POLYMARKET · PREDICTION MARKET · ELON MUSK # TWEETS JUNE 16 - JUNE 23, 2026?
Will Elon Musk post 180-199 tweets from June 16 to June 23, 2026?
37.5¢
62.5¢
▸ Advanced metrics · M2M bundle
polymarket · elon-musk-of-tweets-june-16-june-23-180-199 · fresh · feed 6s old24h sparkline · 60 pts▼ —
realized vol (ann.)
169.54%
max drawdown
5.33%
sharpe
—
ulcer index
3.17%
RMS drawdown
pain index
2.34%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
5.33%
cond. drawdown
gain/pain
2.00
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
2.00
upside/downside
roll spread
2.2 bps
implied (price-only)
bars used
1037
store
spread
—
24h Δ
—
flow lean
—
carry
flat
signalNEUTRALconfidence 20%
Same bundle via M2M API:
/api/m2m/pm-elon-musk-of-tweets-june-16-june-23-180-199/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
37.5¢
NO · live
62.5¢
25 ticks · last 37.50¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.954 / 1.00 bits (95%) · max uncertainty (~50/50)
YES37.5%37.5¢2.67×▬ +0.00pp
NO62.5%62.5¢1.60×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 3300bp moved · peak 600bp · n=24 ticks
6.5s
37.50¢ (37.50%)
62.50¢ (62.50%)
100.00%
0.000pp
$29.5k
$28.5k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 37.50¢
25 NO observations from clob.polymarket.com · last 62.50¢
§2 · Distribution of Δp
n=24
reference line = identity (perfect normality). Heavy upper-right tail = fat positive tail.
§3 · Sample moments
SAMPLE MOMENTS · N=25RIGHT-SKEWED (G₁=0.52)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁0.524right-skewed
−3−10+1+3
EXCESS KURTOSIS · G₂-1.253platykurtic · 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) -0.26 + ADF rejected
HURST EXPONENT [0, 1]
H = 0.850STRONGLY 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
37.50¢
62.50¢
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
YES2.67×(38¢)NO1.60×(63¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.954 bits (95% of max) · maximum uncertainty (~50/50)
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 2026-06-23 16:00 UTC
3days
03hrs
45min
YES →$1.00
NO →$0.00
current: $0.3750 · expected return per side: $0.63 on YES hit · $0.38 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 22.960 pp/day
now3.16d left22.960 pp/day×1.00
−25%2.37d left26.511 pp/day×1.15
−50%1.58d left32.470 pp/day×1.41
−75%18.94h left45.919 pp/day×2.00
−90%7.58h left72.604 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
13 up bars · 5 down · best 3.00% · worst -6.00% · typical |Δ| 1.375%
§10 · Equity curve & underwater drawdown
final equity 1.1114 (11.14%) · max DD -6.04% · time-under-water 13/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest 83.781 · range [-20.72, 93.89] · μ 40.024 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 87.13% · range [87.13%, 308.53%] · μ 175.78% · σ̂ scaled to annualised (×√8760)
latest -0.314 · |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
39.0517
p-VALUE (log scale)
< 0.0001
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
3.4810
p-VALUE (log scale)
0.6288
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
-0.2550
p-VALUE (log scale)
0.9250
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
1.0934
p-VALUE (log scale)
0.2742
KPSS (μ stationarity)
REJECT H₀*H₀: p IS level-stationary
STATISTIC
0.7164
p-VALUE (log scale)
0.0121
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
-0.7306
p-VALUE (log scale)
0.4650
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 ≈ 2.00h (freq 0.500) · concentrates 25.9% of total energy · Σ|X̂|²/n = 4.650e-3
▸ Depth section using sovereign-store price series (1037 bars · effective 1752713 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 3.2 d · σ/bar 0.128pp · expected |Δp| over horizon 1.11ppterminal variance p(1−p) = 0.2344 · n = 1037✓ n = 1037
μ per bar
+0.004pp
average Δp · drift
σ per bar
0.128pp
one-bar volatility · logit-free
Per-day movedaily
0.63pp
σ × √24
Per-horizon move3d
1.11pp
σ × √75.75439333333333
Terminal variancebinary
0.2344
p(1−p) at resolution
Current pricep
37.5¢
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 0.21pp · ES₉₅ 0.26pp · method parametric · drift-correcteddrift +0.004pp/bar · quantised: yes · median step 0.50pp · unique ratio 0.01✓ n = 1037
VaR 95%
0.21pp
1.645·σ (parametric) of Δp
ES 95%
0.26pp
mean of the tail
Max drawdown
5.3pp
peak 37.5¢ → trough 35.5¢
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
37.5%
= price
Decimal oddsEU
2.667
total return per $1
AmericanUS
+167
$100 wins $167
FractionalUK
1.67 / 1
profit per $1 risked
Profit per $100stake
+$166.67
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)
0.954 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.954 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
1.42 bit
self-information
Surprise · NO−log₂(1−p)
0.68 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
98307459114237247062027754282577575390093690690915136660880559027982995193371- NO token ID
70473470942187177363840305842101782468240274740649658467165924494159278824999- Snapshot fetched
- 2026-06-20 12:14:37 UTC
- Snapshot age
- 6.5s
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-20 12:14:44 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
0d715bb66d913618b1b156db9e141dae7c27ff12da3c38883857e99ac0a63b96· 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 16 - June 23, 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
0.375000
(best bid + best ask) / 2
Spread
266.7bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
+0.133
bid-heavy
Imbalance (top-5)
-0.094
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-16-june-23-180-199/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.400823 | 688.62bp | 0.410000 | 4 | FILLED |
| BUY | $10.00K | 0.521072 | 3895.24bp | 0.650000 | 24 | FILLED |
| BUY | $100.00K | 0.828549 | 12094.65bp | 0.990000 | 46 | PARTIAL |
| SELL | $1.00K | 0.359209 | 421.09bp | 0.340000 | 4 | FILLED |
| SELL | $10.00K | 0.057357 | 8470.48bp | 0.010000 | 36 | PARTIAL |
| SELL | $100.00K | 0.057357 | 8470.48bp | 0.010000 | 36 | PARTIAL |
Risk metrics
▸ sovereign store · 1,037 barsperiods/year ≈ 1.75MRealized vol (annualised)
478.13%
σ per bar = 0.003612
Mean return (annualised)
19082.83%
μ per bar = 0.000109
Sharpe (rf=0)
39.91
annualised; risk-free assumed zero
Max drawdown
5.33%
peak 0.38 → trough 0.35 over 133 bars
/api/asset/pm-elon-musk-of-tweets-june-16-june-23-180-199/risk · same metrics, JSON