TradeAgent

TradeAgent is a local-first AI trading workstation prototype. It combines a FastAPI backend, a React frontend, broker-connected market data, a deterministic paper-trading engine, SQLite-backed audit trails, and an LLM-assisted Strategy Studio for creating and backtesting trading ideas.

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

• multi-surface product, not a single demo screen
• 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

Main Dashboard

_{Execution-facing dashboard with live charting, AI analysis, signal panels, and trade journal context.}

Operator Workbench

_{Control plane for engine state, readiness, guardrails, watchlists, audit records, and runtime visibility.}

Strategy Studio

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

Heavyweight Checklist

_{Structured US30/XAUUSD checklist workflow for macro context, go/no-go logic, execution planning, and weighted decision support.}

Earlier prototype snapshots

Main Capabilities

Dashboard

• live market charting
• strategy selection and symbol/timeframe controls
• AI analysis output and manual trade actions
• signals, incidents, intents, and trade journal panels
• broker, engine, and model readiness indicators

Operator Workbench

• engine start/stop, manual scan, reconcile, and recovery
• readiness checks and broker notes
• watchlist management
• persistent config for confidence, daily loss, cooldowns, session filter, and position limits
• paper positions, order intents, audit records, and incident feeds

Strategy Studio

• natural-language strategy chat
• provider/model selection
• draft strategy generation and refinement
• save-to-disk strategy workflow
• saved and draft backtesting
• formatted backtest dashboards and raw result inspection

Heavyweight Checklist

• macro checklist and scenario framework
• US30/XAUUSD decision support
• component confirmation flow
• auto-snapshot integration from backend checklist and calendar endpoints

How The Agent System Works

TradeAgent currently has two AI-related execution surfaces:

• 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.
• Strategy Studio: 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 agent roles still exist conceptually, but the active implementation is a consolidated V2 engine plus a separate Strategy Studio task pipeline.

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: frontend surfaces, FastAPI layer, paper-trading runtime engine, Strategy Studio research flow, and SQLite-backed 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 is paper-only
• live mode can be requested in config, but live 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