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Quantitative Finance Interview Course

Quant Interview Course

Quantitative Finance Interview Course

Quantitative Finance Interview Course

9 Modules

352 Lessons

8 Hours

No Quizzes

The Quantitative Interview Course is designed to help students build a strong foundation in the core concepts tested in quant interviews. It covers key topics such as probability, statistics, programming, stochastic calculus, risk, portfolio theory, and quantitative models, helping students strengthen both their technical understanding and interview readiness.

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Module 1: Introduction to Quantitative Finance

1.1 - Course Overview

2m 9s
1.2.1 - What is Quantitative Finance

2m 15s
1.2.2 - Who is a Quant

1m 48s
1.3 - Role of a Quant in Finance

4m 57s
1.4 - Core Mathematical Foundations

1m 16s
1.5 - Asset Classes

1m 36s
1.6 - Introduction to Finacial Markets

3m 42s
1.6.1 - Primary vs. Secondary Markets

1m 21s
1.6.2 - Type of Market Structures

1m 29s
1.6.3 - Key Market Participants

59s
1.6.4 - Why Financial Market Knowledge is Essential in Quant Finance

1m 34s

Module 2: Probability and Statistics for Quantitative Finance

2.1 - Probability Theory

9s
2.1.1 - Foundations of Probability

53s
2.1.1 - Foundations of Probability

53s
2.1.2 - Sample Spaces, Events, and Sigma-Algebras

3m 10s
2.1.3 - 2.1.3 Axioms of Probability and Key Rules

2m 2s
2.1.4 - Key Rules Derived from Axioms

4m 58s
2.1.5 - Counting Techniques (Combinatorics)

3m 47s
2.1.6 - Random Variables

35s
2.1.7 - Discrete vs. Continuous Random Variables

1m 15s
2.1.8 - PMF vs. PDF — How We Assign Probabilities

3m 15s
2.1.9 - CDF - Cumulative Distribution Function

2m 17s
2.1.10 - Binomial Distribution

3m 6s
2.1.11 - Poisson Distribution

2m 57s
2.1.12 - Geometric Distribution

3m 40s
2.1.13 - Uniform Distribution

2m 22s
2.1.14 - Normal (Gaussian) Distribution

3m 21s
2.1.15 - Lognormal Distribution

2m 26s
2.1.16 - Exponential Distribution

2m 33s
2.1.17 - Financial Applications

53s
2.1.18 - Expectation (Mean)

1m 59s
2.1.19 - Variance

1m 42s
2.1.20 - 2.1.20 Higher-Order Moments

1m 25s
2.1.21 - Covariance

2m 49s
2.1.22 - Correlation

2m 38s
2.1.23 - Summary of Expectation and Higher Moments

1m 35s
2.1.24 - Conditional Probability and Bayes' Theorem - Conditional Probability

1m 16s
2.1.25 - Law of Total Probability – Aggregating Scenarios

2m 1s
2.1.26 - Bayes’ Theorem – Updating Probability Estimates

4m 30s
2.1.27 - Joint, Marginal, and Conditional Distributions

1m 17s
2.1.28 - Joint Distribution

51s
2.1.29 - Marginal Distribution

1m 39s
2.1.30 - Conditional Distribution

1m 15s
2.1.31 - Independence vs Dependence

2m 34s
2.1.32 - Copulas (Intro) – Modeling Dependence Beyond Correlation

2m 31s
2.2.1 - Estimation Theory

1m 48s
2.2.2 - Properties of Estimators

1m 37s
2.2.3 - Maximum Likelihood Estimation (MLE)

1m 50s
2.2.4 - Hypothesis Testing-

8m 15s
2.2.5 - Applications in Quant Finance

3m 58s
2.2.6 - 2.2.6 Regression Analysis - Simple and Multiple Linear Regression

1m 59s
2.2.7 - Time-Series Regression and Autocorrelated Residuals

1m 20s
2.2.8 - OLS Assumptions, Diagnostics, and Model Fit

2m 43s
2.2.9 - Multicollinearity, Heteroscedasticity

2m 32s
2.2.10 - Autocorrelation

2m 25s
2.2.11 - Advanced Topics (Optional - Extra Credit) - Generalized Linear Models (GLMs)

49s
2.2.12 - Non-parametric Methods

58s
2.2.13 - Shrinkage Methods- Ridge and Lasso

1m 45s
2.3 - Applications in Quantitative Finance

8s
2.3.1 - Market Behavior and Return Distributions - Empirical Distribution of Returns

