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report.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'mayanqiong'
from typing import Dict, Optional
import numpy as np
from pandas import DataFrame, Series
from tqsdk.objs import Account, Trade, SecurityAccount, SecurityTrade
from tqsdk.tafunc import get_sharp, get_sortino, get_calmar, _cum_counts
TRADING_DAYS_OF_YEAR = 250
TRADING_DAYS_OF_MONTH = 21
class TqReport(object):
"""
天勤报告类，辅助 web_gui 显示回测统计信息和统计图表
1. 目前只针对 TqSim 账户回测有意义
2. 每份报告针对一组对应的账户截面记录和成交记录
"""
def __init__(self, report_id: str, trade_log: Optional[Dict] = None, quotes: Optional[Dict] = None, account_type: str = "FUTURE"):
"""
本模块为给 TqSim 提供交易成交统计
Args:
report_id (str): 报告Id
trade_log (dict): TqSim 交易结束之后生产的每日账户截面和交易记录
{
'2020-09-01': {
"trades": [],
"account": {},
"positions": {},
'2020-09-02': {....},
}
quotes (dict): 合约信息
Example::
TODO: 补充示例
"""
self.report_id = report_id
self.trade_log = trade_log
self.quotes = quotes
self.account_type = account_type
self.date_keys = sorted(trade_log.keys())
self.account_df, self.trade_df = self._get_df()
# default metrics
self.default_metrics = self._get_default_metrics() if self.account_type == "FUTURE" else self._get_stock_metrics()
def _get_df(self):
type_account = Account if self.account_type == "FUTURE" else SecurityAccount
type_trade = Trade if self.account_type == "FUTURE" else SecurityTrade
account_data = [{'date': dt} for dt in self.date_keys]
for item in account_data:
item.update(self.trade_log[item['date']]['account'])
account_df = DataFrame(data=account_data, columns=['date'] + list(type_account(None).keys()))
trade_array = []
for date in self.date_keys:
trade_array.extend(self.trade_log[date]['trades'])
trade_df = DataFrame(data=trade_array, columns=list(type_trade(None).keys()))
if type_trade == Trade:
trade_df["offset1"] = trade_df["offset"].replace("CLOSETODAY", "CLOSE")
return account_df, trade_df
def _get_default_metrics(self):
if self.account_df.shape[0] > 0:
result = self._get_account_stat_metrics()
result.update(self._get_trades_stat_metrics())
return result
else:
return {
"winning_rate": float('nan'), # 胜率
"profit_loss_ratio": float('nan'), # 盈亏额比例
"ror": float('nan'), # 收益率
"annual_yield": float('nan'), # 年化收益率
"max_drawdown": float('nan'), # 最大回撤
"sharpe_ratio": float('nan'), # 年化夏普率
"sortino_ratio": float('nan'), # 年化索提诺比率
"commission": 0, # 总手续费
"tqsdk_punchline": ""
}
def _get_stock_metrics(self):
if self.account_df.shape[0] > 0:
init_asset = self.account_df.iloc[0]['asset_his']
asset = self.account_df.iloc[-1]['asset']
self.account_df['profit'] = self.account_df['asset'] - self.account_df['asset'].shift(fill_value=init_asset) # 每日收益
self.account_df['is_profit'] = np.where(self.account_df['profit'] > 0, 1, 0) # 是否收益
self.account_df['is_loss'] = np.where(self.account_df['profit'] < 0, 1, 0) # 是否亏损
self.account_df['daily_yield'] = self.account_df['asset'] / self.account_df['asset'].shift(fill_value=init_asset) - 1 # 每日收益率
self.account_df['max_asset'] = self.account_df['asset'].cummax() # 当前单日最大权益
self.account_df['drawdown'] = (self.account_df['max_asset'] - self.account_df['asset']) / self.account_df['max_asset'] # 回撤
