ Advanced Quant Trading Training
✅ Financial Mathematics
✅ Statistics for Finance
✅ Python for Quant Research
✅ Market Data Analysis
✅ Alpha Research
✅ Factor Models
✅ Algorithmic Strategies
✅ Advanced Backtesting
✅ Portfolio Optimization
✅ Risk Management
✅ Machine Learning Fundamentals
✅ Trading Execution
✅ API Concepts
✅ Practical Quant Projects
✅ Professional Research Workflow
✅ Capstone Quant Trading Project
✅ Instructor Support
✅ Certificate of Completion
Who Can Join?
- Quant Trading Learners
- Algorithmic Traders
- Finance Students
- Mathematics Students
- Data Scientists
- Python Developers
- Financial Analysts
- Investment Analysts
- Portfolio Managers
- Risk Analysts
- Traders who want systematic methods
- FinTech Professionals
Python, statistics এবং finance-এর basic knowledge recommended।
After Completing This Course
এই course শেষে আপনি—
- Quantitative Trading-এর complete workflow বুঝতে পারবেন।
- Financial data analyse করতে পারবেন।
- Statistical methods ব্যবহার করে trading signals develop করতে পারবেন।
- Alpha research এবং factor models-এর fundamentals বুঝতে পারবেন।
- Quant strategies design ও backtest করতে পারবেন।
- Algorithmic trading system architecture বুঝতে পারবেন।
- Portfolio construction এবং optimization-এর foundation পাবেন।
- Risk management framework তৈরি করতে পারবেন।
- Performance metrics দিয়ে strategy evaluate করতে পারবেন।
- Machine learning-based trading research-এর fundamentals বুঝতে পারবেন।
- Professional quant research process follow করতে পারবেন।
- Advanced Algorithmic Trading, Quant Research এবং Financial Engineering-এর জন্য strong foundation তৈরি করতে পারবেন।