NOSTRADAMUS · Position Analytics Engine
SIMULATOR Will Lewis Hamilton be the 2026 F1 Drivers' Champion?
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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/pm-will-lewis-hamilton-be-the-2026-f1-drivers-champion page.
▲ YES EDGE · +0.052 · f★ 6.0% · deploy 3.0% · net 4.48pp
§1 · Position economics
YES · Expected P/L per share +0.0523@ model P(YES) = 0.183
P/L per sharemarket pricemodel Pprofit zoneloss zone
Profit is linear in the eventual settlement price.
f★ = 6.02% · g(f★) = 1.090%deploy 3.01% · g = 0.842%
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 @ 0.131 · EV +$301stake $753 · 3.01% of bankroll
Deployed stakestake
$753
3.01% of bankroll
Sharesunits
5,748
each pays $1 if YES
Max payoutwin
$5,748
gross, if win
Max profitwin
+$4,995
net of cost
Max losslose
-$753
binary settles to $0
Payout multiple×
×7.63
$1 → $7.63
Risk:RewardR:R
6.63 : 1
win $6.63 per $1
Expected P/LE[P/L]
+$301
probability-weighted
| Outcome | P(model) | P/L | Contribution |
|---|---|---|---|
| Resolves YES (win) | 18.3% | +$4,995 | +$916 |
| Resolves against (lose) | 81.7% | -$753 | -$615 |
| Expected value | 100.0% | — | +$301 |
What you actually win and lose. The bottom table tabulates probability-weighted P/L by outcome.
§3 · Break-even & cushion
Cushion +5.2 pprelative edge +40.0%
Required win ratebreak-even
13.1%
price = implied probability
Model win rateP(win)
18.3%
what you forecast
Cushionedge
+5.2 pp
margin of safety
Fair pricemodel
0.183
where you think it should trade
The market price equals the win rate you must beat to make money.
§4 · Odds conversion
Implied probabilityP
13.1%
= price
Decimal oddsEU
7.634
total return per $1
AmericanUS
+663
$100 wins $663
FractionalUK
6.63 / 1
profit per $1 risked
Profit per $100stake
+$663.36
clean dollar framing
underdog (+)favorite (-)your price
Five views of the same number.
§4b · Time & annualized return
APR 695% · APY 34387%ROI 40.0% over 21d · 17.4 turns/yr
Time to resolvehorizon
21.0 d
504h capital lockup
Raw ROIper resolve
+40.0%
APR (simple)scaled
+695%
ROI × 365/days
APY (compounded)if redeployed
+34387%
(1+ROI)^(365/d) − 1
Daily expectedper day
+1.61%
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 +4.48 pperosion 14% · break-even w/ fees 13.9%
gross edgefrictionnet edgefee 0 bps · spread 1.50¢
The number that decides whether to trade.
§6 · Sizing menu
Full Kellyf★
$1,506
6.02% · g = 1.090%
Half Kelly½ f★
$753
3.01% · g = 0.842%
Quarter Kelly¼ f★
$376
1.51% · g = 0.507%
Flat 1%1%
$250
1.00% · g = 0.357%
Flat 2%2%
$500
2.00% · g = 0.634%
Flat 5%5%
$1,250
5.00% · g = 1.063%
Recommended¼ f★
$376
survives model error
Quarter-Kelly is the industry default — survives model error far better than full Kelly.
§7 · Information theory
Market entropyH(p)
0.560 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.687 bit
Δ +0.127 bit vs market
Surprise · YES−log₂ p
2.93 bit
self-information
Surprise · NO−log₂(1−p)
0.20 bit
self-information
H(p) peaks at p = 0.5 (one bit of irreducible doubt).
NOISE · D_KL(q ‖ p) = 0.0109 nat (0.0157 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 0.183 · CI [0.08, 0.31] · κ 40.6
Posterior meanE[θ]
0.183
Beta(7.4, 33.1)
95% credible intervalHDI
[0.08, 0.31]
price INSIDE → weak edge
Concentrationκ
40.6
pseudo-obs behind belief
Disagreementvs crowd
+5.2 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] +50.8% · P(YES) 19.8% · VaR₉₅ 100.0%400 paths · 504 bars to resolution
Expected P/Lper $1
+50.76%
P(YES) empiricalq
19.8%
Best pathmax
+663.4%
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 0.85% · ruin rate 10.0%400 paths × 120 bets · f deploy 3.01%
Sharpe / betμ/σ
0.136
μ 1.21% · σ 8.9%
Sortino / betμ/σ↓
0.403
downside-only denominator
VaR 95%5%
-3.0%
per-bet worst-case
CVaR 95%ES
-3.0%
mean tail loss
Max drawdownMDD
-14.2%
Calmar 0.06
Ruin rate≤50%
10.0%
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 -36.8pp · crowd gap -42.0pp
Anchor gapmodel − base
-36.8 pp
Crowd gapprice − base
-42.0 pp
Verdictdiscipline
ANCHORED
Reference-class anchoring prevents narrative-driven blowups.
§11 · Forecast quality (synthetic ledger)
SKILL POSITIVE · in-sample BSS 20.2% · AUC 0.768out-of-sample BSS (5-fold) 20.4% ± 4.2% · Brier 0.1996 · log-loss 0.6017 · n 1600✓ n = 1600
BrierBS
0.1996
lower = better · ō 0.49
BSSvs base
20.2%
improvement over base rate
ReliabilityREL
0.0048
miscalibration · want ↓
ResolutionRES
0.0542
decisiveness · want ↑
Log lossLL
0.6017
cross-entropy
AUCROC
0.768
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 1.62 · expectancy +0.262R180 trades · win 57.8% · Sharpe 0.195
Total P/Lnet
+$11,794
on $45,000 cycled
Win ratehit %
57.8%
104 W / 76 L
Profit factorPF
1.62
$ won / $ lost
Expectancyper trade
+$65.52
avg $ per position
R-expectancyper risk
+0.262R
in units of risk taken
Avg win / losspayoff
$296.09 / -$250.00
ratio 1.18 : 1
Sharpe / traderisk-adj
0.195
μR / σR
Closing line valueCLV
+3.09 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.