POLYMARKET · PREDICTION MARKET · WHAT WILL WTI CRUDE OIL (WTI) HIT IN JUNE 2026?
Will WTI Crude Oil (WTI) hit (LOW) $70 in June?
63.5¢
36.5¢
▸ Advanced metrics · M2M bundle
polymarket · will-wti-dip-to-70-in-june-2026-532-385-742-336-388-358-153-334-926 · fresh · feed 18s old24h sparkline · 60 pts▲ 73.97%
realized vol (ann.)
342.54%
max drawdown
14.88%
sharpe
—
ulcer index
9.10%
RMS drawdown
pain index
7.42%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
14.88%
cond. drawdown
gain/pain
1.85
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
1.85
upside/downside
roll spread
3.1 bps
implied (price-only)
bars used
2000
store
spread
—
24h Δ
73.97%
flow lean
—
carry
flat
signalNEUTRALconfidence 25%
- 24h change +73.97%
Same bundle via M2M API:
/api/m2m/pm-will-wti-dip-to-70-in-june-2026-532-385-742-336-388-358-153-334-926/bundle · venue execution: polymarket →LIVEPOLL0SRCWARMING⏱--:--:-- UTCNEXT8.0sUP0s--:--HIST
▶ 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
63.5¢
NO · live
36.5¢
25 ticks · last 63.50¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.947 / 1.00 bits (95%) · high uncertainty
YES63.5%63.5¢1.57×▬ +0.00pp
NO36.5%36.5¢2.74×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 6300bp moved · peak 1300bp · n=24 ticks
17.7s
63.50¢ (63.50%)
36.50¢ (36.50%)
100.00%
0.000pp
$38.8k
$16.0k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 63.50¢
25 NO observations from clob.polymarket.com · last 36.50¢
§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 · G₁0.405approximately symmetric
−3−10+1+3
EXCESS KURTOSIS · G₂-1.036platykurtic · 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 · ADF rejects unit root
HURST EXPONENT [0, 1]
H = 0.722STRONGLY 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
63.50¢
36.50¢
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
YES1.57×(64¢)NO2.74×(37¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.947 bits (95% 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 · LOWresolves 2026-07-01 03:59 UTC
12days
13hrs
23min
YES →$1.00
NO →$0.00
current: $0.6350 · expected return per side: $0.36 on YES hit · $0.64 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 40.175 pp/day
now12.56d left40.175 pp/day×1.00
−25%9.42d left46.390 pp/day×1.15
−50%6.28d left56.815 pp/day×1.41
−75%3.14d left80.349 pp/day×2.00
−90%1.26d left127.043 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
8 up bars · 8 down · best 13.00% · worst -6.00% · typical |Δ| 2.625%
§10 · Equity curve & underwater drawdown
final equity 1.3094 (30.94%) · max DD -7.03% · time-under-water 19/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest 19.474 · range [-38.21, 58.68] · μ 19.319 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 599.79% · range [0.00%, 599.79%] · μ 332.85% · σ̂ scaled to annualised (×√8760)
latest -0.169 · |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
REJECT H₀**H₀: Δp ~ Normal(μ, σ²)
STATISTIC
10.0844
p-VALUE (log scale)
0.0065
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
3.2339
p-VALUE (log scale)
0.6666
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
-1.1155
p-VALUE (log scale)
0.7091
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
1.5526
p-VALUE (log scale)
0.1205
KPSS (μ stationarity)
REJECT H₀**H₀: p IS level-stationary
STATISTIC
0.8685
p-VALUE (log scale)
0.0049
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
-1.0875
p-VALUE (log scale)
0.2768
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 ≈ 2.18h (freq 0.458) · concentrates 21.2% of total energy · Σ|X̂|²/n = 2.062e-2
▸ Depth section using sovereign-store price series (5000 bars · effective 1752518 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 12.6 d · σ/bar 0.222pp · expected |Δp| over horizon 3.86ppterminal variance p(1−p) = 0.2318 · n = 5000✓ n = 5000
μ per bar
+0.004pp
average Δp · drift
σ per bar
0.222pp
one-bar volatility · logit-free
Per-day movedaily
1.09pp
σ × √24
Per-horizon move13d
3.86pp
σ × √301.3970561111111
Terminal variancebinary
0.2318
p(1−p) at resolution
Current pricep
63.5¢
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 0.36pp · ES₉₅ 0.45pp · method parametric · drift-correcteddrift +0.004pp/bar · quantised: yes · median step 1.00pp · unique ratio 0.01✓ n = 5000
VaR 95%
0.36pp
1.645·σ (parametric) of Δp
ES 95%
0.45pp
mean of the tail
Max drawdown
24.1pp
peak 41.5¢ → trough 31.5¢
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
63.5%
= price
Decimal oddsEU
1.575
total return per $1
AmericanUS
-174
risk $174 to win $100
FractionalUK
0.57 / 1
profit per $1 risked
Profit per $100stake
+$57.48
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)
0.947 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.947 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
0.66 bit
self-information
Surprise · NO−log₂(1−p)
1.45 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
21529844811694103634769529261708925614995182440663576847381881001309676507633- NO token ID
106387691924927322950684339920778501751044503989975509518741646367600816465977- Snapshot fetched
- 2026-06-18 14:35:52 UTC
- Snapshot age
- 17.7s
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-18 14:36:10 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
571b6eb64d5eb98b0602de41074425a22bf380520cc73efe2c7f399a60d6fd7d· deterministic hash of source snapshot- Open data licence
- CC0 / public domain
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Also see: /arb opportunities · RSS feed · more in What will WTI Crude Oil (WTI) hit in June 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
0.635000
(best bid + best ask) / 2
Spread
157.5bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
+0.517
bid-heavy
Imbalance (top-5)
-0.808
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-wti-dip-to-70-in-june-2026-532-385-742-336-388-358-153-334-926/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.649838 | 233.66bp | 0.650000 | 2 | FILLED |
| BUY | $10.00K | 0.732463 | 1534.85bp | 0.860000 | 16 | FILLED |
| BUY | $100.00K | 0.878647 | 3836.96bp | 0.990000 | 25 | PARTIAL |
| SELL | $1.00K | 0.524546 | 1739.44bp | 0.420000 | 17 | FILLED |
| SELL | $10.00K | 0.264061 | 5841.56bp | 0.140000 | 34 | FILLED |
| SELL | $100.00K | 0.089937 | 8583.67bp | 0.010000 | 45 | PARTIAL |
Risk metrics
▸ sovereign store · 5,000 barsperiods/year ≈ 1.75MRealized vol (annualised)
643.73%
σ per bar = 0.004863
Mean return (annualised)
14911.53%
μ per bar = 0.000085
Sharpe (rf=0)
23.16
annualised; risk-free assumed zero
Max drawdown
24.10%
peak 0.41 → trough 0.32 over 328 bars
/api/asset/pm-will-wti-dip-to-70-in-june-2026-532-385-742-336-388-358-153-334-926/risk · same metrics, JSON