High-Frequency Trading: Latency and Execution
High-frequency trading operates at timescales where the speed of light becomes a binding constraint. Understanding the HFT landscape is not just relevant for HFT practitioners — it is essential for any algorithmic trader who wants to understand the execution environment they operate in.
The Latency Stack
Every order you send to an exchange passes through a latency stack:
- 1.Strategy computation: 1–100 microseconds (depends on hardware and algorithm complexity)
- 2.Network transmission: 1–50 microseconds (co-location) to 1–50 milliseconds (remote)
- 3.Exchange matching engine: 1–10 microseconds
- 4.Confirmation receipt: same as transmission, reversed
The total round-trip time for a co-located HFT firm is typically 10–100 microseconds. For a retail trader on a standard internet connection, it's 1–50 milliseconds — 100–1000x slower.
Co-location and Market Access
Co-location means placing your trading servers physically inside the exchange's data center. This minimizes network latency to the exchange matching engine. Major exchanges (NYSE, NASDAQ, CME) offer co-location services for a fee.
For non-HFT algorithmic traders, co-location is rarely necessary. The edge in most systematic strategies operates on timescales of minutes to days, where a few milliseconds of latency is irrelevant.
Execution Quality Metrics
Even if you're not competing on latency, execution quality matters. Key metrics:
Slippage: The difference between the price when your signal fired and the price you actually executed at. For liquid large-cap stocks, this is typically 1–3 basis points. For illiquid small-caps, it can be 10–50 basis points.
Market impact: The price movement caused by your own order. For large orders, this can be significant. Use TWAP or VWAP execution algorithms to minimize market impact.
Fill rate: The percentage of limit orders that get filled. A strategy that looks profitable with 100% fill rate may be unprofitable with a realistic 60–70% fill rate.
Applied Ideas
The frameworks discussed above translate directly into deployable trading logic. Here are concrete next steps for practitioners:
- ▸Backtest first: Validate any signal-generation or risk-management approach with walk-forward analysis before committing capital.
- ▸Start small: Deploy with fractional position sizing and paper-trade for at least one full market cycle.
- ▸Monitor regime shifts: Set automated alerts for when your model detects a regime change — manual review before large rebalances is prudent.
- ▸Iterate on KPIs: Track Sharpe, Sortino, max drawdown, and win rate weekly. If any metric degrades beyond your predefined threshold, pause and re-evaluate.
- ▸Combine signals: The strongest edges come from combining uncorrelated signals — pair the ideas in this post with your existing alpha sources.
Sources & Research
4 articles that informed this post
From Theory to Practice
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