bt_全球大类资产_动量轮转年化289最大回撤248

一.效果

二.实现

1.获取数据

2.添加策略回测，选择20日涨幅最好的标的

import bt
from bt_algos_extend import SelectTopK
signal = data.pct_change(20)  # 20天涨跌幅
# signal.dropna(inplace=True)
all = []
for K in [
    1,
]:
    s = bt.Strategy(
        "bt_全球大类资产_动量轮转K={}".format(K),
        [
            SelectTopK(signal, K),  # 涨幅最好选前K个
            bt.algos.WeighEqually(),
            bt.algos.Rebalance(),
        ],
    )
    all.append(s)
stras = [bt.Backtest(s, data) for s in all]
for bench in ["000300.SH", "510300.SH", "513100.SH", "518880.SH"]:
    data = CSVDataloader.get([bench])
    s = bt.Strategy(
        bench,
        [
            bt.algos.RunOnce(),
            bt.algos.SelectAll(),
            bt.algos.WeighEqually(),
            bt.algos.Rebalance(),
        ],
    )
    stra = bt.Backtest(s, data, name=bench)
    stras.append(stra)
res = bt.run(*stras)

3.回测数据分析

# 获取每日收益
returns = res["bt_全球大类资产_动量轮转K=1"].returns
res.display_monthly_returns('bt_全球大类资产_动量轮转K=1')
import empyrical
print("累计收益：", round(empyrical.cum_returns_final(returns), 3))
print("年化收益：", round(empyrical.annual_return(returns), 3))
print("最大回撤：", round(empyrical.max_drawdown(returns), 3))
print("夏普比", round(empyrical.sharpe_ratio(returns), 3))
print("卡玛比", round(empyrical.calmar_ratio(returns), 3))
import quantstats as qs
qs.reports.basic(returns)