winning-wallet-finder_github/FINDINGS.md

Findings — what works and what doesn't on Polymarket

A research log of an honest attempt to find a systematic, automatable edge on Polymarket using public data. The short version: almost nothing works — the market is efficient — and the one thing that does isn't a money-printer, it's a detection signal.

The goal

Find a repeatable way to make money on Polymarket: identify "smart money" wallets, copy them, farm rewards, or arbitrage — anything systematic and automatable from public data.

Scorecard

Approach	Verdict	Why
Copy high-win-rate wallets	❌ dead	Win rate was an illusion (see below). True rates ~50%. Flat-size copying backtested −48% over a week.
Rank by leaderboard / PnL	❌ dead	Raw PnL is variance; top wallets win ~50% and profit via sizing/timing you can't copy.
LP reward farming	❌ dead	The fat "thin-book" APRs are illusory — Polymarket refunds unearned pool to the sponsor when liquidity is low. Real yield is modest and adverse-selection-dominated.
Binary YES+NO arbitrage	❌ dead	Efficient — min observed sum was 1.001 (the spread). Closed instantly by the engine.
Multi-outcome logical arb	❌ dead	True partitions priced efficiently (min sum 0.999). Apparent "arbs" were non-exclusive market groupings.
Cross-venue arb (Polymarket↔Kalshi)	❌ dead	Venues agree to ~1¢; locking both legs costs >$1 after fees. Real gaps last ~seconds and are taken by bots.
Insider / sharp detection	✅ works	Statistical improbability (z-score of wins vs. odds) is a real, hard-to-fake edge signal. See insider.py.
In-play surge momentum (tape)	❌ dead	The +$24-46/fill was resolution-timing survivorship (bias round 3, below). Chain truth: −$6/fill over 1,108 forward fills — pre-registered kill met 2026-07-22, three days before the funding decision. Wallet-identity null stands.
Sub-5¢ surge longshots	❌ dead	0-for-38 under chain truth; the exploratory scan's 4 "winners" were the same resolution bias.
Crypto oracle fair-value taking	⏳ forward window	Chain resolution grew the sample 30× and flipped the read: noisy-around-zero, slight positive tilt at E≥0.07 (+$1.8-2.3/fill, n≈470-860 fwd). E0.04 tier killed; stricter tiers accumulating — #17.

The big technical findings

1. Win rate on Polymarket is survivorship-biased — badly. The platform only redeems winning shares; losing shares are worth $0 and sit unredeemed in /positions at curPrice 0 forever, never entering /closed-positions. Measuring win rate over /closed-positions alone counts almost only winners. We saw a wallet read 90.6% that was truly 48.3%. A correct win rate must union both endpoints. Lesson: a high reported win rate is a red flag for a measurement bug, not a sharp.

2. Win rate ≠ profit, and PnL ≠ reliability. A wallet winning 54% made millions; the all-time #1 wallet (43% win) was −$3.8M over 90 days. Profit comes from sizing and entry prices, not hit rate.

3. The market is efficient. Six systematic public-data edges, all closed or illusory. There is no turnkey retail edge sitting in public data in 2026 — durable edge requires speed/infrastructure (arb bots), private information, or getting paid to provide liquidity (modest, adverse-selection-dominated).

4. The one real signal: statistical improbability (z-score). Each bet entered at price p has an odds-implied win probability p. A wallet winning far more than Σp is beating the market's own pricing — measured as a z-score and one-sided p-value. This is the rigorous version of the edge metric the whole project was chasing. It distinguishes:

• Sharps — high z, normal entry timing (skill over many bets).
• Insiders — high z + late (pre-resolution) entry + fresh wallet.

Plus funding-cluster linking (à la Bubblemaps / the 2026 60 Minutes investigation): trace each wallet's USDC funders on Polygon and link wallets that share a personal funding hub — judged by the funder's own outbound degree so shared exchanges don't false-link everyone. (See insider.py.)

Practical conclusion

• Don't fund copy-trading, LP farming, or arb based on this work — we tested them and they don't clear.
• Do use insider.py's z-score as a rigorous "who actually has edge" filter, far better than leaderboard or win rate.
• A genuine money-making edge has to come from you — a niche you understand better than the market — with tooling built around it, not from a public-data scanner.
• Legal note: detecting suspected insider trading is fine; trading on material nonpublic information is illegal, and blindly following a suspected insider is not a safe strategy.

