543 lines
23 KiB
Python
543 lines
23 KiB
Python
import sqlite3
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import tempfile
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import unittest
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from datetime import datetime, timedelta
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from pathlib import Path
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from database import ReviewDatabase
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from screener import (
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ADVANCED_CURATED_STRATEGIES,
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CURATED_STRATEGIES,
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FACTOR_FIELDS,
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FACTOR_GROUPS,
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ScreenerEngine,
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_broken_reversal_metrics,
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_earnings_event_rows,
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_popularity_factor_rows,
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_risk_flags,
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_rsi,
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_quarter_periods,
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)
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from server import DashboardService, automatic_screener_jobs
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class CuratedScreenerTests(unittest.TestCase):
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def test_curated_library_contains_original_and_advanced_strategies(self):
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self.assertEqual(19, len(ADVANCED_CURATED_STRATEGIES))
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self.assertEqual(29, len(CURATED_STRATEGIES))
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self.assertEqual(29, len({item["name"] for item in CURATED_STRATEGIES}))
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self.assertTrue(
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{"行业动量轮动", "主力资金行业流入"}.issubset(
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{item["name"] for item in CURATED_STRATEGIES}
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)
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)
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self.assertTrue(
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all(item["formula"]["meta"]["library"] == "curated" for item in CURATED_STRATEGIES)
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)
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self.assertTrue(
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{
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"景气-趋势-拥挤三维行业打分",
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"大小盘/成长价值风格切换(元策略)",
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"业绩超预期漂移(SUE/PEAD)",
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"多因子综合打分(IC动态加权)",
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"热度突增潜伏(另类数据)",
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"机构榜溢价",
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}.issubset({item["name"] for item in CURATED_STRATEGIES})
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)
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def test_every_curated_strategy_explains_environment_and_failure_risk(self):
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for strategy in CURATED_STRATEGIES:
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meta = strategy["formula"]["meta"]
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self.assertTrue(meta.get("suitable_environment"), strategy["name"])
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self.assertTrue(meta.get("failure_risk"), strategy["name"])
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self.assertNotIn("emotion_gate", meta, strategy["name"])
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def test_automatic_curated_jobs_are_not_filtered_by_market_regime(self):
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strategies = [
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{
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"name": "阶段策略",
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"regimes": ["retreat"],
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"formula": {"meta": {"library": "stage"}},
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},
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*CURATED_STRATEGIES,
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]
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for regime in ("ice", "repair", "fermentation", "climax", "divergence", "retreat"):
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jobs = automatic_screener_jobs(strategies, regime)
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curated_names = {
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job["strategy"]["name"] for job in jobs if job["mode"] == "curated"
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}
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self.assertEqual(
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{strategy["name"] for strategy in CURATED_STRATEGIES},
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curated_names,
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regime,
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)
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def test_curated_risk_flags_do_not_reintroduce_regime_gating(self):
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row = {
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"pct_chg": 0,
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"return_10d": 0,
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"volatility_10d": 0,
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"amount_billion": 5,
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}
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self.assertIn("市场处于退潮阶段，策略可能选择空仓", _risk_flags(row, "retreat"))
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self.assertNotIn(
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"市场处于退潮阶段，策略可能选择空仓",
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_risk_flags(row, "retreat", include_regime_risk=False),
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)
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def test_every_curated_formula_uses_supported_factors(self):
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with tempfile.TemporaryDirectory() as root:
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database = ReviewDatabase(Path(root) / "review.db")
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engine = ScreenerEngine(database)
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for strategy in CURATED_STRATEGIES:
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formula = engine.validate_formula(strategy["formula"])
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fields = {
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item["field"]
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for item in formula["filters"] + formula["score"]
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}
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self.assertTrue(fields.issubset(FACTOR_FIELDS), strategy["name"])
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def test_server_gate_blocks_specialized_strategies_until_sources_are_ready(self):
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factor_dates = [f"2026{index + 1:04d}" for index in range(260)]
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health = {
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"market": True,
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"auction": True,
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"benchmark": True,
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"valuation": True,
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"fundamental": True,
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"dividend_history": True,
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"moneyflow_history": True,
