POLYMARKET · PREDICTION MARKET · ELON MUSK # TWEETS JUNE 13 - JUNE 15, 2026?
Will Elon Musk post 65-89 tweets from June 13 to June 15, 2026?
1.6¢
98.5¢
▸ Advanced metrics · M2M bundle
polymarket · elon-musk-of-tweets-june-13-june-15-65-89 · fresh · feed 0s old24h sparkline · 60 pts▼ —
realized vol (ann.)
115.30%
max drawdown
65.93%
sharpe
—
ulcer index
35.84%
RMS drawdown
pain index
32.36%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
64.36%
cond. drawdown
gain/pain
0.68
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
0.68
upside/downside
roll spread
9.3 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-15-65-89/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
1.6¢
NO · live
98.5¢
25 ticks · last 1.35¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.115 / 1.00 bits (12%) · informative — one side favoured
YES1.6%1.6¢64.52×▬ +0.00pp
NO98.5%98.5¢1.02×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 3105bp moved · peak 800bp · n=24 ticks
1ms
1.55¢ (1.55%)
98.45¢ (98.45%)
100.00%
0.000pp
$49.8k
$16.2k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 1.35¢
25 NO observations from clob.polymarket.com · last 98.65¢
§2 · Distribution of Δp
n=24
reference line = identity (perfect normality). Heavy upper-right tail = fat positive tail.
§3 · Sample moments
SAMPLE MOMENTS · N=25LEPTOKURTIC · FAT TAILS (G₂=2.61)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁1.751right-skewed
−3−10+1+3
EXCESS KURTOSIS · G₂2.607leptokurtic · 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: TRENDING · variance ratio > 1
HURST EXPONENT [0, 1]
H = 0.790STRONGLY 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
1.55¢
98.45¢
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
YES64.52×(2¢)NO1.02×(98¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.115 bits (12% 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 · HIGHresolves 2026-06-15 16:00 UTC
0days
22hrs
57min
YES →$1.00
NO →$0.00
current: $0.0155 · expected return per side: $0.98 on YES hit · $0.02 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 30.935 pp/day
now22.96h left30.935 pp/day×1.00
−25%17.22h left35.721 pp/day×1.15
−50%11.48h left43.749 pp/day×1.41
−75%5.74h left61.870 pp/day×2.00
−90%2.30h left97.825 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
3 up bars · 18 down · best 1.00% · worst -8.00% · typical |Δ| 1.294%
§10 · Equity curve & underwater drawdown
final equity 0.7656 (-23.44%) · max DD -23.44% · time-under-water 24/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest -38.932 · range [-199.51, 5.54] · μ -71.211 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 52.50% · range [50.81%, 245.26%] · μ 81.30% · σ̂ scaled to annualised (×√8760)
latest -0.060 · |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)
3 of 6 REJECT · mixed evidence3 reject·3 pass·α = 0.05
Jarque-Bera
REJECT H₀***H₀: Δp ~ Normal(μ, σ²)
STATISTIC
91.9243
p-VALUE (log scale)
< 0.0001
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
9.2116
p-VALUE (log scale)
0.0999
Dickey-Fuller (τ_μ)
REJECT H₀***H₀: p has a unit root (non-stationary)
STATISTIC
-10.8875
p-VALUE (log scale)
< 0.0001
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
0.8305
p-VALUE (log scale)
0.4063
KPSS (μ stationarity)
REJECT H₀**H₀: p IS level-stationary
STATISTIC
0.7620
p-VALUE (log scale)
0.0088
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
0.7030
p-VALUE (log scale)
0.4821
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 ≈ 24.00h (freq 0.042) · concentrates 29.3% of total energy · Σ|X̂|²/n = 4.016e-3
▸ Depth section using sovereign-store price series (2895 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 0.093pp · expected |Δp| over horizon 0.45ppterminal variance p(1−p) = 0.0153 · n = 2895✓ n = 2895
μ per bar
-0.002pp
average Δp · drift
σ per bar
0.093pp
one-bar volatility · logit-free
Per-day movedaily
0.46pp
σ × √24
Per-horizon move1d
0.45pp
σ × √22.963553333333333
Terminal variancebinary
0.0153
p(1−p) at resolution
Current pricep
1.6¢
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 0.16pp · ES₉₅ 0.19pp · method parametric · drift-correcteddrift -0.002pp/bar · quantised: yes · median step 0.10pp · unique ratio 0.01✓ n = 2895
VaR 95%
0.16pp
1.645·σ (parametric) of Δp
ES 95%
0.19pp
mean of the tail
Max drawdown
81.8pp
peak 8.5¢ → trough 1.6¢
Median step
0.10pp
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
1.6%
= price
Decimal oddsEU
64.516
total return per $1
AmericanUS
+6352
$100 wins $6352
FractionalUK
63.52 / 1
profit per $1 risked
Profit per $100stake
+$6351.61
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)
0.115 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.115 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
6.01 bit
self-information
Surprise · NO−log₂(1−p)
0.02 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
105641905835299104459047817633334674191335447674370742557537291253712674139313- NO token ID
97337390511594097928900563096167540297729647602512807863864501521025499567811- 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
3fbe2a67fc808d604ebd1b88b554e897156760d28ff9b49ea773cdfe32a4bd1b· 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
0.013500
(best bid + best ask) / 2
Spread
740.7bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
-0.316
ask-heavy
Imbalance (top-5)
+0.917
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-elon-musk-of-tweets-june-13-june-15-65-89/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.115230 | 75355.53bp | 0.200000 | 23 | FILLED |
| BUY | $10.00K | 0.419463 | 300713.26bp | 0.890000 | 51 | FILLED |
| BUY | $100.00K | 0.865999 | 631480.62bp | 0.998000 | 65 | FILLED |
| SELL | $1.00K | 0.004570 | 6614.86bp | 0.001000 | 9 | PARTIAL |
| SELL | $10.00K | 0.004570 | 6614.86bp | 0.001000 | 9 | PARTIAL |
| SELL | $100.00K | 0.004570 | 6614.86bp | 0.001000 | 9 | PARTIAL |
Risk metrics
▸ sovereign store · 2,895 barsperiods/year ≈ 1.75MRealized vol (annualised)
3372.70%
σ per bar = 0.025474
Mean return (annualised)
-103079.41%
μ per bar = -0.000588
Sharpe (rf=0)
-30.56
annualised; risk-free assumed zero
Max drawdown
81.76%
peak 0.09 → trough 0.02 over 2790 bars
/api/asset/pm-elon-musk-of-tweets-june-13-june-15-65-89/risk · same metrics, JSON