def on_finish(self, timestmap):
self.on_trading_finish(timestmap)
# show the result
if self.draw_result:
import plotly.io as pio
pio.renderers.default = "browser"
reader = AccountStatsReader(trader_names=[self.trader_name])
df = reader.data_df
drawer = Drawer(main_data=NormalData(
df.copy()[['trader_name', 'timestamp', 'all_value']],
category_field='trader_name'))
drawer.draw_line(show=True)
def show_industry_composition(entity_ids, timestamp):
block_df = Block.query_data(provider='eastmoney', filters=[Block.category == 'industry'], index='entity_id')
block_ids = block_df.index.tolist()
block_df = BlockStock.query_data(entity_ids=block_ids, filters=[BlockStock.stock_id.in_(entity_ids)])
s = block_df['name'].value_counts()
cycle_df = pd.DataFrame(columns=s.index, data=[s.tolist()])
cycle_df['entity_id'] = 'stock_cn_industry'
cycle_df['timestamp'] = timestamp
drawer = Drawer(main_df=cycle_df)
drawer.draw_pie(show=True)
def show_month_performance():
dfs = []
for timestamp, df in got_top_performance_by_month(start_timestamp='2005-01-01', list_days=250):
if pd_is_not_null(df):
df = df.reset_index(drop=True)
df['entity_id'] = 'stock_cn_performance'
df['timestamp'] = timestamp
dfs.append(df)
all_df = pd.concat(dfs)
print(all_df)
drawer = Drawer(main_df=all_df)
drawer.draw_scatter(show=True)
def composite_all(data_schema, column, timestamp, entity_ids=None, filters=None):
if type(column) is not str:
column = column.name
if filters:
filters.append([data_schema.timestamp == to_pd_timestamp(timestamp)])
else:
filters = [data_schema.timestamp == to_pd_timestamp(timestamp)]
df = data_schema.query_data(entity_ids=entity_ids, columns=['entity_id', 'timestamp', column], filters=filters,
index='entity_id')
entity_type, exchange, _ = decode_entity_id(df['entity_id'].iloc[0])
pie_df = pd.DataFrame(columns=df.index, data=[df[column].tolist()])
pie_df['entity_id'] = f'{entity_type}_{exchange}_{column}'
pie_df['timestamp'] = timestamp
drawer = Drawer(main_df=pie_df)
drawer.draw_pie(show=True)
def show_industry_composition(entity_ids, timestamp):
block_df = Block.query_data(provider="eastmoney",
filters=[Block.category == "industry"],
index="entity_id")
block_ids = block_df.index.tolist()
block_df = BlockStock.query_data(
entity_ids=block_ids, filters=[BlockStock.stock_id.in_(entity_ids)])
s = block_df["name"].value_counts()
cycle_df = pd.DataFrame(columns=s.index, data=[s.tolist()])
cycle_df["entity_id"] = "stock_cn_industry"
cycle_df["timestamp"] = timestamp
drawer = Drawer(main_df=cycle_df)
drawer.draw_pie(show=True)
def draw(self,
render='html',
file_name=None,
width=None,
height=None,
title=None,
keep_ui_state=True,
annotation_df=None,
target_type: TargetType = TargetType.open_long):
if target_type == TargetType.open_long:
df = self.open_long_df.copy()
elif target_type == TargetType.open_short:
df = self.open_short_df.copy()
df['target_type'] = target_type.value
if pd_is_not_null(df):
df = df.reset_index(drop=False)
drawer = Drawer(df)
drawer.draw_table(width=width, height=height, title=title,
keep_ui_state=keep_ui_state)
def draw_result(self, entity_id):
if self.label_method == "raw":
df = self.kdata_df.loc[[entity_id], ["close"]].copy()
pred_df = self.pred_y.to_frame(name="pred_close")
pred_df = pred_df.loc[[entity_id], :].shift(self.predict_steps)
drawer = Drawer(
main_df=df,
factor_df_list=[pred_df],
)
drawer.draw_line(show=True)
else:
pred_df = self.pred_y.to_frame(name="pred_result").loc[[entity_id], :]
df = self.testing_y.to_frame(name="real_result").loc[[entity_id], :].join(pred_df, how="outer")
drawer = Drawer(main_df=df)
drawer.draw_table()
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)
def composite(entity_id, data_schema, columns, filters=None):
columns = ["entity_id", "timestamp"] + columns
df = data_schema.query_data(entity_id=entity_id, columns=columns, filters=filters)
drawer = Drawer(main_df=df)
drawer.draw_pie(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']
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)
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 draw_line(self, show=True):
drawer = Drawer(main_data=NormalData(
self.data_df.copy()[['trader_name', 'timestamp', 'all_value']],
category_field='trader_name'))
return drawer.draw_line(show=show)
self.filters = filter
super().__init__(Order,
None,
None,
None,
None,
None,
None,
the_timestamp,
start_timestamp,
end_timestamp,
columns,
self.filters,
order,
None,
level,
category_field='trader_name',
time_field='timestamp',
computing_window=None)
if __name__ == '__main__':
reader = AccountStatsReader(trader_names=['000338_ma_trader'])
drawer = Drawer(main_data=NormalData(
reader.data_df.copy()[['trader_name', 'timestamp', 'all_value']],
category_field='trader_name'))
drawer.draw_line()
# the __all__ is generated
__all__ = ['AccountStatsReader', 'OrderReader']
Order,
None,
None,
None,
None,
None,
None,
start_timestamp,
end_timestamp,
columns,
self.filters,
order,
None,
level,
category_field="trader_name",
time_field="timestamp",
computing_window=None,
)
if __name__ == "__main__":
reader = AccountStatsReader(trader_names=["000338_ma_trader"])
drawer = Drawer(
main_data=NormalData(
reader.data_df.copy()[["trader_name", "timestamp", "all_value"]], category_field="trader_name"
)
)
drawer.draw_line()
# the __all__ is generated
__all__ = ["clear_trader", "get_trader_info", "get_order_securities", "AccountStatsReader", "OrderReader"]
