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migration: establish exact preserved app baseline

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leefer
2026-07-30 23:51:48 +08:00
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tests/test_curated_screener.py
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import sqlite3
import tempfile
import unittest
from datetime import datetime, timedelta
from pathlib import Path
from database import ReviewDatabase
from screener import (
ADVANCED_CURATED_STRATEGIES,
CURATED_STRATEGIES,
FACTOR_FIELDS,
FACTOR_GROUPS,
ScreenerEngine,
_broken_reversal_metrics,
_earnings_event_rows,
_popularity_factor_rows,
_risk_flags,
_rsi,
_quarter_periods,
)
from server import DashboardService, automatic_screener_jobs
class CuratedScreenerTests(unittest.TestCase):
def test_curated_library_contains_original_and_advanced_strategies(self):
self.assertEqual(19, len(ADVANCED_CURATED_STRATEGIES))
self.assertEqual(29, len(CURATED_STRATEGIES))
self.assertEqual(29, len({item["name"] for item in CURATED_STRATEGIES}))
self.assertTrue(
{"行业动量轮动", "主力资金行业流入"}.issubset(
{item["name"] for item in CURATED_STRATEGIES}
)
)
self.assertTrue(
all(item["formula"]["meta"]["library"] == "curated" for item in CURATED_STRATEGIES)
)
self.assertTrue(
{
"景气-趋势-拥挤三维行业打分",
"大小盘/成长价值风格切换(元策略)",
"业绩超预期漂移(SUE/PEAD)",
"多因子综合打分(IC动态加权)",
"热度突增潜伏(另类数据)",
"机构榜溢价",
}.issubset({item["name"] for item in CURATED_STRATEGIES})
)
def test_every_curated_strategy_explains_environment_and_failure_risk(self):
for strategy in CURATED_STRATEGIES:
meta = strategy["formula"]["meta"]
self.assertTrue(meta.get("suitable_environment"), strategy["name"])
self.assertTrue(meta.get("failure_risk"), strategy["name"])
self.assertNotIn("emotion_gate", meta, strategy["name"])
def test_automatic_curated_jobs_are_not_filtered_by_market_regime(self):
strategies = [
{
"name": "阶段策略",
"regimes": ["retreat"],
"formula": {"meta": {"library": "stage"}},
},
*CURATED_STRATEGIES,
]
for regime in ("ice", "repair", "fermentation", "climax", "divergence", "retreat"):
jobs = automatic_screener_jobs(strategies, regime)
curated_names = {
job["strategy"]["name"] for job in jobs if job["mode"] == "curated"
}
self.assertEqual(
{strategy["name"] for strategy in CURATED_STRATEGIES},
curated_names,
regime,
)
def test_curated_risk_flags_do_not_reintroduce_regime_gating(self):
row = {
"pct_chg": 0,
"return_10d": 0,
"volatility_10d": 0,
"amount_billion": 5,
}
self.assertIn("市场处于退潮阶段，策略可能选择空仓", _risk_flags(row, "retreat"))
self.assertNotIn(
"市场处于退潮阶段，策略可能选择空仓",
_risk_flags(row, "retreat", include_regime_risk=False),
)
def test_every_curated_formula_uses_supported_factors(self):
with tempfile.TemporaryDirectory() as root:
database = ReviewDatabase(Path(root) / "review.db")
engine = ScreenerEngine(database)
for strategy in CURATED_STRATEGIES:
formula = engine.validate_formula(strategy["formula"])
fields = {
item["field"]
for item in formula["filters"] + formula["score"]
}
self.assertTrue(fields.issubset(FACTOR_FIELDS), strategy["name"])
def test_server_gate_blocks_specialized_strategies_until_sources_are_ready(self):
factor_dates = [f"2026{index + 1:04d}" for index in range(260)]
health = {
