School of Quants

Курс "Школа квантов" (School of Quants) появился по инициативе команды "Школы финансов ЭФ МГУ", ведущей дискуссионной площадки по финансам.

Годовой цикл School of Quants состоит ­из еженедельных мероприятий специалистов финансовой сферы­,­ включает обсуждение теоретических и практических аспектов тем по анализу и моделированию стоимости финансовых продуктов, статистическому и эконометрическому анализу, финансовому программированию, машинному обучению, построению торговых стратегий, риск-менеджменту, а также командный и индивидуальный ситуационный разбор кейсов­.

Структура курса:

1. Стохастические методы (Stochastic Calculus)

  • Brownian Motion specification and construction, correlated Brownian Motions, features of the Brownian Motion path
  • Martingales and their properties, conditional expectations, elements of martingale analysis
  • Ito stochastic integral, Ito calculus, Ito’s lemma
  • Stochastic differential equations, arithmetic Brownian motion, geometric Brownian Motion, mean reverting
  • Options Pricing Models: Binomial Model, Black-Scholes Model

2. Финансовые продукты и рынки (Products & Markets)

  • Equities and Equity DerivativesFX and FX Derivatives
  • Commodity and Commodity Derivatives
  • Models and methods for Valuation of Vanilla Products
  • Models and methods for Valuation of Exotic and Path-Dependent products
  • Fundamentals of Equities, Commodities and FX Volatility Modelling
  • Rates Markets, Swaps and Derivatives
  • Yield Curve EstimationInterest Rate Models
  • Interest Rate Volatility Modelling
  • Valuation of Vanilla Products and Exotic Interest Rate Derivatives
  • Credit Markets, Vanilla Instruments (CDS/CDX), CDX Options and Correlation Trading
  • Structural and Reduced Form credit models

3. Количественные стратегии в финансах (Trading Strategies)

  • Option Greeks and Delta Hedging: Characteristics & Greeks based trading strategies
  • Implied volatility, smile, skew and forward volatility
  • Different types of Momentum (Time series & Cross-sectional)
  • Trend following strategies and Statistical Arbitrage Trading strategy modeling with Python
  • Arbitrage, market making and asset allocation strategies using ETFs
  • Non-linear strategies
  • Code and back-test different strategies on various platforms

4. Статистический анализ в финансах (Advanced Statistics)

  • Data Visualization: Statistics and probability concepts (Bayesian and Frequentist methodologies), moments of data and Central Limit Theorem
  • Applications of statistics: Random Walk Model for predicting future stock prices using simulations and inferring outcomes, Capital Asset Pricing Model
  • Modern Portfolio Theory - statistical approximations of risk/reward
  • Linear regression
  • Time series analysis and statistical functions including autocorrelation function, partial autocorrelation function
  • Maximum likelihood estimation, Akaike Information Criterion
  • Stationarity of time series, Autoregressive Process, Forecasting using ARIMA
  • Difference between ARCH and GARCH and Understanding volatility
  • Ridge Regression and Lasso Regression for prediction optimization

5. Финансовое программирование в Python (Financial Programming in Python)

  • Data types, variables, Python in-built data structures, inbuilt functions, logical operators, and control structures
  • Python concepts for writing functions - functional programming
  • Implement various OOP concepts in Python program - Aggregation, Inheritance, Composition, Encapsulation, and Polymorphism
  • Introduction to some key libraries NumPy, pandas, and matplotlib
  • Back-testing methodologies & techniques and using Random Walk Hypothesis
  • Work on sample strategies, trade the Boring Consumer Stocks in Python

6. Финансовое программирование в C++ (Financial Programming in C++)

  • Basic C/C++ Language and Syntax
  • OOP in C++
  • Inheritance and Polymorphism
  • Generic Programming in C++ and Standard Template Library (STL)
  • An Introduction to Boost C++ Libraries
  • Applications in Computational Finance

7. Машинное обучение в финансах (Machine Learning in Finance)

  • Modeling data with AI, index and predicting next day’s closing price
  • Supervised learning algorithms, Decision Trees & additive modeling
  • Intro to neural networks
  • Natural Language Processing (NLP) and Sentiment Analysis
  • Confusion Matrix framework for monitoring algorithm’s performance
  • Logistic Regression to predict the conditional probability of the market direction. Cross validation
  • Understand principle component analysis and back-test PCA based long/short portfolios
  • Reinforcement Learning in Trading
  • Clustering algorithms

8. Риск-менеджмент и оптимизация портфеля (Risk Management and Portfolio Optimization)

  • Different methodologies of evaluating portfolio & strategy performance
  • Risk Management: Sources of risk, risk limits, risk evaluation & mitigation, risk control systems
  • Basic Risk Management Technics: Parametric Linear VaR Models, Historical Simulation and MonteCarlo Methods, Scenario Analysis and Stress Testing, xVA
  • Trade sizing for individual trading strategy using conventional methodologies, Kelly criterion, Leverage space theorem

Руководитель и координатор курса: Ахмедзянов Руслан Германович


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