Course Overview
Algorithmic Trading (45h) হলো একটি Advanced & Practical Course, যেখানে predefined rules, mathematical models এবং programming ব্যবহার করে systematic trading strategies design, test ও execute করা শেখানো হবে।
এই course-এ Market Data, Trading Signals, Strategy Coding, Backtesting, Portfolio Construction, Risk Management, Position Sizing, Transaction Costs, Slippage, Optimization, Walk-Forward Testing, Paper Trading এবং Trading APIs শেখানো হবে।
Practical Projects-এর মাধ্যমে learners Idea → Data → Signal → Strategy → Backtest → Risk Management → Execution → Monitoring—এই complete algorithmic trading workflow-এর practical understanding অর্জন করবে।
What You Will Learn
✔ Algorithmic Trading Fundamentals
✔ Systematic Trading
✔ Trading Strategy Design
✔ Market Data Analysis
✔ Financial Data Processing
✔ Trading Signals
✔ Technical Indicators
✔ Quantitative Signals
✔ Trend Following
✔ Mean Reversion
✔ Momentum Strategies
✔ Statistical Arbitrage Fundamentals
✔ Strategy Coding with Python
✔ Backtesting
✔ Event-Based Backtesting Fundamentals
✔ Transaction Costs
✔ Slippage
✔ Market Impact Fundamentals
✔ Position Sizing
✔ Risk Management
✔ Portfolio Construction
✔ Portfolio Optimization Fundamentals
✔ Performance Metrics
✔ Sharpe Ratio
✔ Sortino Ratio
✔ Maximum Drawdown
✔ Strategy Optimization
✔ Parameter Testing
✔ Walk-Forward Testing
✔ Out-of-Sample Testing
✔ Overfitting Prevention
✔ Paper Trading
✔ Trading API Fundamentals
✔ Order Execution Concepts
✔ Automated Trading Workflow
✔ Monitoring & Logging
✔ Strategy Deployment Concepts
✔ Algorithmic Trading System Architecture
✔ Professional Trading Projects
Course Curriculum
Module 1 — Introduction to Algorithmic Trading
- What is Algorithmic Trading?
- Algorithmic vs Manual Trading
- Systematic Trading
- Rule-Based Trading
- Quantitative Trading
- Advantages of Algorithmic Trading
- Limitations & Risks
- Trading Automation
- Algorithmic Trading Workflow
- Real-World Applications
Module 2 — Financial Markets & Trading Instruments
- Stock Market
- Forex Market
- Futures
- Options
- ETFs
- Crypto Market Fundamentals
- Spot vs Derivatives
- Market Participants
- Bid & Ask
- Spread
- Liquidity
- Trading Volume
- Market Sessions
Module 3 — Python Environment for Algo Trading
- Python Trading Environment
- Variables & Data Types
- Functions
- Modules
- NumPy
- Pandas
- DataFrames
- Date & Time Handling
- Data Cleaning
- Vectorized Operations
- Writing Reusable Trading Functions
Module 4 — Market Data & Data Engineering
- Historical Market Data
- OHLCV
- Tick Data Fundamentals
- Intraday Data
- Data Frequency
- Data Cleaning
- Missing Data
- Duplicate Data
- Corporate Actions Fundamentals
- Data Alignment
- Resampling
- Data Quality Checks
Module 5 — Financial Returns & Risk Metrics
- Simple Returns
- Log Returns
- Cumulative Returns
- Volatility
- Rolling Volatility
- Correlation
- Covariance
- Beta
- Drawdown
- Risk-Adjusted Performance
Module 6 — Trading Signal Development
- What is a Trading Signal?
- Signal Generation
- Entry Conditions
- Exit Conditions
- Long Signals
- Short Signals
- Signal Confirmation
- Signal Filtering
- Indicator-Based Signals
- Quantitative Signals
- Signal Strength
Module 7 — Technical Trading Strategies
- Moving Average Strategy
- EMA Strategy
- RSI Strategy
- MACD Strategy
- Bollinger Band Strategy
- Breakout Strategy
- Trend-Following Strategy
- Support & Resistance Concepts
- Combining Indicators
- Strategy Rules
Module 8 — Quantitative Trading Strategies
- Momentum
- Mean Reversion
- Pairs Trading Fundamentals
- Statistical Arbitrage Concept
- Z-Score
- Spread Analysis
- Correlation-Based Strategies
- Factor Signals
- Ranking-Based Strategies
- Strategy Selection
Module 9 — Strategy Development Framework
- Define Trading Hypothesis
- Select Market
- Select Data
- Define Variables
- Define Entry Rules
- Define Exit Rules
- Define Risk Rules
- Define Position Size
- Code Strategy
- Document Strategy
Module 10 — Backtesting Fundamentals
- What is Backtesting?
