POLYMARKET · PREDICTION MARKET · WEATHER & CLIMATE

Will the highest temperature in Hong Kong be 28°C on June 14?

YES · live
0.1¢
NO · live
100.0¢

▸ Advanced metrics · M2M bundle

polymarket · highest-temperature-in-hong-kong-on-june-14-2026-28c · fresh · feed 17s old
24h sparkline · 60 pts▼ —
realized vol (ann.)
4.68%
max drawdown
66.67%
sharpe
—
ulcer index
59.19%
RMS drawdown
pain index
53.07%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
66.67%
cond. drawdown
gain/pain
1.00
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
1.00
upside/downside
roll spread
0.0 bps
implied (price-only)
bars used
2000
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-14-2026-28c/bundle · venue execution: polymarket →
LIVEPOLL0SRCWARMING16.5s⏱--:--:-- UTC8NEXT8.0sUP0s--:--HIST0/30
▶ STREAMING·HYPERLIQUID·POLYMARKET·0 POLLS·SRC WARMING·UPTIME 0s·NEXT POLL 8.0s·CC0 OPEN DATA·HYPO.MARKETS·▶ STREAMING·HYPERLIQUID·POLYMARKET·0 POLLS·SRC WARMING·UPTIME 0s·NEXT POLL 8.0s·CC0 OPEN DATA·HYPO.MARKETS·
YES · live
0.1¢
NO · live
100.0¢
YES price · live 24h
n=25 · μ=0.0319 · σ=0.0756 · range [0.0005, 0.2600] · R²=0.396▼ FALLING -99.81%σ EXTREME 236.56%LAST 0.00050.26000.19510.13030.06540.0005μ = 0.0319max 0.2600min 0.0005dataMA(5)OLS R²=0.40μ lineμ ± σ bandmaxminlive endpoint
25 ticks · last 0.05¢
YES / NO split · live
YES 0.1%NO 100.0%NO100.0%99.95¢ · odds 1/1.00
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.006 / 1.00 bits (1%) · informative — one side favoured
YES
0.1%0.1¢2000.00×▬ +0.00pp
NO
100.0%100.0¢1.00×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Per-tick activity · |Δp| in basis points · live
n=24 · Σ=2,715 · μ=113.1 · σ=257.4 · CV=2.28BURSTY · concentratedcumulative energy ↗ · 50% by h=30212425637850μ = 11385050%h1h5h9h13h17h21#1 peak#2-3> μactivequietμ linecum energy
Σ 2715bp moved · peak 850bp · n=24 ticks
Live numerics · pulse on poll
LIVE NUMERICS8 metrics·POLL 0
⏱snapshot age
16.5s
▲YES mid
0.05¢ (0.05%)
▼NO mid
99.95¢ (99.95%)
ΣΣ sides
100.00%
⚖arb gap
0.000pp
$24h vol $
$30.3k
≋liquidity $
$12.4k
◳history points
25 ticks (live)

§1 · 24h price history (YES + NO tokens)

YES price · CLOB mid
n=25 · μ=0.0319 · σ=0.0756 · range [0.0005, 0.2600] · R²=0.396▼ FALLING -99.81%σ EXTREME 236.56%LAST 0.00050.26000.19510.13030.06540.0005μ = 0.0319max 0.2600min 0.0005dataMA(5)OLS R²=0.40μ lineμ ± σ bandmaxmin
25 YES observations from clob.polymarket.com · last 0.05¢
NO price · CLOB mid
n=25 · μ=0.9681 · σ=0.0756 · range [0.7400, 0.9995] · R²=0.396▲ RISING +35.07%σ HIGH 7.81%LAST 0.99950.99950.93460.86980.80490.7400μ = 0.9681max 0.9995min 0.7400dataMA(5)OLS R²=0.40μ lineμ ± σ bandmaxmin
25 NO observations from clob.polymarket.com · last 99.95¢

