mirror of
https://github.com/Xe138/AI-Trader.git
synced 2026-04-01 17:17:24 -04:00
Bill
f104164187
Complete implementation of reasoning logs retrieval system that replaces JSONL file-based logging with database-only storage. Database Changes: - Add trading_sessions table (one record per model-day) - Add reasoning_logs table (conversation history with summaries) - Add session_id column to positions table - Add indexes for query performance Agent Changes: - Add conversation history tracking to BaseAgent - Add AI-powered summary generation using same model - Remove JSONL logging code (_log_message, _setup_logging) - Preserve in-memory conversation tracking ModelDayExecutor Changes: - Create trading session at start of execution - Store reasoning logs with AI-generated summaries - Update session summary after completion - Link positions to sessions via session_id API Changes: - Add GET /reasoning endpoint with filters (job_id, date, model) - Support include_full_conversation parameter - Return both summaries and full conversation on demand - Include deployment mode info in responses Documentation: - Add complete API reference for GET /reasoning - Add design document with architecture details - Add implementation guide with step-by-step tasks - Update Python and TypeScript client examples Testing: - Add 6 tests for conversation history tracking - Add 4 tests for summary generation - Add 5 tests for model_day_executor integration - Add 8 tests for GET /reasoning endpoint - Add 9 integration tests for E2E flow - Update existing tests for schema changes All 32 new feature tests passing. Total: 285 tests passing.
478 lines
17 KiB
Python
478 lines
17 KiB
Python
"""
|
|
Unit tests for api/model_day_executor.py - Single model-day execution.
|
|
|
|
Coverage target: 90%+
|
|
|
|
Tests verify:
|
|
- Executor initialization
|
|
- Trading session execution
|
|
- Result persistence to SQLite
|
|
- Error handling and recovery
|
|
- Position tracking
|
|
- AI reasoning logs
|
|
"""
|
|
|
|
import pytest
|
|
import json
|
|
from unittest.mock import Mock, patch, MagicMock, AsyncMock
|
|
from pathlib import Path
|
|
|
|
|
|
def create_mock_agent(positions=None, last_trade=None, current_prices=None,
|
|
reasoning_steps=None, tool_usage=None, session_result=None,
|
|
conversation_history=None):
|
|
"""Helper to create properly mocked agent."""
|
|
mock_agent = Mock()
|
|
|
|
# Default values
|
|
mock_agent.get_positions.return_value = positions or {"CASH": 10000.0}
|
|
mock_agent.get_last_trade.return_value = last_trade
|
|
mock_agent.get_current_prices.return_value = current_prices or {}
|
|
mock_agent.get_reasoning_steps.return_value = reasoning_steps or []
|
|
mock_agent.get_tool_usage.return_value = tool_usage or {}
|
|
mock_agent.get_conversation_history.return_value = conversation_history or []
|
|
|
|
# Async methods - use AsyncMock
|
|
mock_agent.run_trading_session = AsyncMock(return_value=session_result or {"success": True})
|
|
mock_agent.generate_summary = AsyncMock(return_value="Mock summary")
|
|
mock_agent.summarize_message = AsyncMock(return_value="Mock message summary")
|
|
|
|
# Mock model for summary generation
|
|
mock_agent.model = Mock()
|
|
|
|
return mock_agent
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestModelDayExecutorInitialization:
|
|
"""Test ModelDayExecutor initialization."""
|
|
|
|
def test_init_with_required_params(self, clean_db):
|
|
"""Should initialize with required parameters."""
|
|
from api.model_day_executor import ModelDayExecutor
|
|
|
|
executor = ModelDayExecutor(
|
|
job_id="test-job-123",
|
|
date="2025-01-16",
|
|
model_sig="gpt-5",
|
|
config_path="configs/test.json",
|
|
db_path=clean_db
|
|
)
|
|
|
|
assert executor.job_id == "test-job-123"
|
|
assert executor.date == "2025-01-16"
|
|
assert executor.model_sig == "gpt-5"
|
|
assert executor.config_path == "configs/test.json"
|
|
|
|
def test_init_creates_runtime_config(self, clean_db):
|
|
"""Should create isolated runtime config file."""