Insider detection — what the z-score signal actually found

Building insider.py and sweeping markets (hunt.py, huntwide.py) surfaced genuinely improbable wallets. Out of ~289 scored:

• DREAMBIG. (z=8.9, p≈2e-19) and qcp14 (z=5.3) on the Iran ceasefire market — 45–77% of wins entered <24h before resolution. Textbook insider fingerprint, on exactly the theme the 60 Minutes story covered.
• Famecesgoal (z=9.6) won only 14.5% of bets — but bet longshots and hit +98 above what the odds implied. The clearest "beats the prices it pays" case.

Two refinements proved essential:

• Trade count separates insiders from bots. bjprolo scored z=37 — but on 306,873 lifetime trades. That's a market-maker grinding a tiny systematic edge, not information. Real edge wallets show concentrated z over 1–3k trades.
• Funding-cluster linking (Alchemy, the Bubblemaps "who-funded-whom" step) works only with a personal-hub filter: a shared exchange (everyone uses Coinbase) is not a shared operator. Judge a funder by its own outbound degree.

The copy-trade verdict — in-sample vs out-of-sample

The decisive test: does copying z-selected wallets make money?

• In-sample (copyback.py): copy the edge wallets from May 30, z-weighted, reinvest 100%. Result: +545% in 15 days. Looks incredible — and it's circular (the wallets were selected for winning over that very window). 86% of it came from one wallet; the highest-z pick contributed $23.
• Out-of-sample (oos.py): select wallets using only data through Apr 30, then copy forward May 30→now. Result: +168% — but entirely from one longshot lottery wallet (1.5% pre-period win rate hitting again). The two strongest pre-period signals made $0 forward. Forward hit rate was 27%. That's variance, not edge that persists.

Conclusion: even the one real signal (z-score), when tested for whether you can profit by copying it, fails out-of-sample — joining every other strategy. The detector is valuable for finding anomalous wallets; copying them is not a proven, fundable edge. The live watcher (webhook_receiver.py) exists to gather real forward (out-of-sample) data on these wallets — observe before you size up.

Practical conclusion 2

• Don't fund a copy strategy — both the +545% and +168% are variance/concentration, not repeatable edge.
• Do use the detector to find statistically anomalous wallets and watch them live; judge persistence forward with your own eyes.
• A durable trading edge has to come from you (a niche you know), with this tooling built around your judgment.

The skilled-3% scan, and a clean out-of-sample loss (June 2026)

External validation arrived: an LBS/Yale study (Gomez-Cram, Guo, Kung, Jensen, Apr 2026; SSRN 5910522) over 1.72M accounts found only ~3.14% of traders are genuinely skilled — measured by randomizing each trader's bet directions 10k× (a Monte-Carlo z-score) and requiring out-of-sample persistence. That is exactly this project's z-score + oos.py method, independently confirmed.

Built live/ to operationalize it at scale: enumerate recent liquid markets → cache every candidate's resolved bets locally (~26k wallets / 12.5M bets, so re-scoring at any cutoff is seconds) → a 5-gate funnel (n≥15, z>0, BH-FDR, split-half OOS, MM/bot cap). It surfaced 107 "validated" wallets.

The decisive test. Copying the high-win-rate "favorite-rider" cohort, $1000, no execution lag, June 1→now:

• selected through the test window (look-ahead): 99% win rate, +23.6%.
• selected on pre-June-1 data only (honest): 68% win rate, −7.4% (−19% on the settled portion).

The +23.6% was selection bias. Done cleanly, the favorites lose — a textbook reproduction of the paper's "~60% of lucky winners become losers out-of-sample," now on our own live data. Lesson reinforced: high win rate is the most misleading signal on the platform; favorite-riders are uncopyable. The underdog/value archetype (beats longshot prices) is the only one left worth testing.

Train/test wallet selection, and the capital wall (June 2026)

Built live/strategy.py (train on bets resolved before May 30, validate June 1+) and live/followability.py (entry-time + lead-time + cadence filter). Selecting on copy-ROI + z + monthly consistency + diversification (not win rate) gave 150 wallets; 59/100 stayed profitable forward (p=0.044), and filtering to followable markets lifted it to 49/77 (p=0.011), +23.4% pooled out-of- sample. So a real, persistent, copyable edge does exist — unlike favorites.

Then the reality check (live/pnl_basket.py, live/pnl_focused.py): a $1,000 copier with missed-trade accounting (capital tied in open positions).

