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283 lines
12 KiB
Python
283 lines
12 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import copy
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from qlib.utils import init_instance_by_config
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import warnings
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import pandas as pd
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from .position import BasePosition, InfPosition, Position
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from .report import Report, Indicator
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from .order import Order
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from .exchange import Exchange
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"""
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rtn & earning in the Account
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rtn:
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from order's view
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1.change if any order is executed, sell order or buy order
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2.change at the end of today, (today_clse - stock_price) * amount
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earning
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from value of current position
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earning will be updated at the end of trade date
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earning = today_value - pre_value
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**is consider cost**
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while earning is the difference of two position value, so it considers cost, it is the true return rate
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in the specific accomplishment for rtn, it does not consider cost, in other words, rtn - cost = earning
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"""
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class AccumulatedInfo:
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"""accumulated trading info, including accumulated return\cost\turnover"""
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def __init__(self):
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self.reset()
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def reset(self):
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self.rtn = 0 # accumulated return, do not consider cost
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self.cost = 0 # accumulated cost
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self.to = 0 # accumulated turnover
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def add_return_value(self, value):
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self.rtn += value
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def add_cost(self, value):
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self.cost += value
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def add_turnover(self, value):
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self.to += value
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@property
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def get_return(self):
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return self.rtn
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@property
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def get_cost(self):
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return self.cost
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@property
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def get_turnover(self):
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return self.to
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class Account:
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def __init__(
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self, init_cash: float = 1e9, freq: str = "day", benchmark_config: dict = {}, pos_type: str = "Position"
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):
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self.pos_type = pos_type
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self.init_vars(init_cash, freq, benchmark_config)
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def init_vars(self, init_cash, freq: str, benchmark_config: dict):
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# init cash
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self.init_cash = init_cash
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self.current: BasePosition = init_instance_by_config(
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{
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"class": self.pos_type,
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"kwargs": {"cash": init_cash},
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"module_path": "qlib.backtest.position",
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}
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)
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self.accum_info = AccumulatedInfo()
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self.reset(freq=freq, benchmark_config=benchmark_config, init_report=True)
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def reset_report(self, freq, benchmark_config):
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# portfolio related metrics
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self.report = Report(freq, benchmark_config)
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self.positions = {}
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# trading related matric(e.g. high-frequency trading)
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self.indicator = Indicator()
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def reset(self, freq=None, benchmark_config=None, init_report=False):
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"""reset freq and report of account
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Parameters
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----------
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freq : str, optional
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frequency of account & report, by default None
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benchmark_config : {}, optional
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benchmark config of report, by default None
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init_report : bool, optional
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whether to initialize the report, by default False
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"""
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if freq is not None:
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self.freq = freq
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if benchmark_config is not None:
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self.benchmark_config = benchmark_config
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if freq is not None or benchmark_config is not None or init_report:
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self.reset_report(self.freq, self.benchmark_config)
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def get_positions(self):
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return self.positions
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def get_cash(self):
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return self.current.get_cash()
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def _update_state_from_order(self, order, trade_val, cost, trade_price):
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# update turnover
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self.accum_info.add_turnover(trade_val)
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# update cost
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self.accum_info.add_cost(cost)
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# update return from order
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trade_amount = trade_val / trade_price
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if order.direction == Order.SELL: # 0 for sell
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# when sell stock, get profit from price change
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profit = trade_val - self.current.get_stock_price(order.stock_id) * trade_amount
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self.accum_info.add_return_value(profit) # note here do not consider cost
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elif order.direction == Order.BUY: # 1 for buy
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# when buy stock, we get return for the rtn computing method
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# profit in buy order is to make rtn is consistent with earning at the end of bar
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profit = self.current.get_stock_price(order.stock_id) * trade_amount - trade_val
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self.accum_info.add_return_value(profit) # note here do not consider cost
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def update_order(self, order, trade_val, cost, trade_price):
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if self.current.skip_update():
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# TODO: supporting polymorphism for account
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# updating order for infinite position is meaningless
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return
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# if stock is sold out, no stock price information in Position, then we should update account first, then update current position
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# if stock is bought, there is no stock in current position, update current, then update account
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# The cost will be substracted from the cash at last. So the trading logic can ignore the cost calculation
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if order.direction == Order.SELL:
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# sell stock
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self._update_state_from_order(order, trade_val, cost, trade_price)
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# update current position
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# for may sell all of stock_id
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self.current.update_order(order, trade_val, cost, trade_price)
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else:
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# buy stock
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# deal order, then update state
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self.current.update_order(order, trade_val, cost, trade_price)
