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Courses / Quant Academy / Backtesting Strategies
📘 Beginner ⏱ 45 Hours

Backtesting Strategies

Course Overview

Backtesting Strategies (45h) হলো একটি Advanced & Practical Course, যেখানে historical market data ব্যবহার করে trading strategy-এর performance, reliability এবং robustness systematically evaluate করা শেখানো হবে।

এই course-এ Historical Data, Strategy Coding, Entry & Exit Rules, Transaction Costs, Slippage, Performance Metrics, Drawdown Analysis, Strategy Optimization, Out-of-Sample Testing, Walk-Forward Analysis এবং Monte Carlo Testing শেখানো হবে।

Practical Projects-এর মাধ্যমে learners realistic backtest তৈরি এবং strategy-এর performance ও robustness professionally analyse করার দক্ষতা অর্জন করবে।

What You Will Learn

✔ Backtesting Fundamentals
✔ Historical Market Data
✔ OHLCV Data
✔ Data Cleaning
✔ Strategy Rules
✔ Entry & Exit Logic
✔ Signal Generation
✔ Position Management
✔ Portfolio Simulation
✔ Vectorized Backtesting
✔ Event-Based Backtesting Fundamentals
✔ Transaction Costs
✔ Brokerage Costs
✔ Slippage
✔ Market Impact Fundamentals
✔ Order Execution Assumptions
✔ P&L Calculation
✔ Equity Curve
✔ Drawdown Analysis
✔ Sharpe Ratio
✔ Sortino Ratio
✔ Calmar Ratio
✔ Win Rate
✔ Profit Factor
✔ Risk-Reward Ratio
✔ Look-Ahead Bias
✔ Survivorship Bias
✔ Data Leakage
✔ Data Snooping
✔ Overfitting
✔ Curve Fitting
✔ In-Sample Testing
✔ Out-of-Sample Testing
✔ Walk-Forward Testing
✔ Parameter Optimization
✔ Robustness Testing
✔ Monte Carlo Testing Fundamentals
✔ Stress Testing
✔ Strategy Comparison
✔ Portfolio Backtesting
✔ Risk Management
✔ Professional Backtest Reporting

Course Curriculum

Module 1 — Introduction to Backtesting

  • What is Backtesting?
  • Why Backtesting Matters
  • Backtesting vs Forward Testing
  • Historical Simulation
  • Systematic Strategy Evaluation
  • Backtesting Workflow
  • Advantages
  • Limitations
  • Common Backtesting Mistakes
  • Realistic Expectations

Module 2 — Trading Strategy Fundamentals

  • What is a Trading Strategy?
  • Trading Hypothesis
  • Entry Rules
  • Exit Rules
  • Long Strategy
  • Short Strategy
  • Stop-Loss
  • Take-Profit
  • Position Sizing
  • Strategy Parameters
  • Strategy Documentation

Module 3 — Market Data for Backtesting

  • Historical Market Data
  • OHLCV
  • Daily Data
  • Intraday Data
  • Tick Data Fundamentals
  • Timeframes
  • Adjusted vs Unadjusted Prices
  • Missing Data
  • Duplicate Data
  • Data Alignment
  • Data Quality Checks

Module 4 — Python Environment for Backtesting

  • Python Setup
  • NumPy
  • Pandas
  • DataFrames
  • DateTime
  • Functions
  • Data Structures
  • Financial Calculations
  • Vectorized Operations
  • Reusable Backtesting Functions

Module 5 — Data Preparation

  • Loading Historical Data
  • Cleaning Data
  • Handling Missing Values
  • Sorting Time Series
  • Resampling
  • Adjusting Data
  • Removing Duplicates
  • Handling Corporate Actions Fundamentals
  • Data Validation
  • Preparing Backtest Dataset

Module 6 — Return & Performance Calculations

  • Simple Return
  • Log Return
  • Cumulative Return
  • Daily P&L
  • Trade P&L
  • Portfolio Return
  • Annualized Return
  • Volatility
  • Rolling Return
  • Risk-Adjusted Return

