POLYMARKET · PREDICTION MARKET · TECH & BUSINESS
Will Google have the best AI model at the end of June 2026?
5.5¢
94.5¢
▸ Advanced metrics · M2M bundle
polymarket · will-google-have-the-best-ai-model-at-the-end-of-june-2026 · fresh · feed 0s old24h sparkline · 60 pts▼ -42.11%
realized vol (ann.)
62.39%
max drawdown
42.11%
sharpe
—
ulcer index
28.79%
RMS drawdown
pain index
23.97%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
42.11%
cond. drawdown
gain/pain
0.11
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
0.11
upside/downside
roll spread
6.2 bps
implied (price-only)
bars used
1798
store
spread
—
24h Δ
-42.11%
flow lean
—
carry
flat
signalNEUTRALconfidence 25%
- 24h change -42.11%
Same bundle via M2M API:
/api/m2m/pm-will-google-have-the-best-ai-model-at-the-end-of-june-2026/bundle · venue execution: polymarket →LIVEPOLL0SRCFRESH⏱--:--:-- UTCNEXT8.0sUP0s--:--HIST
▶ STREAMING·HYPERLIQUID·POLYMARKET·0 POLLS·SRC FRESH·UPTIME 0s·NEXT POLL 8.0s·CC0 OPEN DATA·HYPO.MARKETS·▶ STREAMING·HYPERLIQUID·POLYMARKET·0 POLLS·SRC FRESH·UPTIME 0s·NEXT POLL 8.0s·CC0 OPEN DATA·HYPO.MARKETS·
YES · live
5.5¢
NO · live
94.5¢
25 ticks · last 5.50¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.307 / 1.00 bits (31%) · informative — one side favoured
YES5.5%5.5¢18.18×▬ +0.00pp
NO94.5%94.5¢1.06×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 600bp moved · peak 150bp · n=24 ticks
6ms
5.50¢ (5.50%)
94.50¢ (94.50%)
100.00%
0.000pp
$25.7k
$55.8k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 5.50¢
25 NO observations from clob.polymarket.com · last 94.50¢
§2 · Distribution of Δp
n=24
reference line = identity (perfect normality). Heavy upper-right tail = fat positive tail.
§3 · Sample moments
SAMPLE MOMENTS · N=25LEFT-SKEWED (G₁=-0.79)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁-0.787left-skewed
−3−10+1+3
EXCESS KURTOSIS · G₂-1.214platykurtic · thin tails
−30+2+4+6
μ = mean YES probability · σ = standard deviation · 95% CI = μ ± 1.96·SE. Skew/kurt diagnose departure from normality.
§5 · Time-series structure
TIME-SERIES STRUCTUREREGIME: TRENDING · variance ratio > 1
HURST EXPONENT [0, 1]
H = 1.211STRONGLY PERSISTENT
0
anti-persistent0.45
mean-reverting0.5
random walk0.55
persistent1
strongly trending
anti-persistent0.45
mean-reverting0.5
random walk0.55
persistent1
strongly trending
AUTOCORRELATION FUNCTION · ρ(k) for k=1..5
OLS TREND · t-STAT · [-5, +5]
−5 reject−1.960 retain H₀+1.96+5 reject
ρ(k) = lag-k sample autocorrelation · H = R/S Hurst exponent · t = OLS-trend t-statistic. Significance bands at ±2/√n approximate the 95% white-noise envelope. α=0.05 critical |t|=1.96; α=0.01 |t|=2.58.
§6 · Microstructure
MICROSTRUCTURE · MARKET QUALITYPERFECT · ARB-FREE Σ=100.00%
TWO-SIDED PRICING
5.50¢
94.50¢
Σ-SIDES ARBITRAGE TEST
Σ = 100.00% · |1 − Σ| = 0.000pp
24H ACTIVITY · LIQUIDITY
Σ-sides = YES + NO implied probabilities. Perfect arb-free Σ = 100%. |1−Σ| > 2pp suggests synthetic outright arbitrage.
§7 · Position sizing & edge analysis
FAIR MARKET · no edge
Probability scale (YES)
Implied decimal odds
YES18.18×(6¢)NO1.06×(95¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.307 bits (31% of max) · informative — one side strongly favoured
Σ-sides = 100.00% · |1 − Σ| = 0.00pp · tight cross-venue rounding
K* full = (b·p − q)/b · ½K and ¼K are conservative fractions of the full-Kelly bet. Entropy in bits — log₂(2)=1 is maximum uncertainty for a binary market.
