POLYMARKET · PREDICTION MARKET · POLITICS
Will Abelardo de la Espriella win the 2026 Colombian presidential election?
88.5¢
11.5¢
▸ Advanced metrics · M2M bundle
polymarket · will-abelardo-de-la-espriella-win-the-2026-colombian-presidential-election · fresh · feed 0s old24h sparkline · 60 pts▲ 2.31%
realized vol (ann.)
148.78%
max drawdown
4.47%
sharpe
—
ulcer index
1.41%
RMS drawdown
pain index
0.60%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
4.47%
cond. drawdown
gain/pain
1.38
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
1.38
upside/downside
roll spread
0.2 bps
implied (price-only)
bars used
2000
store
spread
—
24h Δ
2.31%
flow lean
—
carry
flat
signalNEUTRALconfidence 25%
- 24h change +2.31%
Same bundle via M2M API:
/api/m2m/pm-will-abelardo-de-la-espriella-win-the-2026-colombian-presidential-election/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
88.5¢
NO · live
11.5¢
25 ticks · last 88.50¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.515 / 1.00 bits (51%) · moderate uncertainty
YES88.5%88.5¢1.13×▬ +0.00pp
NO11.5%11.5¢8.70×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 1200bp moved · peak 400bp · n=24 ticks
6ms
88.50¢ (88.50%)
11.50¢ (11.50%)
100.00%
0.000pp
$85.5k
$114.6k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 88.50¢
25 NO observations from clob.polymarket.com · last 11.50¢
§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=0.54)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁0.541right-skewed
−3−10+1+3
EXCESS KURTOSIS · G₂-1.010platykurtic · 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 · ADF rejects unit root
HURST EXPONENT [0, 1]
H = 0.854STRONGLY 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
88.50¢
11.50¢
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
YES1.13×(89¢)NO8.70×(12¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.515 bits (51% of max) · moderate 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 · LOWresolves 2026-06-21 14:00 UTC
6days
22hrs
53min
YES →$1.00
NO →$0.00
current: $0.8850 · expected return per side: $0.11 on YES hit · $0.89 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 5.894 pp/day
now6.95d left5.894 pp/day×1.00
−25%5.22d left6.806 pp/day×1.15
−50%3.48d left8.335 pp/day×1.41
−75%1.74d left11.788 pp/day×2.00
−90%16.69h left18.639 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
6 up bars · 2 down · best 2.00% · worst -4.00% · typical |Δ| 0.500%
§10 · Equity curve & underwater drawdown
final equity 1.0188 (1.88%) · max DD -4.00% · time-under-water 10/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest -7.642 · range [-15.87, 60.42] · μ 17.111 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 191.05% · range [38.21%, 191.05%] · μ 83.19% · σ̂ scaled to annualised (×√8760)
latest -0.297 · |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
94.1331
p-VALUE (log scale)
< 0.0001
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
5.1738
p-VALUE (log scale)
0.3954
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
-2.3728
p-VALUE (log scale)
0.1580
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
1.0801
p-VALUE (log scale)
0.2801
KPSS (μ stationarity)
REJECT H₀*H₀: p IS level-stationary
STATISTIC
0.4957
p-VALUE (log scale)
0.0426
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
-1.0318
p-VALUE (log scale)
0.3022
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 ≈ 3.43h (freq 0.292) · concentrates 22.3% of total energy · Σ|X̂|²/n = 1.469e-3
▸ Depth section using sovereign-store price series (3597 bars · effective 1752810 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 7.0 d · σ/bar 0.087pp · expected |Δp| over horizon 1.13ppterminal variance p(1−p) = 0.1018 · n = 3597✓ n = 3597
μ per bar
+0.001pp
average Δp · drift
σ per bar
0.087pp
one-bar volatility · logit-free
Per-day movedaily
0.43pp
σ × √24
Per-horizon move7d
1.13pp
σ × √166.88754333333333
Terminal variancebinary
0.1018
p(1−p) at resolution
Current pricep
88.5¢
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 0.14pp · ES₉₅ 0.18pp · method parametric · drift-correcteddrift +0.001pp/bar · quantised: yes · median step 1.00pp · unique ratio 0.00✓ n = 3597
VaR 95%
0.14pp
1.645·σ (parametric) of Δp
ES 95%
0.18pp
mean of the tail
Max drawdown
4.5pp
peak 89.5¢ → trough 85.5¢
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
88.5%
= price
Decimal oddsEU
1.130
total return per $1
AmericanUS
-770
risk $770 to win $100
FractionalUK
0.13 / 1
profit per $1 risked
Profit per $100stake
+$12.99
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)
0.515 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.515 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
0.18 bit
self-information
Surprise · NO−log₂(1−p)
3.12 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
87037476560262355019736573472997730954875286944171789280729969665019434635807- NO token ID
93119283163420984666329627600000273753909488383271496422725436610044958733622- Snapshot fetched
- 2026-06-14 15:06:44 UTC
- Snapshot age
- 6ms
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-14 15:06:44 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
408407d65250a6f8e8da359b93cb20c821a98c66231e6c1c2dd2aefbef47ac7c· 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 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
0.885000
(best bid + best ask) / 2
Spread
113.0bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
+0.180
bid-heavy
Imbalance (top-5)
+0.261
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-will-abelardo-de-la-espriella-win-the-2026-colombian-presidential-election/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.890000 | 56.50bp | 0.890000 | 1 | FILLED |
| BUY | $10.00K | 0.900799 | 178.52bp | 0.910000 | 3 | FILLED |
| BUY | $100.00K | 0.944271 | 669.73bp | 0.980000 | 10 | FILLED |
| SELL | $1.00K | 0.880000 | 56.50bp | 0.880000 | 1 | FILLED |
| SELL | $10.00K | 0.875489 | 107.47bp | 0.870000 | 2 | FILLED |
| SELL | $100.00K | 0.577364 | 3476.12bp | 0.100000 | 64 | FILLED |
Risk metrics
▸ sovereign store · 3,597 barsperiods/year ≈ 1.75MRealized vol (annualised)
132.40%
σ per bar = 0.001000
Mean return (annualised)
1114.18%
μ per bar = 0.000006
Sharpe (rf=0)
8.42
annualised; risk-free assumed zero
Max drawdown
4.47%
peak 0.90 → trough 0.85 over 979 bars
/api/asset/pm-will-abelardo-de-la-espriella-win-the-2026-colombian-presidential-election/risk · same metrics, JSON