From 4b7d64f915d73559bbc9e63d7c3c2e64caa0d2ca Mon Sep 17 00:00:00 2001 From: Your Name Date: Wed, 8 Oct 2025 11:48:08 +0800 Subject: [PATCH] add stock evaluation view with factor analysis and scoring functionality --- app/ui/streamlit_app.py | 7 +- app/ui/views/__init__.py | 2 + app/ui/views/stock_eval.py | 252 +++++++++++++++++++++++++++++++++++++ 3 files changed, 260 insertions(+), 1 deletion(-) create mode 100644 app/ui/views/stock_eval.py diff --git a/app/ui/streamlit_app.py b/app/ui/streamlit_app.py index 84b982c..78a046b 100644 --- a/app/ui/streamlit_app.py +++ b/app/ui/streamlit_app.py @@ -24,6 +24,7 @@ from app.ui.views import ( render_log_viewer, render_market_visualization, render_pool_overview, + render_stock_evaluation, render_tests, render_today_plan, ) @@ -77,7 +78,11 @@ def main() -> None: with tabs[1]: render_pool_overview() with tabs[2]: - render_backtest_review() + backtest_tabs = st.tabs(["回测复盘", "股票评估"]) + with backtest_tabs[0]: + render_backtest_review() + with backtest_tabs[1]: + render_stock_evaluation() with tabs[3]: render_market_visualization() with tabs[4]: diff --git a/app/ui/views/__init__.py b/app/ui/views/__init__.py index e4f945a..df3a5d0 100644 --- a/app/ui/views/__init__.py +++ b/app/ui/views/__init__.py @@ -8,6 +8,7 @@ from .logs import render_log_viewer from .settings import render_config_overview, render_llm_settings, render_data_settings from .tests import render_tests from .dashboard import render_global_dashboard, update_dashboard_sidebar +from .stock_eval import render_stock_evaluation __all__ = [ "render_today_plan", @@ -21,4 +22,5 @@ __all__ = [ "render_tests", "render_global_dashboard", "update_dashboard_sidebar", + "render_stock_evaluation", ] diff --git a/app/ui/views/stock_eval.py b/app/ui/views/stock_eval.py new file mode 100644 index 0000000..62f8a6f --- /dev/null +++ b/app/ui/views/stock_eval.py @@ -0,0 +1,252 @@ +"""股票筛选与评估视图。""" +from datetime import datetime, timedelta +from typing import Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd +import streamlit as st + +from app.features.evaluation import evaluate_factor +from app.features.factors import DEFAULT_FACTORS +from app.features.validation import check_data_sufficiency +from app.utils.config import get_config +from app.utils.data_access import DataBroker +from app.utils.db import db_session + + +def render_stock_evaluation() -> None: + """渲染股票筛选与评估页面。""" + st.subheader("股票筛选与评估") + + # 1. 时间范围选择 + col1, col2 = st.columns(2) + with col1: + end_date = st.date_input( + "评估截止日期", + value=datetime.now().date() - timedelta(days=1), + help="选择评估的截止日期" + ) + with col2: + lookback_days = st.slider( + "回溯天数", + min_value=30, + max_value=360, + value=180, + step=30, + help="选择评估的历史数据长度" + ) + start_date = end_date - timedelta(days=lookback_days) + + # 2. 因子选择 + st.markdown("##### 评估因子选择") + 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_")] + } + + 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=factor.name in selected_factors, + help=factor.description if hasattr(factor, 'description') else None + ): + selected_factors.append(factor.name) + + if not selected_factors: + st.warning("请至少选择一个评估因子") + return + + # 3. 股票池范围 + 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"], + 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: + # 从数据库获取对应指数成分股 + 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. 评估结果 + if st.button("开始评估", disabled=not selected_factors): + with st.spinner("正在评估因子表现..."): + results = [] + for factor_name in selected_factors: + performance = evaluate_factor( + factor_name, + start_date, + end_date, + universe=universe + ) + results.append({ + "因子": factor_name, + "IC均值": f"{performance.ic_mean:.4f}", + "RankIC均值": f"{performance.rank_ic_mean:.4f}", + "IC信息比率": f"{performance.ic_ir:.4f}", + "夏普比率": f"{performance.sharpe_ratio:.4f}" if performance.sharpe_ratio else "N/A", + "换手率": f"{performance.turnover_rate*100:.1f}%" if performance.turnover_rate else "N/A" + }) + + if results: + st.markdown("##### 因子评估结果") + result_df = pd.DataFrame(results) + st.dataframe( + result_df, + hide_index=True, + use_container_width=True + ) + + # 绘制IC均值分布 + ic_means = [float(r["IC均值"]) for r in results] + chart_df = pd.DataFrame({ + "因子": [r["因子"] for r in results], + "IC均值": ic_means + }) + st.bar_chart(chart_df.set_index("因子")) + + # 生成股票评分 + with st.spinner("正在生成股票评分..."): + scores = _calculate_stock_scores( + universe, + selected_factors, + end_date, + ic_means + ) + + if scores: + st.markdown("##### 股票综合评分 (Top 20)") + score_df = pd.DataFrame(scores).sort_values( + "综合评分", + ascending=False + ).head(20) + st.dataframe( + score_df, + hide_index=True, + use_container_width=True + ) + + # 添加入池功能 + if st.button("将Top 20股票加入股票池"): + _add_to_stock_pool( + score_df["股票代码"].tolist(), + end_date + ) + st.success("已成功将选中股票加入股票池！") + + +def _calculate_stock_scores( + universe: Optional[List[str]], + factors: List[str], + eval_date: datetime.date, + factor_weights: List[float] +) -> List[Dict[str, str]]: + """计算股票的综合评分。""" + broker = DataBroker() + + # 标准化权重 + weights = np.array(factor_weights) + weights = weights / np.sum(np.abs(weights)) + + # 获取所有股票的因子值 + stocks = universe or broker.get_all_stocks(eval_date.strftime("%Y%m%d")) + results = [] + + for ts_code in stocks: + if not check_data_sufficiency(ts_code, eval_date.strftime("%Y%m%d")): + continue + + # 获取股票信息 + info = broker.get_stock_info(ts_code) + if not info: + continue + + # 获取因子值 + factor_values = [] + for factor in factors: + value = broker.fetch_latest_factor(ts_code, factor, eval_date) + if value is None: + break + factor_values.append(value) + + if len(factor_values) != len(factors): + continue + + # 计算综合评分 + score = np.dot(factor_values, weights) + + results.append({ + "股票代码": ts_code, + "股票名称": info.get("name", ""), + "行业": info.get("industry", ""), + "综合评分": f"{score:.4f}" + }) + + return results + + +def _add_to_stock_pool( + ts_codes: List[str], + eval_date: datetime.date +) -> None: + """将股票添加到股票池。""" + with db_session() as session: + # 删除已有记录 + session.execute( + """ + DELETE FROM stock_pool + WHERE entry_date = :entry_date + """, + {"entry_date": eval_date} + ) + + # 插入新记录 + values = [ + { + "ts_code": code, + "entry_date": eval_date, + "entry_reason": "factor_evaluation" + } + for code in ts_codes + ] + + session.execute( + """ + INSERT INTO stock_pool (ts_code, entry_date, entry_reason) + VALUES (:ts_code, :entry_date, :entry_reason) + """, + values + ) + + session.commit() \ No newline at end of file