§2 · Distribution of Δp

Histogram of hourly increments
n=24 · 10 bins · μ=-0.0121 · σ=0.0242 · skew=-2.23 (left-skewed) · kurt=3.23 (leptokurtic (fat tails))191410502-8.06ppbin -8.06pp · n=2 · 10.5% peakbin -8.06pp · n=2 · 10.5% peak-7.19pp1-6.31ppbin -6.31pp · n=1 · 5.3% peakbin -6.31pp · n=1 · 5.3% peak-5.44pp-4.56pp-3.69pp-2.81pp1-1.94ppbin -1.94pp · n=1 · 5.3% peakbin -1.94pp · n=1 · 5.3% peak1-1.06ppbin -1.06pp · n=1 · 5.3% peakbin -1.06pp · n=1 · 5.3% peak19-0.19ppbin -0.19pp · n=19 · 100.0% peakbin -0.19pp · n=19 · 100.0% peakμΔ < 0 · loss barsΔ ≈ 0 · flatΔ > 0 · gain barsN(μ,σ²) referenceμ line · ±σ band shaded
n=24
Q-Q plot · standardised Δp vs N(0,1)
n=24 · skew=-2.22 · kurt=3.20 · near 5 / mid 15 / far 4 · OLS slope=0.73 intercept=-0.00LEPTOKURTIC — FAT TAILSTHIN UPPER TAILFAT LOWER TAIL-3σ-3σ-2σ-2σ-1σ-1σ+0σ+0σ+1σ+1σ+2σ+2σ+3σ+3σΔ=-1.51σsample ↓marginal: sample bars + theoretical N(0,1) curve →theoretical Φ⁻¹(p) →↑ sample z-quantile|Δ| < 0.3σ · on the line|Δ| < 1σ · moderate|Δ| ≥ 1σ · outliery = x refOLS fit
reference line = identity (perfect normality). Heavy upper-right tail = fat positive tail.

§3 · Sample moments

Descriptive statistics · 5-number summary · shape diagnostics
SAMPLE MOMENTS · N=25LEPTOKURTIC · FAT TAILS (G₂=3.22)
μ MEAN3.19¢95% CI: [0.23¢, 6.16¢]
σ STD DEV7.56ppσ² = 57.090 · CV = 236.56%
med MEDIAN0.10¢Q₁ 0.05¢ · Q₃ 0.30¢
FIVE-NUMBER SUMMARY · BOX PLOTrange 25.95pp · IQR 0.25pp (1.349σ→10.19pp)
min 0.05¢Q₁ 0.05¢med 0.10¢Q₃ 0.30¢max 26.00¢μ
SKEWNESS · G₁2.160right-skewed
−3−10+1+3
EXCESS KURTOSIS · G₂3.221leptokurtic · fat tails
−30+2+4+6
μ ↔ medianμ > med · right-tailed|μ−med| / σ = 0.41
σ × 1.349 ↔ IQRdiverges from normalratio = 40.77
range ↔ σconcentrated (range < 4σ)range / σ = 3.43
μ = mean YES probability · σ = standard deviation · 95% CI = μ ± 1.96·SE. Skew/kurt diagnose departure from normality.

§5 · Time-series structure

Regime & autocorrelation diagnostics
TIME-SERIES STRUCTUREREGIME: TRENDING · variance ratio > 1
ρ(1) AUTOCORR+0.668positive · momentum
ρ(2) AUTOCORR+0.425lag-2 dependence detected
H · HURST EXPONENT1.078strongly persistent
OLS TREND · t-STAT-3.879significant @ α=0.05
HURST EXPONENT [0, 1]strongly persistent · H = 1.078
H = 1.078STRONGLY PERSISTENT
0
anti-persistent0.45
mean-reverting0.5
random walk0.55
persistent1
strongly trending
AUTOCORRELATION FUNCTION · ρ(k) for k=1..52 SIGNIFICANT LAGS · k=1,2
k=1 ★+0.668k=2+0.425k=3+0.070k=4-0.036k=5-0.0380+1−1+0.41−0.41+ momentum (ρ > +0.41)− reversal (ρ < −0.41)noise (within band)±2/√n threshold
OLS TREND · t-STAT · [-5, +5]SIGNIFICANT @ 1% (|t|=3.88)
−5 reject−1.960 retain H₀+1.96+5 reject
REGIME CLASSIFICATIONTRENDING · variance ratio > 1from Hurst + ρ(1) joint diagnosis
PREDICTABILITY · score 1.00very high · strong structure|ρ(1)| + 2·|H − 0.5| heuristic
TREND SIGNIFICANCESIGNIFICANT @ 1% (|t|=3.88)α=0.05 critical |t|=1.96 · α=0.01 |t|=2.58
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

