WIP: Add decision logging infrastructure
- Add decision_logs table to database schema - Create decision logger module with comprehensive logging - Prepare for decision tracking and audit trail Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
agentson
235
src/logging/decision_logger.py
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235
src/logging/decision_logger.py
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"""Decision logging system with context snapshots for comprehensive audit trail."""
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from __future__ import annotations
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import json
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import sqlite3
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import uuid
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from dataclasses import dataclass
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from datetime import UTC, datetime
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from typing import Any
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@dataclass
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class DecisionLog:
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"""A logged trading decision with context and outcome."""
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decision_id: str
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timestamp: str
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stock_code: str
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market: str
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exchange_code: str
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action: str
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confidence: int
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rationale: str
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context_snapshot: dict[str, Any]
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input_data: dict[str, Any]
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outcome_pnl: float | None = None
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outcome_accuracy: int | None = None
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reviewed: bool = False
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review_notes: str | None = None
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class DecisionLogger:
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"""Logs trading decisions with full context for review and evolution."""
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def __init__(self, conn: sqlite3.Connection) -> None:
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"""Initialize the decision logger with a database connection."""
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self.conn = conn
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def log_decision(
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self,
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stock_code: str,
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market: str,
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exchange_code: str,
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action: str,
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confidence: int,
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rationale: str,
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context_snapshot: dict[str, Any],
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input_data: dict[str, Any],
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) -> str:
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"""Log a trading decision with full context.
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Args:
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stock_code: Stock symbol
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market: Market code (e.g., "KR", "US_NASDAQ")
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exchange_code: Exchange code (e.g., "KRX", "NASDAQ")
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action: Trading action (BUY/SELL/HOLD)
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confidence: Confidence level (0-100)
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rationale: Reasoning for the decision
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context_snapshot: L1-L7 context snapshot at decision time
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input_data: Market data inputs (price, volume, orderbook, etc.)
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Returns:
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decision_id: Unique identifier for this decision
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"""
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decision_id = str(uuid.uuid4())
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timestamp = datetime.now(UTC).isoformat()
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self.conn.execute(
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"""
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INSERT INTO decision_logs (
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decision_id, timestamp, stock_code, market, exchange_code,
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action, confidence, rationale, context_snapshot, input_data
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)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""",
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(
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decision_id,
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timestamp,
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stock_code,
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market,
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exchange_code,
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action,
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confidence,
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rationale,
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json.dumps(context_snapshot),
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json.dumps(input_data),
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),
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)
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self.conn.commit()
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return decision_id
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def get_unreviewed_decisions(
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self, min_confidence: int = 80, limit: int | None = None
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) -> list[DecisionLog]:
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"""Get unreviewed decisions with high confidence.
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Args:
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min_confidence: Minimum confidence threshold (default 80)
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limit: Maximum number of results (None = unlimited)
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Returns:
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List of unreviewed DecisionLog objects
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"""
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query = """
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SELECT
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decision_id, timestamp, stock_code, market, exchange_code,
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action, confidence, rationale, context_snapshot, input_data,
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outcome_pnl, outcome_accuracy, reviewed, review_notes
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FROM decision_logs
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WHERE reviewed = 0 AND confidence >= ?
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ORDER BY timestamp DESC
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"""
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if limit is not None:
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query += f" LIMIT {limit}"
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cursor = self.conn.execute(query, (min_confidence,))
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return [self._row_to_decision_log(row) for row in cursor.fetchall()]
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def mark_reviewed(self, decision_id: str, notes: str) -> None:
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"""Mark a decision as reviewed with notes.
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Args:
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decision_id: Decision identifier
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notes: Review notes and insights
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"""
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self.conn.execute(
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"""
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UPDATE decision_logs
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SET reviewed = 1, review_notes = ?
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WHERE decision_id = ?
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""",
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(notes, decision_id),
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)
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self.conn.commit()
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def update_outcome(
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self, decision_id: str, pnl: float, accuracy: int
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) -> None:
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"""Update the outcome of a decision after trade execution.
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Args:
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decision_id: Decision identifier
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pnl: Actual profit/loss realized
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accuracy: 1 if decision was correct, 0 if wrong
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"""
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self.conn.execute(
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"""
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UPDATE decision_logs
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SET outcome_pnl = ?, outcome_accuracy = ?
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WHERE decision_id = ?
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""",
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(pnl, accuracy, decision_id),
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)
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self.conn.commit()
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def get_decision_by_id(self, decision_id: str) -> DecisionLog | None:
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"""Get a specific decision by ID.
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Args:
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decision_id: Decision identifier
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Returns:
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DecisionLog object or None if not found
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"""
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cursor = self.conn.execute(
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"""
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SELECT
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decision_id, timestamp, stock_code, market, exchange_code,
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action, confidence, rationale, context_snapshot, input_data,
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outcome_pnl, outcome_accuracy, reviewed, review_notes
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FROM decision_logs
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WHERE decision_id = ?
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""",
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(decision_id,),
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)
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row = cursor.fetchone()
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return self._row_to_decision_log(row) if row else None
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def get_losing_decisions(
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self, min_confidence: int = 80, min_loss: float = -100.0
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) -> list[DecisionLog]:
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"""Get high-confidence decisions that resulted in losses.
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Useful for identifying patterns in failed predictions.
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Args:
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min_confidence: Minimum confidence threshold (default 80)
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min_loss: Minimum loss amount (default -100.0, i.e., loss >= 100)
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Returns:
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List of losing DecisionLog objects
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"""
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cursor = self.conn.execute(
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"""
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SELECT
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decision_id, timestamp, stock_code, market, exchange_code,
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action, confidence, rationale, context_snapshot, input_data,
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outcome_pnl, outcome_accuracy, reviewed, review_notes
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FROM decision_logs
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WHERE confidence >= ?
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AND outcome_pnl IS NOT NULL
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AND outcome_pnl <= ?
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ORDER BY outcome_pnl ASC
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""",
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(min_confidence, min_loss),
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)
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return [self._row_to_decision_log(row) for row in cursor.fetchall()]
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def _row_to_decision_log(self, row: tuple[Any, ...]) -> DecisionLog:
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"""Convert a database row to a DecisionLog object.
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Args:
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row: Database row tuple
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Returns:
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DecisionLog object
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"""
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return DecisionLog(
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decision_id=row[0],
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timestamp=row[1],
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stock_code=row[2],
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market=row[3],
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exchange_code=row[4],
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action=row[5],
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confidence=row[6],
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rationale=row[7],
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context_snapshot=json.loads(row[8]),
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input_data=json.loads(row[9]),
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outcome_pnl=row[10],
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outcome_accuracy=row[11],
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reviewed=bool(row[12]),
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review_notes=row[13],
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)
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