"market": True,
"auction": True,
"benchmark": True,
"valuation": True,
"fundamental": True,
"dividend_history": True,
"moneyflow_history": True,
"earnings_events": False,
"popularity": False,
"institutions": False,
}
expected = {
"业绩超预期漂移(SUE/PEAD)": "业绩预告与快报",
"热度突增潜伏(另类数据)": "当日人气榜",
"机构榜溢价": "龙虎榜机构席位",
}
by_name = {strategy["name"]: strategy for strategy in CURATED_STRATEGIES}
for name, missing_label in expected.items():
self.assertEqual(
[missing_label],
DashboardService._strategy_missing_data(
by_name[name], factor_dates, health
),
name,
)
ready_health = {
**health,
"earnings_events": True,
"popularity": True,
"institutions": True,
}
for name in expected:
self.assertEqual(
[],
DashboardService._strategy_missing_data(
by_name[name], factor_dates, ready_health
),
name,
)
def test_factor_groups_cover_every_quant_factor(self):
grouped = [field for fields in FACTOR_GROUPS.values() for field in fields]
self.assertEqual(set(FACTOR_FIELDS), set(grouped))
self.assertEqual(len(grouped), len(set(grouped)))
def test_database_migrates_valuation_and_fundamental_columns(self):
with tempfile.TemporaryDirectory() as root:
path = Path(root) / "review.db"
ReviewDatabase(path)
connection = sqlite3.connect(path)
try:
indicator_columns = {
row[1] for row in connection.execute("PRAGMA table_info(daily_indicators)")
}
tables = {
row[0] for row in connection.execute(
"SELECT name FROM sqlite_master WHERE type='table'"
)
}
finally:
connection.close()
self.assertTrue({"pe_ttm", "pb", "ps_ttm", "dv_ttm"}.issubset(indicator_columns))
self.assertIn("fundamental_indicators", tables)
self.assertIn("benchmark_bars", tables)
self.assertIn("earnings_events", tables)
self.assertIn("popularity_factors", tables)
self.assertIn("lhb_institution_daily", tables)
def test_advanced_strategies_declare_history_and_backtest_contracts(self):
for strategy in ADVANCED_CURATED_STRATEGIES:
meta = strategy["formula"]["meta"]
self.assertGreaterEqual(meta["history_days"], 80, strategy["name"])
self.assertGreaterEqual(meta["backtest_days"], 1, strategy["name"])
self.assertGreater(meta["take_profit"], 0, strategy["name"])
self.assertLess(meta["stop_loss"], 0, strategy["name"])
def test_quarter_periods_stop_at_selected_date(self):
periods = _quarter_periods("20260722", 5)
self.assertEqual(
["20250630", "20250930", "20251231", "20260331", "20260630"],
periods,
)
def test_factor_health_summary_uses_availability_counts(self):
with tempfile.TemporaryDirectory() as root:
database = ReviewDatabase(Path(root) / "review.db")
with database.connect() as connection:
connection.execute(
"INSERT INTO daily_bars (trade_date, ts_code) VALUES (?, ?)",
("20260722", "600000.SH"),
)
connection.execute(
"""
INSERT INTO daily_indicators
(trade_date, ts_code, pe_ttm)
VALUES (?, ?, ?)
""",
("20260722", "600000.SH", 8.5),
)
connection.executemany(
"INSERT INTO daily_indicators (trade_date, ts_code) VALUES (?, ?)",
[(f"{year}1231", f"{year % 100:02d}0000.SZ") for year in range(2022, 2026)],
)
connection.execute(
"INSERT INTO auction_factors (trade_date, ts_code) VALUES (?, ?)",
("20260722", "600000.SH"),
)
connection.executemany(
"""
INSERT INTO fundamental_indicators (end_date, ann_date, ts_code, roe)
VALUES (?, ?, ?, ?)