_ror = asset / init_asset
return {
"start_date": self.account_df.iloc[0]["date"],
"end_date": self.account_df.iloc[-1]["date"],
"init_asset": init_asset,
"asset": init_asset,
"start_asset": init_asset,
"end_asset": asset,
"ror": _ror - 1, # 收益率
"annual_yield": _ror ** (TRADING_DAYS_OF_YEAR / self.account_df.shape[0]) - 1, # 年化收益率
"trading_days": self.account_df.shape[0], # 总交易天数
"cum_profit_days": self.account_df['is_profit'].sum(), # 累计盈利天数
"cum_loss_days": self.account_df['is_loss'].sum(), # 累计亏损天数
"max_drawdown": self.account_df['drawdown'].max(), # 最大回撤
"fee": self.account_df['buy_fee_today'].sum() + self.account_df['sell_fee_today'].sum(), # 总手续费
"buy_times": self.trade_df.loc[self.trade_df["direction"] == "BUY"].shape[0], # 买次数
"sell_times": self.trade_df.loc[self.trade_df["direction"] == "SELL"].shape[0], # 卖次数
"max_cont_profit_days": _cum_counts(self.account_df['is_profit']).max(), # 最大连续盈利天数
"max_cont_loss_days": _cum_counts(self.account_df['is_loss']).max(), # 最大连续亏损天数
"sharpe_ratio": get_sharp(self.account_df['daily_yield']), # 年化夏普率
"calmar_ratio": get_calmar(self.account_df['daily_yield'], self.account_df['drawdown'].max()), # 年化卡玛比率
"sortino_ratio": get_sortino(self.account_df['daily_yield']), # 年化索提诺比率
"tqsdk_punchline": self._get_tqsdk_punchlines(_ror - 1)
}
else:
return {
"profit_loss_ratio": float('nan'), # 盈亏额比例
"ror": float('nan'), # 收益率
"annual_yield": float('nan'), # 年化收益率
"max_drawdown": float('nan'), # 最大回撤
"sharpe_ratio": float('nan'), # 年化夏普率
"sortino_ratio": float('nan'), # 年化索提诺比率
"fee": 0, # 总手续费
"tqsdk_punchline": ""
}
def _get_account_stat_metrics(self):
init_balance = self.account_df.iloc[0]['pre_balance']
balance = self.account_df.iloc[-1]['balance']
self.account_df['profit'] = self.account_df['balance'] - self.account_df['balance'].shift(fill_value=init_balance) # 每日收益
self.account_df['is_profit'] = np.where(self.account_df['profit'] > 0, 1, 0) # 是否收益
self.account_df['is_loss'] = np.where(self.account_df['profit'] < 0, 1, 0) # 是否亏损
self.account_df['daily_yield'] = self.account_df['balance'] / self.account_df['balance'].shift(fill_value=init_balance) - 1 # 每日收益率
self.account_df['max_balance'] = self.account_df['balance'].cummax() # 当前单日最大权益
self.account_df['drawdown'] = (self.account_df['max_balance'] - self.account_df['balance']) / self.account_df['max_balance'] # 回撤
_ror = self.account_df.iloc[-1]['balance'] / self.account_df.iloc[0]['pre_balance']
return {
"start_date": self.account_df.iloc[0]["date"],
"end_date": self.account_df.iloc[-1]["date"],
"init_balance": init_balance,
"balance": balance,
"start_balance": init_balance,
"end_balance": balance,
"ror": _ror - 1, # 收益率
"annual_yield": _ror ** (TRADING_DAYS_OF_YEAR / self.account_df.shape[0]) - 1, # 年化收益率
"trading_days": self.account_df.shape[0], # 总交易天数
"cum_profit_days": self.account_df['is_profit'].sum(), # 累计盈利天数
"cum_loss_days": self.account_df['is_loss'].sum(), # 累计亏损天数
"max_drawdown": self.account_df['drawdown'].max(), # 最大回撤
"commission": self.account_df['commission'].sum(), # 总手续费
"open_times": self.trade_df.loc[self.trade_df["offset1"] == "OPEN"].shape[0], # 开仓次数
