{
  "what_if": "No cheat: count base-missed retail edge at competitor execution prices",
  "blocked": null,
  "python_version": "3.12.3",
  "base_mean_edge": 109.27254289017199,
  "base_unseen": {
    "mean_edge": 98.28063388702982,
    "retail_edge": 126.01907227628422,
    "arb_edge": -27.73843838925235
  },
  "diff": {
    "mean": 67.29920187181038,
    "standard_error": 0.9882898009129557,
    "simulations": 4000,
    "seed_start": 50000,
    "retail": 67.29920187181038,
    "arb": 0.0
  },
  "by_spread": [
    {
      "spread": 1,
      "runs": 1016,
      "diff": 88.36050156667147,
      "standard_error": 2.463779874118278
    },
    {
      "spread": 2,
      "runs": 998,
      "diff": 70.28489544934736,
      "standard_error": 1.887272348581826
    },
    {
      "spread": 3,
      "runs": 1017,
      "diff": 59.89811307480918,
      "standard_error": 1.7180357988983872
    },
    {
      "spread": 4,
      "runs": 969,
      "diff": 49.9090105675683,
      "standard_error": 1.4460984040033003
    }
  ],
  "how": "Simulator commit b244530ee2a6247b5f930830eeca9dd950da0e7e; Python standard library only; no sandbox. Base JSON code SHA256 baef955ec10728839f7c3357016ec9eb0550e7ec689bbd5171b1cdd5c9e37273. Ran python3 -m orderbook_pm_challenge run base.py --simulations 600 --seed-start 1000 --workers 4 --json, then --simulations 4000 --seed-start 50000. Both reference means reproduced exactly. No market rule or base strategy code changed for the count. In a scratch runner, subclassed PredictionMarket, intercepted retail _fill_order calls before invoking the original method, and cached probability passed to execute_arbitrage. Snapshotted each side quote price/sequence/remaining size immediately before every retail order. Wrapped RetailFlow.generate_orders to associate original SELL notional with its unchanged quantity conversion. Recorded every order side, notional, true p, requested sell quantity, participant/competitor fill quantities and executed notionals, each fill price, reason and edge; recorded residual unspent BUY cash and unfilled SELL quantity. Per-run compressed ledgers stayed in scratch only. Sum signed q*(x-p) for retail BUYs hitting competitor asks and q*(p-x) for retail SELLs hitting competitor bids, with no positive-part clipping. No unfilled flow valued at invented prices. diff is this additive accounting credit, not a score change from a new trading policy. Compute paired original-score-plus-credit minus original-score per seed algebraically as that seed credit; SE=sample standard deviation of per-run credits/sqrt(n), with ddof=1. Stratify on sampled competitor spread_ticks, not observed bid/ask width; zone is the true probability at execution. Numeric summaries and output generated with Python and json.dump. All numbers below are local measurements, not external leaderboard results. Metric means and standard errors use all runs in the indicated spread, including zero-opportunity runs. spread=null means all spreads. Zone bounds refer to contemporaneous true probability, before that retail order. Reason taxonomy: no_quote=no resting base order on the required passive side immediately before this retail order (after arbitrage and any earlier retail orders); size_ran_out=a base quote with priority over this competitor fill existed at order start and was consumed earlier within the same order; price_behind_strict=every initial base quote had strictly worse price; same_price_fifo=initial best base price tied but competitor had earlier time priority. Tiny orders count as quotes. The probe_only diagnostic means every initial side