mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-21 19:27:36 +08:00
5b73b80293
* Fix the Errors/Warnings when building Qlib's documentation * Fix * Fix * Empty * Test CI * Add doc compiling checking to CI * Fix * Tries to be consistent with Makefile Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
102 lines
3.7 KiB
Python
102 lines
3.7 KiB
Python
# Copyright (c) Microsoft Corporation.
|
|
# Licensed under the MIT License.
|
|
"""
|
|
This strategy is not well maintained
|
|
"""
|
|
|
|
|
|
from .order_generator import OrderGenWInteract
|
|
from .signal_strategy import WeightStrategyBase
|
|
import copy
|
|
|
|
|
|
class SoftTopkStrategy(WeightStrategyBase):
|
|
def __init__(
|
|
self,
|
|
model,
|
|
dataset,
|
|
topk,
|
|
order_generator_cls_or_obj=OrderGenWInteract,
|
|
max_sold_weight=1.0,
|
|
risk_degree=0.95,
|
|
buy_method="first_fill",
|
|
trade_exchange=None,
|
|
level_infra=None,
|
|
common_infra=None,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Parameters
|
|
----------
|
|
topk : int
|
|
top-N stocks to buy
|
|
risk_degree : float
|
|
position percentage of total value buy_method:
|
|
|
|
rank_fill: assign the weight stocks that rank high first(1/topk max)
|
|
average_fill: assign the weight to the stocks rank high averagely.
|
|
"""
|
|
super(SoftTopkStrategy, self).__init__(
|
|
model, dataset, order_generator_cls_or_obj, trade_exchange, level_infra, common_infra, **kwargs
|
|
)
|
|
self.topk = topk
|
|
self.max_sold_weight = max_sold_weight
|
|
self.risk_degree = risk_degree
|
|
self.buy_method = buy_method
|
|
|
|
def get_risk_degree(self, trade_step=None):
|
|
"""get_risk_degree
|
|
Return the proportion of your total value you will used in investment.
|
|
Dynamically risk_degree will result in Market timing
|
|
"""
|
|
# It will use 95% amount of your total value by default
|
|
return self.risk_degree
|
|
|
|
def generate_target_weight_position(self, score, current, trade_start_time, trade_end_time):
|
|
"""
|
|
Parameters
|
|
----------
|
|
score:
|
|
pred score for this trade date, pd.Series, index is stock_id, contain 'score' column
|
|
current:
|
|
current position, use Position() class
|
|
trade_date:
|
|
trade date
|
|
|
|
generate target position from score for this date and the current position
|
|
|
|
The cache is not considered in the position
|
|
"""
|
|
# TODO:
|
|
# If the current stock list is more than topk(eg. The weights are modified
|
|
# by risk control), the weight will not be handled correctly.
|
|
buy_signal_stocks = set(score.sort_values(ascending=False).iloc[: self.topk].index)
|
|
cur_stock_weight = current.get_stock_weight_dict(only_stock=True)
|
|
|
|
if len(cur_stock_weight) == 0:
|
|
final_stock_weight = {code: 1 / self.topk for code in buy_signal_stocks}
|
|
else:
|
|
final_stock_weight = copy.deepcopy(cur_stock_weight)
|
|
sold_stock_weight = 0.0
|
|
for stock_id in final_stock_weight:
|
|
if stock_id not in buy_signal_stocks:
|
|
sw = min(self.max_sold_weight, final_stock_weight[stock_id])
|
|
sold_stock_weight += sw
|
|
final_stock_weight[stock_id] -= sw
|
|
if self.buy_method == "first_fill":
|
|
for stock_id in buy_signal_stocks:
|
|
add_weight = min(
|
|
max(1 / self.topk - final_stock_weight.get(stock_id, 0), 0.0),
|
|
sold_stock_weight,
|
|
)
|
|
final_stock_weight[stock_id] = final_stock_weight.get(stock_id, 0.0) + add_weight
|
|
sold_stock_weight -= add_weight
|
|
elif self.buy_method == "average_fill":
|
|
for stock_id in buy_signal_stocks:
|
|
final_stock_weight[stock_id] = final_stock_weight.get(stock_id, 0.0) + sold_stock_weight / len(
|
|
buy_signal_stocks
|
|
)
|
|
else:
|
|
raise ValueError("Buy method not found")
|
|
return final_stock_weight
|