Anatomy of one trade: every station between idea and evidence.

Before the click

The trade must already be sized, slotted, and cleared.

A MARS trade doesn't begin at the entry — it begins inside an envelope that existed before the setup appeared. The throttle has already resolved the cycle's authorized tier, pool percent, and per-trade average risk; the Cycle Command Console knows which of the cycle's four slots are open and how much capacity open exposure has already consumed; and the operator's discretionary work — branch identification, volatility context, setup validity — happens inside those boundaries. The Trade Launch Checklist is the final gate: session, approved asset, news danger, spread, gate state, throttle-approved risk, cycle slot, ATR regime, branch selection, exit model, stop logic, position size, fees, and loss acceptability, verified in sequence. Only a fully cleared checklist reaches the broker.

The live phase

Execution, then immediate preservation.

The click itself is the smallest station. Execution goes through the broker; TradeZella begins its black-box capture; and the operator preserves what the platforms can't — screenshots of the higher timeframe, the trigger timeframe, and any scanner or AI evidence, taken now because Saturday's review becomes guesswork without them. Then the paper station: a Trade Capture Document sheet opened for this trade, context and adherence recorded while they're still facts rather than memories. From this point the trade is simultaneously a market position and a data record under construction, and the system treats both identities with equal seriousness.

The open middle

While it lives, the trade is exposure first and opinion never.

Between entry and close, the system's interest in the trade is deliberately narrow: what does it cost the account if it stops out right now? That's active risk, and it drives everything — the console carries it, smart capacity sizes fresh trades around it, and a stop moved to breakeven takes it to zero, releasing capacity. Floating R — the unrealized P&L — is tracked and explicitly demoted: context only, never treated as realized EV proof. The demotion matters because floating profit is the single most persuasive number in trading, and every bad mid-week decision wants to cite it. The lifecycle's answer is structural: until the trade closes, it contributes exposure to the risk system and nothing to the evidence system.

• Active risk is what the trade can still take from the account; floating R is what it might give. Only the first governs anything.
• A breakeven'd trade still occupies its cycle slot — capacity and slots are separate resources.
• Management follows the branch's exit model selected before entry; mid-trade branch conversion is identity drift, not adaptation.

After the close

The trade's second life as a row.

The close ends the market position and begins the record's final assembly. Outcome fields complete the capture sheet — final R, hit flags, breakeven status, MAE, MFE, duration, fees including swap. On Saturday the sheet crosses the paper bridge: transferred into the CP3 Journal, verified against TradeZella, compliance marks closed. And then the trade's real career starts. That one row now feeds the weekly hit rates, the branch EV, the fee-drag ratios, the adherence rate, the drawdown track, the MAE/MFE efficiency read, the variant attribution — and through them, eventually, the throttle inputs that size some future cycle. The trade took minutes to execute. As evidence, it works forever.

The key idea

The lifecycle is the doctrine, made physical.

Every abstraction in the platform pages lands somewhere in this transit: authority hierarchy is the envelope that preceded the setup; the paper bridge is the capture station; open-exposure doctrine is the living middle; journal integrity is the Saturday close. A system is only as real as what happens to one ordinary trade on one ordinary day — and in MARS, what happens is that a discretionary idea enters a governed envelope, leaves a complete evidentiary trail, and exits as a row the entire analytical stack can trust. Multiply by sixteen a week, and that's the machine.