Module 7 — Building a Basic Backtesting Engine

  • Backtesting Architecture
  • Data Layer
  • Signal Layer
  • Position Layer
  • Portfolio Layer
  • P&L Layer
  • Performance Layer
  • Trade Log
  • Equity Curve
  • Basic Backtest Implementation

Module 8 — Entry & Exit Logic

  • Buy Signal
  • Sell Signal
  • Entry Price
  • Exit Price
  • Position Tracking
  • Holding Period
  • Multiple Trades
  • Long Positions
  • Short Positions Fundamentals
  • Trade Execution Rules

Module 9 — Technical Strategy Backtesting

  • Moving Average Strategy
  • EMA Strategy
  • RSI Strategy
  • MACD Strategy
  • Bollinger Bands
  • Breakout Strategy
  • Trend Following
  • Mean Reversion
  • Momentum
  • Strategy Comparison

Module 10 — Portfolio Backtesting

  • Single Asset Backtest
  • Multi-Asset Backtest
  • Portfolio Weights
  • Asset Allocation
  • Rebalancing
  • Portfolio Return
  • Portfolio Volatility
  • Correlation
  • Portfolio Drawdown
  • Portfolio Performance

Module 11 — Transaction Costs & Slippage

  • Why Costs Matter
  • Brokerage
  • Commission
  • Bid-Ask Spread
  • Slippage
  • Market Impact Fundamentals
  • Fixed Transaction Cost
  • Percentage-Based Cost
  • Cost Modeling
  • Net vs Gross Return

Module 12 — Realistic Execution Modeling

  • Market Orders
  • Limit Orders
  • Stop Orders
  • Order Timing
  • Execution Price
  • Liquidity
  • Partial Execution Fundamentals
  • Delayed Execution
  • Execution Assumptions
  • Realistic Backtesting

Module 13 — Performance Metrics

  • Total Return
  • Annualized Return
  • CAGR
  • Volatility
  • Sharpe Ratio
  • Sortino Ratio
  • Calmar Ratio
  • Win Rate
  • Average Win
  • Average Loss
  • Profit Factor
  • Expectancy
  • Risk-Reward Ratio

Module 14 — Drawdown & Risk Analysis

  • What is Drawdown?
  • Maximum Drawdown
  • Average Drawdown
  • Drawdown Duration
  • Recovery Period
  • Rolling Drawdown
  • Risk of Ruin Fundamentals
  • Portfolio Exposure
  • Risk per Trade
  • Strategy Risk Profile

Module 15 — Equity Curve Analysis

  • Building Equity Curve
  • Cumulative P&L
  • Equity Highs
  • Drawdown Curve
  • Performance Stability
  • Monthly Returns
  • Yearly Returns
  • Return Distribution
  • Performance Consistency

Module 16 — Backtesting Biases

  • Look-Ahead Bias
  • Survivorship Bias
  • Selection Bias
  • Data Snooping
  • Data Leakage
  • Overfitting
  • Curve Fitting
  • Incorrect Benchmarking
  • Unrealistic Assumptions
  • Bias Detection

Module 17 — Overfitting & Curve Fitting

  • What is Overfitting?
  • Model Complexity
  • Too Many Parameters
  • Historical Data Fitting
  • In-Sample Performance
  • Out-of-Sample Performance
  • Parameter Stability
  • Robust Strategy Design
  • Avoiding Curve Fitting

Module 18 — In-Sample & Out-of-Sample Testing

  • Training Period
  • Validation Period
  • Testing Period
  • Data Splitting
  • In-Sample Optimization
  • Out-of-Sample Evaluation
  • Performance Comparison
  • Model Validation
  • Generalization

Module 19 — Walk-Forward Testing

  • What is Walk-Forward Testing?
  • Rolling Window
  • Expanding Window
  • Training Period
  • Testing Period
  • Parameter Recalibration
  • Walk-Forward Optimization
  • Out-of-Sample Validation
  • Strategy Stability

