长期年化20%，回撤小于20%是ETF大类资产配置+轮动一个合理且舒服的状态。

这里的动量及止损，还有优化方向，作为星球的作业：

其实追求长期年化20%，回撤小于20%是一个合理且舒服的状态。

使用streamlit搭建量化界面，相当容易。

后面部署到服务器供大家直接回测使用：

核心代码如下：

import streamlit as st
import pandas as pd
import numpy as np
from common import mongo_utils
import datetime as dt
import time
from datetime import datetime

def get_etf_list():
    items = mongo_utils.get_db()['universe'].find({'type':'etf'}, {'_id':0, 'symbol': 1, 'name':1})

    df = pd.DataFrame(list(items))
    df['title'] = df['name'] + '-' + df['symbol']
    return list(df['title'])

st.title('创建你自己的AI量化策略')

import streamlit as st

basic, rules, backtest = st.tabs(["回测基础配置", "回测规则", "权重与调仓"])

with basic:
    name = st.text_input(label='请输入策略名称', value='策略1')
    template = st.selectbox(
        '请选择策略类型',
        ('大类资产配置', '轮动'))

    symbols = st.multiselect(
        '请选择etfs',
        get_etf_list(),
        [], placeholder='请选择etfs')

    col1, col2 = st.columns(2)
    with col1:
        dt_start = st.date_input("起始日期", dt.date(2010, 1, 1))
    with col2:
        dt_end = st.date_input("结束日期", datetime.now().date())

    benchmark = st.selectbox('请选择比较基准',options=get_etf_list())

with rules:
    df = pd.DataFrame(
        [
            {"command": "st.selectbox", "rating": 4, "is_widget": True},
            {"command": "st.balloons", "rating": 5, "is_widget": False},
            {"command": "st.time_input", "rating": 3, "is_widget": True},
        ]
    )
    edited_df = st.data_editor(df)

with backtest:
    weights = st.selectbox(
        '请选择权重分配',
        ('等权-WeightEqually', '固定权重-WeightFix'))

    if weights == '固定权重':
        count = len(symbols)
        if count > 0:
            values = [str(1/count) for _ in range(count)]
            fix_weights = st.text_input('请配置固定权重，,分隔开,个数与etf数量一致','.'.join(values))

    period = st.selectbox(
        '请选择调仓周期：',
        ('每天', '每周','每月','每季','每年'), index=0)



progress_text = "Operation in progress. Please wait."
my_bar = st.progress(0, text=progress_text)

if st.button("开始回测"):
    from quantlab.engine.engine import Task
    task = Task()
    task.name = name
    task.start_date = dt_start.strftime('%Y%m%d')
    task.end_date = dt_end.strftime('%Y%m%d')

    true_symbols = []
    for s in symbols:
        true_symbols.append(s.split('-')[1])
    task.symbols = true_symbols
    task.template = template
    task.weights = weights.split('-')[1]
    task.period = period
    task.benchmark = benchmark.split('-')[1]

    from quantlab.engine.engine import Engine

    e = Engine(task)
    e.run()
    df_ratio, df_equities = e.analysis()
    print(df_ratio)

    #for percent_complete in range(100):
    #    time.sleep(0.01)
    #    my_bar.progress(percent_complete + 1, text=progress_text)
    #    time.sleep(1)
    #    my_bar.empty()



    chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])

    st.line_chart(df_equities)
    st.write(df_ratio)