Data, models and new computation in markets.
This is not “AI that predicts the market”. These are tools for analysis, features, risk, execution and research — with real risks: overfitting, black boxes, data leakage, fragility and governance.
Para qué sirve
This discipline studies how large data sets, machine learning and advanced computation (including quantum in research phase) support analysis, forecasting, risk management, execution and strategy building.
| Area | Applications | Limits |
|---|---|---|
| Classical ML | Features, classification, regression | Overfitting, regime change |
| Deep learning / LLM | News NLP, agents, summarisation | Black box, hallucination |
| Alternative data | Satellite, card, web, social | Cost, quality, survivorship |
| Quantum (research) | Portfolio opt, pricing, Monte Carlo | Immature hardware, hybrid classical |
In quantum finance the most studied areas are portfolio optimisation, derivatives pricing, Monte Carlo simulations and quantum ML — today often in hybrid workflows, not immediate replacement of classical systems.
Core concepts
| Término | In Cyclepedia |
|---|---|
| AI / ML / deep learning | |
| Big data / alternative data | |
| NLP / sentiment / news analytics | |
| Feature engineering / selection | |
| Train / test / validation set | |
| Cross-validation / data leakage | |
| Overfitting / black box / XAI | |
| Regime detection / anomaly detection | |
| Quantum computing / QML | |
| Post-quantum security |
Infrastructure: Matching engine · Execution latency · Market Maker
Related tools
| Tool | Role |
|---|---|
| Data pipeline (tick → feature) | ML preparation |
| ML backtest with purge/embargo | Avoid leakage |
| LLM + RAG on financial documents | Assisted research |
| Agentic workflows | Analysis automation (with supervision) |
| Quantum simulators (Qiskit, etc.) | QML prototypes |
| MLOps / drift monitoring | Model production |
Researchers, desks and institutions
| Figure / entity | Contribution |
|---|---|
| Jim Simons | Renaissance, statistics at scale |
| Marcos López de Prado | ML in finance, validation |
| D.E. Shaw / David Shaw | Computational finance |
| Two Sigma / AQR | Quant + alternative data |
| Doyne Farmer / J.-P. Bouchaud | Complexity and microstructure |
| IBM Quantum / academic research | Quantum finance prototypes |
| Patrick Rebentrost, Seth Lloyd, Marco Pistoia | Quantum algorithms for finance |
Recommended path
- Quantitative trading — quant foundation
- Backtest · Sample size — data and samples
- Systematic trading — models and metrics
- Overfitting · Out of sample — ML backtest and leakage
- Forward test · Paper trading — validation
- Market regime — context and drift
- Anti-scam — risks, fake bots, unrealistic promises
Prerequisite: Systematic trading.
Enlaces
- Systematic trading
- Volume analysis — microstructure and high-frequency data
- Anti-scam — fraudulent “AI trading”