feat: implement L1-L7 context tree for multi-layered memory management
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Implements Pillar 2 (Multi-layered Context Management) with a 7-tier hierarchical memory system from real-time market data to generational trading wisdom. ## New Modules - `src/context/layer.py`: ContextLayer enum and metadata config - `src/context/store.py`: ContextStore for CRUD operations - `src/context/aggregator.py`: Bottom-up aggregation (L7→L6→...→L1) ## Database Changes - Added `contexts` table for hierarchical data storage - Added `context_metadata` table for layer configuration - Indexed by layer, timeframe, and updated_at for fast queries ## Context Layers - L1 (Legacy): Cumulative wisdom (kept forever) - L2 (Annual): Yearly metrics (10 years retention) - L3 (Quarterly): Strategy pivots (3 years) - L4 (Monthly): Portfolio rebalancing (2 years) - L5 (Weekly): Stock selection (1 year) - L6 (Daily): Trade logs (90 days) - L7 (Real-time): Live market data (7 days) ## Tests - 18 new tests in `tests/test_context.py` - 100% coverage on context modules - All 72 tests passing (54 existing + 18 new) ## Documentation - Added `docs/context-tree.md` with comprehensive guide - Updated `CLAUDE.md` architecture section - Includes usage examples and best practices Closes #15 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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agentson
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src/context/aggregator.py
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src/context/aggregator.py
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"""Context aggregation logic for rolling up data from lower to higher layers."""
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from __future__ import annotations
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import sqlite3
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from datetime import UTC, datetime
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from typing import Any
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from src.context.layer import ContextLayer
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from src.context.store import ContextStore
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class ContextAggregator:
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"""Aggregates context data from lower (finer) to higher (coarser) layers."""
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def __init__(self, conn: sqlite3.Connection) -> None:
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"""Initialize the aggregator with a database connection."""
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self.conn = conn
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self.store = ContextStore(conn)
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def aggregate_daily_from_trades(self, date: str | None = None) -> None:
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"""Aggregate L6 (daily) context from trades table.
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Args:
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date: Date in YYYY-MM-DD format. If None, uses today.
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"""
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if date is None:
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date = datetime.now(UTC).date().isoformat()
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# Calculate daily metrics from trades
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cursor = self.conn.execute(
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"""
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SELECT
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COUNT(*) as trade_count,
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SUM(CASE WHEN action = 'BUY' THEN 1 ELSE 0 END) as buys,
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SUM(CASE WHEN action = 'SELL' THEN 1 ELSE 0 END) as sells,
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SUM(CASE WHEN action = 'HOLD' THEN 1 ELSE 0 END) as holds,
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AVG(confidence) as avg_confidence,
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SUM(pnl) as total_pnl,
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COUNT(DISTINCT stock_code) as unique_stocks,
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SUM(CASE WHEN pnl > 0 THEN 1 ELSE 0 END) as wins,
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SUM(CASE WHEN pnl < 0 THEN 1 ELSE 0 END) as losses
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FROM trades
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WHERE DATE(timestamp) = ?
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""",
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(date,),
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)
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row = cursor.fetchone()
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if row and row[0] > 0: # At least one trade
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trade_count, buys, sells, holds, avg_conf, total_pnl, stocks, wins, losses = row
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# Store daily metrics in L6
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self.store.set_context(ContextLayer.L6_DAILY, date, "trade_count", trade_count)
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self.store.set_context(ContextLayer.L6_DAILY, date, "buys", buys)
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self.store.set_context(ContextLayer.L6_DAILY, date, "sells", sells)
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self.store.set_context(ContextLayer.L6_DAILY, date, "holds", holds)
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self.store.set_context(
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ContextLayer.L6_DAILY, date, "avg_confidence", round(avg_conf, 2)
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)
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self.store.set_context(
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ContextLayer.L6_DAILY, date, "total_pnl", round(total_pnl, 2)
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)
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self.store.set_context(ContextLayer.L6_DAILY, date, "unique_stocks", stocks)
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win_rate = round(wins / max(wins + losses, 1) * 100, 2)
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self.store.set_context(ContextLayer.L6_DAILY, date, "win_rate", win_rate)
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def aggregate_weekly_from_daily(self, week: str | None = None) -> None:
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"""Aggregate L5 (weekly) context from L6 (daily).
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Args:
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week: Week in YYYY-Www format (ISO week). If None, uses current week.
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"""
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if week is None:
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week = datetime.now(UTC).strftime("%Y-W%V")
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# Get all daily contexts for this week
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cursor = self.conn.execute(
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"""
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SELECT key, value FROM contexts
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WHERE layer = ? AND timeframe LIKE ?
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""",
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(ContextLayer.L6_DAILY.value, f"{week[:4]}-%"), # All days in the year
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)
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# Group by key and collect all values
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import json
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from collections import defaultdict
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daily_data: dict[str, list[Any]] = defaultdict(list)
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for row in cursor.fetchall():
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daily_data[row[0]].append(json.loads(row[1]))
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if daily_data:
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# Sum all PnL values
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if "total_pnl" in daily_data:
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total_pnl = sum(daily_data["total_pnl"])
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self.store.set_context(
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ContextLayer.L5_WEEKLY, week, "weekly_pnl", round(total_pnl, 2)
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)
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# Average all confidence values
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if "avg_confidence" in daily_data:
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conf_values = daily_data["avg_confidence"]
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avg_conf = sum(conf_values) / len(conf_values)
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self.store.set_context(
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ContextLayer.L5_WEEKLY, week, "avg_confidence", round(avg_conf, 2)
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)
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def aggregate_monthly_from_weekly(self, month: str | None = None) -> None:
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"""Aggregate L4 (monthly) context from L5 (weekly).