quote had remaining quantity <=0.010000001; it is size-based and can include residual main orders, rather than asserting original order provenance. Arbitrage-removed quotes and depletion in previous retail orders enter no_quote. These definitions are disjoint at fill level. Probability zones: [0,0.01),[0.01,0.05),[0.05,0.2),[0.2,0.8),[0.8,0.95),[0.95,0.99),[0.99,1]. Metric records below: missed_edge_per_run and standard_error, jointly by spread, reason, and probability zone; reason=all sums all four reasons. These are edge units, not dollars of order notional.",
  "clean": "Clean observational count: all 4000 instrumented SimulationResult dictionaries equal originals exactly, including cash, inventories, retail edge, arbitrage edge and fill count. No extra strategy information, liquidity, capital, queue priority, execution, or altered RNG draws. The requested three-reason split is incomplete under FIFO: same-price competitor priority is a genuine fourth reason, reported separately instead of mislabeled as worse price. This is a frozen-path gross retail opportunity ceiling under the task definition, not a clean implementable counterfactual nor a rigorous bound on all dynamically changed strategies. The additive credit ignores collateral, lost existing fills, changed competitor replenishment, downstream inventory and new arbitrage losses. Unexecuted retail remainder is disclosed but excluded because it has no competitor execution price. Nothing fabricated; no missing inputs or failed required measurements.",
  "realizable": "Extra retail capture requires genuine quotes before the probability move and before the retail arrival, so the entire accounting credit cannot be captured costlessly. The no_quote bucket includes quotes swept by arbitrage before retail, as well as quotes absent by design; there is no strategy callback between those events, so this count does not distinguish those subcauses or establish that an empty side can safely be filled. Missing quotes near the price boundary may need covered YES inventory and earlier cheap acquisition; that locks capital and loses on adverse jumps. Exhausted main sizes may justify deeper secondary orders or selective size only where incremental retail tail edge exceeds stale-quote loss; exhaustion of probe-sized orders often reflects absence of a substantial main quote and is not evidence to scale probes; the diagnostic uses remaining size and may include tiny main-order residuals. Strict price losses require improving the competitor or adding a ladder; this sacrifices spread or reserves more cash and inventory. FIFO losses require establishing and keeping orders before the competitor arrives, such as a standing second rung ahead of future replenishments; joining its price afterward does not grant priority. Wider asks can collect some overflow with less adverse selection than scaling the best ask, but only after better-price and earlier-time orders fill. The real attempt below uses unreserved YES for a second ask at the competitor price. Its measured paired out-of-sample result, including all inventory, collateral, competition and arbitrage feedback, is the realizable evidence; the larger count is not a promised gain. Training used only seeds 4000..4599, a 600-run subset of the permitted 4000..7999 range; the winner was frozen before unseen evaluation.",
  "attempt": {
    "name": "covered second ask at competitor price",
    "selected": "tail5",
    "diff": {
      "mean": -4.741292586289525,
      "standard_error": 0.21334474576811202,
      "retail": -0.6231006123494998,
      "arb": -4.118191973937099,