"close_times": self.trade_df.loc[self.trade_df["offset1"] == "CLOSE"].shape[0], # 平仓次数
"daily_risk_ratio": self.account_df['risk_ratio'].mean(), # 提供日均风险度
"max_cont_profit_days": _cum_counts(self.account_df['is_profit']).max(), # 最大连续盈利天数
"max_cont_loss_days": _cum_counts(self.account_df['is_loss']).max(), # 最大连续亏损天数
"sharpe_ratio": get_sharp(self.account_df['daily_yield']), # 年化夏普率
"calmar_ratio": get_calmar(self.account_df['daily_yield'], self.account_df['drawdown'].max()), # 年化卡玛比率
"sortino_ratio": get_sortino(self.account_df['daily_yield']), # 年化索提诺比率
"tqsdk_punchline": self._get_tqsdk_punchlines(_ror - 1)
}
def _get_trades_stat_metrics(self):
"""
根据成交手数计算 胜率，盈亏额比例
self.quotes 主要需要合约乘数，用于计算盈亏额
"""
trade_array = []
for date in self.date_keys:
for trade in self.trade_log[date]['trades']:
# 每一行都是 1 手的成交记录
trade_array.extend([{
"symbol": f"{trade['exchange_id']}.{trade['instrument_id']}",
"direction": trade["direction"],
"offset": "CLOSE" if trade["offset"] == "CLOSETODAY" else trade["offset"],
"price": trade["price"]
} for i in range(trade['volume'])])
trade_df = DataFrame(data=trade_array, columns=['symbol', 'direction', 'offset', 'price'])
profit_volumes = 0 # 盈利手数
loss_volumes = 0 # 亏损手数
profit_value = 0 # 盈利额
loss_value = 0 # 亏损额
all_symbols = trade_df['symbol'].drop_duplicates()
for symbol in all_symbols:
for direction in ["BUY", "SELL"]:
open_df = self._get_sub_df(trade_df, symbol, dir=direction, offset='OPEN')
close_df = self._get_sub_df(trade_df, symbol, dir=("SELL" if direction == "BUY" else "BUY"), offset='CLOSE')
close_df['profit'] = (close_df['price'] - open_df['price']) * (1 if direction == "BUY" else -1)
profit_volumes += close_df.loc[close_df['profit'] >= 0].shape[0] # 盈利手数
loss_volumes += close_df.loc[close_df['profit'] < 0].shape[0] # 亏损手数
profit_value += close_df.loc[close_df['profit'] >= 0, 'profit'].sum() * self.quotes[symbol]['volume_multiple']
loss_value += close_df.loc[close_df['profit'] < 0, 'profit'].sum() * self.quotes[symbol]['volume_multiple']
winning_rate = profit_volumes / (profit_volumes + loss_volumes) if profit_volumes + loss_volumes else 0
profit_pre_volume = profit_value / profit_volumes if profit_volumes else 0
loss_pre_volume = loss_value / loss_volumes if loss_volumes else 0
profit_loss_ratio = abs(profit_pre_volume / loss_pre_volume) if loss_pre_volume else float("inf")
return {
"profit_volumes": profit_volumes,
"loss_volumes": loss_volumes,
"profit_value": profit_value,
"loss_value": loss_value,
"winning_rate": winning_rate,
"profit_loss_ratio": profit_loss_ratio
}
def _get_tqsdk_punchlines(self, ror):
tqsdk_punchlines = [
'幸好是模拟账户，不然你就亏完啦',
'触底反弹,与其执迷修改参数，不如改变策略思路去天勤官网策略库进修',
'越挫越勇，不如去天勤量化官网策略库进修',
'不要灰心，少侠重新来过',
'策略看来小有所成',
'策略看来的得心应手',
'策略看来春风得意，堪比当代索罗斯',
'策略看来独孤求败，小心过拟合噢'
]
ror_level = [i for i, k in enumerate([-1, -0.5, -0.2, 0, 0.2, 0.5, 1]) if ror < k]
if len(ror_level) > 0:
return tqsdk_punchlines[ror_level[0]]
else:
return tqsdk_punchlines[-1]
def _get_sub_df(self, origin_df, symbol, dir, offset):
df = origin_df.where(
(origin_df['symbol'] == symbol) & (origin_df['offset'] == offset) & (origin_df['direction'] == dir))
df.dropna(inplace=True)
df.reset_index(drop=True, inplace=True)
return df
