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

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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

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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

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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

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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

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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

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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

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Module 7 — Quantitative Research Process

• Research Question
• Market Hypothesis
• Data Collection
• Data Analysis
• Signal Discovery
• Strategy Development
• Backtesting
• Validation
• Performance Evaluation
• Research Documentation

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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

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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

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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

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Module 11 — Algorithmic Trading System Development

• Strategy Architecture
• Data Layer
• Signal Layer
• Strategy Layer
• Risk Layer
• Execution Layer
• Portfolio Layer
• Monitoring Layer
• Automated Workflow

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Module 12 — Advanced Backtesting

• Backtesting Framework
• Historical Simulation
• Event-Based Backtesting
• Portfolio Backtesting
• Transaction Costs
• Slippage
• Market Impact
• Execution Modeling
• Performance Analysis

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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

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Module 14 — Portfolio Construction

• Portfolio Theory Fundamentals
• Asset Allocation
• Portfolio Weighting
• Diversification
• Correlation Analysis
• Portfolio Return
• Portfolio Risk
• Rebalancing
• Multi-Asset Strategies

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Module 15 — Portfolio Optimization

• Optimization Concepts
• Risk-Return Tradeoff
• Mean-Variance Optimization
• Efficient Frontier Fundamentals
• Minimum Variance Portfolio
• Risk-Based Allocation
• Optimization Limitations

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Module 16 — Risk Management

• Trading Risk
• Portfolio Risk
• Position Sizing
• Stop Loss
• Exposure Management
• Volatility Targeting
• Drawdown Control
• Risk Limits
• Risk Monitoring

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Module 17 — Performance Measurement

• Return Analysis
• Sharpe Ratio
• Sortino Ratio
• Information Ratio
• Alpha
• Beta
• Maximum Drawdown
• Calmar Ratio
• Profit Factor
• Risk Adjusted Performance

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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

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Module 19 — Feature Engineering for Trading

• Price Features
• Volume Features
• Technical Features
• Statistical Features
• Rolling Features
• Lag Features
• Feature Selection
• Feature Importance

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Module 20 — Trading Execution & Market Microstructure

• Market Structure
• Liquidity
• Bid-Ask Spread
• Order Types
• Execution Algorithms Fundamentals
• Slippage
• Market Impact
• Execution Quality

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Module 21 — Trading API & Automation

• Trading API Concept
• Market Data API
• Order API
• Authentication
• Paper Trading
• Automated Execution Workflow
• Error Handling
• Monitoring

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Module 22 — Professional Quant Trading Workflow

• Idea Generation
• Data Research
• Signal Development
• Strategy Coding
• Backtesting
• Validation
• Portfolio Construction
• Risk Analysis
• Execution
• Monitoring
• Performance Review

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Practical Quant Trading Projects

Project 1 — Quant Research Pipeline

Complete workflow:

Market Data → Cleaning → Analysis → Signal Discovery → Strategy → Backtest → Report

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Project 2 — Momentum Quant Strategy

Develop:

• Asset Ranking
• Signal Generation
• Portfolio Selection
• Backtesting
• Performance Analysis

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Project 3 — Mean Reversion Model

Build:

• Statistical Model
• Entry Signal
• Exit Signal
• Risk Rules
• Backtesting

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Project 4 — Statistical Arbitrage Strategy

Develop:

• Pair Selection
• Spread Calculation
• Z-Score Analysis
• Trading Signal
• Performance Evaluation

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Project 5 — Factor-Based Portfolio

Create:

• Factor Scores
• Asset Ranking
• Portfolio Construction
• Rebalancing System

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Project 6 — Machine Learning Trading Model

Build fundamentals:

• Feature Creation
• Dataset Preparation
• Model Training
• Prediction Analysis
• Evaluation

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Project 7 — Complete Quant Trading System

Final project:

Data → Research → Alpha Signal → Strategy → Backtest → Optimization → Portfolio → Risk → Report

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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

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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।

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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।