# -*- coding: utf-8 -*-
"""
author: zengbin93
email: zeng_bin8888@163.com
create_dt: 2023/08/02 22:20
describe: 按持仓权重回测
"""
import numpy as np
import pandas as pd
import plotly.express as px
from loguru import logger
from pathlib import Path
from typing import Union, AnyStr, Callable
from czsc.traders.base import CzscTrader
from czsc.utils.io import save_json
from czsc.utils.stats import daily_performance, evaluate_pairs
[docs]def get_ensemble_weight(trader: CzscTrader, method: Union[AnyStr, Callable] = 'mean'):
"""获取 CzscTrader 中所有 positions 按照 method 方法集成之后的权重
:param trader: CzscTrader
缠论交易者
:param method: str or callable
集成方法，可选值包括：'mean', 'max', 'min', 'vote'
也可以传入自定义的函数，函数的输入为 dict，key 为 position.name，value 为 position.pos, 样例输入：
{'多头策略A': 1, '多头策略B': 1, '空头策略A': -1}
:param kwargs:
:return: pd.DataFrame
columns = ['dt', 'symbol', 'weight', 'price']
"""
logger.info(f"trader positions: {[p.name for p in trader.positions]}")
dfp = pd.DataFrame()
for p in trader.positions:
p_pos = pd.DataFrame(p.holds)
if dfp.empty:
dfp = p_pos.copy()
else:
assert dfp['dt'].equals(p_pos['dt'])
dfp = dfp.merge(p_pos[['dt', 'pos']], on='dt', how='left')
dfp.rename(columns={'pos': p.name}, inplace=True)
pos_cols = [c for c in dfp.columns if c not in ['dt', 'weight', 'price']]
if callable(method):
dfp['weight'] = dfp[pos_cols].apply(lambda x: method(x.to_dict()), axis=1)
else:
method = method.lower()
if method == "mean":
dfp['weight'] = dfp[pos_cols].mean(axis=1)
elif method == "max":
dfp['weight'] = dfp[pos_cols].max(axis=1)
elif method == "min":
dfp['weight'] = dfp[pos_cols].min(axis=1)
elif method == "vote":
dfp['weight'] = dfp[pos_cols].apply(lambda x: np.sign(np.sum(x)), axis=1)
else:
raise ValueError(f"method {method} not supported")
dfp['symbol'] = trader.symbol
logger.info(f"trader weight decribe: {dfp['weight'].describe().round(4).to_dict()}")
return dfp[['dt', 'symbol', 'weight', 'price']].copy()
[docs]class WeightBacktest:
"""持仓权重回测"""
def __init__(self, dfw, digits=2, **kwargs) -> None:
"""持仓权重回测
:param dfw: pd.DataFrame, columns = ['dt', 'symbol', 'weight', 'price'], 持仓权重数据，其中
dt 为K线结束时间，
symbol 为合约代码，
weight 为K线结束时间对应的持仓权重，
price 为结束时间对应的交易价格，可以是当前K线的收盘价，或者下一根K线的开盘价，或者未来N根K线的TWAP、VWAP等
数据样例如下：
=================== ======== ======== =======
dt symbol weight price
=================== ======== ======== =======
2019-01-02 09:01:00 DLi9001 0.5 961.695
2019-01-02 09:02:00 DLi9001 0.25 960.72
2019-01-02 09:03:00 DLi9001 0.25 962.669
2019-01-02 09:04:00 DLi9001 0.25 960.72
2019-01-02 09:05:00 DLi9001 0.25 961.695
=================== ======== ======== =======
:param digits: int, 权重列保留小数位数
:param kwargs:
- fee_rate: float，单边交易成本，包括手续费与冲击成本, 默认为 0.0002
- res_path: str，回测结果保存路径，默认为 "weight_backtest"
"""
self.kwargs = kwargs
self.dfw = dfw.copy()
self.digits = digits
self.fee_rate = kwargs.get('fee_rate', 0.0002)
self.dfw['weight'] = self.dfw['weight'].round(digits)
self.symbols = list(self.dfw['symbol'].unique().tolist())
