POLYMARKET · PREDICTION MARKET · POLITICS
Will Rafael López Aliaga win the 2026 Peruvian presidential election?
0.1¢
99.9¢
▸ Advanced metrics · M2M bundle
polymarket · will-rafael-lpez-aliaga-win-the-2026-peruvian-presidential-election · fresh · feed 10s old24h sparkline · 60 pts▼ —
realized vol (ann.)
2.09%
max drawdown
33.33%
sharpe
—
ulcer index
12.87%
RMS drawdown
pain index
4.97%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
33.33%
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
2000
store
spread
—
24h Δ
—
flow lean
—
carry
flat
signalNEUTRALconfidence 20%
Same bundle via M2M API:
/api/m2m/pm-will-rafael-lpez-aliaga-win-the-2026-peruvian-presidential-election/bundle · venue execution: polymarket →LIVEPOLL0SRCWARMING⏱--:--:-- UTCNEXT8.0sUP0s--:--HIST
▶ STREAMING·HYPERLIQUID·POLYMARKET·0 POLLS·SRC WARMING·UPTIME 0s·NEXT POLL 8.0s·CC0 OPEN DATA·HYPO.MARKETS·▶ STREAMING·HYPERLIQUID·POLYMARKET·0 POLLS·SRC WARMING·UPTIME 0s·NEXT POLL 8.0s·CC0 OPEN DATA·HYPO.MARKETS·
YES · live
0.1¢
NO · live
99.9¢
25 ticks · last 0.15¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.016 / 1.00 bits (2%) · informative — one side favoured
YES0.1%0.1¢666.67×▬ +0.00pp
NO99.9%99.9¢1.00×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 10bp moved · peak 5bp · n=24 ticks
10.3s
0.15¢ (0.15%)
99.85¢ (99.85%)
100.00%
0.000pp
$52.5k
$147.9k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 0.15¢
25 NO observations from clob.polymarket.com · last 99.85¢
§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=6.76)
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁-2.912left-skewed
−3−10+1+3
EXCESS KURTOSIS · G₂6.757leptokurtic · 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: MEAN-REVERTING · ADF rejects unit root
HURST EXPONENT [0, 1]
H = 1.597STRONGLY 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
0.15¢
99.85¢
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
YES666.67×(0¢)NO1.00×(100¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.016 bits (2% 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.
§9 · Hourly return heatmap
1 up bars · 1 down · best 0.05% · worst -0.05% · typical |Δ| 0.004%
§10 · Equity curve & underwater drawdown
final equity 1.0000 (-0.00%) · max DD -0.05% · time-under-water 8/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest 38.210 · range [-38.21, 38.21] · μ 0.000 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 1.91% · range [0.00%, 2.96%] · μ 1.03% · σ̂ scaled to annualised (×√8760)
latest -0.033 · |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 5 REJECT · mixed evidence2 reject·3 pass·1 n/a·α = 0.05
Jarque-Bera
REJECT H₀***H₀: Δp ~ Normal(μ, σ²)
STATISTIC
132.2500
p-VALUE (log scale)
< 0.0001
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
7.0909
p-VALUE (log scale)
0.2128
Dickey-Fuller (τ_μ)
REJECT H₀*H₀: p has a unit root (non-stationary)
STATISTIC
-2.8723
p-VALUE (log scale)
0.0488
Wald-Wolfowitz runs
N/An/aH₀: Sign sequence of Δ is random
STATISTIC
—
p-VALUE (log scale)
—
KPSS (μ stationarity)
FAIL TO REJECTnsH₀: p IS level-stationary
STATISTIC
0.1721
p-VALUE (log scale)
0.4056
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
-0.8868
p-VALUE (log scale)
0.3752
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 ≈ 4.00h (freq 0.250) · concentrates 16.7% of total energy · Σ|X̂|²/n = 2.500e-7
▸ Depth section using sovereign-store price series (2826 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 0.3 d · σ/bar 0.001pp · expected |Δp| over horizon 0.00ppterminal variance p(1−p) = 0.0015 · n = 2826✓ n = 2826
μ per bar
+0.000pp
average Δp · drift
σ per bar
0.001pp
one-bar volatility · logit-free
Per-day movedaily
0.01pp
σ × √24
Per-horizon move0d
0.00pp
σ × √6
Terminal variancebinary
0.0015
p(1−p) at resolution
Current pricep
0.1¢
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 0.00pp · ES₉₅ 0.00pp · method parametric · drift-correcteddrift +0.000pp/bar · quantised: yes · median step 0.05pp · unique ratio 0.00✓ n = 2826
VaR 95%
0.00pp
1.645·σ (parametric) of Δp
ES 95%
0.00pp
mean of the tail
Max drawdown
33.3pp
peak 0.1¢ → trough 0.1¢
Median step
0.05pp
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
0.1%
= price
Decimal oddsEU
666.667
total return per $1
AmericanUS
+66567
$100 wins $66567
FractionalUK
665.67 / 1
profit per $1 risked
Profit per $100stake
+$66566.67
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)
0.016 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.016 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
9.38 bit
self-information
Surprise · NO−log₂(1−p)
0.00 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
91464516107556165907566387619157396733019262339196802152206657889196699256450- NO token ID
25945415793931624559794421130954173506522916582203134613102786444988212496867- Snapshot fetched
- 2026-06-14 11:08:50 UTC
- Snapshot age
- 10.3s
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-14 11:09:00 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
f66f0c762e228cced881c9fab4e637ad4faa7877dd06f1abd1a10ea0267648cf· 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
0.001500
(best bid + best ask) / 2
Spread
6666.7bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
-1.000
ask-heavy
Imbalance (top-5)
-0.998
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-rafael-lpez-aliaga-win-the-2026-peruvian-presidential-election/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.009878 | 55850.09bp | 0.177000 | 25 | FILLED |
| BUY | $10.00K | 0.085063 | 557086.93bp | 0.827000 | 36 | FILLED |
| BUY | $100.00K | 0.458983 | 3049885.85bp | 0.948000 | 46 | FILLED |
| SELL | $1.00K | 0.001000 | 3333.33bp | 0.001000 | 1 | PARTIAL |
| SELL | $10.00K | 0.001000 | 3333.33bp | 0.001000 | 1 | PARTIAL |
| SELL | $100.00K | 0.001000 | 3333.33bp | 0.001000 | 1 | PARTIAL |
Risk metrics
▸ sovereign store · 2,826 barsperiods/year ≈ 1.75MRealized vol (annualised)
1428.58%
σ per bar = 0.010790
Mean return (annualised)
-0.00%
μ per bar = -0.000000
Sharpe (rf=0)
-0.00
annualised; risk-free assumed zero
Max drawdown
33.33%
peak 0.00 → trough 0.00 over 1625 bars
/api/asset/pm-will-rafael-lpez-aliaga-win-the-2026-peruvian-presidential-election/risk · same metrics, JSON