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risk_cal.py
160 lines (121 loc) · 6.94 KB
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risk_cal.py
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# -*- coding: utf-8 -*-
from __future__ import division
import copy
from collections import OrderedDict
import pandas as pd
import numpy as np
from .risk import Risk
from .. import const
class RiskCal(object):
def __init__(self, trading_params, data_proxy):
self.data_proxy = data_proxy
self.start_date = trading_params.start_date
self.trading_index = trading_params.trading_calendar
self.trading_days_cnt = len(self.trading_index)
self.strategy_total_daily_returns = np.full(self.trading_days_cnt, np.nan)
self.benchmark_total_daily_returns = np.full(self.trading_days_cnt, np.nan)
self.strategy_current_daily_returns = None
self.benchmark_current_daily_returns = None
self.strategy_total_returns = np.full(self.trading_days_cnt, np.nan)
self.benchmark_total_returns = np.full(self.trading_days_cnt, np.nan)
self.strategy_current_total_returns = None
self.benchmark_current_total_returns = None
self.strategy_annualized_returns = np.full(self.trading_days_cnt, np.nan)
self.benchmark_annualized_returns = np.full(self.trading_days_cnt, np.nan)
self.strategy_current_annualized_returns = None
self.benchmark_current_annualized_returns = None
self.risk = Risk()
self.daily_risks = OrderedDict()
self.current_max_returns = -np.inf
self.current_max_drawdown = 0
# FIXME might change daily?
self.risk_free_rate = data_proxy.get_yield_curve(self.trading_index[0], self.trading_index[-1])
def calculate(self, date, strategy_daily_returns, benchmark_daily_returns):
idx = self.latest_idx = self.trading_index.get_loc(date)
# daily
self.strategy_total_daily_returns[idx] = strategy_daily_returns
self.benchmark_total_daily_returns[idx] = benchmark_daily_returns
self.strategy_current_daily_returns = self.strategy_total_daily_returns[:idx + 1]
self.benchmark_current_daily_returns = self.benchmark_total_daily_returns[:idx + 1]
self.days_cnt = len(self.strategy_current_daily_returns)
days_pass_cnt = (date - self.start_date).days + 1
# risk
# self.riskfree_total_returns = self.risk_free_rate / self.days_cnt * const.DAYS_CNT.TRADING_DAYS_A_YEAR
self.riskfree_total_returns = self.risk_free_rate
# total
self.strategy_total_returns[idx] = (1. + self.strategy_current_daily_returns).prod() - 1
self.benchmark_total_returns[idx] = (1. + self.benchmark_current_daily_returns).prod() - 1
self.strategy_current_total_returns = self.strategy_total_returns[:idx + 1]
self.benchmark_current_total_returns = self.benchmark_total_returns[:idx + 1]
# annual
self.strategy_annualized_returns[idx] = (1 + self.strategy_current_total_returns[-1]) ** (
const.DAYS_CNT.DAYS_A_YEAR / days_pass_cnt) - 1
self.benchmark_annualized_returns[idx] = (1 + self.benchmark_current_total_returns[-1]) ** (
const.DAYS_CNT.DAYS_A_YEAR / days_pass_cnt) - 1
self.strategy_current_annualized_returns = self.strategy_annualized_returns[:idx + 1]
self.benchmark_current_annualized_returns = self.benchmark_annualized_returns[:idx + 1]
if self.strategy_current_total_returns[-1] > self.current_max_returns:
self.current_max_returns = self.strategy_current_total_returns[-1]
risk = self.risk
risk.volatility = self.cal_volatility()
risk.max_drawdown = self.cal_max_drawdown()
risk.tracking_error = self.cal_tracking_error()
risk.information_rate = self.cal_information_rate(risk.volatility)
risk.downside_risk = self.cal_downside_risk()
risk.beta = self.cal_beta()
risk.alpha = self.cal_alpha()
risk.sharpe = self.cal_sharpe()
risk.sortino = self.cal_sortino()
self.daily_risks[date] = copy.deepcopy(risk)
def cal_volatility(self):
daily_returns = self.strategy_current_daily_returns
if len(daily_returns) <= 1:
return 0.
volatility = const.DAYS_CNT.TRADING_DAYS_A_YEAR ** 0.5 * np.std(daily_returns, ddof=1)
return volatility
def cal_max_drawdown(self):
today_return = self.strategy_current_total_returns[-1]
today_drawdown = (1. + today_return) / (1. + self.current_max_returns) - 1.
if today_drawdown < self.current_max_drawdown:
self.current_max_drawdown = today_drawdown
return self.current_max_drawdown
def cal_tracking_error(self):
diff = self.strategy_current_daily_returns - self.benchmark_current_daily_returns
return ((diff * diff).sum() / len(diff)) ** 0.5 * const.DAYS_CNT.TRADING_DAYS_A_YEAR ** 0.5
def cal_information_rate(self, volatility):
strategy_rets = self.strategy_current_daily_returns.sum() / len(self.strategy_current_daily_returns) * const.DAYS_CNT.TRADING_DAYS_A_YEAR
benchmark_rets = self.benchmark_current_daily_returns.sum() / len(self.benchmark_current_daily_returns) * const.DAYS_CNT.TRADING_DAYS_A_YEAR
return (strategy_rets - benchmark_rets) / volatility
def cal_alpha(self):
beta = self.risk.beta
strategy_rets = self.strategy_current_daily_returns.sum() / len(self.strategy_current_daily_returns) * const.DAYS_CNT.TRADING_DAYS_A_YEAR
benchmark_rets = self.benchmark_current_daily_returns.sum() / len(self.benchmark_current_daily_returns) * const.DAYS_CNT.TRADING_DAYS_A_YEAR
alpha = strategy_rets - (self.riskfree_total_returns + beta * (benchmark_rets - self.riskfree_total_returns))
return alpha
def cal_beta(self):
if len(self.strategy_current_daily_returns) <= 1:
return 0.
cov = np.cov(np.vstack([
self.strategy_current_daily_returns,
self.benchmark_current_daily_returns,
]), ddof=1)
beta = cov[0][1] / cov[1][1]
return beta
def cal_sharpe(self):
volatility = self.risk.volatility
strategy_rets = self.strategy_current_daily_returns.sum() / len(self.strategy_current_daily_returns) * const.DAYS_CNT.TRADING_DAYS_A_YEAR
sharpe = (strategy_rets - self.riskfree_total_returns) / volatility
return sharpe
def cal_sortino(self):
strategy_rets = self.strategy_current_daily_returns.sum() / len(self.strategy_current_daily_returns) * const.DAYS_CNT.TRADING_DAYS_A_YEAR
downside_risk = self.risk.downside_risk
sortino = (strategy_rets - self.riskfree_total_returns) / downside_risk
return sortino
def cal_downside_risk(self):
mask = self.strategy_current_daily_returns < self.benchmark_current_daily_returns
diff = self.strategy_current_daily_returns[mask] - self.benchmark_current_daily_returns[mask]
if len(diff) <= 1:
return 0.
return ((diff * diff).sum() / len(diff)) ** 0.5 * const.DAYS_CNT.TRADING_DAYS_A_YEAR ** 0.5
def __repr__(self):
return "RiskCal({0})".format(self.__dict__)