| name | quant-trade |
| description | Jane Street-style quantitative trading knowledge system. Covers market making (Avellaneda-Stoikov, spread, inventory), ETF arbitrage (creation/redemption, NAV, AP mechanics), statistical arbitrage (cointegration, IC/ICIR, alpha decay), options and vol trading (surface, Greeks, gamma-theta), risk management (Kelly, drawdown, CVaR), alpha research (backtesting, overfitting), and market microstructure (VPIN, order flow, adverse selection). Sourced from Jane Street PDFs + quant finance literature. TRIGGER: market making, ETF arb, stat arb, vol trading, quant risk, alpha research, VPIN, Jane Street philosophy, systematic trading strategy design.
|
Quant Trade — Jane Street Mental Model
The Jane Street meme: trading is applied probability theory at industrial scale.
Every position is a bet; every bet is priced by expected value; every edge is measured, monitored, and managed.
The Jane Street Operating System
Five axioms that govern all decisions:
- EV first — before entering any trade, compute E[P&L]. If EV ≤ 0, don't trade
- Risk is a cost — wider spread = compensation for inventory risk and adverse selection
- Condition on fill — when your passive order fills, update your fair value estimate downward (you're probably wrong)
- Technology + math + domain = edge — no single leg is sufficient alone
- Porous boundaries — traders, researchers, and engineers share the same goal: find and exploit pricing inefficiencies
Domain Map → Reference Files
Load the relevant file based on user request:
| Domain | Load |
|---|
| Market making, bid/offer, inventory | references/market-making.md |
| ETF arbitrage, creation/redemption, AP | references/etf-arbitrage.md |
| Statistical arb, pairs, cointegration, IC | references/stat-arb.md |
| Options, vol surface, Greeks, hedging | references/options-vol.md |
| Risk management, Kelly, position sizing | references/risk-management.md |
| Alpha research, backtesting, overfitting | references/alpha-research.md |
| Microstructure, order flow, VPIN | references/microstructure.md |
Decision Framework (apply to any quant problem)
1. What is the fair value / EV of this instrument?
2. What is my edge (why am I right and the market is wrong)?
3. What is the risk (inventory, adverse selection, model error)?
4. How do I hedge residual risk at lowest cost?
5. What is my sizing? (Fractional Kelly on true edge estimate)
6. What updates my belief? (Fill = adverse selection signal)
Jane Street's Actual Business
- Primary model: ETF market making + arbitrage across 200+ exchanges globally
- Asset classes: Equities, fixed income, options, ETFs, commodities, crypto
- Quant stack: OCaml for correctness + Python/ML for research + custom hardware for execution
- Research model: traders and researchers co-located, rapid feedback loop between model and live trading
- ML approach: deep learning for alpha generation; "one-size-fits-all" solutions are rejected — use whatever works