|
|
from api.model_day_executor import ModelDayExecutor
|
|
|
|
with patch("api.model_day_executor.RuntimeConfigManager") as mock_runtime:
|
|
mock_instance = Mock()
|
|
mock_instance.create_runtime_config.return_value = "/tmp/runtime_test.json"
|
|
mock_runtime.return_value = mock_instance
|
|
|
|
executor = ModelDayExecutor(
|
|
job_id="test-job-123",
|
|
date="2025-01-16",
|
|
model_sig="gpt-5",
|
|
config_path="configs/test.json",
|
|
db_path=clean_db
|
|
)
|
|
|
|
# Verify runtime config created
|
|
mock_instance.create_runtime_config.assert_called_once_with(
|
|
job_id="test-job-123",
|
|
model_sig="gpt-5",
|
|
date="2025-01-16"
|
|
)
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestModelDayExecutorExecution:
|
|
"""Test trading session execution."""
|
|
|
|
def test_execute_success(self, clean_db, sample_job_data):
|
|
"""Should execute trading session and write results to DB."""
|
|
from api.model_day_executor import ModelDayExecutor
|
|
from api.job_manager import JobManager
|
|
|
|
# Create job and job_detail
|
|
manager = JobManager(db_path=clean_db)
|
|
job_id = manager.create_job(
|
|
config_path="configs/test.json",
|
|
date_range=["2025-01-16"],
|
|
models=["gpt-5"]
|
|
)
|
|
|
|
# Mock agent execution
|
|
mock_agent = create_mock_agent(
|
|
positions={"AAPL": 10, "CASH": 7500.0},
|
|
current_prices={"AAPL": 250.0},
|
|
session_result={"success": True, "total_steps": 15, "stop_signal_received": True}
|
|
)
|
|
|
|
with patch("api.model_day_executor.RuntimeConfigManager") as mock_runtime:
|
|
mock_instance = Mock()
|
|
mock_instance.create_runtime_config.return_value = "/tmp/runtime_test.json"
|
|
mock_runtime.return_value = mock_instance
|
|
|
|
executor = ModelDayExecutor(
|
|
job_id=job_id,
|
|
date="2025-01-16",
|
|
model_sig="gpt-5",
|
|
config_path="configs/test.json",
|
|
db_path=clean_db
|
|
)
|
|
|
|
# Mock the _initialize_agent method
|
|
with patch.object(executor, '_initialize_agent', return_value=mock_agent):
|
|
result = executor.execute()
|
|
|
|
assert result["success"] is True
|
|
assert result["job_id"] == job_id
|
|
assert result["date"] == "2025-01-16"
|
|
assert result["model"] == "gpt-5"
|
|
|
|
# Verify job_detail status updated
|
|
progress = manager.get_job_progress(job_id)
|
|
assert progress["completed"] == 1
|
|
|
|
def test_execute_failure_updates_status(self, clean_db):
|
|
"""Should update status to failed on execution error."""
|
|
from api.model_day_executor import ModelDayExecutor
|
|
from api.job_manager import JobManager
|
|
|
|
# Create job
|
|
manager = JobManager(db_path=clean_db)
|
|
job_id = manager.create_job(
|
|
config_path="configs/test.json",
|
|
date_range=["2025-01-16"],
|
|
models=["gpt-5"]
|
|
)
|
|
|
|
# Mock agent to raise error
|
|
with patch("api.model_day_executor.RuntimeConfigManager") as mock_runtime:
|
|
mock_instance = Mock()
|
|
mock_instance.create_runtime_config.return_value = "/tmp/runtime_test.json"
|
|
mock_runtime.return_value = mock_instance
|
|
|
|
executor = ModelDayExecutor(
|
|
job_id=job_id,
|
|
date="2025-01-16",
|
|
model_sig="gpt-5",
|
|
config_path="configs/test.json",
|
|
db_path=clean_db
|
|
)
|
|
|
|
# Mock _initialize_agent to raise error
|
|
with patch.object(executor, '_initialize_agent', side_effect=Exception("Agent initialization failed")):
|
|
result = executor.execute()
|
|
|
|
assert result["success"] is False
|
|
assert "error" in result
|
|
|
|
# Verify job_detail marked as failed
|
|
progress = manager.get_job_progress(job_id)
|
|
assert progress["failed"] == 1
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestModelDayExecutorDataPersistence:
|
|
"""Test result persistence to SQLite."""
