def trade_by_strategy(self, omega):
logging.info("the step is {}".format(self._steps))
logging.debug("the raw omega is {}".format(omega))
future_price = np.concatenate((np.ones(1), self.__get_matrix_y()))
pv_after_commission = calculate_pv_after_commission(
omega, self._last_omega, self._commission_rate)
portfolio_change = pv_after_commission * np.dot(omega, future_price)
self._total_capital *= portfolio_change
self._last_omega = pv_after_commission * omega * future_price / portfolio_change
logging.debug("the portfolio change this period is : {}".format(
portfolio_change))
self.__test_pc_vector.append(portfolio_change)
def trade_by_strategy(self, omega):
logging.info("the step is {}".format(self._steps))
logging.debug("the raw omega is {}".format(omega))
future_price = np.concatenate((np.ones(1), self.__get_matrix_y()))
pv_after_commission = calculate_pv_after_commission(omega, self._last_omega, self._commission_rate)
portfolio_change = pv_after_commission * np.dot(omega, future_price)
self._total_capital *= portfolio_change
self._last_omega = pv_after_commission * omega * \
future_price /\
portfolio_change
logging.debug("the portfolio change this period is : {}".format(portfolio_change))
self.__test_pc_vector.append(portfolio_change)
def trade_by_online_strategy(self, omega):
logging.debug("LAST OMEGA IS {}".format(omega))
real_output = self.__get_matrix_last_y()
#print("Last Value : {} Shape : {}".format(real_output, real_output.shape))
future_price = np.concatenate((np.ones(1), real_output))
logging.info("FUTURE PRICE : {}".format(future_price))
pv_after_commission = calculate_pv_after_commission(
omega, self._last_omega, self._commission_rate)
portfolio_change = pv_after_commission * np.dot(omega, future_price)
self._total_capital *= portfolio_change
self._last_omega = pv_after_commission * omega * \
future_price /\
portfolio_change
logging.debug("TOTAL PORTFOLIO CHANGE: {}".format(portfolio_change))
self.__test_pc_vector.append(portfolio_change)
def trade_by_strategy(self, omega):
logging.info("the step is {}".format(self._steps))
logging.debug("the raw omega is {}".format(omega))
#vector of ones and the relative future price
future_price = np.concatenate((np.ones(1), self.__get_matrix_y()))
#returns a numerically calculated commission
pv_after_commission = calculate_pv_after_commission(
omega, self._last_omega, self._commission_rate)
#portfolio change
portfolio_change = pv_after_commission * np.dot(omega, future_price)
self._total_capital *= portfolio_change
#set the new last omega to be equal to the new portfolio after commission and price changes
self._last_omega = pv_after_commission * omega * \
future_price /\
portfolio_change
logging.debug("the new omega is {}".format(self._last_omega))
logging.debug("the portfolio change this period is : {}".format(
portfolio_change))
self.__test_pc_vector.append(portfolio_change)
self.__test_updated_omega.append(self._last_omega)
def trade_by_strategy(self, omega):
#test
# omega[0,] = np.clip(omega[0,],-1,1)
omega[0, ] = np.tanh(omega[0, ])
logging.info("the step is {}".format(self._steps))
# test
logging.info("omega is {}".format(omega))
# test
logging.debug("the raw omega is {}".format(omega))
future_price = np.concatenate((np.ones(1), self.__get_matrix_y()))
pv_after_commission = calculate_pv_after_commission(
omega, self._last_omega, self._commission_rate)
#test
portfolio_change = pv_after_commission * np.dot(
omega, future_price) + np.multiply(
np.abs(np.clip(omega[0, ], -1, 0)), 0.645)
self._total_capital *= portfolio_change
self._last_omega = pv_after_commission * omega * future_price + np.abs(
np.clip(omega[0, ], -1, 0)) * 0.645 / portfolio_change
logging.debug("the portfolio change this period is : {}".format(
portfolio_change))
self.__test_pc_vector.append(portfolio_change)
def trade_by_strategy(self, omega):
# execute trade once, output the change of omega and portfolio
logging.info("the step is {}".format(self._steps))
logging.debug("the raw omega is {}".format(omega))
future_price = np.concatenate(
(np.ones(1), self.__get_matrix_y()
)) # np.ones(1) as BCT, which is a baseline asset
# the pv after paying commission fee
pv_after_commission = calculate_pv_after_commission(
omega, self._last_omega, self._commission_rate)
turn_over = np.sum(np.abs(omega - self._last_omega))
self.__turn_over.append(turn_over)
portfolio_change = pv_after_commission * np.dot(
omega, future_price) # portfolio change ratio
self._total_capital *= portfolio_change
# * indicates dot product， np.dot indicates x product
self._last_omega = pv_after_commission * omega * \
future_price /\
portfolio_change # to regularize the last_omega
logging.debug("the portfolio change this period is : {}".format(
portfolio_change))
self.__test_pc_vector.append(portfolio_change)