Market quality · two-sided pricing · activity
MICROSTRUCTURE · MARKET QUALITYPERFECT · ARB-FREE Σ=100.00%
MARKET ID2513937
SLUGhighest-temperature-in-hong-kong-on-june-14-2026-28c
CATEGORYWeather & Climate
TWO-SIDED PRICINGFAVOURS NO (100¢)
PRIMARY · YES0.05¢implied prob 0.05% · decimal odds 2000.00×
COUNTER · NO99.95¢implied prob 99.95% · decimal odds 1.00×
0.05¢
99.95¢
Σ-SIDES ARBITRAGE TESTPERFECT · ARB-FREE Σ=100.00%
0%50%100% · target110%
Σ = 100.00% · |1 − Σ| = 0.000pp
24H ACTIVITY · LIQUIDITYACTIVE
24H VOLUME30.34k USD 24h
LIQUIDITY12.42k USD
MARKET QUALITYPERFECT · ARB-FREE Σ=100.00%|1−Σ| ≤ 0.5pp ⇒ fair · > 2pp ⇒ inefficient
PRICING SKEWFAVOURS NO (100¢)|primary − counter| = 0.999 · entropy 0.006 bits
LIQUIDITY DEPTHACTIVE100k+ deep · 10k+ active · 1k+ modest · 100+ thin
Σ-sides = YES + NO implied probabilities. Perfect arb-free Σ = 100%. |1−Σ| > 2pp suggests synthetic outright arbitrage.

§7 · Position sizing & edge analysis

Probability split · YES vs NO · Kelly · entropy · arbitrage
FAIR MARKET · no edge
YES 0.1%NO 100.0%YES p̂0.1%H = 0.006 / 1.00 bits
Probability scale (YES)
0%25%50%
fair75%100%
Implied decimal odds
YES2000.00×(0¢)NO1.00×(100¢)
Kelly bet-size (% of bankroll) K* = 0.00%
K* full
0.00%
½K half
0.00%
¼K quarter
0.00%
Entropy H(p̂) = 0.006 bits (1% of max) · informative — one side strongly favoured
0 (certain)0.250.50.751.00 (max)
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

Time decay & theta projection
⏱ URGENCY · CRITICALresolves 2026-06-14 12:00 UTC
0days
00hrs
52min
52 minutes·θ = 37.016 pp/day
YES →$1.00(P = 0.1%)
NO →$0.00(P = 100.0%)
current: $0.0005 · expected return per side: $1.00 on YES hit · $0.00 on NO hit
0%25%50%75%100%YES $1NO $0NOW+0.4hRESOLVESP projection · σ=7.56% · path funnel to settle at YES=1 or NO=0
Theta progression · θ ∝ σ / √t_remainingθ_now = 37.016 pp/day
now0.87h left
37.016 pp/day×1.00
−25%0.65h left
42.742 pp/day×1.15
−50%0.43h left
52.348 pp/day×1.41
−75%0.22h left
74.031 pp/day×2.00
−90%0.09h left
117.054 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

24-hour signed Δp grid · green = up · red = down
HOURLY RETURN HEATMAP · n=24 bars · best 0.25% · worst -8.50% · typical |Δ| 1.13%BEARISH SESSION -25.95%BEST+0.25%6hWORST-8.50%2hTYPICAL |Δ|1.13%mean absoluteCUMULATIVE-25.95%Σ signed ΔSTREAK▬ 0flat-runASIA · 00-08 UTCμ -3.71% · Σ -25.95%EUROPE · 08-16 UTCμ +0.01% · Σ +0.10%US · 16-24 UTCμ -0.01% · Σ -0.10%CUMULATIVE Δ PATH · final -25.95%+0.00%-25.95%-1.50% · 1h-1.50% · 1h-1.50%1h-8.50% · 2h-8.50% · 2h-8.50%2h▼ WORST-6.50% · 3h-6.50% · 3h-6.50%3h-8.00% · 4h-8.00% · 4h-8.00%4h-1.45% · 5h-1.45% · 5h-1.45%5h0.25% · 6h0.25% · 6h0.25%6h★ BEST-0.25% · 7h-0.25% · 7h-0.25%7h0.10% · 8h0.10% · 8h0.10%8h0.15% · 9h0.15% · 9h0.15%9h-0.05% · 10h-0.05% · 10h-0.05%10h0.00% · 11h0.00% · 11h·11h0.00% · 12h0.00% · 12h·12h-0.20% · 13h-0.20% · 13h-0.20%13h0.05% · 14h0.05% · 14h0.05%14h0.05% · 15h0.05% · 15h0.05%15h-0.10% · 16h-0.10% · 16h-0.10%16h0.00% · 17h0.00% · 17h·17h0.00% · 18h0.00% · 18h·18h0.00% · 19h0.00% · 19h·19h0.00% · 20h0.00% · 20h·20h0.00% · 21h0.00% · 21h·21h0.00% · 22h0.00% · 22h·22h0.00% · 23h0.00% · 23h·23h0.00% · 24h0.00% · 24h·24hTIME PATTERNEurope-led (+0.10%)RUNSup max 2 · down max 5BREADTH21% up · 38% down · 42% flat
5 up bars · 9 down · best 0.25% · worst -8.50% · typical |Δ| 1.131%

