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Courses / Quant Academy / Quantitative Finance Professional
📘 Beginner ⏱ 60 Hours

Quantitative Finance Professional

Course Overview

Quantitative Finance হলো mathematics, statistics, programming এবং financial theory ব্যবহার করে investment, pricing, risk management এবং financial decision-making-এর complex problems solve করার একটি advanced field।

Quantitative Finance Professional (60 Hours) courseটি finance, mathematics, statistics, economics, computer science এবং related backgrounds-এর learners যারা professional-level quantitative finance skills develop করতে চান তাদের জন্য designed করা হয়েছে।

এই course-এ আপনি Quantitative Finance Foundations, Probability, Statistics, Financial Mathematics, Time Series, Regression, Portfolio Theory, Risk Management, Derivatives, Option Pricing, Black-Scholes Model, Monte Carlo Simulation, Value at Risk (VaR), Fixed Income, Quantitative Trading Fundamentals, Financial Modeling এবং Python-based Quantitative Analysis practical projects-এর মাধ্যমে শিখবেন।

Course-এর লক্ষ্য হলো learners-কে financial theory এবং quantitative techniques একসাথে ব্যবহার করে financial markets, investments, derivatives এবং risk-এর data-driven analysis করার জন্য প্রস্তুত করা।

What You Will Learn

✔ Quantitative Finance Fundamentals
✔ Financial Mathematics
✔ Probability for Finance
✔ Statistical Methods
✔ Financial Data Analysis
✔ Random Variables
✔ Probability Distributions
✔ Expected Value & Variance
✔ Covariance & Correlation
✔ Regression Analysis
✔ Time-Series Analysis
✔ Financial Returns
✔ Volatility Modeling
✔ Portfolio Theory
✔ Modern Portfolio Theory
✔ Efficient Frontier
✔ CAPM Fundamentals
✔ Factor Models
✔ Risk Management
✔ Value at Risk (VaR)
✔ Expected Shortfall
✔ Monte Carlo Simulation
✔ Derivatives Fundamentals
✔ Forwards & Futures
✔ Options
✔ Option Payoff Analysis
✔ Black-Scholes Model
✔ Greeks
✔ Binomial Option Pricing
✔ Fixed Income Fundamentals
✔ Bond Pricing
✔ Yield & Duration
✔ Quantitative Trading Fundamentals
✔ Backtesting Fundamentals
✔ Python for Quantitative Finance
✔ Financial Modeling
✔ Model Validation
✔ Scenario & Sensitivity Analysis
✔ Professional Quantitative Finance Projects

Course Curriculum

Module 1 — Quantitative Finance Foundations

  • What is Quantitative Finance?
  • Role of Mathematics in Finance
  • Role of Statistics
  • Role of Programming
  • Financial Markets
  • Asset Classes
  • Quantitative vs Fundamental Analysis
  • Quantitative Finance Applications
  • Career Paths in Quant Finance
  • Quantitative Finance Workflow

Module 2 — Mathematical Foundations for Finance

  • Algebra for Finance
  • Exponents
  • Logarithms
  • Functions
  • Equations
  • Financial Formulas
  • Mathematical Notation
  • Sequences & Series
  • Compounding
  • Discounting
  • Continuous Compounding
  • Mathematical Applications in Finance

Module 3 — Probability for Quantitative Finance

  • Probability Fundamentals
  • Sample Space
  • Events
  • Conditional Probability
  • Independent Events
  • Bayes’ Theorem
  • Random Variables
  • Expected Value
  • Variance
  • Covariance
  • Probability-Based Financial Decisions

Module 4 — Probability Distributions

  • Discrete Distributions
  • Continuous Distributions
  • Normal Distribution
  • Lognormal Distribution
  • Binomial Distribution
  • Poisson Distribution
  • Exponential Distribution
  • Expected Value
  • Variance
  • Distribution Selection
  • Financial Applications

Module 5 — Statistical Methods for Finance

  • Descriptive Statistics
  • Mean
  • Median
  • Variance
  • Standard Deviation
  • Skewness
  • Kurtosis
  • Correlation
  • Covariance
  • Z-Score
  • Confidence Intervals
  • Hypothesis Testing
  • Statistical Significance

Module 6 — Financial Returns

  • Price vs Return
  • Simple Return
  • Log Return
  • Daily Return
  • Monthly Return
  • Annual Return
  • Cumulative Return
  • Expected Return
  • Return Distribution
  • Risk-Return Relationship

Module 7 — Time-Series Analysis

  • What is Time-Series Data?
  • Trend
  • Seasonality
  • Stationarity
  • Autocorrelation
  • Moving Average
  • Rolling Statistics
  • Lag Variables
  • Financial Price Series
  • Return Series
  • Time-Series Applications

