enhance factor evaluation with async progress tracking and UI updates
This commit is contained in:
sam
parent
4e7a56567b
commit
07c76d7674
@ -80,14 +80,37 @@ def evaluate_factor(
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"""
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performance = FactorPerformance(factor_name)
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# 计算因子值
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factor_results = compute_factor_range(
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start_date,
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end_date,
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factors=[FactorSpec(factor_name, 0)],
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ts_codes=universe
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# 导入进度状态模块
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from app.ui.progress_state import factor_progress
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# 开始因子计算进度
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factor_progress.start_calculation(
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total_securities=len(universe) if universe else 0,
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message=f"开始评估因子 {factor_name}"
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)
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try:
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# 计算因子值
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factor_results = compute_factor_range(
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start_date,
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end_date,
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factors=[FactorSpec(factor_name, 0)],
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ts_codes=universe
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)
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# 因子计算完成
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factor_progress.complete_calculation(
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message=f"因子 {factor_name} 评估完成"
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)
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except Exception as e:
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# 因子计算失败
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factor_progress.complete_calculation(
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message=f"因子 {factor_name} 评估失败: {str(e)}",
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success=False
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)
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raise
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# 按日期分组
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date_groups: Dict[date, List[FactorResult]] = {}
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for result in factor_results:
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@ -469,51 +469,45 @@ class ExtendedFactors:
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raise ValueError(f"因子 {factor_name} 没有对应的计算实现")
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def compute_all_factors(self,
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close_series: Sequence[float],
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volume_series: Sequence[float]) -> Dict[str, float]:
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"""计算所有已注册的扩展因子值
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close_series: List[float],
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volume_series: List[float],
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ts_code: str,
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trade_date: str) -> Dict[str, float | None]:
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"""计算所有扩展因子
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Args:
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close_series: 收盘价序列，从新到旧排序
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volume_series: 成交量序列，从新到旧排序
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close_series: 收盘价序列
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volume_series: 成交量序列
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ts_code: 股票代码
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trade_date: 交易日期
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Returns:
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Dict[str, float]: 因子名称到因子值的映射字典，
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只包含成功计算的因子值
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Note:
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该方法会尝试计算所有已注册的因子，失败的因子将被忽略。
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如果所有因子计算都失败，将返回空字典。
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因子名称到因子值的映射
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"""
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results = {}
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success_count = 0
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total_count = len(self.factor_specs)
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for factor_name in self.factor_specs:
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value = self.compute_factor(factor_name, close_series, volume_series)
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if value is not None:
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# 验证因子值是否在合理范围内
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validated_value = validate_factor_value(
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factor_name, value, "unknown", "unknown"
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)
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if validated_value is not None:
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results[factor_name] = validated_value
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success_count += 1
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else:
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LOGGER.debug(
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"因子值验证失败 factor=%s value=%f",
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factor_name, value,
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extra=LOG_EXTRA
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)
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for factor_spec in EXTENDED_FACTORS:
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try:
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factor_name = factor_spec.name
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factor_value = self.compute_factor(factor_name, close_series, volume_series)
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# 关闭因子计算完成日志打印
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# LOGGER.info(
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# "因子计算完成 total=%d success=%d failed=%d",
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# total_count,
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# success_count,
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# total_count - success_count,
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# extra=LOG_EXTRA
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# )
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# 验证因子值
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if factor_value is not None:
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# 使用真实的 ts_code 和 trade_date 进行验证
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validate_factor_value(factor_name, factor_value, ts_code, trade_date)
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results[factor_name] = factor_value
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except Exception as e:
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LOGGER.debug(
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"因子计算失败 factor=%s ts_code=%s date=%s err=%s",
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factor_spec.name,
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ts_code,
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trade_date,
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str(e),
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extra=LOG_EXTRA,
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)
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results[factor_spec.name] = None
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return results
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@ -332,14 +332,12 @@ def _compute_batch_factors(
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# 批次化数据可用性检查
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available_codes = _check_batch_data_availability(broker, ts_codes, trade_date, specs)
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# 更新UI进度状态
