POLYMARKET · PREDICTION MARKET · POLITICS
Will Luiz Inácio Lula da Silva win the 2026 Brazilian presidential election?
51.5¢
48.5¢
▸ Advanced metrics · M2M bundle
polymarket · will-luiz-incio-lula-da-silva-win-the-2026-brazilian-presidential-election · fresh · feed 12s old24h sparkline · 60 pts▼ —
realized vol (ann.)
59.20%
max drawdown
1.90%
sharpe
—
ulcer index
0.69%
RMS drawdown
pain index
0.25%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
—
ret / ulcer
CDaR 95%
1.90%
cond. drawdown
gain/pain
3.00
Σgain / Σ|loss|
sterling
—
ret / CDaR
omega (θ=0)
3.00
upside/downside
roll spread
0.4 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-luiz-incio-lula-da-silva-win-the-2026-brazilian-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
51.5¢
NO · live
48.5¢
25 ticks · last 50.50¢
Σ 100.00% · fair
Σ-sides total = 100.00% (tight rounding)
H(p) entropy = 0.999 / 1.00 bits (100%) · max uncertainty (~50/50)
YES51.5%51.5¢1.94×▬ +0.00pp
NO48.5%48.5¢2.06×▬ +0.00pp
Σ 100.00% · arb gap 0.00pp
Σ 700bp moved · peak 200bp · n=24 ticks
11.6s
51.50¢ (51.50%)
48.50¢ (48.50%)
100.00%
0.000pp
$31.9k
$221.9k
25 ticks (live)
§1 · 24h price history (YES + NO tokens)
25 YES observations from clob.polymarket.com · last 50.50¢
25 NO observations from clob.polymarket.com · last 49.50¢
§2 · Distribution of Δp
n=24
reference line = identity (perfect normality). Heavy upper-right tail = fat positive tail.
§3 · Sample moments
SAMPLE MOMENTS · N=25APPROXIMATELY NORMAL · WELL-BEHAVED
FIVE-NUMBER SUMMARY · BOX PLOT
SKEWNESS · G₁0.098approximately symmetric
−3−10+1+3
EXCESS KURTOSIS · G₂-0.825mesokurtic · normal-like
−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 = 1.291STRONGLY 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
51.50¢
48.50¢
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
YES1.94×(52¢)NO2.06×(49¢)
Kelly bet-size (% of bankroll) K* = 0.00%
Entropy H(p̂) = 0.999 bits (100% 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 · DISTANTresolves 2026-10-04 00:00 UTC
111days
12hrs
50min
YES →$1.00
NO →$0.00
current: $0.5150 · expected return per side: $0.48 on YES hit · $0.52 on NO hit
Theta progression · θ ∝ σ / √t_remainingθ_now = 7.386 pp/day
now111.53d left7.386 pp/day×1.00
−25%83.65d left8.529 pp/day×1.15
−50%55.77d left10.446 pp/day×1.41
−75%27.88d left14.773 pp/day×2.00
−90%11.15d left23.358 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
4 up bars · 2 down · best 2.00% · worst -1.00% · typical |Δ| 0.292%
§10 · Equity curve & underwater drawdown
final equity 1.0300 (3.00%) · max DD -1.99% · time-under-water 3/25 bars
§11 · Rolling-window statistics (w = 6 bars)
latest -20.722 · range [-20.72, 85.44] · μ 29.431 · positive Sharpe = excess-return-per-risk earned by buying-and-holding through this window
latest 70.46% · range [0.00%, 76.42%] · μ 44.04% · σ̂ scaled to annualised (×√8760)
latest -0.010 · |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
17.9847
p-VALUE (log scale)
0.0001
Ljung-Box(h=5)
FAIL TO REJECTnsH₀: No serial autocorrelation up to lag 5
STATISTIC
4.0203
p-VALUE (log scale)
0.5485
Dickey-Fuller (τ_μ)
FAIL TO REJECTnsH₀: p has a unit root (non-stationary)
STATISTIC
-1.5136
p-VALUE (log scale)
0.5269
Wald-Wolfowitz runs
FAIL TO REJECTnsH₀: Sign sequence of Δ is random
STATISTIC
-1.7678
p-VALUE (log scale)
0.0771
KPSS (μ stationarity)
