184 lines
6.6 KiB
Python
184 lines
6.6 KiB
Python
import streamlit as st
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import pandas as pd
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import plotly.express as px
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from datetime import datetime, timedelta
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def _check_openbb_installed() -> bool:
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try:
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# OpenBB v4 推荐用法: from openbb import obb
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from openbb import obb # noqa: F401
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return True
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except Exception:
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return False
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def _load_price_data(symbol: str, days: int = 365) -> pd.DataFrame:
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"""Fetch OHLCV using OpenBB v4 when available; otherwise return demo/synthetic data."""
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end = datetime.utcnow().date()
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start = end - timedelta(days=days)
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# 优先使用 OpenBB v4
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try:
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from openbb import obb
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# 先尝试股票路由
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try:
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out = obb.equity.price.historical(
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symbol,
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start_date=str(start),
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end_date=str(end),
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)
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except Exception:
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out = None
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# 若股票无数据，再尝试 ETF 路由
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if out is None or (hasattr(out, "is_empty") and out.is_empty):
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try:
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out = obb.etf.price.historical(
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symbol,
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start_date=str(start),
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end_date=str(end),
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)
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except Exception:
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out = None
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if out is not None:
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if hasattr(out, "to_df"):
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df = out.to_df()
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elif hasattr(out, "to_dataframe"):
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df = out.to_dataframe()
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else:
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# 兜底: 有些 provider 返回可序列化对象
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df = pd.DataFrame(out) # type: ignore[arg-type]
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# 规格化列名
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if not isinstance(df, pd.DataFrame) or df.empty:
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raise ValueError("OpenBB 返回空数据")
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# 有的表以 index 为日期
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if 'date' in df.columns:
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df['Date'] = pd.to_datetime(df['date'])
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elif df.index.name in ('date', 'Date') or isinstance(df.index, pd.DatetimeIndex):
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df = df.copy()
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df['Date'] = pd.to_datetime(df.index)
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else:
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# 尝试查找常见日期列
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for cand in ['timestamp', 'time', 'datetime']:
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if cand in df.columns:
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df['Date'] = pd.to_datetime(df[cand])
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break
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# 归一化收盘价列
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close_col = None
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for cand in ['adj_close', 'close', 'Close', 'price', 'close_price', 'c']:
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if cand in df.columns:
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close_col = cand
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break
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if close_col is None:
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raise ValueError("未找到收盘价列")
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df['Close'] = pd.to_numeric(df[close_col], errors='coerce')
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# 仅保留需要列并清洗
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if 'Date' not in df.columns:
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raise ValueError("未找到日期列")
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df = df[['Date', 'Close']].dropna()
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df = df.sort_values('Date').reset_index(drop=True)
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# 限定时间窗口（有些 provider 可能返回更长区间）
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df = df[df['Date'].dt.date.between(start, end)]
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if df.empty:
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raise ValueError("清洗后为空")
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return df
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except Exception:
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# 如果 OpenBB 不可用或调用失败，进入本地演示/合成数据兜底
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pass
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# Fallback to demo from examples/data
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try:
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from pathlib import Path
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root = Path(__file__).resolve().parents[2]
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demo_map = {
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'AAPL': root / 'examples' / 'data' / 'demo_results_aapl.json',
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'MSFT': root / 'examples' / 'data' / 'demo_results_msft.json',
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'TSLA': root / 'examples' / 'data' / 'demo_results_tsla.json',
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}
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path = demo_map.get(symbol.upper())
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if path and path.exists():
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df = pd.read_json(path)
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if 'date' in df.columns:
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df['Date'] = pd.to_datetime(df['date'])
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if 'close' in df.columns:
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df['Close'] = df['close']
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df = df[['Date', 'Close']].dropna().sort_values('Date').reset_index(drop=True)
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# 裁剪到时间窗口
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df = df[df['Date'].dt.date.between(start, end)]
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return df
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except Exception:
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pass
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# Last resort: minimal synthetic data（避免 FutureWarning）
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dates = pd.date_range(end=end, periods=min(days, 180))
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return pd.DataFrame({
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'Date': dates,
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'Close': pd.Series(range(len(dates))).rolling(5).mean().bfill()
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})
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def _kpis_from_df(df: pd.DataFrame) -> dict:
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if df.empty or 'Close' not in df.columns:
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return {"最新价": "-", "近30日涨幅": "-", "最大回撤(近90日)": "-"}
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latest = float(df['Close'].iloc[-1])
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last_30 = df.tail(30)
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if len(last_30) > 1:
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pct_30 = (last_30['Close'].iloc[-1] / last_30['Close'].iloc[0] - 1) * 100
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else:
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pct_30 = 0.0
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# max drawdown over last 90 days
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lookback = df.tail(90)['Close']
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roll_max = lookback.cummax()
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drawdown = (lookback / roll_max - 1).min() * 100
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return {
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"最新价": f"{latest:,.2f}",
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"近30日涨幅": f"{pct_30:.2f}%",
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"最大回撤(近90日)": f"{drawdown:.2f}%",
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}
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def render_openbb_tab():
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st.write("使用 OpenBB（如可用）或演示数据展示市场概览。")
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col_a, col_b = st.columns([2, 1])
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with col_b:
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symbol = st.text_input("股票/ETF 代码", value="AAPL")
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days = st.slider("时间窗口（天）", 90, 720, 365, step=30)
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obb_ready = _check_openbb_installed()
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if obb_ready:
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st.success("OpenBB 已安装 ✅")
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else:
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st.info("未检测到 OpenBB，将使用演示数据。可在 requirements.txt 中加入 openbb 后安装启用。")
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with col_a:
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df = _load_price_data(symbol, days)
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if df is None or df.empty:
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st.warning("未获取到数据")
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return
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# 绘制收盘价
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if 'Date' in df.columns and 'Close' in df.columns:
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fig = px.line(df, x='Date', y='Close', title=f"{symbol.upper()} 收盘价")
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st.plotly_chart(fig, use_container_width=True)
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else:
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st.dataframe(df.head())
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# KPI 卡片
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st.markdown("#### 关键指标")
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kpis = _kpis_from_df(df)
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k1, k2, k3 = st.columns(3)
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k1.metric("最新价", kpis["最新价"])
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k2.metric("近30日涨幅", kpis["近30日涨幅"])
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k3.metric("最大回撤(近90日)", kpis["最大回撤(近90日)"])
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# 未来：基本面、新闻、情绪等组件占位
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with st.expander("🚧 更多组件（即将推出）"):
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st.write("基本面卡片、新闻与情绪、宏观指标、策略筛选等将逐步接入。") |