|
|
|
|
def test_writes_position_to_database(self, clean_db):
|
|
"""Should write position record to SQLite."""
|
|
from api.model_day_executor import ModelDayExecutor
|
|
from api.job_manager import JobManager
|
|
from api.database import get_db_connection
|
|
|
|
# Create job
|
|
manager = JobManager(db_path=clean_db)
|
|
job_id = manager.create_job(
|
|
config_path="configs/test.json",
|
|
date_range=["2025-01-16"],
|
|
models=["gpt-5"]
|
|
)
|
|
|
|
# Mock successful execution
|
|
mock_agent = create_mock_agent(
|
|
positions={"AAPL": 10, "CASH": 7500.0},
|
|
last_trade={"action": "buy", "symbol": "AAPL", "amount": 10, "price": 250.0},
|
|
current_prices={"AAPL": 250.0},
|
|
session_result={"success": True, "total_steps": 10}
|
|
)
|
|
|
|
with patch("api.model_day_executor.RuntimeConfigManager") as mock_runtime:
|
|
mock_instance = Mock()
|
|
mock_instance.create_runtime_config.return_value = "/tmp/runtime_test.json"
|
|
mock_runtime.return_value = mock_instance
|
|
|
|
executor = ModelDayExecutor(
|
|
job_id=job_id,
|
|
date="2025-01-16",
|
|
model_sig="gpt-5",
|
|
config_path="configs/test.json",
|
|
db_path=clean_db
|
|
)
|
|
|
|
with patch.object(executor, '_initialize_agent', return_value=mock_agent):
|
|
executor.execute()
|
|
|
|
# Verify position written to database
|
|
conn = get_db_connection(clean_db)
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute("""
|
|
SELECT job_id, date, model, action_id, action_type
|
|
FROM positions
|
|
WHERE job_id = ? AND date = ? AND model = ?
|
|
""", (job_id, "2025-01-16", "gpt-5"))
|
|
|
|
row = cursor.fetchone()
|
|
assert row is not None
|
|
assert row[0] == job_id
|
|
assert row[1] == "2025-01-16"
|
|
assert row[2] == "gpt-5"
|
|
|
|
conn.close()
|
|
|
|
def test_writes_holdings_to_database(self, clean_db):
|
|
"""Should write holdings records to SQLite."""
|
|
from api.model_day_executor import ModelDayExecutor
|
|
from api.job_manager import JobManager
|
|
from api.database import get_db_connection
|
|
|
|
# Create job
|
|
manager = JobManager(db_path=clean_db)
|
|
job_id = manager.create_job(
|
|
config_path="configs/test.json",
|
|
date_range=["2025-01-16"],
|
|
models=["gpt-5"]
|
|
)
|
|
|
|
# Mock successful execution
|
|
mock_agent = create_mock_agent(
|
|
positions={"AAPL": 10, "MSFT": 5, "CASH": 7500.0},
|
|
current_prices={"AAPL": 250.0, "MSFT": 300.0},
|
|
session_result={"success": True}
|
|
)
|
|
|
|
with patch("api.model_day_executor.RuntimeConfigManager") as mock_runtime:
|
|
mock_instance = Mock()
|
|
mock_instance.create_runtime_config.return_value = "/tmp/runtime_test.json"
|
|
mock_runtime.return_value = mock_instance
|
|
|
|
executor = ModelDayExecutor(
|
|
job_id=job_id,
|
|
date="2025-01-16",
|
|
model_sig="gpt-5",
|
|
config_path="configs/test.json",
|
|
db_path=clean_db
|
|
)
|
|
|
|
with patch.object(executor, '_initialize_agent', return_value=mock_agent):
|
|
executor.execute()
|
|
|
|
# Verify holdings written
|
|
conn = get_db_connection(clean_db)
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute("""
|
|
SELECT h.symbol, h.quantity
|
|
FROM holdings h
|
|
JOIN positions p ON h.position_id = p.id
|
|
WHERE p.job_id = ? AND p.date = ? AND p.model = ?
