NOSTRADAMUS · Position Analytics Engine
SIMULATOR Will Trump speak to Vladimir Putin in June?
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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-trump-speak-to-vladimir-putin-in-june page.
▲ YES EDGE · +0.006 · f★ 23.1% · deploy 11.5% · net -0.15pp
§1 · Position economics
YES · Expected P/L per share +0.0060@ model P(YES) = 0.980
P/L per sharemarket pricemodel Pprofit zoneloss zone
Profit is linear in the eventual settlement price.
f★ = 23.08% · g(f★) = 0.077%deploy 11.54% · g = 0.056%
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 @ 0.974 · EV +$18stake $2,885 · 11.54% of bankroll
Deployed stakestake
$2,885
11.54% of bankroll
Sharesunits
2,962
each pays $1 if YES
Max payoutwin
$2,962
gross, if win
Max profitwin
+$77
net of cost
Max losslose
-$2,885
binary settles to $0
Payout multiple×
×1.03
$1 → $1.03
Risk:RewardR:R
0.03 : 1
win $0.03 per $1
Expected P/LE[P/L]
+$18
probability-weighted
| Outcome | P(model) | P/L | Contribution |
|---|---|---|---|
| Resolves YES (win) | 98.0% | +$77 | +$75 |
| Resolves against (lose) | 2.0% | -$2,885 | -$58 |
| Expected value | 100.0% | — | +$18 |
What you actually win and lose. The bottom table tabulates probability-weighted P/L by outcome.
§3 · Break-even & cushion
Cushion +0.6 pprelative edge +0.6%
Required win ratebreak-even
97.4%
price = implied probability
Model win rateP(win)
98.0%
what you forecast
Cushionedge
+0.6 pp
margin of safety
Fair pricemodel
0.980
where you think it should trade
The market price equals the win rate you must beat to make money.
§4 · Odds conversion
Implied probabilityP
97.4%
= price
Decimal oddsEU
1.027
total return per $1
AmericanUS
-3746
risk $3746 to win $100
FractionalUK
0.03 / 1
profit per $1 risked
Profit per $100stake
+$2.67
clean dollar framing
underdog (+)favorite (-)your price
Five views of the same number.
§4b · Time & annualized return
APR 11% · APY 11%ROI 0.6% over 21d · 17.4 turns/yr
Time to resolvehorizon
21.0 d
504h capital lockup
Raw ROIper resolve
+0.6%
APR (simple)scaled
+11%
ROI × 365/days
APY (compounded)if redeployed
+11%
(1+ROI)^(365/d) − 1
Daily expectedper day
+0.03%
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 -0.15 pperosion 125% · break-even w/ fees 98.1%
gross edgefrictionnet edgefee 0 bps · spread 1.50¢
The number that decides whether to trade.
§6 · Sizing menu
Full Kellyf★
$5,769
23.08% · g = 0.077%
Half Kelly½ f★
$2,885
11.54% · g = 0.056%
Quarter Kelly¼ f★
$1,442
5.77% · g = 0.032%
Flat 1%1%
$250
1.00% · g = 0.006%
Flat 2%2%
$500
2.00% · g = 0.012%
Flat 5%5%
$1,250
5.00% · g = 0.028%
Recommended¼ f★
$1,442
survives model error
Quarter-Kelly is the industry default — survives model error far better than full Kelly.
§7 · Information theory
Market entropyH(p)
0.174 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.141 bit
Δ -0.032 bit vs market
Surprise · YES−log₂ p
0.04 bit
self-information
Surprise · NO−log₂(1−p)
5.27 bit
self-information
H(p) peaks at p = 0.5 (one bit of irreducible doubt).
NOISE · D_KL(q ‖ p) = 0.0008 nat (0.0011 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 0.980 · CI [0.70, 1.00] · κ 4.4
Posterior meanE[θ]
0.980
Beta(4.4, 0.1)
95% credible intervalHDI
[0.70, 1.00]
price INSIDE → weak edge
Concentrationκ
4.4
pseudo-obs behind belief
Disagreementvs crowd
+0.6 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] +0.4% · P(YES) 97.8% · VaR₉₅ -2.7%400 paths · 504 bars to resolution
Expected P/Lper $1
+0.36%
P(YES) empiricalq
97.8%
Best pathmax
+2.7%
Worst pathmin
-100.0%
VaR 95%5%
-2.7%
CVaR 95%ES
41.3%
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 -0.22% · ruin rate 8.0%400 paths × 120 bets · f deploy 11.54%
Sharpe / betμ/σ
-0.071
μ -0.16% · σ 2.3%
Sortino / betμ/σ↓
-0.014
downside-only denominator
VaR 95%5%
0.3%
per-bet worst-case
CVaR 95%ES
-0.2%
mean tail loss
Max drawdownMDD
-27.2%
Calmar -0.01
Ruin rate≤50%
8.0%
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 +40.9pp · crowd gap +40.3pp
Anchor gapmodel − base
+40.9 pp
Crowd gapprice − base
+40.3 pp
Verdictdiscipline
ANCHORED
Reference-class anchoring prevents narrative-driven blowups.
§11 · Forecast quality (synthetic ledger)
SKILL POSITIVE · in-sample BSS 22.1% · AUC 0.776out-of-sample BSS (5-fold) 22.3% ± 2.0% · Brier 0.1948 · log-loss 0.5833 · n 1600✓ n = 1600
BrierBS
0.1948
lower = better · ō 0.50
BSSvs base
22.1%
improvement over base rate
ReliabilityREL
0.0037
miscalibration · want ↓
ResolutionRES
0.0581
decisiveness · want ↑
Log lossLL
0.5833
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 1.12 · expectancy +0.059R180 trades · win 50.6% · Sharpe 0.040
Total P/Lnet
+$2,638
on $45,000 cycled
Win ratehit %
50.6%
91 W / 89 L
Profit factorPF
1.12
$ won / $ lost
Expectancyper trade
+$14.65
avg $ per position
R-expectancyper risk
+0.059R
in units of risk taken
Avg win / losspayoff
$273.49 / -$250.00
ratio 1.09 : 1
Sharpe / traderisk-adj
0.040
μR / σR
Closing line valueCLV
+2.78 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.