NOSTRADAMUS · Position Analytics Engine

SIMULATOR Russia x Ukraine ceasefire agreement by June 30, 2026?

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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-russia-x-ukraine-ceasefire-agreement-by-june-30-2026 page.

SIDE

MARKET PRICE (YES) 0.043

MODEL P(YES) 0.076

YOUR FILL (YES) 0.043

KELLY FRACTION 0.50

MODEL σ 0.060

BANKROLL (USDC)

DAYS TO RESOLVE

FEE (bps)

SPREAD (¢)

LIQUIDITY ($)

SCENARIO PRESET

▲ YES EDGE · +0.034 · f★ 3.5% · deploy 1.8% · net 2.64pp

§1 · Position economics

Payoff diagram · binary contract P/L vs resolution

YES · Expected P/L per share +0.0339@ model P(YES) = 0.076

P/L per sharemarket pricemodel Pprofit zoneloss zone

Profit is linear in the eventual settlement price.

Kelly growth curve · g(f) with f★ and deployed f markers

f★ = 3.54% · g(f★) = 1.149%deploy 1.77% · g = 0.913%

g(f)f★ optimumdeployed fgrowth zone

Underbet leaves growth on the table; overbet destroys capital. The interior maximum is f★.

§2 · The trade ticket

Trade ticket · dollar outcomes at this stake

YES @ 0.043 · EV +$352stake $442 · 1.77% of bankroll

Deployed stakestake

$442

1.77% of bankroll

Sharesunits

10,402

each pays $1 if YES

Max payoutwin

$10,402

gross, if win

Max profitwin

+$9,960

net of cost

Max losslose

-$442

binary settles to $0

Payout multiple×

×23.53

$1 → $23.53

Risk:RewardR:R

22.53 : 1

win $22.53 per $1

Expected P/LE[P/L]

+$352

probability-weighted

Outcome	P(model)	P/L	Contribution
Resolves YES (win)	7.6%	+$9,960	+$761
Resolves against (lose)	92.4%	-$442	-$408
Expected value	100.0%	—	+$352

What you actually win and lose. The bottom table tabulates probability-weighted P/L by outcome.

§3 · Break-even & cushion

Break-even & cushion · margin of safety

Cushion +3.4 pprelative edge +79.7%

Required win ratebreak-even

4.3%

price = implied probability

Model win rateP(win)

7.6%

what you forecast

Cushionedge

+3.4 pp

margin of safety

Fair pricemodel

0.076

where you think it should trade

The market price equals the win rate you must beat to make money.

§4 · Odds conversion

Implied probability, decimal, American, fractional

Implied probabilityP

4.3%

= price

Decimal oddsEU

23.529

total return per $1

AmericanUS

+2253

$100 wins $2253

FractionalUK

22.53 / 1

profit per $1 risked

Profit per $100stake

+$2252.94

clean dollar framing

underdog (+)favorite (-)your price

Five views of the same number.

§4b · Time & annualized return

Time & APR · capital lockup vs annualized return

APR 1385% · APY 2651098%ROI 79.7% over 21d · 17.4 turns/yr

Time to resolvehorizon

21.0 d

504h capital lockup

Raw ROIper resolve

+79.7%

APR (simple)scaled

+1385%

ROI × 365/days

APY (compounded)if redeployed

+2651098%

(1+ROI)^(365/d) − 1

Daily expectedper day

+2.83%

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

Cost waterfall · gross edge → net of friction

Net edge +2.64 pperosion 22% · break-even w/ fees 5.0%

gross edgefrictionnet edgefee 0 bps · spread 1.50¢

The number that decides whether to trade.

§6 · Sizing menu

Sizing menu · disciplined deployment

Full Kellyf★

$884

3.54% · g = 1.149%

Half Kelly½ f★

$442

1.77% · g = 0.913%

Quarter Kelly¼ f★

$221

0.88% · g = 0.567%

Flat 1%1%

$250

1.00% · g = 0.623%

Flat 2%2%

$500

2.00% · g = 0.975%

Flat 5%5%

$1,250

5.00% · g = 1.024%

Recommended¼ f★

$221

survives model error

Quarter-Kelly is the industry default — survives model error far better than full Kelly.