§8 · Time decay & θ projection
⏱ URGENCY · DISTANTresolves 2026-06-30 00:00 UTC
15days
08hrs
53min
YES →$1.00
NO →$0.00
current: $0.0550 · expected return per side: $0.94 on YES hit · $0.06 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 7.870 pp/day
now15.37d left7.870 pp/day×1.00
−25%11.53d left9.088 pp/day×1.15
−50%7.69d left11.130 pp/day×1.41
−75%3.84d left15.740 pp/day×2.00
−90%1.54d left24.888 pp/day×3.16
θ approximation: σ/√T (expected daily move magnitude). The cone shows ±√(p̂(1−p̂)) widening as time decays, funneling to {0, 1} at resolution. Theta accelerates as √(t_left)→0.
§9 · Hourly return heatmap
2 up bars · 5 down · best 1.00% · worst -1.50% · typical |Δ| 0.250%
§10 · Equity curve & underwater drawdown
final equity 0.9702 (-2.98%) · max DD -3.95% · time-under-water 9/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest -30.208 · range [-103.04, 38.21] · μ -25.298 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 48.33% · range [0.00%, 72.50%] · μ 30.19% · σ̂ scaled to annualised (×√8760)
latest -0.521 · |ρ| > 0.3 ⇒ regime with persistence (ρ > 0) or reversal (ρ < 0) · |ρ| ≤ 0.1 = consistent with random walk
§12 · Hypothesis tests (α = 0.05)
2 of 6 REJECT · mixed evidence2 reject·4 pass·α = 0.05
Jarque-Bera
REJECT H₀**H₀: Δp ~ Normal(μ, σ²)
STATISTIC
10.9067
p-VALUE (log scale)
0.0043
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
3.7941
p-VALUE (log scale)
0.5816
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
0.3554
p-VALUE (log scale)
0.9801
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
0.1519
p-VALUE (log scale)
0.8793
KPSS (μ stationarity)
REJECT H₀*H₀: p IS level-stationary
STATISTIC
0.6496
p-VALUE (log scale)
0.0181
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
1.4376
p-VALUE (log scale)
0.1505
Each row states an explicit null H₀, the test statistic, an approximated p-value, and the decision. REJECT means evidence against H₀. KPSS complements ADF (rejecting both ⇒ ambiguous; rejecting one ⇒ clean verdict).
§13 · Spectral analysis (DFT periodogram)
dominant period ≈ 24.00h (freq 0.042) · concentrates 25.1% of total energy · Σ|X̂|²/n = 2.812e-4
▸ Depth section using sovereign-store price series (1798 bars · effective 1752908 bars/year) — annualisation reflects native polling cadence, not upstream timeframes.
§14 · Honest position analytics
A binary-market analytics module framed in horizon time (days to resolution, not annualised). Estimators that need a model probability q as a first-class input (Kelly, KL divergence, Bayesian posterior, Mark-to-Market MC) only render when q is provided externally. Sweep an exploratory q at the interactive simulator →
§15 · Horizon returns
Horizon 15.4 d · σ/bar 0.047pp · expected |Δp| over horizon 0.91ppterminal variance p(1−p) = 0.0520 · n = 1798✓ n = 1798
μ per bar
-0.002pp
average Δp · drift
σ per bar
0.047pp
one-bar volatility · logit-free
Per-day movedaily
0.23pp
σ × √24
Per-horizon move15d
0.91pp
σ × √368.88475555555556
Terminal variancebinary
0.0520
p(1−p) at resolution
Current pricep
5.5¢
latest snapshot
Note: annualised Sharpe/Sortino are omitted — they are not meaningful for a bounded fixed-horizon binary contract that snaps to {0, 1} at resolution.
Annualised metrics are intentionally omitted — they don't apply to bounded probability series that resolve at a fixed date.
§16 · Tail risk
VaR₉₅ 0.08pp · ES₉₅ 0.10pp · method parametric · drift-correcteddrift -0.002pp/bar · quantised: yes · median step 0.50pp · unique ratio 0.00✓ n = 1798
VaR 95%
0.08pp
1.645·σ (parametric) of Δp
ES 95%
0.10pp
mean of the tail
Max drawdown
42.1pp
peak 9.5¢ → trough 5.5¢
Median step
0.50pp
price bucket granularity
Price series is bucketed (cent grid). Empirical quantiles collapse to grid points — parametric N(0, σ²) used instead.
