NOSTRADAMUS · Position Analytics Engine

SIMULATOR Iran agrees to end enrichment of uranium by June 30?

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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-iran-agrees-to-end-enrichment-of-uranium-by-june-30 page.

SIDE

MARKET PRICE (YES) 0.450

MODEL P(YES) 0.474

YOUR FILL (YES) 0.450

KELLY FRACTION 0.50

MODEL σ 0.060

BANKROLL (USDC)

DAYS TO RESOLVE

FEE (bps)

SPREAD (¢)

LIQUIDITY ($)

SCENARIO PRESET

▲ YES EDGE · +0.024 · f★ 4.4% · deploy 2.2% · net 1.65pp

§1 · Position economics

Payoff diagram · binary contract P/L vs resolution

YES · Expected P/L per share +0.0240@ model P(YES) = 0.474

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★ = 4.37% · g(f★) = 0.116%deploy 2.18% · g = 0.087%

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 @ 0.450 · EV +$29stake $546 · 2.18% of bankroll

Deployed stakestake

$546

2.18% of bankroll

Sharesunits

1,213

each pays $1 if YES

Max payoutwin

$1,213

gross, if win

Max profitwin

+$667

net of cost

Max losslose

-$546

binary settles to $0

Payout multiple×

×2.22

$1 → $2.22

Risk:RewardR:R

1.22 : 1

win $1.22 per $1

Expected P/LE[P/L]

+$29

probability-weighted

Outcome	P(model)	P/L	Contribution
Resolves YES (win)	47.4%	+$667	+$316
Resolves against (lose)	52.6%	-$546	-$287
Expected value	100.0%	—	+$29

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 +2.4 pprelative edge +5.3%

Required win ratebreak-even

45.0%

price = implied probability

Model win rateP(win)

47.4%

what you forecast

Cushionedge

+2.4 pp

margin of safety

Fair pricemodel

0.474

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

45.0%

= price

Decimal oddsEU

2.222

total return per $1

AmericanUS

+122

$100 wins $122

FractionalUK

1.22 / 1

profit per $1 risked

Profit per $100stake

+$122.22

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 93% · APY 147%ROI 5.3% over 21d · 17.4 turns/yr

Time to resolvehorizon

21.0 d

504h capital lockup

Raw ROIper resolve

+5.3%

APR (simple)scaled

+93%

ROI × 365/days

APY (compounded)if redeployed

+147%

(1+ROI)^(365/d) − 1

Daily expectedper day

+0.25%

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 +1.65 pperosion 31% · break-even w/ fees 45.8%

gross edgefrictionnet edgefee 0 bps · spread 1.50¢

The number that decides whether to trade.

§6 · Sizing menu

Sizing menu · disciplined deployment

Full Kellyf★

$1,091

4.37% · g = 0.116%

Half Kelly½ f★

$546

2.18% · g = 0.087%

Quarter Kelly¼ f★

$273

1.09% · g = 0.051%

Flat 1%1%

$250

1.00% · g = 0.047%

Flat 2%2%

$500

2.00% · g = 0.082%

Flat 5%5%

$1,250

5.00% · g = 0.114%

Recommended¼ f★

$273

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)

0.993 bit

max 1.0 at p = 0.5

Your entropyH(q)

0.998 bit

Δ +0.005 bit vs market

Surprise · YES−log₂ p

1.15 bit

self-information

Surprise · NO−log₂(1−p)

0.86 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) = 0.0012 nat (0.0017 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 0.474 · CI [0.36, 0.59] · κ 68.3

Posterior meanE[θ]

0.474

Beta(32.4, 35.9)

95% credible intervalHDI

[0.36, 0.59]

price INSIDE → weak edge

Concentrationκ

68.3

pseudo-obs behind belief

Disagreementvs crowd

+2.4 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] +6.1% · P(YES) 47.8% · VaR₉₅ 100.0%400 paths · 504 bars to resolution

Expected P/Lper $1

+6.11%

P(YES) empiricalq

47.8%

Best pathmax

+122.2%

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 0.09% · ruin rate 0.0%400 paths × 120 bets · f deploy 2.18%

Sharpe / betμ/σ

0.043

μ 0.10% · σ 2.4%

Sortino / betμ/σ↓

0.048

downside-only denominator

VaR 95%5%

-2.2%

per-bet worst-case

CVaR 95%ES

-2.2%

mean tail loss

Max drawdownMDD

-4.8%

Calmar 0.02

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

Probability stack · base rate vs crowd vs model

ANCHORED · supported by convictionanchor gap -9.5pp · crowd gap -11.9pp

Anchor gapmodel − base

-9.5 pp

Crowd gapprice − base

-11.9 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 17.3% · AUC 0.753out-of-sample BSS (5-fold) 17.6% ± 1.4% · Brier 0.2067 · log-loss 0.6179 · n 1600✓ n = 1600

BrierBS

0.2067

lower = better · ō 0.49

BSSvs base

17.3%

improvement over base rate

ReliabilityREL

0.0059

miscalibration · want ↓

ResolutionRES

0.0494

decisiveness · want ↑

Log lossLL

0.6179

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

BLEEDING · PF 0.94 · expectancy -0.030R180 trades · win 50.0% · Sharpe -0.028

Total P/Lnet

-$1,364

on $45,000 cycled

Win ratehit %

50.0%

90 W / 90 L

Profit factorPF

0.94

$ won / $ lost

Expectancyper trade

-$7.58

avg $ per position

R-expectancyper risk

-0.030R

in units of risk taken

Avg win / losspayoff

$234.84 / -$250.00

ratio 0.94 : 1

Sharpe / traderisk-adj

-0.028

μR / σR

Closing line valueCLV

+3.16 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.