mirror of
https://github.com/Xe138/AI-Trader.git
synced 2026-04-02 09:37:23 -04:00
Bill
fb9583b374
Major architecture transformation from batch-only to API service with
database persistence for Windmill integration.
## REST API Implementation
- POST /simulate/trigger - Start simulation jobs
- GET /simulate/status/{job_id} - Monitor job progress
- GET /results - Query results with filters (job_id, date, model)
- GET /health - Service health checks
## Database Layer
- SQLite persistence with 6 tables (jobs, job_details, positions,
holdings, reasoning_logs, tool_usage)
- Foreign key constraints with cascade deletes
- Replaces JSONL file storage
## Backend Components
- JobManager: Job lifecycle management with concurrency control
- RuntimeConfigManager: Thread-safe isolated runtime configs
- ModelDayExecutor: Single model-day execution engine
- SimulationWorker: Date-sequential, model-parallel orchestration
## Testing
- 102 unit and integration tests (85% coverage)
- Database: 98% coverage
- Job manager: 98% coverage
- API endpoints: 81% coverage
- Pydantic models: 100% coverage
- TDD approach throughout
## Docker Deployment
- Dual-mode: API server (persistent) + batch (one-time)
- Health checks with 30s interval
- Volume persistence for database and logs
- Separate entrypoints for each mode
## Validation Tools
- scripts/validate_docker_build.sh - Build validation
- scripts/test_api_endpoints.sh - Complete API testing
- scripts/test_batch_mode.sh - Batch mode validation
- DOCKER_API.md - Deployment guide
- TESTING_GUIDE.md - Testing procedures
## Configuration
- API_PORT environment variable (default: 8080)
- Backwards compatible with existing configs
- FastAPI, uvicorn, pydantic>=2.0 dependencies
Co-Authored-By: AI Assistant <noreply@example.com>
460 lines
12 KiB
Python
460 lines
12 KiB
Python
"""
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Pydantic data models for AI-Trader API.
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This module defines:
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- Request models (input validation)
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- Response models (output serialization)
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- Nested models for complex data structures
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"""
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from pydantic import BaseModel, Field
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from typing import Optional, List, Dict, Literal, Any
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from datetime import datetime
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# ==================== Request Models ====================
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class TriggerSimulationRequest(BaseModel):
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"""Request model for POST /simulate/trigger endpoint."""
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config_path: str = Field(
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default="configs/default_config.json",
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description="Path to configuration file"
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)
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class Config:
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json_schema_extra = {
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"example": {
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"config_path": "configs/default_config.json"
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}
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}
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class ResultsQueryParams(BaseModel):
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"""Query parameters for GET /results endpoint."""
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date: str = Field(
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...,
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pattern=r"^\d{4}-\d{2}-\d{2}$",
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description="Date in YYYY-MM-DD format"
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)
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model: Optional[str] = Field(
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None,
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description="Model signature filter (optional)"
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)
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detail: Literal["minimal", "full"] = Field(
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default="minimal",
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description="Response detail level"
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)
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class Config:
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json_schema_extra = {
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"example": {
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"date": "2025-01-16",
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"model": "gpt-5",
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"detail": "minimal"
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}
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}
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# ==================== Nested Response Models ====================
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class JobProgress(BaseModel):
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"""Progress tracking for simulation jobs."""
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total_model_days: int = Field(
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...,
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description="Total number of model-days to execute"
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)
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completed: int = Field(
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...,
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description="Number of model-days completed"
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)
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failed: int = Field(
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...,
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description="Number of model-days that failed"
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)
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current: Optional[Dict[str, str]] = Field(
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None,
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description="Currently executing model-day (if any)"
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)
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details: Optional[List[Dict]] = Field(
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None,
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description="Detailed progress for each model-day"
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)
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class Config:
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json_schema_extra = {
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"example": {
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"total_model_days": 4,
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"completed": 2,
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"failed": 0,
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"current": {"date": "2025-01-16", "model": "gpt-5"},
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"details": [
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{
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"date": "2025-01-16",
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"model": "gpt-5",
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"status": "completed",
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"duration_seconds": 45.2
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}
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]
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}
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}
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class DailyPnL(BaseModel):
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"""Daily profit and loss metrics."""