|
|
ORDER BY h.symbol
|
|
""", (job_id, "2025-01-16", "gpt-5"))
|
|
|
|
holdings = cursor.fetchall()
|
|
assert len(holdings) == 3
|
|
assert holdings[0][0] == "AAPL"
|
|
assert holdings[0][1] == 10.0
|
|
|
|
conn.close()
|
|
|
|
def test_writes_reasoning_logs(self, clean_db):
|
|
"""Should write AI reasoning logs to SQLite."""
|
|
from api.model_day_executor import ModelDayExecutor
|
|
from api.job_manager import JobManager
|
|
from api.database import get_db_connection
|
|
|
|
# Create job
|
|
manager = JobManager(db_path=clean_db)
|
|
job_id = manager.create_job(
|
|
config_path="configs/test.json",
|
|
date_range=["2025-01-16"],
|
|
models=["gpt-5"]
|
|
)
|
|
|
|
# Mock execution with reasoning
|
|
mock_agent = create_mock_agent(
|
|
positions={"CASH": 10000.0},
|
|
reasoning_steps=[
|
|
{"step": 1, "reasoning": "Analyzing market data"},
|
|
{"step": 2, "reasoning": "Evaluating risk"}
|
|
],
|
|
session_result={
|
|
"success": True,
|
|
"total_steps": 5,
|
|
"stop_signal_received": True,
|
|
"reasoning_summary": "Market analysis indicates upward trend"
|
|
}
|
|
)
|
|
|
|
with patch("api.model_day_executor.RuntimeConfigManager") as mock_runtime:
|
|
mock_instance = Mock()
|
|
mock_instance.create_runtime_config.return_value = "/tmp/runtime_test.json"
|
|
mock_runtime.return_value = mock_instance
|
|
|
|
executor = ModelDayExecutor(
|
|
job_id=job_id,
|
|
date="2025-01-16",
|
|
model_sig="gpt-5",
|
|
config_path="configs/test.json",
|
|
db_path=clean_db
|
|
)
|
|
|
|
with patch.object(executor, '_initialize_agent', return_value=mock_agent):
|
|
executor.execute()
|
|
|
|
# NOTE: Reasoning logs are now stored differently (see test_model_day_executor_reasoning.py)
|
|
# This test is deprecated but kept to ensure backward compatibility
|
|
pytest.skip("Test deprecated - reasoning logs schema changed. See test_model_day_executor_reasoning.py")
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestModelDayExecutorCleanup:
|
|
"""Test cleanup operations."""
|
|
|
|
def test_cleanup_runtime_config_on_success(self, clean_db):
|
|
"""Should cleanup runtime config after successful execution."""
|
|
from api.model_day_executor import ModelDayExecutor
|
|
from api.job_manager import JobManager
|
|
|
|
manager = JobManager(db_path=clean_db)
|
|
job_id = manager.create_job(
|
|
config_path="configs/test.json",
|
|
date_range=["2025-01-16"],
|
|
models=["gpt-5"]
|
|
)
|
|
|
|
mock_agent = create_mock_agent(
|
|
positions={"CASH": 10000.0},
|
|
session_result={"success": True}
|
|
)
|
|
|
|
with patch("api.model_day_executor.RuntimeConfigManager") as mock_runtime:
|
|
mock_instance = Mock()
|
|
mock_instance.create_runtime_config.return_value = "/tmp/runtime.json"
|
|
mock_runtime.return_value = mock_instance
|
|
|
|
executor = ModelDayExecutor(
|
|
job_id=job_id,
|
|
date="2025-01-16",
|
|
model_sig="gpt-5",
|
|
config_path="configs/test.json",
|
|
db_path=clean_db
|
|
)
|
|
|
|
with patch.object(executor, '_initialize_agent', return_value=mock_agent):
|
|
executor.execute()
|
|
|
|
# Verify cleanup called
|
|
mock_instance.cleanup_runtime_config.assert_called_once_with("/tmp/runtime.json")
|
|
|
|
def test_cleanup_runtime_config_on_failure(self, clean_db):
|
|
"""Should cleanup runtime config even after failure."""
