POLYMARKET · PREDICTION MARKET · WILL CHINA INVADE TAIWAN BY END OF 2026?
Will China invade Taiwan by end of 2026?
6.2¢
93.8¢
▸ Advanced metrics · M2M bundle
polymarket · will-china-invade-taiwan-before-2027 · fresh · feed 0s old24h sparkline · 60 pts▼ -3.91%
realized vol (ann.)
3.63%
max drawdown
1.60%
sharpe
—
ulcer index
1.47%
RMS drawdown
pain index
1.36%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
1.60%
cond. drawdown
gain/pain
0.33
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
0.33
upside/downside
roll spread
0.2 bps
implied (price-only)
bars used
2000
store
spread
—
24h Δ
-3.91%
flow lean
—
carry
flat
signalNEUTRALconfidence 25%
- 24h change -3.91%
Same bundle via M2M API:
/api/m2m/pm-will-china-invade-taiwan-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
6.2¢
NO · live
93.8¢
25 ticks · last 6.15¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.333 / 1.00 bits (33%) · informative — one side favoured
YES6.2%6.2¢16.26×▬ +0.00pp
NO93.8%93.8¢1.07×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 85bp moved · peak 10bp · n=24 ticks
2ms
6.15¢ (6.15%)
93.85¢ (93.85%)
100.00%
0.000pp
$126.2k
$547.3k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 6.15¢
25 NO observations from clob.polymarket.com · last 93.85¢
§2 · Distribution of Δp
n=24
reference line = identity (perfect normality). Heavy upper-right tail = fat positive tail.
§3 · Sample moments
SAMPLE MOMENTS · N=25PLATYKURTIC · THIN TAILS (G₂=-1.73)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁0.133approximately symmetric
−3−10+1+3
EXCESS KURTOSIS · G₂-1.726platykurtic · 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: MEAN-REVERTING · ρ(1) -0.61 + ADF rejected
HURST EXPONENT [0, 1]
H = 1.002STRONGLY 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
6.15¢
93.85¢
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
YES16.26×(6¢)NO1.07×(94¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.333 bits (33% 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
199days
07hrs
55min
YES →$1.00
NO →$0.00
current: $0.0615 · expected return per side: $0.94 on YES hit · $0.06 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 0.579 pp/day
now199.33d left0.579 pp/day×1.00
−25%149.50d left0.669 pp/day×1.15
−50%99.66d left0.819 pp/day×1.41
−75%49.83d left1.159 pp/day×2.00
−90%19.93d left1.832 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
4 up bars · 7 down · best 0.10% · worst -0.10% · typical |Δ| 0.035%
§10 · Equity curve & underwater drawdown
final equity 0.9975 (-0.25%) · max DD -0.30% · time-under-water 22/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest 0.000 · range [-104.64, 19.10] · μ -26.984 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 0.00% · range [0.00%, 9.20%] · μ 4.58% · 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)
2 of 6 REJECT · mixed evidence2 reject·4 pass·α = 0.05
Jarque-Bera
FAIL TO REJECTnsH₀: Δp ~ Normal(μ, σ²)
STATISTIC
0.0301
p-VALUE (log scale)
0.9851
Ljung-Box(h=5)
REJECT H₀**H₀: No serial autocorrelation up to lag 5
STATISTIC
18.9445
p-VALUE (log scale)
0.0021
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
-1.1369
p-VALUE (log scale)
0.7011
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
1.3229
p-VALUE (log scale)
0.1859
KPSS (μ stationarity)
REJECT H₀**H₀: p IS level-stationary
STATISTIC
0.8231
p-VALUE (log scale)
0.0063
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
-1.5542
p-VALUE (log scale)
0.1201
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 34.5% of total energy · Σ|X̂|²/n = 3.625e-6
▸ Depth section using sovereign-store price series (3799 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 199.3 d · σ/bar 0.005pp · expected |Δp| over horizon 0.31ppterminal variance p(1−p) = 0.0577 · n = 3799✓ n = 3799
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 move199d
0.31pp
σ × √4783.917649444445
Terminal variancebinary
0.0577
p(1−p) at resolution
Current pricep
6.2¢
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 0.01pp · ES₉₅ 0.01pp · method parametric · drift-correcteddrift -0.000pp/bar · quantised: yes · median step 0.05pp · unique ratio 0.00✓ n = 3799
VaR 95%
0.01pp
1.645·σ (parametric) of Δp
ES 95%
0.01pp
mean of the tail
Max drawdown
4.7pp
peak 6.5¢ → trough 6.2¢
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
Implied probabilityP
6.2%
= price
Decimal oddsEU
16.260
total return per $1
AmericanUS
+1526
$100 wins $1526
FractionalUK
15.26 / 1
profit per $1 risked
Profit per $100stake
+$1526.02
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)
0.333 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.333 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
4.02 bit
self-information
Surprise · NO−log₂(1−p)
0.09 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
94559586571241563470235664821564670251180951772614764383113614156422396181162- NO token ID
90772332434487149264114862115632028379978765245278600275169585501290867536237- Snapshot fetched
- 2026-06-14 16:04:56 UTC
- Snapshot age
- 2ms
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-14 16:04:56 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
a0c9f6fab1370c1175b1ee85abf533d6fe04be25dc27fc06955e5682e43867f8· deterministic hash of source snapshot- Open data licence
- CC0 / public domain
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Also see: /arb opportunities · RSS feed · more in Will China invade Taiwan by end of 2026?
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
0.061500
(best bid + best ask) / 2
Spread
162.6bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
+0.824
bid-heavy
Imbalance (top-5)
+0.750
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-china-invade-taiwan-before-2027/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.062203 | 114.26bp | 0.063000 | 2 | FILLED |
| BUY | $10.00K | 0.071067 | 1555.57bp | 0.075000 | 14 | FILLED |
| BUY | $100.00K | 0.198747 | 22316.53bp | 0.530000 | 104 | FILLED |
| SELL | $1.00K | 0.060889 | 99.36bp | 0.060000 | 2 | FILLED |
| SELL | $10.00K | 0.060088 | 229.64bp | 0.060000 | 2 | FILLED |
| SELL | $100.00K | 0.003550 | 9422.82bp | 0.001000 | 59 | PARTIAL |
Risk metrics
▸ sovereign store · 3,799 barsperiods/year ≈ 1.75MRealized vol (annualised)
94.70%
σ per bar = 0.000715
Mean return (annualised)
-1839.03%
μ per bar = -0.000010
Sharpe (rf=0)
-19.42
annualised; risk-free assumed zero
Max drawdown
4.65%
peak 0.06 → trough 0.06 over 1473 bars
/api/asset/pm-will-china-invade-taiwan-before-2027/risk · same metrics, JSON