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Merge branch 'nested_decision_exe' of https://github.com/microsoft/qlib into rl-dummy

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v-mingzhehan
2021-07-19 04:18:17 +00:00
parent 25ff62f542 92f2891664
commit 62583ea6ec
5 changed files with 75 additions and 23 deletions
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47
qlib/backtest/exchange.py
View File

@@ -2,6 +2,7 @@
# Licensed under the MIT License.
from qlib.backtest.position import Position
import random
import logging
from typing import List, Tuple, Union
@@ -281,6 +282,8 @@ class Exchange:
"""
Deal order when the actual transaction
the results section in `Order` will be changed.
:param order: Deal the order.
:param trade_account: Trade account to be updated after dealing the order.
:param position: position to be updated after dealing the order.
@@ -343,6 +346,7 @@ class Exchange:
`None`: if the stock is suspended `None` may be returned
`float`: return factor if the factor exists
"""
assert (start_time is not None and end_time is not None, "the time range must be given")
if stock_id not in self.quote:
return None
return resam_ts_data(self.quote[stock_id]["$factor"], start_time, end_time, method=ts_data_last)
@@ -505,20 +509,56 @@ class Exchange:
)
return value
def get_amount_of_trade_unit(self, factor):
def _get_factor_or_raise_erorr(self, factor: float = None, stock_id: str = None, start_time=None, end_time=None):
"""Please refer to the docs of get_amount_of_trade_unit"""
if factor is None:
if stock_id is not None and start_time is not None and end_time is not None :
factor = self.get_factor(stock_id=stock_id, start_time=start_time, end_time=end_time)
else:
raise ValueError(f"`factor` and (`stock_id`, `start_time`, `end_time`) can't both be None")
return factor
def get_amount_of_trade_unit(self, factor: float = None, stock_id: str = None, start_time=None, end_time=None):
"""
get the trade unit of amount based on **factor**
the factor can be given directly or calculated in given time range and stock id.
`factor` has higher priority than `stock_id`, `start_time` and `end_time`
Parameters
----------
factor : float
the adjusted factor
stock_id : str
the id of the stock
start_time :
the start time of trading range
end_time :
the end time of trading range
"""
if not self.trade_w_adj_price and self.trade_unit is not None:
factor = self._get_factor_or_raise_erorr(factor=factor,
stock_id=stock_id,
start_time=start_time,
end_time=end_time)
return self.trade_unit / factor
else:
return None
def round_amount_by_trade_unit(self, deal_amount, factor):
def round_amount_by_trade_unit(self, deal_amount, factor: float = None, stock_id: str = None, start_time=None, end_time=None):
"""Parameter
Please refer to the docs of get_amount_of_trade_unit
deal_amount : float, adjusted amount
factor : float, adjusted factor
return : float, real amount
"""
if not self.trade_w_adj_price and self.trade_unit is not None:
# the minimal amount is 1. Add 0.1 for solving precision problem.
factor = self._get_factor_or_raise_erorr(factor=factor,
stock_id=stock_id,
start_time=start_time,
end_time=end_time)
return (deal_amount * factor + 0.1) // self.trade_unit * self.trade_unit / factor
return deal_amount
@@ -529,7 +569,7 @@ class Exchange:
else:
return deal_amount
def _calc_trade_info_by_order(self, order, position):
def _calc_trade_info_by_order(self, order, position: Position):
"""
Calculation of trade info
@@ -541,6 +581,7 @@ class Exchange:
"""
trade_price = self.get_deal_price(order.stock_id, order.start_time, order.end_time, direction=order.direction)
order.factor = self.get_factor(order.stock_id, order.start_time, order.end_time)
if order.direction == Order.SELL:
# sell
if position is not None:

20
qlib/backtest/order.py
View File

@@ -43,16 +43,24 @@ class Order:
presents the weight factor assigned in Exchange()
"""
# 1) time invariant values
# - they are set by users and is time-invariant.
stock_id: str
amount: float # `amount` is a non-negative value
amount: float # `amount` is a non-negative and adjusted value
direction: int
# 2) time variant values:
# - Users may want to set these values when using lower level APIs
# - If users don't, TradeDecisionWO will help users to set them
# The interval of the order which belongs to (NOTE: this is not the expected order dealing range time)
start_time: pd.Timestamp
end_time: pd.Timestamp
direction: int
factor: float
# 3) results
# - users should not care about these values
# - they are set by the backtest system after finishing the results.
deal_amount: Optional[float] = None # `deal_amount` is a non-negative value
factor: Optional[float] = None
# FIXME:
# for compatible now.
@@ -145,9 +153,9 @@ class OrderHelper:
**adjusted trading amount**
direction : OrderDir
trading direction
start_time : Union[str, pd.Timestamp]
start_time : Union[str, pd.Timestamp] (optional)
The interval of the order which belongs to
end_time : Union[str, pd.Timestamp]
end_time : Union[str, pd.Timestamp] (optional)
The interval of the order which belongs to
Returns
@@ -159,13 +167,13 @@ class OrderHelper:
start_time = pd.Timestamp(start_time)
if end_time is not None:
end_time = pd.Timestamp(end_time)
# NOTE: factor is a value belongs to the results section. User don't have to care about it when creating orders
return Order(
stock_id=code,
amount=amount,
start_time=start_time,
end_time=end_time,
direction=direction,
factor=self.exchange.get_factor(code, start_time, end_time),
)

