POLYMARKET · PREDICTION MARKET · POLITICS
Will Iván Cepeda Castro place 1st in Bogotá in the second round of the 2026 Colombia presidential election?
43.0¢
57.0¢
▸ Advanced metrics · M2M bundle
polymarket · will-ivan-cepeda-castro-place-1st-in-bogota-in-the-second-round-of-the-2026-colombia-presidential-election-20260604170547990 · fresh · feed 9s old24h sparkline · 60 pts▼ —
realized vol (ann.)
51.70%
max drawdown
2.30%
sharpe
—
ulcer index
0.85%
RMS drawdown
pain index
0.56%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
1.24%
cond. drawdown
gain/pain
1.00
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
1.00
upside/downside
roll spread
0.0 bps
implied (price-only)
bars used
985
store
spread
—
24h Δ
—
flow lean
—
carry
flat
signalNEUTRALconfidence 20%
Same bundle via M2M API:
/api/m2m/pm-will-ivan-cepeda-castro-place-1st-in-bogota-in-the-second-round-of-the-2026-colombia-presidential-election-20260604170547990/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
43.0¢
NO · live
57.0¢
25 ticks · last 43.00¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.986 / 1.00 bits (99%) · max uncertainty (~50/50)
YES43.0%43.0¢2.33×▬ +0.00pp
NO57.0%57.0¢1.75×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 1650bp moved · peak 500bp · n=24 ticks
9.1s
43.00¢ (43.00%)
57.00¢ (57.00%)
100.00%
0.000pp
$24.1k
$48.8k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 43.00¢
25 NO observations from clob.polymarket.com · last 57.00¢
§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=-1.15)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁-0.034approximately symmetric
−3−10+1+3
EXCESS KURTOSIS · G₂-1.155platykurtic · 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 = 0.889STRONGLY 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
43.00¢
57.00¢
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
YES2.33×(43¢)NO1.75×(57¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.986 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 2026-06-22 03:59 UTC
1days
16hrs
08min
YES →$1.00
NO →$0.00
current: $0.4300 · expected return per side: $0.57 on YES hit · $0.43 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 11.368 pp/day
now1.67d left11.368 pp/day×1.00
−25%1.25d left13.127 pp/day×1.15
−50%20.07h left16.077 pp/day×1.41
−75%10.03h left22.737 pp/day×2.00
−90%4.01h left35.950 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
7 up bars · 4 down · best 3.00% · worst -5.00% · typical |Δ| 0.688%
§10 · Equity curve & underwater drawdown
final equity 1.0129 (1.29%) · max DD -5.95% · time-under-water 18/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest 11.736 · range [-19.27, 59.86] · μ 26.678 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 62.20% · range [38.21%, 227.34%] · μ 104.25% · σ̂ scaled to annualised (×√8760)
latest -0.248 · |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
81.7243
p-VALUE (log scale)
< 0.0001
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
1.9511
p-VALUE (log scale)
0.8569
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
-1.2289
p-VALUE (log scale)
0.6596
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
-0.0630
p-VALUE (log scale)
0.9498
KPSS (μ stationarity)
REJECT H₀*H₀: p IS level-stationary
STATISTIC
0.5153
p-VALUE (log scale)
0.0382
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
0.5549
p-VALUE (log scale)
0.5789
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 ≈ 6.00h (freq 0.167) · concentrates 23.8% of total energy · Σ|X̂|²/n = 2.333e-3
▸ Depth section using sovereign-store price series (985 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 1.7 d · σ/bar 0.039pp · expected |Δp| over horizon 0.25ppterminal variance p(1−p) = 0.2451 · n = 985✓ n = 985
μ per bar
+0.000pp
average Δp · drift
σ per bar
0.039pp
one-bar volatility · logit-free
Per-day movedaily
0.19pp
σ × √24
Per-horizon move2d
0.25pp
σ × √40.13826694444444
Terminal variancebinary
0.2451
p(1−p) at resolution
Current pricep
43.0¢
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 0.06pp · ES₉₅ 0.08pp · method parametric · drift-correcteddrift +0.000pp/bar · quantised: yes · median step 0.50pp · unique ratio 0.00✓ n = 985
VaR 95%
0.06pp
1.645·σ (parametric) of Δp
ES 95%
0.08pp
mean of the tail
Max drawdown
2.3pp
peak 43.5¢ → trough 42.5¢
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
43.0%
= price
Decimal oddsEU
2.326
total return per $1
AmericanUS
+133
$100 wins $133
FractionalUK
1.33 / 1
profit per $1 risked
Profit per $100stake
+$132.56
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)
0.986 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.986 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
1.22 bit
self-information
Surprise · NO−log₂(1−p)
0.81 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
51986373652466077523760166908992553996156559541187887086591038114806922945350- NO token ID
47637365543602963920225948010612073178457366051413011063071015330636241156349- Snapshot fetched
- 2026-06-20 11:50:32 UTC
- Snapshot age
- 9.1s
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-20 11:50:42 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
11a4c4f956c4a51abb4517257e336e57aee3c6f66a3f6c4d9b5da71c845f358f· deterministic hash of source snapshot- Open data licence
- CC0 / public domain
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Also see: /arb opportunities · RSS feed · more in Politics
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
0.430000
(best bid + best ask) / 2
Spread
465.1bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
+0.105
bid-heavy
Imbalance (top-5)
-0.529
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-ivan-cepeda-castro-place-1st-in-bogota-in-the-second-round-of-the-2026-colombia-presidential-election-20260604170547990/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.440000 | 232.56bp | 0.440000 | 1 | FILLED |
| BUY | $10.00K | 0.459199 | 679.05bp | 0.510000 | 7 | FILLED |
| BUY | $100.00K | 0.782348 | 8194.15bp | 0.960000 | 42 | FILLED |
| SELL | $1.00K | 0.413130 | 392.32bp | 0.400000 | 3 | FILLED |
| SELL | $10.00K | 0.232960 | 4582.34bp | 0.100000 | 27 | FILLED |
| SELL | $100.00K | 0.060458 | 8594.01bp | 0.010000 | 35 | PARTIAL |
Risk metrics
▸ sovereign store · 985 barsperiods/year ≈ 1.75MRealized vol (annualised)
120.51%
σ per bar = 0.000910
Mean return (annualised)
-0.00%
μ per bar = -0.000000
Sharpe (rf=0)
-0.00
annualised; risk-free assumed zero
Max drawdown
2.30%
peak 0.43 → trough 0.42 over 483 bars
/api/asset/pm-will-ivan-cepeda-castro-place-1st-in-bogota-in-the-second-round-of-the-2026-colombia-presidential-election-20260604170547990/risk · same metrics, JSON