NOSTRADAMUS · Position Analytics Engine
SIMULATOR Will Roberto Sánchez Palomino win the 2026 Peruvian presidential election?
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A live, interactive instrument for dissecting a single binary position. Sweep the inputs and watch every indicator recompute — payoff geometry, Kelly growth, Bayesian posterior, KL divergence, cost waterfall, Monte-Carlo equity fan, forecast calibration. Companion to the live /feed/pm-will-roberto-snchez-palomino-win-the-2026-peruvian-presidential-election page.
▲ YES EDGE · +0.010 · f★ 1.0% · deploy 0.5% · net 0.20pp
§1 · Position economics
YES · Expected P/L per share +0.0095@ model P(YES) = 0.020
P/L per sharemarket pricemodel Pprofit zoneloss zone
Profit is linear in the eventual settlement price.
f★ = 0.96% · g(f★) = 0.343%deploy 0.48% · g = 0.275%
g(f)f★ optimumdeployed fgrowth zone
Underbet leaves growth on the table; overbet destroys capital. The interior maximum is f★.
§2 · The trade ticket
YES @ 0.010 · EV +$109stake $120 · 0.48% of bankroll
Deployed stakestake
$120
0.48% of bankroll
Sharesunits
11,430
each pays $1 if YES
Max payoutwin
$11,430
gross, if win
Max profitwin
+$11,310
net of cost
Max losslose
-$120
binary settles to $0
Payout multiple×
×95.24
$1 → $95.24
Risk:RewardR:R
94.24 : 1
win $94.24 per $1
Expected P/LE[P/L]
+$109
probability-weighted
| Outcome | P(model) | P/L | Contribution |
|---|---|---|---|
| Resolves YES (win) | 2.0% | +$11,310 | +$226 |
| Resolves against (lose) | 98.0% | -$120 | -$118 |
| Expected value | 100.0% | — | +$109 |
What you actually win and lose. The bottom table tabulates probability-weighted P/L by outcome.
§3 · Break-even & cushion
Cushion +1.0 pprelative edge +90.5%
Required win ratebreak-even
1.0%
price = implied probability
Model win rateP(win)
2.0%
what you forecast
Cushionedge
+1.0 pp
margin of safety
Fair pricemodel
0.020
where you think it should trade
The market price equals the win rate you must beat to make money.
§4 · Odds conversion
Implied probabilityP
1.0%
= price
Decimal oddsEU
95.238
total return per $1
AmericanUS
+9424
$100 wins $9424
FractionalUK
94.24 / 1
profit per $1 risked
Profit per $100stake
+$9423.81
clean dollar framing
underdog (+)favorite (-)your price
Five views of the same number.
§4b · Time & annualized return
APR 1573% · APY 7309571%ROI 90.5% over 21d · 17.4 turns/yr
Time to resolvehorizon
21.0 d
504h capital lockup
Raw ROIper resolve
+90.5%
APR (simple)scaled
+1573%
ROI × 365/days
APY (compounded)if redeployed
+7309571%
(1+ROI)^(365/d) − 1
Daily expectedper day
+3.12%
geometric, per day held
Capital turns/yrvelocity
×17.4
how often this slot recycles
simple APRcompounded APYyour horizon
Rank positions by APR, not raw ROI. A thin edge tomorrow beats a fat edge next year.
§5 · Costs & net edge
Net edge +0.20 pperosion 79% · break-even w/ fees 1.8%
gross edgefrictionnet edgefee 0 bps · spread 1.50¢
The number that decides whether to trade.
§6 · Sizing menu
Full Kellyf★
$240
0.96% · g = 0.343%
Half Kelly½ f★
$120
0.48% · g = 0.275%
Quarter Kelly¼ f★
$60
0.24% · g = 0.172%
Flat 1%1%
$250
1.00% · g = 0.343%
Flat 2%2%
$500
2.00% · g = 0.139%
Flat 5%5%
$1,250
5.00% · g = -1.542%
Recommended¼ f★
$60
survives model error
Quarter-Kelly is the industry default — survives model error far better than full Kelly.
§7 · Information theory
Market entropyH(p)
0.084 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.141 bit
Δ +0.057 bit vs market
Surprise · YES−log₂ p
6.57 bit
self-information
Surprise · NO−log₂(1−p)
0.02 bit
self-information
H(p) peaks at p = 0.5 (one bit of irreducible doubt).