53s
2.3.2 - Fat Tails and Kurtosis

2m 14s
2.3.3 - Stationarity and Time Series Diagnostics

1m 38s
2.3.4 - Risk Management - Value at Risk (VaR)

3m 30s
2.3.5 - Expected Shortfall (Conditional VaR)

1m 58s
2.3.6 - Stress Testing

2m 8s
2.3.7 - Types of Derivatives

23s
2.3.8 - Options

51s
2.3.9 - Futures

1m 9s
2.3.10 - Other Derivatives

1m 19s
2.3.11 - Black-Scholes Model- Foundation of Option Pricing

2m 35s
2.3.12 - Derivative Pricing

1m 16s
2.3.13 - Monte Carlo Simulation (preview)

1m 50s
2.3.14 - How It Relates to Monte Carlo Tree Search (MCTS)

1m 29s
2.3.15 - Monte Carlo Tree Search (MCTS)

28s
2.3.16 - Step 1 - Selection

54s
2.3.17 - Step 2 - Expansion

32s
2.3.18 - Step 3 - Simulation (Rollout)

53s
2.3.19 - Step 4 - Backpropagation

1m 11s
2.3.20 - Quantitative Trading - Signal Generation and Backtesting Basics

2m 14s
2.3.21 - Types of Signals

2m 52s
2.3.22 - What is Backtesting

2m 6s
2.3.23 - Steps to Build a Backtester

1m 58s
2.3.24 - Framework- Building and Evaluating a Quantitative Strategy - Part 1

3m 46s
2.3.25 - Framework- Building and Evaluating a Quantitative Strategy - Part 2

2m 41s
2.3.26 - Real-World Quant Strategy- Pairs Trading (MCD vs. YUM) - Part 1

2m 35s
2.3.27 - Real-World Quant Strategy- Pairs Trading (MCD vs. YUM) - Part 2

2m 16s
2.3.28 - Evaluating Strategy Significance

2m 21s
2.4.1 - Binomial Distribution - Drill #15

7m 30s
2.4.2 - Covariance from a Joint Distribution - Drill #24

5m 33s
2.4.3 - Bayes’ Rule and False Positives - Drill #30

8m 38s
2.4.4 - Designing a Monte Carlo Risk Model - Drill #42

12m 17s

Module 3: Interview Preparation

3.1 - Quant Finance Interview Guide
3.1.1 - Interview Preparation (Quantitative Focus)

43s
3.1.2 - Problem-Solving & Brain Teasers

8s
3.1.3 - Categories of Teasers - Estimation Questions

3m 17s
3.1.4 - Logic Puzzles

2m 8s
3.1.5 - Combinatorics Questions

2m 6s
3.1.6 - Trick Questions & Lateral Thinking

2m 27s
3.1.7 - Framework for Solving Quant Brain Teasers

2m 25s
3.1.8 - Make Simplifying Assumptions

1m 6s
3.1.9 - Use Bounds or Estimation

1m 26s
3.1.10 - Check Edge Cases and Sanity

1m 34s
3.2 - Advanced Probability & Expected Value

7s
3.2.1 - Advanced Probability & Expected Value - Expected Value (EV)

2m 25s
3.2.2 - Bayes' Theorem

4m 1s
3.2.3 - Markov Chains

1m 37s
3.2.4 - Stopping Problems (Optimal Stopping Theory)

3m 11s
3.2.5 - Probability Distributions - Part 1

1m 58s
3.2.6 - Probability Distributions - Part 2

2m 33s
3.3.1 - Finance Flavored Estimation - Drill #48

8m 2s
3.3.2 - Range Bounding Estimate - Drill #49

9m 54s
3.3.3 - Assumption Sensitivity Estimate - Drill #50

5m
3.3.4 - Step-by-Step Estimation Walkthrough - Drill #51

5m 48s
3.3.5 - Bad Answer vs. Strong Answer - Drill #52

7m 33s
3.3.6 - Observation Puzzle - Drill #58

4m 58s
3.3.7 - Multi-Step Strategy Puzzle - Drill #60

5m 20s
3.3.8 - Full Logic Puzzle Walkthrough - Drill #61

6m 54s
3.3.9 - Bad Answer vs. Strong Answer - Drill #62

5m 35s
3.3.10 - Bad Answer vs. Strong Answer - Drill #72

7m 25s
3.3.11 - Lateral-Strategy Puzzle - Drill #80

8m 15s
3.3.12 - Full Reframe Walkthrough - Drill #81

6m 19s
4.3.13 - Finance Signal Problem - Drill #86

11m 29s

Module 4: Programming

4.1 - Intro to Programming

52s
4.1.1 - Data Structures and Algorithms (DSA)

