Strategy & conditions
The ruleset, written down: conditions grouped by context, each carrying a weight, versioned so a change can be judged later.
About
What it contains,
and how it thinks.
AAR Business Center is a private environment for running a business on structure. Two things are worth explaining: what is actually inside it, and how the analytical layer reaches its conclusions.
01
What AAR BC contains
One environment holding the whole loop — from the rules, through the decisions, to what they returned.
Checklists
The same conditions, rendered as the checklist a decision is worked through — one definition, used everywhere.
Setups & trade history
Every scored decision is stored with the answers it was scored on, then given its real outcome once it closes.
Performance tracking
Expectancy, win rate, profit factor, drawdown and streaks, recomputed from the record on every load.
Condition analysis
Per-condition statistics with the sample size attached, so a thin edge never gets to look like a proven one.
Trading calendar
Daily results by the day they closed — trade count, R, and money when a cash value for 1R is configured.
Data in and out
CSV export, and an import that validates the whole file before a single row is written.
Configurable workspace
Forty-one preset themes plus a full colour editor covering every token in the interface, including the grade scale.
02
How the intelligence works
Nothing here is predictive. It is descriptive statistics over your own closed decisions, reported with the evidence it rests on.
Expectancy
total R ÷ number of closed trades
The average result the strategy returns per decision, in R. It is the single number that says whether the process pays.
Maximum drawdown
lowest point below the running equity peak
The equity curve is walked in the order trades closed, tracking the peak. The largest fall from a peak is the drawdown.
Condition impact
expectancy with − expectancy without
For each condition, results are split by how it was answered. The gap between the two sides is what that condition has historically been worth.
Conditions whose presence lines up with better results. These are the parts of the process actually doing work.
Conditions whose impact is negative. The honest reading is that the rule, as written, is not earning its place.
Total weight
Each condition carries a weight, and each answer a multiplier. The score is the weighted signal normalised against the total weight of everything actually answered, mapped onto 0–100 and then onto a grade from D to A+.
score = ((Σ w·m ÷ Σ w) + 1) ÷ 2 × 100
Answering everything neutral lands on exactly 50. Marking a condition not applicable removes it from both sides, so it never quietly drags the result down.
Calibrate weights
Weights start as judgement. Once a condition has enough closed trades on both sides, its weight can instead be derived from measured expectancy — each suggestion scaling that condition's impact against the strongest one.
- Only conditions past their own sample threshold are included.
- A condition whose impact is negative falls to the floor rather than earning weight.
- Calibration is always an explicit action — nothing rewrites itself.
- Anything under threshold is flagged, never presented as settled.
Performance, visualised
Equity curve
Cumulative performance over time, in the order trades closed.
Cumulative R
The running total of R across the whole record.
Expectancy
The average expected outcome per decision.
R-multiple distribution
How outcomes spread across R bands — a fat left tail says stops are being widened.