Induction Day - May 11
What you need to know to build-up and manage a trading system
- Programming Foundation (1.5h)
Programming 101
Tutors
- Programming tools in R, Python and Matlab
- User-defined functions
- Data I/O and financial providers download
- Plotting functions
- Functions for random and stochastic variables
- Quantitative Analysis Intro (2.0h)
Manuele Monti
- Time series analysis
- Returns and Normality test (+coding)
- Correlation, Co-Integration Analysis (+coding)
- Basic Stochastic processes (Random Walk, Brownian Motion, GBM, Mean Reversion)
- Trading System Structure (2.0h)
Giordano Frezza
- Trading system process: from the idea to the automatic trading
- Data base management
- Fees: spread, market impact, brokers
- Risk and performance quantitative parameters (+ coding)
- Objective/predictor, optimization, multi objective function (+ coding)
- IN/OUT and smoothing analysis (+ coding)
- Backtesting and execution (+ coding)
- Updating and Handling Risk Management in Automated systems
- Risk Management (1.5h)
Manuele Monti
- Risk Metrics, factors
- Risk models and measures (VaR, ES, CVaR, PaR, CFaR)
- Primer on risk management analytical and numerical techniques
Qtrade Bootcamp - May 12
Trading Strategies
- Trend-following strategies (2.0h)
Trend-following strategies
Giordano Frezza
- Rho analysis, time series dependence based on autocorrelation (Levich-Rizzo) (+ coding)
- Trend following trading system example (+ coding)
- Dispersion Trading and Correlation Modelling (2.5h)
Simone Siragusa (2.5h)
- Volatility Modelling (+coding)
- Variance pitfalls
- Exponential smoothing
- GARCH and Leverage effect
- Realized variance
- Correlation Modelling (+coding)
- Value at risk and the needs of covariance
- Cluster analysis
- Modelling Conditional covariance and correlation
- Monte Carlo Analysis with different covariance matrixes
- Implied volatility arbitrage and the case of dispersion trading
- Correlation risk and hedge fund returns
- Statistical Arbitrage via Kalman Filter(2.0h)
Rocco Mosconi, Mattia Manzoni
- Kalman filter: a latent variable model applied to a systematic trading strategy
- An alternative to cointegration
- An “in house” custom solution (+ coding)
- Backtesting
Qtrade Bootcamp - May 13
Statistical Arbitrage
- Statistical Arbitrage (7.0h)
Statistical Arbitrage
Ernest P.Chan (7.0h)
- Stationarity and cointegration of time series
- Stationarity and mean-reversion: the practical benefits.
- Cointegration vs correlation.
- Mean-reversion trading of pairs and triplets
- Finding hedge ratio through linear regression (LR).
- Order-dependence of hedge ratio based on LR.
- Finding hedge ratio through Johansen test.
- Case study: The breakdown of cointegration of GLD-GDX, the economic reasons and the remedy.
- Half-life of mean-reversion
- Practical importance of half-life.
- The Ornstein-Uhlenbeck formula.
- Risk management of mean-reversion strategies
- Index arbitrage
- Trading an ETF against a basket of its component stocks.
- Constructing a basket : linear regression, constrained optimization
- Long-short portfolio
- Long-short portfolio strategy of stocks in the S&P 500
Qtrade Bootcamp - May 14
Artificial Intelligence and Portfolio Optimization
- Forecasting and Artificial Intelligence Based Strategies (4.0h)
Forecasting & Artificial Intelligence Based Strategies
Ernest P.Chan
- General paradigm of machine learning.
- AI techniques
- Stepwise linear regression
- Classification and regression trees (CART)
- Neural networks
- Genetic algorithm
- Bayesian networks
- Support Vector Machines (SVM)
- Predicting returns of a portfolio using stepwise linear regression, CART, neural network, and SVM (+coding)
- Portfolio Optimization (3.0h)
Ernest P.Chan
- Markowitz mean-variance optimization as applied to strategies.
- Theoretical derivation of Kelly formula.
- Exercise: Testing the implications of Kelly formula.
- Exercise: Finding the optimal allocations of N strategies based on Kelly formula.
- Simpler ways to allocate leverage.
- Exercise: Experimenting with variations of the optimization scheme to achieve better out-of-sample performance.
- Portfolio Optimization
Qtrade Bootcamp - May 15
Market Making, Volume Impact and High Frequency Trading
- Market Making Strategies (3.0h)
Yiran Liu (3.0h)
Introduction
- Definition of Market Maker
- Difference between Proprietary Trading and Market Making
- Let’s start a simple Market Making Shop (business Model Market Maker)
- Profit and Risk of our Business
Market Making Strategies
- Plain Market Making (Non Offset, Full Offset, Direction and Timing)
- Risk Analysis of such strategies: Adverse selection
- Other source of Market Making costs
Optimal Control Problem
- A Toy Example of optimal control
- Hamilton-Jacobi_Bellman Equation
- Feynman Kac Theorem
- Solving HUB Equation
Modelling Key Components
- Market Model
- Order Arriving Model
- Inventory Model
- Spread Model
- Utility Function
- Extend the Optimal Control Problem
Numerical Solution (+coding)
- Assemble the Component Models into a whole system
- Simulation of Client Order Arrivals and Market Dynamics
- A test of applying same model on real market price
Statistical results and analysis
- Factor analysis + coding, chart and tables
What to consider if we want to use the pure math idea into production
- May try to improve Price Model
- More sophisticated directional betting factor
- More Factors to put in to control, such as spread
- API Broker connection
Michele Bogliardi
- Propietary APIs
- APIs vs strategies (i.e. Spread Trading, HFT)
- Python, R and Matlab APIs
- Example: Matlab API in multiple Spread Trading
- Market Volumes Analysis (1.0h)
Antonio Lengua
- Mechanic of market volume
- From discretional to systematic trading
- HFT based on Order Imbalance (1.5h)
Rocco Mosconi, Mattia Manzoni
- Quantitative Trading Strategies based on High Frequency Data
- The leading informative content of order imbalance indicator
- The role of sampling rule with high frequency data: Time vs. Volume Clock approaches