• Broad 10-wallet basket: the wallets fire 1,210 markets in June; $1,000 can follow only ~2–13% of them. At realistic stakes it loses (−$384 to −$800); the gains sit in the trades you couldn't afford ($14k–$153k "missed"). Capital, not edge, is the binding constraint.
• Focused + conviction: copy only 1–2 top wallets and only their larger-stake (≥$200) bets → trade count drops to ~30–40, $1,000 affords them all, and it clears: +91% to +247% across stakes, stable, no blowup.

Lesson: a small-bankroll copier cannot follow a skilled wallet's whole feed — the edge is only capturable by concentrating on few wallets' high-conviction bets. The live tracker (jaxperro.com/trading) now runs exactly that config.

The repeatable profile: conviction bets + a timing gate (the best result)

Refining the above: instead of all bets, score wallets on their HIGH-CONVICTION bets only — the top 20% of each wallet's own stake sizes (per-wallet p80). This replaced the original flat >= $200 cutoff (2026-06-22): p80 reproduces flat-$200's win-rate lift across the sharps while adapting to each wallet's scale — a whale's $200 bet isn't conviction, a minnow's is. The top wallets win 70-80% of their big bets on genuinely-uncertain (~0.4-0.6 priced) markets — real edge, not favorite-riding — and it persists. live/conviction_scan.py (train pre-June, validate June) under p80 finds 218 wallets matching the profile; forward, 62/83 stayed profitable (p≈0), +16.0% pooled. A reproducible class, not a fluke. (The original flat-$200 run found 69 wallets, 25/37 forward, +11.7%.)

Then the decisive copyability filter, live/validate_timing.py: a near-100% win rate is only useful if we can mirror it. The tell is entry->resolution lead time on winning conviction bets — this is a copyability heuristic, NOT proof of inside information (a short lead can be a genuine insider or just someone good at fast-resolving markets; we can't tell, and for copy purposes it doesn't matter). Of the 218, the gate drops the "last-minute" wallets (median lead <24h — you can't get the trade in before resolution), then a 30-day-active filter, leaving ~31 validated copyable sharps (watch_sharps.json) with multi-day leads. The standout 0x60ec1744… held 80% win over 1,017 forward conviction bets; even the suspiciously-perfect 0x72e1… (99/100% win) enters ~7 days early — a real forecaster, clearly not last-minute. These 50 are surfaced live on jaxperro.com/trading.

Lesson: score conviction bets, not all bets; require avg entry ~0.4-0.6 (edge, not favorites); and gate on lead time to drop last-minute (un-mirrorable) wallets. That funnel produces a copyable, forward-validated set — the strongest evidence in this project that followable skill exists.

Copy P&L: position win% ≠ copyability (the scalper trap, 2026-06-23)

The biggest caveat on the whole sharps table: a high conviction win% does not mean you can profit copying the wallet. The win%/record are computed from curPrice ≥ 0.5 on resolved positions — a position snapshot. For a high-frequency scalper that massively over-counts: he buys ~$0.50, sells seconds later for ~+$1, and the snapshot records a "win" even though he never held to resolution. ArbTraderRookie shows ~100% conviction win (398-2) yet a flat-$50 copy of his conviction bets, held to authoritative clob resolution (winner by token_id), nets −$790 (held 0-19) — two independent replays agree (live portfolio −$687 ≈ standalone clob −$790).

So validate_timing.display_stats now also computes copy_pnl — what a flat-$50 copier actually realizes since June 1: replay their conviction entries, mirror their exits, settle held bets at clob resolution. This is surfaced as the Copy P&L column on the dashboard (default sort). The verdict it delivers: most "sharps" lose when copied. Of the ~31, only a handful are copy-positive — Kruto2027 +$1,184 and fortuneking +$430 (true hold-to-resolution betters); names that looked great on win% (iohihoo 88.7% → −$749, ArbTrader 99.5% → −$790) are scalpers that bleed. The live tracker now follows fortuneking + Kruto2027 — the two copy-positive wallets — at $50/trade.

Lesson: judge a copy target by Copy P&L (a trade-replay with real resolution), never by position win%. Conviction must be measured at the position level (a wallet's total stake in a market), not per individual buy — a scalper splits one position across many small buys, so a per-trade threshold copies far more (and worse) bets than intended.

