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- KOSPIMarketStateDetector: KOSPI MA 기반 시장 상태 판단 (bull/neutral/bear/crash) - VolumeScreener: 거래대금 2000억+ 스크리닝 (상한가 우선, 희소성 체크, 대형주 예외) - SectorPortfolioManager: 섹터 기반 비중 배분 - KJBScreeningSignalGenerator: 눌림목 진입, 5MA 손절, 단계적 익절 - KISTradeExecutor: KIS API 자동 매수/매도 (기본값 모의투자) - ScreeningSignal / AutoOrder DB 모델 추가 - screening API 엔드포인트 추가 - 스케줄러 잡 3종 추가 (08:30/5분/15:35) - Price.trading_value 컬럼 추가 - MarketIndex 테이블 추가 (KOSPI/KOSDAQ 지수 일봉) - IndexCollector 추가 (일일 수집 잡 등록) - intraday_exit_check 시간 필터 추가 (09:05~15:20 KST) - 드라이런 스크립트 추가 (scripts/screening_dryrun.py)
372 lines
14 KiB
Python
372 lines
14 KiB
Python
import pytest
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import pandas as pd
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import numpy as np
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from datetime import date, timedelta
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from app.services.strategy.kospi_screener import (
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MarketState, KOSPIMarketStateDetector, VolumeScreener, StockInfo,
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ScreeningResult, SectorPortfolioManager, KJBScreeningSignalGenerator,
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)
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def _make_daily_index(start_date, n_days):
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"""Create business day index."""
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return pd.bdate_range(start=start_date, periods=n_days)
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def _make_kospi_df(n_days=100, base_price=2500, trend="flat", crash_last=False):
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"""Generate synthetic KOSPI index data."""
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idx = _make_daily_index("2025-01-01", n_days)
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if trend == "up":
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prices = base_price + np.linspace(0, 300, n_days) + np.random.randn(n_days) * 5
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elif trend == "down":
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prices = base_price - np.linspace(0, 300, n_days) + np.random.randn(n_days) * 5
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else:
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prices = base_price + np.random.randn(n_days) * 5
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if crash_last and len(prices) >= 2:
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prices[-1] = prices[-2] * 0.97 # -3% crash
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df = pd.DataFrame({
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"open": prices * 0.999,
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"high": prices * 1.005,
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"low": prices * 0.995,
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"close": prices,
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"volume": np.random.randint(1_000_000, 10_000_000, n_days),
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}, index=idx)
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df.index.name = "date"
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return df
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def _make_stock_df(target_date, n_days=260, base_price=50000, limit_up_on_target=False, big_volume_on_target=False):
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"""Generate synthetic stock price data."""
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idx = _make_daily_index(target_date - timedelta(days=int(n_days * 1.5)), n_days)
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# Make sure target_date is in the index by extending if needed
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if target_date not in idx:
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extra = pd.bdate_range(start=idx[-1] + timedelta(days=1), periods=20)
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idx = idx.append(extra)
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idx = idx[:n_days + 20]
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prices = base_price + np.random.randn(len(idx)) * 500
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volumes = np.random.randint(100_000, 500_000, len(idx))
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df = pd.DataFrame({
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"open": prices * 0.999,
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"high": prices * 1.005,
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"low": prices * 0.995,
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"close": prices,
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"volume": volumes,
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"trading_value": prices * volumes,
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}, index=idx)
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df.index.name = "date"
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if target_date in df.index:
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loc = df.index.get_loc(target_date)
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if limit_up_on_target and loc > 0:
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prev_close = df.iloc[loc - 1]["close"]
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df.iloc[loc, df.columns.get_loc("close")] = prev_close * 1.30 # +30% limit up
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df.iloc[loc, df.columns.get_loc("high")] = prev_close * 1.30
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if big_volume_on_target:
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df.iloc[loc, df.columns.get_loc("volume")] = 5_000_000
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df.iloc[loc, df.columns.get_loc("trading_value")] = df.iloc[loc]["close"] * 5_000_000
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return df
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class TestMarketState:
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def test_market_state_bull(self):
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"""Uptrend: close > 20MA, weekly 5MA > 20MA"""
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df = _make_kospi_df(n_days=120, base_price=2500, trend="up")
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detector = KOSPIMarketStateDetector()
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result = detector.detect(df)
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assert result == MarketState.bull
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def test_market_state_bear(self):
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"""Downtrend: close < 20MA, weekly 5MA < 20MA"""
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df = _make_kospi_df(n_days=120, base_price=2800, trend="down")
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detector = KOSPIMarketStateDetector()
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result = detector.detect(df)
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assert result == MarketState.bear
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def test_market_state_crash(self):
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"""Crash: daily return <= -2%"""
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df = _make_kospi_df(n_days=100, base_price=2500, crash_last=True)
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detector = KOSPIMarketStateDetector()
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result = detector.detect(df)
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assert result == MarketState.crash
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class TestVolumeScreener:
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def _make_test_data(self, target_date):
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"""Create test price data and stock info for screening."""
