How exoTrader Works

This page explains exoTrader as a governed execution process from signal intake to order lifecycle and telemetry.

Process Overview

Each cycle follows a fixed chain:

1. Run preflight checks and acquire cycle lock.
2. Build runtime context from market, account and order state.
3. Refresh higher-timeframe context when live mode requires it.
4. Compute position economics and confluence state.
5. Apply safety validation before execution routing.
6. Route hold/buy/sell logic through lifecycle controls.
7. Publish telemetry, diagnostics and optional auto-settings actions.
8. Persist cycle state and release lock.

1) Data Intake and Preconditions

exoTrader consumes bid/ask, candle arrays, open orders, fills and account balances. It requires a minimum candle history and exits early when prerequisites are not satisfied.

2) Signal Confluence Layer

Primary direction context is generated from WaveTrend logic with optional RSI and Bollinger confirmations. This stack aims to reduce low-quality entries and exits by requiring aligned conditions.

3) Break-Even and Position Economics

exoTrader computes aggregate break-even and position economics from order history. This context feeds buy restrictions, partial sell logic and PnL quality controls.

4) Risk and Behaviour Gating

Before posting, exoTrader applies:

• break-even filters,
• trend protection checks,
• max position/capital controls,
• buy/sell cooldown timers,
• exchange minimum notional checks.

5) Order Lifecycle Controls

Execution supports post-only and standard paths, orderbook positioning (where relevant), refresh windows and stale-order cleanup rules. These controls are designed to improve determinism and reduce unwanted fills.

6) Performance and State Layer

The strategy tracks operational and financial metrics such as PnL, volume ratios, drawdown context and runtime timings. State is persisted to JSON for continuity and diagnostics.

Runtime Flow (Code-Mapped)

This flow is mapped to the live cycle orchestration in quanta_exotrader.js and shows the operational control path used in production.

flowchart TD
    A([Cycle Start]) --> B{"Preflight OK?"}
    B -- "No" --> B1["Skip cycle and log reason"] --> Z([Cycle End])
    B -- "Yes" --> C["Acquire cycle lock (runCycleBusy)"]

    C --> D["Build runtime context<br/>strategyVariables()"]
    D --> E{"Live mode?"}
    E -- "Yes" --> E1["Refresh higher-timeframe candles"]
    E -- "No" --> F
    E1 --> F["Compute position economics<br/>aggregate BEP and profitable-unit state"]

    F --> G["Generate confluence state<br/>WaveTrend, RSI/BB filters, trend and BEP gates"]
    G --> H{"Safety checks pass?"}
    H -- "No" --> H1["Abort execution path"] --> Y
    H -- "Yes" --> I["Execution Router (orderCentre)"]

    I --> I1{"Signal = Hold (0)?"}
    I1 -- "Yes" --> I2["Optional order cleanup (ORDER_KILL)"] --> J
    I1 -- "No" --> I3{"Signal = Buy (-1)<br/>or Sell (1)?"}
    I3 -- "Buy" --> K["Buy path<br/>choke window, depth/cancel rules, post buy if eligible"]
    I3 -- "Sell" --> L["Sell path<br/>choke window, depth/cancel rules, post sell if eligible"]
    K --> J
    L --> J

    J["Publish operator telemetry<br/>trade lines, sidebar and chart icons"] --> M{"AUTO_SETTINGS trigger?"}
    M -- "Yes" --> M1["Auto-adapt trend settings"]
    M -- "No" --> N
    M1 --> N
    N --> O{"BALANCE_OUTPUT enabled?"}
    O -- "Yes" --> O1["Write balance snapshot JSON"]
    O -- "No" --> P
    O1 --> P["Persist cycle state<br/>timing, trend state and financial ratios"]

    P --> Y([Cycle Complete])
    Y --> R["Release cycle lock<br/>finally: runCycleBusy=false"]
    R --> Z

Strategic Outcome Mapping

exoTrader’s operating model is designed to support:

• disciplined signal quality,
• controlled risk and inventory behaviour,
• deterministic execution management,
• measurable runtime and performance transparency.

These are design objectives, not guaranteed outcomes.