NOSTRADAMUS · Position Analytics Engine

SIMULATOR New York Y vs Toronto Winner?

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-kxmlbgame-26jun141337nyytor-nyy page.

▲ YES EDGE · +0.001 · f★ 0.1% · deploy 0.1% · net -0.69pp

§1 · Position economics

Payoff diagram · binary contract P/L vs resolution
YES · Expected P/L per share +0.0006@ model P(YES) = 0.551
-1.00-0.50+0.00+0.50+1.000.000.200.400.600.801.00price 0.550model 0.551YES 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★ = 0.13% · g(f★) = 0.000%deploy 0.07% · g = 0.000%
-2.00%-1.50%-1.00%-0.49%0.01%0%8%16%24%32%40%f★ optimumdeployfraction 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 @ 0.550 · EV +$0stake $16 · 0.07% of bankroll
Deployed stakestake
$16
0.07% of bankroll
Sharesunits
30
each pays $1 if YES
Max payoutwin
$30
gross, if win
Max profitwin
+$13
net of cost
Max losslose
-$16
binary settles to $0
Payout multiple×
×1.82
$1 → $1.82
Risk:RewardR:R
0.82 : 1
win $0.82 per $1
Expected P/LE[P/L]
+$0
probability-weighted
OutcomeP(model)P/LContribution
Resolves YES (win)55.1%+$13+$7
Resolves against (lose)44.9%-$16-$7
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 +0.1 pprelative edge +0.1%
Required win ratebreak-even
55.0%
price = implied probability
Model win rateP(win)
55.1%
what you forecast
Cushionedge
+0.1 pp
margin of safety
Fair pricemodel
0.551
where you think it should trade
-60-3003060020406080100you @ 55.0%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
55.0%
= price
Decimal oddsEU
1.818
total return per $1
AmericanUS
-122
risk $122 to win $100
FractionalUK
0.82 / 1
profit per $1 risked
Profit per $100stake
+$81.82
clean dollar framing
-1000-5000+500+1000020406080100you · 55.0%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 2% · APY 2%ROI 0.1% over 21d · 17.4 turns/yr
Time to resolvehorizon
21.0 d
504h capital lockup
Raw ROIper resolve
+0.1%
APR (simple)scaled
+2%
ROI × 365/days
APY (compounded)if redeployed
+2%
(1+ROI)^(365/d) − 1
Daily expectedper day
+0.01%
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 -0.69 pperosion 1270% · break-even w/ fees 55.8%
-1.0pp-0.7pp-0.5pp-0.3pp-0.1pp0.2pp+0.06Gross edge-0.75- ½ spread+0.00- entry fee+0.00- exit fee-0.69Net 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★
$33
0.13% · g = 0.000%
Half Kelly½ f★
$16
0.07% · g = 0.000%
Quarter Kelly¼ f★
$8
0.03% · g = 0.000%
Flat 1%1%
$250
1.00% · g = -0.003%
Flat 2%2%
$500
2.00% · g = -0.014%
Flat 5%5%
$1,250
5.00% · g = -0.098%
Recommended¼ f★
$8
survives model error
$0$369$738$1,106$1,475$33Full Kelly0.13%$16Half Kelly0.07%$8Quarter Kelly0.03%$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)
0.993 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.993 bit
Δ -0.000 bit vs market
Surprise · YES−log₂ p
0.86 bit
self-information
Surprise · NO−log₂(1−p)
1.15 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
-0.002-0.001-0.0000.0010.0010.0006YES branch-0.0006NO 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 0.551 · CI [0.43, 0.67] · κ 67.7
Posterior meanE[θ]
0.551
Beta(37.3, 30.4)
95% credible intervalHDI
[0.43, 0.67]
price INSIDE → weak edge
Concentrationκ
67.7
pseudo-obs behind belief
Disagreementvs crowd
+0.1 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] +1.4% · P(YES) 55.8% · VaR₉₅ 100.0%400 paths · 504 bars to resolution
Expected P/Lper $1
+1.36%
P(YES) empiricalq
55.8%
Best pathmax
+81.8%
Worst pathmin
-100.0%
VaR 95%5%
100.0%
CVaR 95%ES
100.0%
25¢50¢75¢100¢084168252336420504entry 55.0¢model q 55.1¢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 -0.00% · ruin rate 0.0%400 paths × 120 bets · f deploy 0.50%
Sharpe / betμ/σ
0.006
μ 0.00% · σ 0.5%
Sortino / betμ/σ↓
0.005
downside-only denominator
VaR 95%5%
-0.5%
per-bet worst-case
CVaR 95%ES
-0.5%
mean tail loss
Max drawdownMDD
-1.1%
Calmar -0.00
Ruin rate≤50%
0.0%
P(equity ever ≤ 50%)
0.87×0.93×0.98×1.04×1.09×1.14×020406080100120startruin 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 +11.4pp · crowd gap +11.3pp
0%20%40%60%80%100%Reference base rate43.7%Market price55.0%Model P(YES)55.1%
Anchor gapmodel − base
+11.4 pp
Crowd gapprice − base
+11.3 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 18.2% · AUC 0.757out-of-sample BSS (5-fold) 18.4% ± 3.4% · Brier 0.2045 · log-loss 0.6046 · n 1600n = 1600
BrierBS
0.2045
lower = better · ō 0.50
BSSvs base
18.2%
improvement over base rate
ReliabilityREL
0.0049
miscalibration · want ↓
ResolutionRES
0.0498
decisiveness · want ↑
Log lossLL
0.6046
cross-entropy
AUCROC
0.757
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 = 0.757false positive ratetrue positive rate0.0000.0750.1500.2250.3000.250UNC0.050RES0.005REL0.205BRIERcontribution
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 1.40 · expectancy +0.166R180 trades · win 58.9% · Sharpe 0.149
Total P/Lnet
+$7,489
on $45,000 cycled
Win ratehit %
58.9%
106 W / 74 L
Profit factorPF
1.40
$ won / $ lost
Expectancyper trade
+$41.61
avg $ per position
R-expectancyper risk
+0.166R
in units of risk taken
Avg win / losspayoff
$245.18 / -$250.00
ratio 0.98 : 1
Sharpe / traderisk-adj
0.149
μR / σR
Closing line valueCLV
+3.44 pp
avg edge vs close
-$659$1,650$3,958$6,267$8,57603672108144180trade #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 · kxmlbgame-26jun141337nyytor-nyy · fresh · feed 3s old
24h sparkline · 60 pts
realized vol (ann.)
125.43%
max drawdown
3.64%
sharpe
ulcer index
0.27%
RMS drawdown
pain index
0.03%
mean drawdown
mod. VaR 95%
0.13%
Cornish-Fisher
martin ratio
ret / ulcer
CDaR 95%
0.03%
cond. drawdown
gain/pain
1.14
Σgain / Σ|loss|
sterling
ret / CDaR
omega (θ=0)
1.14
upside/downside
roll spread
53.6 bps
implied (price-only)
bars used
425
store
spread
183.5 bps
24h Δ
flow lean
carry
flat
signalNEUTRALconfidence 20%
Same bundle via M2M API: /api/m2m/kalshi-kxmlbgame-26jun141337nyytor-nyy/bundle · venue execution: kalshi