NOISE · D_KL(q ‖ p) = 0.0034 nat (0.0050 bit)belief ≈ market — stand down
YES contributionNO contributionbelief ‖ marketnoise
Zero KL ⇒ you know nothing the crowd doesn't.
§8 · Bayesian inference
MARKET PRICE INSIDE 95% CIposterior μ 0.020 · CI [0.00, 0.30] · κ 4.4
Posterior meanE[θ]
0.020
Beta(0.1, 4.4)
95% credible intervalHDI
[0.00, 0.30]
price INSIDE → weak edge
Concentrationκ
4.4
pseudo-obs behind belief
Disagreementvs crowd
+0.0 pp
posterior − price
market prior (dashed)model posterior95% credible bandmarket price
When the market price falls outside the 95% credible interval, your disagreement is statistically meaningful.
§9 · Tail risk · Monte-Carlo (mode A · single position to resolution)
E[P/L] +90.5% · P(YES) 2.0% · VaR₉₅ 100.0%400 paths · 504 bars to resolution
Expected P/Lper $1
+90.48%
P(YES) empiricalq
2.0%
Best pathmax
+9423.8%
Worst pathmin
-100.0%
VaR 95%5%
100.0%
CVaR 95%ES
100.0%
median path25/75 + 5/95 bandsentry pricemodel q
Logit-space mean-reverting walk + terminal flip with probability q. Answers: 'what happens to THIS one position'. Distinct from the repeated-edge fan below.
§9b · Tail risk · Monte-Carlo (mode B · repeated independent edges)
Median CAGR/bet 1.13% · ruin rate 0.0%400 paths × 120 bets · f deploy 0.50%
Sharpe / betμ/σ
0.147
μ 1.35% · σ 9.2%
Sortino / betμ/σ↓
2.704
downside-only denominator
VaR 95%5%
-0.5%
per-bet worst-case
CVaR 95%ES
-0.5%
mean tail loss
Max drawdownMDD
-11.8%
Calmar 0.10
Ruin rate≤50%
0.0%
P(equity ever ≤ 50%)
median25/75 band5/95 bandruin line
Answers a different question: 'if I could find this exact edge forever, what is the bankroll trajectory'. Compounds 120 sequential resolutions which is NOT what happens to a single position.
§10 · Base-rate & macro context
ANCHORED · supported by convictionanchor gap -46.4pp · crowd gap -47.3pp
Anchor gapmodel − base
-46.4 pp
Crowd gapprice − base
-47.3 pp
Verdictdiscipline
ANCHORED
Reference-class anchoring prevents narrative-driven blowups.
§11 · Forecast quality (synthetic ledger)
SKILL POSITIVE · in-sample BSS 24.5% · AUC 0.791out-of-sample BSS (5-fold) 24.6% ± 1.7% · Brier 0.1884 · log-loss 0.5642 · n 1600✓ n = 1600
BrierBS
0.1884
lower = better · ō 0.52
BSSvs base
24.5%
improvement over base rate
ReliabilityREL
0.0023
miscalibration · want ↓
ResolutionRES
0.0640
decisiveness · want ↑
Log lossLL
0.5642
cross-entropy
AUCROC
0.791
0.5 coin · 1.0 oracle
calibration curveROCUNC (irreducible)RES (skill, ↑)REL (miscalib, ↓)
Computed on a seeded synthetic forecast ledger. Reseed (⟳) to redraw.
§12 · Journal vitals (synthetic ledger)
PROFITABLE · PF 1.02 · expectancy +0.011R180 trades · win 46.1% · Sharpe 0.009
Total P/Lnet
+$495
on $45,000 cycled
Win ratehit %
46.1%
83 W / 97 L
Profit factorPF
1.02
$ won / $ lost
Expectancyper trade
+$2.75
avg $ per position
R-expectancyper risk
+0.011R
in units of risk taken
Avg win / losspayoff
$298.14 / -$250.00
ratio 1.19 : 1
Sharpe / traderisk-adj
0.009
μR / σR
Closing line valueCLV
+2.64 pp
avg edge vs close
cumulative P/Lprofitable zonered zonesynthetic · seeded from asset
The scorecard every trader checks. Synthetic ledger seeded from the asset slug — recomputes against your real fill history once wired.