Quantitative Investment (QI)
Workflow:
-
Problem Domain
Defines what is being modeled or analyzed.
| Category | Examples |
|---|---|
| Asset Pricing | Equities, Fixed Income, Derivatives |
| Risk Management | Credit Risk, Market Risk, Liquidity Risk |
| Portfolio Construction | Optimization, Diversification, Allocation |
| Trading Strategies | Market Making, Statistical Arbitrage, Momentum |
| Corporate Finance | Valuation, Capital Structure, Cost of Capital |
| Macroeconomic Analysis | FX, Interest Rates, Commodities |
Financial Data
Involves sourcing, cleaning, storing, and preprocessing financial data.
| Subsystem | Examples |
|---|---|
| Market Data | Price ticks, order books, historical OHLCV |
| Fundamentals | Balance sheets, earnings, macroeconomic indicators |
| Alternative Data | Satellite, sentiment, ESG, geolocation |
| Data Engineering | Pipelines, feature stores, real-time ingestion |
| Preprocessing | Normalization, missing value imputation, feature extraction |
Quantitative Strategy
...
| Strategy Type | Methodology | Primary Data | Model/Algorithm Examples | Notes / Risks |
|---|---|---|---|---|
| Statistical Arbitrage | Mean reversion between correlated assets | Price, returns, volume | Pairs trading, cointegration tests, Kalman filter | Relies on stable statistical relationships |
| Momentum / Trend | Buy winners, sell losers | Time-series prices | Moving averages, breakout models, regression | Suffers in sideways markets |
| Mean Reversion | Exploit short-term price mispricings | Price, intraday ticks | Bollinger Bands, Ornstein–Uhlenbeck processes | Requires strong reversion signal and tight execution |
| High-Frequency Trading | Microstructure-based statistical edges | Tick data, order book | Market making, latency arbitrage | Requires ultra-low latency; high tech cost |
| Machine Learning-Based | Predictive modeling from features | Prices, news, alt data | Random Forests, XGBoost, LSTM, RL | High overfitting risk; interpretability challenges |
| Event-Driven | React to news, earnings, macro events | News feeds, filings, EPS | NLP models, earnings drift models | Hard to model rare events |
| Market Microstructure | Exploit order flow and execution patterns | Order book, trade prints | Queue models, imbalance metrics | Short horizon; high execution complexity |
| Index Arbitrage | ETF vs index/futures pricing gaps | Real-time NAVs, quotes | Basket replication, futures pricing | Needs precise and fast pricing |
| Cross-Asset Arbitrage | Exploit mispricing across assets | FX, equities, rates | Cross-hedging, regression/correlation | Depends on market linkage strength |
| Volatility Arbitrage | Implied vs. realized volatility | Option chain, realized vols | GARCH, stochastic volatility, Monte Carlo | Vega/gamma exposure; hedging necessary |
| Quant Macro | Use macroeconomic indicators systemically | GDP, CPI, interest rates | Dynamic factor models, PCA, VAR | Lagging signals; regime dependence |
| Sentiment-Based | Social/media opinion analysis | Social media, news, forums | NLP models, sentiment indexes | Noisy and manipulable data |
| Liquidity Provision | Passive spread capture via quoting | Tick data, volume | Inventory control, optimal spread models | Adverse selection and inventory risk |
| Options Strategies | Complex trades on volatility & Greeks | Option prices and Greeks | Delta-neutral, straddles, gamma scalping | Multidimensional risk exposure |
| Calendar/Seasonal | Use recurring temporal market patterns | Historical prices | Fourier series, date-based regression | Weak alpha; often crowded |
| Fractal/Chaos Models | Use nonlinear and self-similar patterns | Price time series | Hurst exponent, Mandelbrot fractals | Not widely accepted in practice |
| Reinforcement Learning | Learn trading policies through simulation | Environment feedback loop | DQN, PPO, Actor-Critic | Unstable learning; poor generalization |
| Execution Algorithms | Minimize market impact/slippage | Market depth, volume | VWAP, TWAP, POV, IS | Not alpha-generating; used for cost control |
| Information Arbitrage | Exploit early or private info access | Proprietary feeds, alt data | Text mining, data scraping pipelines | Regulatory/legal gray area |
Quantitative Modelling
| Modeling Domain | Techniques / Examples | Typical Applications |
|---|---|---|
| Time Series Analysis | - ARIMA, SARIMA - GARCH, EGARCH - Cointegration & stationarity tests |
Forecasting returns, volatility modeling |
| Factor Models | - CAPM, Fama-French 3/5-factor models - Principal Component Analysis (PCA) |
Asset pricing, portfolio construction |
| Machine Learning & AI | - Supervised: linear/logistic regression, SVM, trees - Unsupervised: clustering, PCA - Reinforcement Learning (RL) |
Alpha generation, strategy optimization |
| Stochastic Calculus | - Black-Scholes, Ito calculus - Monte Carlo simulation - Binomial/trinomial trees |
Derivative pricing, volatility modeling |
Portfolio Optimization
- Mean-variance optimization (Markowitz model).
