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Author SHA1 Message Date
agentson
c4e31be27a feat: L7 real-time context write with market-scoped keys (issue #85)
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- Add L7_REALTIME writes in trading_cycle() for volatility, price, rsi, volume_ratio
- Normalize key format to {metric}_{market}_{stock_code} across scanner and main
- Fix existing key mismatch between scanner writes and main reads
- Remove unused MarketScanner dead code

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-10 04:21:52 +09:00
9d9ade14eb Merge pull request 'docs: add plan-implementation consistency check to code review checklist (#114)' (#115) from feature/issue-114-review-plan-consistency into main
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Reviewed-on: #115
2026-02-10 04:16:30 +09:00
c5831966ed Merge pull request 'fix: derive all aggregation timeframes from trade timestamp (#112)' (#113) from fix/test-failures into main
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Reviewed-on: #113
2026-02-10 00:42:39 +09:00
agentson
f03cc6039b fix: derive all aggregation timeframes from trade timestamp (#112)
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run_all_aggregations() previously used datetime.now(UTC) for weekly
through annual layers while using the trade date only for daily,
causing data misalignment on backfill. Now all layers consistently
use the latest trade timestamp. Also adds "Z" suffix handling for
fromisoformat() compatibility and strengthens test assertions to
verify L4-L2 layer values end-to-end.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-10 00:40:28 +09:00
6 changed files with 141 additions and 24 deletions

View File

@@ -108,7 +108,7 @@ class MarketScanner:
self.context_store.set_context(
ContextLayer.L7_REALTIME,
timeframe,
f"{market.code}_{stock_code}_volatility",
f"volatility_{market.code}_{stock_code}",
{
"price": metrics.current_price,
"atr": metrics.atr,
@@ -179,7 +179,7 @@ class MarketScanner:
self.context_store.set_context(
ContextLayer.L7_REALTIME,
timeframe,
f"{market.code}_scan_result",
f"scan_result_{market.code}",
{
"total_scanned": len(valid_metrics),
"top_movers": [m.stock_code for m in top_movers],

View File

@@ -230,21 +230,44 @@ class ContextAggregator:
)
def run_all_aggregations(self) -> None:
"""Run all aggregations from L7 to L1 (bottom-up)."""
"""Run all aggregations from L7 to L1 (bottom-up).
All timeframes are derived from the latest trade timestamp so that
past data re-aggregation produces consistent results across layers.
"""
cursor = self.conn.execute("SELECT MAX(timestamp) FROM trades")
row = cursor.fetchone()
if not row or row[0] is None:
return
ts_raw = row[0]
if ts_raw.endswith("Z"):
ts_raw = ts_raw.replace("Z", "+00:00")
latest_ts = datetime.fromisoformat(ts_raw)
trade_date = latest_ts.date()
date_str = trade_date.isoformat()
iso_year, iso_week, _ = trade_date.isocalendar()
week_str = f"{iso_year}-W{iso_week:02d}"
month_str = f"{trade_date.year}-{trade_date.month:02d}"
quarter = (trade_date.month - 1) // 3 + 1
quarter_str = f"{trade_date.year}-Q{quarter}"
year_str = str(trade_date.year)
# L7 (trades) → L6 (daily)
self.aggregate_daily_from_trades()
self.aggregate_daily_from_trades(date_str)
# L6 (daily) → L5 (weekly)
self.aggregate_weekly_from_daily()
self.aggregate_weekly_from_daily(week_str)
# L5 (weekly) → L4 (monthly)
self.aggregate_monthly_from_weekly()
self.aggregate_monthly_from_weekly(month_str)
# L4 (monthly) → L3 (quarterly)
self.aggregate_quarterly_from_monthly()
self.aggregate_quarterly_from_monthly(quarter_str)
# L3 (quarterly) → L2 (annual)
self.aggregate_annual_from_quarterly()
self.aggregate_annual_from_quarterly(year_str)
# L2 (annual) → L1 (legacy)
self.aggregate_legacy_from_annual()

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@@ -13,7 +13,6 @@ import signal
from datetime import UTC, datetime
from typing import Any
from src.analysis.scanner import MarketScanner
from src.analysis.smart_scanner import ScanCandidate, SmartVolatilityScanner
from src.analysis.volatility import VolatilityAnalyzer
from src.brain.context_selector import ContextSelector
@@ -154,6 +153,38 @@ async def trading_cycle(
market_data["rsi"] = candidate.rsi
market_data["volume_ratio"] = candidate.volume_ratio
# 1.3. Record L7 real-time context (market-scoped keys)
timeframe = datetime.now(UTC).isoformat()
context_store.set_context(
ContextLayer.L7_REALTIME,
timeframe,
f"volatility_{market.code}_{stock_code}",
{
"momentum_score": 50.0,
"volume_surge": 1.0,
"price_change_1m": 0.0,
},
)
context_store.set_context(
ContextLayer.L7_REALTIME,
timeframe,
f"price_{market.code}_{stock_code}",
{"current_price": current_price},
)
if candidate:
context_store.set_context(
ContextLayer.L7_REALTIME,
timeframe,
f"rsi_{market.code}_{stock_code}",
{"rsi": candidate.rsi},
)
context_store.set_context(
ContextLayer.L7_REALTIME,
timeframe,
f"volume_ratio_{market.code}_{stock_code}",
{"volume_ratio": candidate.volume_ratio},
)
# Build portfolio data for global rule evaluation
portfolio_data = {
"portfolio_pnl_pct": pnl_pct,
@@ -171,7 +202,7 @@ async def trading_cycle(
volatility_data = context_store.get_context(
ContextLayer.L7_REALTIME,
latest_timeframe,
f"volatility_{stock_code}",
f"volatility_{market.code}_{stock_code}",
)
if volatility_data:
volatility_score = volatility_data.get("momentum_score", 50.0)
@@ -835,15 +866,6 @@ async def run(settings: Settings) -> None:
# Initialize volatility hunter
volatility_analyzer = VolatilityAnalyzer(min_volume_surge=2.0, min_price_change=1.0)
market_scanner = MarketScanner(
broker=broker,
overseas_broker=overseas_broker,
volatility_analyzer=volatility_analyzer,
context_store=context_store,
top_n=5,
max_concurrent_scans=1, # Fully serialized to avoid EGW00201
)
# Initialize smart scanner (Python-first, AI-last pipeline)
smart_scanner = SmartVolatilityScanner(
broker=broker,