      "simulations": 4000,
      "seed_start": 50000
    },
    "by_spread": [
      {
        "spread": 1,
        "runs": 1016,
        "diff": -2.643038021154626,
        "standard_error": 0.5082629247275045,
        "retail": 0.22382331326437,
        "arb": -2.8668613344195863
      },
      {
        "spread": 2,
        "runs": 998,
        "diff": -4.739079516107916,
        "standard_error": 0.41207303969234915,
        "retail": -0.6888455975666326,
        "arb": -4.050233918538978
      },
      {
        "spread": 3,
        "runs": 1017,
        "diff": -6.105193790658309,
        "standard_error": 0.42838078719461764,
        "retail": -1.0231594250015734,
        "arb": -5.082034365651131
      },
      {
        "spread": 4,
        "runs": 969,
        "diff": -5.512136505149432,
        "standard_error": 0.3201815058410014,
        "retail": -1.023514854568111,
        "arb": -4.488621650576883
      }
    ],
    "mean_edge": 93.5393413007403,
    "failures": 0,
    "tuning": {
      "training": {
        "tail5": {
          "mean": -4.1551845695395,
          "standard_error": 0.6304435465708207,
          "retail": -0.3832119729766667,
          "arb": -3.771972596550167
        },
        "tail20": {
          "mean": -6.5596624790751665,
          "standard_error": 1.1556879090874193,
          "retail": -0.8621145737676666,
          "arb": -5.6975479052899995
        }
      },
      "selected": "tail5",
      "simulations": 600,
      "seed_start": 4000,
      "base_mean_edge": 100.61264031170599,
      "mean_edges": {
        "tail5": 96.45745574216649,
        "tail20": 94.05297783263083
      }
    },
    "implementation": "Insert the following block immediately before the original probes comment, with all other base code unchanged. It reserves only surplus covered YES after retained and newly placed main asks, uses the original order manager on the next step, never assumes priority over existing competitor orders, and does not add cash. Tail orders can persist only if the original manager chooses to keep them.",
    "inserted_code": "        # A second ask at the competitor price uses only YES left after all main asks.\n        # Subtract entire ask quantities, even uncovered ones, to stay conservative.\n        if 1 <= ca <= 99 and ca > ask_t:\n            already = sum(o.remaining_quantity for o in asks if o.order_id in kept_ids)\n            already += sum(a.quantity for a in out if isinstance(a, PlaceOrder) and a.side is Side.SELL)\n            spare_yes = max(0.0, Y - already - 0.2)\n            tail_q = fl(min(spare_yes, 5.0 / (ca / 100.0)))\n            if tail_q >= 0.01:\n                tail_id = self._oid('tail')\n                out.append(PlaceOrder(side=Side.SELL, price_ticks=ca, quantity=tail_q, client_order_id=tail_id))\n                self.res[tail_id] = 0.0\n\n",
    "code_sha256": "b8b322f70c0d0dd82076818d66d5869e3d4aaee03ed8085b910fdb91a97056d0"
  },
  "tried": [
    "600-seed scorer reproduction: mean edge=109.27254289017199; seeds 1000..1599; failures=0; expected difference=0.0.",
    "4000-seed unseen reproduction: mean edge=98.28063388702982; seeds 50000..53999; failures=0; expected difference=0.0.",
    "Instrumentation versus original: 4000/4000 full SimulationResult dictionaries exactly equal; actual score difference mean=0.0, standard_error=0.0; actual retail difference=0.0; actual arb difference=0.0. Separately in each competitor spread 1,2,3,4: actual score difference mean=0.0, standard_error=0.0.",