Module 20 — Strategy Optimization

  • What is Optimization?
  • Strategy Parameters
  • Parameter Search
  • Grid Search
  • Parameter Ranges
  • Optimization Objectives
  • Sharpe Optimization
  • Drawdown Constraints
  • Return Optimization
  • Robust Parameter Selection

Module 21 — Robustness Testing

  • What is Robustness?
  • Parameter Stability
  • Different Time Periods
  • Different Markets
  • Different Timeframes
  • Cost Sensitivity
  • Slippage Sensitivity
  • Execution Sensitivity
  • Strategy Stability
  • Robustness Checklist

Module 22 — Monte Carlo Testing

  • Monte Carlo Fundamentals
  • Randomized Trade Sequences
  • Resampling
  • Return Simulation
  • Drawdown Simulation
  • Probability of Loss
  • Risk Estimation
  • Confidence Intervals
  • Strategy Reliability

Module 23 — Stress Testing

  • Market Crash Scenarios
  • High Volatility
  • Low Liquidity
  • Increased Transaction Costs
  • Increased Slippage
  • Large Drawdowns
  • Extreme Market Conditions
  • Strategy Stress Test
  • Risk Interpretation

Module 24 — Strategy Comparison

  • Comparing Multiple Strategies
  • Return Comparison
  • Risk Comparison
  • Sharpe Comparison
  • Drawdown Comparison
  • Win Rate Comparison
  • Profit Factor Comparison
  • Consistency
  • Risk-Adjusted Performance
  • Strategy Selection

Module 25 — Professional Backtest Workflow

  • Trading Idea
  • Historical Data
  • Data Cleaning
  • Strategy Definition
  • Coding
  • Backtesting
  • Cost Modeling
  • Performance Analysis
  • Bias Checking
  • Optimization
  • Out-of-Sample Testing
  • Walk-Forward Testing
  • Robustness Testing
  • Final Backtest Report

Practical Backtesting Projects

📊 Project 1 — Moving Average Strategy Backtest

একটি SMA/EMA strategy-এর জন্য complete backtest তৈরি করা হবে:

Historical Data → Indicator → Signal → Position → P&L → Equity Curve → Performance

📈 Project 2 — RSI Strategy Backtest

RSI-based entry ও exit rules ব্যবহার করে strategy performance analyse করা হবে।

Analysis-এ থাকবে:

  • Total Return
  • Win Rate
  • Drawdown
  • Sharpe Ratio
  • Profit Factor

📉 Project 3 — Bollinger Band Mean Reversion

Bollinger Bands ব্যবহার করে একটি mean-reversion strategy backtest করা হবে এবং বিভিন্ন parameter-এর performance compare করা হবে।

🚀 Project 4 — Momentum Strategy Backtest

Multiple assets-এর historical return ব্যবহার করে momentum strategy তৈরি ও backtest করা হবে।

💼 Project 5 — Multi-Asset Portfolio Backtest

একাধিক asset নিয়ে portfolio backtest করা হবে এবং—

  • Portfolio Return
  • Volatility
  • Correlation
  • Drawdown
  • Sharpe Ratio

analyse করা হবে।

⚙️ Project 6 — Transaction Cost & Slippage Model

একটি profitable-looking strategy-তে বিভিন্ন brokerage, spread ও slippage assumptions যোগ করে Gross vs Net Performance compare করা হবে।

🧪 Project 7 — In-Sample vs Out-of-Sample Test

Historical dataset split করে strategy-এর training/optimization এবং unseen data-এর performance compare করা হবে।

🔄 Project 8 — Walk-Forward Backtest

Rolling training/testing windows ব্যবহার করে strategy-এর robustness evaluate করা হবে।

🎯 Project 9 — Parameter Optimization

একটি strategy-এর multiple parameters test করে optimal এবং stable parameter range identify করার process শেখানো হবে।

🎲 Project 10 — Monte Carlo Strategy Test

Historical trade results resample করে সম্ভাব্য future equity curves এবং drawdown scenarios simulate করা হবে।

