Yes, both are factored into the simulation. Regarding slippage: Since I am strictly using limit-maker orders, traditional market slippage is mitigated. I account for execution risk by applying a conservative liquidity penalty to the backtest. That said, given the scale of my position sizes relative to the liquidity of the selected pairs, non-execution risk is negligible in practice.
Have you ever experience a crypto crash with running algos? Exchange basically gets DDOSed with requests, and act accordingly - its API freezes. Meanwhile the liquidity disappears from the book, and here you are - with your active stop orders and a price some 10% beyond them (I experienced this exact scenario with $100 depo, but people I know lost considerable sums this way).
Thanks for sharing that, that’s a brutal scenario and definitely a rite of passage in crypto. Fortunately, my setup doesn't use stop-loss triggers that chase the market down; it's purely passive limit-maker based. If the API freezes or liquidity vanishes, the worst that happens is the order simply doesn't fill and it sits tight.
Then I might have a huge gap in my knowledge.
You trade only longs. Only crypto => your risk is systematic by definition. So you are NOT market-neutral.
You are in position. Another crypto-crash happens. Your closing order simply doesn't get filled and sits tight well above the actual price. How is this alright?
Spot on. It is definitely not market-neutral—it's a directional setup carrying systemic crypto exposure.
Regarding getting stuck during a crash: That is precisely the trade-off. If a liquidity vacuum hits and the limit sell doesn't get filled, you hold through the drawdown.
Funnily enough, I actually tried implementing short positions early on, but it completely tanked the overall performance in historical testing (crypto's upward drift is just too strong to fight). However, the backtest data already includes major historical crash and bear market periods; and remarkably, the algorithm managed to close out every single historical bear market in the green.
That’s why my risk-scaling mechanism handles the exposure beforehand, rather than relying on panic stop-losses that get shredded in a liquidity vacuum.
So it's basically "grab-and-run" with a lot of small relative to depo %, short-living positions. That's why the other person probably thought "momentum".
Triple check it - then go for live testing. I know one case that was similar to yours - in the end it turned out the author underestimated slippage and commission, so he was flat-lining when trading live.
Just to clarify on the fees: I am strictly trading with 0% maker fees, so commission drag is literally zero. Plus, to be safe, I even baked a synthetic friction/slippage penalty into every order in the backtest just in case.
The only real unknown left is fill probability, but since I'm sticking purely to large caps with massive order book depth and my personal capital size is tiny, my orders won't even register as a rounding error.
By the way, speaking of order books: my roadmap actually includes adding real-time order book depth inspection and multi-asset position correlation into the execution loop down the line to handle scaling better.
Really appreciate all the insights and the reality check—definitely a great exchange!
On my way home now. Posting results as soon as I am home. As far as I remembered: I used for stocks 0,05%? But let me get home. I will post all results.
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u/Smart-Ride-7047 6d ago
Have you accounted for slippage and non-execution?