§10 · Equity curve & underwater drawdown

Cumulative compounded return + running peak-to-trough
EQUITY & DRAWDOWN ANALYSIS · n=25 barsSEVERE DRAWDOWN -23.60%FINAL-23.60%MAX DD-23.60%RECOVERYONGOING · 24 barsMAX RUN-UP+0.00%UNDERWATER24/25 (96%)STREAK▬ 0EQUITY CURVE · end 0.7640 · peak 1.0000 · range [0.7640, 1.0000]1.00000.7640break-even = 1★ PEAK 1.0000UNDERWATER DRAWDOWN · max -23.60% · severe0%-23.60%▼ TROUGH -23.60%TOP DRAWDOWN PERIODS · 1 total#1 -23.60%bar 2-25 · 24 bars · ONGOINGDD SEVERITYsevere (max -23.60%)RECOVERYongoing · 24 barsTIME UNDER WATER96% of session · 24/25 bars
final equity 0.7640 (-23.60%) · max DD -23.60% · time-under-water 24/25 bars

§11 · Rolling-window statistics (w = 6 bars)

Rolling annualised Sharpe ratio · green positive · red negative
n=19 · +1 / −13 (5% positive) · μ=-26.92 · σ=32.91UNPROFITABLE STRATEGYLAST 0.00 (+0.82σ vs μ)105.0452.520.00-52.52-105.04μ = -26.92-105.04-105.04-94.70-94.70-67.74-67.74-44.44-44.44-30.81-30.8117.8217.82-5.60-5.600.000.00-6.73-6.73-25.01-25.01-31.73-31.73-31.73-31.73-31.73-31.730.000.00-15.87-15.87-38.21-38.210.000.000.000.000.000.00v > 0 · positivev < 0 · negativeμ mean lineμ ± σ bandlatest bar (outlined)
latest 0.000 · range [-105.04, 17.82] · μ -26.923 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
Rolling annualised volatility (%)
n=19 · μ=80.9964 · σ=140.9701 · range [0.0000, 376.9300] · R²=0.567▼ FALLING -100.00%σ EXTREME 174.04%LAST 0.0000376.9300282.6975188.465094.23250.0000μ = 80.9964max 376.9300min 0.0000dataMA(3)OLS R²=0.57μ lineμ ± σ bandmaxmin
latest 0.00% · range [0.00%, 376.93%] · μ 81.00% · σ̂ scaled to annualised (×√8760)
Rolling lag-1 autocorrelation ρ(1)
n=19 · +5 / −11 (26% positive) · μ=-0.101 · σ=0.282MEAN-REVERSIONLAST 0.000 (+0.36σ vs μ)0.5180.2590.000-0.259-0.518μ = -0.1010.1120.1120.4670.4670.4680.4680.1290.129-0.217-0.217-0.518-0.518-0.164-0.1640.1000.100-0.293-0.293-0.271-0.271-0.351-0.351-0.420-0.420-0.282-0.282-0.167-0.167-0.489-0.489-0.033-0.0330.0000.0000.0000.0000.0000.000v > 0 · positivev < 0 · negativeμ mean lineμ ± σ bandlatest bar (outlined)
latest 0.000 · |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)

Formal inference at 5% significance
5 of 6 REJECT · mixed evidence5 reject·1 pass·α = 0.05
𝒩

Jarque-Bera

REJECT H₀***

H₀: Δp ~ Normal(μ, σ²)