🏆 Final Project — Professional Strategy Validation

Course শেষে একটি complete backtesting & validation framework তৈরি করা হবে:

Strategy Idea → Data Preparation → Strategy Coding → Backtest → Transaction Costs → Slippage → Performance Metrics → Bias Check → Optimization → Out-of-Sample Test → Walk-Forward → Monte Carlo → Robustness Report

Course Features

✅ 45 Hours Professional Training

✅ বাংলা + English Explanation

✅ Intermediate to Advanced Level

✅ Python-Based Backtesting

✅ Financial Market Data

✅ Data Cleaning

✅ Strategy Development

✅ Entry & Exit Logic

✅ Technical Strategy Backtesting

✅ Quantitative Strategy Backtesting

✅ Portfolio Backtesting

✅ Transaction Cost Modeling

✅ Slippage Modeling

✅ Realistic Execution

✅ Performance Metrics

✅ Sharpe Ratio

✅ Sortino Ratio

✅ Maximum Drawdown

✅ Profit Factor

✅ Equity Curve Analysis

✅ Backtesting Bias Detection

✅ Overfitting Prevention

✅ In-Sample Testing

✅ Out-of-Sample Testing

✅ Walk-Forward Testing

✅ Strategy Optimization

✅ Robustness Testing

✅ Monte Carlo Testing

✅ Stress Testing

✅ Strategy Comparison

✅ Practical Backtesting Projects

✅ Professional Strategy Validation Capstone

✅ Instructor Support

✅ Class Recording (যদি প্রযোজ্য হয়)

✅ Certificate of Completion (যদি প্রদান করা হয়)

Who Can Join?

  • Stock Market Traders
  • Forex Traders
  • Futures Traders
  • Crypto Trading Learners
  • Algorithmic Traders
  • Quantitative Finance Learners
  • Finance Students
  • Financial Analysts
  • Investment Analysts
  • Quantitative Analysts
  • Python Programmers
  • Data Analysts
  • Portfolio Analysts
  • Risk Analysts
  • FinTech Professionals
  • যারা Trading Strategy test করতে চান
  • যারা Algorithmic Trading শেখার পর strategy validation করতে চান

Basic Python এবং financial market knowledge recommended।

After Completing This Course

এই course শেষ করার পর আপনি—

  • Trading strategy-এর জন্য professional backtesting framework বুঝতে পারবেন।
  • Historical market data prepare ও clean করতে পারবেন।
  • Python ব্যবহার করে trading strategy backtest করতে পারবেন।
  • Entry এবং exit logic implement করতে পারবেন।
  • Single এবং multi-asset portfolio backtest করতে পারবেন।
  • Brokerage, transaction costs এবং slippage model করতে পারবেন।
  • Gross ও Net trading performance-এর difference analyse করতে পারবেন।
  • Equity curve এবং drawdown analyse করতে পারবেন।
  • Sharpe Ratio, Sortino Ratio, Calmar Ratio, Win Rate ও Profit Factor calculate করতে পারবেন।
  • Look-ahead bias, survivorship bias, data leakage ও data snooping identify করতে পারবেন।
  • Overfitting ও curve fitting-এর ঝুঁকি বুঝতে পারবেন।
  • In-Sample এবং Out-of-Sample testing করতে পারবেন।
  • Walk-Forward Analysis ব্যবহার করে strategy validate করতে পারবেন।
  • Strategy parameters optimize ও compare করতে পারবেন।
  • Different markets, timeframes ও cost assumptions-এর মাধ্যমে robustness test করতে পারবেন।
  • Monte Carlo simulation ব্যবহার করে সম্ভাব্য risk ও drawdown scenarios analyse করতে পারবেন।
  • Stress testing-এর মাধ্যমে extreme market conditions evaluate করতে পারবেন।
  • Multiple trading strategies-এর risk-adjusted performance compare করতে পারবেন।
  • একটি professional backtest report তৈরি করতে পারবেন।
  • Live deployment-এর আগে strategy validation-এর জন্য systematic workflow তৈরি করতে পারবেন।