NOSTRADAMUS · Position Analytics Engine
SIMULATOR India vs Pakistan Winner?
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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/kalshi-kxwt20match-26jun140930pakind-pak page.
▲ YES EDGE · +0.025 · f★ 2.5% · deploy 1.3% · net 1.75pp
§1 · Position economics
YES · Expected P/L per share +0.0250@ model P(YES) = 0.035
P/L per sharemarket pricemodel Pprofit zoneloss zone
Profit is linear in the eventual settlement price.
f★ = 2.52% · g(f★) = 1.913%deploy 1.26% · g = 1.609%
g(f)f★ optimumdeployed fgrowth zone
Underbet leaves growth on the table; overbet destroys capital. The interior maximum is f★.
§2 · The trade ticket
YES @ 0.010 · EV +$787stake $315 · 1.26% of bankroll
Deployed stakestake
$315
1.26% of bankroll
Sharesunits
31,530
each pays $1 if YES
Max payoutwin
$31,530
gross, if win
Max profitwin
+$31,214
net of cost
Max losslose
-$315
binary settles to $0
Payout multiple×
×100.00
$1 → $100.00
Risk:RewardR:R
99.00 : 1
win $99.00 per $1
Expected P/LE[P/L]
+$787
probability-weighted
| Outcome | P(model) | P/L | Contribution |
|---|---|---|---|
| Resolves YES (win) | 3.5% | +$31,214 | +$1,092 |
| Resolves against (lose) | 96.5% | -$315 | -$304 |
| Expected value | 100.0% | — | +$787 |
What you actually win and lose. The bottom table tabulates probability-weighted P/L by outcome.
§3 · Break-even & cushion
Cushion +2.5 pprelative edge +249.7%
Required win ratebreak-even
1.0%
price = implied probability
Model win rateP(win)
3.5%
what you forecast
Cushionedge
+2.5 pp
margin of safety
Fair pricemodel
0.035
where you think it should trade
The market price equals the win rate you must beat to make money.
§4 · Odds conversion
Implied probabilityP
1.0%
= price
Decimal oddsEU
100.000
total return per $1
AmericanUS
+9900
$100 wins $9900
FractionalUK
99.00 / 1
profit per $1 risked
Profit per $100stake
+$9900.00
clean dollar framing
underdog (+)favorite (-)your price
Five views of the same number.
§4b · Time & annualized return
APR 4340% · APY 282014497134%ROI 249.7% over 21d · 17.4 turns/yr
Time to resolvehorizon
21.0 d
504h capital lockup
Raw ROIper resolve
+249.7%
APR (simple)scaled
+4340%
ROI × 365/days
APY (compounded)if redeployed
+282014497134%
(1+ROI)^(365/d) − 1
Daily expectedper day
+6.14%
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
Net edge +1.75 pperosion 30% · break-even w/ fees 1.8%
gross edgefrictionnet edgefee 0 bps · spread 1.50¢
The number that decides whether to trade.
§6 · Sizing menu
Full Kellyf★
$631
2.52% · g = 1.913%
Half Kelly½ f★
$315
1.26% · g = 1.609%
Quarter Kelly¼ f★
$158
0.63% · g = 1.086%
Flat 1%1%
$250
1.00% · g = 1.437%
Flat 2%2%
$500
2.00% · g = 1.869%
Flat 5%5%
$1,250
5.00% · g = 1.287%
Recommended¼ f★
$158
survives model error
Quarter-Kelly is the industry default — survives model error far better than full Kelly.
§7 · Information theory
Market entropyH(p)
0.081 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.219 bit
Δ +0.138 bit vs market
Surprise · YES−log₂ p
6.64 bit
self-information
Surprise · NO−log₂(1−p)
0.01 bit
self-information
H(p) peaks at p = 0.5 (one bit of irreducible doubt).
NOISE · D_KL(q ‖ p) = 0.0191 nat (0.0276 bit)belief ≈ market — stand down
YES contributionNO contributionbelief ‖ marketnoise
Zero KL ⇒ you know nothing the crowd doesn't.
§8 · Bayesian inference
MARKET PRICE INSIDE 95% CIposterior μ 0.035 · CI [0.00, 0.23] · κ 8.4
Posterior meanE[θ]
0.035
Beta(0.3, 8.1)
95% credible intervalHDI
[0.00, 0.23]
price INSIDE → weak edge
Concentrationκ
8.4
pseudo-obs behind belief
Disagreementvs crowd
+1.5 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)
E[P/L] +250.0% · P(YES) 3.5% · VaR₉₅ 100.0%400 paths · 504 bars to resolution
Expected P/Lper $1
+250.00%
P(YES) empiricalq
3.5%
Best pathmax
+9900.0%
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)
Median CAGR/bet 2.89% · ruin rate 8.3%400 paths × 120 bets · f deploy 1.26%
Sharpe / betμ/σ
0.178
μ 4.82% · σ 27.0%
Sortino / betμ/σ↓
3.821
downside-only denominator
VaR 95%5%
-1.3%
per-bet worst-case
CVaR 95%ES
-1.3%
mean tail loss
Max drawdownMDD
-22.4%
Calmar 0.13
Ruin rate≤50%
8.3%
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
ANCHORED · supported by convictionanchor gap -47.9pp · crowd gap -50.4pp
Anchor gapmodel − base
-47.9 pp
Crowd gapprice − base
-50.4 pp
Verdictdiscipline
ANCHORED
Reference-class anchoring prevents narrative-driven blowups.
§11 · Forecast quality (synthetic ledger)
SKILL POSITIVE · in-sample BSS 21.7% · AUC 0.774out-of-sample BSS (5-fold) 21.7% ± 2.3% · Brier 0.1958 · log-loss 0.5835 · n 1600✓ n = 1600
BrierBS
0.1958
lower = better · ō 0.50
BSSvs base
21.7%
improvement over base rate
ReliabilityREL
0.0030
miscalibration · want ↓
ResolutionRES
0.0585
decisiveness · want ↑
Log lossLL
0.5835
cross-entropy
AUCROC
0.774
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)
PROFITABLE · PF 1.07 · expectancy +0.034R180 trades · win 53.9% · Sharpe 0.030
Total P/Lnet
+$1,512
on $45,000 cycled
Win ratehit %
53.9%
97 W / 83 L
Profit factorPF
1.07
$ won / $ lost
Expectancyper trade
+$8.40
avg $ per position
R-expectancyper risk
+0.034R
in units of risk taken
Avg win / losspayoff
$229.51 / -$250.00
ratio 0.92 : 1
Sharpe / traderisk-adj
0.030
μR / σR
Closing line valueCLV
+2.82 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.