5s
4.1.2 - Data Structures and Algorithms (DSA)

1m
4.1.3 - Common Time Complexities and Their Meanings - Part 1

1m 18s
4.1.4 - Common Time Complexities and Their Meanings - Part 2

59s
4.1.5 - Arrays & Strings

1m 11s
4.1.6 - Linked Lists

59s
4.1.7 - Stacks

1m 37s
4.1.8 - Queues

1m 30s
4.1.9 - Hash Maps & Hash Sets

1m 21s
4.1.10 - Trees & Binary Search Trees (BSTs)

1m 25s
4.1.11 - Heaps (Priority Queues)

1m 16s
4.1.12 - Graphs

1m 36s
4.1.13 - Trie (Prefix Tree)

1m 43s
4.1.14 - Union-Find (Disjoint Set Union – DSU)

1m 22s
4.1.15 - Core Algorithms & Techniques - Sorting Algorithms

2m 3s
4.1.16 - Searching Algorithms

1m 34s
4.1.17 - Recursion and Divide & Conquer

1m 7s
4.1.18 - Dynamic Programming (DP)

1m 43s
4.1.19 - Graph Algorithms - Part 1

1m 25s
4.1.20 - Graph Algorithms - Part 2

2m 1s
4.1.21 - Graph Algorithms - Part 3

1m 19s
4.1.22 - Backtracking

1m 38s
4.1.23 - Greedy Algorithms

1m 56s
4.2 - Essential Python Libraries for Quant Finance

10s
4.2.1 - NumPy – Numerical Computing

1m 22s
4.2.2 - Pandas – Data Manipulation

1m 26s
4.2.3 - Scikit-Learn – Machine Learning

1m 12s
4.2.4 - Statsmodels – Statistical Modeling

1m 15s
4.2.5 - CVXPY – Convex Optimization in Python

2m 16s
4.2.6 - Matplotlib – Data Visualization in Python

1m 57s
4.2.7 - yfinance – Access Yahoo Finance Data

2m 16s
4.3 - Financial Databases and Data Sources

7s
4.3.1 - Bloomberg Terminal

1m 40s
4.3.2 - WRDS (Wharton Research Data Services)

1m 33s
4.3.3 - Quandl (Nasdaq Data Link)

1m 9s
4.3.4 - Yahoo Finance (and similar free sources)

57s
4.4 - Solutions to Practice Problems

9s
4.4.1 - Practice Problems

1m 37s
4.4.2 - Merging Two Sorted Arrays (Sorting)

2m 14s
4.4.3 - Binary Search in Sorted Array (Searching)

1m 15s
4.4.4 - Nth Fibonacci Number (Recursion + Memoization)

44s
4.4.5 - Climbing Stairs (Dynamic Programming)

1m 6s
4.4.6 - Coin Change – Minimum Coins (DP)

1m 6s
4.4.7 - House Robber (Dynamic Programming)

1m 52s
4.4.8 - Number of Islands (Graph Traversal – DFS)

1m 59s
4.4.9 - Interval Scheduling (Greedy Activity Selection)

2m 2s
4.4.10 - Binary Search (Searching)

2m 2s
4.4.11 - Fibonacci Number (Recursion with Memoization)

1m 15s

Module 5: Quantitative Risk and Portfolio Theory

5.1 - Quantitative Risk and Portfolio Theory

7s
5.1.1 - Modern Portfolio Theory

8s
5.1.2 - Risk-Return Tradeoff - Part 1

1m 46s
5.1.3 - Risk-Return Tradeoff - Part 2

1m 10s
5.1.4 - Variance-Covariance Matrix and Portfolio Variance - Part 1

48s
5.1.5 - Variance-Covariance Matrix and Portfolio Variance - Part 2

48s
5.1.6 - Efficient Frontier and Optimal Portfolios - Part 1

1m 42s
5.1.7 - Efficient Frontier and Optimal Portfolios - Part 2

57s
5.1.8 - CAPM - Assumptions, Equation, and the Security Market Line (SML) - Part 1