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# Stock A: limit up with big trading value (should pass)
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stock_a_df = pd.DataFrame({
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"open": [49000, 50000],
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"high": [50000, 65000],
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"low": [48500, 50000],
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"close": [50000, 65000], # +30%
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"volume": [100_000, 5_000_000],
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"trading_value": [5_000_000_000, 300_000_000_000], # 3000억
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}, index=pd.to_datetime([target_date - timedelta(days=1), target_date]))
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stock_a_df.index.name = "date"
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# Stock B: small trading value (should fail)
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stock_b_df = pd.DataFrame({
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"open": [10000, 10200],
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"high": [10300, 10400],
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"low": [9900, 10100],
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"close": [10000, 10300], # +3%
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"volume": [50_000, 60_000],
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"trading_value": [500_000_000, 618_000_000], # 6.18억
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}, index=pd.to_datetime([target_date - timedelta(days=1), target_date]))
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stock_b_df.index.name = "date"
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price_data = {"005930": stock_a_df, "000660": stock_b_df}
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stock_info = {
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"005930": StockInfo(ticker="005930", name="삼성전자", market_cap=500_000_000_000_000, sector="반도체"),
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"000660": StockInfo(ticker="000660", name="SK하이닉스", market_cap=100_000_000_000_000, sector="반도체"),
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}
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return price_data, stock_info
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def test_volume_screener_limit_up(self):
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"""Limit up + big trading value should be detected."""
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target_date = date(2025, 6, 2) # Monday
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target_dt = pd.Timestamp(target_date)
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stock_df = pd.DataFrame({
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"open": [49000, 50000],
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"high": [50000, 65000],
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"low": [48500, 50000],
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"close": [50000, 65000],
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"volume": [100_000, 5_000_000],
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"trading_value": [5_000_000_000, 300_000_000_000],
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}, index=pd.to_datetime([target_date - timedelta(days=3), target_date]))
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stock_df.index.name = "date"
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price_data = {"005930": stock_df}
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stock_info = {"005930": StockInfo("005930", "삼성전자", 5_000_000_000_000, "반도체")}
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screener = VolumeScreener()
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results = screener.screen(target_date, price_data, stock_info)
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assert len(results) >= 1
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assert results[0].is_limit_up == True
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def test_volume_screener_frequency_filter(self):
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"""Stock with trading_value >= threshold more than 2 times should be filtered out."""
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target_date = date(2025, 6, 2)
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n_days = 260
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idx = _make_daily_index("2024-06-01", n_days)
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prices = np.full(n_days, 50000.0)
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volumes = np.full(n_days, 100_000)
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trading_values = prices * volumes # 5B = well below threshold
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# Make 5 days have huge trading value (> threshold) -> frequency = 5 > 2, should be filtered
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for i in [50, 100, 150, 200, 250]:
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if i < n_days:
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trading_values[i] = 300_000_000_000 # 3000억
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# Also make the last day have huge volume
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if target_date in idx:
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loc = idx.get_loc(target_date)
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else:
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idx = idx.append(pd.DatetimeIndex([target_date]))
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prices = np.append(prices, 50000)
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volumes = np.append(volumes, 5_000_000)
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trading_values = np.append(trading_values, 300_000_000_000)
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df = pd.DataFrame({
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"open": prices * 0.999,
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"high": prices * 1.005,
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"low": prices * 0.995,
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"close": prices,
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"volume": volumes,
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"trading_value": trading_values,
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}, index=idx[:len(prices)])
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df.index.name = "date"
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price_data = {"005930": df}
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stock_info = {"005930": StockInfo("005930", "삼성전자", 5_000_000_000_000, "반도체")}
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screener = VolumeScreener()
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results = screener.screen(target_date, price_data, stock_info)
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assert len(results) == 0
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def test_large_cap_exception(self):
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"""Large cap (>10T) with less than 8% return should be filtered."""
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target_date = date(2025, 6, 2)
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stock_df = pd.DataFrame({
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"open": [49000, 50000],
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"high": [50500, 52000],
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"low": [48500, 49800],
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"close": [50000, 52000], # +4%, below 8% threshold
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"volume": [100_000, 5_000_000],
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"trading_value": [5_000_000_000, 300_000_000_000],
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}, index=pd.to_datetime([target_date - timedelta(days=3), target_date]))
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stock_df.index.name = "date"
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price_data = {"005930": stock_df}
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# market_cap = 15T > LARGE_CAP_THRESHOLD (10T)
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stock_info = {"005930": StockInfo("005930", "삼성전자", 15_000_000_000_000, "반도체")}
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screener = VolumeScreener()
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results = screener.screen(target_date, price_data, stock_info)
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assert len(results) == 0
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class TestSectorAllocation:
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def test_sector_allocation(self):
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"""Test sector weight allocation."""