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# 更新UI进度状态 - 开始处理批次
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if total_securities > 0:
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current_progress = processed_securities + len(available_codes)
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progress_percentage = (current_progress / total_securities) * 100
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factor_progress.update_progress(
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current_securities=current_progress,
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current_securities=processed_securities,
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current_batch=batch_index + 1,
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message=f"处理批次 {batch_index + 1}/{total_batches} - 证券 {current_progress}/{total_securities} ({progress_percentage:.1f}%)"
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message=f"开始处理批次 {batch_index + 1}/{total_batches}"
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)
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for i, ts_code in enumerate(ts_codes):
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@ -379,8 +377,8 @@ def _compute_batch_factors(
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else:
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validation_stats["skipped"] += 1
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# 每处理10个证券更新一次进度
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if (i + 1) % 10 == 0 and total_securities > 0:
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# 每处理1个证券更新一次进度，确保实时性
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if total_securities > 0:
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current_progress = processed_securities + i + 1
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progress_percentage = (current_progress / total_securities) * 100
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factor_progress.update_progress(
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@ -397,6 +395,16 @@ def _compute_batch_factors(
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)
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validation_stats["skipped"] += 1
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# 批次处理完成，更新最终进度
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if total_securities > 0:
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final_progress = processed_securities + len(ts_codes)
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progress_percentage = (final_progress / total_securities) * 100
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factor_progress.update_progress(
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current_securities=final_progress,
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current_batch=batch_index + 1,
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message=f"批次 {batch_index + 1}/{total_batches} 处理完成 - 证券 {final_progress}/{total_securities} ({progress_percentage:.1f}%)"
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)
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return batch_results
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@ -676,7 +684,7 @@ def _compute_security_factors(
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# 计算扩展因子值
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calculator = ExtendedFactors()
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extended_factors = calculator.compute_all_factors(close_series, volume_series)
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extended_factors = calculator.compute_all_factors(close_series, volume_series, ts_code, trade_date)
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results.update(extended_factors)
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# 计算情感因子
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@ -10,7 +10,12 @@ class FactorProgressState:
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def __init__(self):
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"""初始化进度状态"""
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if 'factor_progress' not in st.session_state:
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# 确保session_state中有factor_progress属性
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self._ensure_initialized()
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def _ensure_initialized(self) -> None:
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"""确保进度状态已初始化"""
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if not hasattr(st.session_state, 'factor_progress'):
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st.session_state.factor_progress = {
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'current': 0,
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'total': 0,
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@ -99,6 +104,7 @@ class FactorProgressState:
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Returns:
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进度信息字典
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"""
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self._ensure_initialized()
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return st.session_state.factor_progress.copy()
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def reset(self) -> None:
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@ -66,9 +66,6 @@ def main() -> None:
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st.error(f"❌ 自动数据更新失败：{exc}")
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render_global_dashboard()
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# 显示因子计算进度
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render_factor_progress()
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tabs = st.tabs(["今日计划", "投资池/仓位", "回测与复盘", "行情可视化", "日志钻取", "数据与设置", "自检测试"])
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LOGGER.debug(
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@ -1,6 +1,8 @@
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"""股票筛选与评估视图。"""
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Tuple
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import threading
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import time
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import numpy as np
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import pandas as pd
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@ -136,10 +138,32 @@ def render_stock_evaluation() -> None:
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)
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# 4. 评估结果
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if st.button("开始评估", disabled=not selected_factors):
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with st.spinner("正在评估因子表现..."):
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# 初始化会话状态
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if 'evaluation_thread' not in st.session_state:
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st.session_state.evaluation_thread = None
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if 'evaluation_results' not in st.session_state:
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st.session_state.evaluation_results = None
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if 'evaluation_status' not in st.session_state:
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st.session_state.evaluation_status = 'idle' # idle, running, completed, error
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if 'current_factor' not in st.session_state:
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st.session_state.current_factor = ''
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if 'progress' not in st.session_state:
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st.session_state.progress = 0
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# 异步评估函数
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def run_evaluation_async():
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try:
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st.session_state.evaluation_status = 'running'
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results = []
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for factor_name in selected_factors:
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for i, factor_name in enumerate(selected_factors):
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st.session_state.current_factor = factor_name
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st.session_state.progress = (i / len(selected_factors)) * 100
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# 模拟进度更新（实际计算中进度会在evaluate_factor内部更新）
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time.sleep(0.1) # 让UI有机会更新