    "Unit checks passed for BUY and SELL: no side quote, strict worse price, exhausted better quote, same-price FIFO, SELL exact edge signs/quantities and no_quote after previous-order exhaustion; four pilot full-result equalities; all 4000 run ledgers reconcile retail edge to within 1e-6 and missed edge to within 1e-7.",
    "Real mechanism training, only seeds 4000..4599: {\"training\":{\"tail5\":{\"mean\":-4.1551845695395,\"standard_error\":0.6304435465708207,\"retail\":-0.3832119729766667,\"arb\":-3.771972596550167},\"tail20\":{\"mean\":-6.5596624790751665,\"standard_error\":1.1556879090874193,\"retail\":-0.8621145737676666,\"arb\":-5.6975479052899995}},\"selected\":\"tail5\",\"simulations\":600,\"seed_start\":4000,\"base_mean_edge\":100.61264031170599,\"mean_edges\":{\"tail5\":96.45745574216649,\"tail20\":94.05297783263083}}",
    "Exhaustion decomposition (probe_only means all initial side quantities <=0.010000001, including possible tiny residuals): {\"spread\":null,\"runs\":4000,\"probe_only_mean\":22.453429013674405,\"probe_only_standard_error\":0.321434736051472,\"other_size_mean\":26.305860164723114,\"other_size_standard_error\":0.5798839791949729}",
    "Exhaustion decomposition (probe_only means all initial side quantities <=0.010000001, including possible tiny residuals): {\"spread\":1,\"runs\":1016,\"probe_only_mean\":36.98672820922787,\"probe_only_standard_error\":0.8036137413192226,\"other_size_mean\":22.70174956302165,\"other_size_standard_error\":1.3195417003191814}",
    "Exhaustion decomposition (probe_only means all initial side quantities <=0.010000001, including possible tiny residuals): {\"spread\":2,\"runs\":998,\"probe_only_mean\":23.208597453953093,\"probe_only_standard_error\":0.5676724869875139,\"other_size_mean\":28.920148464379544,\"other_size_standard_error\":1.1621006518221668}",
    "Exhaustion decomposition (probe_only means all initial side quantities <=0.010000001, including possible tiny residuals): {\"spread\":3,\"runs\":1017,\"probe_only_mean\":16.30493618375988,\"probe_only_standard_error\":0.4285130182544576,\"other_size_mean\":28.426612228701554,\"other_size_standard_error\":1.1393482391108918}",
    "Exhaustion decomposition (probe_only means all initial side quantities <=0.010000001, including possible tiny residuals): {\"spread\":4,\"runs\":969,\"probe_only_mean\":12.890505506907255,\"probe_only_standard_error\":0.37816266466577414,\"other_size_mean\":25.166450256782458,\"other_size_standard_error\":0.9644175385499378}",
    "{\"spread\":null,\"reason\":\"no_quote\",\"zone\":\"all\",\"missed_edge_per_run\":14.89348514182232,\"standard_error\":0.26563847428473975}",
    "{\"spread\":null,\"reason\":\"no_quote\",\"zone\":\"[0,0.01)\",\"missed_edge_per_run\":0.16518100744221148,\"standard_error\":0.023598735541559245}",
    "{\"spread\":null,\"reason\":\"no_quote\",\"zone\":\"[0.01,0.05)\",\"missed_edge_per_run\":1.5604753543202845,\"standard_error\":0.10349708215576281}",
    "{\"spread\":null,\"reason\":\"no_quote\",\"zone\":\"[0.05,0.2)\",\"missed_edge_per_run\":5.134228693281906,\"standard_error\":0.1650815082246398}",
    "{\"spread\":null,\"reason\":\"no_quote\",\"zone\":\"[0.2,0.8)\",\"missed_edge_per_run\":7.091888889171922,\"standard_error\":0.11806518273097012}",
    "{\"spread\":null,\"reason\":\"no_quote\",\"zone\":\"[0.8,0.95)\",\"missed_edge_per_run\":0.7114529383535169,\"standard_error\":0.022123574810037648}",
    "{\"spread\":null,\"reason\":\"no_quote\",\"zone\":\"[0.95,0.99)\",\"missed_edge_per_run\":0.21771087988793195,\"standard_error\":0.00786218660393812}",
    "{\"spread\":null,\"reason\":\"no_quote\",\"zone\":\"[0.99,1]\",\"missed_edge_per_run\":0.012547379364546037,\"standard_error\":0.0018564745975520043}",