""",
[
("20251231", "20260430", f"{index:06d}.SZ", 10.0)
for index in range(100)
],
)
connection.executemany(
"INSERT INTO benchmark_bars (trade_date, ts_code, close) VALUES (?, ?, ?)",
[(f"2026{index + 1:04d}", "000300.SH", 4000 + index) for index in range(60)],
)
health = database.factor_health_summary("20260722")
self.assertTrue(health["market"])
self.assertTrue(health["auction"])
self.assertTrue(health["valuation"])
self.assertTrue(health["fundamental"])
self.assertTrue(health["dividend_history"])
self.assertTrue(health["benchmark"])
self.assertEqual(health["valuation_rows"], 1)
self.assertEqual(health["fundamental_rows"], 100)
self.assertEqual(health["dividend_years"], 5)
def test_moneyflow_health_requires_the_latest_five_market_dates(self):
with tempfile.TemporaryDirectory() as root:
database = ReviewDatabase(Path(root) / "review.db")
dates = [f"202607{day:02d}" for day in range(20, 25)]
database.upsert_daily_bars([
{
"trade_date": trade_date, "ts_code": "600000.SH",
"open": 10, "high": 10.2, "low": 9.8, "close": 10,
"pct_chg": 0, "vol": 1000, "amount": 100000,
}
for trade_date in dates
])
database.upsert_moneyflow([
{"trade_date": "20260105", "ts_code": "600000.SH", "net_mf_amount": 10}
] * 5)
self.assertFalse(database.factor_health_summary(dates[-1])["moneyflow_history"])
database.upsert_moneyflow([
{"trade_date": trade_date, "ts_code": "600000.SH", "net_mf_amount": 10}
for trade_date in dates
])
health = database.factor_health_summary(dates[-1])
self.assertTrue(health["moneyflow_history"])
self.assertEqual(health["moneyflow_dates"], 5)
def test_technical_helpers_detect_rsi_and_daily_reversal_path(self):
self.assertLess(_rsi([10, 9, 8, 7, 6, 5, 4], 6), 1)
rows = [
{"close": 10, "high": 10, "vol": 100},
{"close": 11, "high": 11, "vol": 120},
{"close": 12, "high": 12, "vol": 130},
{"close": 11.2, "high": 11.8, "vol": 100},
{"close": 12.5, "high": 12.5, "vol": 140},
]
metrics = _broken_reversal_metrics(
rows, [False, True, True, False, True], "600000", "示例"
)
self.assertEqual(metrics["signal"], 1)
self.assertEqual(metrics["days"], 1)
def test_factor_builder_generates_long_window_and_benchmark_factors(self):
with tempfile.TemporaryDirectory() as root:
database = ReviewDatabase(Path(root) / "review.db")
database.upsert_stock_master([
{
"ts_code": "600000.SH", "name": "趋势样本", "industry": "银行",
"market": "主板", "list_date": "20000101",
}
])
dates = []
cursor = datetime(2025, 6, 1)
while len(dates) < 260:
if cursor.weekday() < 5:
dates.append(cursor.strftime("%Y%m%d"))
cursor += timedelta(days=1)
bars = []
benchmarks = []
indicators = []
for index, trade_date in enumerate(dates):
close = 10 + index * 0.05
bars.append({
"trade_date": trade_date, "ts_code": "600000.SH",
"open": close - 0.02, "high": close + 0.08, "low": close - 0.08,
"close": close, "pct_chg": 0.25, "vol": 1000 + index,
"amount": 200000,
})
benchmarks.append({
"trade_date": trade_date, "ts_code": "000300.SH",
"close": 4000 + index, "pct_chg": 0.02,
})
if index >= 250:
indicators.append({
"trade_date": trade_date, "ts_code": "600000.SH",
"turnover_rate": 2, "volume_ratio": 1,
})
database.upsert_daily_bars(bars)
database.upsert_benchmark_bars(benchmarks)
database.upsert_daily_indicators(indicators)
factors, actual_date = ScreenerEngine(database).build_factors(
dates[-1], history_days=260
)
self.assertEqual(actual_date, dates[-1])
self.assertEqual(len(factors), 1)
factor = factors[0]
self.assertEqual(factor["ma_bull_alignment"], 1)
self.assertEqual(factor["rs_high_120"], 1)
self.assertGreater(factor["momentum_60_5"], 0)