- Risk parity, Black-Litterman model.
Risk Management & Metrics
Risk Metrics:
- Value at Risk (VaR), Conditional VaR (CVaR).
- Maximum drawdown, Sharpe ratio, Sortino ratio.
Stress Testing & Scenario Analysis:
- Historical simulations, hypothetical scenarios.
Computational Method
Executes models and strategies using computing resources.
| Component | Examples |
|---|---|
| Numerical Methods | Monte Carlo, Finite Differences, Optimization Solvers |
| Backtesting | Event-driven simulation engines, walk-forward analysis |
| Execution Systems | Smart order routing, slippage control |
| Infrastructure | HPC clusters, cloud, GPU, low-latency systems |
Execution & Trading Strategies
Algorithmic Trading:
- High-frequency trading (HFT), statistical arbitrage.
- Market-making strategies.
Backtesting:
- Walk-forward analysis, survivorship bias checks.
Performance Evaluation
Assesses results and provides adaptive feedback for strategy improvement.
| Metric Category | Examples |
|---|---|
| Performance | Sharpe Ratio, Alpha, IR, CAGR |
| Risk | VaR, Drawdown, Beta, CVaR |
| Robustness | Sensitivity, stress testing, overfitting diagnostics |
| Compliance | Regulatory limits, ESG alignment, auditability |
Decision & Execution
Implements strategies and makes real-time decisions.
| Subsystem | Examples |
|---|---|
| Signal Processing | Trade entry/exit, hedge triggers |
| Order Management | VWAP, TWAP, iceberg orders |
| Risk Controls | Kill switches, position limits |
| Trade Execution | Algorithmic trading, DMA, broker APIs |
Governance & Strategic Integration
Links quant systems to business strategy and external constraints.
| Area | Examples |
|---|---|
| Regulatory | Basel III, MiFID II, Dodd-Frank |
| Strategic Fit | Alignment with investment goals, capital requirements |
| Reporting & Auditing | Model audit trails, compliance logs |
| Human Oversight | Analyst review, explainability, override systems |
Key Challenges
- Overfitting (in ML models).
- Non-stationarity of financial data.
- Regulatory and ethical considerations.
References
- Quantitative Analysis (QA)
- Algorithmic Trading
- https://sungwookle.github.io/research/2105121010/img/AI+for+Trading+Learning+Nanodegree+Program+Syllabus.pdf
- https://learn-udacity.top/1/AI-for-Trading-T/index.html
- https://github.com/jseluis/artificial_intelligence_for_trading
- https://github.com/wilsonfreitas/awesome-quant
- https://github.com/cantaro86/Financial-Models-Numerical-Methods
- https://python.quantecon.org/intro.html
- https://julia.quantecon.org/intro.html
- https://github.com/nkaz001/hftbacktest
- https://github.com/letianzj/QuantResearch/tree/master
- https://github.com/tradytics/eiten
- https://github.com/je-suis-tm/quant-trading
- https://www.quantconnect.com/
- https://numer.ai/
- https://quantocracy.com/
- https://www.janestreet.com/puzzles/
- https://github.com/stefan-jansen/machine-learning-for-trading/tree/main/01_machine_learning_for_trading