def __init__(
self,
codes=None,
the_timestamp: Union[str, pd.Timestamp] = None,
start_timestamp: Union[str, pd.Timestamp] = None,
end_timestamp: Union[str, pd.Timestamp] = None,
columns: List = [
IndexMoneyFlow.net_inflows, IndexMoneyFlow.net_inflow_rate,
IndexMoneyFlow.net_main_inflows,
IndexMoneyFlow.net_main_inflow_rate
],
filters: List = [],
order: object = None,
limit: int = None,
provider: str = 'sina',
level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,
category_field: str = 'entity_id',
time_field: str = 'timestamp',
keep_all_timestamp: bool = False,
fill_method: str = 'ffill',
effective_number: int = 10,
scorer: Scorer = RankScorer(ascending=True)
) -> None:
super().__init__(IndexMoneyFlow, None, 'index', None, codes,
the_timestamp, start_timestamp, end_timestamp,
columns, filters, order, limit, provider, level,
category_field, time_field, keep_all_timestamp,
fill_method, effective_number, scorer)
def __init__(
self,
provider: str = 'sina',
entity_provider: str = 'sina',
the_timestamp: Union[str, pd.Timestamp] = None,
start_timestamp: Union[str, pd.Timestamp] = None,
end_timestamp: Union[str, pd.Timestamp] = None,
columns: List = [
BlockMoneyFlow.net_inflows, BlockMoneyFlow.net_main_inflows
],
category=BlockCategory.industry.value,
window=20,
scorer: Scorer = RankScorer(ascending=True)) -> None:
df = Block.query_data(provider=entity_provider,
filters=[Block.category == category])
entity_ids = df['entity_id'].tolist()
self.window = window
super().__init__(BlockMoneyFlow,
Block,
provider=provider,
entity_provider=entity_provider,
entity_ids=entity_ids,
the_timestamp=the_timestamp,
start_timestamp=start_timestamp,
end_timestamp=end_timestamp,
columns=columns,
scorer=scorer)
def __init__(self,
entity_schema: EntityMixin = Stock,
provider: str = None,
entity_provider: str = None,
entity_ids: List[str] = None,
exchanges: List[str] = None,
codes: List[str] = None,
the_timestamp: Union[str, pd.Timestamp] = None,
start_timestamp: Union[str, pd.Timestamp] = None,
end_timestamp: Union[str, pd.Timestamp] = None,
filters: List = None,
order: object = None,
limit: int = None,
level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,
category_field: str = 'entity_id',
time_field: str = 'timestamp',
computing_window: int = None,
keep_all_timestamp: bool = False,
fill_method: str = 'ffill',
effective_number: int = None,
transformer: Transformer = None,
accumulator: Accumulator = None,
need_persist: bool = False,
dry_run: bool = False) -> None:
super().__init__(Stock1dKdata, entity_schema, provider,
entity_provider, entity_ids, exchanges, codes,
the_timestamp, start_timestamp, end_timestamp,
[Stock1dKdata.turnover], filters, order, limit, level,
category_field, time_field, computing_window,
keep_all_timestamp, fill_method, effective_number,
transformer, accumulator, need_persist, dry_run,
RankScorer(ascending=True))
class VolFactor(TechnicalFactor, ScoreFactor):
scorer = RankScorer(ascending=True)
def __init__(self,
region: Region,
entity_schema: Type[EntityMixin] = Stock,
provider: Provider = Provider.Default,
entity_ids: List[str] = None,
exchanges: List[str] = None,
codes: List[str] = None,
the_timestamp: Union[str, pd.Timestamp] = None,
start_timestamp: Union[str, pd.Timestamp] = None,
end_timestamp: Union[str, pd.Timestamp] = None,
filters: List = None,
order: object = None,
limit: int = None,
level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY,
category_field: str = 'entity_id',
time_field: str = 'timestamp',
computing_window: int = None,
keep_all_timestamp: bool = False,
fill_method: str = 'ffill',
effective_number: int = None,
transformer: Transformer = None,
accumulator: Accumulator = None,
need_persist: bool = False,
dry_run: bool = False,
factor_name: str = None,
clear_state: bool = False,
not_load_data: bool = False,
adjust_type: Union[AdjustType, str] = None) -> None:
super().__init__(region, entity_schema, provider, entity_ids,
exchanges, codes, the_timestamp, start_timestamp,
end_timestamp, ['turnover'], filters, order, limit,
level, category_field, time_field, computing_window,
keep_all_timestamp, fill_method, effective_number,
transformer, accumulator, need_persist, dry_run,
factor_name, clear_state, not_load_data, adjust_type)
def pre_compute(self):
super().pre_compute()
self.pipe_df = self.pipe_df[['turnover']]