Capital recycling & the $1k book (2026-06-23)

The $1,000 paper book (live/portfolio.py → portfolio.json, rendered at jaxperro.com/trading) surfaced two things:

• "Saturation" was mostly a measurement artifact. The old browser replay froze capital in positions whose resolution date the data-api didn't return, so it skipped bets it could afford (340 phantom misses on a 4-wallet book). Computing the book off the cache — which stores each bet's resolution time (res_t) — frees cash at the true resolution moment: misses dropped to ~0 and the book recycled ~23× over the window. With real money this isn't even a problem (cash returns on redemption); it was purely the paper sim mis-measuring.
• More wallets help only up to the bankroll's slot count. A combo backtest over the copy-positive holders showed returns rise with basket size until peak concurrent demand exceeds ~$1k ÷ $50 = 20 slots, after which a high-volume wallet just crowds out the others. So: pick wallets that fit the bankroll, favor fast-resolving markets (capital velocity > bet size on $1k), and don't diversify past what you can fund. Two well-chosen holders beat four that overflow.

The holder blind spot: two data bugs that hid the best copy targets (2026-07-04)

A clean re-run of the May→June train/test on a trusted subset of the cache overturned two earlier verdicts. Two mechanisms were poisoning the data:

1. The res_t = ts fallback. When the data-api omits endDate on a closed position, insider.resolved_bets stores the wallet's sell time as res_t and won = curPrice >= 0.5 at pull time — so a scalper's sold-at-profit position looks like a resolved win at a fake resolution time. ArbTraderRookie's 1,997 cached rows were 100% this. Fix: live/trust.py — only trust rows whose res_t matches the market's modal res_t across ≥2 wallets (endDate rows agree; sell-time rows scatter), market over, wallet pulled after resolution, resolved not False. 13.5M of 19.2M rows pass.
2. The held-leg window bug. validate_timing's Jun-1→now replay only counted held bets entered and resolved inside the window; a ~7-day-lead holder always showed held 0-0, ~20 unresolved and failed held_n>=8. And before the 2026-07-02 winner=False settle fix, those unresolved held bets were booked as losses — which is exactly where the "scalper trap" numbers for iohihoo (−$749) and ArbTrader (−$790) came from. Those two verdicts were bug artifacts, not scalper traps. (ArbTrader still deserved rejection pre-fix — his cache stats were res_t=ts poison — but his real trade record was a ~160h-lead holder.)

What the clean test found (select on ≤May trusted rows only, validate on June, fees+slip): population baseline −1.4%/bet; the existing profile +8.7% pooled; adding a whole-book z gate (z_all > 2) roughly doubles it at every tier; a practical top-basket (also gated on med conviction stake ≥ $50 and holder/borderline lead verdicts) went +80% pooled, 7/7 wallets profitable. A capital-aware $1k replay of the 8-wallet pre-June basket did +504% in June (118 bets, 97W-21L, fees+slip, 53 missed for cash) — with the three informed holders a combined 62-0 and two basket members losing money (toosmart 4-12), so the selection is good, not magic.

Where the edge lives: the top holders (Stavenson, whale 0x73afc816… with $20–120k clips, iohihoo; ArbTraderRookie until 2026-07-03) bet low-tier tennis (ITF/qualifiers/Wimbledon doubles) and tier-3 esports (CCT CS, Dota 2 EPL) at ~0.5 entries, win 95–100%, enter ~160h before resolution, and hold. That's informed money — plausibly match-fixing-adjacent — which is copyable precisely because of the long lead. The regime risk is real and demonstrated: ArbTraderRookie was wiped from every data-api endpoint mid-analysis on 2026-07-03. Treat every month of this edge as possibly its last; re-select weekly; never size as if the 100% win rates are permanent.

Lesson: selection metrics are only as honest as the rows they read. Gate on trusted rows, judge held edges on windows longer than the wallet's lead time, and add z_all — skill must show in the whole book, not just the big bets.

Making P&L equal reality — the survivorship correction, finished (2026-07-08)

The sharps table's All-Time P&L had been a won × entry × size reconstruction, and decomposing it against each wallet's Polymarket profile (lb-api /profit, the PM P&L column) exposed it diverging by up to 10× — and flipping signs. Four distinct bugs, each earned by decomposing an outlier:

1. A 2,000-row pull cap (max_pages=40) truncated high-volume wallets — ewww1's 4,088 positions ($409k) showed as 740 ($40k).
2. Both-sides positions double-dropped — one-per-market dedup kept the winning leg and silently dropped the paired losing leg (suraxy: +$35k of phantom profit).
3. initialValue = 0 on big longshot winners mis-sized the reconstruction.
4. Corrupt near-epoch res_t rows polluted the sums.