REJECT H₀**H₀: p IS level-stationary
STATISTIC
0.7606
p-VALUE (log scale)
0.0089
Variance ratio q=3
FAIL TO REJECTnsH₀: Δp is a random walk · VR = 1
STATISTIC
0.2608
p-VALUE (log scale)
0.7942
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 ≈ 12.00h (freq 0.083) · concentrates 31.1% of total energy · Σ|X̂|²/n = 4.500e-4
▸ Depth section using sovereign-store price series (2746 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 111.5 d · σ/bar 0.038pp · expected |Δp| over horizon 1.98ppterminal variance p(1−p) = 0.2498 · n = 2746✓ n = 2746
μ per bar
+0.001pp
average Δp · drift
σ per bar
0.038pp
one-bar volatility · logit-free
Per-day movedaily
0.19pp
σ × √24
Per-horizon move112d
1.98pp
σ × √2676.8344280555557
Terminal variancebinary
0.2498
p(1−p) at resolution
Current pricep
51.5¢
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 0.06pp · ES₉₅ 0.08pp · method parametric · drift-correcteddrift +0.001pp/bar · quantised: yes · median step 1.00pp · unique ratio 0.00✓ n = 2746
VaR 95%
0.06pp
1.645·σ (parametric) of Δp
ES 95%
0.08pp
mean of the tail
Max drawdown
1.9pp
peak 52.5¢ → trough 51.5¢
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
51.5%
= price
Decimal oddsEU
1.942
total return per $1
AmericanUS
-106
risk $106 to win $100
FractionalUK
0.94 / 1
profit per $1 risked
Profit per $100stake
+$94.17
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)
0.999 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.999 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
0.96 bit
self-information
Surprise · NO−log₂(1−p)
1.04 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
30630994248667897740988010928640156931882346081873066002335460180076741328029- NO token ID
79191939610100241429039499950443680906623179487184628479206155805558220344190- Snapshot fetched
- 2026-06-14 11:09:44 UTC
- Snapshot age
- 11.6s
- History points
- 25 CLOB mids
- Page rendered
- 2026-06-14 11:09:56 UTC
- Storage policy
- no persistence — fetched on every request
- SHA-256 attestation
51ab53d6400cff9aa008ede4ed3c728be8f1fd7e5d41b695576666f41f0a7584· 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
0.505000
(best bid + best ask) / 2
Spread
198.0bp
(bestAsk − bestBid) / mid
Imbalance (whole book)
-0.510
ask-heavy
Imbalance (top-5)
+0.533
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-luiz-incio-lula-da-silva-win-the-2026-brazilian-presidential-election/slippage?size=10000&side=buy
| Side | Notional | Avg fill | Slippage | Worst fill | Levels | Status |
|---|---|---|---|---|---|---|
| BUY | $1.00K | 0.510000 | 99.01bp | 0.510000 | 1 | FILLED |
| BUY | $10.00K | 0.527768 | 450.85bp | 0.540000 | 4 | FILLED |
| BUY | $100.00K | 0.752750 | 4905.93bp | 0.890000 | 36 | FILLED |
| SELL | $1.00K | 0.500000 | 99.01bp | 0.500000 | 1 | FILLED |
| SELL | $10.00K | 0.492501 | 247.51bp | 0.490000 | 2 | FILLED |
| SELL | $100.00K | 0.442249 | 1242.59bp | 0.390000 | 12 | FILLED |
Risk metrics
▸ sovereign store · 2,746 barsperiods/year ≈ 1.75MRealized vol (annualised)
98.65%
σ per bar = 0.000745
Mean return (annualised)
2529.23%
μ per bar = 0.000014
Sharpe (rf=0)
25.64
annualised; risk-free assumed zero
Max drawdown
1.90%
peak 0.53 → trough 0.52 over 414 bars
/api/asset/pm-will-luiz-incio-lula-da-silva-win-the-2026-brazilian-presidential-election/risk · same metrics, JSON