"""因子计算页面。""" from datetime import datetime, timedelta from typing import List, Optional import streamlit as st from app.features.factors import compute_factors, DEFAULT_FACTORS, FactorSpec from app.ui.progress_state import factor_progress from app.utils.data_access import DataBroker from app.utils.db import db_session def _get_latest_trading_date() -> datetime.date: """获取数据库中的最新交易日期""" with db_session() as session: result = session.execute( """ SELECT trade_date FROM daily_basic WHERE trade_date <= :today GROUP BY trade_date ORDER BY trade_date DESC LIMIT 1 """, {"today": datetime.now().strftime("%Y%m%d")} ).fetchone() if result and result[0]: return datetime.strptime(str(result[0]), "%Y%m%d").date() return datetime.now().date() - timedelta(days=1) def _get_all_stocks() -> List[str]: """获取所有股票代码""" try: # 从daily表获取所有股票代码 with db_session() as session: latest_date = _get_latest_trading_date() result = session.execute( """ SELECT DISTINCT ts_code FROM daily WHERE trade_date = :trade_date """, {"trade_date": latest_date.strftime("%Y%m%d")} ).fetchall() return [row[0] for row in result] if result else [] except Exception as e: st.error(f"获取股票列表失败: {str(e)}") return [] def render_factor_calculation() -> None: """渲染因子计算页面。""" st.subheader("📊 因子计算") st.caption("计算指定日期范围的因子值") # 1. 时间范围选择 col1, col2 = st.columns(2) with col1: latest_date = _get_latest_trading_date() end_date = st.date_input( "计算截止日期", value=latest_date, help="选择因子计算的截止日期" ) with col2: lookback_days = st.slider( "回溯天数", min_value=1, max_value=365, value=30, step=1, help="选择计算的历史数据长度" ) start_date = end_date - timedelta(days=lookback_days) st.info(f"计算范围: {start_date} 至 {end_date} (共{lookback_days}天)") # 2. 因子选择 st.markdown("##### 选择要计算的因子") # 按因子类型分组 factor_groups = { "动量类因子": [f for f in DEFAULT_FACTORS if f.name.startswith("mom_")], "波动率类因子": [f for f in DEFAULT_FACTORS if f.name.startswith("volat_")], "换手率类因子": [f for f in DEFAULT_FACTORS if f.name.startswith("turn_")], "估值类因子": [f for f in DEFAULT_FACTORS if f.name.startswith("val_")], "量价类因子": [f for f in DEFAULT_FACTORS if f.name.startswith("volume_")], "市场类因子": [f for f in DEFAULT_FACTORS if f.name.startswith("market_")], "其他因子": [f for f in DEFAULT_FACTORS if not any(f.name.startswith(prefix) for prefix in ["mom_", "volat_", "turn_", "val_", "volume_", "market_"])] } selected_factors = [] for group_name, factors in factor_groups.items(): if factors: st.markdown(f"###### {group_name}") cols = st.columns(3) for i, factor in enumerate(factors): if cols[i % 3].checkbox( factor.name, value=True, # 默认全选 help=factor.description if hasattr(factor, 'description') else None, key=f"factor_checkbox_{factor.name}_{group_name}" # 添加唯一key ): selected_factors.append(factor) if not selected_factors: st.warning("请至少选择一个因子进行计算") return # 3. 股票池选择 st.markdown("##### 股票池范围") pool_type = st.radio( "选择股票池", ["全部A股", "沪深300", "中证500", "中证1000", "自定义"], index=0, horizontal=True ) universe: Optional[List[str]] = None if pool_type != "全部A股": broker = DataBroker() if pool_type == "自定义": custom_codes = st.text_area( "输入股票代码列表(每行一个)", help="请输入股票代码，每行一个，例如: 000001.SZ" ) if custom_codes: universe = [ code.strip() for code in custom_codes.split("\n") if code.strip() ] else: index_code = { "沪深300": "000300.SH", "中证500": "000905.SH", "中证1000": "000852.SH" }[pool_type] universe = broker.get_index_stocks( index_code, end_date.strftime("%Y%m%d") ) # 4. 