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Documentation Index

Fetch the complete documentation index at: https://condor.hummingbot.org/llms.txt

Use this file to discover all available pages before exploring further.

A Trading Agent is an autonomous, file-backed entity that runs on a fixed tick interval, reasons about the market with an LLM, and translates its decisions into real trades through Hummingbot executors.
Trading Agent Architecture

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Design Philosophy

The Trading Agents Standard separates what LLMs do well—reasoning under uncertainty—from what traditional software does well—reliable, repeatable execution.
LayerTypeWhat It Does
AgenticLLM reasoning (OODA loop)Observes market, orients context, decides action, acts via tools
ExecutionDeterministic PythonRuns executors, tracks positions, enforces risk limits
The result is an agent that thinks like a discretionary trader but acts like a systematic one, with full auditability across every tick.

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Core Principle: Executor-Based Trading

Agents only act through Hummingbot executors. Each agent spawns executors with its own controller_id == agent_id, which provides:
  1. Isolation: Two agents on the same account never see or touch each other’s executors
  2. Virtual Portfolio: Each agent gets its own positions, breakeven prices, realized/unrealized P&L
  3. Position Handover: When an executor closes with keep_position=true, the agent retains the inventory

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Mental Model

An agent is a folder on disk and a tick loop in memory. The folder is its long-term memory; providers give it short-term situational awareness; the LLM is its decision function; executors are its hands; the risk engine is the wrist it can’t move past.
Everything an agent owns is tagged with its controller_id, making the system safely composable: any number of agents can share the same exchange account without stepping on each other.

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Core Components

ComponentPurposeDocumentation
ArchitectureFile structure, tick engine, providersagent.md, journals, learnings
SessionsSession management, snapshotsCross-session memory
InventoryPosition tracking and P&LVirtual portfolio
ExecutorsTrading operationsTypes, lifecycle, keep_position
MCP ToolsLLM tool accessMarket data, trading

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Run Modes

ModeBehavior
dry_runOne tick, no trading. Pure reasoning test. Saves experiment snapshot.
run_onceOne tick with trading. Manual single-shot execution.
loopStandard mode. Ticks every frequency_sec until stopped. Creates session with full journal.

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Quick Start

Via Telegram: 
/agent → Create New Agent → Configure → Start
Programmatically: 
from condor.trading_agent.engine import TickEngine
from condor.trading_agent.strategy import StrategyStore

store = StrategyStore()
strategy = store.get_by_slug("my_strategy")

engine = TickEngine(
    strategy=strategy,
    config={
        "execution_mode": "loop",
        "frequency_sec": 60,
        "server_name": "binance_main",
        "total_amount_quote": 500,
        "risk_limits": {
            "max_total_exposure_quote": 1500,
            "max_drawdown_pct": 5,
            "max_open_executors": 4,
        },
    },
)

await engine.start()

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Session Continuity

The ~/condor directory stores all agent state, and Condor uses ACP (Agent Client Protocol) to connect to your LLM. This means you can:
  • Start a conversation on Telegram
  • Continue it in Claude Code
  • Switch to the web dashboard
Same session, same agent state, same conversation history.