POLYMARKET · PREDICTION MARKET · CHINA X TAIWAN MILITARY CLASH BEFORE 2027?
China x Taiwan military clash before 2027?
7.5¢
92.5¢
▸ Advanced metrics · M2M bundle
polymarket · china-x-taiwan-military-clash-before-2027 · fresh · feed 0s old24h sparkline · 60 pts▼ —
realized vol (ann.)
0.00%
max drawdown
0.00%
sharpe
—
ulcer index
0.00%
RMS drawdown
pain index
0.00%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
0.00%
cond. drawdown
gain/pain
—
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
1.00
upside/downside
roll spread
0.0 bps
implied (price-only)
bars used
967
store
spread
—
24h Δ
—
flow lean
—
carry
flat
signalNEUTRALconfidence 20%
Same bundle via M2M API:
/api/m2m/pm-china-x-taiwan-military-clash-before-2027/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
7.5¢
NO · live
92.5¢
25 ticks · last 7.50¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.384 / 1.00 bits (38%) · informative — one side favoured
YES7.5%7.5¢13.33×▬ +0.00pp
NO92.5%92.5¢1.08×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 250bp moved · peak 50bp · n=24 ticks
183ms
7.50¢ (7.50%)
92.50¢ (92.50%)
100.00%
0.000pp
$23.5k
$50.9k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 7.50¢
25 NO observations from clob.polymarket.com · last 92.50¢
§2 · Distribution of Δp
n=24
reference line = identity (perfect normality). Heavy upper-right tail = fat positive tail.
§3 · Sample moments
SAMPLE MOMENTS · N=25STRONGLY RIGHT-SKEWED (G₁=1.74)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁1.745right-skewed
−3−10+1+3
EXCESS KURTOSIS · G₂1.092leptokurtic · 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: MEAN-REVERTING · ρ(1) -0.40 + ADF rejected
HURST EXPONENT [0, 1]
H = 0.792STRONGLY 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
7.50¢
92.50¢
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
YES13.33×(8¢)NO1.08×(93¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.384 bits (38% 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 2026-12-31 00:00 UTC
193days
12hrs
11min
YES →$1.00
NO →$0.00
current: $0.0750 · expected return per side: $0.93 on YES hit · $0.07 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 0.917 pp/day
now193.51d left0.917 pp/day×1.00
−25%145.13d left1.058 pp/day×1.15
−50%96.75d left1.296 pp/day×1.41
−75%48.38d left1.833 pp/day×2.00
−90%19.35d left2.898 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 · 3 down · best 0.50% · worst -0.50% · typical |Δ| 0.104%
§10 · Equity curve & underwater drawdown
final equity 0.9950 (-0.50%) · max DD -0.50% · time-under-water 24/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest 0.000 · range [-38.21, 20.72] · μ -4.192 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 0.00% · range [0.00%, 35.23%] · μ 13.10% · σ̂ scaled to annualised (×√8760)
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)
4 of 6 REJECT · mixed evidence4 reject·2 pass·α = 0.05
Jarque-Bera
REJECT H₀*H₀: Δp ~ Normal(μ, σ²)
STATISTIC
6.3802
p-VALUE (log scale)
0.0412
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
8.2489
p-VALUE (log scale)
0.1418
Dickey-Fuller (τ_μ)
REJECT H₀***H₀: p has a unit root (non-stationary)
STATISTIC
-4.5583
p-VALUE (log scale)
0.0004
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
1.7457
p-VALUE (log scale)
0.0809
KPSS (μ stationarity)
REJECT H₀*H₀: p IS level-stationary
STATISTIC
0.6213
p-VALUE (log scale)
0.0207
Variance ratio q=3
REJECT H₀*H₀: Δp is a random walk · VR = 1
STATISTIC
-2.3624
p-VALUE (log scale)
0.0182
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 ≈ 2.40h (freq 0.417) · concentrates 23.2% of total energy · Σ|X̂|²/n = 6.250e-5
▸ Depth section using sovereign-store price series (5000 bars · effective 1752616 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 193.5 d · σ/bar 0.062pp · expected |Δp| over horizon 4.26ppterminal variance p(1−p) = 0.0694 · n = 5000✓ n = 5000
μ per bar
-0.000pp
average Δp · drift
σ per bar
0.062pp
one-bar volatility · logit-free
Per-day movedaily
0.31pp
σ × √24
Per-horizon move194d
4.26pp
σ × √4644.197422777777
Terminal variancebinary
0.0694
p(1−p) at resolution
Current pricep
7.5¢
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 0.10pp · ES₉₅ 0.13pp · method parametric · drift-correcteddrift -0.000pp/bar · quantised: yes · median step 0.50pp · unique ratio 0.00✓ n = 5000
VaR 95%
0.10pp
1.645·σ (parametric) of Δp
ES 95%
0.13pp
mean of the tail
Max drawdown
21.1pp
peak 9.5¢ → trough 7.5¢
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
7.5%
= price
Decimal oddsEU
13.333
total return per $1
AmericanUS
+1233
$100 wins $1233
FractionalUK
12.33 / 1
profit per $1 risked
Profit per $100stake
+$1233.33
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)
0.384 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.384 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
3.74 bit
self-information
Surprise · NO−log₂(1−p)
0.11 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
104812032440315568595318241870590180481232715981192720184511444933968102038477- NO token ID
50488227317031565004575684525878022626020008256198844799050708993499540064859- Snapshot fetched
- 2026-06-20 11:48:08 UTC
- Snapshot age
- 183ms
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-20 11:48:09 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
9aeac61cf0537911cc15be5010050001809cce19cfbe32e0997883dad675e3d2· deterministic hash of source snapshot- Open data licence
- CC0 / public domain
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Also see: /arb opportunities · RSS feed · more in China x Taiwan military clash before 2027?
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
0.075000
(best bid + best ask) / 2
Spread
1333.3bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
-0.304
ask-heavy
Imbalance (top-5)
+0.688
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-china-x-taiwan-military-clash-before-2027/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.101478 | 3530.40bp | 0.120000 | 5 | FILLED |
| BUY | $10.00K | 0.277009 | 26934.53bp | 0.560000 | 37 | FILLED |
| BUY | $100.00K | 0.688202 | 81760.30bp | 0.920000 | 63 | FILLED |
| SELL | $1.00K | 0.052319 | 3024.20bp | 0.040000 | 4 | FILLED |
| SELL | $10.00K | 0.021573 | 7123.61bp | 0.010000 | 7 | PARTIAL |
| SELL | $100.00K | 0.021573 | 7123.61bp | 0.010000 | 7 | PARTIAL |
Risk metrics
▸ sovereign store · 5,000 barsperiods/year ≈ 1.75MRealized vol (annualised)
930.91%
σ per bar = 0.007032
Mean return (annualised)
-8287.63%
μ per bar = -0.000047
Sharpe (rf=0)
-8.90
annualised; risk-free assumed zero
Max drawdown
21.05%
peak 0.10 → trough 0.07 over 3418 bars
/api/asset/pm-china-x-taiwan-military-clash-before-2027/risk · same metrics, JSON