STATISTIC
40.8202
p-VALUE (log scale)
< 0.0001
α
10⁻⁴10⁻³10⁻²10⁻¹1
p < α · rejection zonenon-normal · fat tails or skew present
ρ

Ljung-Box(h=5)

REJECT H₀**

H₀: No serial autocorrelation up to lag 5

STATISTIC
17.4702
p-VALUE (log scale)
0.0038
α
10⁻⁴10⁻³10⁻²10⁻¹1
p < α · rejection zoneserial dependence detected
Ψ

Dickey-Fuller (τ_μ)

REJECT H₀***

H₀: p has a unit root (non-stationary)

STATISTIC
-5.2801
p-VALUE (log scale)
< 0.0001
α
10⁻⁴10⁻³10⁻²10⁻¹1
p < α · rejection zonestationary · mean-reverting (crit ≈ -2.86)
±

Wald-Wolfowitz runs

FAIL TO REJECTns

H₀: Sign sequence of Δ is random

STATISTIC
-0.2616
p-VALUE (log scale)
0.7936
α
10⁻⁴10⁻³10⁻²10⁻¹1
p ≥ α · null retainedsigns appear random (7 runs)
χ

KPSS (μ stationarity)

REJECT H₀*

H₀: p IS level-stationary

STATISTIC
0.4812
p-VALUE (log scale)
0.0459
α
10⁻⁴10⁻³10⁻²10⁻¹1
p < α · rejection zonenon-stationary (crit 0.463)
χ

Variance ratio q=3

REJECT H₀***

H₀: Δp is a random walk · VR = 1

STATISTIC
4.0081
p-VALUE (log scale)
< 0.0001
α
10⁻⁴10⁻³10⁻²10⁻¹1
p < α · rejection zoneVR 2.220 → trending
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)

Power spectrum of Δp · ‖X̂(k)‖²/n
n=12 bins · noise floor μ=6.53e-4 · top T=24.00h (32.5%) · top-3 cover 76.9%BROADBAND · 3 CYCLEScumulative energy ↗ (3 bins above 2× noise)2.5e-31.9e-31.3e-36.4e-40.0e+0μ noise floor2× noise (significance)period 24.0 · power 2.55e-3 · 32.5% energyperiod 24.0 · power 2.55e-3 · 32.5% energyperiod 12.0 · power 2.10e-3 · 26.7% energyperiod 12.0 · power 2.10e-3 · 26.7% energyperiod 8.0 · power 1.38e-3 · 17.6% energyperiod 8.0 · power 1.38e-3 · 17.6% energyperiod 6.0 · power 7.17e-4 · 9.1% energyperiod 6.0 · power 7.17e-4 · 9.1% energyperiod 4.8 · power 3.09e-4 · 3.9% energyperiod 4.8 · power 3.09e-4 · 3.9% energyperiod 4.0 · power 5.73e-5 · 0.7% energyperiod 4.0 · power 5.73e-5 · 0.7% energyperiod 3.4 · power 1.24e-7 · 0.0% energyperiod 3.4 · power 1.24e-7 · 0.0% energyperiod 3.0 · power 6.37e-5 · 0.8% energyperiod 3.0 · power 6.37e-5 · 0.8% energyperiod 2.7 · power 1.35e-4 · 1.7% energyperiod 2.7 · power 1.35e-4 · 1.7% energyperiod 2.4 · power 1.68e-4 · 2.1% energyperiod 2.4 · power 1.68e-4 · 2.1% energyperiod 2.2 · power 1.84e-4 · 2.3% energyperiod 2.2 · power 1.84e-4 · 2.3% energyperiod 2.0 · power 1.79e-4 · 2.3% energyperiod 2.0 · power 1.79e-4 · 2.3% energy50% by T=12.0h#1 dominantT=24.00h#2T=12.00h#3T=8.00hT=2hT=3hT=4hT=6hT=8hT=12hT=16hT=24h← shorter cycle (high freq · Nyquist=½) · period T (bars per cycle) · longer cycle (low freq · 1/n) →#1 dominant#2 peak#3 peak> 2× noisenoiseμ floor2μ sig.cum energy
dominant period ≈ 24.00h (freq 0.042) · concentrates 32.5% of total energy · Σ|X̂|²/n = 7.840e-3

