NOSTRADAMUS · Position Analytics Engine
SIMULATOR Will Tessa Johanna Brockmann win the Brockmann vs Parks: Qualification Round 1 match?
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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-kxwtamatch-26jun13bropar-bro page.
▲ YES EDGE · +0.001 · f★ 0.5% · deploy 0.3% · net -0.64pp
§1 · Position economics
YES · Expected P/L per share +0.0011@ model P(YES) = 0.791
P/L per sharemarket pricemodel Pprofit zoneloss zone
Profit is linear in the eventual settlement price.
f★ = 0.50% · g(f★) = 0.000%deploy 0.25% · 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
YES @ 0.790 · EV +$0stake $63 · 0.25% of bankroll
Deployed stakestake
$63
0.25% of bankroll
Sharesunits
80
each pays $1 if YES
Max payoutwin
$80
gross, if win
Max profitwin
+$17
net of cost
Max losslose
-$63
binary settles to $0
Payout multiple×
×1.27
$1 → $1.27
Risk:RewardR:R
0.27 : 1
win $0.27 per $1
Expected P/LE[P/L]
+$0
probability-weighted
| Outcome | P(model) | P/L | Contribution |
|---|---|---|---|
| Resolves YES (win) | 79.1% | +$17 | +$13 |
| Resolves against (lose) | 20.9% | -$63 | -$13 |
| 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
Cushion +0.1 pprelative edge +0.1%
Required win ratebreak-even
79.0%
price = implied probability
Model win rateP(win)
79.1%
what you forecast
Cushionedge
+0.1 pp
margin of safety
Fair pricemodel
0.791
where you think it should trade
The market price equals the win rate you must beat to make money.
§4 · Odds conversion
Implied probabilityP
79.0%
= price
Decimal oddsEU
1.266
total return per $1
AmericanUS
-376
risk $376 to win $100
FractionalUK
0.27 / 1
profit per $1 risked
Profit per $100stake
+$26.58
clean dollar framing
underdog (+)favorite (-)your price
Five views of the same number.
§4b · Time & 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
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 -0.64 pperosion 709% · break-even w/ fees 79.8%
gross edgefrictionnet edgefee 0 bps · spread 1.50¢
The number that decides whether to trade.
§6 · Sizing menu
Full Kellyf★
$126
0.50% · g = 0.000%
Half Kelly½ f★
$63
0.25% · g = 0.000%
Quarter Kelly¼ f★
$31
0.13% · g = 0.000%
Flat 1%1%
$250
1.00% · g = 0.000%
Flat 2%2%
$500
2.00% · g = -0.003%
Flat 5%5%
$1,250
5.00% · g = -0.027%
Recommended¼ f★
$31
survives model error
Quarter-Kelly is the industry default — survives model error far better than full Kelly.
§7 · Information theory
Market entropyH(p)
0.741 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.739 bit
Δ -0.002 bit vs market
Surprise · YES−log₂ p
0.34 bit
self-information
Surprise · NO−log₂(1−p)
2.25 bit
self-information
H(p) peaks at p = 0.5 (one bit of irreducible doubt).
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
MARKET PRICE INSIDE 95% CIposterior μ 0.791 · CI [0.66, 0.90] · κ 44.9
Posterior meanE[θ]
0.791
Beta(35.5, 9.4)
95% credible intervalHDI
[0.66, 0.90]
price INSIDE → weak edge
Concentrationκ
44.9
pseudo-obs behind belief
Disagreementvs crowd
+0.1 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] +0.6% · P(YES) 79.5% · VaR₉₅ 100.0%400 paths · 504 bars to resolution
Expected P/Lper $1
+0.63%
P(YES) empiricalq
79.5%
Best pathmax
+26.6%
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 0.00% · ruin rate 0.0%400 paths × 120 bets · f deploy 0.50%
Sharpe / betμ/σ
0.006
μ 0.00% · σ 0.3%
Sortino / betμ/σ↓
0.003
downside-only denominator
VaR 95%5%
-0.5%
per-bet worst-case
CVaR 95%ES
-0.5%
mean tail loss
Max drawdownMDD
-0.8%
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
ANCHORED · supported by convictionanchor gap +24.1pp · crowd gap +24.0pp
Anchor gapmodel − base
+24.1 pp
Crowd gapprice − base
+24.0 pp
Verdictdiscipline
ANCHORED
Reference-class anchoring prevents narrative-driven blowups.
§11 · Forecast quality (synthetic ledger)
SKILL POSITIVE · in-sample BSS 21.8% · AUC 0.776out-of-sample BSS (5-fold) 21.8% ± 2.0% · Brier 0.1953 · log-loss 0.5856 · n 1600✓ n = 1600
BrierBS
0.1953
lower = better · ō 0.48
BSSvs base
21.8%
improvement over base rate
ReliabilityREL
0.0033
miscalibration · want ↓
ResolutionRES
0.0571
decisiveness · want ↑
Log lossLL
0.5856
cross-entropy
AUCROC
0.776
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 1.19 · expectancy +0.090R180 trades · win 52.8% · Sharpe 0.074
Total P/Lnet
+$4,029
on $45,000 cycled
Win ratehit %
52.8%
95 W / 85 L
Profit factorPF
1.19
$ won / $ lost
Expectancyper trade
+$22.38
avg $ per position
R-expectancyper risk
+0.090R
in units of risk taken
Avg win / losspayoff
$266.09 / -$250.00
ratio 1.06 : 1
Sharpe / traderisk-adj
0.074
μR / σR
Closing line valueCLV
+2.08 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.