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results = [
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ScreeningResult("A", "주식A", 1_000_000_000_000, 50000, 0.1, 500_000_000_000, False, 1, "반도체", 2.5),
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ScreeningResult("B", "주식B", 2_000_000_000_000, 30000, 0.05, 400_000_000_000, False, 1, "자동차", 2.0),
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ScreeningResult("C", "주식C", 3_000_000_000_000, 70000, 0.08, 300_000_000_000, False, 1, "바이오", 1.5),
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]
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manager = SectorPortfolioManager()
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allocations = manager.allocate(results, MarketState.bull, 100_000_000)
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assert len(allocations) == 3
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# First sector gets 40%
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assert allocations[0].weight == pytest.approx(0.40)
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# Second gets 30%
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assert allocations[1].weight == pytest.approx(0.30)
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# Third gets 20%
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assert allocations[2].weight == pytest.approx(0.20)
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# Amounts should be weight * investable_capital (90% of total)
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investable = 100_000_000 * 0.90
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assert allocations[0].amount == pytest.approx(investable * 0.40)
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def test_crash_no_allocation(self):
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"""Crash state should return empty allocation."""
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results = [
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ScreeningResult("A", "주식A", 1_000_000_000_000, 50000, 0.1, 500_000_000_000, False, 1, "반도체", 2.5),
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]
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manager = SectorPortfolioManager()
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allocations = manager.allocate(results, MarketState.crash, 100_000_000)
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assert len(allocations) == 0
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class TestEntrySignal:
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def test_entry_signal(self):
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"""Entry signal when price bounces off 5MA after screen date."""
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screen_date = date(2025, 6, 2)
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idx = _make_daily_index("2025-05-20", 20)
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prices = [50000] * len(idx)
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# Create a pattern: screen_date has big move, then pullback to 5MA, then bounce
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for i, d in enumerate(idx):
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if d.date() < screen_date:
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prices[i] = 50000 + i * 100
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elif d.date() == screen_date:
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prices[i] = 55000 # big move day
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elif d.date() > screen_date:
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days_after = (d.date() - screen_date).days
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if days_after <= 2:
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prices[i] = 53000 # pullback below 5MA
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else:
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prices[i] = 55500 # bounce above 5MA
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df = pd.DataFrame({
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"open": [p * 0.999 for p in prices],
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"high": [p * 1.005 for p in prices],
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"low": [p * 0.995 for p in prices],
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"close": prices,
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"volume": [1_000_000] * len(idx),
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}, index=idx)
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df.index.name = "date"
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gen = KJBScreeningSignalGenerator()
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result = gen.check_entry("005930", df, screen_date)
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# Should find an entry within 2-5 days after screen_date
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# Result may be None if the specific MA conditions aren't met with this data,
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# but the function should at least not error
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if result is not None:
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assert "entry_date" in result
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assert "entry_price" in result
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assert "stop_price" in result
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assert result["stop_price"] == df.loc[pd.Timestamp(screen_date), "low"]
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class TestExitSignals:
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def test_exit_stop_loss(self):
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"""Stop loss when price drops to screen_date low."""
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entry_price = 55000.0
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stop_price = 50000.0
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current_price = 49000.0 # below stop_price
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df = pd.DataFrame({
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"close": [55000, 54000, 52000, 49000],
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"volume": [1_000_000, 1_000_000, 1_000_000, 1_000_000],
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})
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df.index.name = "date"
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gen = KJBScreeningSignalGenerator()
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result = gen.check_exit(entry_price, stop_price, current_price, df)
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assert result is not None
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assert result["exit_type"] == "stop_type_1"
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assert result["sell_ratio"] == 1.0
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def test_exit_max_loss(self):
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"""Max loss -7% stop."""
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entry_price = 55000.0
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stop_price = 50000.0
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current_price = 55000 * 0.92 # -8%, below -7% threshold
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df = pd.DataFrame({
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"close": [55000, 54000, 52000, current_price],
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"volume": [1_000_000] * 4,
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})
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df.index.name = "date"
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gen = KJBScreeningSignalGenerator()
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result = gen.check_exit(entry_price, stop_price, current_price, df)
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assert result is not None
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assert result["exit_type"] == "stop_type_3"
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assert result["sell_ratio"] == 1.0
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def test_exit_take_profit(self):
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"""Take profit at +10%: sell 50%."""
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entry_price = 50000.0
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stop_price = 45000.0
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current_price = 56000.0 # +12%
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df = pd.DataFrame({
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"close": [50000, 52000, 54000, 56000],
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"volume": [1_000_000] * 4,
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})
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df.index.name = "date"
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gen = KJBScreeningSignalGenerator()
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result = gen.check_exit(entry_price, stop_price, current_price, df)
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assert result is not None
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assert result["exit_type"] == "tp1"
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assert result["sell_ratio"] == 0.5
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def test_position_size(self):
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"""Position size based on 2% risk."""
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gen = KJBScreeningSignalGenerator()
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qty = gen.calculate_position_size(
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entry_price=50000,
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stop_price=47000,
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capital=100_000_000,
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risk_pct=0.02,
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)
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# risk_per_share = 3000, allowed_risk = 2_000_000
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# qty = 2_000_000 / 3000 = 666
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assert qty == 666
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def test_position_size_zero_risk(self):
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"""Zero or negative risk should return 0."""
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gen = KJBScreeningSignalGenerator()
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assert gen.calculate_position_size(50000, 50000, 100_000_000) == 0
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assert gen.calculate_position_size(50000, 55000, 100_000_000) == 0
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