POLYMARKET · PREDICTION MARKET · WEATHER & CLIMATE
Will the highest temperature in Hong Kong be 29°C on June 15?
92.0¢
8.0¢
▸ Advanced metrics · M2M bundle
polymarket · highest-temperature-in-hong-kong-on-june-15-2026-29c · fresh · feed 0s old24h sparkline · 60 pts▼ —
realized vol (ann.)
2470.83%
max drawdown
7.19%
sharpe
—
ulcer index
2.89%
RMS drawdown
pain index
1.16%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
7.19%
cond. drawdown
gain/pain
9.08
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
9.08
upside/downside
roll spread
40.7 bps
implied (price-only)
bars used
310
store
spread
—
24h Δ
—
flow lean
—
carry
flat
signalNEUTRALconfidence 20%
Same bundle via M2M API:
/api/m2m/pm-highest-temperature-in-hong-kong-on-june-15-2026-29c/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
92.0¢
NO · live
8.0¢
25 ticks · last 97.50¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.402 / 1.00 bits (40%) · informative — one side favoured
YES92.0%92.0¢1.09×▬ +0.00pp
NO8.0%8.0¢12.50×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 16600bp moved · peak 5450bp · n=24 ticks
5ms
92.00¢ (92.00%)
8.00¢ (8.00%)
100.00%
0.000pp
$38.4k
$5.0k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 97.50¢
25 NO observations from clob.polymarket.com · last 2.50¢
§2 · Distribution of Δp
n=24
reference line = identity (perfect normality). Heavy upper-right tail = fat positive tail.
§3 · Sample moments
SAMPLE MOMENTS · N=25LEPTOKURTIC · FAT TAILS (G₂=2.90)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁1.644right-skewed
−3−10+1+3
EXCESS KURTOSIS · G₂2.905leptokurtic · fat 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: MARTINGALE · UNPREDICTABLE
HURST EXPONENT [0, 1]
H = 0.783STRONGLY 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
92.00¢
8.00¢
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
YES1.09×(92¢)NO12.50×(8¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.402 bits (40% 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 · HIGHresolves 2026-06-15 12:00 UTC
0days
06hrs
26min
YES →$1.00
NO →$0.00
current: $0.9200 · expected return per side: $0.08 on YES hit · $0.92 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 82.032 pp/day
now6.44h left82.032 pp/day×1.00
−25%4.83h left94.722 pp/day×1.15
−50%3.22h left116.011 pp/day×1.41
−75%1.61h left164.064 pp/day×2.00
−90%0.64h left259.408 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
13 up bars · 7 down · best 54.50% · worst -22.00% · typical |Δ| 6.917%
§10 · Equity curve & underwater drawdown
final equity 1.5305 (53.05%) · max DD -34.87% · time-under-water 16/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest 27.679 · range [-36.02, 71.45] · μ 18.433 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 2584.59% · range [100.00%, 2590.90%] · μ 718.18% · σ̂ scaled to annualised (×√8760)
latest -0.012 · |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)
1 of 6 REJECT · mixed evidence1 reject·5 pass·α = 0.05
Jarque-Bera
REJECT H₀***H₀: Δp ~ Normal(μ, σ²)
STATISTIC
115.6203
p-VALUE (log scale)
< 0.0001
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
2.3495
p-VALUE (log scale)
0.8009
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
-0.9472
p-VALUE (log scale)
0.7714
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
1.4724
p-VALUE (log scale)
0.1409
KPSS (μ stationarity)
FAIL TO REJECTnsH₀: p IS level-stationary
STATISTIC
0.4154
p-VALUE (log scale)
0.0705
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
-0.3730
p-VALUE (log scale)
0.7091
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 ≈ 4.00h (freq 0.250) · concentrates 15.6% of total energy · Σ|X̂|²/n = 2.179e-1
▸ Depth section using sovereign-store price series (310 bars · effective 1753395 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 0.3 d · σ/bar 1.867pp · expected |Δp| over horizon 4.74ppterminal variance p(1−p) = 0.0736 · n = 310✓ n = 310
μ per bar
+0.157pp
average Δp · drift
σ per bar
1.867pp
one-bar volatility · logit-free
Per-day movedaily
9.14pp
σ × √24
Per-horizon move0d
4.74pp
σ × √6.440111944444444
Terminal variancebinary
0.0736
p(1−p) at resolution
Current pricep
92.0¢
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 2.91pp · ES₉₅ 3.69pp · method parametric · drift-correcteddrift +0.157pp/bar · quantised: yes · median step 5.50pp · unique ratio 0.02✓ n = 310
VaR 95%
2.91pp
1.645·σ (parametric) of Δp
ES 95%
3.69pp
mean of the tail
Max drawdown
7.2pp
peak 83.5¢ → trough 77.5¢
Median step
5.50pp
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
92.0%
= price
Decimal oddsEU
1.087
total return per $1
AmericanUS
-1150
risk $1150 to win $100
FractionalUK
0.09 / 1
profit per $1 risked
Profit per $100stake
+$8.70
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)
0.402 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.402 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
0.12 bit
self-information
Surprise · NO−log₂(1−p)
3.64 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
62206187304311511066404748957048308114840214593170589502016591380727698205866- NO token ID
44873952444259915158415438992166076888418206560616079112098258125716443815895- Snapshot fetched
- 2026-06-15 05:33:35 UTC
- Snapshot age
- 5ms
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-15 05:33:35 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
b35492d2a1685387dbdb7efc1a93e5ba206a0ee130c10095d1c4e12769d526db· deterministic hash of source snapshot- Open data licence
- CC0 / public domain
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Also see: /arb opportunities · RSS feed · more in Weather & Climate
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
0.973500
(best bid + best ask) / 2
Spread
133.5bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
+0.736
bid-heavy
Imbalance (top-5)
-0.639
ask-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-highest-temperature-in-hong-kong-on-june-15-2026-29c/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.985075 | 118.90bp | 0.990000 | 2 | FILLED |
| BUY | $10.00K | 0.992573 | 195.92bp | 0.999000 | 5 | PARTIAL |
| BUY | $100.00K | 0.992573 | 195.92bp | 0.999000 | 5 | PARTIAL |
| SELL | $1.00K | 0.960058 | 138.08bp | 0.960000 | 3 | FILLED |
| SELL | $10.00K | 0.242782 | 7506.09bp | 0.001000 | 33 | PARTIAL |
| SELL | $100.00K | 0.242782 | 7506.09bp | 0.001000 | 33 | PARTIAL |
Risk metrics
▸ sovereign store · 310 barsperiods/year ≈ 1.75MRealized vol (annualised)
4082.28%
σ per bar = 0.030829
Mean return (annualised)
425029.51%
μ per bar = 0.002424
Sharpe (rf=0)
104.12
annualised; risk-free assumed zero
Max drawdown
7.19%
peak 0.83 → trough 0.78 over 47 bars
/api/asset/pm-highest-temperature-in-hong-kong-on-june-15-2026-29c/risk · same metrics, JSON