The fix that killed all four at once: stop reconstructing, and sum Polymarket's own realizedPnl per closed position over the wallet's full history (cache.closed_exits, incremental). This is the wallet's realized track record — what a copier mirroring their buy/sell/hold actually banks — and it needs no size/entry/timestamp, so both-sides, iv=0, and bad res_t all become moot, and it sums to PM by construction.

Then the deeper one — the founding survivorship bias, live inside PM itself. A residual gap remained: PM /profit is itself survivorship-biased. Bets that lost, went to $0, and were never redeemed sit in /positions at curPrice 0 — real losses, but PM under-counts them unevenly (it subtracts Coteykens' $52k of abandoned losers to land at $14k = our number, but does not subtract oliman2's $161k, leaving PM at $112k against a true $20k). So _open_split now folds those decided-but-unredeemed positions into the realized total, leaving only genuinely in-flight positions in a new Open P&L column. The result: where our All-Time reads below PM, PM is the biased number and ours is the truth. oliman2 $181k → $20k, JuiceFarm $380k → $32k — wallets that looked elite on redeemed-only P&L are mediocre once you count the bets they walked away from. Full-list check: 27 of 31 sharps match PM within a few percent, 4 are honestly-lower, and — the correctness signal — zero over-count.

Lesson: a wallet's redeemed P&L flatters "sell your winners, abandon your losers." The honest record counts the abandoned losers; the profile number doesn't. Rank on realized-including-abandoned, and read the open book as a separate risk.

Choosing the month's follow set from corrected data (Set D, 2026-07-08)

With P&L finally honest, the follow set was rebuilt by simulation. Ranking on the signals that actually predict forward copy profit — 2-month Copy P&L (validated outside the backtest window), 30-day conviction form, copyable lead (no sub-hour snipers), a clean open book, and moderate bet size (a $1k book can't follow a $3k-clip wallet) — then backtesting candidate portfolios:

set	equity (30d, $1k)	W–L	missed
old live set	$15,359	250–79	0
5 high-lead big-bettors	$3,661	44–18	144 (capital-capped)
Set D (6 moderate-bet)	$27,799	279–75	0

Set D = LSB1, imwalkinghere, Kruto2027, 42021, 0xbadaf319, BikesAreTheBikes — the sweet spot where return, a 79% decided win rate, full capture (0 missed), and diversification all peak. Two rules fell out: moderate-bet wallets beat big-bettors on a small book (the whales get capital-capped, 144 missed), and imwalkinghere + LSB1 are irreplaceable (dropping them halves the return). The backtest is in-sample (a ceiling), but every Set D wallet also clears the out-of-window Copy P&L signal — that's what separates it from curve-fitting. The live July book remains the only out-of-sample truth.

Superseded the same day by Set E, after the alignment audit below found the replay itself was still dropping and mislabeling bets.

Aligning the three books — backtest ↔ bot ↔ Polymarket (2026-07-08)

The live bot showed Kruto2027 mirror-sells the backtest didn't have. Pulling that thread found the replay was silently losing real bets three ways:

1. Stale entry maps — the daily freshen reset the bets and exits cursors but never pulled_entries (14-day TTL), so any market a wallet first entered since the last entries pull had no first_buy and the replay dropped its bets entirely (if not et: continue) — not won, not lost, not open. Gone. The recovered bets included hidden LOSSES — the stale backtest was flattering.
2. res_t can't detect in-play sells — it's endDate metadata (game-day midnight, sometimes future), so the exit < res_t − 300 test never fired on in-play markets and every pre-resolution sell booked as held-to-resolution. Fixed with the price test: a redeem prints exactly the payout; a mid print is a sell (booked at the wallet's actual exit price — which also self-corrects poisoned won flags).
3. Resolved round trips vanished — the round-trip path skipped resolved-on-chain markets assuming "the cache row will cover it", but rows with bogus forward res_t never qualify. Now redeem-closes book at chain truth and sell-closes mirror the sell.

Plus one parity fix: Set wallets now replay on the bot's pinned floors (copybot.paper.json), not a recomputed p80 that drifts a few percent and takes different bets.

Proof of alignment: matching every settled bet in the bot's real book against the backtest row-by-row — 7/9 agree exactly, 0 absent (was 0/9 agree before the fixes). The 2 disagreements are genuine execution divergence (the sharp sold on a fast-resolving market, the bot couldn't catch the exit and rode to resolution) — each book correctly records what happened to it, and that divergence class is permanent.

The honest price: the 30d Set D replay fell $29.1k → $17.4k as the flattering artifacts (phantom held-to-$1.00 winners, missing salvage exits, hidden losses) came out.