self.assertEqual(factor["momentum_60_5_rank"], 0)
def test_factor_builder_generates_sector_momentum_and_five_day_flow(self):
with tempfile.TemporaryDirectory() as root:
database = ReviewDatabase(Path(root) / "review.db")
stocks = [
("600001.SH", "动量样本", "电子", 0.16, 180),
("600002.SH", "对照样本", "银行", 0.02, -40),
]
database.upsert_stock_master([
{
"ts_code": code, "name": name, "industry": industry,
"market": "主板", "list_date": "20000101",
}
for code, name, industry, _, _ in stocks
])
dates = []
cursor = datetime(2026, 4, 1)
while len(dates) < 80:
if cursor.weekday() < 5:
dates.append(cursor.strftime("%Y%m%d"))
cursor += timedelta(days=1)
bars = []
for index, trade_date in enumerate(dates):
for code, _, _, slope, _ in stocks:
close = 10 + index * slope
bars.append({
"trade_date": trade_date, "ts_code": code,
"open": close - 0.03, "high": close + 0.08,
"low": close - 0.08, "close": close,
"pct_chg": slope, "vol": 1000 + index,
"amount": 300000,
})
database.upsert_daily_bars(bars)
database.upsert_daily_indicators([
{
"trade_date": dates[-1], "ts_code": code,
"turnover_rate": 2, "volume_ratio": 1,
"circ_mv": 1000000, "total_mv": 1500000,
}
for code, *_ in stocks
])
database.upsert_moneyflow([
{
"trade_date": trade_date, "ts_code": code,
"net_mf_amount": daily_flow,
}
for trade_date in dates[-5:]
for code, _, _, _, daily_flow in stocks
])
factors, _ = ScreenerEngine(database).build_factors(
dates[-1], history_days=80
)
by_code = {item["ts_code"]: item for item in factors}
leader = by_code["600001.SH"]
laggard = by_code["600002.SH"]
self.assertGreater(leader["return_20d"], laggard["return_20d"])
self.assertEqual(leader["sector_momentum_rank"], 1)
self.assertEqual(laggard["sector_momentum_rank"], 0)
self.assertGreater(leader["net_flow_5d_million"], 0)
self.assertLess(laggard["net_flow_5d_million"], 0)
self.assertEqual(leader["sector_flow_rank"], 1)
def test_stage_three_event_and_composite_factors_are_date_scoped(self):
with tempfile.TemporaryDirectory() as root:
database = ReviewDatabase(Path(root) / "review.db")
stocks = [
("600001.SH", "成长样本", "电子", 0.08),
("600002.SH", "价值样本", "银行", 0.02),
]
database.upsert_stock_master([
{
"ts_code": code, "name": name, "industry": industry,
"market": "主板", "list_date": "20000101",
}
for code, name, industry, _ in stocks
])
dates = []
cursor = datetime(2026, 3, 1)
while len(dates) < 80:
if cursor.weekday() < 5:
dates.append(cursor.strftime("%Y%m%d"))
cursor += timedelta(days=1)
database.upsert_daily_bars([
{
"trade_date": trade_date, "ts_code": code,
"open": 10 + index * slope - 0.02,
"high": 10 + index * slope + 0.08,
"low": 10 + index * slope - 0.08,
"close": 10 + index * slope,
"pct_chg": slope, "vol": 1000 + index, "amount": 300000,
}
for index, trade_date in enumerate(dates)
for code, _, _, slope in stocks
])
database.upsert_daily_indicators([
{
"trade_date": dates[-1], "ts_code": "600001.SH",
"turnover_rate": 3, "volume_ratio": 1.4, "total_mv": 900000,
"circ_mv": 700000, "pe_ttm": 25, "pb": 3, "ps_ttm": 4,
},
{
"trade_date": dates[-1], "ts_code": "600002.SH",
"turnover_rate": 1, "volume_ratio": 0.9, "total_mv": 5000000,
"circ_mv": 4000000, "pe_ttm": 8, "pb": 0.8, "ps_ttm": 1,
},
])
database.upsert_fundamental_indicators([
{
"end_date": "20260331", "ann_date": dates[-10],
"ts_code": "600001.SH", "roe": 16, "roic": 13,
"grossprofit_margin": 35, "netprofit_yoy": 45, "or_yoy": 30,
},
{
"end_date": "20260331", "ann_date": dates[-10],
"ts_code": "600002.SH", "roe": 9, "roic": 7,
"grossprofit_margin": 18, "netprofit_yoy": 5, "or_yoy": 3,
},
])
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()