- Historical Simulation
- Strategy Execution Logic
- Entry & Exit
- Position Tracking
- Portfolio Value
- Equity Curve
- Trade Log
- Profit & Loss
- Performance Analysis
Module 11 — Advanced Backtesting
- Event-Based Backtesting Fundamentals
- Vectorized Backtesting
- Daily vs Intraday Backtesting
- Position Management
- Multiple Positions
- Portfolio-Level Backtesting
- Rebalancing
- Transaction Costs
- Slippage
- Execution Assumptions
Module 12 — Backtesting Biases
- Look-Ahead Bias
- Survivorship Bias
- Data Snooping
- Selection Bias
- Leakage
- Overfitting
- Curve Fitting
- Unrealistic Execution
- Poor Data Quality
- How to Improve Backtest Reliability
Module 13 — Trading Performance Analysis
- Total Return
- Annualized Return
- Volatility
- Sharpe Ratio
- Sortino Ratio
- Calmar Ratio Fundamentals
- Maximum Drawdown
- Win Rate
- Profit Factor
- Average Trade
- Risk-Reward Ratio
- Equity Curve Analysis
Module 14 — Risk Management
- Trading Risk
- Portfolio Risk
- Risk per Trade
- Stop-Loss
- Take-Profit
- Position Sizing
- Volatility-Based Position Sizing
- Maximum Position Size
- Portfolio Exposure
- Daily Loss Limit
- Maximum Drawdown Limit
Module 15 — Position Sizing
- Fixed Position Size
- Fixed Fractional Position Sizing
- Risk-Based Position Sizing
- Volatility-Based Sizing
- Kelly Criterion Fundamentals
- Position Limits
- Leverage Fundamentals
- Exposure Management
- Portfolio Allocation
Module 16 — Portfolio Construction
- Multi-Asset Strategies
- Asset Allocation
- Position Weights
- Portfolio Return
- Portfolio Volatility
- Correlation Matrix
- Diversification
- Portfolio Risk
- Rebalancing
- Portfolio-Level Performance
Module 17 — Strategy Optimization
- What is Strategy Optimization?
- Strategy Parameters
- Parameter Search
- Grid Search Concept
- Parameter Stability
- In-Sample Testing
- Out-of-Sample Testing
- Robustness Testing
- Avoiding Curve Fitting
Module 18 — Walk-Forward Analysis
- What is Walk-Forward Testing?
- Training Period
- Testing Period
- Rolling Windows
- Expanding Windows
- Parameter Recalibration
- Out-of-Sample Validation
- Strategy Stability
- Practical Walk-Forward Testing
Module 19 — Machine Learning for Trading Fundamentals
- Machine Learning in Trading
- Features
- Labels
- Training Data
- Testing Data
- Classification Fundamentals
- Regression Fundamentals
- Prediction vs Signal
- Model Evaluation
- Overfitting Risks
Note: এই moduleটি introductory level; advanced machine learning model development-এর জন্য separate specialization recommended।
Module 20 — Execution & Market Microstructure
- Trading Execution
- Market Order
- Limit Order
- Stop Order
- Bid-Ask Spread
- Liquidity
- Slippage
- Market Impact
- Order Timing
- Execution Quality
- Basic Market Microstructure
Module 21 — Trading API Fundamentals
- What is a Trading API?
- API Architecture
- Authentication
- Market Data API
- Order API
- Account Information
- Position Data
- Order Status
- API Errors
- Rate Limits
- API Security
Module 22 — Paper Trading
- What is Paper Trading?
- Live Market Data
- Simulated Orders
- Strategy Monitoring
- Paper Portfolio
- P&L Tracking
- Execution Testing
- Error Detection
- Strategy Validation
- Transition to Live Environment
Module 23 — Automated Trading System Architecture
- Data Layer
- Strategy Layer
- Signal Layer
- Risk Layer
- Execution Layer
- Portfolio Layer
- Logging
- Monitoring
- Error Handling
- System Workflow
Module 24 — Monitoring & Risk Controls
- Live Strategy Monitoring
- Position Monitoring
- P&L Monitoring
- Drawdown Monitoring
- Risk Alerts
- Trade Logs
- System Health
- Data Failure Detection
- API Failure Handling
- Emergency Stop Concept
Module 25 — Professional Algorithmic Trading Workflow
- Trading Idea
- Research
- Data Collection
- Data Cleaning
- Signal Development
- Strategy Coding
- Backtesting
- Optimization
- Walk-Forward Testing
- Paper Trading
- Risk Validation
- Deployment Planning
- Monitoring
- Performance Review
Practical Algorithmic Trading Projects
📊 Project 1 — Moving Average Algorithm
Python ব্যবহার করে একটি trend-following algorithm তৈরি করা হবে:
Market Data → SMA/EMA → Signal → Position → P&L → Backtest
📈 Project 2 — RSI-Based Algorithm
RSI-এর predefined rules ব্যবহার করে automated buy/sell signal তৈরি করা হবে এবং historical performance test করা হবে।
📉 Project 3 — Mean Reversion Algorithm
Bollinger Bands ও Z-Score concept ব্যবহার করে একটি basic mean-reversion strategy তৈরি করা হবে।
