def get_trading_signals_figure(order_reader: OrderReader,
entity_id: str,
start_timestamp=None,
end_timestamp=None,
adjust_type=None):
entity_type, _, _ = decode_entity_id(entity_id)
data_schema = get_kdata_schema(entity_type=entity_type,
level=order_reader.level,
adjust_type=adjust_type)
if not start_timestamp:
start_timestamp = order_reader.start_timestamp
if not end_timestamp:
end_timestamp = order_reader.end_timestamp
kdata_reader = DataReader(
entity_ids=[entity_id],
data_schema=data_schema,
entity_schema=zvt_context.tradable_schema_map.get(entity_type),
start_timestamp=start_timestamp,
end_timestamp=end_timestamp,
level=order_reader.level,
)
# generate the annotation df
order_reader.move_on(timeout=0)
df = order_reader.data_df.copy()
df = df[df.entity_id == entity_id].copy()
if pd_is_not_null(df):
df["value"] = df["order_price"]
df["flag"] = df["order_type"].apply(lambda x: order_type_flag(x))
df["color"] = df["order_type"].apply(lambda x: order_type_color(x))
print(df.tail())
drawer = Drawer(main_df=kdata_reader.data_df, annotation_df=df)
return drawer.draw_kline(show=False, height=800)
def compare(entity_ids, schema_map_columns: dict = None, chart_type: ChartType = ChartType.line):
entity_type_map_ids = _group_entity_ids(entity_ids=entity_ids)
dfs = []
for entity_type in entity_type_map_ids:
if schema_map_columns:
for schema in schema_map_columns:
columns = ["entity_id", "timestamp"] + schema_map_columns.get(schema)
df = schema.query_data(entity_ids=entity_type_map_ids.get(entity_type), columns=columns)
dfs.append(df)
else:
schema = get_kdata_schema(entity_type=entity_type)
df = schema.query_data(entity_ids=entity_type_map_ids.get(entity_type))
dfs.append(df)
all_df = pd.concat(dfs)
if schema_map_columns:
drawer = Drawer(main_df=all_df)
drawer.draw(main_chart=chart_type, show=True)
else:
drawer = Drawer(main_df=all_df, sub_df_list=[all_df[["entity_id", "timestamp", "turnover"]].copy()])
drawer.draw_kline(show=True)
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,
accumulator: Accumulator = None, need_persist: bool = False, dry_run: bool = False,
adjust_type: Union[AdjustType, str] = None, window=30) -> None:
self.adjust_type = adjust_type
transformer = TopBottomTransformer(window=window)
super().__init__(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, adjust_type)
if __name__ == '__main__':
factor = TopBottomFactor(codes=['601318'], start_timestamp='2005-01-01',
end_timestamp=now_pd_timestamp(),
level=IntervalLevel.LEVEL_1DAY, window=120)
print(factor.factor_df)
data_reader1 = DataReader(codes=['601318'], data_schema=Stock1dKdata, entity_schema=Stock)
drawer = Drawer(main_df=data_reader1.data_df, factor_df_list=[factor.factor_df[['top', 'bottom']]])
drawer.draw_kline()
# the __all__ is generated
__all__ = ['TopBottomTransformer', 'TopBottomFactor']
self.adjust_type = adjust_type
transformer = TopBottomTransformer(window=window)
super().__init__(entity_schema, provider, entity_provider, entity_ids,
exchanges, codes, 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, only_compute_factor,
factor_name, clear_state, only_load_factor,
adjust_type)
if __name__ == '__main__':
factor = TopBottomFactor(codes=['601318'],
start_timestamp='2005-01-01',
end_timestamp=now_pd_timestamp(),
level=IntervalLevel.LEVEL_1DAY,
window=120)
print(factor.factor_df)
data_reader1 = DataReader(codes=['601318'],
data_schema=Stock1dKdata,
entity_schema=Stock)
drawer = Drawer(main_df=data_reader1.data_df,
factor_df_list=[factor.factor_df[['top', 'bottom']]])
drawer.draw_kline(show=True)
# the __all__ is generated
__all__ = ['TopBottomTransformer', 'TopBottomFactor']
def compare(
entity_ids=None,
codes=None,
schema=None,
columns=None,
schema_map_columns: dict = None,
chart_type: ChartType = ChartType.line,
start_timestamp=None,
scale_value: int = None,
):
"""
compare indicators(columns) of entities
:param entity_ids:
:param codes:
:param schema:
:param columns:
:param schema_map_columns: key represents schema, value represents columns
:param chart_type: "line", "area", "scatter", default "line"
:param start_timestamp: "
:param scale_value: compare with same value which scaled to scale_value
"""
dfs = []
# default compare kdata
if schema_map_columns is None and schema is None:
entity_type_map_ids = _group_entity_ids(entity_ids=entity_ids)
for entity_type in entity_type_map_ids:
schema = get_kdata_schema(entity_type=entity_type)
df = schema.query_data(
entity_ids=entity_type_map_ids.get(entity_type),
start_timestamp=start_timestamp)
dfs.append(df)
all_df = pd.concat(dfs)
drawer = Drawer(main_df=all_df,
sub_df_list=[
all_df[["entity_id", "timestamp",
"turnover"]].copy()
])
drawer.draw_kline(show=True, scale_value=scale_value)
else:
if schema_map_columns:
for schema in schema_map_columns:
columns = ["entity_id", "timestamp"
] + schema_map_columns.get(schema)
df = schema.query_data(entity_ids=entity_ids,
codes=codes,
columns=columns,
start_timestamp=start_timestamp)
dfs.append(df)
elif schema:
columns = ["entity_id", "timestamp"] + columns
df = schema.query_data(entity_ids=entity_ids,
codes=codes,
columns=columns,
start_timestamp=start_timestamp)
dfs.append(df)
all_df = pd.concat(dfs)
drawer = Drawer(main_df=all_df)
drawer.draw(main_chart=chart_type, show=True, scale_value=scale_value)