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performance = evaluate_factor(
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factor_name,
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start_date,
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@ -154,52 +178,88 @@ def render_stock_evaluation() -> None:
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"夏普比率": f"{performance.sharpe_ratio:.4f}" if performance.sharpe_ratio else "N/A",
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"换手率": f"{performance.turnover_rate*100:.1f}%" if performance.turnover_rate else "N/A"
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})
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if results:
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st.markdown("##### 因子评估结果")
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result_df = pd.DataFrame(results)
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st.session_state.evaluation_results = results
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st.session_state.evaluation_status = 'completed'
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st.session_state.progress = 100
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except Exception as e:
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st.session_state.evaluation_status = 'error'
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st.session_state.evaluation_error = str(e)
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# 显示进度
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if st.session_state.evaluation_status == 'running':
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st.info(f"正在评估因子: {st.session_state.current_factor}")
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st.progress(st.session_state.progress / 100)
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elif st.session_state.evaluation_status == 'completed':
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st.success("因子评估完成！")
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elif st.session_state.evaluation_status == 'error':
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st.error(f"评估失败: {st.session_state.evaluation_error}")
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# 开始评估按钮
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if st.button("开始评估", disabled=not selected_factors or st.session_state.evaluation_status == 'running'):
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# 重置状态
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st.session_state.evaluation_results = None
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st.session_state.evaluation_status = 'running'
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st.session_state.progress = 0
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# 启动异步线程
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thread = threading.Thread(target=run_evaluation_async)
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thread.daemon = True
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thread.start()
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st.session_state.evaluation_thread = thread
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# 强制重新运行以显示进度
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st.rerun()
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# 显示结果
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if st.session_state.evaluation_results:
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results = st.session_state.evaluation_results
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st.markdown("##### 因子评估结果")
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result_df = pd.DataFrame(results)
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st.dataframe(
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result_df,
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hide_index=True,
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use_container_width=True
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)
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# 绘制IC均值分布
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ic_means = [float(r["IC均值"]) for r in results]
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chart_df = pd.DataFrame({
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"因子": [r["因子"] for r in results],
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"IC均值": ic_means
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})
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st.bar_chart(chart_df.set_index("因子"))
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# 生成股票评分
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with st.spinner("正在生成股票评分..."):
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scores = _calculate_stock_scores(
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universe,
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selected_factors,
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end_date,
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ic_means
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)
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if scores:
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st.markdown("##### 股票综合评分 (Top 20)")
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score_df = pd.DataFrame(scores).sort_values(
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"综合评分",
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ascending=False
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).head(20)
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st.dataframe(
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result_df,
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score_df,
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hide_index=True,
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use_container_width=True
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)
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# 绘制IC均值分布
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ic_means = [float(r["IC均值"]) for r in results]
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chart_df = pd.DataFrame({
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"因子": [r["因子"] for r in results],
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"IC均值": ic_means
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})
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st.bar_chart(chart_df.set_index("因子"))
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# 生成股票评分
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with st.spinner("正在生成股票评分..."):
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scores = _calculate_stock_scores(
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universe,
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selected_factors,
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end_date,
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ic_means
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# 添加入池功能
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if st.button("将Top 20股票加入股票池"):
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_add_to_stock_pool(
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score_df["股票代码"].tolist(),
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end_date
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)
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if scores:
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st.markdown("##### 股票综合评分 (Top 20)")
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score_df = pd.DataFrame(scores).sort_values(
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"综合评分",
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ascending=False
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).head(20)
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st.dataframe(
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score_df,
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hide_index=True,
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use_container_width=True
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)
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# 添加入池功能
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if st.button("将Top 20股票加入股票池"):
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_add_to_stock_pool(
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score_df["股票代码"].tolist(),
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end_date
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)
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st.success("已成功将选中股票加入股票池！")
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st.success("已成功将选中股票加入股票池！")
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def _calculate_stock_scores(
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