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self._update_state_from_order(order, trade_val, cost, trade_price)
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def update_bar_count(self):
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"""at the end of the trading bar, update holding bar, count of stock"""
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# update holding day count
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if not self.current.skip_update():
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self.current.add_count_all(bar=self.freq)
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def update_current(self, trade_start_time, trade_end_time, trade_exchange):
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"""update current to make rtn consistent with earning at the end of bar"""
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# update price for stock in the position and the profit from changed_price
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if not self.current.skip_update():
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stock_list = self.current.get_stock_list()
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for code in stock_list:
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# if suspend, no new price to be updated, profit is 0
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if trade_exchange.check_stock_suspended(code, trade_start_time, trade_end_time):
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continue
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bar_close = trade_exchange.get_close(code, trade_start_time, trade_end_time)
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self.current.update_stock_price(stock_id=code, price=bar_close)
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def update_report(self, trade_start_time, trade_end_time):
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"""update position history, report"""
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# calculate earning
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# account_value - last_account_value
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# for the first trade date, account_value - init_cash
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# self.report.is_empty() to judge is_first_trade_date
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# get last_account_value, last_total_cost, last_total_turnover
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if self.report.is_empty():
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last_account_value = self.init_cash
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last_total_cost = 0
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last_total_turnover = 0
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else:
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last_account_value = self.report.get_latest_account_value()
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last_total_cost = self.report.get_latest_total_cost()
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last_total_turnover = self.report.get_latest_total_turnover()
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# get now_account_value, now_stock_value, now_earning, now_cost, now_turnover
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now_account_value = self.current.calculate_value()
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now_stock_value = self.current.calculate_stock_value()
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now_earning = now_account_value - last_account_value
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now_cost = self.accum_info.get_cost - last_total_cost
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now_turnover = self.accum_info.get_turnover - last_total_turnover
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# update report for today
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# judge whether the the trading is begin.
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# and don't add init account state into report, due to we don't have excess return in those days.
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self.report.update_report_record(
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trade_start_time=trade_start_time,
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trade_end_time=trade_end_time,
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account_value=now_account_value,
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cash=self.current.position["cash"],
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return_rate=(now_earning + now_cost) / last_account_value,
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# here use earning to calculate return, position's view, earning consider cost, true return
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# in order to make same definition with original backtest in evaluate.py
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total_turnover=self.accum_info.get_turnover,
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turnover_rate=now_turnover / last_account_value,
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total_cost=self.accum_info.get_cost,
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cost_rate=now_cost / last_account_value,
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stock_value=now_stock_value,
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)
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# set now_account_value to position
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self.current.position["now_account_value"] = now_account_value
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self.current.update_weight_all()
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# update positions
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# note use deepcopy
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self.positions[trade_start_time] = copy.deepcopy(self.current)
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def update_bar_end(
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self,
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trade_start_time: pd.Timestamp,
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trade_end_time: pd.Timestamp,
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trade_exchange: Exchange,
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atomic: bool,
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generate_report: bool = False,
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trade_info: list = None,
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inner_order_indicators: Indicator = None,
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indicator_config: dict = {},
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):
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"""update account at each trading bar step
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Parameters
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----------
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trade_start_time : pd.Timestamp
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closed start time of step
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trade_end_time : pd.Timestamp
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closed end time of step
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trade_exchange : Exchange
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trading exchange, used to update current
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atomic : bool
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whether the trading executor is atomic, which means there is no higher-frequency trading executor inside it
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- if atomic is True, calculate the indicators with trade_info
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- else, aggregate indicators with inner indicators
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generate_report : bool, optional
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whether to generate report, by default False
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trade_info : List[(Order, float, float, float)], optional
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trading information, by default None
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- necessary if atomic is True
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- list of tuple(order, trade_val, trade_cost, trade_price)
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inner_order_indicators : Indicator, optional
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indicators of inner executor, by default None
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- necessary if atomic is False
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- used to aggregate outer indicators
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indicator_config : dict, optional
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config of calculating indicators, by default {}
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"""
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if atomic is True and trade_info is None:
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raise ValueError("trade_info is necessary in atomic executor")
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elif atomic is False and inner_order_indicators is None:
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raise ValueError("inner_order_indicators is necessary in unatomic executor")
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if generate_report:
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# report is portfolio related analysis
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# TODO: `update_bar_count` and `update_current` should placed in Position and be merged.
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self.update_bar_count()
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self.update_current(trade_start_time, trade_end_time, trade_exchange)
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self.update_report(trade_start_time, trade_end_time)
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# indicator is trading (e.g. high-frequency order execution) related analysis
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self.indicator.clear()
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if atomic:
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self.indicator.update_order_indicators(trade_start_time, trade_end_time, trade_info, trade_exchange)
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else:
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self.indicator.agg_order_indicators(inner_order_indicators, indicator_config)
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self.indicator.cal_trade_indicators(trade_start_time, self.freq, indicator_config)
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self.indicator.record(trade_start_time)
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