NOSTRADAMUS · Position Analytics Engine

SIMULATOR Will Bitcoin dip to $52,500 in June?

← Back to live dashboard Embed card OG preview Top movers Arb

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-bitcoin-dip-to-52pt5k-in-june-2026-885 page.

SIDE

MARKET PRICE (YES) 0.077

MODEL P(YES) 0.075

YOUR FILL (YES) 0.077

KELLY FRACTION 0.50

MODEL σ 0.060

BANKROLL (USDC)

DAYS TO RESOLVE

FEE (bps)

SPREAD (¢)

LIQUIDITY ($)

SCENARIO PRESET

▼ NO EDGE YES · DO NOT TRADE

§1 · Position economics

Payoff diagram · binary contract P/L vs resolution

YES · Expected P/L per share -0.0021@ model P(YES) = 0.075

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★ = 0.00% · g(f★) = 0.000%deploy 0.00% · g = 0.000%

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 @ 0.077 · EV +$0stake $0 · 0.00% of bankroll

Deployed stakestake

0.00% of bankroll

Sharesunits

each pays $1 if YES

Max payoutwin

gross, if win

Max profitwin

+$0

net of cost

Max losslose

-$0

binary settles to $0

Payout multiple×

×12.90

$1 → $12.90

Risk:RewardR:R

11.90 : 1

win $11.90 per $1

Expected P/LE[P/L]

+$0

probability-weighted

Outcome	P(model)	P/L	Contribution
Resolves YES (win)	7.5%	+$0	+$0
Resolves against (lose)	92.5%	-$0	-$0
Expected value	100.0%	—	+$0

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 -0.2 pprelative edge -2.7%

Required win ratebreak-even

7.8%

price = implied probability

Model win rateP(win)

7.5%

what you forecast

Cushionedge

-0.2 pp

margin of safety

Fair pricemodel

0.075

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

7.8%

= price

Decimal oddsEU

12.903

total return per $1

AmericanUS

+1190

$100 wins $1190

FractionalUK

11.90 / 1

profit per $1 risked

Profit per $100stake

+$1190.32

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 -47% · APY -38%ROI -2.7% over 21d · 17.4 turns/yr

Time to resolvehorizon

21.0 d

504h capital lockup

Raw ROIper resolve

-2.7%

APR (simple)scaled

-47%

ROI × 365/days

APY (compounded)if redeployed

-38%

(1+ROI)^(365/d) − 1

Daily expectedper day

-0.13%

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 -0.96 pperosion 0% · break-even w/ fees 8.5%

gross edgefrictionnet edgefee 0 bps · spread 1.50¢

The number that decides whether to trade.

§6 · Sizing menu

Sizing menu · disciplined deployment

Full Kellyf★

0.00% · g = 0.000%

Half Kelly½ f★

0.00% · g = 0.000%

Quarter Kelly¼ f★

0.00% · g = 0.000%

Flat 1%1%

$250

1.00% · g = -0.081%

Flat 2%2%

$500

2.00% · g = -0.258%

Flat 5%5%

$1,250

5.00% · g = -1.221%

Recommended¼ f★

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)

0.393 bit

max 1.0 at p = 0.5

Your entropyH(q)

0.386 bit

Δ -0.008 bit vs market

Surprise · YES−log₂ p

3.69 bit

self-information

Surprise · NO−log₂(1−p)

0.12 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) = 0.0000 nat (0.0000 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 0.075 · CI [0.00, 0.23] · κ 18.4

Posterior meanE[θ]

0.075

Beta(1.4, 17.0)

95% credible intervalHDI

[0.00, 0.23]

price INSIDE → weak edge

Concentrationκ

18.4

pseudo-obs behind belief

Disagreementvs crowd

-0.2 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] +25.8% · P(YES) 9.8% · VaR₉₅ 100.0%400 paths · 504 bars to resolution

Expected P/Lper $1

+25.81%

P(YES) empiricalq

9.8%

Best pathmax

+1190.3%

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 -0.03% · ruin rate 0.0%400 paths × 120 bets · f deploy 0.50%

Sharpe / betμ/σ

-0.001

μ -0.00% · σ 1.7%

Sortino / betμ/σ↓

-0.002

downside-only denominator

VaR 95%5%

-0.5%

per-bet worst-case

CVaR 95%ES

-0.5%

mean tail loss

Max drawdownMDD

-7.1%

Calmar -0.00

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 -39.4pp · crowd gap -39.1pp

Anchor gapmodel − base

-39.4 pp

Crowd gapprice − base

-39.1 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 19.1% · AUC 0.762out-of-sample BSS (5-fold) 19.2% ± 2.0% · Brier 0.2021 · log-loss 0.6010 · n 1600✓ n = 1600

BrierBS

0.2021

lower = better · ō 0.50

BSSvs base

19.1%

improvement over base rate

ReliabilityREL

0.0044

miscalibration · want ↓

ResolutionRES

0.0515

decisiveness · want ↑

Log lossLL

0.6010

cross-entropy

AUCROC

0.762

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 1.15 · expectancy +0.065R180 trades · win 56.7% · Sharpe 0.063

Total P/Lnet

+$2,903

on $45,000 cycled

Win ratehit %

56.7%

102 W / 78 L

Profit factorPF

1.15

$ won / $ lost

Expectancyper trade

+$16.13

avg $ per position

R-expectancyper risk

+0.065R

in units of risk taken

Avg win / losspayoff

$219.63 / -$250.00

ratio 0.88 : 1

Sharpe / traderisk-adj

0.063

μR / σR

Closing line valueCLV

+3.01 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.