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.

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 scanner: cache-backed skilled-3% finder + watchlist + daily refresh + dashboard. See live/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.