Backtesting with L3 data in very low-liquidity market
Backtesting with L3 data in very low-liquidity market
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Dyon J Don Kiwi van Vreumingen · External communityPost link
External question — Quantitative Finance Stack Exchange
Author: Dyon J Don Kiwi van Vreumingen
Original post: https://quant.stackexchange.com/questions/85863
License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/
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I'm new to algo trading and I'm developing an automated trading strategy in a very slow and illiquid market (not your typical HFT setting). For example, it is common for there to be less than five orders on one side of the order book (not five orders per price level, five orders in total), and sometimes one side is even completely empty.
I have access to L3 data of this market, but I'm wondering whether it would make sense at all to develop a backtesting framework for this market since the market impact of a single order can be significant. Can anybody shed more light on what would be an appropriate way to approach this?
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Deniz Kara · External communityPost link
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Author: Deniz Kara
Original post: https://quant.stackexchange.com/a/85876
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yes, it can still make sense to backtest it. the problem is that in a market this illiquid, execution is probably more important than the signal itself.
with L3 data, i'd build a market-replay simulator rather than a conventional backtester. you want to model queue position, cancellations, partial fills, new orders arriving, market orders consuming liquidity, latency, and the impact of your own orders.
i'd also run sensitivity tests on those assumptions. if the strategy only works with zero latency or optimistic queue placement, that's a warning sign. Similarly, test different order sizes. If going from 1 unit to 2-3 units destroys the edge, you've learned something about the strategy's capacity.
so the question isn't really whether backtesting is possible. it's whether your execution model is realistic enough that the simulated P&L tells you anything useful. L3 data gives you a good basis for doing that, but i'd treat the result as a range of plausible outcomes rather than a precise equity curve.
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Quoted from Forex.com.bd-Editorial External answer — Quantitative Finance Stack Exchange Author: Deniz Kara Source score (net votes, not local likes): 0 Original post: https://quant.stackexchange.com/a/85876 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. yes, it can still make sense to backtest it. the problem is that in a market this illiquid, execution is probably more important than the signal itself. with L3 data, i'd build a market-replay simulator rather than a conventional backtester. you want to model queue position, cancellations, partial fills, new orders arriving, market orders consuming liquidity, latency, and the impact of your own orders. i'd also run sensitivity tests on those assumptions. if the strategy only works with zero latency or optimistic queue placement, that's a warning sign. Similarly, test different order sizes. If going from 1 unit to 2-3 units destroys the edge, you've learned something about the strategy's capacity. so the question isn't really whether backtesting is possible. it's whether your execution model is realistic enough that the simulated P&L tells you anything useful. L3 data gives you a good basis for doing that, but i'd treat the result as a range of plausible outcomes rather than a precise equity curve.
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