3m 31s
5.1.9 - CAPM - Assumptions, Equation, and the Security Market Line (SML) - Part 2

46s
5.2 - Risk Metrics in Practice

11s
5.2.1 - Value-at-Risk (VaR)- Parametric, Historical, and Monte Carlo

2m 45s
5.2.2 - Conditional VaR (CVaR) - Expected Shortfall

1m 28s
5.2.3 - Drawdown, Sharpe Ratio, Sortino Ratio - Part 1

2m 12s
5.2.4 - Drawdown, Sharpe Ratio, Sortino Ratio - Part 2

1m 5s
5.2.5 - Stress Testing and Scenario Analysis

1m 49s
5.3.1 - Portfolio Optimization

1m 38s
5.3.2 - Lagrange Multipliers in Optimization

1m 20s
5.3.3 - Practical Constraints- Transaction Costs, Liquidity

1m 13s
5.3.4 - Use of Convex Optimization Libraries (e.g., CVXPY) - Part 1

1m 36s
5.3.5 - Use of Convex Optimization Libraries (e.g., CVXPY) - Part 2

34s
5.3.6 - Use of Convex Optimization Libraries (e.g., CVXPY) - Part 3

42s
5.3.7 - Use of Convex Optimization Libraries (e.g., CVXPY) - Part 4

40s
5.4 - Factor Models in Risk

12s
5.4.1 - Single and Multi-Factor Models - Part 1

2m 54s
5.4.2 - Single and Multi-Factor Models - Part 2

2m 29s
5.4.3 - Risk Decomposition

2m 35s
5.4.4 - Factor Exposures

1m 20s
5.4.5 - Application- Barra risk model - Definition

1m 10s
5.4.6 - How Does Barra Actually Work - Part 1

2m 3s
5.4.7 - How Does Barra Actually Work - Part 2

1m 38s
5.4.8 - How Does Barra Actually Work - Part 3

58s
5.5.1 - CAPM and the SML - Drill #101

6m 39s
5.5.2 - Stress Testing

6m 54s
5.5.3 - Barra-Style Risk - Drill #122

5m 17s

Module 6: Models

6.1 - Time Series Models

6s
6.1.1 - Introduction to Time Series in Quant Finance

1m 8s
6.1.2 - Key Applications in Practice

1m 6s
6.1.3 - Stationarity and Why It Matters

1m 57s
6.1.4 - Visual Examples

30s
6.1.5 - Real-World Examples

1m
6.1.6 - ACF and PACF Explained

1m 21s
6.1.7 - AR, MA, ARMA, ARIMA - 1. AR(p) – Autoregressive Model

1m 36s
6.1.8 - MA(q) – Moving Average Model

1m 16s
6.1.9 - ARMA(p, q) – Combo Model

1m 2s
6.1.10 - ARIMA(p, d, q) – Integrated Model

1m 30s
6.1.11 - ACF-PACF Identification Strategy

1m 23s
6.1.12 - Model Fitting (Using AIC-BIC for Selection)

1m 46s
6.1.13 - Forecasting Next Values with Confidence Intervals

1m 24s
6.1.14 - Python Example Using statsmodels - Step 1 - Import and Prepare Data

44s
6.1.15 - Step 2- Plot ACF and PACF

1m 13s
6.1.16 - Step 3 - Fit the ARIMA Model

1m 11s
6.1.17 - Step 4 - Forecast Future Values

34s
6.1.18 - Step 5 - Plot Forecast with Confidence Interval

43s
6.1.19 - Define Heteroskedasticity- Time-Varying Volatility

2m 58s
6.1.20 - ARCH – Modeling Volatility Using Past Shocks

1m 31s
6.1.21 - Why It Matters in Finance

1m 11s
6.1.22 - What GARCH Adds - More Memory, Fewer Parameters

1m 33s
6.1.23 - Comparison- Black-Scholes vs. GARCH

2m 6s
6.1.24 - Example Forecasting Next-Day Volatility for SPY - Part 1

1m 5s
6.1.25 - Example Forecasting Next-Day Volatility for SPY - Part 2

1m 31s
6.2 - Machine Learning Models for Prediction

10s
6.2.1 - What is Supervised Learning

44s
6.2.2 - Supervised Learning in Quant Finance

1m 18s
6.2.3 - Prediction vs. Interpretability Tradeoff

2m 1s
6.2.4 - Decision Trees, Random Forests, and Gradient Boosting - Decision Trees