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profit: float = Field(
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...,
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description="Daily profit in dollars"
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)
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return_pct: float = Field(
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...,
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description="Daily return percentage"
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)
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portfolio_value: float = Field(
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...,
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description="Total portfolio value"
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)
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class Config:
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json_schema_extra = {
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"example": {
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"profit": 150.50,
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"return_pct": 1.51,
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"portfolio_value": 10150.50
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}
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}
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class Trade(BaseModel):
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"""Individual trade record."""
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id: int = Field(
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...,
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description="Trade sequence ID"
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)
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action: str = Field(
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...,
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description="Trade action (buy/sell)"
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)
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symbol: str = Field(
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...,
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description="Stock symbol"
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)
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amount: int = Field(
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...,
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description="Number of shares"
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)
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price: Optional[float] = Field(
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None,
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description="Trade price per share"
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)
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total: Optional[float] = Field(
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None,
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description="Total trade value"
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)
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class Config:
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json_schema_extra = {
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"example": {
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"id": 1,
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"action": "buy",
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"symbol": "AAPL",
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"amount": 10,
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"price": 255.88,
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"total": 2558.80
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}
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}
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class AIReasoning(BaseModel):
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"""AI reasoning and decision-making summary."""
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total_steps: int = Field(
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...,
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description="Total reasoning steps taken"
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)
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stop_signal_received: bool = Field(
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...,
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description="Whether AI sent stop signal"
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)
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reasoning_summary: str = Field(
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...,
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description="Summary of AI reasoning"
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)
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tool_usage: Dict[str, int] = Field(
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...,
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description="Tool usage counts"
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)
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class Config:
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json_schema_extra = {
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"example": {
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"total_steps": 15,
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"stop_signal_received": True,
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"reasoning_summary": "Market analysis indicates...",
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"tool_usage": {
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"search": 3,
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"get_price": 5,
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"math": 2,
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"trade": 1
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}
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}
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}
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class ModelResult(BaseModel):
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"""Simulation results for a single model on a single date."""
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model: str = Field(
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...,
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description="Model signature"
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)
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positions: Dict[str, float] = Field(
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...,
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description="Current positions (symbol: quantity)"
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)
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daily_pnl: DailyPnL = Field(
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...,
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description="Daily P&L metrics"
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)
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trades: Optional[List[Trade]] = Field(
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None,
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description="Trades executed (detail=full only)"
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)
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ai_reasoning: Optional[AIReasoning] = Field(
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None,
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description="AI reasoning summary (detail=full only)"
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)
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log_file_path: Optional[str] = Field(
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None,
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description="Path to detailed log file (detail=full only)"
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)
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class Config:
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json_schema_extra = {
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"example": {
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"model": "gpt-5",
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"positions": {
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"AAPL": 10,
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"MSFT": 5,
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"CASH": 7500.0
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},
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"daily_pnl": {
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"profit": 150.50,
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"return_pct": 1.51,
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"portfolio_value": 10150.50
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}
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}
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}
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# ==================== Response Models ====================
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class TriggerSimulationResponse(BaseModel):
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"""Response model for POST /simulate/trigger endpoint."""
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job_id: str = Field(
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...,
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description="Unique job identifier"
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)
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status: str = Field(
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...,
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description="Job status (accepted/running/current)"
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)
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date_range: List[str] = Field(
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...,
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description="Dates to be simulated"
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)
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models: List[str] = Field(
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...,
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description="Models to execute"
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)
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created_at: str = Field(
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...,
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description="Job creation timestamp (ISO 8601)"
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)
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message: str = Field(
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...,
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description="Human-readable status message"
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)
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progress: Optional[JobProgress] = Field(
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None,
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description="Progress (if job already running)"
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)
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class Config:
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json_schema_extra = {
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"example": {
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"job_id": "550e8400-e29b-41d4-a716-446655440000",
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"status": "accepted",
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"date_range": ["2025-01-16", "2025-01-17"],
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"models": ["gpt-5", "claude-3.7-sonnet"],
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"created_at": "2025-01-20T14:30:00Z",
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"message": "Simulation job queued successfully"
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}
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}
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class JobStatusResponse(BaseModel):
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"""Response model for GET /simulate/status/{job_id} endpoint."""
