add value risk factors with valuation and risk penalty calculations
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@ -15,6 +15,7 @@ from app.utils.logging import get_logger
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# 导入扩展因子模块
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from app.features.extended_factors import ExtendedFactors
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from app.features.sentiment_factors import SentimentFactors
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from app.features.value_risk_factors import ValueRiskFactors
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# 导入因子验证功能
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from app.features.validation import check_data_sufficiency, detect_outliers
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@ -83,6 +84,8 @@ DEFAULT_FACTORS: List[FactorSpec] = [
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FactorSpec("sent_impact", 0), # 新闻影响力
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FactorSpec("sent_market", 20), # 市场情绪指数
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FactorSpec("sent_divergence", 0), # 行业情绪背离度
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# 风险和估值因子
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FactorSpec("risk_penalty", 0), # 风险惩罚因子
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]
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@ -525,6 +528,40 @@ def _compute_security_factors(
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extended_factors = calculator.compute_all_factors(close_series, volume_series)
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results.update(extended_factors)
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# 计算情感因子
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sentiment_calculator = SentimentFactors()
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sentiment_factors = sentiment_calculator.compute_stock_factors(broker, ts_code, trade_date)
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if sentiment_factors:
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results.update(sentiment_factors)
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# 计算风险和估值因子
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value_risk_calculator = ValueRiskFactors()
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# 计算val_multiscore
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val_multiscore = value_risk_calculator.compute_val_multiscore(
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pe=latest_fields.get("daily_basic.pe"),
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pb=latest_fields.get("daily_basic.pb"),
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ps=latest_fields.get("daily_basic.ps"),
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dv=latest_fields.get("daily_basic.dv_ratio")
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)
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if val_multiscore is not None:
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results["val_multiscore"] = val_multiscore
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# 计算risk_penalty
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volat_20 = results.get("volat_20")
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turnover = latest_fields.get("daily_basic.turnover_rate")
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current_price = latest_fields.get("daily.close")
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avg_price = rolling_mean(close_series, 20) if len(close_series) >= 20 else None
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risk_penalty = value_risk_calculator.compute_risk_penalty(
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volatility=volat_20,
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turnover=turnover,
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price=current_price,
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avg_price=avg_price
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)
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if risk_penalty is not None:
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results["risk_penalty"] = risk_penalty
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# 确保返回结果不为空
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if not any(v is not None for v in results.values()):
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return {}
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274
app/features/value_risk_factors.py
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274
app/features/value_risk_factors.py
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@ -0,0 +1,274 @@
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"""Value and risk factor implementations."""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Dict, Optional, Sequence
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import numpy as np
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from app.core.indicators import normalize
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from app.utils.logging import get_logger
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LOGGER = get_logger(__name__)
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LOG_EXTRA = {"stage": "value_risk_factors"}
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class ValueRiskFactors:
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"""Value and risk factor calculation implementation.
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This class implements:
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1. Multi-dimensional valuation score (val_multiscore)
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2. Risk penalty factor (risk_penalty)
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"""
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def __init__(self):
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"""Initialize the calculator"""
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# Weights for different valuation metrics
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self.valuation_weights = {
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'pe': 0.3, # PE ratio weight
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'pb': 0.3, # PB ratio weight
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'ps': 0.2, # PS ratio weight
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'dv': 0.2 # Dividend yield weight
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}
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def compute_val_multiscore(self,
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pe: Optional[float],
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pb: Optional[float],
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ps: Optional[float],
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dv: Optional[float]) -> Optional[float]:
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"""Compute multi-dimensional valuation score
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Args:
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pe: PE ratio
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pb: PB ratio
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ps: PS ratio
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dv: Dividend yield
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Returns:
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Normalized valuation score in [-1, 1] range,
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where -1 indicates overvalued and 1 indicates undervalued
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"""
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try:
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scores = []
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weights = []
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# PE ratio score (inverted)
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if pe is not None and pe > 0:
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pe_score = -normalize(pe, factor=25.0) # Center around PE=25
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scores.append(pe_score)
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weights.append(self.valuation_weights['pe'])
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# PB ratio score (inverted)
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if pb is not None and pb > 0:
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pb_score = -normalize(pb, factor=2.0) # Center around PB=2
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scores.append(pb_score)
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weights.append(self.valuation_weights['pb'])
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# PS ratio score (inverted)
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if ps is not None and ps > 0:
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ps_score = -normalize(ps, factor=2.0) # Center around PS=2
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scores.append(ps_score)
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weights.append(self.valuation_weights['ps'])
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# Dividend yield score
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if dv is not None and dv >= 0:
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dv_score = normalize(dv, factor=0.03) # Center around 3% yield
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scores.append(dv_score)
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weights.append(self.valuation_weights['dv'])
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if not scores:
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return None
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# Normalize weights
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weights = np.array(weights)
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weights = weights / weights.sum()
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# Weighted average score
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return float(np.average(scores, weights=weights))
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except Exception as e:
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LOGGER.error(
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"Error calculating val_multiscore: %s",
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str(e),
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exc_info=True,
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extra=LOG_EXTRA
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)
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return None
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def compute_risk_penalty(self,
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volatility: Optional[float],
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turnover: Optional[float],
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price: Optional[float],
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avg_price: Optional[float]) -> Optional[float]:
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"""Compute risk penalty factor
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Args:
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volatility: Historical volatility
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turnover: Turnover rate
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price: Current price
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avg_price: Moving average price (e.g. 20-day MA)
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Returns:
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Risk penalty score in [0, 1] range,
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where 0 indicates low risk and 1 indicates high risk
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"""
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try:
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penalties = []
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# Volatility penalty
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if volatility is not None:
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vol_penalty = normalize(volatility, factor=0.3) # Baseline 30% annualized vol
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penalties.append(vol_penalty)
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# Turnover penalty
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if turnover is not None:
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turn_penalty = normalize(turnover, factor=5.0) # Baseline 500% turnover
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penalties.append(turn_penalty)
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# Price deviation penalty
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if price is not None and avg_price is not None and avg_price > 0:
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deviation = abs(price / avg_price - 1.0)
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dev_penalty = normalize(deviation, factor=0.1) # Baseline 10% deviation
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penalties.append(dev_penalty)
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if not penalties:
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return None
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# Average of all penalties
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return float(np.mean(penalties))
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except Exception as e:
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LOGGER.error(
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"Error calculating risk_penalty: %s",
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str(e),
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exc_info=True,
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extra=LOG_EXTRA
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)
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return None
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def compute_batch(self,
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broker,
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ts_codes: list[str],
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trade_date: str,
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batch_size: int = 100) -> None:
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"""Batch compute factors for multiple stocks
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Args:
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broker: Data broker instance
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ts_codes: List of stock codes
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trade_date: Trading date
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batch_size: Batch size for processing
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"""
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# Prepare SQL statements
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columns = ['risk_penalty', 'val_multiscore']
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insert_columns = ['ts_code', 'trade_date', 'updated_at'] + columns
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placeholders = ','.join('?' * len(insert_columns))
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update_clause = ', '.join(
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f"{column}=excluded.{column}"
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for column in ['updated_at'] + columns
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)
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sql = (
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f"INSERT INTO factors ({','.join(insert_columns)}) "
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f"VALUES ({placeholders}) "
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f"ON CONFLICT (ts_code, trade_date) DO UPDATE SET {update_clause}"
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)
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from datetime import datetime, timezone
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now = datetime.now(timezone.utc).isoformat()
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# Process in batches
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processed = 0
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for i in range(0, len(ts_codes), batch_size):
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batch = ts_codes[i:i + batch_size]
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values = []
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for ts_code in batch:
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try:
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# Get required data
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data = broker.get_stock_data(
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ts_code,
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trade_date,
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fields=[
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'daily_basic.pe',
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'daily_basic.pb',
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'daily_basic.ps',
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'daily_basic.dv_ratio',
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'factors.volat_20',
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'daily_basic.turnover_rate',
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'daily.close',
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'daily_basic.pe_ttm'
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],
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limit=1
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)
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if not data:
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continue
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current = data[0]
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# Get 20-day average price
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hist_data = broker.get_stock_data(
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ts_code,
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trade_date,
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fields=['daily.close'],
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limit=20
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)
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if not hist_data or len(hist_data) < 20:
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continue
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avg_price = np.mean([d['daily.close'] for d in hist_data])
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# Calculate factors
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risk_penalty = self.compute_risk_penalty(
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volatility=current.get('factors.volat_20'),
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turnover=current.get('daily_basic.turnover_rate'),
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price=current.get('daily.close'),
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avg_price=avg_price
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)
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val_multiscore = self.compute_val_multiscore(
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pe=current.get('daily_basic.pe_ttm'),
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pb=current.get('daily_basic.pb'),
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ps=current.get('daily_basic.ps'),
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dv=current.get('daily_basic.dv_ratio')
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)
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values.append((
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ts_code,
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trade_date,
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now,
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risk_penalty,
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val_multiscore
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))
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except Exception as e:
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LOGGER.error(
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"Error processing stock: %s error=%s",
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ts_code,
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str(e),
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exc_info=True,
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extra=LOG_EXTRA
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)
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continue
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if values:
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try:
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with broker.db.write_session() as session:
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session.executemany(sql, values)
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processed += len(values)
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except Exception as e:
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LOGGER.error(
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"Error saving batch results: %s",
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str(e),
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exc_info=True,
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extra=LOG_EXTRA
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)
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LOGGER.info(
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"Batch processing completed: processed %d/%d stocks",
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processed,
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len(ts_codes),
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extra=LOG_EXTRA
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)
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