POLYMARKET · PREDICTION MARKET · WHAT IRANIAN DEMANDS WILL TRUMP AGREE TO BY JUNE 30?
Will Trump agree to withdraw troops from the Iranian region by June 30?
36.0¢
64.0¢
▸ Advanced metrics · M2M bundle
polymarket · will-trump-agree-to-withdraw-troops-from-the-iranian-region-by-june-30 · fresh · feed 1s old24h sparkline · 60 pts▼ —
realized vol (ann.)
216.38%
max drawdown
28.00%
sharpe
—
ulcer index
9.42%
RMS drawdown
pain index
6.72%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
23.15%
cond. drawdown
gain/pain
0.22
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
0.22
upside/downside
roll spread
3.0 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-will-trump-agree-to-withdraw-troops-from-the-iranian-region-by-june-30/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
36.0¢
NO · live
64.0¢
25 ticks · last 36.50¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.943 / 1.00 bits (94%) · high uncertainty
YES36.0%36.0¢2.78×▬ +0.00pp
NO64.0%64.0¢1.56×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 5700bp moved · peak 950bp · n=24 ticks
621ms
36.00¢ (36.00%)
64.00¢ (64.00%)
100.00%
0.000pp
$45.4k
$44.3k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 36.50¢
25 NO observations from clob.polymarket.com · last 63.50¢
§2 · Distribution of Δp
n=24
reference line = identity (perfect normality). Heavy upper-right tail = fat positive tail.
§3 · Sample moments
SAMPLE MOMENTS · N=25PLATYKURTIC · THIN TAILS (G₂=-1.04)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁-0.006approximately symmetric
−3−10+1+3
EXCESS KURTOSIS · G₂-1.037platykurtic · 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 = 1.138STRONGLY 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
36.00¢
64.00¢
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
YES2.78×(36¢)NO1.56×(64¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.943 bits (94% of max) · high uncertainty
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
12hrs
50min
YES →$1.00
NO →$0.00
current: $0.3600 · expected return per side: $0.64 on YES hit · $0.36 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 31.635 pp/day
now15.54d left31.635 pp/day×1.00
−25%11.65d left36.529 pp/day×1.15
−50%7.77d left44.738 pp/day×1.41
−75%3.88d left63.270 pp/day×2.00
−90%1.55d left100.038 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
6 up bars · 10 down · best 9.50% · worst -6.00% · typical |Δ| 2.375%
§10 · Equity curve & underwater drawdown
final equity 0.9657 (-3.43%) · max DD -21.55% · time-under-water 18/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest -71.804 · range [-71.80, 86.48] · μ -14.448 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 233.83% · range [87.13%, 551.34%] · μ 305.18% · σ̂ scaled to annualised (×√8760)
latest -0.473 · |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)
ALL TESTS PASS · data behaves as nominal0 reject·6 pass·α = 0.05
Jarque-Bera
FAIL TO REJECTnsH₀: Δp ~ Normal(μ, σ²)
STATISTIC
4.0286
p-VALUE (log scale)
0.1334
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
2.2735
p-VALUE (log scale)
0.8119
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
-1.2266
p-VALUE (log scale)
0.6607
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
-1.3868
p-VALUE (log scale)
0.1655
KPSS (μ stationarity)
FAIL TO REJECTnsH₀: p IS level-stationary
STATISTIC
0.2285
p-VALUE (log scale)
0.3070
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
0.7421
p-VALUE (log scale)
0.4580
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 ≈ 4.80h (freq 0.208) · concentrates 22.9% of total energy · Σ|X̂|²/n = 1.532e-2
▸ Depth section using sovereign-store price series (2805 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
Horizon 15.5 d · σ/bar 0.166pp · expected |Δp| over horizon 3.20ppterminal variance p(1−p) = 0.2304 · n = 2805✓ n = 2805
μ per bar
-0.006pp
average Δp · drift
σ per bar
0.166pp
one-bar volatility · logit-free
Per-day movedaily
0.81pp
σ × √24
Per-horizon move16d
3.20pp
σ × √372.84748194444444
Terminal variancebinary
0.2304
p(1−p) at resolution
Current pricep
36.0¢
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 0.28pp · ES₉₅ 0.35pp · method parametric · drift-correcteddrift -0.006pp/bar · quantised: yes · median step 1.00pp · unique ratio 0.00✓ n = 2805
VaR 95%
0.28pp
1.645·σ (parametric) of Δp
ES 95%
0.35pp
mean of the tail
Max drawdown
35.1pp
peak 55.5¢ → trough 36.0¢
Median step
1.00pp
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
36.0%
= price
Decimal oddsEU
2.778
total return per $1
AmericanUS
+178
$100 wins $178
FractionalUK
1.78 / 1
profit per $1 risked
Profit per $100stake
+$177.78
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)
0.943 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.943 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
1.47 bit
self-information
Surprise · NO−log₂(1−p)
0.64 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
92488220621909882531791113518945145991276504934055990507865455181231063950386- NO token ID
115566518660618076147767448009521309994996011530870689615716487478287310049118- Snapshot fetched
- 2026-06-14 11:09:08 UTC
- Snapshot age
- 621ms
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-14 11:09:09 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
eecb56454012c4c8aa2503bd39f4ce29ad94d0796abeaffd84d6943ce9327478· deterministic hash of source snapshot- Open data licence
- CC0 / public domain
§∞-2 · Related markets · explore more
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Also see: /arb opportunities · RSS feed · more in What Iranian demands will Trump agree to by June 30?
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
0.365000
(best bid + best ask) / 2
Spread
274.0bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
+0.230
bid-heavy
Imbalance (top-5)
-0.610
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-will-trump-agree-to-withdraw-troops-from-the-iranian-region-by-june-30/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.370000 | 136.99bp | 0.370000 | 1 | FILLED |
| BUY | $10.00K | 0.438551 | 2015.08bp | 0.660000 | 21 | FILLED |
| BUY | $100.00K | 0.796750 | 11828.77bp | 0.970000 | 42 | FILLED |
| SELL | $1.00K | 0.322286 | 1170.24bp | 0.310000 | 6 | FILLED |
| SELL | $10.00K | 0.156728 | 5706.09bp | 0.080000 | 27 | FILLED |
| SELL | $100.00K | 0.065661 | 8201.07bp | 0.010000 | 34 | PARTIAL |
Risk metrics
▸ sovereign store · 2,805 barsperiods/year ≈ 1.75MRealized vol (annualised)
481.17%
σ per bar = 0.003634
Mean return (annualised)
-25346.06%
μ per bar = -0.000145
Sharpe (rf=0)
-52.68
annualised; risk-free assumed zero
Max drawdown
35.14%
peak 0.56 → trough 0.36 over 2700 bars
/api/asset/pm-will-trump-agree-to-withdraw-troops-from-the-iranian-region-by-june-30/risk · same metrics, JSON