NOSTRADAMUS · Position Analytics Engine

SIMULATOR Will Reza Pahlavi visit Iran before Jan 1, 2027?

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/kalshi-kxpahlavivisita-27jan01 page.

▼ NO EDGE YES · DO NOT TRADE

§1 · Position economics

Payoff diagram · binary contract P/L vs resolution
YES · Expected P/L per share +0.0000@ model P(YES) = 0.091
-1.00-0.50+0.00+0.50+1.000.000.200.400.600.801.00price 0.091model 0.091YES resolution priceP/L per $1 contract
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%
-2.00%-1.50%-1.00%-0.49%0.01%0%8%16%24%32%40%fraction of bankroll fexpected log-growth g(f)
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 @ 0.091 · EV +$0stake $0 · 0.00% of bankroll
Deployed stakestake
$0
0.00% of bankroll
Sharesunits
0
each pays $1 if YES
Max payoutwin
$0
gross, if win
Max profitwin
+$0
net of cost
Max losslose
-$0
binary settles to $0
Payout multiple×
×10.99
$1 → $10.99
Risk:RewardR:R
9.99 : 1
win $9.99 per $1
Expected P/LE[P/L]
+$0
probability-weighted
OutcomeP(model)P/LContribution
Resolves YES (win)9.1%+$0+$0
Resolves against (lose)90.9%-$0-$0
Expected value100.0%+$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 +0.0 pprelative edge +0.0%
Required win ratebreak-even
9.1%
price = implied probability
Model win rateP(win)
9.1%
what you forecast
Cushionedge
+0.0 pp
margin of safety
Fair pricemodel
0.091
where you think it should trade
-60-3003060020406080100you @ 9.1%market price (%)cushion (pp)
The market price equals the win rate you must beat to make money.

§4 · Odds conversion

Implied probability, decimal, American, fractional
Implied probabilityP
9.1%
= price
Decimal oddsEU
10.989
total return per $1
AmericanUS
+999
$100 wins $999
FractionalUK
9.99 / 1
profit per $1 risked
Profit per $100stake
+$998.90
clean dollar framing
-1000-5000+500+1000020406080100you · 9.1%implied probability (%)American odds
underdog (+)favorite (-)your price
Five views of the same number.

