POLYMARKET · PREDICTION MARKET · ELON MUSK # TWEETS JUNE 13 - JUNE 15, 2026?
Will Elon Musk post 40-64 tweets from June 13 to June 15, 2026?
31.5¢
68.5¢
▸ Advanced metrics · M2M bundle
polymarket · elon-musk-of-tweets-june-13-june-15-40-64 · fresh · feed 0s old24h sparkline · 60 pts▼ —
realized vol (ann.)
629.17%
max drawdown
47.56%
sharpe
—
ulcer index
24.69%
RMS drawdown
pain index
20.81%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
40.88%
cond. drawdown
gain/pain
1.23
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
1.23
upside/downside
roll spread
2.1 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-elon-musk-of-tweets-june-13-june-15-40-64/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
31.5¢
NO · live
68.5¢
25 ticks · last 31.00¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.899 / 1.00 bits (90%) · high uncertainty
YES31.5%31.5¢3.17×▬ +0.00pp
NO68.5%68.5¢1.46×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 6250bp moved · peak 900bp · n=24 ticks
1ms
31.50¢ (31.50%)
68.50¢ (68.50%)
100.00%
0.000pp
$48.3k
$7.2k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 31.00¢
25 NO observations from clob.polymarket.com · last 69.00¢
§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=-1.52)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁0.407approximately symmetric
−3−10+1+3
EXCESS KURTOSIS · G₂-1.523platykurtic · 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: INDETERMINATE · weak signal at n=24
HURST EXPONENT [0, 1]
H = 1.085STRONGLY 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
31.50¢
68.50¢
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
YES3.17×(32¢)NO1.46×(69¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.899 bits (90% 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 · HIGHresolves 2026-06-15 16:00 UTC
0days
22hrs
57min
YES →$1.00
NO →$0.00
current: $0.3150 · expected return per side: $0.69 on YES hit · $0.32 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 50.631 pp/day
now22.96h left50.631 pp/day×1.00
−25%17.22h left58.464 pp/day×1.15
−50%11.48h left71.603 pp/day×1.41
−75%5.74h left101.262 pp/day×2.00
−90%2.30h left160.109 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
5 up bars · 13 down · best 8.00% · worst -9.00% · typical |Δ| 2.604%
§10 · Equity curve & underwater drawdown
final equity 0.8090 (-19.10%) · max DD -27.67% · time-under-water 21/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest -1.924 · range [-89.24, 22.09] · μ -38.542 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 379.51% · range [98.16%, 494.08%] · μ 325.11% · σ̂ scaled to annualised (×√8760)
latest -0.328 · |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)
1 of 6 REJECT · mixed evidence1 reject·5 pass·α = 0.05
Jarque-Bera
FAIL TO REJECTnsH₀: Δp ~ Normal(μ, σ²)
STATISTIC
3.0909
p-VALUE (log scale)
0.2132
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
0.9727
p-VALUE (log scale)
0.9628
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
-1.2548
p-VALUE (log scale)
0.6478
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
-0.7517
p-VALUE (log scale)
0.4522
KPSS (μ stationarity)
REJECT H₀**H₀: p IS level-stationary
STATISTIC
0.7479
p-VALUE (log scale)
0.0095
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
-0.0588
p-VALUE (log scale)
0.9531
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 23.0% of total energy · Σ|X̂|²/n = 1.623e-2
▸ Depth section using sovereign-store price series (2408 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 1.0 d · σ/bar 0.447pp · expected |Δp| over horizon 2.14ppterminal variance p(1−p) = 0.2158 · n = 2408✓ n = 2408
μ per bar
+0.000pp
average Δp · drift
σ per bar
0.447pp
one-bar volatility · logit-free
Per-day movedaily
2.19pp
σ × √24
Per-horizon move1d
2.14pp
σ × √22.96354277777778
Terminal variancebinary
0.2158
p(1−p) at resolution
Current pricep
31.5¢
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 0.74pp · ES₉₅ 0.92pp · method parametric · drift-correcteddrift +0.000pp/bar · quantised: yes · median step 0.50pp · unique ratio 0.01✓ n = 2408
VaR 95%
0.74pp
1.645·σ (parametric) of Δp
ES 95%
0.92pp
mean of the tail
Max drawdown
47.6pp
peak 41.0¢ → trough 21.5¢
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
31.5%
= price
Decimal oddsEU
3.175
total return per $1
AmericanUS
+217
$100 wins $217
FractionalUK
2.17 / 1
profit per $1 risked
Profit per $100stake
+$217.46
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)
0.899 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.899 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
1.67 bit
self-information
Surprise · NO−log₂(1−p)
0.55 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
105577993048948109580713045276679542431413340762946402420115963849346865805857- NO token ID
39577234398414241613289748679241795149829793792284485248363350884887923488707- Snapshot fetched
- 2026-06-14 17:02:11 UTC
- Snapshot age
- 1ms
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-14 17:02:11 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
5e4ada5f3d66942b4133ee9a68e2cf6ee38e1888a6398acacb4781f07925ce8f· 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 Elon Musk # tweets June 13 - June 15, 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
0.310000
(best bid + best ask) / 2
Spread
645.2bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
-0.610
ask-heavy
Imbalance (top-5)
-0.001
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-elon-musk-of-tweets-june-13-june-15-40-64/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.385885 | 2447.91bp | 0.480000 | 13 | FILLED |
| BUY | $10.00K | 0.732359 | 13624.48bp | 0.940000 | 33 | FILLED |
| BUY | $100.00K | 0.944820 | 20478.08bp | 0.990000 | 38 | PARTIAL |
| SELL | $1.00K | 0.239013 | 2289.90bp | 0.210000 | 10 | FILLED |
| SELL | $10.00K | 0.100973 | 6742.81bp | 0.010000 | 27 | PARTIAL |
| SELL | $100.00K | 0.100973 | 6742.81bp | 0.010000 | 27 | PARTIAL |
Risk metrics
▸ sovereign store · 2,408 barsperiods/year ≈ 1.75MRealized vol (annualised)
1920.63%
σ per bar = 0.014507
Mean return (annualised)
0.00%
μ per bar = 0.000000
Sharpe (rf=0)
0.00
annualised; risk-free assumed zero
Max drawdown
47.56%
peak 0.41 → trough 0.21 over 1459 bars
/api/asset/pm-elon-musk-of-tweets-june-13-june-15-40-64/risk · same metrics, JSON