Module 8 — Regression & Factor Models

  • Linear Regression
  • Multiple Regression
  • Regression Coefficients
  • R-Squared
  • Residual Analysis
  • Regression Assumptions
  • Financial Applications
  • CAPM
  • Beta
  • Alpha
  • Factor Models
  • Risk Factor Analysis

Module 9 — Portfolio Theory

  • Portfolio Fundamentals
  • Expected Portfolio Return
  • Portfolio Variance
  • Portfolio Standard Deviation
  • Covariance Matrix
  • Correlation
  • Diversification
  • Efficient Frontier
  • Minimum Variance Portfolio
  • Optimal Portfolio Concept
  • Risk-Return Analysis

Module 10 — Modern Portfolio Theory

  • Markowitz Portfolio Theory
  • Efficient Frontier
  • Portfolio Optimization
  • Risk Minimization
  • Return Maximization
  • Constraints
  • Asset Allocation
  • Diversification
  • Portfolio Comparison
  • Practical Portfolio Optimization

Module 11 — CAPM & Asset Pricing

  • Capital Asset Pricing Model
  • Systematic Risk
  • Unsystematic Risk
  • Beta
  • Expected Return
  • Risk-Free Rate
  • Market Risk Premium
  • Security Market Line
  • Alpha
  • CAPM Applications
  • Model Limitations

Module 12 — Quantitative Risk Management

  • Financial Risk Fundamentals
  • Market Risk
  • Credit Risk
  • Liquidity Risk
  • Operational Risk
  • Risk Measurement
  • Risk Factors
  • Risk Limits
  • Risk Monitoring
  • Quantitative Risk Framework

Module 13 — Value at Risk (VaR)

  • What is VaR?
  • VaR Interpretation
  • Confidence Level
  • Time Horizon
  • Historical VaR
  • Parametric VaR
  • Monte Carlo VaR
  • VaR Applications
  • VaR Limitations
  • Portfolio VaR

Module 14 — Expected Shortfall

  • What is Expected Shortfall?
  • VaR vs Expected Shortfall
  • Tail Risk
  • Expected Loss
  • Extreme Market Events
  • Portfolio Tail Risk
  • Risk Interpretation
  • Practical Risk Analysis

Module 15 — Monte Carlo Simulation

  • What is Monte Carlo Simulation?
  • Random Sampling
  • Simulation Process
  • Probability Models
  • Generating Scenarios
  • Simulating Asset Prices
  • Risk Simulation
  • Option Pricing Applications
  • Portfolio Simulation
  • Simulation Interpretation

Module 16 — Derivatives Fundamentals

  • What are Derivatives?
  • Forwards
  • Futures
  • Options
  • Swaps Fundamentals
  • Underlying Assets
  • Payoff
  • Profit & Loss
  • Hedging
  • Speculation
  • Arbitrage Concept

Module 17 — Forwards & Futures

  • Forward Contracts
  • Futures Contracts
  • Contract Pricing
  • Payoff Analysis
  • Long Position
  • Short Position
  • Hedging
  • Futures Margin Concept
  • Mark-to-Market Fundamentals
  • Practical Examples

Module 18 — Options Fundamentals

  • Call Option
  • Put Option
  • European vs American Options
  • Strike Price
  • Expiration
  • Premium
  • Intrinsic Value
  • Time Value
  • Option Payoff
  • Profit & Loss
  • Option Strategies Fundamentals

Module 19 — Binomial Option Pricing

  • Binomial Tree
  • Up & Down Movement
  • Risk-Neutral Probability
  • Option Payoff
  • Backward Induction
  • Call Valuation
  • Put Valuation
  • Multi-Period Binomial Model
  • Practical Option Pricing

Module 20 — Black-Scholes Model

  • Black-Scholes Framework
  • Model Assumptions
  • Stock Price
  • Strike Price
  • Time to Maturity
  • Volatility
  • Risk-Free Rate
  • Call Pricing
  • Put Pricing
  • Model Interpretation
  • Practical Applications
  • Model Limitations

Module 21 — Option Greeks

  • What are Greeks?
  • Delta
  • Gamma
  • Theta
  • Vega
  • Rho
  • Price Sensitivity
  • Volatility Sensitivity
  • Time Decay
  • Hedging Applications
  • Greeks Interpretation

Module 22 — Fixed Income Quantitative Analysis

  • Bond Fundamentals
  • Bond Pricing
  • Yield
  • Coupon
  • Zero-Coupon Bonds
  • Yield Curve
  • Spot Rate
  • Forward Rate
  • Discount Factors
  • Fixed Income Risk