§7 · Information theory

Binary entropy · uncertainty in bits

Market entropyH(p)

0.254 bit

max 1.0 at p = 0.5

Your entropyH(q)

0.389 bit

Δ +0.136 bit vs market

Surprise · YES−log₂ p

4.56 bit

self-information

Surprise · NO−log₂(1−p)

0.06 bit

self-information

H(p) peaks at p = 0.5 (one bit of irreducible doubt).

KL divergence · upper bound on exploitable edge

NOISE · D_KL(q ‖ p) = 0.0115 nat (0.0166 bit)belief ≈ market — stand down

YES contributionNO contributionbelief ‖ marketnoise

Zero KL ⇒ you know nothing the crowd doesn't.

§8 · Bayesian inference

Bayesian posterior · prior + evidence → belief with 95% CI

MARKET PRICE INSIDE 95% CIposterior μ 0.076 · CI [0.01, 0.23] · κ 18.6

Posterior meanE[θ]

0.076

Beta(1.4, 17.2)

95% credible intervalHDI

[0.01, 0.23]

price INSIDE → weak edge

Concentrationκ

18.6

pseudo-obs behind belief

Disagreementvs crowd

+3.4 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)

Mark-to-market MC · single position held to resolution

E[P/L] +70.6% · P(YES) 7.2% · VaR₉₅ 100.0%400 paths · 504 bars to resolution

Expected P/Lper $1

+70.59%

P(YES) empiricalq

7.2%

Best pathmax

+2252.9%

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)

Monte-Carlo equity fan · this profile, repeated 400× independently

Median CAGR/bet 1.03% · ruin rate 8.8%400 paths × 120 bets · f deploy 1.77%

Sharpe / betμ/σ

0.139

μ 1.57% · σ 11.3%

Sortino / betμ/σ↓

0.887

downside-only denominator

VaR 95%5%

-1.8%

per-bet worst-case

CVaR 95%ES

-1.8%

mean tail loss

Max drawdownMDD

-20.7%

Calmar 0.05

Ruin rate≤50%

8.8%

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

Probability stack · base rate vs crowd vs model

ANCHORED · supported by convictionanchor gap -50.3pp · crowd gap -53.6pp

Anchor gapmodel − base

-50.3 pp

Crowd gapprice − base

-53.6 pp

Verdictdiscipline

ANCHORED

Reference-class anchoring prevents narrative-driven blowups.

§11 · Forecast quality (synthetic ledger)

Brier · Murphy decomposition · reliability · ROC

SKILL POSITIVE · in-sample BSS 17.5% · AUC 0.753out-of-sample BSS (5-fold) 17.5% ± 1.6% · Brier 0.2061 · log-loss 0.6109 · n 1600✓ n = 1600

BrierBS

0.2061

lower = better · ō 0.48

BSSvs base

17.5%

improvement over base rate

ReliabilityREL

0.0053

miscalibration · want ↓

ResolutionRES

0.0499

decisiveness · want ↑

Log lossLL

0.6109

cross-entropy

AUCROC

0.753

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)

Track record · win rate · PF · expectancy · CLV · equity curve

PROFITABLE · PF 1.31 · expectancy +0.146R180 trades · win 52.8% · Sharpe 0.096

Total P/Lnet

+$6,574

on $45,000 cycled

Win ratehit %

52.8%

95 W / 85 L

Profit factorPF

1.31

$ won / $ lost

Expectancyper trade

+$36.52

avg $ per position

R-expectancyper risk

+0.146R

in units of risk taken

Avg win / losspayoff

$292.88 / -$250.00

ratio 1.17 : 1

Sharpe / traderisk-adj

0.096

μR / σR

Closing line valueCLV

+2.81 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.