Set E (deployed 2026-07-08): with the replay finally honest, all 35 sharps were re-ranked by individual 30d copy replay, and combined sets tested in a shared book (one position per market, shared cash):

set	equity (30d, $1k)	note
top-4 only	$12,777	pruning alone loses carry
Set D (control)	$17,362
Set E (7)	$24,378 (+2338%)	every member positive
Set E + lma0o0o0o	$24,437	carries a −$1,775 wallet — rejected

Set E = LSB1, imwalkinghere, Kruto2027, 0xbadaf319 + gkmgkldfmg, AIcAIc, 1kto1m. Dropped: 42021 (+16% on 22 bets), BikesAreTheBikes (+12%). Rejected on the audit evidence: oliman2 (true lifetime ~$19k, not PM's $112k; +21% to copy with 22 stuck-open) and leegunner (elite lifetime +$274k but negative to copy — 7.6-day holds kill compounding). Newcomer caveat: gkmgkldfmg (z=2.05) and 1kto1m (z=2.4) sit near the selection gate floor — their seats are earned on a strong month, not deep statistical edge; AIcAIc's held-win is only 42% (his profit is sell-timing, the most lag-fragile edge class). They are the demotion watch-list, in that order.

The refund harvesters — a new sharp archetype (2026-07-08)

Splitting SOLD out of the record columns (same W/L/R/S taxonomy as the bot and backtest; win% is now held-outcomes only) exposed something the sign-based tally had been calling "wins": two of the highest-z wallets in the list are refund-harvesting machines. The signature is exits at exactly $0.50 to float precision — only refund redemptions print there — at enormous scale:

• 0xb0E43B… (z=20.2, "94.4% all-time" under the old tally): 797 exact-0.5 redeems = $148k of his $218k lifetime P&L. True held record: 76W–31L.
• ArbTraderRookie (z=29, "99%"): 1,150 of the same.

The strategy: buy ITF tennis totals just under 50¢, harvest the chronic ITF cancellation/retirement rate (50/50 refunds pay $0.50/share). The edge is real, clever, and structurally uncopyable — it clears 1–2¢/share and a taker copy pays ~0.75¢ fee each way plus slippage into it (honest replay: +6% and −5% respectively). This closes the loop on the project's oldest lesson: win rate lies, z finds real skill, and only the fee-and-lag replay decides whether the skill transfers to a follower.

Also fixed in the same pass: _open_split now classifies decided-unredeemed positions by the data-api's redeemable flag (exact, set at on-chain resolution for winners and losers alike) instead of price-pinning — verified by reproducing PM's per-position books to the dollar on the two biggest All-Time-vs-PM divergences.

The calibration experiment (started 2026-07-08)

Everything above makes the accounting honest. It does not make the +2338% forecast honest: Set E is an in-sample maximum (ranked and assembled on the same 30-day window it's scored on — winner's curse applies), and the replay's fill model (their price +0.5%, always filled) ignores adverse selection — the market moves fastest on exactly the bets where the sharp knew something, and thin ITF books won't always fill a FAK copy at size. The one piece of measured ground truth — the old paper book's +$229.79 (~23%) over two buggy weeks vs. four-figure replay percentages for the same era — says the live-to-model discount is large.

So the paper book was reset to a fresh $1,000 on 2026-07-08 (old book archived in git history + archive/copybot_fills.pre-reset-2026-07-08.jsonl) running Set E with every fix live from day one. The measured ratio between this book and the published backtest over the coming weeks is the number that sizes real money — not the replay percentage. Bank-size note for that decision: the replay compounds faster on smaller banks (--bank 500 → +2728% vs $1k's +2103%) because 4%-of-equity stakes hit the never-bigger-than- their-bet ceilings later — percentage returns from small books are the most optimistic view, discount accordingly.

The tape era opens: first RTDS findings (2026-07-20)

CORRECTED 2026-07-22: the Study-A/B EV figures in this section were inflated by resolution-timing survivorship (see the next section). The structural findings — sharp screen, identity-null, crater physics — survive; the surge P&L numbers do not.

