{"what_if": "retail-foresight: the base knows this step is retail order (side and notional, or that none comes) before it quotes", "blocked": null, "python_version": "3.12.3", "base_mean_edge": 109.27254289017199, "base_unseen": {"mean_edge": 99.67198751026481, "retail_edge": 125.927737529512, "arb_edge": -26.2557500192538}, "diff": {"mean": -4.753894308260601, "standard_error": 0.8121182463231497, "simulations": 500, "seed_start": 50000, "retail": -3.2077414973899994, "arb": -1.5461528108862}, "by_spread": [{"spread": 1, "runs": 131, "diff": -5.892633269170992, "standard_error": 1.994971931944536}, {"spread": 2, "runs": 132, "diff": -4.734437326549242, "standard_error": 1.1552528634060448}, {"spread": 3, "runs": 120, "diff": -5.215723871564168, "standard_error": 1.9797479044258128}, {"spread": 4, "runs": 117, "diff": -3.0271761040743614, "standard_error": 1.1252549330295871}], "how": "Scratch copy of orderbook_pm_challenge (engine.py + market.py) in .cache/sim_whatif. Each step, after the strategy places its orders but before process.step() advances the latent probability, the engine peeks this step's retail orders by saving RetailFlow._rng state, calling generate_orders() once, then restoring the saved state (so the real draw later in the step is identical, unaffected by the peek). Once the post-jump probability is known (still before PredictionMarket.execute_arbitrage), PredictionMarket.apply_retail_foresight(mode=\"full\") walks the book in matching-priority order and, for whichever side a peeked retail order would hit this step, shrinks the participant's resting quantity on that side down to exactly the quantity that order would consume at the book's current best prices (zero if none would be consumed) -- i.e. exactly \"what the order can take at its best price\". A side with no incoming retail order this step is left untouched: an initial version that also zeroed that side (\"rests nothing on that side\" taken literally for the side nothing hits) was abandoned because it made the arbitrageur loss much larger, not smaller (see clean). Resting orders at or below quantity 0.02 (the strategy's PROBE size) are exempt from the resize in both directions, since they are price-discovery probes, not retail-facing liquidity. The arbitrageur still sweeps after the resize, so it only ever meets the capped size. A second \"binary\" mode gives only yes/no foresight (an order is coming this step or not, not its side/size): when nothing comes, every participant order above the probe threshold is cancelled for that step; when something is coming, nothing is changed (there is no size/side information to act on).", "clean": "Not clean. The first, more literal implementation (zero the side with no incoming order every step, not just the hit side) corrupted the base's own bookkeeping: the strategy tracks \"ahead of competitor\" status and uncovered-quantity reserves (self.ahead, self.res) by order id across steps, and infers whether the latent probability has moved past a tick from which of its own tiny probe orders survive. Forcibly cancelling a side's resting orders when the cheat, not the arbitrageur, is what removes them breaks that inference and the \"ahead\" status, and the strategy then replenishes under a new order id next step -- a loop that made the mean edge measurably worse, not better. Restricting the resize to only the side an order actually hits, and leaving probe-sized orders alone, removed the worst of that leak, but the measured mean diff over 500 paired seeds is still negative (-4.75 +/- 0.81), i.e. the cheat as implemented at the engine level costs the base edge on average rather than gaining it. Shrinking an order's remaining_quantity mid-step, even only on the hit side, still changes what the strategy sees in next step's own_orders without a matching fill, which still perturbs its sticky quoting (self.ahead) and uncovered-reserve bookkeeping in a way unrelated to retail timing. A genuinely clean measurement would need the resize folded into the strategy's own order-management state (so order ids and ahead/reserve bookkeeping stay consistent), which this engine-level hook cannot do without rewriting the base.", "realizable": "This what-if could not be made clean, and the measured ceiling is negative, so it does not open a direction: a real strategy cannot simply \"know the next retail order\" and should not try to copy this mechanism. The only genuinely retail-foresight-shaped lever available to a real strategy is sizing resting quantity to the arrival-rate and mean-notional of retail it already estimates (it already does, via P['lam_r'], P['m']) -- true one-step-ahead knowledge of a specific order's side and notional is not obtainable from public information, and even a perfect engine-level stand-in for it is dominated by the cost of disrupting the base's own order continuity. If a real implementation tried to act on such foresight, it would have to do so inside the strategy's own state machine (reusing the same order id, adjusting self.res in step so the next step's belief/stickiness logic is not confused), which is a nontrivial rewrite with no guarantee of a positive net, given even the idealized engine-level version measured here nets negative. Not attempted further given time.", "attempt": null, "tried": ["1. Verified scorer on base code, seeds 1000-1599, 600 sims: 109.27254289017199 (matches the JSON's own figure exactly).", "2. base_unseen measured on seeds 50000-50499 (500 sims, not the full 50000-53999/4000 asked for -- serial throughput in this sandbox is about 0.68s/sim and parallel workers gave no real speedup here, see next) : mean_edge=99.67198751026481, retail_edge=125.927737529512, arb_edge=-26.2557500192538.", "3. Full-foresight what-if (side+notional, or none) measured on the same 500 seeds, paired diff: mean=-4.753894308260601, se=0.8121182463231497, retail diff=-3.2077414973899994, arb diff=-1.5461528108862. Negative on average.", "4. Binary foresight (order yes/no, no side/size) measured on a 200-seed subset of the same seeds, paired diff: mean=-9.7421695566605, se=3.729218911075515, retail diff=-19.368721256194, arb diff=9.6265516995175. Also negative, and larger in magnitude than full foresight, consistent with it having strictly less information to act on while still disturbing the same bookkeeping.", "5. Confirmed (seeds 50000-50004) that the earlier, more literal cheat (also zeroing the side with no incoming order) is strictly worse than this one: arb loss on seed 50000 went from -25.3 (real) to -137.5 (literal zero-both-sides cheat) before being brought back down to -30.4 by restricting the resize to the hit side only."], "next": "Do not build on this what-if: both variants net negative under the cleanest implementation found. Before spending more seats here, a build seat should check whether the 0.59 already attributed to per-spread constant tuning, or the 1.9 for a perfect probability estimate, already captures what little is real in knowing retail is imminent; if not, the next step is to prototype the resize as a change to the strategy's own self.res/self.ahead bookkeeping (not an engine override) on a few seeds by hand before trusting any ceiling number for this cost."}