FTO is not a tool you open, it is what runs inside your sessions when a contract needs evaluating. Each version is evaluated on dates it never saw, and reported on its own.
Start with the question you actually have. DeriveAI can read current conditions, research the market, evaluate a contract, run an optimization, and bring your own Scorecard history into the same session.
Asked
Why is SPY 30-day IV elevated?
Answered from
Live option chain
Price history
FTO-1 output
Read the environment first
When your question is really whether anything is worth acting on right now, DeriveAI evaluates the current opportunity environment before showing you a ranking. If the evidence is weak, it can say so.
Opportunity environment
Read before ranking
Possible reads
Favorable
Conditions support evaluation
Selective
Some evidence, not broad
Wait
Evidence does not support acting
See which contracts are actually worth it
Optimizer scores every contract in the chain and ranks them by their probability of reaching target before stopping out. When nothing clears the bar, it says so, and tells you why.
Chain scanned
Every listed contract
Shortlist, ranked
1
SPY 560C
30 DTE
2
SPY 555C
30 DTE
3
NVDA 140C
14 DTE
See the odds, not a score
FTO scores a contract against a history of graded trade outcomes and returns a probability, not a rating. FTO-1 answers it for single contracts; FTO-1.5 also picks the exits and reads spreads.
FTO-1
Outcome model
Doubles before it halves?
Returns
A probability
The calibration behind it
The model version
Bring your own history into the next decision
Scorecard learns from your graded decision history and surfaces relevant patterns when you evaluate new opportunities, without changing the model’s underlying probability.
Decision quality
Last 90 days
J
A
S
O
N
D
How we evaluate the models
Calibration
We compare predicted probabilities with realized outcomes to understand whether the numbers behave the way they claim to.
0.0301
FTO-1 calibration gap, July chronological test
Model decay
Model edge changes. We monitor performance drift over time instead of assuming a model that worked before will keep working the same way.
Tracked
Rolling accuracy, drift
Versions
Model versions are evaluated separately over stated periods and conditions so changes in behavior remain visible.
Versioned
FTO-1 and FTO-1.5
The record
Checkable, not claimed
Measured on a temporal holdout — dates the model never saw in training. Not live trading, and excluding transaction costs, slippage and portfolio effects.
41.59%
FTO-1 target-hit rate, highest-scored 1%
94 of the 226 most selective job-level picks reached the fixed target before the stop or expiry.
0.0301
FTO-1 calibration gap
Measured across 15 probability groups on the July chronological test; lower is better.
100,908
Candidate contracts in the July test
22,569 opportunities across 26 assets and 23 trading days, using entry-only information.
No. DeriveAI is research software, not a feed of picks, entries or exits. A session helps you investigate markets and contracts, Optimizer ranks contracts by their odds, and FTO is the probability behind that ranking.
Each of those is an input to your judgment. You make the decision, and DeriveAI helps you understand the odds.
It is the estimated chance the contract reaches its target before it stops out, learned from graded trade outcomes. A 60% estimate is not a promise about one trade; it is a number that should be judged across many comparable outcomes.
That is why we focus on calibration: whether predicted probabilities line up with realized outcomes over time, rather than reducing the model to a single headline accuracy number.
By inspecting the evidence rather than taking our word for it. DeriveAI is designed to publish model performance over time, including probability calibration, model versions, evaluation periods and the number of graded outcomes behind the record.
Advanced AI shouldn’t require blind trust. The goal is to make the model’s behavior understandable enough to evaluate.
We expect it to. Our own research into model decay found that a model’s edge is not a permanent property of it — performance rises, falls, recovers and drifts as market conditions change. So models are versioned, monitored after deployment, and retrained on a schedule rather than trusted indefinitely.
When model behavior changes, the evaluation record should change with it. The point is not to preserve a flattering number; it is to show how the system is behaving now.
No. You need to be comfortable reading a probability and deciding what to do with it. The workspace, Optimizer and FTO are meant to be legible to any trader who understands that markets involve risk.
The research is there if you want it, model decay, adaptive ensembles, calibration, but nothing in the product asks you to read it first.
Scorecard grades every recommendation you have been given and looks at the pattern: the probabilities you tend to accept, the expiries and strategies you favour, when your positions peak, and what waiting has cost. From that it shows you where your decisions have been strongest and weakest.
You control what any of that becomes. Nothing from your Scorecard is shared publicly unless you choose to share it.
No. DeriveAI is not a broker or an adviser, does not execute trades, and nothing it produces is a recommendation to buy or sell a security. Options trading carries substantial risk of loss and is not suitable for every investor.
Model output is an estimate of probability from historical outcomes. Past model performance does not indicate future results, and you are responsible for your own decisions.
“Show me the odds. Show me why.”
DeriveAI is built to make uncertainty more legible: the probability, the evidence behind it, and how the model has behaved over time, so your judgment has something concrete to work with.
41.59%
FTO-1 target-hit rate, highest-scored 1% of 226 picks