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"earnings_events": False,
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"popularity": False,
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"institutions": False,
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}
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expected = {
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"业绩超预期漂移(SUE/PEAD)": "业绩预告与快报",
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"热度突增潜伏(另类数据)": "当日人气榜",
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"机构榜溢价": "龙虎榜机构席位",
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}
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by_name = {strategy["name"]: strategy for strategy in CURATED_STRATEGIES}
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for name, missing_label in expected.items():
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self.assertEqual(
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[missing_label],
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DashboardService._strategy_missing_data(
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by_name[name], factor_dates, health
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),
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name,
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)
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ready_health = {
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**health,
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"earnings_events": True,
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"popularity": True,
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"institutions": True,
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}
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for name in expected:
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self.assertEqual(
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[],
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DashboardService._strategy_missing_data(
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by_name[name], factor_dates, ready_health
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),
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name,
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)
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def test_factor_groups_cover_every_quant_factor(self):
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grouped = [field for fields in FACTOR_GROUPS.values() for field in fields]
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self.assertEqual(set(FACTOR_FIELDS), set(grouped))
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self.assertEqual(len(grouped), len(set(grouped)))
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def test_database_migrates_valuation_and_fundamental_columns(self):
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with tempfile.TemporaryDirectory() as root:
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path = Path(root) / "review.db"
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ReviewDatabase(path)
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connection = sqlite3.connect(path)
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try:
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indicator_columns = {
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row[1] for row in connection.execute("PRAGMA table_info(daily_indicators)")
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}
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tables = {
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row[0] for row in connection.execute(
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"SELECT name FROM sqlite_master WHERE type='table'"
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)
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}
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finally:
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connection.close()
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self.assertTrue({"pe_ttm", "pb", "ps_ttm", "dv_ttm"}.issubset(indicator_columns))
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self.assertIn("fundamental_indicators", tables)
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self.assertIn("benchmark_bars", tables)
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self.assertIn("earnings_events", tables)
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self.assertIn("popularity_factors", tables)
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self.assertIn("lhb_institution_daily", tables)
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def test_advanced_strategies_declare_history_and_backtest_contracts(self):
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for strategy in ADVANCED_CURATED_STRATEGIES:
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meta = strategy["formula"]["meta"]
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self.assertGreaterEqual(meta["history_days"], 80, strategy["name"])
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self.assertGreaterEqual(meta["backtest_days"], 1, strategy["name"])
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self.assertGreater(meta["take_profit"], 0, strategy["name"])
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self.assertLess(meta["stop_loss"], 0, strategy["name"])
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def test_quarter_periods_stop_at_selected_date(self):
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periods = _quarter_periods("20260722", 5)
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self.assertEqual(
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["20250630", "20250930", "20251231", "20260331", "20260630"],
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periods,
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)
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def test_factor_health_summary_uses_availability_counts(self):
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with tempfile.TemporaryDirectory() as root:
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database = ReviewDatabase(Path(root) / "review.db")
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with database.connect() as connection:
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connection.execute(
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"INSERT INTO daily_bars (trade_date, ts_code) VALUES (?, ?)",
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("20260722", "600000.SH"),
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)
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connection.execute(
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"""
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INSERT INTO daily_indicators
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(trade_date, ts_code, pe_ttm)
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VALUES (?, ?, ?)
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""",
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("20260722", "600000.SH", 8.5),
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)
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connection.executemany(
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"INSERT INTO daily_indicators (trade_date, ts_code) VALUES (?, ?)",
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[(f"{year}1231", f"{year % 100:02d}0000.SZ") for year in range(2022, 2026)],
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)
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connection.execute(
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"INSERT INTO auction_factors (trade_date, ts_code) VALUES (?, ?)",
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("20260722", "600000.SH"),
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)
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connection.executemany(
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"""
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INSERT INTO fundamental_indicators (end_date, ann_date, ts_code, roe)
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VALUES (?, ?, ?, ?)