▸ Depth section using sovereign-store price series (2681 bars · effective 1752810 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

Returns · per bar / per day / per horizon
Horizon 0.3 d · σ/bar 0.005pp · expected |Δp| over horizon 0.01ppterminal variance p(1−p) = 0.0005 · n = 2681✓ n = 2681
μ per bar
-0.000pp
average Δp · drift
σ per bar
0.005pp
one-bar volatility · logit-free
Per-day movedaily
0.02pp
σ × √24
Per-horizon move0d
0.01pp
σ × √6
Terminal variancebinary
0.0005
p(1−p) at resolution
Current pricep
0.1¢
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

VaR · ES · max drawdown
VaR₉₅ 0.01pp · ES₉₅ 0.01pp · method parametric · drift-correcteddrift -0.000pp/bar · quantised: yes · median step 0.05pp · unique ratio 0.00✓ n = 2681
VaR 95%
0.01pp
1.645·σ (parametric) of Δp
ES 95%
0.01pp
mean of the tail
Max drawdown
80.0pp
peak 0.3¢ → trough 0.1¢
Median step
0.05pp
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

Odds conversion · every dialect a bettor thinks in
Implied probabilityP
0.1%
= price
Decimal oddsEU
2000.000
total return per $1
AmericanUS
+199900
$100 wins $199900
FractionalUK
1999.00 / 1
profit per $1 risked
Profit per $100stake
+$199900.00
clean dollar framing
-1000-5000+500+1000020406080100you · 0.1%implied probability (%)American odds
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

Binary entropy · uncertainty as bits of information
Market entropyH(p)
0.006 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.006 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
10.97 bit
self-information
Surprise · NO−log₂(1−p)
0.00 bit
self-information
0.000.260.530.791.050.00.20.40.60.81.0marketmodelprobabilityH (bits)
Market entropy only — model entropy requires an external q.

§19 · Model-dependent surfaces

§ Edge / Kelly / KL · no model probability provided

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
11625770451053782946397064257000531684137846277241186102111818498461224876425
NO token ID
16549693073097224866866468970479851601556964368645106962709047772259221184260
Snapshot fetched
2026-06-14 11:07:38 UTC
Snapshot age
16.5s
History points
25 CLOB mids
Page rendered
2026-06-14 11:07:54 UTC
Storage policy
no persistence — fetched on every request
SHA-256 attestation
0230c252a33c96e1a4ec383d11e9170ec57144f129658eb6ebff347a9891165d · deterministic hash of source snapshot
Open data licence
CC0 / public domain

§∞-2 · Related markets · explore more

HL PREDGermany94.4¢HL PREDSpain90.4¢HL PREDUruguay66.6¢POLYMARKETWill Australia win the 2026 FIFA World Cup?0¢POLYMARKETWill Scotland win the 2026 FIFA World Cup?0¢POLYMARKETUS x Iran permanent peace deal by June 15, 2026?23¢HL PERPBTC-USD perpetual$64,474HL PERPETH-USD perpetual$1,672.9HL PERPHYPE-USD perpetual$60.76

Also see: /arb opportunities · RSS feed · more in Weather & Climate

Market depth

▸ live order book · Polymarket YES
Depth 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
—
(best bid + best ask) / 2
Spread
—
(bestAsk − bestBid) / mid
Imbalance (whole book)
-1.000
ask-heavy
Imbalance (top-5)
-1.000
ask-heavy top-of-book

Slippage scenarios

▸ live book walk · Polymarket YES

Simulating 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-14-2026-28c/slippage?size=10000&side=buy

SideNotionalAvg fillSlippageWorst fillLevelsStatus
BUY$1.00K————ERR
BUY$10.00K————ERR
BUY$100.00K————ERR
SELL$1.00K————ERR
SELL$10.00K————ERR
SELL$100.00K————ERR

Risk metrics

▸ sovereign store · 2,681 barsperiods/year ≈ 1.75M
Realized vol (annualised)
6246.76%
σ per bar = 0.047183
Mean return (annualised)
-105262.67%
μ per bar = -0.000601
Sharpe (rf=0)
-16.85
annualised; risk-free assumed zero
Max drawdown
80.00%
peak 0.00 → trough 0.00 over 583 bars

/api/asset/pm-highest-temperature-in-hong-kong-on-june-14-2026-28c/risk · same metrics, JSON