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feature/is
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feature/is
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c4e31be27a | ||
| 9d9ade14eb | |||
| c5831966ed | |||
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f03cc6039b |
@@ -108,7 +108,7 @@ class MarketScanner:
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self.context_store.set_context(
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ContextLayer.L7_REALTIME,
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timeframe,
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f"{market.code}_{stock_code}_volatility",
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f"volatility_{market.code}_{stock_code}",
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{
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"price": metrics.current_price,
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"atr": metrics.atr,
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@@ -179,7 +179,7 @@ class MarketScanner:
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self.context_store.set_context(
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ContextLayer.L7_REALTIME,
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timeframe,
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f"{market.code}_scan_result",
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f"scan_result_{market.code}",
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{
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"total_scanned": len(valid_metrics),
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"top_movers": [m.stock_code for m in top_movers],
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@@ -230,21 +230,44 @@ class ContextAggregator:
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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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"""Run all aggregations from L7 to L1 (bottom-up).
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All timeframes are derived from the latest trade timestamp so that
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past data re-aggregation produces consistent results across layers.
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"""
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cursor = self.conn.execute("SELECT MAX(timestamp) FROM trades")
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row = cursor.fetchone()
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if not row or row[0] is None:
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return
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ts_raw = row[0]
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if ts_raw.endswith("Z"):
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ts_raw = ts_raw.replace("Z", "+00:00")
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latest_ts = datetime.fromisoformat(ts_raw)
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trade_date = latest_ts.date()
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date_str = trade_date.isoformat()
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iso_year, iso_week, _ = trade_date.isocalendar()
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week_str = f"{iso_year}-W{iso_week:02d}"
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month_str = f"{trade_date.year}-{trade_date.month:02d}"
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quarter = (trade_date.month - 1) // 3 + 1
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quarter_str = f"{trade_date.year}-Q{quarter}"
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year_str = str(trade_date.year)
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# L7 (trades) → L6 (daily)
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self.aggregate_daily_from_trades()
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self.aggregate_daily_from_trades(date_str)
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# L6 (daily) → L5 (weekly)
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self.aggregate_weekly_from_daily()
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self.aggregate_weekly_from_daily(week_str)
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# L5 (weekly) → L4 (monthly)
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self.aggregate_monthly_from_weekly()
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self.aggregate_monthly_from_weekly(month_str)
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# L4 (monthly) → L3 (quarterly)
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self.aggregate_quarterly_from_monthly()
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self.aggregate_quarterly_from_monthly(quarter_str)
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# L3 (quarterly) → L2 (annual)
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self.aggregate_annual_from_quarterly()
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self.aggregate_annual_from_quarterly(year_str)
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# L2 (annual) → L1 (legacy)
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self.aggregate_legacy_from_annual()
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44
src/main.py
44
src/main.py
@@ -13,7 +13,6 @@ import signal
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from datetime import UTC, datetime
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from typing import Any
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from src.analysis.scanner import MarketScanner
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from src.analysis.smart_scanner import ScanCandidate, SmartVolatilityScanner
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from src.analysis.volatility import VolatilityAnalyzer
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from src.brain.context_selector import ContextSelector
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@@ -154,6 +153,38 @@ async def trading_cycle(
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market_data["rsi"] = candidate.rsi
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market_data["volume_ratio"] = candidate.volume_ratio
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# 1.3. Record L7 real-time context (market-scoped keys)
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timeframe = datetime.now(UTC).isoformat()
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context_store.set_context(
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ContextLayer.L7_REALTIME,
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timeframe,
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f"volatility_{market.code}_{stock_code}",
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{
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"momentum_score": 50.0,
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"volume_surge": 1.0,
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"price_change_1m": 0.0,
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},
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)
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context_store.set_context(
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ContextLayer.L7_REALTIME,
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timeframe,
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f"price_{market.code}_{stock_code}",
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{"current_price": current_price},
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)
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if candidate:
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context_store.set_context(
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ContextLayer.L7_REALTIME,
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timeframe,
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f"rsi_{market.code}_{stock_code}",
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{"rsi": candidate.rsi},
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)
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context_store.set_context(
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ContextLayer.L7_REALTIME,
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timeframe,
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f"volume_ratio_{market.code}_{stock_code}",
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{"volume_ratio": candidate.volume_ratio},
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)
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# Build portfolio data for global rule evaluation
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portfolio_data = {
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"portfolio_pnl_pct": pnl_pct,
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@@ -171,7 +202,7 @@ async def trading_cycle(
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volatility_data = context_store.get_context(
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ContextLayer.L7_REALTIME,
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latest_timeframe,
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f"volatility_{stock_code}",
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f"volatility_{market.code}_{stock_code}",
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)
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if volatility_data:
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volatility_score = volatility_data.get("momentum_score", 50.0)
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@@ -835,15 +866,6 @@ async def run(settings: Settings) -> None:
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# Initialize volatility hunter
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volatility_analyzer = VolatilityAnalyzer(min_volume_surge=2.0, min_price_change=1.0)
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market_scanner = MarketScanner(
