Lesson 4 · 16 min
Microstructure & trading infrastructure
Exchanges, brokers, spreads, and how research simulation differs from live trading.
VenuesBrokersCosts
Market microstructure is the mechanics of how orders become trades — queues, spreads, and latency. You do not need to build a exchange to learn quant trading, but you must know where simulation ends and real infrastructure begins.
Quant platform
- Quant Buffet backtest lab
- QuantConnect / LEAN (library code)
- Python + pandas locally
Research layer: signals, backtests, and strategy articles.
| Concept | Plain English |
|---|---|
| Bid–ask spread | Gap between best buy and sell price — hidden cost when you trade. |
| Slippage | Fill price worse than expected; Quant Buffet models 2 bps per side. |
| Latency | Delay from signal to fill; matters for HFT, less for monthly ETF rotation. |
| Partial fill | Order only partly executed — engine scales buys if cash is insufficient. |
Research stack vs live stack
| Layer | Backtest lab | Live trading |
|---|---|---|
| Signal time | Daily close | Intraday or daily — your choice |
| Execution price | Close + 2 bps slippage model | Bid/ask at broker |
| Commission | 5 bps per fill | Broker fee schedule |
| Capital | Virtual $100,000 | Real cash & margin |
| Failure mode | Python exception | Rejected order, partial fill, outage |
Infrastructure checklist for live trading
- Broker account — e.g. Interactive Brokers for ETFs, crypto exchange for BTC.
- Market data subscription — free delayed vs paid real-time.
- Order routing — desktop, API, or platform like QuantConnect.
- Risk limits — max position size, max daily loss, kill switch.
- Logging & reconciliation — compare fills to what your model expected.