用 Streamlit + QuantDash 搭建你的专属「个人多市场资产扫描器」

pip install streamlit plotly pandas quantdash

import streamlit as st
import pandas as pd
import plotly.graph_objects as go
from quantdash import QuantDash

# 1. 页面基本配置
st.set_page_config(layout="wide", page_title="多市场资产扫描看板", page_icon="📊")

st.title("📊 个人多市场资产扫描器")
st.caption("基于 QuantDash 金融数据 API 与 Streamlit 快速构建的技术分析复盘看板")

# 2. 侧边栏参数配置
st.sidebar.header("🔧 系统配置与参数")
api_key = st.sidebar.text_input("QuantDash API Key", type="password", help="请输入你的 QuantDash 平台 Token")
symbol = st.sidebar.text_input("股票代码", value="00700.HK", help="支持 A股（如 600519.SH）、港股（如 00700.HK）、美股（如 AAPL.US）")

start_date = st.sidebar.date_input("开始日期", value=pd.to_datetime("2024-01-01"))
sma_period = st.sidebar.slider("简单均线 (SMA) 周期", min_value=5, max_value=120, value=20)

# 3. 核心逻辑：数据拉取与可视化
if not api_key:
    st.info("💡 请先在左侧边栏配置您的 QuantDash API Key 启动看板。")
else:
    # 初始化 SDK 客户端
    qd = QuantDash(api_key=api_key)
    
    with st.spinner("正在向 QuantDash 请求最新行情数据..."):
        try:
            # 调用 SDK 获取前复权 K 线
            df = qd.stock.get_kline(
                symbol=symbol,
                start_date=start_date.strftime("%Y-%m-%d"),
                adjust="forward"
            )
            
            # 数据安全排序并计算移动平均线
            df['date'] = pd.to_datetime(df['date'])
            df = df.sort_values(by="date")
            df['SMA'] = df['close'].rolling(window=sma_period).mean()
            
            # 4. 指标卡片面板展示
            latest_data = df.iloc[-1]
            prev_data = df.iloc[-2] if len(df) > 1 else latest_data
            
            price_change = latest_data['close'] - prev_data['close']
            price_change_ratio = (price_change / prev_data['close']) * 100
            
            col1, col2, col3 = st.columns(3)
            col1.metric(
                label=f"{symbol} 最新价格", 
                value=f"${latest_data['close']:.2f}", 
                delta=f"{price_change:+.2f} ({price_change_ratio:+.2f}%)"
            )
            col2.metric(
                label="今日成交量", 
                value=f"{int(latest_data['volume']):,}"
            )
            col3.metric(
                label=f"当前 {sma_period}日均线", 
                value=f"${latest_data['SMA']:.2f}" if not pd.isna(latest_data['SMA']) else "计算中..."
            )
            
            # 5. 绘制交互式 Plotly K线与指标图
            fig = go.Figure()
            
            # 添加 Candlestick 走势
            fig.add_trace(go.Candlestick(
                x=df['date'],
                open=df['open'],
                high=df['high'],
                low=df['low'],
                close=df['close'],
                name="K线走势"
            ))
            
            # 添加均线折线
            fig.add_trace(go.Scatter(
                x=df['date'],
                y=df['SMA'],
                line=dict(color='gold', width=1.5),
                name=f"{sma_period}日 均线"
            ))
            
            fig.update_layout(
                xaxis_rangeslider_visible=False,
                height=550,
                title=f"{symbol} 走势技术面透视",
                template="plotly_dark", # 深色系专业外观
                margin=dict(l=20, r=20, t=50, b=20)
            )
            
            st.plotly_chart(fig, use_container_width=True)
            
            # 6. 数据源明细展示
            with st.expander("📂 点击展开原始行情数据明细"):
                st.dataframe(
                    df.sort_values(by="date", ascending=False), 
                    use_container_width=True
                )
                
        except Exception as e:
            st.error(f"⚠️ 数据获取或计算失败，原因: {e}")

streamlit run app.py