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.

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.

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.
• 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.