Reverse-engineered where the high-edge wallets (edge_z > 2) on pm_short_term_trader_accuracy actually make their money. The naive “Binance leads, book lags, exploit the lag” thesis from short-term-accuracy and lag-probe failed M0 backtesting at 46% win rate. The real edge is not latency arb. This note replaces the strategy thesis.

Primary case study: 0x75cc3b63a2f2423085e10706c78b494017b93ce1. Cross-validated on the other 8 wallets in the cohort.

Correction (2026-05-30) — 0x75cc3b is wallet #2, not the top trader

The original RE pass framed 0x75cc3b as “the top trader” because they had the densest trade history (~39k 5m-updown buys → easiest to reverse-engineer). A proper cohort-wide ranking on 30-day ROI in the same band+window (5–10¢, tiw [0.66, 0.93]) puts them at #2 (+40.1% / 30d). The actual #1 is 0xb27bc932bf8110d8f78e55da7d5f0497a18b5b82 at +48.1% / 30d. And 0x75cc3b is also the cohort outlier in behavior: 4 of 5 top wallets are buy-only / hold-to-resolution, but 0x75cc3b sells 28.1% of the time. The basic longshot template does describe the cohort (so the RE captured real edge), but several specific parameters in this note are biased to 0x75cc3b’s personal pattern. See Cohort study (2026-05-30) — actual sweet spot is 12–16¢ and [[longshot-strategy#Deep-dive on the real #1 (0xb27bc932) — three candidate tunes]] for the updated cohort picture.

headline

The book’s own structural under-pricing of residual volatility late in a 5-minute window is the edge — especially on the cheap-tail side. The book treats nearly-dead outcomes as deader than they are. BTC-vs-book lag is real (see lag-probe: p50 300 ms) but it isn’t what these wallets are harvesting.

what 0x75cc3b actually does — two lanes

Both lanes operate only in the last third of a 5-minute window (time_in_window > 0.66), 5m markets only, cross-asset (BTC + ETH + SOL + XRP).

Lane 1 — late-window longshot reversion

Buy at best-ask ≤ $0.30 when an outcome looks “almost dead.” The book over-prices the deadness; residual underlying volatility flips outcomes more often than the 5%-of-implied book suggests.

Dollar-weighted ROI by entry price band:

price bandROI
$0.00–0.10+132.8%
$0.10–0.30+25.0%
$0.30–0.50(decreasing)
$0.90–1.00~+1%

Monotonic from cheap to expensive.

The phase × price heatmap is the killer:

phase$0.00–0.10 ROI
late-window+222%
early-window−68%

Same band, same wallet, opposite sign. Timing is half the edge.

Lane 2 — late-window momentum-chase

When the book has bid an outcome up ≥ 2¢ in the last 30 s and we’re in the last third of the window, take the offer (cap ~$0.85).

  • Hit rate: 73.4%
  • Edge: +4.7¢

The book lags its own recent moves; consume the slow liquidity.

what was wrong about 0x75cc3b before

was believedactually
3,397 markets28,696 BUYs across 43,734 trades. pm_short_term_trader_accuracy.n_markets counts distinct outcomes touched, not trades.
BTC onlyBTC + ETH + SOL + XRP updown markets
Median entry 15% into windowMedian 41% for 5m, 44% for 15m — fairly uniform; edge is concentrated late, but trades are not
One strategyAt least two lanes (longshot + momentum), 5m-specific

generalizes — partially

walletpatternstrength
0xd189664cPUREST: 613 late-window 5m longshot trades at 9.4K PnL on $12K invested**strongest single-wallet validation
0x8c901f67, 0x20d2309c, 0xeebde7a0same pattern, weaker (0xeebde7a0 looks like spread-capturing MM)confirms lane 1
0x7523cafc, 0xb27bc932DIFFERENT strategy: favorites-only in 15m marketsat least 2 distinct edge-bearing strategies coexist in the cohort

what to avoid (per the data)

  • 15m BUY Up at any price — loses for every asset
  • 5m early-window low prices (early × $0–0.10 = −68% ROI)
  • 5m mid-window deep favorites — basically zero edge with capital tied up

strategy v2 implications

Redesign the bot around:

  • Signal source: book state + time-in-window. NOT Binance vs book.
  • Universe: cross-asset by default (BTC + ETH + SOL + XRP), 5m markets primarily.
  • Lanes: longshot reversion (price ≤ 0.85).
  • Infrastructure: less about Binance feed microseconds, more about staying connected to a fast Polymarket book feed and computing late-window reversal probabilities. The eu-west-2 colocation argument from short-term-accuracy still holds — we still need to be fast on the CLOB — but the signal is endogenous to the book, not the BTC tape.

This deprecates the M0 model from the lag-probe capture as the primary strategy. The lag measurement (p50 300 ms book-lags-Binance) is still a true fact about the market; it just isn’t where this cohort’s money comes from.

caveats

  • No external price tape used in this analysis — momentum was inferred from Polymarket’s own book activity. Whether “book lags its own activity” vs “book lags Chainlink/Binance” needs a synced feed to disambiguate.
  • Order-book depth/spread unobserved; only fills visible. Maker vs taker not distinguished — 0xeebde7a0’s 20+ trades/outcome suggests maker behavior (spread capture) rather than pure direction.
  • pm_trades is restricted to ~119 watched wallets; “momentum” computed over that subset is a proxy, not the full book.
  • 7-day observation window — niche cells (e.g. late deep-longshot, n=478) carry ~1–2¢ edge uncertainty per cell.
  • longshot-strategy — the pm-arb implementation + tuning of Lane 1 (longshot reversion); paper strategy validated against 0x75cc3b’s actual trades, three structural tunes shipped
  • short-term-accuracy — the earlier (now-corrected) latency-arb framing
  • lag-probe — measured the lag we thought was the edge; still informs the execution infra need
  • copy-trade-algorithm — sibling algo, all-category; same survivorship-bias caveats apply
  • polymarket-fetch — overview / schema / CLI