From 8a4ca05155eddeaba6ed8c5bf4ea018411f484a9 Mon Sep 17 00:00:00 2001 From: sam Date: Sun, 5 Oct 2025 21:02:07 +0800 Subject: [PATCH] update --- app/ui/streamlit_app.py | 266 ++++++++++++++++++++-------------------- 1 file changed, 130 insertions(+), 136 deletions(-) diff --git a/app/ui/streamlit_app.py b/app/ui/streamlit_app.py index 181d48d..f974fa0 100644 --- a/app/ui/streamlit_app.py +++ b/app/ui/streamlit_app.py @@ -2470,6 +2470,130 @@ def render_llm_settings() -> None: st.caption("部门配置存储为独立 LLM 参数，执行时会自动套用对应 Provider 的连接信息。") +def render_market_visualization() -> None: + """Render a standalone market data visualization dashboard.""" + + st.header("股票行情可视化") + options = _load_stock_options() + default_code = options[0] if options else "000001.SZ" + + if options: + selection = st.selectbox("选择股票", options, index=0) + ts_code = _parse_ts_code(selection) + LOGGER.debug("选择股票：%s", ts_code, extra=LOG_EXTRA) + else: + ts_code = st.text_input("输入股票代码（如 000001.SZ）", value=default_code).strip().upper() + LOGGER.debug("输入股票：%s", ts_code, extra=LOG_EXTRA) + + col_start, col_end = st.columns(2) + default_start = date.today() - timedelta(days=180) + start_date = col_start.date_input("开始日期", value=default_start, key="viz_start") + end_date = col_end.date_input("结束日期", value=date.today(), key="viz_end") + LOGGER.debug("行情可视化日期范围：%s-%s", start_date, end_date, extra=LOG_EXTRA) + + if start_date > end_date: + LOGGER.warning("无效日期范围：%s>%s", start_date, end_date, extra=LOG_EXTRA) + st.error("开始日期不能晚于结束日期") + return + + with st.spinner("正在加载行情数据..."): + try: + df = _load_daily_frame(ts_code, start_date, end_date) + except Exception as exc: # noqa: BLE001 + LOGGER.exception("加载行情数据失败", extra=LOG_EXTRA) + st.error(f"读取数据失败：{exc}") + return + + if df.empty: + LOGGER.warning("指定区间无行情数据：%s %s-%s", ts_code, start_date, end_date, extra=LOG_EXTRA) + st.warning("未查询到该区间的交易数据，请确认数据库已拉取对应日线。") + return + + price_df = df[["close"]].rename(columns={"close": "收盘价"}) + volume_df = df[["vol"]].rename(columns={"vol": "成交量(手)"}) + + sampled = price_df.resample("3D").last().dropna() if price_df.shape[0] > 180 else price_df + volume_sampled = volume_df.resample("3D").mean().dropna() if volume_df.shape[0] > 180 else volume_df + + first_close = sampled.iloc[0, 0] + last_close = sampled.iloc[-1, 0] + delta_abs = last_close - first_close + delta_pct = (delta_abs / first_close * 100) if first_close else 0.0 + + metric_col1, metric_col2, metric_col3 = st.columns(3) + metric_col1.metric("最新收盘价", f"{last_close:.2f}", delta=f"{delta_abs:+.2f}") + metric_col2.metric("区间涨跌幅", f"{delta_pct:+.2f}%") + metric_col3.metric("平均成交量", f"{volume_sampled['成交量(手)'].mean():.0f}") + + df_reset = df.reset_index().rename(columns={ + "trade_date": "交易日", + "open": "开盘价", + "high": "最高价", + "low": "最低价", + "close": "收盘价", + "vol": "成交量(手)", + "amount": "成交额(千元)", + }) + df_reset["成交额(千元)"] = df_reset["成交额(千元)"] / 1000 + + numeric_columns = ["开盘价", "最高价", "最低价", "收盘价", "成交量(手)", "成交额(千元)"] + for col in numeric_columns: + if col in df_reset.columns: + df_reset[col] = pd.to_numeric(df_reset[col], errors="coerce") + df_reset["交易日"] = pd.to_datetime(df_reset["交易日"]) + + candle_fig = go.Figure( + data=[ + go.Candlestick( + x=df_reset["交易日"], + open=df_reset["开盘价"], + high=df_reset["最高价"], + low=df_reset["最低价"], + close=df_reset["收盘价"], + name="K线", + ) + ] + ) + candle_fig.update_layout(height=420, margin=dict(l=10, r=10, t=40, b=10)) + st.plotly_chart(candle_fig, width="stretch") + + vol_fig = px.bar( + df_reset, + x="交易日", + y="成交量(手)", + labels={"成交量(手)": "成交量(手)"}, + title="成交量", + ) + vol_fig.update_layout(height=280, margin=dict(l=10, r=10, t=40, b=10)) + st.plotly_chart(vol_fig, width="stretch") + + amt_fig = px.bar( + df_reset, + x="交易日", + y="成交额(千元)", + labels={"成交额(千元)": "成交额(千元)"}, + title="成交额", + ) + amt_fig.update_layout(height=280, margin=dict(l=10, r=10, t=40, b=10)) + st.plotly_chart(amt_fig, width="stretch") + + df_reset["月份"] = df_reset["交易日"].dt.to_period("M").astype(str) + df_reset["收盘价"] = pd.to_numeric(df_reset["收盘价"], errors="coerce") + box_fig = px.box( + df_reset, + x="月份", + y="收盘价", + points="outliers", + title="月度收盘价分布", + ) + box_fig.update_layout(height=320, margin=dict(l=10, r=10, t=40, b=10)) + st.plotly_chart(box_fig, width="stretch") + + st.caption("提示：成交量单位为手，成交额以千元显示。箱线图按月展示收盘价分布。") + st.dataframe(df_reset.tail(20), width="stretch") + LOGGER.info("行情可视化完成，展示行数=%s", len(df_reset), extra=LOG_EXTRA) + + def render_tests() -> None: LOGGER.info("渲染自检页面", extra=LOG_EXTRA) st.header("自检测试") @@ -2627,138 +2751,6 @@ def render_tests() -> None: finally: progress_bar.progress(1.0) - st.divider() - st.subheader("股票行情可视化") - options = _load_stock_options() - default_code = options[0] if options else "000001.SZ" - - if options: - selection = st.selectbox("选择股票", options, index=0) - ts_code = _parse_ts_code(selection) - LOGGER.debug("选择股票：%s", ts_code, extra=LOG_EXTRA) - else: - ts_code = st.text_input("输入股票代码（如 000001.SZ）", value=default_code).strip().upper() - LOGGER.debug("输入股票：%s", ts_code, extra=LOG_EXTRA) - - viz_col1, viz_col2 = st.columns(2) - default_start = date.today() - timedelta(days=180) - start_date = viz_col1.date_input("开始日期", value=default_start, key="viz_start") - end_date = viz_col2.date_input("结束日期", value=date.today(), key="viz_end") - LOGGER.debug("行情可视化日期范围：%s-%s", start_date, end_date, extra=LOG_EXTRA) - - if start_date > end_date: - LOGGER.warning("无效日期范围：%s>%s", start_date, end_date, extra=LOG_EXTRA) - st.error("开始日期不能晚于结束日期") - return - - with st.spinner("正在加载行情数据..."): - try: - df = _load_daily_frame(ts_code, start_date, end_date) - except Exception as exc: # noqa: BLE001 - LOGGER.exception("加载行情数据失败", extra=LOG_EXTRA) - st.error(f"读取数据失败：{exc}") - return - - if df.empty: - LOGGER.warning("指定区间无行情数据：%s %s-%s", ts_code, start_date, end_date, extra=LOG_EXTRA) - st.warning("未查询到该区间的交易数据，请确认数据库已拉取对应日线。") - return - - price_df = df[["close"]].rename(columns={"close": "收盘价"}) - volume_df = df[["vol"]].rename(columns={"vol": "成交量(手)"}) - - if price_df.shape[0] > 180: - sampled = price_df.resample('3D').last().dropna() - else: - sampled = price_df - - if volume_df.shape[0] > 180: - volume_sampled = volume_df.resample('3D').mean().dropna() - else: - volume_sampled = volume_df - - first_close = sampled.iloc[0, 0] - last_close = sampled.iloc[-1, 0] - delta_abs = last_close - first_close - delta_pct = (delta_abs / first_close * 100) if first_close else 0.0 - - metric_col1, metric_col2, metric_col3 = st.columns(3) - metric_col1.metric("最新收盘价", f"{last_close:.2f}", delta=f"{delta_abs:+.2f}") - metric_col2.metric("区间涨跌幅", f"{delta_pct:+.2f}%") - metric_col3.metric("平均成交量", f"{volume_sampled['成交量(手)'].mean():.0f}") - - df_reset = df.reset_index().rename(columns={ - "trade_date": "交易日", - "open": "开盘价", - "high": "最高价", - "low": "最低价", - "close": "收盘价", - "vol": "成交量(手)", - "amount": "成交额(千元)", - }) - df_reset["成交额(千元)"] = df_reset["成交额(千元)"] / 1000 - - # 确保所有列的数据类型正确，避免PyArrow序列化错误 - numeric_columns = ["开盘价", "最高价", "最低价", "收盘价", "成交量(手)", "成交额(千元)"] - for col in numeric_columns: - if col in df_reset.columns: - df_reset[col] = pd.to_numeric(df_reset[col], errors='coerce') - - # 确保日期列是datetime类型 - df_reset["交易日"] = pd.to_datetime(df_reset["交易日"]) - - candle_fig = go.Figure( - data=[ - go.Candlestick( - x=df_reset["交易日"], - open=df_reset["开盘价"], - high=df_reset["最高价"], - low=df_reset["最低价"], - close=df_reset["收盘价"], - name="K线", - ) - ] - ) - candle_fig.update_layout(height=420, margin=dict(l=10, r=10, t=40, b=10)) - st.plotly_chart(candle_fig, width='stretch') - - vol_fig = px.bar( - df_reset, - x="交易日", - y="成交量(手)", - labels={"成交量(手)": "成交量(手)"}, - title="成交量", - ) - vol_fig.update_layout(height=280, margin=dict(l=10, r=10, t=40, b=10)) - st.plotly_chart(vol_fig, width='stretch') - - amt_fig = px.bar( - df_reset, - x="交易日", - y="成交额(千元)", - labels={"成交额(千元)": "成交额(千元)"}, - title="成交额", - ) - amt_fig.update_layout(height=280, margin=dict(l=10, r=10, t=40, b=10)) - st.plotly_chart(amt_fig, width='stretch') - - df_reset["月份"] = df_reset["交易日"].dt.to_period("M").astype(str) - # 确保收盘价列是数值类型 - df_reset["收盘价"] = pd.to_numeric(df_reset["收盘价"], errors='coerce') - box_fig = px.box( - df_reset, - x="月份", - y="收盘价", - points="outliers", - title="月度收盘价分布", - ) - box_fig.update_layout(height=320, margin=dict(l=10, r=10, t=40, b=10)) - st.plotly_chart(box_fig, width='stretch') - - st.caption("提示：成交量单位为手，成交额以千元显示。箱线图按月展示收盘价分布。") - st.dataframe(df_reset.tail(20), width='stretch') - LOGGER.info("行情可视化完成，展示行数=%s", len(df_reset), extra=LOG_EXTRA) - st.divider() st.subheader("LLM 接口测试") st.json(llm_config_snapshot()) @@ -2889,10 +2881,10 @@ def main() -> None: st.error(f"❌ 自动数据更新失败：{exc}") render_global_dashboard() - tabs = st.tabs(["今日计划", "回测与复盘", "日志钻取", "数据与设置", "自检测试"]) + tabs = st.tabs(["今日计划", "回测与复盘", "行情可视化", "日志钻取", "数据与设置", "自检测试"]) LOGGER.debug( "Tabs 初始化完成：%s", - ["今日计划", "回测与复盘", "日志钻取", "数据与设置", "自检测试"], + ["今日计划", "回测与复盘", "行情可视化", "日志钻取", "数据与设置", "自检测试"], extra=LOG_EXTRA, ) with tabs[0]: @@ -2900,8 +2892,10 @@ def main() -> None: with tabs[1]: render_backtest_review() with tabs[2]: - render_log_viewer() + render_market_visualization() with tabs[3]: + render_log_viewer() + with tabs[4]: st.header("系统设置") settings_tabs = st.tabs(["配置概览", "LLM 设置", "投资组合", "数据源"]) @@ -2917,7 +2911,7 @@ def main() -> None: with settings_tabs[3]: render_data_settings() - with tabs[4]: + with tabs[5]: render_tests()