NOSTRADAMUS · Position Analytics Engine
SIMULATOR Will Elon Musk post <40 tweets from June 13 to June 15, 2026?
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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-elon-musk-of-tweets-june-13-june-15-0-39 page.
▲ YES EDGE · +0.029 · f★ 11.8% · deploy 5.9% · net 2.15pp
§1 · Position economics
YES · Expected P/L per share +0.0290@ model P(YES) = 0.784
P/L per sharemarket pricemodel Pprofit zoneloss zone
Profit is linear in the eventual settlement price.
f★ = 11.83% · g(f★) = 0.234%deploy 5.91% · g = 0.173%
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 @ 0.755 · EV +$57stake $1,479 · 5.91% of bankroll
Deployed stakestake
$1,479
5.91% of bankroll
Sharesunits
1,958
each pays $1 if YES
Max payoutwin
$1,958
gross, if win
Max profitwin
+$480
net of cost
Max losslose
-$1,479
binary settles to $0
Payout multiple×
×1.32
$1 → $1.32
Risk:RewardR:R
0.32 : 1
win $0.32 per $1
Expected P/LE[P/L]
+$57
probability-weighted
| Outcome | P(model) | P/L | Contribution |
|---|---|---|---|
| Resolves YES (win) | 78.4% | +$480 | +$376 |
| Resolves against (lose) | 21.6% | -$1,479 | -$319 |
| Expected value | 100.0% | — | +$57 |
What you actually win and lose. The bottom table tabulates probability-weighted P/L by outcome.
§3 · Break-even & cushion
Cushion +2.9 pprelative edge +3.8%
Required win ratebreak-even
75.5%
price = implied probability
Model win rateP(win)
78.4%
what you forecast
Cushionedge
+2.9 pp
margin of safety
Fair pricemodel
0.784
where you think it should trade
The market price equals the win rate you must beat to make money.
§4 · Odds conversion
Implied probabilityP
75.5%
= price
Decimal oddsEU
1.325
total return per $1
AmericanUS
-308
risk $308 to win $100
FractionalUK
0.32 / 1
profit per $1 risked
Profit per $100stake
+$32.45
clean dollar framing
underdog (+)favorite (-)your price
Five views of the same number.
§4b · Time & annualized return
APR 67% · APY 92%ROI 3.8% over 21d · 17.4 turns/yr
Time to resolvehorizon
21.0 d
504h capital lockup
Raw ROIper resolve
+3.8%
APR (simple)scaled
+67%
ROI × 365/days
APY (compounded)if redeployed
+92%
(1+ROI)^(365/d) − 1
Daily expectedper day
+0.18%
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 +2.15 pperosion 26% · break-even w/ fees 76.3%
gross edgefrictionnet edgefee 0 bps · spread 1.50¢
The number that decides whether to trade.
§6 · Sizing menu
Full Kellyf★
$2,957
11.83% · g = 0.234%
Half Kelly½ f★
$1,479
5.91% · g = 0.173%
Quarter Kelly¼ f★
$739
2.96% · g = 0.100%
Flat 1%1%
$250
1.00% · g = 0.037%
Flat 2%2%
$500
2.00% · g = 0.071%
Flat 5%5%
$1,250
5.00% · g = 0.154%
Recommended¼ f★
$739
survives model error
Quarter-Kelly is the industry default — survives model error far better than full Kelly.
§7 · Information theory
Market entropyH(p)
0.803 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.753 bit
Δ -0.050 bit vs market
Surprise · YES−log₂ p
0.41 bit
self-information
Surprise · NO−log₂(1−p)
2.03 bit
self-information
H(p) peaks at p = 0.5 (one bit of irreducible doubt).
NOISE · D_KL(q ‖ p) = 0.0023 nat (0.0034 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 0.784 · CI [0.66, 0.89] · κ 46.0
Posterior meanE[θ]
0.784
Beta(36.1, 9.9)
95% credible intervalHDI
[0.66, 0.89]
price INSIDE → weak edge
Concentrationκ
46.0
pseudo-obs behind belief
Disagreementvs crowd
+2.9 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] +2.6% · P(YES) 77.5% · VaR₉₅ 100.0%400 paths · 504 bars to resolution
Expected P/Lper $1
+2.65%
P(YES) empiricalq
77.5%
Best pathmax
+32.5%
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 0.17% · ruin rate 1.8%400 paths × 120 bets · f deploy 5.91%
Sharpe / betμ/σ
0.067
μ 0.22% · σ 3.2%
Sortino / betμ/σ↓
0.036
downside-only denominator
VaR 95%5%
-5.9%
per-bet worst-case
CVaR 95%ES
-5.9%
mean tail loss
Max drawdownMDD
-8.0%
Calmar 0.02
Ruin rate≤50%
1.8%
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 +26.0pp · crowd gap +23.1pp
Anchor gapmodel − base
+26.0 pp
Crowd gapprice − base
+23.1 pp
Verdictdiscipline
ANCHORED
Reference-class anchoring prevents narrative-driven blowups.
§11 · Forecast quality (synthetic ledger)
SKILL POSITIVE · in-sample BSS 19.6% · AUC 0.764out-of-sample BSS (5-fold) 19.6% ± 2.1% · Brier 0.2010 · log-loss 0.6005 · n 1600✓ n = 1600
BrierBS
0.2010
lower = better · ō 0.49
BSSvs base
19.6%
improvement over base rate
ReliabilityREL
0.0049
miscalibration · want ↓
ResolutionRES
0.0524
decisiveness · want ↑
Log lossLL
0.6005
cross-entropy
AUCROC
0.764
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 1.32 · expectancy +0.139R180 trades · win 56.7% · Sharpe 0.121
Total P/Lnet
+$6,235
on $45,000 cycled
Win ratehit %
56.7%
102 W / 78 L
Profit factorPF
1.32
$ won / $ lost
Expectancyper trade
+$34.64
avg $ per position
R-expectancyper risk
+0.139R
in units of risk taken
Avg win / losspayoff
$252.31 / -$250.00
ratio 1.01 : 1
Sharpe / traderisk-adj
0.121
μR / σR
Closing line valueCLV
+2.26 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.