POLYMARKET · PREDICTION MARKET · ELON MUSK # TWEETS JUNE 12 - JUNE 19, 2026?
Will Elon Musk post 200-219 tweets from June 12 to June 19, 2026?
44.5¢
55.5¢
▸ Advanced metrics · M2M bundle
polymarket · elon-musk-of-tweets-june-12-june-19-200-219 · fresh · feed 3s old24h sparkline · 60 pts▲ 4.71%
realized vol (ann.)
265.57%
max drawdown
17.48%
sharpe
—
ulcer index
9.23%
RMS drawdown
pain index
6.71%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
17.48%
cond. drawdown
gain/pain
1.48
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
1.48
upside/downside
roll spread
1.7 bps
implied (price-only)
bars used
2000
store
spread
—
24h Δ
4.71%
flow lean
—
carry
flat
signalNEUTRALconfidence 25%
- 24h change +4.71%
Same bundle via M2M API:
/api/m2m/pm-elon-musk-of-tweets-june-12-june-19-200-219/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
44.5¢
NO · live
55.5¢
25 ticks · last 44.50¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.991 / 1.00 bits (99%) · max uncertainty (~50/50)
YES44.5%44.5¢2.25×▬ +0.00pp
NO55.5%55.5¢1.80×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 4400bp moved · peak 700bp · n=24 ticks
2.6s
44.50¢ (44.50%)
55.50¢ (55.50%)
100.00%
0.000pp
$53.0k
$26.3k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 44.50¢
25 NO observations from clob.polymarket.com · last 55.50¢
§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=-1.25)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁0.273approximately symmetric
−3−10+1+3
EXCESS KURTOSIS · G₂-1.251platykurtic · 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: MARTINGALE · UNPREDICTABLE
HURST EXPONENT [0, 1]
H = 0.913STRONGLY 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
44.50¢
55.50¢
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
YES2.25×(45¢)NO1.80×(56¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.991 bits (99% 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 · MEDIUMresolves 2026-06-19 16:00 UTC
1days
03hrs
42min
YES →$1.00
NO →$0.00
current: $0.4450 · expected return per side: $0.55 on YES hit · $0.45 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 20.004 pp/day
now1.15d left20.004 pp/day×1.00
−25%20.78h left23.099 pp/day×1.15
−50%13.86h left28.290 pp/day×1.41
−75%6.93h left40.008 pp/day×2.00
−90%2.77h left63.258 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
10 up bars · 8 down · best 7.00% · worst -4.00% · typical |Δ| 1.833%
§10 · Equity curve & underwater drawdown
final equity 1.0124 (1.24%) · max DD -6.87% · time-under-water 19/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest -40.188 · range [-63.40, 69.28] · μ -0.551 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 181.65% · range [132.36%, 358.85%] · μ 243.50% · σ̂ scaled to annualised (×√8760)
latest -0.001 · |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
2.1761
p-VALUE (log scale)
0.3369
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
6.7442
p-VALUE (log scale)
0.2394
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
-1.5091
p-VALUE (log scale)
0.5290
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
0.0547
p-VALUE (log scale)
0.9564
KPSS (μ stationarity)
FAIL TO REJECTnsH₀: p IS level-stationary
STATISTIC
0.4175
p-VALUE (log scale)
0.0696
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
0.5555
p-VALUE (log scale)
0.5786
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 34.3% of total energy · Σ|X̂|²/n = 8.525e-3
▸ Depth section using sovereign-store price series (5000 bars · effective 1752421 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 1.2 d · σ/bar 0.199pp · expected |Δp| over horizon 1.05ppterminal variance p(1−p) = 0.2470 · n = 5000✓ n = 5000
μ per bar
+0.001pp
average Δp · drift
σ per bar
0.199pp
one-bar volatility · logit-free
Per-day movedaily
0.98pp
σ × √24
Per-horizon move1d
1.05pp
σ × √27.71249583333333
Terminal variancebinary
0.2470
p(1−p) at resolution
Current pricep
44.5¢
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 0.33pp · ES₉₅ 0.41pp · method parametric · drift-correcteddrift +0.001pp/bar · quantised: yes · median step 1.00pp · unique ratio 0.00✓ n = 5000
VaR 95%
0.33pp
1.645·σ (parametric) of Δp
ES 95%
0.41pp
mean of the tail
Max drawdown
23.2pp
peak 47.5¢ → trough 36.5¢
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
44.5%
= price
Decimal oddsEU
2.247
total return per $1
AmericanUS
+125
$100 wins $125
FractionalUK
1.25 / 1
profit per $1 risked
Profit per $100stake
+$124.72
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)
0.991 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.991 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
1.17 bit
self-information
Surprise · NO−log₂(1−p)
0.85 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
13780147397713152295057581886128876904101974770903165050624556728213927568844- NO token ID
59527075485812177139800211164881558198121541406238529864844292752474637183323- Snapshot fetched
- 2026-06-18 12:17:12 UTC
- Snapshot age
- 2.6s
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-18 12:17:15 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
92b27dd96c08bd9e5d64411c8dc80b913e4d9fce33b08a00fcbb4539a0d82047· 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 12 - June 19, 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
0.445000
(best bid + best ask) / 2
Spread
224.7bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
-0.267
ask-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-12-june-19-200-219/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.457575 | 282.58bp | 0.460000 | 2 | FILLED |
| BUY | $10.00K | 0.516797 | 1613.42bp | 0.630000 | 16 | FILLED |
| BUY | $100.00K | 0.808961 | 8178.90bp | 0.990000 | 41 | PARTIAL |
| SELL | $1.00K | 0.434518 | 235.56bp | 0.420000 | 3 | FILLED |
| SELL | $10.00K | 0.138478 | 6888.13bp | 0.010000 | 43 | PARTIAL |
| SELL | $100.00K | 0.138478 | 6888.13bp | 0.010000 | 43 | PARTIAL |
Risk metrics
▸ sovereign store · 5,000 barsperiods/year ≈ 1.75MRealized vol (annualised)
618.85%
σ per bar = 0.004675
Mean return (annualised)
3301.77%
μ per bar = 0.000019
Sharpe (rf=0)
5.34
annualised; risk-free assumed zero
Max drawdown
23.16%
peak 0.47 → trough 0.36 over 2413 bars
/api/asset/pm-elon-musk-of-tweets-june-12-june-19-200-219/risk · same metrics, JSON