2026-06-13 13:09:56 -04:00
# 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 ** −
| **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 −
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.
2026-06-13 16:59:18 -04:00
## 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–
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–
- **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.
2026-06-18 11:16:20 -06:00
## 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, ** −
(−
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.
2026-06-18 16:38:41 -06:00
## 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– **loses** (−
− –
**Capital, not edge, is the binding constraint.**
- **Focused + conviction:** copy only 1–
(≥$200) bets → trade count drops to ~30–
**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.*
2026-06-19 09:43:27 -06:00
## 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 — their large (>= $200) stakes. 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) found **69 wallets** matching the profile; forward, **25/37 stayed
profitable (p=0.024), +11.7% pooled**. A reproducible class, not a fluke.
Then the decisive copyability filter, `live/validate_timing.py` : a near-100% win
rate is either foresight (copyable) or last-second information (uncopyable). The
tell is **entry->resolution lead time on winning conviction bets** . Of the 69,
**21 were insiders** (median lead < 6h — you can't mirror them), leaving **23
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, not an insider. These 23 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 insiders. That funnel produces a
copyable, forward-validated set — the strongest evidence in this project that
followable skill exists.*
2026-06-13 13:09:56 -04:00
## Repo layout
2026-06-13 16:59:18 -04:00
- `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).
2026-06-13 13:09:56 -04:00
- `smart_money.py` — data foundation + dashboard (true-win-rate scanner).
2026-06-18 11:16:20 -06:00
- `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` .
2026-06-13 13:09:56 -04:00
- `archive/` — the strategies that didn't work, kept for reference. See
`archive/README.md` .