    "{\"spread\":null,\"reason\":\"price_behind_strict\",\"zone\":\"all\",\"missed_edge_per_run\":2.309640823091095,\"standard_error\":0.07916239048533599}",
    "{\"spread\":null,\"reason\":\"price_behind_strict\",\"zone\":\"[0,0.01)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":null,\"reason\":\"price_behind_strict\",\"zone\":\"[0.01,0.05)\",\"missed_edge_per_run\":2.0109208269953447,\"standard_error\":0.07108556412939737}",
    "{\"spread\":null,\"reason\":\"price_behind_strict\",\"zone\":\"[0.05,0.2)\",\"missed_edge_per_run\":0.29871999609575034,\"standard_error\":0.018425715146797094}",
    "{\"spread\":null,\"reason\":\"price_behind_strict\",\"zone\":\"[0.2,0.8)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":null,\"reason\":\"price_behind_strict\",\"zone\":\"[0.8,0.95)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":null,\"reason\":\"price_behind_strict\",\"zone\":\"[0.95,0.99)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":null,\"reason\":\"price_behind_strict\",\"zone\":\"[0.99,1]\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":null,\"reason\":\"same_price_fifo\",\"zone\":\"all\",\"missed_edge_per_run\":1.336786728499442,\"standard_error\":0.060166809823749154}",
    "{\"spread\":null,\"reason\":\"same_price_fifo\",\"zone\":\"[0,0.01)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":null,\"reason\":\"same_price_fifo\",\"zone\":\"[0.01,0.05)\",\"missed_edge_per_run\":1.3359778249512764,\"standard_error\":0.060074293693992524}",
    "{\"spread\":null,\"reason\":\"same_price_fifo\",\"zone\":\"[0.05,0.2)\",\"missed_edge_per_run\":0.0008089035481656898,\"standard_error\":0.00080890354816569}",
    "{\"spread\":null,\"reason\":\"same_price_fifo\",\"zone\":\"[0.2,0.8)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":null,\"reason\":\"same_price_fifo\",\"zone\":\"[0.8,0.95)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":null,\"reason\":\"same_price_fifo\",\"zone\":\"[0.95,0.99)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":null,\"reason\":\"same_price_fifo\",\"zone\":\"[0.99,1]\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":null,\"reason\":\"size_ran_out\",\"zone\":\"all\",\"missed_edge_per_run\":48.75928917839752,\"standard_error\":0.7432615835379859}",
    "{\"spread\":null,\"reason\":\"size_ran_out\",\"zone\":\"[0,0.01)\",\"missed_edge_per_run\":5.570687806775988,\"standard_error\":0.3553121808322863}",
    "{\"spread\":null,\"reason\":\"size_ran_out\",\"zone\":\"[0.01,0.05)\",\"missed_edge_per_run\":4.58476689849392,\"standard_error\":0.24467872088730397}",
    "{\"spread\":null,\"reason\":\"size_ran_out\",\"zone\":\"[0.05,0.2)\",\"missed_edge_per_run\":13.331609305686639,\"standard_error\":0.34388220233184996}",
    "{\"spread\":null,\"reason\":\"size_ran_out\",\"zone\":\"[0.2,0.8)\",\"missed_edge_per_run\":22.629140472263224,\"standard_error\":0.300764106377573}",
    "{\"spread\":null,\"reason\":\"size_ran_out\",\"zone\":\"[0.8,0.95)\",\"missed_edge_per_run\":1.8938463357999664,\"standard_error\":0.05147823909092986}",
    "{\"spread\":null,\"reason\":\"size_ran_out\",\"zone\":\"[0.95,0.99)\",\"missed_edge_per_run\":0.36311005028586224,\"standard_error\":0.015191993087844628}",
    "{\"spread\":null,\"reason\":\"size_ran_out\",\"zone\":\"[0.99,1]\",\"missed_edge_per_run\":0.3861283090919215,\"standard_error\":0.01983212268877749}",
    "{\"spread\":null,\"reason\":\"all\",\"zone\":\"[0,0.01)\",\"missed_edge_per_run\":5.7358688142182,\"standard_error\":0.36250616442469547}",