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""",
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[
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("20251231", "20260430", f"{index:06d}.SZ", 10.0)
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for index in range(100)
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],
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)
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connection.executemany(
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"INSERT INTO benchmark_bars (trade_date, ts_code, close) VALUES (?, ?, ?)",
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[(f"2026{index + 1:04d}", "000300.SH", 4000 + index) for index in range(60)],
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)
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health = database.factor_health_summary("20260722")
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self.assertTrue(health["market"])
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self.assertTrue(health["auction"])
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self.assertTrue(health["valuation"])
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self.assertTrue(health["fundamental"])
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self.assertTrue(health["dividend_history"])
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self.assertTrue(health["benchmark"])
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self.assertEqual(health["valuation_rows"], 1)
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self.assertEqual(health["fundamental_rows"], 100)
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self.assertEqual(health["dividend_years"], 5)
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def test_moneyflow_health_requires_the_latest_five_market_dates(self):
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with tempfile.TemporaryDirectory() as root:
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database = ReviewDatabase(Path(root) / "review.db")
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dates = [f"202607{day:02d}" for day in range(20, 25)]
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database.upsert_daily_bars([
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{
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"trade_date": trade_date, "ts_code": "600000.SH",
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"open": 10, "high": 10.2, "low": 9.8, "close": 10,
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"pct_chg": 0, "vol": 1000, "amount": 100000,
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}
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for trade_date in dates
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])
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database.upsert_moneyflow([
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{"trade_date": "20260105", "ts_code": "600000.SH", "net_mf_amount": 10}
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] * 5)
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self.assertFalse(database.factor_health_summary(dates[-1])["moneyflow_history"])
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database.upsert_moneyflow([
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{"trade_date": trade_date, "ts_code": "600000.SH", "net_mf_amount": 10}
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for trade_date in dates
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])
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health = database.factor_health_summary(dates[-1])
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self.assertTrue(health["moneyflow_history"])
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self.assertEqual(health["moneyflow_dates"], 5)
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def test_technical_helpers_detect_rsi_and_daily_reversal_path(self):
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self.assertLess(_rsi([10, 9, 8, 7, 6, 5, 4], 6), 1)
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rows = [
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{"close": 10, "high": 10, "vol": 100},
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{"close": 11, "high": 11, "vol": 120},
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{"close": 12, "high": 12, "vol": 130},
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{"close": 11.2, "high": 11.8, "vol": 100},
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{"close": 12.5, "high": 12.5, "vol": 140},
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]
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metrics = _broken_reversal_metrics(
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rows, [False, True, True, False, True], "600000", "示例"
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)
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self.assertEqual(metrics["signal"], 1)
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self.assertEqual(metrics["days"], 1)
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def test_factor_builder_generates_long_window_and_benchmark_factors(self):
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with tempfile.TemporaryDirectory() as root:
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database = ReviewDatabase(Path(root) / "review.db")
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database.upsert_stock_master([
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{
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"ts_code": "600000.SH", "name": "趋势样本", "industry": "银行",
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"market": "主板", "list_date": "20000101",
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}
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])
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dates = []
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cursor = datetime(2025, 6, 1)
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while len(dates) < 260:
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if cursor.weekday() < 5:
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dates.append(cursor.strftime("%Y%m%d"))
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cursor += timedelta(days=1)
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bars = []
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benchmarks = []
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indicators = []
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for index, trade_date in enumerate(dates):
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close = 10 + index * 0.05
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bars.append({
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"trade_date": trade_date, "ts_code": "600000.SH",
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"open": close - 0.02, "high": close + 0.08, "low": close - 0.08,
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"close": close, "pct_chg": 0.25, "vol": 1000 + index,
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"amount": 200000,
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})
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benchmarks.append({
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"trade_date": trade_date, "ts_code": "000300.SH",
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"close": 4000 + index, "pct_chg": 0.02,