Three days of the recorder's firehose in live/rtds.duckdb (13.8M fills, 160k wallets, every fill including the losers) killed the survivorship problem at the source and produced four findings in one day — details, code, and frozen parameters in research/ (silo'd from the bots; see its README):

1. The tape sharp screen works, and its resolution proxy is exact. Terminal-VWAP convergence (≥0.97/≤0.03, 2h quiet, sibling veto) agreed with on-chain CTF payout vectors on 742 of 742 validated bets. First run (live/tape_sharps.py): 2,360 wallets with ≥8 resolved held bets, 25 copyable candidates at z 4.0–5.5 (discrete conviction entries, sports/ esports in-play profile — the Set-E archetype), cleanly separated from an uncopyable algo-flow tier (z 9–12, 10–37 fills/bet, $M volumes: the crater-sweepers). Benchmark sanity: benched sharps scored positive, benched disappointments negative.

2. Wallet identity might not matter (the null that redirects the program). Study A (#16): a $300/60s net-flow surge into an in-play sports/esports market at 0.10–0.90 simulates to ~+$24/$100-fill after fees at our measured execution — but 10 activity-matched random wallet sets produce the SAME EV (+23.85 pooled vs +23.68 informed). The herd's lean is the signal; who leans adds nothing so far. Hypothesis revised at freeze: surge momentum primary, identity lift secondary. Verdict comes only from the forward ledger (research nightly, pre-registered thresholds).

3. The oracle edge is real on paper and mostly untakeable — worse at size. Study B (#17): fair value from the venue's own settlement tick feed flags ~9k mispricings/21h, but 86% die as FAK craters (crypto makers requote <4s), and the fillable remainder shows winner's-curse inversion — demanding a 10¢ edge loses $31/fill. Nothing froze (no cell hit 30 fills); candidate v2 uses are inverted: copy filter / maker-side.

4. Craters refill on a clock, and it's niche-shaped. 775k crater prints: crypto refills within 4s 94% of the time, esports 83% by 10s, sports needs ~25s, geo/politics tails run minutes. Shipped straight into the bots as per-niche fak_retry waits (the flat 10s was calibrated to nothing).

Execution realism note for everything above: the research simulator is fitted on the live bot's own 29 labeled attempts (fills + FAK misses) and carries a measured −2¢/fill optimism bias — every pre-registered pass threshold sits at least 2× above it.

Survivorship bias, round three: resolution timing kills Study A (2026-07-22)

The forward ledger said the surge signal earned +$35–46/fill. The truth was −$6/fill. The gap was a third, subtler survivorship mechanism — and the way it was caught is the real finding.

The mechanism. The ledger graded fills with the tape's proxy-resolution and skipped "pending" markets. But tape resolution is win-biased: when our side wins, the losing sibling goes quiet and the market resolves into the ledger within hours. When our side loses, the winning sibling keeps printing at 99¢ until close — the sibling-veto keeps the market "alive" and the loss hides in the ignored pending bucket. Wins scored same-day; losses waited. Jul-21 audit: tape-resolved fills hit 81% (+$46/fill); the 329 "pending" fills, chain-resolved on the spot, hit 26% (−$49.61/fill). Combined: 53%, ≈ breakeven-negative.

How it was caught: an independent instrument refused to agree. The surge paper harness (wwf-surgebot — real-time, $5 stakes, graded nightly against CTF payout vectors from day one) read 57.5% while the ledger read 73–81%. Decomposition ruled out selection (same-trigger cohorts hit alike) and entry prices (~1¢ effect); what remained was the scorer itself. The same pattern as rounds one (unredeemed losers) and two (res_t poison): a too-good number met a measurement that couldn't be sweet-talked.

The fix (research/forward.py payouts_for(), d34a4c5): chain-truth overlay is now mandatory for every arm — tape proxy first, CTF payout vectors for the remainder, refunds as scratches. Full recompute:

arm	corrected verdict
Study A surge (#16)	KILL met: −$6.03/fill over 1,108 forward fills; even the fit day negative. Identity-null unchanged (controls deflated equally).
sub-5¢ longshots	0-for-38 — the scan's "winners" were the same bias.
Study B oracle (#17)	Sample ×30 under chain resolution (crypto sprints were veto-stuck). Now noisy-zero with a positive tilt at E≥0.07; the 4¢ tier killed, stricter tiers accumulating.

What the pre-registration bought. The kill line was written 2026-07-20, before any forward data; when the corrected numbers crossed it there was nothing to argue about and no money at risk — the $100 deployment plan (#19) was three days from its funding gate. Total cost of the false edge: $0 real, ~2 days of compute, one paper book that ended at $99-and-change.

