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Update docs and fix tabnet
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Jactus
@@ -31,16 +31,16 @@ class BaseStrategy:
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Parameters
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-----------
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score_series : pd.Seires
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stock_id , score
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stock_id , score.
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current : Position()
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current state of position
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DO NOT directly change the state of current
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current state of position.
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DO NOT directly change the state of current.
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trade_exchange : Exchange()
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trade exchange
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trade exchange.
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pred_date : pd.Timestamp
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predict date
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predict date.
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trade_date : pd.Timestamp
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trade date
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trade date.
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"""
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pass
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@@ -49,11 +49,11 @@ class BaseStrategy:
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Parameters
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-----------
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score_series : pd.Series
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stock_id , score
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stock_id , score.
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pred_date : pd.Timestamp
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oredict date
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oredict date.
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trade_date : pd.Timestamp
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trade date
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trade date.
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"""
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pass
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@@ -67,7 +67,7 @@ class BaseStrategy:
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"""
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This method only be used in 'online' module, it will generate the *args to initial the strategy.
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:param
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mode : model used in 'online' module
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mode : model used in 'online' module.
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"""
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return {}
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@@ -82,7 +82,7 @@ class StrategyWrapper:
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def __init__(self, inner_strategy):
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"""__init__
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:param inner_strategy: set the inner strategy
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:param inner_strategy: set the inner strategy.
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"""
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self.inner_strategy = inner_strategy
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@@ -99,9 +99,9 @@ class AdjustTimer:
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Responsible for timing of position adjusting
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This is designed as multiple inheritance mechanism due to:
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- the is_adjust may need access to the internel state of a strategy
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- the is_adjust may need access to the internel state of a strategy.
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- it can be reguard as a enhancement to the existing strategy
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- it can be reguard as a enhancement to the existing strategy.
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"""
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# adjust position in each trade date
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@@ -146,12 +146,12 @@ class WeightStrategyBase(BaseStrategy, AdjustTimer):
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Parameters
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-----------
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score : pd.Series
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pred score for this trade date, index is stock_id, contain 'score' column
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pred score for this trade date, index is stock_id, contain 'score' column.
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current : Position()
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current position
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current position.
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trade_exchange : Exchange()
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trade_date : pd.Timestamp
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trade date
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trade date.
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"""
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raise NotImplementedError()
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@@ -160,13 +160,13 @@ class WeightStrategyBase(BaseStrategy, AdjustTimer):
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Parameters
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-----------
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score_series : pd.Seires
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stock_id , score
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stock_id , score.
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current : Position()
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current of account
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current of account.
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trade_exchange : Exchange()
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exchange
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exchange.
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trade_date : pd.Timestamp
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date
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date.
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"""
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# judge if to adjust
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if not self.is_adjust(trade_date):
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@@ -206,26 +206,26 @@ class TopkDropoutStrategy(BaseStrategy, ListAdjustTimer):
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Parameters
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-----------
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topk : int
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The number of stocks in the portfolio
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the number of stocks in the portfolio.
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n_drop : int
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number of stocks to be replaced in each trading date
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number of stocks to be replaced in each trading date.
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method_sell : str
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dropout method_sell, random/bottom
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dropout method_sell, random/bottom.
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method_buy : str
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dropout method_buy, random/top
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dropout method_buy, random/top.
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risk_degree : float
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position percentage of total value
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position percentage of total value.
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thresh : int
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minimun holding days since last buy singal of the stock
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minimun holding days since last buy singal of the stock.
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hold_thresh : int
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minimum holding days
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before sell stock , will check current.get_stock_count(order.stock_id) >= self.thresh
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before sell stock , will check current.get_stock_count(order.stock_id) >= self.thresh.
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only_tradable : bool
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will the strategy only consider the tradable stock when buying and selling.
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if only_tradable:
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strategy will make buy sell decision without checking the tradable state of the stock
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strategy will make buy sell decision without checking the tradable state of the stock.
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else:
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strategy will make decision with the tradable state of the stock info and avoid buy and sell them
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strategy will make decision with the tradable state of the stock info and avoid buy and sell them.
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"""
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super(TopkDropoutStrategy, self).__init__()
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ListAdjustTimer.__init__(self, kwargs.get("adjust_dates", None))
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@@ -245,7 +245,7 @@ class TopkDropoutStrategy(BaseStrategy, ListAdjustTimer):
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def get_risk_degree(self, date):
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"""get_risk_degree
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Return the proportion of your total value you will used in investment.
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Dynamically risk_degree will result in Market timing
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Dynamically risk_degree will result in Market timing.
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"""
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# It will use 95% amoutn of your total value by default
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return self.risk_degree
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@@ -257,15 +257,15 @@ class TopkDropoutStrategy(BaseStrategy, ListAdjustTimer):
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Parameters
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-----------
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score_series : pd.Series
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stock_id , score
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stock_id , score.
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current : Position()
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current of account
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current of account.
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trade_exchange : Exchange()
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exchange
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exchange.
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pred_date : pd.Timestamp
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predict date
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predict date.
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trade_date : pd.Timestamp
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trade date
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trade date.
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"""
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if not self.is_adjust(trade_date):
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return []
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