    "{\"spread\":null,\"reason\":\"all\",\"zone\":\"[0.01,0.05)\",\"missed_edge_per_run\":9.492140904760827,\"standard_error\":0.36219813863621236}",
    "{\"spread\":null,\"reason\":\"all\",\"zone\":\"[0.05,0.2)\",\"missed_edge_per_run\":18.765366898612463,\"standard_error\":0.4784184325071125}",
    "{\"spread\":null,\"reason\":\"all\",\"zone\":\"[0.2,0.8)\",\"missed_edge_per_run\":29.721029361435146,\"standard_error\":0.38539928109870264}",
    "{\"spread\":null,\"reason\":\"all\",\"zone\":\"[0.8,0.95)\",\"missed_edge_per_run\":2.605299274153483,\"standard_error\":0.06819766702827562}",
    "{\"spread\":null,\"reason\":\"all\",\"zone\":\"[0.95,0.99)\",\"missed_edge_per_run\":0.5808209301737942,\"standard_error\":0.02001931470547132}",
    "{\"spread\":null,\"reason\":\"all\",\"zone\":\"[0.99,1]\",\"missed_edge_per_run\":0.3986756884564675,\"standard_error\":0.02034338852800961}",
    "{\"spread\":1,\"reason\":\"no_quote\",\"zone\":\"all\",\"missed_edge_per_run\":24.666484388208815,\"standard_error\":0.7941882364627256}",
    "{\"spread\":1,\"reason\":\"no_quote\",\"zone\":\"[0,0.01)\",\"missed_edge_per_run\":0.3182324046839831,\"standard_error\":0.07006718787626491}",
    "{\"spread\":1,\"reason\":\"no_quote\",\"zone\":\"[0.01,0.05)\",\"missed_edge_per_run\":3.9377896974387383,\"standard_error\":0.37077400526916415}",
    "{\"spread\":1,\"reason\":\"no_quote\",\"zone\":\"[0.05,0.2)\",\"missed_edge_per_run\":8.227851214354056,\"standard_error\":0.5030437098459345}",
    "{\"spread\":1,\"reason\":\"no_quote\",\"zone\":\"[0.2,0.8)\",\"missed_edge_per_run\":10.60419616263125,\"standard_error\":0.3266557136174743}",
    "{\"spread\":1,\"reason\":\"no_quote\",\"zone\":\"[0.8,0.95)\",\"missed_edge_per_run\":1.1972720941365382,\"standard_error\":0.0666833096931864}",
    "{\"spread\":1,\"reason\":\"no_quote\",\"zone\":\"[0.95,0.99)\",\"missed_edge_per_run\":0.3648311542753674,\"standard_error\":0.02449569351219838}",
    "{\"spread\":1,\"reason\":\"no_quote\",\"zone\":\"[0.99,1]\",\"missed_edge_per_run\":0.016311660688879587,\"standard_error\":0.0025631152231509837}",
    "{\"spread\":1,\"reason\":\"price_behind_strict\",\"zone\":\"all\",\"missed_edge_per_run\":2.632209047228945,\"standard_error\":0.1824964062786024}",
    "{\"spread\":1,\"reason\":\"price_behind_strict\",\"zone\":\"[0,0.01)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":1,\"reason\":\"price_behind_strict\",\"zone\":\"[0.01,0.05)\",\"missed_edge_per_run\":2.2356156248288084,\"standard_error\":0.1613614518150394}",
    "{\"spread\":1,\"reason\":\"price_behind_strict\",\"zone\":\"[0.05,0.2)\",\"missed_edge_per_run\":0.3965934224001369,\"standard_error\":0.040865854934462896}",
    "{\"spread\":1,\"reason\":\"price_behind_strict\",\"zone\":\"[0.2,0.8)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":1,\"reason\":\"price_behind_strict\",\"zone\":\"[0.8,0.95)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":1,\"reason\":\"price_behind_strict\",\"zone\":\"[0.95,0.99)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":1,\"reason\":\"price_behind_strict\",\"zone\":\"[0.99,1]\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":1,\"reason\":\"same_price_fifo\",\"zone\":\"all\",\"missed_edge_per_run\":1.3733303589842083,\"standard_error\":0.11503481717667384}",
    "{\"spread\":1,\"reason\":\"same_price_fifo\",\"zone\":\"[0,0.01)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":1,\"reason\":\"same_price_fifo\",\"zone\":\"[0.01,0.05)\",\"missed_edge_per_run\":1.3733303589842083,\"standard_error\":0.11503481717667384}",