Standing rules extracted: (1) any scorer that can say "pending" must prove pending is outcome-neutral, or chain-resolve it — on Polymarket it never is neutral, because market liveness itself encodes the outcome; (2) every study needs one instrument that doesn't share the scorer's assumptions (the paper harness earns its keep even when — especially when — it disagrees); (3) at 20–50× payoffs, no small sample means anything: the sub-5¢ "edge" survived a 31-fill scan and died at 38.

The measurement split + the data moat (2026-07-22, same night)

Round three's kill landed while a parallel line of work was rebuilding the instruments; the two converged into an architecture change and one more corrected verdict.

One instrument can't sample and rehearse at once. The v1 surge book halted at its pre-registered −50% line the same evening. First diagnosis blamed the cash gate for adverse selection — the ~2% subsample it could afford read −$9/fill vs "+$41/fill" for the full signal. Round three dissolved that comparator (the +$41 was the resolved-only bias; chain truth −$6/fill, right next to the subsample's −$9): the book wasn't sampling badly, the signal was dead. The structural lesson survives the autopsy either way: a physically cash-gated harness measures a bankroll's shadow, not a signal. Both harnesses now run measurement-mode — every trigger attempted at the ledger's stake, and bankroll specs replayed OFFLINE over the recorded attempt stream (surge_book_replay.py), where gating can never touch the sample it's judged on.

No scalp was hiding in the corpse. The markout-exit study (chain-true; its res_tok v0 self-corrected within hours — same round-three mechanism, caught by the same law): 1,146 forward fills, hold −$5.92/fill, and every exit horizon negative too (best −$4.18 at +30m). The cohort split is the microstructure finding: winners drift monotonically to +$44 held; the losses the old scorer hid bleed from minute one (−$6@60s → −$44@2h). Surge moments are symmetric information events, priced in immediately — after fees and worst-print entry there is nothing for a taker at ANY horizon. Whatever earns money at those moments is on the other side of the book (maker study: staged, tape-sim first).

Two more scorer artifacts, disclosed on #17 before they could bite: the tape scorer reads sprint strikes from the FUTURE window-open tick for pre-window prints (live gates ts ≥ t₀; 404 such prints suppressed in an 8.5-min shakedown), and unlabeled non-sprint tokens default to the yes-side, turning No-side dust prints into fake ~+0.99-edge events (the harness reads labels from the trade payload itself — same rule, better input).

The data moat, hardened (DATA LAW, research/README). Everything accrues Mac-independent on Fly volumes (recorder: 25GB at 4%, ~3+ weeks of offline headroom; harness streams: attempts with top-5 asks + top-3 bids + latency, markout book re-reads at +60/300/1800s per fill, durable settle logs immune to state trims; daily volume snapshots ×5). The Mac is only the grader/archiver, and its two offline failure modes are closed: forward.py now backfills any tape-covered day the ledger has never seen, and settle logs mean trims can never rotate evidence away. meta_snap.py snapshots every active market nightly (~12k: end dates, tags, token→outcome maps) — τ becomes knowable AT TRIGGER for every tape trigger, and the label-gap artifact class dies at the source. Tests are additive, never subtractive: raw streams are append-only, analysis reads only, and a study that needs different data records a NEW stream.

Repo layout

• insider.py — the detector: z-score/p-value, timing/freshness/sizing signals, Alchemy funding-cluster ring detection.
• hunt.py / huntwide.py — market sweeps that surface edge wallets.
• copyback.py / oos.py — in-sample and out-of-sample copy-trade backtests.
• webhook_receiver.py — push-based live trade watcher (Alchemy → Discord).
• smart_money.py — data foundation + dashboard (true-win-rate scanner).
• live/ — current system: cache-backed finder + copy-positive-holder sharps selection (conviction_scan.py + validate_timing.py → watch_sharps.json, ranked by Copy P&L) + $1k paper book (portfolio.py → portfolio.json) + daily refresh. The dashboard (jaxperro repo) renders those two JSON feeds. See live/README.md. Copy execution (copybot.py, sync_floors.py) is a separate, in-progress system — this finder is selection + tracking only.
• research/ — the tape-era edge factory (SILO'd from the bots): read-only RTDS loaders, execution sim calibrated on the live ledger, pre-registered studies (#16 surge momentum, #17 oracle fair value), nightly forward ledger. Verdicts come from research/forward_ledger.jsonl only. See research/README.md.
• wide/ — bulk subgraph→DuckDB scanner (survivorship-bias-free, all wallets); public subgraph frozen at Jan 2026, so historical-only. See wide/README.md.
• archive/ — the strategies that didn't work, kept for reference. See archive/README.md.