jaxperro
34d02956d8
Moved the 8 tested-and-failed strategy tools into archive/ (copytrade, backtest, edge_research, lookback, table_77, lp_screener, lp_paper, xarb) with an archive/README explaining each. Root now holds the keepers: insider.py (made self-sufficient — dropped the copytrade load_json dependency) and smart_money.py (data foundation). New FINDINGS.md is the honest scorecard: six systematic public-data edges all efficient/illusory, the win-rate survivorship-bias finding, and the one real signal (z-score improbability + funding clustering). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
4.4 KiB
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.
Repo layout
insider.py— the keeper: z-score/p-value detection, timing/freshness/sizing signals, and Alchemy funding-cluster ring detection.smart_money.py— data foundation + dashboard (true-win-rate scanner).archive/— the strategies that didn't work, kept for reference. Seearchive/README.md.