§4b · Time & annualized return

Time & APR · capital lockup vs annualized return
APR 0% · APY 0%ROI 0.0% over 21d · 17.4 turns/yr
Time to resolvehorizon
21.0 d
504h capital lockup
Raw ROIper resolve
+0.0%
APR (simple)scaled
+0%
ROI × 365/days
APY (compounded)if redeployed
+0%
(1+ROI)^(365/d) − 1
Daily expectedper day
+0.00%
geometric, per day held
Capital turns/yrvelocity
×17.4
how often this slot recycles
0%11%22%33%44%55%121416180100120now 21ddays to resolutionannualized return (capped 1000%)
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 -0.75 pperosion 0% · break-even w/ fees 9.8%
-1.0pp-0.8pp-0.6pp-0.4pp-0.1pp0.1pp+0.00Gross edge-0.75- ½ spread+0.00- entry fee+0.00- exit fee-0.75Net edgeEV / share (pp)
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
0.00% · g = 0.000%
Half Kelly½ f★
$0
0.00% · g = 0.000%
Quarter Kelly¼ f★
$0
0.00% · g = 0.000%
Flat 1%1%
$250
1.00% · g = -0.047%
Flat 2%2%
$500
2.00% · g = -0.179%
Flat 5%5%
$1,250
5.00% · g = -0.976%
Recommended¼ f★
$0
survives model error
$0$369$738$1,106$1,475$0Full Kelly0.00%$0Half Kelly0.00%$0Quarter Kelly0.00%$250Flat 1%1.00%$500Flat 2%2.00%$1,250Flat 5%5.00%
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)
0.440 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.440 bit
Δ +0.000 bit vs market
Surprise · YES−log₂ p
3.46 bit
self-information
Surprise · NO−log₂(1−p)
0.14 bit
self-information
0.000.260.530.791.050.00.20.40.60.81.0marketmodelprobabilityH (bits)
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
-0.001-0.0000.0000.0010.0010.0000YES branch0.0000NO branchΣKL = 0.0000 natKL contribution (nat)
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 0.091 · CI [0.01, 0.24] · κ 22.0
Posterior meanE[θ]
0.091
Beta(2.0, 20.0)
95% credible intervalHDI
[0.01, 0.24]
price INSIDE → weak edge
Concentrationκ
22.0
pseudo-obs behind belief
Disagreementvs crowd
+0.0 pp
posterior − price
0.000.200.400.600.801.00marketposterior μprobability θposterior density
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] -12.1% · P(YES) 8.0% · VaR₉₅ 100.0%400 paths · 504 bars to resolution
Expected P/Lper $1
-12.09%
P(YES) empiricalq
8.0%
Best pathmax
+998.9%
Worst pathmin
-100.0%
VaR 95%5%
100.0%
CVaR 95%ES
100.0%
25¢50¢75¢100¢084168252336420504entry 9.1¢model q 9.1¢bars until resolutionprice path
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 -0.01% · ruin rate 0.0%400 paths × 120 bets · f deploy 0.50%
Sharpe / betμ/σ
0.005
μ 0.01% · σ 1.6%
Sortino / betμ/σ↓
0.016
downside-only denominator
VaR 95%5%
-0.5%
per-bet worst-case
CVaR 95%ES
-0.5%
mean tail loss
Max drawdownMDD
-6.2%
Calmar -0.00
Ruin rate≤50%
0.0%
P(equity ever ≤ 50%)
0.72×0.86×1.01×1.15×1.29×1.43×020406080100120startruin 50%bet #bankroll multiple
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 -42.7pp · crowd gap -42.7pp
0%20%40%60%80%100%Reference base rate51.8%Market price9.1%Model P(YES)9.1%
Anchor gapmodel − base
-42.7 pp
Crowd gapprice − base
-42.7 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 19.2% · AUC 0.763out-of-sample BSS (5-fold) 19.2% ± 2.1% · Brier 0.2020 · log-loss 0.6039 · n 1600n = 1600
BrierBS
0.2020
lower = better · ō 0.51
BSSvs base
19.2%
improvement over base rate
ReliabilityREL
0.0049
miscalibration · want ↓
ResolutionRES
0.0528
decisiveness · want ↑
Log lossLL
0.6039
cross-entropy
AUCROC
0.763
0.5 coin · 1.0 oracle
0.00.20.40.60.81.00.00.20.40.60.81.0stated probability fobserved frequency ō0.00.20.40.60.81.00.00.20.40.60.81.0AUC = 0.763false positive ratetrue positive rate0.0000.0750.1500.2250.3000.250UNC0.053RES0.005REL0.202BRIERcontribution
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 0.98 · expectancy -0.010R180 trades · win 49.4% · Sharpe -0.009
Total P/Lnet
-$460
on $45,000 cycled
Win ratehit %
49.4%
89 W / 91 L
Profit factorPF
0.98
$ won / $ lost
Expectancyper trade
-$2.56
avg $ per position
R-expectancyper risk
-0.010R
in units of risk taken
Avg win / losspayoff
$250.45 / -$250.00
ratio 1.00 : 1
Sharpe / traderisk-adj
-0.009
μR / σR
Closing line valueCLV
+2.64 pp
avg edge vs close
-$2,012-$1,011-$10$990$1,99103672108144180trade #cumulative P/L (USD)
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.

▸ Advanced metrics · M2M bundle

kalshi · kxpahlavivisita-27jan01 · fresh · feed 22s old
24h sparkline · 60 pts -8.08%
realized vol (ann.)
68.96%
max drawdown
17.27%
sharpe
ulcer index
11.71%
RMS drawdown
pain index
8.81%
mean drawdown
mod. VaR 95%
0.00%
Cornish-Fisher
martin ratio
ret / ulcer
CDaR 95%
17.27%
cond. drawdown
gain/pain
0.83
Σgain / Σ|loss|
sterling
ret / CDaR
omega (θ=0)
0.83
upside/downside
roll spread
2.1 bps
implied (price-only)
bars used
804
store
spread
733.0 bps
24h Δ
-8.08%
flow lean
carry
flat
signalNEUTRALconfidence 25%
  • 24h change -8.08%
Same bundle via M2M API: /api/m2m/kalshi-kxpahlavivisita-27jan01/bundle · venue execution: kalshi