Decision Agent

The Decision Agent (src/agent/) is the cognitive center of Rez. It orchestrates the flow between raw technical data and final trade execution using Large Language Models (LLMs).

The TradingAgent Architecture

The TradingAgent class encapsulates the logic for prompt construction, LLM interaction, and tool-calling orchestration.

Initial Context Construction

When a decision is requested, the agent assembles a comprehensive context payload:

• Account Context: Total value, current leverage, and available cash.

• Position Context: Entry price, current PnL, and liquidation price of open trades.

• Market Context: A snapshots of indicators (EMA, RSI, MACD, Volume) for the assets being considered.

The ReAct Cycle Sequence

The interaction follows a strict multi-turn sequence to ensure the model has all necessary data before deciding.

Output Formatting & Contract

To ensure the backend can reliably execute LLM decisions, Rez uses Structured Outputs.

JSON Schema Enforcement

The agent requests data in a strict JSON format:

{
  "reasoning": "...",
  "summary": "Short bullish thesis...",
  "trade_decisions": [
    {
      "asset": "BTC",
      "action": "buy",
      "percentage": 50,
      "leverage": 5,
      "tp_price": 68000,
      "sl_price": 64000
    }
  ]
}

Sanitization Fallback

If the primary model (e.g., GPT-4o) provides malformed JSON, the agent triggers a secondary Sanitizer Loop. A smaller model (gpt-4o-mini) is used to "clean" the raw response into the valid schema without altering the trade intent.

Model Flexibility

Thanks to the OpenRouter integration, the Decision Agent can be configured to use any state-of-the-art model (DeepSeek, Grok, Claude, GPT-4) simply by updating the config.json.