8
qlib/contrib/evaluate.py
View File

@@ -73,20 +73,20 @@ def indicator_analysis(df, method="mean"):
Parameters
----------
df : pandas.DataFrame
columns: like ['pa', 'pos', 'ffr', 'amount', 'value'].
columns: like ['pa', 'pos', 'ffr', 'deal_amount', 'value'].
Necessary fields:
- 'pa' is the price advantage in trade indicators
- 'pos' is the positive rate in trade indicators
- 'ffr' is the fulfill rate in trade indicators
Optional fields:
- 'amount' is the total deal amount, only necessary when method is 'amount_weighted'
- 'deal_amount' is the total deal deal_amount, only necessary when method is 'amount_weighted'
- 'value' is the total trade value, only necessary when method is 'value_weighted'
index: Index(datetime)
method : str, optional
statistics method of pa/ffr, by default "mean"
- if method is 'mean', count the mean statistical value of each trade indicator
- if method is 'amount_weighted', count the amount weighted mean statistical value of each trade indicator
- if method is 'amount_weighted', count the deal_amount weighted mean statistical value of each trade indicator
- if method is 'value_weighted', count the value weighted mean statistical value of each trade indicator
Note: statistics method of pos is always "mean"
@@ -97,7 +97,7 @@ def indicator_analysis(df, method="mean"):
"""
weights_dict = {
"mean": df["count"],
"amount_weighted": df["amount"].abs(),
"amount_weighted": df["deal_amount"].abs(),
"value_weighted": df["value"].abs(),
}
if method not in weights_dict:

2
qlib/contrib/strategy/model_strategy.py
View File

@@ -194,7 +194,6 @@ class TopkDropoutStrategy(ModelStrategy):
start_time=trade_start_time,
end_time=trade_end_time,
direction=Order.SELL, # 0 for sell, 1 for buy
factor=factor,
)
# is order executable
if self.trade_exchange.check_order(sell_order):
@@ -231,7 +230,6 @@ class TopkDropoutStrategy(ModelStrategy):
start_time=trade_start_time,
end_time=trade_end_time,
direction=Order.BUY, # 1 for buy
factor=factor,
)
buy_order_list.append(buy_order)
return TradeDecisionWO(sell_order_list + buy_order_list, self)

21
qlib/contrib/strategy/rule_strategy.py
View File

@@ -63,7 +63,9 @@ class TWAPStrategy(BaseStrategy):
stock_id=order.stock_id, start_time=trade_start_time, end_time=trade_end_time
):
continue
_amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(order.factor)
_amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(stock_id=order.stock_id,
start_time=order.start_time,
end_time=order.end_time)
_order_amount = None
# considering trade unit
if _amount_trade_unit is None:
@@ -99,7 +101,6 @@ class TWAPStrategy(BaseStrategy):
start_time=trade_start_time,
end_time=trade_end_time,
direction=order.direction, # 1 for buy
factor=order.factor,
)
order_list.append(_order)
return TradeDecisionWO(order_list=order_list, strategy=self)
@@ -168,7 +169,9 @@ class SBBStrategyBase(BaseStrategy):
self.trade_trend[order.stock_id] = _pred_trend
continue
# get amount of one trade unit
_amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(order.factor)
_amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(stock_id=order.stock_id,
start_time=order.start_time,
end_time=order.end_time)
if _pred_trend == self.TREND_MID:
_order_amount = None
# considering trade unit
@@ -201,7 +204,6 @@ class SBBStrategyBase(BaseStrategy):
start_time=trade_start_time,
end_time=trade_end_time,
direction=order.direction,
factor=order.factor,
)
order_list.append(_order)
@@ -248,7 +250,6 @@ class SBBStrategyBase(BaseStrategy):
start_time=trade_start_time,
end_time=trade_end_time,
direction=order.direction, # 1 for buy
factor=order.factor,
)
order_list.append(_order)
else:
@@ -267,7 +268,6 @@ class SBBStrategyBase(BaseStrategy):
start_time=trade_start_time,
end_time=trade_end_time,
direction=order.direction, # 1 for buy
factor=order.factor,
)
order_list.append(_order)
@@ -471,7 +471,9 @@ class ACStrategy(BaseStrategy):
if sig_sam is None or np.isnan(sig_sam):
# no signal, TWAP
_amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(order.factor)
_amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(stock_id=order.stock_id,
start_time=order.start_time,
end_time=order.end_time)
if _amount_trade_unit is None:
# divide the order into equal parts, and trade one part
_order_amount = self.trade_amount[order.stock_id] / (trade_len - trade_step)
@@ -492,7 +494,10 @@ class ACStrategy(BaseStrategy):
np.sinh(kappa * (trade_len - trade_step)) - np.sinh(kappa * (trade_len - trade_step - 1))
) / np.sinh(kappa * trade_len)
_order_amount = order.amount * amount_ratio
_order_amount = self.trade_exchange.round_amount_by_trade_unit(_order_amount, order.factor)
_order_amount = self.trade_exchange.round_amount_by_trade_unit(_order_amount,
stock_id=order.stock_id,
start_time=order.start_time,
end_time=order.end_time)
if order.direction == order.SELL:
# sell all amount at last