NOSTRADAMUS · Position Analytics Engine
SIMULATOR Will OpenAI IPO by June 30 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-will-openai-ipo-by-june-30-2026 page.
▲ YES EDGE · +0.022 · f★ 2.3% · deploy 1.1% · net 1.50pp
§1 · Position economics
YES · Expected P/L per share +0.0225@ model P(YES) = 0.026
P/L per sharemarket pricemodel Pprofit zoneloss zone
Profit is linear in the eventual settlement price.
f★ = 2.25% · g(f★) = 2.778%deploy 1.13% · g = 2.433%
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 @ 0.004 · EV +$1,582stake $282 · 1.13% of bankroll
Deployed stakestake
$282
1.13% of bankroll
Sharesunits
70,450
each pays $1 if YES
Max payoutwin
$70,450
gross, if win
Max profitwin
+$70,168
net of cost
Max losslose
-$282
binary settles to $0
Payout multiple×
×250.00
$1 → $250.00
Risk:RewardR:R
249.00 : 1
win $249.00 per $1
Expected P/LE[P/L]
+$1,582
probability-weighted
| Outcome | P(model) | P/L | Contribution |
|---|---|---|---|
| Resolves YES (win) | 2.6% | +$70,168 | +$1,856 |
| Resolves against (lose) | 97.4% | -$282 | -$274 |
| Expected value | 100.0% | — | +$1,582 |
What you actually win and lose. The bottom table tabulates probability-weighted P/L by outcome.
§3 · Break-even & cushion
Cushion +2.2 pprelative edge +561.3%
Required win ratebreak-even
0.4%
price = implied probability
Model win rateP(win)
2.6%
what you forecast
Cushionedge
+2.2 pp
margin of safety
Fair pricemodel
0.026
where you think it should trade
The market price equals the win rate you must beat to make money.
§4 · Odds conversion
Implied probabilityP
0.4%
= price
Decimal oddsEU
250.000
total return per $1
AmericanUS
+24900
$100 wins $24900
FractionalUK
249.00 / 1
profit per $1 risked
Profit per $100stake
+$24900.00
clean dollar framing
underdog (+)favorite (-)your price
Five views of the same number.
§4b · Time & annualized return
APR 9757% · APY 18189739663194156%ROI 561.3% over 21d · 17.4 turns/yr
Time to resolvehorizon
21.0 d
504h capital lockup
Raw ROIper resolve
+561.3%
APR (simple)scaled
+9757%
ROI × 365/days
APY (compounded)if redeployed
+18189739663194156%
(1+ROI)^(365/d) − 1
Daily expectedper day
+9.41%
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 +1.50 pperosion 33% · break-even w/ fees 1.1%
gross edgefrictionnet edgefee 0 bps · spread 1.50¢
The number that decides whether to trade.
§6 · Sizing menu
Full Kellyf★
$564
2.25% · g = 2.778%
Half Kelly½ f★
$282
1.13% · g = 2.433%
Quarter Kelly¼ f★
$141
0.56% · g = 1.769%
Flat 1%1%
$250
1.00% · g = 2.328%
Flat 2%2%
$500
2.00% · g = 2.764%
Flat 5%5%
$1,250
5.00% · g = 1.882%
Recommended¼ f★
$141
survives model error
Quarter-Kelly is the industry default — survives model error far better than full Kelly.
§7 · Information theory
Market entropyH(p)
0.038 bit
max 1.0 at p = 0.5
Your entropyH(q)
0.176 bit
Δ +0.139 bit vs market
Surprise · YES−log₂ p
7.97 bit
self-information
Surprise · NO−log₂(1−p)
0.01 bit
self-information
H(p) peaks at p = 0.5 (one bit of irreducible doubt).
SIGNAL · D_KL(q ‖ p) = 0.0278 nat (0.0401 bit)exploitable edge present
YES contributionNO contributionbelief ‖ marketsignal
Zero KL ⇒ you know nothing the crowd doesn't.
§8 · Bayesian inference
MARKET PRICE INSIDE 95% CIposterior μ 0.026 · CI [0.00, 0.25] · κ 6.2
Posterior meanE[θ]
0.026
Beta(0.2, 6.0)
95% credible intervalHDI
[0.00, 0.25]
price INSIDE → weak edge
Concentrationκ
6.2
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] +337.5% · P(YES) 1.8% · VaR₉₅ 100.0%400 paths · 504 bars to resolution
Expected P/Lper $1
+337.50%
P(YES) empiricalq
1.8%
Best pathmax
+24900.0%
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 4.59% · ruin rate 6.5%400 paths × 120 bets · f deploy 1.13%
Sharpe / betμ/σ
0.192
μ 10.97% · σ 57.1%
Sortino / betμ/σ↓
9.734
downside-only denominator
VaR 95%5%
-1.1%
per-bet worst-case
CVaR 95%ES
-1.1%
mean tail loss
Max drawdownMDD
-23.8%
Calmar 0.19
Ruin rate≤50%
6.5%
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 -54.2pp · crowd gap -56.4pp
Anchor gapmodel − base
-54.2 pp
Crowd gapprice − base
-56.4 pp
Verdictdiscipline
ANCHORED
Reference-class anchoring prevents narrative-driven blowups.
§11 · Forecast quality (synthetic ledger)
SKILL POSITIVE · in-sample BSS 20.7% · AUC 0.770out-of-sample BSS (5-fold) 20.7% ± 2.2% · Brier 0.1981 · log-loss 0.5855 · n 1600✓ n = 1600
BrierBS
0.1981
lower = better · ō 0.52
BSSvs base
20.7%
improvement over base rate
ReliabilityREL
0.0033
miscalibration · want ↓
ResolutionRES
0.0549
decisiveness · want ↑
Log lossLL
0.5855
cross-entropy
AUCROC
0.770
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 1.10 · expectancy +0.048R180 trades · win 50.6% · Sharpe 0.039
Total P/Lnet
+$2,164
on $45,000 cycled
Win ratehit %
50.6%
91 W / 89 L
Profit factorPF
1.10
$ won / $ lost
Expectancyper trade
+$12.02
avg $ per position
R-expectancyper risk
+0.048R
in units of risk taken
Avg win / losspayoff
$268.28 / -$250.00
ratio 1.07 : 1
Sharpe / traderisk-adj
0.039
μR / σR
Closing line valueCLV
+2.69 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.