def metrics(self, **kwargs):
self.default_metrics.update(kwargs)
return [{
self.report_id: {"metrics": self.default_metrics.copy()}
}]
def full(self):
data = self.metrics()
data += self.daily_balance()
data += self.daily_profit()
data += self.drawdown()
data += self.sharp_rolling()
data += self.sortino_rolling()
# data += self.calmar_rolling()
return data
def daily_balance(self):
"""每日资金曲线"""
return [{
self.report_id: {
"charts": {
"daily_balance": {
"title": {
"left": 'center',
"text": "每日账户资金"
},
"xAxis": {
"type": 'category',
"data": self.account_df['date'].to_dict()
},
"yAxis": {
"type": 'value',
"min": 'dataMin',
"max": 'dataMax',
},
"series": {
"0": {
"data": self.account_df['balance'].map(lambda x: '%.2f' % x).to_dict(),
"type": 'line'
}
}
}
}
}
}]
def daily_profit(self):
"""每日盈亏"""
profit = Series(np.where(self.account_df['profit'] >= 0, self.account_df['profit'], float('nan'))) # 收益
loss = Series(np.where(self.account_df['profit'] < 0, self.account_df['profit'], float('nan'))) # 亏损
return [{
self.report_id: {
"charts": {
"daily_profit": {
"title": {
"left": 'center',
"text": "每日盈亏"
},
"xAxis": {
"type": 'category',
"data": self.account_df['date'].to_dict()
},
"yAxis": {
"type": 'value'
},
"series": {
"0": {
"data": profit.to_dict(),
"type": 'bar',
"itemStyle": {
"color": "#ee6666"
},
"stack": 'one',
},
"1": {
"data": loss.to_dict(),
"type": 'bar',
"itemStyle": {
"color": "#91cc75"
},
"stack": 'one',
}
}
}
}
}
}]
def drawdown(self):
"""回撤"""
return [{
self.report_id: {
"charts": {
"drawdown": {
"title": {
"left": 'center',
"text": "回撤"
},
"xAxis": {
"type": 'category',
"data": self.account_df['date'].to_dict()
},
"yAxis": {
"type": 'value'
},
"series": {
"0": {
"data": self.account_df['drawdown'].to_dict(),
"type": 'line'
}
}
}
}
}
}]
def sharp_rolling(self):
"""滚动夏普比率图表"""
rolling_sharp = self.account_df['daily_yield'].rolling(TRADING_DAYS_OF_MONTH).apply(get_sharp)
return [{
self.report_id: {
"charts": {
"sharp_rolling": {
"title": {
"left": 'center',
"text": "滚动夏普比率图表"
},
"xAxis": {
"type": 'category',
"data": self.account_df['date'].to_dict()
},
"yAxis": {
"type": 'value'
},
"series": {
"0": {
"data": rolling_sharp.to_dict(),
"type": 'line'
}
}
}
}
}
}]
def sortino_rolling(self):
"""滚动索提诺比率图表"""
rolling_sortino = self.account_df['daily_yield'].rolling(TRADING_DAYS_OF_MONTH).apply(get_sortino)
return [{
self.report_id: {
"charts": {
"sortino_rolling": {
"title": {
"left": 'center',
"text": "滚动索提诺比率图表"
},
"xAxis": {
"type": 'category',
"data": self.account_df['date'].to_dict()
},
"yAxis": {
"type": 'value'
},
"series": {
"0": {
"name": "滚动索提诺比率图表",
"data": rolling_sortino.to_dict(),
"type": 'line'
}
}
}
}
}
}]
def calmar_rolling(self):
"""滚动卡玛比率图表"""
rolling_calmar = self.account_df['daily_yield'].rolling(TRADING_DAYS_OF_MONTH).apply(
lambda x: get_calmar(x, self.account_df.loc[x.index]['drawdown'].max()))
return [{
self.report_id: {
"charts": {
"calmar_rolling": {
"title": {
"left": 'center',
"text": "滚动卡玛比率图表"
},
"xAxis": {
"type": 'category',
"data": self.account_df['date'].to_dict()
},
"yAxis": {
"type": 'value'
},
"series": {
"0": {
"data": rolling_calmar.to_dict(),
"type": 'line'
},
}
}
}
}
}]