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e610a2f ee37d63 e610a2f ee37d63 e610a2f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | import numpy as np
import pandas as pd
from models.predictor import (
FEATURE_COLUMNS,
OLDWANG_WEIGHT_OVERLAY,
_apply_oldwang_weight_overlay,
_build_features,
_build_oldwang_context,
)
OLDWANG_FEATURES = [
"oldwang_triple_bull",
"oldwang_triple_bear",
"oldwang_ma5_hold",
"oldwang_trust_ma10_guard",
"oldwang_trust_ma10_broken",
"oldwang_foreign_ma20_guard",
"oldwang_foreign_ma20_broken",
"oldwang_volume_spike",
"oldwang_volume_high_break",
"oldwang_volume_low_guard",
"oldwang_volume_low_break",
"oldwang_gap_guard",
"oldwang_gap_filled",
"oldwang_bull_score",
"oldwang_bear_score",
]
def _oldwang_frame(n: int = 80) -> pd.DataFrame:
close = pd.Series(np.linspace(100, 145, n))
volume = np.full(n, 10_000.0)
volume[50] = 45_000.0
df = pd.DataFrame(
{
"open": close.shift(1).fillna(close.iloc[0]) + 0.2,
"high": close + 1.5,
"low": close - 1.0,
"close": close,
"volume": volume,
"ma5": close.rolling(5, min_periods=1).mean(),
"ma10": close.rolling(10, min_periods=1).mean(),
"ma20": close.rolling(20, min_periods=1).mean(),
"ma60": close.rolling(60, min_periods=1).mean(),
"rsi": 55.0,
"bb_pct_b": 0.5,
"k": 50.0,
"d": 48.0,
"volume_ratio": pd.Series(volume).rolling(20, min_periods=1).mean() / 10_000.0,
"atr_ratio": 0.02,
"obv_trend": 0.01,
"volatility_20d": 0.02,
"foreign_net": 1_000.0,
"trust_net": 500.0,
"dealer_net": 0.0,
"institutional_net": 1_500.0,
}
)
df["macd"] = np.linspace(-1, 1, n)
df["macd_signal"] = np.linspace(-0.8, 0.7, n)
df["macd_hist"] = df["macd"] - df["macd_signal"]
return df
def test_oldwang_features_are_production_columns_and_non_null():
df = _oldwang_frame()
feat = _build_features(df)
for col in OLDWANG_FEATURES:
assert col in FEATURE_COLUMNS
assert col in feat.columns
assert feat[col].replace([np.inf, -np.inf], np.nan).notna().all()
def test_oldwang_bull_context_activates_on_uptrend_with_institutional_buying():
df = _oldwang_frame()
feat = _build_features(df)
context = _build_oldwang_context(df, feat)
assert feat["oldwang_triple_bull"].tail(10).max() == 1.0
assert feat["oldwang_trust_ma10_guard"].tail(10).max() == 1.0
assert feat["oldwang_foreign_ma20_guard"].tail(10).max() == 1.0
assert feat["oldwang_bull_score"].tail(10).max() >= 3.0
assert context["bull_score"] >= 3.0
assert any("三陽開泰" in reason for reason in context["bull_reasons"])
assert any("外資" in reason for reason in context["bull_reasons"])
assert context["key_levels"]["ma20"] is not None
def test_oldwang_context_reports_volume_and_gap_risks():
df = _oldwang_frame()
feat = _build_features(df)
feat.loc[feat.index[-1], "oldwang_volume_low_break"] = 1.0
feat.loc[feat.index[-1], "oldwang_gap_filled"] = 1.0
feat.loc[feat.index[-1], "oldwang_bear_score"] = 2.0
context = _build_oldwang_context(df, feat)
assert context["bear_score"] == 2.0
assert any("爆大量" in reason for reason in context["risk_reasons"])
assert any("缺口回補" in reason for reason in context["risk_reasons"])
def test_oldwang_weight_overlay_applies_validated_top40_weights():
buy, sell, meta = _apply_oldwang_weight_overlay(
buy_prob=0.55,
sell_prob=0.30,
oldwang_context={"bull_score": 3, "bear_score": 1},
config=OLDWANG_WEIGHT_OVERLAY,
)
assert buy == 0.66
assert sell == 0.30
assert meta["applied"] is True
assert meta["validation_stock_count"] == 40
def test_oldwang_weight_overlay_applies_validated_bear_gate():
buy, sell, meta = _apply_oldwang_weight_overlay(
buy_prob=0.70,
sell_prob=0.20,
oldwang_context={"bull_score": 1, "bear_score": 3},
config=OLDWANG_WEIGHT_OVERLAY,
)
assert buy == 0.0
assert sell == 0.20
assert meta["applied"] is True
def test_oldwang_weight_overlay_can_be_disabled():
buy, sell, meta = _apply_oldwang_weight_overlay(
buy_prob=0.55,
sell_prob=0.30,
oldwang_context={"bull_score": 3, "bear_score": 1},
enabled=False,
)
assert buy == 0.55
assert sell == 0.30
assert meta["applied"] is False
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