self.res_path = Path(kwargs.get('res_path', "weight_backtest"))
self.res_path.mkdir(exist_ok=True, parents=True)
logger.add(self.res_path.joinpath("weight_backtest.log"), rotation="1 week")
logger.info(f"持仓权重回测参数：digits={digits}, fee_rate={self.fee_rate}，res_path={self.res_path}，kwargs={kwargs}")
[docs] def get_symbol_daily(self, symbol):
"""获取某个合约的每日收益率
:param symbol: str，合约代码
:return: pd.DataFrame，品种每日收益率，
columns = ['date', 'symbol', 'edge', 'return', 'cost']
其中
date 为交易日，
symbol 为合约代码，
edge 为每日收益率，
return 为每日收益率减去交易成本后的真实收益，
cost 为交易成本
数据样例如下：
========== ======== ============ ============ =======
date symbol edge return cost
========== ======== ============ ============ =======
2019-01-02 DLi9001 0.00230261 0.00195919 0.00085
2019-01-03 DLi9001 0.00425589 0.00310589 0.00115
2019-01-04 DLi9001 -0.0014209 -0.0024709 0.00105
2019-01-07 DLi9001 0.000988305 -0.000111695 0.0011
2019-01-08 DLi9001 -0.0004743 -0.0016243 0.00115
========== ======== ============ ============ =======
"""
dfs = self.dfw[self.dfw['symbol'] == symbol].copy()
dfs['edge'] = dfs['weight'] * (dfs['price'].shift(-1) / dfs['price'] - 1)
dfs['cost'] = abs(dfs['weight'].shift(1) - dfs['weight']) * self.fee_rate
dfs['edge_post_fee'] = dfs['edge'] - dfs['cost']
daily = dfs.groupby(dfs['dt'].dt.date).agg({'edge': 'sum', 'edge_post_fee': 'sum', 'cost': 'sum'}).reset_index()
daily['symbol'] = symbol
daily.rename(columns={'edge_post_fee': 'return', 'dt': 'date'}, inplace=True)
daily = daily[['date', 'symbol', 'edge', 'return', 'cost']]
return daily
[docs] def get_symbol_pairs(self, symbol):
"""获取某个合约的开平交易记录"""
dfs = self.dfw[self.dfw['symbol'] == symbol].copy()
dfs['volume'] = (dfs['weight'] * pow(10, self.digits)).astype(int)
dfs['bar_id'] = list(range(1, len(dfs)+1))
# 根据权重变化生成开平仓记录
operates = []
def __add_operate(dt, bar_id, volume, price, operate):
for _ in range(abs(volume)):
op = {'bar_id': bar_id, "dt": dt, "price": price, "operate": operate}
operates.append(op)
rows = dfs.to_dict(orient='records')
# 处理第一个 row
if rows[0]['volume'] > 0:
__add_operate(rows[0]['dt'], rows[0]['bar_id'], rows[0]['volume'], rows[0]['price'], operate='开多')
elif rows[0]['volume'] < 0:
__add_operate(rows[0]['dt'], rows[0]['bar_id'], rows[0]['volume'], rows[0]['price'], operate='开空')
# 处理后续 rows
for row1, row2 in zip(rows[:-1], rows[1:]):
if row1['volume'] >= 0 and row2['volume'] >= 0:
# 多头仓位变化对应的操作
if row2['volume'] > row1['volume']:
__add_operate(row2['dt'], row2['bar_id'], row2['volume'] - row1['volume'], row2['price'], operate='开多')
elif row2['volume'] < row1['volume']:
__add_operate(row2['dt'], row2['bar_id'], row1['volume'] - row2['volume'], row2['price'], operate='平多')
elif row1['volume'] <= 0 and row2['volume'] <= 0:
# 空头仓位变化对应的操作
if row2['volume'] > row1['volume']:
__add_operate(row2['dt'], row2['bar_id'], row1['volume'] - row2['volume'], row2['price'], operate='平空')
elif row2['volume'] < row1['volume']:
__add_operate(row2['dt'], row2['bar_id'], row2['volume'] - row1['volume'], row2['price'], operate='开空')
elif row1['volume'] >= 0 and row2['volume'] <= 0:
# 多头转换成空头对应的操作
__add_operate(row2['dt'], row2['bar_id'], row1['volume'], row2['price'], operate='平多')
__add_operate(row2['dt'], row2['bar_id'], row2['volume'], row2['price'], operate='开空')
elif row1['volume'] <= 0 and row2['volume'] >= 0:
# 空头转换成多头对应的操作
__add_operate(row2['dt'], row2['bar_id'], row1['volume'], row2['price'], operate='平空')
__add_operate(row2['dt'], row2['bar_id'], row2['volume'], row2['price'], operate='开多')
pairs, opens =[], []
for op in operates:
if op['operate'] in ['开多', '开空']:
opens.append(op)
continue
assert op['operate'] in ['平多', '平空']
open_op = opens.pop()
if open_op['operate'] == '开多':
p_ret = round((op['price'] - open_op['price']) / open_op['price'] * 10000, 2)
p_dir = '多头'
else:
p_ret = round((open_op['price'] - op['price']) / open_op['price'] * 10000, 2)
p_dir = '空头'
pair = {"标的代码": symbol, "交易方向": p_dir,
"开仓时间": open_op['dt'], "平仓时间": op['dt'],
"开仓价格": open_op['price'], "平仓价格": op['price'],
"持仓K线数": op['bar_id'] - open_op['bar_id'] + 1,
"事件序列": f"{open_op['operate']} -> {op['operate']}",
"持仓天数": (op['dt'] - open_op['dt']).days,
"盈亏比例": p_ret}
pairs.append(pair)
df_pairs = pd.DataFrame(pairs)
return df_pairs
[docs] def backtest(self):
"""回测所有合约的收益率"""
res = {}
for symbol in self.symbols:
daily = self.get_symbol_daily(symbol)
pairs = self.get_symbol_pairs(symbol)
res[symbol] = {"daily": daily, "pairs": pairs}
pd.to_pickle(res, self.res_path.joinpath("res.pkl"))
logger.info(f"回测结果已保存到 {self.res_path.joinpath('res.pkl')}")
# 品种等权费后日收益率
dret = pd.concat([v['daily'] for v in res.values()], ignore_index=True)
dret = pd.pivot_table(dret, index='date', columns='symbol', values='return').fillna(0)
dret['total'] = dret[list(res.keys())].mean(axis=1)
stats = {"开始日期": dret.index.min().strftime("%Y%m%d"), "结束日期": dret.index.max().strftime("%Y%m%d")}
stats.update(daily_performance(dret['total']))
logger.info(f"品种等权费后日收益率：{stats}")
dret.to_excel(self.res_path.joinpath("daily_return.xlsx"), index=True)
logger.info(f"品种等权费后日收益率已保存到 {self.res_path.joinpath('daily_return.xlsx')}")
# 品种等权费后日收益率资金曲线绘制
dret = dret.cumsum()
fig = px.line(dret, y=dret.columns.to_list(), title="费后日收益率资金曲线")
fig.for_each_trace(lambda trace: trace.update(visible=True if trace.name == 'total' else 'legendonly'))
fig.write_html(self.res_path.joinpath("daily_return.html"))
logger.info(f"费后日收益率资金曲线已保存到 {self.res_path.joinpath('daily_return.html')}")
# 所有开平交易记录的表现
dfp = pd.concat([v['pairs'] for v in res.values()], ignore_index=True)
pairs_stats = evaluate_pairs(dfp)
pairs_stats = {k: v for k, v in pairs_stats.items() if k in ['单笔收益', '持仓K线数', '交易胜率', '持仓天数']}
logger.info(f"所有开平交易记录的表现：{pairs_stats}")
stats.update(pairs_stats)
logger.info(f"策略评价：{stats}")
save_json(stats, self.res_path.joinpath("stats.json"))
res['stats'] = stats
return res