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fix: preserve NaNs in cross-sectional ranks

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Gahow Wang
2026-04-18 16:14:25 +08:00
parent aa053605de
commit 40ec3b828a
3 changed files with 18 additions and 8 deletions
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10
strategies/momentum_quality.py
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@@ -46,9 +46,13 @@ class MomentumQualityStrategy(Strategy):
inv_dd = rolling_max_dd(data, self.quality_window) inv_dd = rolling_max_dd(data, self.quality_window)
# --- Cross-sectional ranking --- # --- Cross-sectional ranking ---
mom_rank = momentum.rank(axis=1, pct=True, na_option="bottom") # na_option="keep" so NaN stocks stay NaN in the composite. With
con_rank = consistency.rank(axis=1, pct=True, na_option="bottom") # "bottom" + default ascending=True, NaN entries receive pct=1.0 and
dd_rank = inv_dd.rank(axis=1, pct=True, na_option="bottom") # the additive composite ends up maximal for NaN rows, silently
# selecting pre-IPO / delisted names as "top".
mom_rank = momentum.rank(axis=1, pct=True, na_option="keep")
con_rank = consistency.rank(axis=1, pct=True, na_option="keep")
dd_rank = inv_dd.rank(axis=1, pct=True, na_option="keep")
# Composite: momentum 50%, consistency 25%, drawdown 25% # Composite: momentum 50%, consistency 25%, drawdown 25%
scores = 0.50 * mom_rank + 0.25 * con_rank + 0.25 * dd_rank scores = 0.50 * mom_rank + 0.25 * con_rank + 0.25 * dd_rank

6
strategies/multi_factor.py
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@@ -49,8 +49,10 @@ class MultiFactorStrategy(Strategy):
value = stock.rolling(self.value_period).min() / stock value = stock.rolling(self.value_period).min() / stock
# --- Cross-sectional ranking (each row ranked across assets) --- # --- Cross-sectional ranking (each row ranked across assets) ---
mom_rank = momentum.rank(axis=1, pct=True, na_option="bottom") # na_option="keep" so NaN stocks (pre-IPO / delisted / masked) stay
val_rank = value.rank(axis=1, pct=True, na_option="bottom") # NaN in the composite score instead of being assigned pct=1.0.
mom_rank = momentum.rank(axis=1, pct=True, na_option="keep")
val_rank = value.rank(axis=1, pct=True, na_option="keep")
scores = mom_rank + val_rank # combined score, higher = better scores = mom_rank + val_rank # combined score, higher = better
# --- Select top_n assets per row --- # --- Select top_n assets per row ---

10
strategies/recovery_momentum.py
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@@ -36,9 +36,13 @@ class RecoveryMomentumStrategy(Strategy):
# Factor 2: 12-1 month momentum # Factor 2: 12-1 month momentum
momentum = data.shift(self.mom_skip).pct_change(self.mom_lookback - self.mom_skip) momentum = data.shift(self.mom_skip).pct_change(self.mom_lookback - self.mom_skip)
# Cross-sectional percentile ranks # Cross-sectional percentile ranks. na_option="keep" is critical:
rec_rank = recovery.rank(axis=1, pct=True, na_option="bottom") # with "bottom" + ascending=True default, NaN stocks get pct=1.0, so
mom_rank = momentum.rank(axis=1, pct=True, na_option="bottom") # non-member / delisted / pre-IPO names leak into the composite as
# "top" candidates. "keep" propagates NaN and the final ascending=False
# + "bottom" rank pushes them to the end where they are not selected.
rec_rank = recovery.rank(axis=1, pct=True, na_option="keep")
mom_rank = momentum.rank(axis=1, pct=True, na_option="keep")
# Composite score (50/50) # Composite score (50/50)
composite = 0.5 * rec_rank + 0.5 * mom_rank composite = 0.5 * rec_rank + 0.5 * mom_rank