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})
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if index >= 250:
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indicators.append({
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"trade_date": trade_date, "ts_code": "600000.SH",
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"turnover_rate": 2, "volume_ratio": 1,
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})
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database.upsert_daily_bars(bars)
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database.upsert_benchmark_bars(benchmarks)
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database.upsert_daily_indicators(indicators)
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factors, actual_date = ScreenerEngine(database).build_factors(
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dates[-1], history_days=260
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)
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self.assertEqual(actual_date, dates[-1])
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self.assertEqual(len(factors), 1)
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factor = factors[0]
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self.assertEqual(factor["ma_bull_alignment"], 1)
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self.assertEqual(factor["rs_high_120"], 1)
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self.assertGreater(factor["momentum_60_5"], 0)
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self.assertEqual(factor["momentum_60_5_rank"], 0)
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def test_factor_builder_generates_sector_momentum_and_five_day_flow(self):
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with tempfile.TemporaryDirectory() as root:
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database = ReviewDatabase(Path(root) / "review.db")
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stocks = [
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("600001.SH", "动量样本", "电子", 0.16, 180),
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("600002.SH", "对照样本", "银行", 0.02, -40),
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]
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database.upsert_stock_master([
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{
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"ts_code": code, "name": name, "industry": industry,
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"market": "主板", "list_date": "20000101",
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}
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for code, name, industry, _, _ in stocks
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])
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dates = []
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cursor = datetime(2026, 4, 1)
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while len(dates) < 80:
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if cursor.weekday() < 5:
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dates.append(cursor.strftime("%Y%m%d"))
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cursor += timedelta(days=1)
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bars = []
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for index, trade_date in enumerate(dates):
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for code, _, _, slope, _ in stocks:
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close = 10 + index * slope
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bars.append({
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"trade_date": trade_date, "ts_code": code,
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"open": close - 0.03, "high": close + 0.08,
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"low": close - 0.08, "close": close,
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"pct_chg": slope, "vol": 1000 + index,
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"amount": 300000,
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})
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database.upsert_daily_bars(bars)
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database.upsert_daily_indicators([
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{
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"trade_date": dates[-1], "ts_code": code,
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"turnover_rate": 2, "volume_ratio": 1,
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"circ_mv": 1000000, "total_mv": 1500000,
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}
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for code, *_ in stocks
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])
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database.upsert_moneyflow([
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{
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"trade_date": trade_date, "ts_code": code,
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"net_mf_amount": daily_flow,
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}
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for trade_date in dates[-5:]
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for code, _, _, _, daily_flow in stocks
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])
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factors, _ = ScreenerEngine(database).build_factors(
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dates[-1], history_days=80
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)
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by_code = {item["ts_code"]: item for item in factors}
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leader = by_code["600001.SH"]
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laggard = by_code["600002.SH"]
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self.assertGreater(leader["return_20d"], laggard["return_20d"])
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self.assertEqual(leader["sector_momentum_rank"], 1)
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self.assertEqual(laggard["sector_momentum_rank"], 0)
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self.assertGreater(leader["net_flow_5d_million"], 0)
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self.assertLess(laggard["net_flow_5d_million"], 0)
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self.assertEqual(leader["sector_flow_rank"], 1)
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def test_stage_three_event_and_composite_factors_are_date_scoped(self):
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with tempfile.TemporaryDirectory() as root:
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database = ReviewDatabase(Path(root) / "review.db")
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stocks = [
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("600001.SH", "成长样本", "电子", 0.08),
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("600002.SH", "价值样本", "银行", 0.02),
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]
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database.upsert_stock_master([
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{
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"ts_code": code, "name": name, "industry": industry,