Module 23 — Duration & Convexity

  • Macaulay Duration
  • Modified Duration
  • Duration-Based Risk
  • Interest Rate Sensitivity
  • Convexity
  • Price-Yield Relationship
  • Bond Portfolio Risk
  • Practical Applications

Module 24 — Interest Rate Modeling Fundamentals

  • Interest Rate Risk
  • Yield Curve
  • Term Structure
  • Short-Term Rates
  • Forward Rates
  • Rate Scenarios
  • Interest Rate Sensitivity
  • Basic Interest Rate Models
  • Financial Applications

Module 25 — Quantitative Trading Fundamentals

  • What is Quantitative Trading?
  • Trading Strategies
  • Rule-Based Trading
  • Signal Generation
  • Momentum
  • Mean Reversion
  • Statistical Arbitrage Concept
  • Risk Management
  • Position Sizing
  • Trading Constraints

Module 26 — Backtesting

  • What is Backtesting?
  • Historical Data
  • Strategy Rules
  • Entry & Exit
  • Transaction Costs
  • Slippage
  • Performance Metrics
  • Sharpe Ratio
  • Maximum Drawdown
  • Overfitting
  • Backtesting Limitations

Module 27 — Python for Quantitative Finance

  • Python Environment
  • Variables & Data Types
  • Lists & Dictionaries
  • Functions
  • NumPy Fundamentals
  • Pandas Fundamentals
  • DataFrames
  • Data Cleaning
  • Financial Data Structures
  • Basic Visualization
  • Statistical Calculations
  • Financial Modeling with Python

Module 28 — Python-Based Financial Analysis

  • Loading Financial Data
  • Return Calculation
  • Volatility Calculation
  • Correlation Matrix
  • Rolling Statistics
  • Portfolio Analysis
  • Regression
  • Risk Metrics
  • Financial Data Visualization
  • Automated Analysis

Module 29 — Quantitative Financial Modeling

  • Financial Model Structure
  • Model Inputs
  • Assumptions
  • Risk Factors
  • Scenario Analysis
  • Sensitivity Analysis
  • Valuation
  • Model Calibration
  • Model Validation
  • Model Risk

Module 30 — Professional Quantitative Finance Workflow

  • Financial Problem Definition
  • Data Collection
  • Data Cleaning
  • Statistical Analysis
  • Model Selection
  • Model Development
  • Backtesting
  • Validation
  • Risk Analysis
  • Interpretation
  • Reporting
  • Final Decision

Practical Quantitative Finance Projects

📊 Project 1 — Financial Return & Risk Analysis

Historical asset data ব্যবহার করে—

  • Returns
  • Mean Return
  • Standard Deviation
  • Volatility
  • Skewness
  • Kurtosis

calculate করে risk-return profile তৈরি করা হবে।

📈 Project 2 — Portfolio Optimization

Multiple assets ব্যবহার করে—

  • Expected Return
  • Covariance Matrix
  • Portfolio Risk
  • Correlation
  • Efficient Frontier

analyse করে portfolio optimization-এর practical implementation করা হবে।

🧮 Project 3 — CAPM Analysis

একটি selected asset-এর জন্য—

  • Beta
  • Market Return
  • Risk-Free Rate
  • Market Risk Premium
  • Expected Return
  • Alpha

calculate ও interpret করা হবে।

⚠️ Project 4 — Value at Risk Model

একটি portfolio-এর জন্য Historical VaR, Parametric VaR এবং basic Monte Carlo VaR estimate করে risk comparison করা হবে।

🎲 Project 5 — Monte Carlo Simulation

Random scenarios ব্যবহার করে asset price বা portfolio value simulate করা হবে এবং probability-based risk analysis করা হবে।

📉 Project 6 — Option Pricing

একটি option-এর জন্য—

  • Strike Price
  • Expiration
  • Volatility
  • Risk-Free Rate
  • Underlying Price

ব্যবহার করে Binomial এবং Black-Scholes approaches দিয়ে option pricing করা হবে।

📊 Project 7 — Option Greeks Analysis

Option-এর Delta, Gamma, Theta, Vega এবং Rho calculate করে price এবং risk sensitivity analyse করা হবে।

🏦 Project 8 — Bond Risk Analysis

একটি bond-এর—

  • Price
  • Yield
  • Duration
  • Convexity

calculate করে interest-rate risk evaluate করা হবে।

💻 Project 9 — Python Quantitative Analysis

Python ব্যবহার করে—

  • Financial Data
  • Returns
  • Volatility
  • Correlation
  • Portfolio Risk
  • Statistical Analysis

automate করা হবে।

📈 Project 10 — Quantitative Trading Backtest

একটি basic rule-based strategy-এর historical backtest তৈরি করা হবে এবং—

  • Return
  • Sharpe Ratio
  • Drawdown
  • Win/Loss
  • Transaction Cost

সহ performance evaluate করা হবে।

🏆 Final Project — Quantitative Finance Professional Capstone

একটি complete quantitative finance case solve করা হবে:

Financial Data → Data Cleaning → Return Analysis → Risk → Portfolio → Factor Analysis → VaR → Monte Carlo → Derivative Pricing → Python Analysis → Strategy/Model Testing → Final Quantitative Report

Course Features

✅ 60 Hours Professional Training

✅ বাংলা + English Explanation

✅ Advanced / Professional Level

✅ Financial Mathematics

✅ Statistics for Finance

✅ Probability & Distributions

✅ Financial Return Analysis

✅ Time-Series Analysis

✅ Regression & Factor Models

✅ Portfolio Theory

✅ Portfolio Optimization

✅ CAPM

✅ Quantitative Risk Management

✅ Value at Risk (VaR)

✅ Expected Shortfall

✅ Monte Carlo Simulation

✅ Derivatives

✅ Futures & Forwards

✅ Options

✅ Binomial Option Pricing

✅ Black-Scholes Model

✅ Option Greeks

✅ Fixed Income Analysis

✅ Bond Pricing

✅ Duration & Convexity

✅ Yield Curve Fundamentals

✅ Quantitative Trading Fundamentals

✅ Backtesting

✅ Python for Quantitative Finance

✅ NumPy & Pandas Fundamentals

✅ Financial Data Analysis

✅ Quantitative Financial Modeling

✅ Scenario & Sensitivity Analysis

✅ Model Validation

✅ Real-World Financial Cases

✅ Practical Quantitative Projects

✅ Professional Capstone Project

✅ Instructor Support

✅ Class Recording (যদি প্রযোজ্য হয়)

✅ Certificate of Completion (যদি প্রদান করা হয়)

Who Can Join?

  • Finance Students
  • Economics Students
  • Mathematics Students
  • Statistics Students
  • Computer Science Students
  • Financial Analysts
  • Quantitative Analysts
  • Investment Analysts
  • Risk Analysts
  • Portfolio Analysts
  • Banking Professionals
  • Investment Professionals
  • Data Analysts
  • Data Scientists
  • FinTech Professionals
  • Traders
  • Financial Modelers
  • যারা Quantitative Finance Professional হতে চান

Basic Finance, Mathematics এবং Statistics knowledge strongly recommended। Basic Python knowledge থাকলে সুবিধা হবে।

After Completing This Course

এই course শেষ করার পর আপনি—

  • Quantitative Finance-এর core concepts বুঝতে পারবেন।
  • Probability ও Statistics financial applications-এ ব্যবহার করতে পারবেন।
  • Financial returns calculate ও analyse করতে পারবেন।
  • Volatility এবং statistical risk measure করতে পারবেন।
  • Correlation, covariance এবং regression ব্যবহার করতে পারবেন।
  • Time-series financial data analyse করতে পারবেন।
  • Portfolio return ও risk calculate করতে পারবেন।
  • Modern Portfolio Theory এবং Efficient Frontier-এর fundamentals বুঝতে পারবেন।
  • CAPM, Beta এবং factor-based analysis করতে পারবেন।
  • VaR এবং Expected Shortfall-এর fundamentals ব্যবহার করতে পারবেন।
  • Monte Carlo Simulation ব্যবহার করে financial scenarios generate করতে পারবেন।
  • Futures, forwards এবং options-এর fundamentals বুঝতে পারবেন।
  • Binomial ও Black-Scholes models-এর মাধ্যমে basic option pricing করতে পারবেন।
  • Option Greeks ব্যবহার করে sensitivity ও risk analyse করতে পারবেন।
  • Bond pricing, yield, duration এবং convexity analyse করতে পারবেন।
  • Quantitative trading strategy-এর basic framework বুঝতে পারবেন।
  • Historical data ব্যবহার করে basic strategy backtesting করতে পারবেন।
  • Python, NumPy এবং Pandas ব্যবহার করে financial data analyse করতে পারবেন।
  • Quantitative financial models তৈরি ও validate করার foundation পাবেন।
  • Scenario ও sensitivity analysis করতে পারবেন।
  • Quantitative results interpret করে professional financial insights তৈরি করতে পারবেন।
  • Advanced Quantitative Research, Risk Management, Algorithmic Trading এবং Financial Engineering-এর জন্য strong foundation তৈরি করতে পারবেন।