|
|
from api.model_day_executor import ModelDayExecutor
|
|
from api.job_manager import JobManager
|
|
|
|
manager = JobManager(db_path=clean_db)
|
|
job_id = manager.create_job(
|
|
config_path="configs/test.json",
|
|
date_range=["2025-01-16"],
|
|
models=["gpt-5"]
|
|
)
|
|
|
|
with patch("api.model_day_executor.RuntimeConfigManager") as mock_runtime:
|
|
mock_instance = Mock()
|
|
mock_instance.create_runtime_config.return_value = "/tmp/runtime.json"
|
|
mock_runtime.return_value = mock_instance
|
|
|
|
executor = ModelDayExecutor(
|
|
job_id=job_id,
|
|
date="2025-01-16",
|
|
model_sig="gpt-5",
|
|
config_path="configs/test.json",
|
|
db_path=clean_db
|
|
)
|
|
|
|
# Mock _initialize_agent to raise error
|
|
with patch.object(executor, '_initialize_agent', side_effect=Exception("Agent failed")):
|
|
executor.execute()
|
|
|
|
# Verify cleanup called even on failure
|
|
mock_instance.cleanup_runtime_config.assert_called_once_with("/tmp/runtime.json")
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestModelDayExecutorPositionCalculations:
|
|
"""Test position and P&L calculations."""
|
|
|
|
def test_calculates_portfolio_value(self, clean_db):
|
|
"""Should calculate total portfolio value."""
|
|
from api.model_day_executor import ModelDayExecutor
|
|
from api.job_manager import JobManager
|
|
from api.database import get_db_connection
|
|
|
|
manager = JobManager(db_path=clean_db)
|
|
job_id = manager.create_job(
|
|
config_path="configs/test.json",
|
|
date_range=["2025-01-16"],
|
|
models=["gpt-5"]
|
|
)
|
|
|
|
mock_agent = create_mock_agent(
|
|
positions={"AAPL": 10, "CASH": 7500.0}, # 10 shares @ $250 = $2500
|
|
current_prices={"AAPL": 250.0},
|
|
session_result={"success": True}
|
|
)
|
|
|
|
with patch("api.model_day_executor.RuntimeConfigManager") as mock_runtime:
|
|
mock_instance = Mock()
|
|
mock_instance.create_runtime_config.return_value = "/tmp/runtime_test.json"
|
|
mock_runtime.return_value = mock_instance
|
|
|
|
executor = ModelDayExecutor(
|
|
job_id=job_id,
|
|
date="2025-01-16",
|
|
model_sig="gpt-5",
|
|
config_path="configs/test.json",
|
|
db_path=clean_db
|
|
)
|
|
|
|
with patch.object(executor, '_initialize_agent', return_value=mock_agent):
|
|
executor.execute()
|
|
|
|
# Verify portfolio value calculated correctly
|
|
conn = get_db_connection(clean_db)
|
|
cursor = conn.cursor()
|
|
|
|
cursor.execute("""
|
|
SELECT portfolio_value
|
|
FROM positions
|
|
WHERE job_id = ? AND date = ? AND model = ?
|
|
""", (job_id, "2025-01-16", "gpt-5"))
|
|
|
|
row = cursor.fetchone()
|
|
assert row is not None
|
|
# Portfolio value should be 2500 (stocks) + 7500 (cash) = 10000
|
|
assert row[0] == 10000.0
|
|
|
|
conn.close()
|
|
|
|
|
|
# Coverage target: 90%+ for api/model_day_executor.py
|