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Args:
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month: Month in YYYY-MM format. If None, uses current month.
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"""
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if month is None:
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month = datetime.now(UTC).strftime("%Y-%m")
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# Get all weekly contexts for this month
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cursor = self.conn.execute(
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"""
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SELECT key, value FROM contexts
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WHERE layer = ? AND timeframe LIKE ?
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""",
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(ContextLayer.L5_WEEKLY.value, f"{month[:4]}-W%"),
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)
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# Group by key and collect all values
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import json
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from collections import defaultdict
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weekly_data: dict[str, list[Any]] = defaultdict(list)
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for row in cursor.fetchall():
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weekly_data[row[0]].append(json.loads(row[1]))
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if weekly_data:
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# Sum all weekly PnL values
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if "weekly_pnl" in weekly_data:
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total_pnl = sum(weekly_data["weekly_pnl"])
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self.store.set_context(
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ContextLayer.L4_MONTHLY, month, "monthly_pnl", round(total_pnl, 2)
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)
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def aggregate_quarterly_from_monthly(self, quarter: str | None = None) -> None:
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"""Aggregate L3 (quarterly) context from L4 (monthly).
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Args:
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quarter: Quarter in YYYY-Qn format. If None, uses current quarter.
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"""
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if quarter is None:
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from datetime import datetime
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now = datetime.now(UTC)
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q = (now.month - 1) // 3 + 1
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quarter = f"{now.year}-Q{q}"
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# Get all monthly contexts for this quarter
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# Q1: 01-03, Q2: 04-06, Q3: 07-09, Q4: 10-12
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q_num = int(quarter.split("-Q")[1])
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months = [f"{quarter[:4]}-{m:02d}" for m in range((q_num - 1) * 3 + 1, q_num * 3 + 1)]
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total_pnl = 0.0
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for month in months:
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monthly_pnl = self.store.get_context(
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ContextLayer.L4_MONTHLY, month, "monthly_pnl"
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)
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if monthly_pnl is not None:
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total_pnl += monthly_pnl
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self.store.set_context(
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ContextLayer.L3_QUARTERLY, quarter, "quarterly_pnl", round(total_pnl, 2)
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)
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def aggregate_annual_from_quarterly(self, year: str | None = None) -> None:
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"""Aggregate L2 (annual) context from L3 (quarterly).
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Args:
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year: Year in YYYY format. If None, uses current year.
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"""
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if year is None:
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year = str(datetime.now(UTC).year)
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# Get all quarterly contexts for this year
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total_pnl = 0.0
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for q in range(1, 5):
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quarter = f"{year}-Q{q}"
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quarterly_pnl = self.store.get_context(
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ContextLayer.L3_QUARTERLY, quarter, "quarterly_pnl"
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)
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if quarterly_pnl is not None:
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total_pnl += quarterly_pnl
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self.store.set_context(
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ContextLayer.L2_ANNUAL, year, "annual_pnl", round(total_pnl, 2)
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)
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def aggregate_legacy_from_annual(self) -> None:
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"""Aggregate L1 (legacy) context from all L2 (annual) data."""
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# Get all annual PnL
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cursor = self.conn.execute(
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"""
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SELECT timeframe, value FROM contexts
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WHERE layer = ? AND key = ?
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ORDER BY timeframe
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""",
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(ContextLayer.L2_ANNUAL.value, "annual_pnl"),
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)
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import json
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annual_data = [(row[0], json.loads(row[1])) for row in cursor.fetchall()]
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if annual_data:
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total_pnl = sum(pnl for _, pnl in annual_data)
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years_traded = len(annual_data)
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avg_annual_pnl = total_pnl / years_traded
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# Store in L1 (single "LEGACY" timeframe)
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self.store.set_context(
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ContextLayer.L1_LEGACY, "LEGACY", "total_pnl", round(total_pnl, 2)
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)
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self.store.set_context(
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ContextLayer.L1_LEGACY, "LEGACY", "years_traded", years_traded
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)
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self.store.set_context(
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ContextLayer.L1_LEGACY,
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"LEGACY",
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"avg_annual_pnl",
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round(avg_annual_pnl, 2),
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)
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def run_all_aggregations(self) -> None:
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"""Run all aggregations from L7 to L1 (bottom-up)."""
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# L7 (trades) → L6 (daily)
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self.aggregate_daily_from_trades()
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# L6 (daily) → L5 (weekly)
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self.aggregate_weekly_from_daily()
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# L5 (weekly) → L4 (monthly)
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self.aggregate_monthly_from_weekly()
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# L4 (monthly) → L3 (quarterly)
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self.aggregate_quarterly_from_monthly()
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# L3 (quarterly) → L2 (annual)
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self.aggregate_annual_from_quarterly()
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# L2 (annual) → L1 (legacy)
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self.aggregate_legacy_from_annual()
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