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"market": "主板", "list_date": "20000101",
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}
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for code, name, industry, _ in stocks
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])
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dates = []
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cursor = datetime(2026, 3, 1)
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while len(dates) < 80:
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if cursor.weekday() < 5:
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dates.append(cursor.strftime("%Y%m%d"))
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cursor += timedelta(days=1)
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database.upsert_daily_bars([
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{
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"trade_date": trade_date, "ts_code": code,
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"open": 10 + index * slope - 0.02,
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"high": 10 + index * slope + 0.08,
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"low": 10 + index * slope - 0.08,
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"close": 10 + index * slope,
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"pct_chg": slope, "vol": 1000 + index, "amount": 300000,
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}
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for index, trade_date in enumerate(dates)
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for code, _, _, slope in stocks
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])
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database.upsert_daily_indicators([
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{
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"trade_date": dates[-1], "ts_code": "600001.SH",
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"turnover_rate": 3, "volume_ratio": 1.4, "total_mv": 900000,
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"circ_mv": 700000, "pe_ttm": 25, "pb": 3, "ps_ttm": 4,
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},
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{
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"trade_date": dates[-1], "ts_code": "600002.SH",
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"turnover_rate": 1, "volume_ratio": 0.9, "total_mv": 5000000,
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"circ_mv": 4000000, "pe_ttm": 8, "pb": 0.8, "ps_ttm": 1,
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},
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])
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database.upsert_fundamental_indicators([
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{
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"end_date": "20260331", "ann_date": dates[-10],
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"ts_code": "600001.SH", "roe": 16, "roic": 13,
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"grossprofit_margin": 35, "netprofit_yoy": 45, "or_yoy": 30,
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},
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{
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"end_date": "20260331", "ann_date": dates[-10],
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"ts_code": "600002.SH", "roe": 9, "roic": 7,
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"grossprofit_margin": 18, "netprofit_yoy": 5, "or_yoy": 3,
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},
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])
|
|
database.upsert_earnings_events([{
|
|
"end_date": "20260331", "ann_date": dates[-3],
|
|
"ts_code": "600001.SH", "forecast_profit": 100,
|
|
"actual_profit": 125, "surprise_pct": 25,
|
|
"revenue_yoy": 30, "netprofit_yoy": 45,
|
|
"source": "forecast+express",
|
|
}])
|
|
database.upsert_popularity_factors([{
|
|
"trade_date": dates[-1], "ts_code": "600001.SH",
|
|
"ths_rank": 5, "dc_rank": 8, "combined_score": 75,
|
|
"rank_change": 12, "dual_source": True,
|
|
}])
|
|
database.upsert_lhb_institutions([{
|
|
"trade_date": dates[-1], "ts_code": "600001.SH",
|
|
"exalter": "机构专用", "buy": 80_000_000,
|
|
"sell": 20_000_000, "net_buy": 60_000_000,
|
|
}])
|
|
|
|
factors, actual_date = ScreenerEngine(database).build_factors(
|
|
dates[-1], history_days=80
|
|
)
|
|
by_code = {item["ts_code"]: item for item in factors}
|
|
factor = by_code["600001.SH"]
|
|
self.assertEqual(actual_date, dates[-1])
|
|
self.assertEqual(factor["earnings_days_since_announce"], 2)
|
|
self.assertEqual(factor["earnings_surprise_pct"], 25)
|
|
self.assertEqual(factor["popularity_score"], 75)
|
|
self.assertEqual(factor["popularity_dual_source"], 1)
|
|
self.assertEqual(factor["institution_net_buy_million"], 60)
|
|
self.assertEqual(factor["institution_seat_count"], 1)
|
|
self.assertIsNotNone(factor["sector_composite_score"])
|
|
self.assertIsNotNone(factor["style_fit_score"])
|
|
self.assertIsNotNone(factor["multi_factor_composite"])
|
|
health = database.factor_health_summary(dates[-1])
|
|
self.assertTrue(health["earnings_events"])
|
|
self.assertTrue(health["popularity"])
|
|
self.assertTrue(health["institutions"])
|
|
|
|
def test_stage_three_sources_normalize_units_and_rank_changes(self):
|
|
earnings = _earnings_event_rows(
|
|
[{
|
|
"ts_code": "600001.SH", "ann_date": "20260401",
|
|
"end_date": "20260331", "net_profit_min": 10000,
|
|
"net_profit_max": 12000,
|
|
}],
|
|
[{
|
|
"ts_code": "600001.SH", "ann_date": "20260420",
|
|
"end_date": "20260331", "n_income": 132_000_000,
|
|
"yoy_net_profit": 30, "yoy_sales": 18,
|
|
}],
|
|
"20260420",
|
|
)
|
|
self.assertEqual(len(earnings), 1)
|
|
self.assertEqual(round(earnings[0]["actual_profit"]), 13200)
|
|
self.assertEqual(round(earnings[0]["surprise_pct"]), 20)
|
|
|
|
popularity = _popularity_factor_rows(
|
|
"20260420",
|
|
[{"data_type": "热股", "ts_code": "600001.SH", "rank": 5}],
|
|
[{"data_type": "A股市场", "ts_code": "600001.SH", "rank": 8}],
|
|
[{"data_type": "热股", "ts_code": "600001.SH", "rank": 20}],
|
|
[{"data_type": "A股市场", "ts_code": "600001.SH", "rank": 30}],
|
|
)
|
|
self.assertEqual(len(popularity), 1)
|
|
self.assertEqual(popularity[0]["rank_change"], 15)
|
|
self.assertTrue(popularity[0]["dual_source"])
|
|
|
|
def test_screen_reports_signal_health(self):
|
|
with tempfile.TemporaryDirectory() as root:
|
|
database = ReviewDatabase(Path(root) / "review.db")
|
|
engine = ScreenerEngine(database)
|
|
formula = {
|
|
"universe": {"exclude_st": True, "listed_days_min": 0},
|
|
"filters": [{"field": "pct_chg", "op": ">", "value": 0}],
|
|
"score": [{"field": "amount_billion", "weight": 1, "direction": "desc"}],
|
|
"limit": 5,
|
|
"min_score": 0,
|
|
}
|
|
result = engine.screen(
|
|
0, "20260724", formula, "repair", "健康检查", False,
|
|
mode="curated",
|
|
prepared_factors=[{
|
|
"ts_code": "600000.SH", "code": "600000", "name": "浦发银行",
|
|
"sector": "银行", "listed_days": 1000, "pct_chg": 1,
|
|
"amount_billion": 5, "price": 10, "return_5d": 1,
|
|
"volume_ratio_5d": 1, "sector_strength": 50,
|
|
}],
|
|
prepared_date="20260724",
|
|
)
|
|
health = result["meta"]["health"]
|
|
self.assertEqual(health["status"], "normal")
|
|
self.assertEqual(health["signal_count"], 1)
|
|
self.assertEqual(health["coverage"], 100)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|