feat: Allow stop prices (gain/loss) in trading agent decisions#1
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feat: Allow stop prices (gain/loss) in trading agent decisions#1
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This PR implements robust management for Stop Gain/Loss prices, ensuring these critical protective limits are **maintained and consistently applied** across trading sessions and subsequent LLM decisions. * **LLM Definition:** LLM response extended to include new Stop Gain/Loss price fields. * **Persistent Storage:** Defined stop prices are stored in session memory and persisted via database snapshot. * **Session Continuity:** Loads persisted stop prices when resuming a session, restoring trading strategy limits. * **Informed Decisions:** Existing stop prices are fed back into the LLM context to inform and assist subsequent trading actions.
- Add multi-stage Dockerfile.cloud (bun SPA + Python backend) - Serve React SPA from FastAPI via FRONTEND_BUILD_DIR env var - Add docker-compose.yml and vm-setup.sh for GCE VM - Add GitHub Actions workflow deploying via SSH to 34.142.253.116 - Remove Cloud Run / OCI configs Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Summary
biome.json,agent.tsconstants)Changes
trading/models.pystop_gain_priceandstop_loss_pricefields to position modelportfolio/interfaces.py&in_memory.pycoordinator.pyruntime.pycomposer.py/system_prompt.pyfrontend/StrategyAgentconstantTest plan
make test(Python unit tests pass)deploy.yml→ Docker image built → VM restarted →GET /api/v1/healthzreturns 200🤖 Generated with Claude Code