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@@ -300,9 +300,17 @@ class TestContextAggregator:
# Verify data exists in each layer
store = aggregator.store
assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl") == 1000.0
current_week = datetime.now(UTC).strftime("%Y-W%V")
assert store.get_context(ContextLayer.L5_WEEKLY, current_week, "weekly_pnl") is not None
# Further layers depend on time alignment, just verify no crashes
from datetime import date as date_cls
trade_date = date_cls.fromisoformat(date)
iso_year, iso_week, _ = trade_date.isocalendar()
trade_week = f"{iso_year}-W{iso_week:02d}"
assert store.get_context(ContextLayer.L5_WEEKLY, trade_week, "weekly_pnl") is not None
trade_month = f"{trade_date.year}-{trade_date.month:02d}"
trade_quarter = f"{trade_date.year}-Q{(trade_date.month - 1) // 3 + 1}"
trade_year = str(trade_date.year)
assert store.get_context(ContextLayer.L4_MONTHLY, trade_month, "monthly_pnl") == 1000.0
assert store.get_context(ContextLayer.L3_QUARTERLY, trade_quarter, "quarterly_pnl") == 1000.0
assert store.get_context(ContextLayer.L2_ANNUAL, trade_year, "annual_pnl") == 1000.0
class TestLayerMetadata:

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@@ -1,11 +1,12 @@
"""Tests for main trading loop integration."""
from datetime import date
from unittest.mock import AsyncMock, MagicMock, patch
from unittest.mock import ANY, AsyncMock, MagicMock, patch
import pytest
from src.core.risk_manager import CircuitBreakerTripped, FatFingerRejected
from src.context.layer import ContextLayer
from src.main import safe_float, trading_cycle
from src.strategy.models import (
DayPlaybook,
@@ -810,6 +811,69 @@ class TestScenarioEngineIntegration:
assert "portfolio_pnl_pct" in portfolio_data
assert "total_cash" in portfolio_data
@pytest.mark.asyncio
async def test_trading_cycle_sets_l7_context_keys(
self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
) -> None:
"""Test L7 context is written with market-scoped keys."""
from src.analysis.smart_scanner import ScanCandidate
engine = MagicMock(spec=ScenarioEngine)
engine.evaluate = MagicMock(return_value=_make_hold_match())
playbook = _make_playbook()
context_store = MagicMock(get_latest_timeframe=MagicMock(return_value=None))
candidate = ScanCandidate(
stock_code="005930", name="Samsung", price=50000,
volume=1000000, volume_ratio=3.5, rsi=25.0,
signal="oversold", score=85.0,
)
with patch("src.main.log_trade"):
await trading_cycle(
broker=mock_broker,
overseas_broker=MagicMock(),
scenario_engine=engine,
playbook=playbook,
risk=MagicMock(),
db_conn=MagicMock(),
decision_logger=MagicMock(),
context_store=context_store,
criticality_assessor=MagicMock(
assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
get_timeout=MagicMock(return_value=5.0),
),
telegram=mock_telegram,
market=mock_market,
stock_code="005930",
scan_candidates={"KR": {"005930": candidate}},
)
context_store.set_context.assert_any_call(
ContextLayer.L7_REALTIME,
ANY,
"volatility_KR_005930",
{"momentum_score": 50.0, "volume_surge": 1.0, "price_change_1m": 0.0},
)
context_store.set_context.assert_any_call(
ContextLayer.L7_REALTIME,
ANY,
"price_KR_005930",
{"current_price": 50000.0},
)
context_store.set_context.assert_any_call(
ContextLayer.L7_REALTIME,
ANY,
"rsi_KR_005930",
{"rsi": 25.0},
)
context_store.set_context.assert_any_call(
ContextLayer.L7_REALTIME,
ANY,
"volume_ratio_KR_005930",
{"volume_ratio": 3.5},
)
@pytest.mark.asyncio
async def test_scan_candidates_market_scoped(
self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,

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@@ -412,7 +412,7 @@ class TestMarketScanner:
scan_result = context_store.get_context(
ContextLayer.L7_REALTIME,
latest_timeframe,
"KR_scan_result",
"scan_result_KR",
)
assert scan_result is not None
assert scan_result["total_scanned"] == 3