计算选项 st.markdown("##### 计算选项") skip_existing = st.checkbox( "跳过已计算的因子", value=True, help="如果勾选，将跳过数据库中已存在的因子计算结果" ) # 5. 同步计算函数 def run_factor_calculation_sync(): """同步执行因子计算""" # 计算参数 total_stocks = len(universe) if universe else len(_get_all_stocks()) total_batches = len(selected_factors) try: # 执行因子计算 results = [] for i, factor in enumerate(selected_factors): # 更新批次进度 factor_progress.update_progress( current_securities=0, current_batch=i+1, message=f"正在计算因子: {factor.name}" ) # 计算单个交易日的因子 current_date = start_date while current_date <= end_date: try: # 计算指定日期的因子 daily_results = compute_factors( current_date, [factor], ts_codes=universe, skip_existing=skip_existing ) results.extend(daily_results) except Exception as e: # 记录错误但不中断计算 error_msg = f"计算因子 {factor.name} 在日期 {current_date} 时出错: {str(e)}" print(f"ERROR: {error_msg}") current_date += timedelta(days=1) # 计算完成 factor_progress.complete_calculation(f"因子计算完成！共计算 {len(results)} 条因子记录") return { 'success': True, 'results': results, 'factors': [f.name for f in selected_factors], 'date_range': f"{start_date} 至 {end_date}", 'stock_count': len(set(r.ts_code for r in results)) if results else 0, 'message': f"因子计算完成！共计算 {len(results)} 条因子记录" } except Exception as e: # 计算失败 factor_progress.error_occurred(f"因子计算失败: {str(e)}") return { 'success': False, 'error': str(e), 'message': f"因子计算失败: {str(e)}" } # 6. 开始计算按钮 if st.button("开始计算因子", disabled=not selected_factors): # 重置状态 if 'factor_calculation_results' in st.session_state: st.session_state.factor_calculation_results = None if 'factor_calculation_error' in st.session_state: st.session_state.factor_calculation_error = None # 初始化进度状态 total_stocks = len(universe) if universe else len(_get_all_stocks()) factor_progress.start_calculation( total_securities=total_stocks, total_batches=len(selected_factors) ) # 直接调用同步计算函数 result = run_factor_calculation_sync() # 处理计算结果 if result['success']: st.session_state.factor_calculation_results = { 'results': result['results'], 'factors': result['factors'], 'date_range': result['date_range'], 'stock_count': result['stock_count'] } st.success("✅ 因子计算完成！") else: st.session_state.factor_calculation_error = result['error'] st.error(f"❌ 因子计算失败: {result['error']}") # 7. 显示计算结果 if 'factor_calculation_results' in st.session_state and st.session_state.factor_calculation_results: results = st.session_state.factor_calculation_results st.success("✅ 因子计算完成！") col1, col2, col3 = st.columns(3) with col1: st.metric("计算因子数量", len(results['factors'])) with col2: st.metric("涉及股票数量", results['stock_count']) with col3: st.metric("计算时间范围", results['date_range']) # 显示计算详情 with st.expander("查看计算详情"): if results['results']: # 转换为DataFrame显示 import pandas as pd df_data = [] for result in results['results']: for factor_name, value in result.values.items(): df_data.append({ '日期': result.trade_date, '股票代码': result.ts_code, '因子名称': factor_name, '因子值': value }) if df_data: df = pd.DataFrame(df_data) st.dataframe(df.head(100), width="stretch") # 只显示前100条 st.info(f"共 {len(df_data)} 条因子记录（显示前100条）") else: st.info("没有找到因子计算结果") # 8. 移除异步线程检查逻辑（已改为同步模式） # 9. 显示错误信息 if 'factor_calculation_error' in st.session_state and st.session_state.factor_calculation_error: st.error(f"❌ 因子计算失败: {st.session_state.factor_calculation_error}")