A single model can make you feel precise and still leave you exposed.
In Auster, the most reliable workflow is not one tool. It is a sequence: forecast range, event risk, Black Scholes check, then Monte Carlo distribution. This is how I turn those four views into one decision table I can actually trade.
1) Forecast sets the map
I begin in Forecast and write down the expected move and range for my holding period. Then I pin my own strike breakpoints against that map.
- Which price region is most probable by expiry
- Which region causes my largest convex loss
- Where I need price to hold for the thesis to stay valid
2) Events test the path, not just the destination
Event risk changes path behavior even when the final destination stays similar. I check scheduled catalysts inside my window:
- Earnings releases
- Macro prints like CPI, NFP, FOMC
- Sector leaders that can move correlated names
Correlation spillover is where many short-dated positions break. Your name does not need to report for your book to get repriced.
3) Black Scholes is the pricing sanity layer
I use the Black Scholes output in Auster as a consistency check. If model value and live mark diverge materially, I treat that as a prompt to verify assumptions before execution.
4) Monte Carlo reveals shape of risk
Monte Carlo is where the truth usually gets uncomfortable. I focus on three outputs:
- Probability the position improves from current mark
- P25/P50/P75 portfolio outcomes
- Tail concentration near max-loss zones
Many trades that look attractive in headline expected value are still bad trades when downside is too concentrated in realistic paths.
5) Convert analysis into actions
Before I place a trade, I write three rules:
- Hold rule: condition that keeps the position intact
- Adjust rule: condition for rolling, hedging, or resizing
- Exit rule: condition that closes the trade without negotiation
That keeps the workflow quantitative under pressure, not emotional under pressure.