    "{\"spread\":1,\"reason\":\"same_price_fifo\",\"zone\":\"[0.05,0.2)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":1,\"reason\":\"same_price_fifo\",\"zone\":\"[0.2,0.8)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":1,\"reason\":\"same_price_fifo\",\"zone\":\"[0.8,0.95)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":1,\"reason\":\"same_price_fifo\",\"zone\":\"[0.95,0.99)\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":1,\"reason\":\"same_price_fifo\",\"zone\":\"[0.99,1]\",\"missed_edge_per_run\":0.0,\"standard_error\":0.0}",
    "{\"spread\":1,\"reason\":\"size_ran_out\",\"zone\":\"all\",\"missed_edge_per_run\":59.68847777224951,\"standard_error\":1.7470103136134032}",
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    "{\"spread\":4,\"reason\":\"all\",\"zone\":\"[0.8,0.95)\",\"missed_edge_per_run\":2.0317538777689594,\"standard_error\":0.10558249940453684}",
    "{\"spread\":4,\"reason\":\"all\",\"zone\":\"[0.95,0.99)\",\"missed_edge_per_run\":0.47785177647180366,\"standard_error\":0.04196043794525423}",
    "{\"spread\":4,\"reason\":\"all\",\"zone\":\"[0.99,1]\",\"missed_edge_per_run\":0.35037635495456154,\"standard_error\":0.03781944103620647}",
    "Fill amounts per run (actual fill notional=quantity*execution price; requested SELL notional is converted at max(p,0.05), so it need not equal execution notional): {\"BUY_missed_edge\":35.88926315280113,\"SELL_missed_edge\":31.409938719009258,\"competitor_notional\":1269.990467225,\"competitor_quantity\":4903.63063,\"no_quote_notional\":604.3863576,\"no_quote_quantity\":1975.2342225,\"participant_notional\":763.4707881749999,\"participant_quantity\":6833.276737500005,\"price_behind_strict_notional\":11.4732799,\"price_behind_strict_quantity\":374.9042525,\"probe_only_size_edge\":22.453429013674405,\"requested_notional\":2276.0580648980495,\"retail_edge_reconstructed\":126.0190722762935,\"same_price_fifo_notional\":2.26292575,\"same_price_fifo_quantity\":226.292575,\"size_ran_out_notional\":651.867903975,\"size_ran_out_quantity\":2327.19958,\"unfilled_sell_quantity\":1559.3927475,\"unspent_buy_notional\":83.06459757271763}",
    "Whole-sample order/fill counts: {\"BUY_orders\":1018915.0,\"SELL_orders\":1019510.0,\"no_quote_fills\":562565.0,\"orders\":2038425.0,\"price_behind_strict_fills\":17234.0,\"same_price_fifo_fills\":9990.0,\"size_ran_out_fills\":528515.0}",
    "Accounting credited mean score=165.5798357588402; this is original score plus observed competitor retail edge, with no trade or cash credit to the strategy."
  ],
  "next": "Reject the tested second ask as a net improvement: unseen paired mean=-4.741292586289525, SE=0.21334474576811202. Largest missed category: size_ran_out at 48.75928917839752 edge/run; largest probability zone: [0.2,0.8) at 29.721029361435146 edge/run. Start with joint marginal sizing across the best quote and one wider level, using the base belief, retail order reach probabilities, remaining covered YES, collateral opportunity cost and total jump loss. Compare that allocation against merely increasing the best quote; an arbitrary covered second ask is insufficient. Retain wider orders only when their expected retail tail benefit exceeds stale loss and the cost of reserving inventory. FIFO alone has only the separately measured 1.336786728499442 edge/run gross opportunity, so it cannot close the 4.2-point gap by itself. Do not scale probes from the size bucket; separate probe-sized residuals and main-size exhaustion first. Tune only on 4000..7999 and use a fresh holdout; 50000..53999 has now been examined. Require a positive paired net score, including arbitrage and displaced retail, before a build advances."
}