Empirical quantiles unless the price series is bucketed (PM cent grid), in which case parametric N(0, σ²) is used to avoid grid collapse.
§17 · Odds conversion
Implied probabilityP
5.5%
= price
Decimal oddsEU
18.182
total return per $1
AmericanUS
+1718
$100 wins $1718
FractionalUK
17.18 / 1
profit per $1 risked
Profit per $100stake
+$1718.18
clean dollar framing
underdog (+)favorite (-)your price
Price → implied probability → decimal odds → American moneyline → fractional. Five views of the same number, plus the moneyline curve.
§18 · Binary entropy
Market entropyH(p)
0.307 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.307 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
4.18 bit
self-information
Surprise · NO−log₂(1−p)
0.08 bit
self-information
Market entropy only — model entropy requires an external q.
§19 · Model-dependent surfaces
External model required
The position-economics, Kelly, KL-divergence, Bayesian and Monte-Carlo surfaces require a model probability q as input — a number independent of the market price p.
The previous build defaulted q to a tape-momentum heuristic derived from p; that produces apparent edge that is structurally guaranteed to be small and is not a useful skill signal. The auto-derived path has been removed.
To explore these surfaces with a hypothetical q, open the interactive simulator and drag the MODEL P(YES) slider. To wire a real model, POST to the NOSTRADAMUS hook (TBD) or pass ?q=… on the simulator URL.
§∞ · Provenance & attestation
- Upstream (snapshot)
- gamma-api.polymarket.com
- Upstream (history)
- clob.polymarket.com
- YES token ID
48636515238425021804591740157603774575693183581120073077366926452048719239424- NO token ID
55343546711440719852073804567266677523610031228702521608443447223099518953270- Snapshot fetched
- 2026-06-14 15:06:54 UTC
- Snapshot age
- 6ms
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-14 15:06:54 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
4037d988577a8b3a1df0dba4814c098b0fa2b846afe21c4fb93651e074b335ff· deterministic hash of source snapshot- Open data licence
- CC0 / public domain
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Also see: /arb opportunities · RSS feed · more in Tech & Business
Market depth
▸ live order book · Polymarket YESDepth within 1bp
$0
bid $0 · ask $0
Depth within 5bp
$0
bid $0 · ask $0
Depth within 10bp
$0
bid $0 · ask $0
Depth within 50bp
$0
bid $0 · ask $0
Mid price
0.055000
(best bid + best ask) / 2
Spread
1818.2bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
-0.716
ask-heavy
Imbalance (top-5)
+0.822
bid-heavy top-of-book
Slippage scenarios
▸ live book walk · Polymarket YESSimulating a market order at three notionals against the live book. Slippage = avg execution price vs. mid, in basis points. Worst fill = price of the deepest level touched. Live JSON: /api/asset/pm-will-google-have-the-best-ai-model-at-the-end-of-june-2026/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.085812 | 5602.21bp | 0.100000 | 5 | FILLED |
| BUY | $10.00K | 0.197803 | 25964.10bp | 0.530000 | 44 | FILLED |
| BUY | $100.00K | 0.674633 | 112660.55bp | 0.980000 | 73 | FILLED |
| SELL | $1.00K | 0.043088 | 2165.73bp | 0.040000 | 2 | FILLED |
| SELL | $10.00K | 0.020272 | 6314.10bp | 0.010000 | 5 | PARTIAL |
| SELL | $100.00K | 0.020272 | 6314.10bp | 0.010000 | 5 | PARTIAL |
Risk metrics
▸ sovereign store · 1,798 barsperiods/year ≈ 1.75MRealized vol (annualised)
903.15%
σ per bar = 0.006821
Mean return (annualised)
-53313.34%
μ per bar = -0.000304
Sharpe (rf=0)
-59.03
annualised; risk-free assumed zero
Max drawdown
42.11%
peak 0.10 → trough 0.06 over 1099 bars
/api/asset/pm-will-google-have-the-best-ai-model-at-the-end-of-june-2026/risk · same metrics, JSON