🚀 Project 4 — Momentum Strategy
Multiple assets-এর historical performance ব্যবহার করে ranking-based momentum strategy develop করা হবে।
🔄 Project 5 — Pairs Trading Fundamentals
দুটি related assets-এর price relationship analyse করে basic pairs trading framework তৈরি করা হবে।
⚙️ Project 6 — Complete Backtesting Engine
একটি Python-based backtesting framework তৈরি করা হবে যেখানে থাকবে:
- Market Data
- Entry Signal
- Exit Signal
- Position
- P&L
- Transaction Cost
- Slippage
- Equity Curve
- Drawdown
- Performance Metrics
⚠️ Project 7 — Risk Management Engine
Trading system-এর জন্য—
- Position Size
- Stop-Loss
- Risk per Trade
- Maximum Exposure
- Maximum Drawdown
- Daily Loss Limit
implement করা হবে।
💼 Project 8 — Multi-Asset Portfolio Algorithm
একাধিক asset-এর জন্য algorithmic portfolio তৈরি করা হবে এবং—
- Allocation
- Correlation
- Portfolio Return
- Portfolio Risk
- Rebalancing
analyse করা হবে।
🧪 Project 9 — Walk-Forward Strategy Testing
একটি strategy-এর জন্য In-Sample → Out-of-Sample → Walk-Forward testing process তৈরি করা হবে।
🤖 Project 10 — Paper Trading System
Trading API/paper environment-এর concept ব্যবহার করে একটি basic automated trading workflow তৈরি করা হবে:
Market Data → Signal → Risk Check → Order Logic → Position → Monitoring
🏆 Final Project — Complete Algorithmic Trading System
Course শেষে একটি professional-style algorithmic trading project তৈরি করা হবে:
Market Data → Data Processing → Signal Generation → Strategy → Position Sizing → Risk Management → Backtesting → Optimization → Walk-Forward Testing → Paper Trading → Performance Report
Course Features
✅ 45 Hours Professional Training
✅ বাংলা + English Explanation
✅ Intermediate to Advanced Level
✅ Python for Algorithmic Trading
✅ Market Data Analysis
✅ Trading Signal Development
✅ Technical Strategies
✅ Quantitative Strategies
✅ Trend Following
✅ Mean Reversion
✅ Momentum
✅ Pairs Trading Fundamentals
✅ Strategy Coding
✅ Backtesting
✅ Advanced Backtesting Concepts
✅ Transaction Costs
✅ Slippage
✅ Backtesting Biases
✅ Performance Analysis
✅ Sharpe Ratio
✅ Maximum Drawdown
✅ Risk Management
✅ Position Sizing
✅ Portfolio Construction
✅ Strategy Optimization
✅ Walk-Forward Testing
✅ Out-of-Sample Testing
✅ Trading API Fundamentals
✅ Paper Trading
✅ Automated Trading Architecture
✅ Monitoring & Logging
✅ Practical Projects
✅ Complete Algo Trading Capstone
✅ Instructor Support
✅ Class Recording (যদি প্রযোজ্য হয়)
✅ Certificate of Completion (যদি প্রদান করা হয়)
Who Can Join?
- Traders
- Stock Market Traders
- Forex Traders
- Futures Traders
- Crypto Trading Learners
- Finance Students
- Quantitative Finance Students
- Financial Analysts
- Investment Analysts
- Quantitative Analysts
- Python Programmers
- Software Developers
- Data Analysts
- Portfolio Analysts
- Risk Analysts
- FinTech Professionals
- যারা Algorithmic Trading শিখতে চান
- যারা Manual Trading থেকে Systematic Trading-এ যেতে চান
Basic Python এবং financial market knowledge recommended।
After Completing This Course
এই course শেষ করার পর আপনি—
Advanced Quantitative Trading ও Algorithmic Trading development-এর জন্য strong practical foundation তৈরি করতে পারবেন।
Algorithmic Trading-এর core concepts বুঝতে পারবেন।
Manual trading ও systematic trading-এর difference বুঝতে পারবেন।
Python দিয়ে trading strategy develop করতে পারবেন।
Historical market data process ও analyse করতে পারবেন।
Technical এবং quantitative signals তৈরি করতে পারবেন।
Trend-following, momentum এবং mean-reversion strategy develop করতে পারবেন।
Basic pairs trading framework বুঝতে পারবেন।
Trading strategy backtest করতে পারবেন।
Transaction costs এবং slippage model করতে পারবেন।
Look-ahead bias, survivorship bias, data leakage এবং overfitting identify করতে পারবেন।
Sharpe Ratio, Sortino Ratio, Maximum Drawdown, Win Rate ও Profit Factor দিয়ে strategy evaluate করতে পারবেন।
Risk per trade এবং position sizing calculate করতে পারবেন।
Portfolio-level risk ও exposure manage করতে পারবেন।
Multi-asset algorithmic portfolio তৈরি করতে পারবেন।
Strategy optimization ও parameter testing করতে পারবেন।
In-Sample, Out-of-Sample এবং Walk-Forward testing করতে পারবেন।
Trading API এবং paper trading workflow বুঝতে পারবেন।
Automated trading system-এর basic architecture design করতে পারবেন।
Monitoring, logging এবং basic risk controls implement করার foundation পাবেন।
একটি complete algorithmic trading research-to-testing workflow তৈরি করতে পারবেন।