1m 28s
6.2.5 - Decision Trees - Real-World Example

50s
6.2.6 - Random Forests

58s
6.2.7 - Random Forests - Real-World Example

41s
6.2.8 - Gradient Boosting (XGBoost, LightGBM)

59s
6.2.9 - Gradient Boosting (XGBoost, LightGBM) - Real-World Examples

46s
6.2.10 - Use Cases in Forecasting and Feature Ranking - Part 1

1m 32s
6.2.11 - Use Cases in Forecasting and Feature Ranking - Part 2

1m 14s
6.2.12 - Use Cases in Forecasting and Feature Ranking - Part 3

1m 7s
6.2.13 - Model Tuning and Cross-Validation - Overfitting vs. Underfitting

58s
6.2.14 - Hyperparameter Tuning- Grid Search vs. Random Search

2m 11s
6.2.15 - k-Fold Cross-Validation (CV)

1m 9s
6.2.16 - Feature Engineering for Financial Data - Part 1

45s
6.2.17 - Feature Engineering for Financial Data - Part 2

1m 28s
6.2.18 - Feature Engineering for Financial Data - Part 3

1m 26s
6.2.19 - Feature Engineering for Financial Data - Part 4

1m 6s
6.3 - Asset Pricing Models

8s
6.3.1 - Introduction to Asset Pricing Models - Definition

1m 36s
6.3.2 - Common Types of Asset Pricing Models

1m 17s
6.3.3 - CAPM — Assumptions and Formula - Definition

59s
6.3.4 - Real-World Example (Simple)

52s
6.3.5 - Intuition Behind the Formula

1m 7s
6.3.6 - CAPM Assumptions (Simplified)

1m 46s
6.3.7 - CAPM in Real Finance Work

1m 48s
6.3.8 - Empirical Limitations of CAPM - Part 1

1m 33s
6.3.9 - Empirical Limitations of CAPM - Part 2

1m 54s
6.3.10 - Empirical Limitations of CAPM - Part 3

2m 57s
6.3.11 - Fama-French 3-Factor and 5-Factor Models - Part 1

1m 12s
6.3.12 - Fama-French 3-Factor and 5-Factor Models - Part 2

2m 55s
6.3.13 - Fama-French 3-Factor and 5-Factor Models - Part 3

1m 7s
6.3.14 - Black-Scholes as a Pricing Model

1m 49s
6.3.15 - What Are the Inputs in the Formula

1m 15s
6.3.16 - Real-Life Example

2m 51s
6.3.17 - Why Is This Model Important in Finance-

2m 11s
6.3.18 - Black-Scholes Surface vs. Real Market Prices

3m 10s
6.4 - Risk Models

6s
6.4.1 - Revisiting VaR and CVaR from a Modeling Perspective

1m 11s
6.4.2 - CVaR – Conditional Value at Risk

1m 3s
6.4.3 - Parametric vs. Non-Parametric Methods

1m 48s
6.4.4 - Part 1 - Real-World Use- Risk Budgeting at a Hedge Fund

1m 40s
6.4.5 - Part 2 - Real-World Use- Risk Budgeting at a Hedge Fund

1m 50s
6.4.6 - Moody’s KMV Model — Structural Credit Risk

2m 20s
6.4.7 - Credit Spreads and Default Probabilities

3m 32s
6.4.8 - How Market Prices Imply Default Probability

1m 38s
6.5.1 - Credit Spreads - Drill #143

7m 44s
6.5.2 - Event-Driven FX - Drill #144

7m 31s
6.5.3 - Overfit Diagnosis - Drill #145

7m 11s
6.5.4 - Underfit Diagnosis - Drill #146

6m 23s
6.5.5 - Regime Shift - Drill #147

5m 56s
6.5.6 - Parameter Instability - Drill #148

6m 17s
6.5.7 - Regression Output - Drill #149

5m 54s
6.5.8 - GARCH Persistence - Drill #150

5m 31s
6.5.9 - AUC vs. Calibration - Drill #151

7m 9s
6.5.10 - VaR Backtesting - Drill #152

6m 43s

Module 7: Machine Learning for Quant Finance

Module 8: Stochastic Calculus and Financial Modeling

Quant Finance Practice Problems

9.1 - Quant Finance Practice Problems

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