I/Win rate · active trading
Decide what you want to earn. The engine computes the path.
Every setup runs through thousands of simulated futures before risk is committed. Monte Carlo turns an edge into a win rate, a win rate into risk parameters, and risk parameters into an expected return you can choose — directly in the options chain.
Win rate
57.0%
Trades
0
Session P&L
+0.0%
Each tick is one trade from a 57% edge. Monte Carlo replays this thousands of times to estimate the stable win rate and equity curve before capital is committed.
II/Data processing
01
Warehouse inputs
volatility · drift · regime · flow · win-rate priors
02
Monte Carlo engine
1,400+ GBM paths · seeded · reproducible
03
Distribution read
percentiles · P(up) · tail risk · Kelly size
04
Options chain map
P(ITM) · expected return per strike
A · Price path
Inputs
spot · σ · μ · horizon
Outputs
terminal distribution · percentile bands · expected price
B · Strategy outcome
Inputs
win rate · avg win/loss · frequency · size
Outputs
P(profit) · drawdown · risk of ruin · equity distribution
C · Trade quality
Inputs
signal · liquidity · regime · sentiment · structure
Outputs
conviction · noise · confidence interval · quality grade
III · Simulation
Geometric Brownian motion draws path after path from the asset's volatility and drift. The engine keeps every step — median, likely band, and tails — so you see the full shape of what could happen over the horizon, not a single price target.
IV · Distribution
When the simulations finish, terminal prices collapse into a probability distribution. That histogram is the object the engine reads — where mass clusters, where the tails sit, and how much of the future finishes above or below spot.
V · Risk parameters
Expected price, probability of finishing up, outcome bands, downside tail, and Kelly-optimal size all fall out of the same simulated record. No round numbers, no gut feel — just statistics from the futures you already ran.
Expected price · 1Y
8.5% vs spot
Probability up
paths finishing above spot
10–90% band
where 80% of outcomes land
Downside tail
10th percentile · stop/size input
Kelly size
half-Kelly · capped
Risk of ruin proxy
share of adverse terminal paths
VI · Options chain
The same distribution prices every strike. Black–Scholes supplies premium and delta; Monte Carlo supplies P(ITM) and expected return. Choose the payoff you want — the engine shows the trade that targets it and the odds you are paying for.
| Strike | Premium | Delta | P(ITM) | Exp. return |
|---|---|---|---|---|
| 92.5 | $10.58 | 0.76 | 75% | +13% |
| 97.5 | $7.39 | 0.63 | 62% | +16% |
| 100.0 | $6.06 | 0.56 | 55% | +18% |
| 102.5 | $4.91 | 0.49 | 47% | +19% |
| 107.5 | $3.11 | 0.35 | 34% | +23% |
| 112.5 | $1.87 | 0.24 | 23% | +28% |
+25%target
$107.5 call+60%target
$112.5 call+120%target
$112.5 callName the return you want. The engine maps it to the strike whose simulated distribution targets it — and shows the probability you are paying for.
VII/The quantitative stack
Monte Carlo
GBM paths · strategy replay · conviction under noise
Black–Scholes
chain premiums · delta · greeks for strike selection
Kelly criterion
position size from modeled edge
GARCH / Bayesian
vol forecast · signal fusion · conviction update
Trade evaluation
aggregates all modules → grade A–F
Historical edge and conviction replay trade by trade until a stable rate emerges — live in the hero simulation.
The distribution sets bands, tails, and size before any order is staged.
Every strike carries modeled P(ITM) and expected return so you pick the outcome, not guess the path.