NOSTRADAMUS · Position Analytics Engine
SIMULATOR Will Billy Horschel win the RBC Canadian Open?
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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-kxpgatour-rbbcan26-bhor page.
▲ YES EDGE · +0.020 · f★ 2.1% · deploy 1.0% · net 1.28pp
§1 · Position economics
YES · Expected P/L per share +0.0203@ model P(YES) = 0.043
P/L per sharemarket pricemodel Pprofit zoneloss zone
Profit is linear in the eventual settlement price.
f★ = 2.08% · g(f★) = 0.733%deploy 1.04% · g = 0.586%
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 @ 0.023 · EV +$230stake $260 · 1.04% of bankroll
Deployed stakestake
$260
1.04% of bankroll
Sharesunits
11,314
each pays $1 if YES
Max payoutwin
$11,314
gross, if win
Max profitwin
+$11,054
net of cost
Max losslose
-$260
binary settles to $0
Payout multiple×
×43.48
$1 → $43.48
Risk:RewardR:R
42.48 : 1
win $42.48 per $1
Expected P/LE[P/L]
+$230
probability-weighted
| Outcome | P(model) | P/L | Contribution |
|---|---|---|---|
| Resolves YES (win) | 4.3% | +$11,054 | +$479 |
| Resolves against (lose) | 95.7% | -$260 | -$249 |
| Expected value | 100.0% | — | +$230 |
What you actually win and lose. The bottom table tabulates probability-weighted P/L by outcome.
§3 · Break-even & cushion
Cushion +2.0 pprelative edge +88.4%
Required win ratebreak-even
2.3%
price = implied probability
Model win rateP(win)
4.3%
what you forecast
Cushionedge
+2.0 pp
margin of safety
Fair pricemodel
0.043
where you think it should trade
The market price equals the win rate you must beat to make money.
§4 · Odds conversion
Implied probabilityP
2.3%
= price
Decimal oddsEU
43.478
total return per $1
AmericanUS
+4248
$100 wins $4248
FractionalUK
42.48 / 1
profit per $1 risked
Profit per $100stake
+$4247.83
clean dollar framing
underdog (+)favorite (-)your price
Five views of the same number.
§4b · Time & annualized return
APR 1537% · APY 6058420%ROI 88.4% over 21d · 17.4 turns/yr
Time to resolvehorizon
21.0 d
504h capital lockup
Raw ROIper resolve
+88.4%
APR (simple)scaled
+1537%
ROI × 365/days
APY (compounded)if redeployed
+6058420%
(1+ROI)^(365/d) − 1
Daily expectedper day
+3.06%
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 +1.28 pperosion 37% · break-even w/ fees 3.0%
gross edgefrictionnet edgefee 0 bps · spread 1.50¢
The number that decides whether to trade.
§6 · Sizing menu
Full Kellyf★
$520
2.08% · g = 0.733%
Half Kelly½ f★
$260
1.04% · g = 0.586%
Quarter Kelly¼ f★
$130
0.52% · g = 0.366%
Flat 1%1%
$250
1.00% · g = 0.573%
Flat 2%2%
$500
2.00% · g = 0.732%
Flat 5%5%
$1,250
5.00% · g = 0.030%
Recommended¼ f★
$130
survives model error
Quarter-Kelly is the industry default — survives model error far better than full Kelly.
§7 · Information theory
Market entropyH(p)
0.158 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.257 bit
Δ +0.099 bit vs market
Surprise · YES−log₂ p
5.44 bit
self-information
Surprise · NO−log₂(1−p)
0.03 bit
self-information
H(p) peaks at p = 0.5 (one bit of irreducible doubt).
NOISE · D_KL(q ‖ p) = 0.0073 nat (0.0106 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 0.043 · CI [0.00, 0.22] · κ 10.5
Posterior meanE[θ]
0.043
Beta(0.5, 10.1)
95% credible intervalHDI
[0.00, 0.22]
price INSIDE → weak edge
Concentrationκ
10.5
pseudo-obs behind belief
Disagreementvs crowd
+2.0 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] +41.3% · P(YES) 3.3% · VaR₉₅ 100.0%400 paths · 504 bars to resolution
Expected P/Lper $1
+41.30%
P(YES) empiricalq
3.3%
Best pathmax
+4247.8%
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 0.84% · ruin rate 2.8%400 paths × 120 bets · f deploy 1.04%
Sharpe / betμ/σ
0.135
μ 1.37% · σ 10.2%
Sortino / betμ/σ↓
1.320
downside-only denominator
VaR 95%5%
-1.0%
per-bet worst-case
CVaR 95%ES
-1.0%
mean tail loss
Max drawdownMDD
-18.9%
Calmar 0.04
Ruin rate≤50%
2.8%
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 -51.3pp · crowd gap -53.3pp
Anchor gapmodel − base
-51.3 pp
Crowd gapprice − base
-53.3 pp
Verdictdiscipline
ANCHORED
Reference-class anchoring prevents narrative-driven blowups.
§11 · Forecast quality (synthetic ledger)
SKILL POSITIVE · in-sample BSS 19.1% · AUC 0.761out-of-sample BSS (5-fold) 19.2% ± 1.8% · Brier 0.2020 · log-loss 0.6024 · n 1600✓ n = 1600
BrierBS
0.2020
lower = better · ō 0.49
BSSvs base
19.1%
improvement over base rate
ReliabilityREL
0.0047
miscalibration · want ↓
ResolutionRES
0.0516
decisiveness · want ↑
Log lossLL
0.6024
cross-entropy
AUCROC
0.761
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 +0.032R180 trades · win 52.2% · Sharpe 0.029
Total P/Lnet
+$1,455
on $45,000 cycled
Win ratehit %
52.2%
94 W / 86 L
Profit factorPF
1.07
$ won / $ lost
Expectancyper trade
+$8.08
avg $ per position
R-expectancyper risk
+0.032R
in units of risk taken
Avg win / losspayoff
$244.20 / -$250.00
ratio 0.98 : 1
Sharpe / traderisk-adj
0.029
μR / σR
Closing line valueCLV
+2.65 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.