TradeAgent

TradeAgent is a local-first trading workstation organized into three connected areas: Trade, Build & Test, and System. It combines a FastAPI backend, React frontend, broker-connected market data, deterministic paper execution, SQLite-backed audit trails, and LLM-assisted research.

The project is intended to show AI product engineering rather than prompt-only experimentation: operator controls, explicit risk boundaries, persistent state, testing, research workflows, and a UI that supports the full operating loop.

What It Demonstrates

• one methodological workflow across Trade, Build & Test, and System
• deterministic paper execution with explicit guardrails
• LLM-assisted strategy drafting, editing, and backtesting
• persistent runtime, incidents, intents, positions, and audit history
• broker-connected market data and trading context
• architecture that separates operator workflows, runtime execution, and research tooling

Product Gallery

Trade

_{Daily trading workspace with live charting, selected-market context, explicit strategy analysis, signals, positions, and journal context.}

Build & Test

_{LLM-assisted research workflow with strategy drafting, saved strategies, formatted metrics, equity curve, and trade-level backtest output.}

Earlier prototype snapshots

Main Capabilities

Trade

• live charting, selected-market context, and explicit strategy rules
• watchlist, symbol, timeframe, and strategy selection
• signal review, paper orders, positions, and trade journal
• broker, market-data, engine, and model status

Build & Test

• measurable hypothesis and strategy lifecycle
• natural-language research assistance with visible generated rules
• saved and draft backtesting with fees and slippage
• event-outcome calibration and original-versus-shadow replay
• paper evidence and controlled promotion decisions

System

• engine start/stop, one-shot scan, reconciliation, and recovery
• readiness and connection diagnostics
• risk, session, stop, cooldown, and loss controls
• decision, trade, engine-event, and incident audit trails

How The Agent System Works

TradeAgent has one trading runtime and one separate research assistant:

• Runtime trading engine: one orchestrated paper-trading loop scans a watchlist, fetches bars, runs a deterministic strategy, passes the result through risk and sizing checks, and records intents and paper-trade audit history.
• Build & Test research pipeline: an LLM-assisted research workflow can chat, draft strategy code, backtest drafts or saved files, and save strategies into backend/strategies_generated/.

The repo is best described as an agent-inspired, service-oriented design rather than a swarm of independently deployed worker agents. The product workflow is consolidated into Trade, Build & Test, and System, while the backend retains explicit runtime and research boundaries.

See ARCHITECTURE.md for the current diagrams, agent-role mapping, runtime flow, and documentation of what is active versus legacy.

Architecture At A Glance

_{Current-state architecture: Trade, Build & Test, System, FastAPI services, deterministic paper runtime, and SQLite-backed audit memory.}

Tech Stack

• FastAPI
• React 19 + Vite + TypeScript
• SQLite
• cTrader Open API integration
• Ollama and Gemini-ready model routing for Strategy Studio
• Recharts and lightweight-charts

Quick Start

One-command local startup

cmd /c call start-local.cmd

This starts:

• backend on http://127.0.0.1:4000
• frontend on http://127.0.0.1:5173

Manual startup

Backend:

set APP_START_CTRADER_ON_BOOT=1
set APP_WARM_OLLAMA_ON_BOOT=1
set OLLAMA_URL=http://127.0.0.1:11434
set PYTHONPATH=%CD%
C:\Users\mohag\miniconda3\python.exe -m uvicorn backend.app:app --host 127.0.0.1 --port 4000

Frontend:

cd frontend
set VITE_API_BASE=http://127.0.0.1:4000
npm.cmd run dev -- --host 127.0.0.1 --port 5173

Verification

Verified locally on April 15, 2026:

python -m pytest backend\tests -q
cd frontend
npm.cmd run build

Result:

• 52 backend tests passed
• frontend production build passed

Documentation

• ARCHITECTURE.md: current system architecture, runtime flow, agent-role mapping, diagrams, and active boundaries
• docs/operations/local-run.md: local startup, environment flags, verification commands, and troubleshooting
• docs/TRADEAGENT.md: lightweight documentation index and migration note

Current Constraints

• autonomous execution supports local paper positions and explicitly enabled cTrader demo-account orders
• cTrader execution stays blocked until the API confirms the configured account has isLive = false; live-account execution remains intentionally blocked
• broker connectivity and market data depend on the local cTrader/Open API environment
• Strategy Studio quality depends on the configured local or remote model

Why It Works As A Portfolio Project

This repo shows more than model integration. It shows how AI features can be placed inside a product with operational boundaries, state, observability, recovery paths, and a clear separation between research tooling and execution logic.

License

MIT