Latency & Execution Modeling
In the theoretical world of most backtesters, execution is instantaneous. In the physical world, data must travel from the exchange to your strategy, your strategy must process that data, and your order must travel back to the exchange. This "Round-trip Time" (RTT) is known as Latency.
For high-frequency or intraday strategies, latency is often the difference between a high-performing system and a broken one.
The 50ms Gap
Even with a high-speed connection, a retail trader often faces 50ms to 200ms of latency. In a volatile market like the Nasdaq (NQ), price can move several ticks in that fraction of a second. If your strategy relies on capturing single-tick movements, it may be mathematically impossible to trade live.
The Latency Stack
Professional modeling requires breaking latency down into three distinct components:
- Data Latency: The time it takes for a price update to travel from the exchange to your strategy engine.
- Compute Latency: The time it takes for your AlgoLift logic nodes to process the new data and trigger an "Entry" signal.
- Execution Latency: The time it takes for your order to reach the broker's matching engine and receive a "Fill" confirmation.
Modeling Price Drift
Slippage is the direct result of latency. While you are "in flight" to the exchange, other market participants are hitting the Bid and Ask.
AlgoLift allows you to inject Artificial Latency into your simulations. By setting a "Compute Delay" or "Network Lag" in the backtest settings, you force the engine to look ahead by X milliseconds before granting a fill. This reveals "Price Drift"—showing you exactly how much profit is lost while your order is simply traveling through the wire.
Under the Hood: Precision Timestamps
AlgoLift's backtest engine utilizes Microsecond Timestamps. Because we process the raw historical tape, we can simulate what happens when you "Wait" for 100ms. We don't just guess the price change; we move the simulation forward to the exact tick that occurred 100ms after your signal.
Latency-Sensitivity Analysis
A robust strategy should be "Latency-Insensitive."
- The Test: Run your backtest with 0ms latency, then run it again with 250ms latency.
- The Result: If the Profit Factor drops significantly with just a small amount of added lag, your strategy is too fragile for the real world. You are "fighting for scraps" that only high-frequency firms with co-located servers can capture.
Mitigation: Limit Orders vs. Market Orders
Latency affects different order types in different ways:
- Market Orders: High latency leads to worse fill prices (Slippage).
- Limit Orders: High latency leads to "Missed Fills." You might reach the exchange too late, and the liquidity you wanted is already gone.
- AlgoLift Simulation: Our engine models both outcomes, allowing you to determine which order type is most resilient to your expected network conditions.
Pro Tip: The 'Fat' Edge
Don't build strategies that require "Winning the Race." Aim for a "Fat Edge"—strategies that capture moves large enough that a 100ms delay or a 1-tick slip doesn't significantly impact the overall expectancy.