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broker=broker,
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overseas_broker=overseas_broker,
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volatility_analyzer=volatility_analyzer,
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context_store=context_store,
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top_n=5,
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max_concurrent_scans=1, # Fully serialized to avoid EGW00201
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)
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# Initialize smart scanner (Python-first, AI-last pipeline)
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smart_scanner = SmartVolatilityScanner(
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broker=broker,
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@@ -300,9 +300,17 @@ class TestContextAggregator:
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# Verify data exists in each layer
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store = aggregator.store
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assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl") == 1000.0
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current_week = datetime.now(UTC).strftime("%Y-W%V")
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assert store.get_context(ContextLayer.L5_WEEKLY, current_week, "weekly_pnl") is not None
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# Further layers depend on time alignment, just verify no crashes
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from datetime import date as date_cls
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trade_date = date_cls.fromisoformat(date)
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iso_year, iso_week, _ = trade_date.isocalendar()
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trade_week = f"{iso_year}-W{iso_week:02d}"
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assert store.get_context(ContextLayer.L5_WEEKLY, trade_week, "weekly_pnl") is not None
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trade_month = f"{trade_date.year}-{trade_date.month:02d}"
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trade_quarter = f"{trade_date.year}-Q{(trade_date.month - 1) // 3 + 1}"
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trade_year = str(trade_date.year)
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assert store.get_context(ContextLayer.L4_MONTHLY, trade_month, "monthly_pnl") == 1000.0
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assert store.get_context(ContextLayer.L3_QUARTERLY, trade_quarter, "quarterly_pnl") == 1000.0
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assert store.get_context(ContextLayer.L2_ANNUAL, trade_year, "annual_pnl") == 1000.0
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class TestLayerMetadata:
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@@ -1,11 +1,12 @@
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"""Tests for main trading loop integration."""
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from datetime import date
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from unittest.mock import AsyncMock, MagicMock, patch
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from unittest.mock import ANY, AsyncMock, MagicMock, patch
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import pytest
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from src.core.risk_manager import CircuitBreakerTripped, FatFingerRejected
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from src.context.layer import ContextLayer
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from src.main import safe_float, trading_cycle
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from src.strategy.models import (
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DayPlaybook,
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@@ -810,6 +811,69 @@ class TestScenarioEngineIntegration:
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assert "portfolio_pnl_pct" in portfolio_data
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assert "total_cash" in portfolio_data
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@pytest.mark.asyncio
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async def test_trading_cycle_sets_l7_context_keys(
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self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
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) -> None:
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"""Test L7 context is written with market-scoped keys."""
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from src.analysis.smart_scanner import ScanCandidate
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engine = MagicMock(spec=ScenarioEngine)
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engine.evaluate = MagicMock(return_value=_make_hold_match())
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playbook = _make_playbook()
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context_store = MagicMock(get_latest_timeframe=MagicMock(return_value=None))
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candidate = ScanCandidate(
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stock_code="005930", name="Samsung", price=50000,
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volume=1000000, volume_ratio=3.5, rsi=25.0,
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signal="oversold", score=85.0,
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)
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with patch("src.main.log_trade"):
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await trading_cycle(
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broker=mock_broker,
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overseas_broker=MagicMock(),
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scenario_engine=engine,
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playbook=playbook,
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risk=MagicMock(),
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db_conn=MagicMock(),
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decision_logger=MagicMock(),
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context_store=context_store,
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criticality_assessor=MagicMock(
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assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
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get_timeout=MagicMock(return_value=5.0),
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),
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telegram=mock_telegram,
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market=mock_market,
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stock_code="005930",
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scan_candidates={"KR": {"005930": candidate}},
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)
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context_store.set_context.assert_any_call(
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ContextLayer.L7_REALTIME,
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ANY,
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"volatility_KR_005930",
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{"momentum_score": 50.0, "volume_surge": 1.0, "price_change_1m": 0.0},
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)
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context_store.set_context.assert_any_call(
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ContextLayer.L7_REALTIME,
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ANY,
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"price_KR_005930",
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{"current_price": 50000.0},
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)
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context_store.set_context.assert_any_call(
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ContextLayer.L7_REALTIME,
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ANY,
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"rsi_KR_005930",
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{"rsi": 25.0},
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)
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context_store.set_context.assert_any_call(
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ContextLayer.L7_REALTIME,
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ANY,
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"volume_ratio_KR_005930",
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{"volume_ratio": 3.5},
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)
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@pytest.mark.asyncio
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async def test_scan_candidates_market_scoped(
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self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
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@@ -412,7 +412,7 @@ class TestMarketScanner:
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scan_result = context_store.get_context(
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ContextLayer.L7_REALTIME,
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latest_timeframe,
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"KR_scan_result",
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"scan_result_KR",
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)
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assert scan_result is not None
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assert scan_result["total_scanned"] == 3
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