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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>
76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
"""Context layer definitions for multi-tier memory management."""
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from __future__ import annotations
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from dataclasses import dataclass
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from enum import Enum
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class ContextLayer(str, Enum):
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"""7-tier context hierarchy from real-time to generational."""
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L1_LEGACY = "L1_LEGACY" # Cumulative/generational wisdom
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L2_ANNUAL = "L2_ANNUAL" # Yearly performance
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L3_QUARTERLY = "L3_QUARTERLY" # Quarterly strategy adjustments
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L4_MONTHLY = "L4_MONTHLY" # Monthly rebalancing
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L5_WEEKLY = "L5_WEEKLY" # Weekly stock selection
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L6_DAILY = "L6_DAILY" # Daily trade logs
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L7_REALTIME = "L7_REALTIME" # Real-time market data
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@dataclass(frozen=True)
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class LayerMetadata:
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"""Metadata for each context layer."""
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layer: ContextLayer
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description: str
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retention_days: int | None # None = keep forever
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aggregation_source: ContextLayer | None # Parent layer for aggregation
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# Layer configuration
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LAYER_CONFIG: dict[ContextLayer, LayerMetadata] = {
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ContextLayer.L1_LEGACY: LayerMetadata(
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layer=ContextLayer.L1_LEGACY,
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description="Cumulative trading history and core lessons learned across generations",
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retention_days=None, # Keep forever
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aggregation_source=ContextLayer.L2_ANNUAL,
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),
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ContextLayer.L2_ANNUAL: LayerMetadata(
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layer=ContextLayer.L2_ANNUAL,
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description="Yearly returns, Sharpe ratio, max drawdown, win rate",
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retention_days=365 * 10, # 10 years
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aggregation_source=ContextLayer.L3_QUARTERLY,
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),
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ContextLayer.L3_QUARTERLY: LayerMetadata(
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layer=ContextLayer.L3_QUARTERLY,
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description="Quarterly strategy adjustments, market phase detection, sector rotation",
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retention_days=365 * 3, # 3 years
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aggregation_source=ContextLayer.L4_MONTHLY,
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),
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ContextLayer.L4_MONTHLY: LayerMetadata(
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layer=ContextLayer.L4_MONTHLY,
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description="Monthly portfolio rebalancing, risk exposure, drawdown recovery",
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retention_days=365 * 2, # 2 years
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aggregation_source=ContextLayer.L5_WEEKLY,
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),
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ContextLayer.L5_WEEKLY: LayerMetadata(
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layer=ContextLayer.L5_WEEKLY,
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description="Weekly stock selection, sector focus, volatility regime",
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retention_days=365, # 1 year
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aggregation_source=ContextLayer.L6_DAILY,
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),
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ContextLayer.L6_DAILY: LayerMetadata(
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layer=ContextLayer.L6_DAILY,
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description="Daily trade logs, P&L, market summaries, decision accuracy",
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retention_days=90, # 90 days
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aggregation_source=ContextLayer.L7_REALTIME,
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),
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ContextLayer.L7_REALTIME: LayerMetadata(
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layer=ContextLayer.L7_REALTIME,
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description="Real-time positions, quotes, orderbook, volatility, live P&L",
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retention_days=7, # 7 days (real-time data is ephemeral)
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aggregation_source=None, # No aggregation source (leaf layer)
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),
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}
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