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job_id: str = Field(
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...,
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description="Job identifier"
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)
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status: str = Field(
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...,
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description="Job status (pending/running/completed/partial/failed)"
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)
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date_range: List[str] = Field(
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...,
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description="Dates being simulated"
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)
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models: List[str] = Field(
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...,
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description="Models being executed"
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)
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progress: JobProgress = Field(
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...,
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description="Execution progress"
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)
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created_at: str = Field(
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...,
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description="Job creation timestamp"
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)
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updated_at: Optional[str] = Field(
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None,
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description="Last update timestamp"
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)
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completed_at: Optional[str] = Field(
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None,
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description="Job completion timestamp"
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)
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total_duration_seconds: Optional[float] = Field(
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None,
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description="Total execution duration"
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)
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class Config:
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json_schema_extra = {
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"example": {
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"job_id": "550e8400-e29b-41d4-a716-446655440000",
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"status": "running",
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"date_range": ["2025-01-16", "2025-01-17"],
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"models": ["gpt-5"],
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"progress": {
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"total_model_days": 2,
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"completed": 1,
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"failed": 0,
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"current": {"date": "2025-01-17", "model": "gpt-5"}
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},
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"created_at": "2025-01-20T14:30:00Z"
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}
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}
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class ResultsResponse(BaseModel):
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"""Response model for GET /results endpoint."""
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date: str = Field(
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...,
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description="Trading date"
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)
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results: List[ModelResult] = Field(
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...,
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description="Results for each model"
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)
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class Config:
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json_schema_extra = {
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"example": {
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"date": "2025-01-16",
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"results": [
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{
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"model": "gpt-5",
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"positions": {"AAPL": 10, "CASH": 7500.0},
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"daily_pnl": {
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"profit": 150.50,
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"return_pct": 1.51,
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"portfolio_value": 10150.50
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}
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}
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]
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}
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}
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class HealthCheckResponse(BaseModel):
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"""Response model for GET /health endpoint."""
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status: str = Field(
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...,
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description="Overall health status (healthy/unhealthy)"
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)
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timestamp: str = Field(
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...,
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description="Health check timestamp"
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)
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services: Dict[str, Dict] = Field(
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...,
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description="Status of each service"
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)
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storage: Dict[str, Any] = Field(
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...,
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description="Storage status"
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)
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database: Dict[str, Any] = Field(
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...,
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description="Database status"
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)
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class Config:
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json_schema_extra = {
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"example": {
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"status": "healthy",
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"timestamp": "2025-01-20T14:30:00Z",
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"services": {
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"mcp_math": {"status": "up", "url": "http://localhost:8000/mcp"},
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"mcp_search": {"status": "up", "url": "http://localhost:8001/mcp"}
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},
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"storage": {
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"data_directory": "/app/data",
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"writable": True,
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"free_space_mb": 15234
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},
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"database": {
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"status": "connected",
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"path": "/app/data/jobs.db"
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}
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}
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}
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class ErrorResponse(BaseModel):
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"""Standard error response model."""
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error: str = Field(
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...,
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description="Error code/type"
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)
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message: str = Field(
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...,
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description="Human-readable error message"
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)
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details: Optional[Dict] = Field(
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None,
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description="Additional error details"
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)
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class Config:
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json_schema_extra = {
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"example": {
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"error": "invalid_date",
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"message": "Date must be in YYYY-MM-DD format",
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"details": {"provided": "2025/01/16"}
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}
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}
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