def __init__(self,
region: Region,
entity_id,
timestamp,
window=100,
level=IntervalLevel.LEVEL_1DAY,
entity_schema=Stock,
range=0.3,
std=1) -> None:
self.entity_id = entity_id
self.window = window
data_schema = get_kdata_schema(EntityType(
entity_schema.__name__.lower()),
level=level)
self.df = get_kdata(region=region,
entity_id=entity_id,
level=level,
end_timestamp=timestamp,
order=data_schema.timestamp.desc(),
limit=window,
columns=['volume', 'open', 'close', 'high', 'low'])
self.range = range
self.std = std
def __init__(
self,
region: Region,
entity_schema: EntityMixin = Stock,
provider: Provider = Provider.Default,
entity_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,
columns: List = [
'id', 'entity_id', 'timestamp', 'level', 'open', 'close',
'high', 'low'
],
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 = 10,
keep_all_timestamp: bool = False,
fill_method: str = 'ffill',
effective_number: int = None,
need_persist: bool = True,
dry_run: bool = True,
# added fields
short_window: int = 5,
long_window: int = 10) -> None:
self.factor_schema = get_ma_state_stats_schema(entity_type=EntityType(
entity_schema.__name__.lower()),
level=level)
self.short_window = short_window
self.long_window = long_window
transformer: Transformer = MaTransformer(
windows=[short_window, long_window], cal_change_pct=True)
accumulator = MaAccumulator(short_window=short_window,
long_window=long_window)
super().__init__(region, entity_schema, provider, entity_provider,
entity_ids, exchanges, codes, the_timestamp,
start_timestamp, end_timestamp, columns, filters,
order, limit, level, category_field, time_field,
computing_window, keep_all_timestamp, fill_method,
effective_number, transformer, accumulator,
need_persist, dry_run)
def register(cls):
# register the entity
if issubclass(cls, EntityMixin):
entity_type_ = entity_type
if not entity_type:
entity_type_ = EntityType(cls.__name__.lower())
if entity_type_ not in zvt_context.entity_types:
zvt_context.entity_types.append(entity_type_)
zvt_context.entity_schema_map[entity_type_] = cls
add_to_map_list(the_map=zvt_context.entity_map_schemas,
key=entity_type,
value=cls)
return cls
def __init__(self,
region: Region,
entity_schema: EntityMixin = Stock,
provider: Provider = Provider.Default,
entity_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,
columns: List = [
'id', 'entity_id', 'timestamp', 'level', 'open', 'close',
'high', 'low'
],
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 = MacdTransformer(),
accumulator: Accumulator = None,
need_persist: bool = False,
dry_run: bool = False,
adjust_type: Union[AdjustType, str] = None) -> None:
self.adjust_type = adjust_type
self.data_schema = get_kdata_schema(EntityType(
entity_schema.__name__.lower()),
level=level,
adjust_type=adjust_type)
if transformer:
self.indicator_cols = transformer.indicators
super().__init__(self.data_schema, region, entity_schema, provider,
entity_provider, entity_ids, exchanges, codes,
the_timestamp, start_timestamp, end_timestamp,
columns, filters, order, limit, level, category_field,
time_field, computing_window, keep_all_timestamp,
fill_method, effective_number, transformer,
accumulator, need_persist, dry_run)
def get_ref_vector(region: Region,
entity_id,
end,
window=100,
level=IntervalLevel.LEVEL_1DAY,
entity_schema=Stock):
data_schema = get_kdata_schema(EntityType(entity_schema.__name__.lower()),
level=level)
df = get_kdata(region=region,
entity_id=entity_id,
level=level,
end_timestamp=end,
order=data_schema.timestamp.desc(),
limit=window,
columns=['close', 'volume'])
exp_data = np.zeros((window, 2))
exp_data[:, 0] = df['close']
exp_data[:, 1] = df['volume']
return exp_data
def decode_entity_id(entity_id: str):
result = entity_id.split('_')
entity_type = EntityType(result[0].lower())
exchange = result[1]
code = ''.join(result[2:])
return entity_type, exchange, code