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Add cross-venue scanner (xarb.py); verdict: PM<->Kalshi is efficient

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xarb.py pulls Polymarket (Gamma) + Kalshi (elections API, ~65k markets),
matches the same contract (token overlap + same resolution month + exact
numeric match on thresholds/scores/dates), and computes both arb directions
with Kalshi's 0.07*P*(1-P) taker fee.

Verified verdict: no retail cross-venue arb. On liquid, identical, cleanly-
matched contracts the venues agree to ~1c and locking both sides costs >$1
after fees (worked example: Brazil-Morocco BTTS, PM 0.46/0.47 vs Kalshi
0.47/0.48 -> every direction negative). The big apparent edges are false
matches, illiquid wide-spread markets, or stale snapshot timing.

README now records the full project conclusion: six systematic public-data
edges tested, all efficient/illusory. Durable edge needs speed/infra, private
information, or liquidity provision -- not a turnkey public-data scanner.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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2026-06-13 12:31:34 -04:00
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@@ -21,6 +21,7 @@ live), and backtest the strategy. Zero dependencies — Python 3 stdlib only
| `backtest.py` | Replay a watchlist over a recent window and mark outcomes. |
| `lp_screener.py` | Rank reward-eligible markets by risk-adjusted LP yield (pool ÷ competition, penalized by volatility). |
| `lp_paper.py` | Paper liquidity-provision loop — simulate quoting on the live book, track **net = rewards − adverse selection**. |
| `xarb.py` | Cross-venue scanner — match the same event on Polymarket vs Kalshi and flag price gaps. |
## Run the dashboard
@@ -249,3 +250,32 @@ pessimistic on fill rate); rewards accrue by score-share of each pool. Net P&L,
per-market breakdown, and Discord summaries let it run for days to see whether
the edge survives mean-reversion. **Only if net stays clearly positive does a
real, funded, hosted bot make sense.**
## Cross-venue arbitrage: Polymarket ↔ Kalshi (`xarb.py`)
The last relative-value lane: buy YES on one venue + NO on the other for < $1
(net of fees) = locked profit. Kalshi's public API (`api.elections.kalshi.com`)
exposes ~65k markets; `xarb.py` pulls both venues, matches the same event
(token overlap + same resolution month + **exact numeric match** on
thresholds/scores/dates so we compare the same *contract*, not just the same
event), and computes both arb directions with Kalshi's `0.07·P·(1−P)` taker fee.
**Verdict: efficient — no retail arb.** On liquid, cleanly-matched, identical
contracts the two venues agree to **~1¢**, and locking both sides costs **>$1
after fees.** Worked example (live): *Brazil vs Morocco — Both Teams To Score*
priced PM 0.46/0.47 vs Kalshi 0.47/0.48; every arb direction nets **negative**.
The large "edges" the scanner surfaces are artifacts: false matches (same event,
different sub-question), illiquid wide-spread markets (exact-score, props), or
stale snapshot timing. Matches the documented reality that real gaps last
~seconds and are taken by bots watching 10k+ markets.
### The bottom line across the whole project
Six systematic, public-data edges tested — copy-trading, win-rate ranking, LP
reward farming, binary arb, multi-outcome logical arb, and cross-venue arb —
**all efficient or illusory.** Polymarket in 2026 does not hand a retail bot a
turnkey edge. Durable edge requires *speed/infra* (competing with pro arb bots),
*genuine private information* (a niche you know better than the market), or
*getting paid to provide a service* (liquidity, at modest adverse-selection-
dominated yields). The most valuable output here is knowing that before funding
any of it.