ma state

examples/reports/report_state.py

@@ -0,0 +1,109 @@
+# -*- coding: utf-8 -*-
+import datetime
+import logging
+import time
+
+import eastmoneypy
+from apscheduler.schedulers.background import BackgroundScheduler
+
+from zvdata.api import get_entities
+from zvdata.utils.time_utils import to_pd_timestamp
+from zvt import init_log
+from zvt.domain import Stock1dKdata, StockValuation, Stock
+from zvt.factors import TargetSelector
+from zvt.factors.ma.ma_factor import VolumeUpMa250Factor
+from zvt.factors.ma.ma_stats import MaStateStatsFactor
+from zvt.informer.informer import EmailInformer
+
+logger = logging.getLogger(__name__)
+
+sched = BackgroundScheduler()
+
+
+@sched.scheduled_job('cron', hour=17, minute=30, day_of_week='mon-fri')
+def report_state():
+    while True:
+        error_count = 0
+        email_action = EmailInformer(ssl=True)
+
+        try:
+            latest_day: Stock1dKdata = Stock1dKdata.query_data(order=Stock1dKdata.timestamp.desc(), limit=1,
+                                                               return_type='domain')
+            # target_date = latest_day[0].timestamp
+            target_date = to_pd_timestamp('2020-01-02')
+
+            # 计算均线
+            my_selector = TargetSelector(start_timestamp='2018-01-01', end_timestamp=target_date)
+            # add the factors
+            factor1 = VolumeUpMa250Factor(start_timestamp='2018-01-01', end_timestamp=target_date)
+
+            my_selector.add_filter_factor(factor1)
+
+            my_selector.run()
+
+            long_stocks = my_selector.get_open_long_targets(timestamp=target_date)
+
+            msg = 'no targets'
+            # 过滤亏损股
+            # check StockValuation data
+            pe_date = target_date - datetime.timedelta(10)
+            if StockValuation.query_data(start_timestamp=pe_date, limit=1, return_type='domain'):
+                positive_df = StockValuation.query_data(provider='joinquant', entity_ids=long_stocks,
+                                                        start_timestamp=pe_date,
+                                                        filters=[StockValuation.pe > 0],
+                                                        columns=['entity_id'])
+                bad_stocks = set(long_stocks) - set(positive_df['entity_id'].tolist())
+                if bad_stocks:
+                    stocks = get_entities(provider='joinquant', entity_schema=Stock, entity_ids=bad_stocks,
+                                          return_type='domain')
+                    info = [f'{stock.name}({stock.code})' for stock in stocks]
+                    msg = '亏损股:' + ' '.join(info) + '\n'
+
+                long_stocks = set(positive_df['entity_id'].tolist())
+
+            if long_stocks:
+                ma_state = MaStateStatsFactor(entity_ids=long_stocks, start_timestamp='2016-01-01',
+                                              end_timestamp=target_date, persist_factor=False)
+                for entity_id, df in ma_state.factor_df.groupby(level=0):
+                    if df['current_pct'].max() >= 0.3:
+                        long_stocks.remove(entity_id)
+
+            if long_stocks:
+                stocks = get_entities(provider='joinquant', entity_schema=Stock, entity_ids=long_stocks,
+                                      return_type='domain')
+                # add them to eastmoney
+                try:
+                    try:
+                        eastmoneypy.del_group('real')
+                    except:
+                        pass
+                    eastmoneypy.create_group('real')
+                    for stock in stocks:
+                        eastmoneypy.add_to_group(stock.code, group_name='real')
+                except Exception as e:
+                    email_action.send_message("[email protected]", f'report state error',
+                                              'report state error:{}'.format(e))
+
+                info = [f'{stock.name}({stock.code})' for stock in stocks]
+                msg = msg + '盈利股:' + ' '.join(info) + '\n'
+
+            logger.info(msg)
+            email_action.send_message('[email protected]', f'{target_date} 放量突破年线state选股结果', msg)
+            break
+        except Exception as e:
+            logger.exception('report state error:{}'.format(e))
+            time.sleep(60 * 3)
+            error_count = error_count + 1
+            if error_count == 10:
+                email_action.send_message("[email protected]", f'report state error',
+                                          'report state error:{}'.format(e))
+
+
+if __name__ == '__main__':
+    init_log('report_state.log')
+
+    report_state()
+
+    sched.start()
+
+    sched._thread.join()

requirements.txt

@@ -1,4 +1,4 @@
-zvdata == 1.2.2
+zvdata == 1.2.3
 requests == 2.20.1
 SQLAlchemy == 1.2.14
 pandas == 0.24.2

setup.py

@@ -36,7 +36,7 @@
     # For a discussion on single-sourcing the version across setup.py and the
     # project code, see
     # https://packaging.python.org/en/latest/single_source_version.html
-    version='0.7.7',  # Required
+    version='0.7.8',  # Required
 
     # This is a one-line description or tagline of what your project does. This
     # corresponds to the "Summary" metadata field:
@@ -119,7 +119,7 @@
     # For an analysis of "install_requires" vs pip's requirements files see:
     # https://packaging.python.org/en/latest/requirements.html
 
-    install_requires=['zvdata>=1.2.2', 'requests>=2.20.1', 'SQLAlchemy>=1.2.14', 'pandas>=0.24.2',
+    install_requires=['zvdata>=1.2.3', 'requests>=2.20.1', 'SQLAlchemy>=1.2.14', 'pandas>=0.24.2',
                       'arrow>=0.11.0', 'marshmallow >= 3.2.2', 'tzlocal>=1.5.1', 'xlrd>=1.1.0', 'apscheduler>=3.4.0',
                       'jqdatasdk', 'demjson>=2.2.4', 'marshmallow-sqlalchemy>=0.19.0', 'ccxt>=1.17.191',
                       'plotly>=4.1.0', 'simplejson>=3.16.0',

zvt/domain/factors/stock_1d_ma_stats.py

@@ -24,7 +24,7 @@ class Stock1dMaStateStats(Stock1dMaStateStatsBase, Mixin):
     # 此轮维持状态的最大值，即切换点
     total_count = Column(Integer)
     # 涨跌幅
-    total_pct = Column(Integer)
+    # total_pct = Column(Integer)
 
 
 register_schema(providers=['zvt'], db_name='stock_1d_ma_stats', schema_base=Stock1dMaStateStatsBase)

zvt/drawer/drawer.py

@@ -5,26 +5,34 @@
 import plotly.graph_objs as go
 from plotly.subplots import make_subplots
 
-from zvdata.api import decode_entity_id, get_data
+from zvdata.api import decode_entity_id
 from zvdata.normal_data import NormalData
+from zvdata.reader import DataReader
 from zvdata.utils.pd_utils import pd_is_not_null
 from zvdata.utils.time_utils import now_time_str, TIME_FORMAT_ISO8601
 from zvt import zvt_env
-from zvt.domain import Stock1dKdata, Stock1dMaStateStats
+from zvt.domain import Stock1dKdata, Stock1dMaStateStats, Stock
 
 
 class Drawer(object):
     def __init__(self,
                  main_df: pd.DataFrame = None,
+                 factor_df: pd.DataFrame = None,
                  sub_df: pd.DataFrame = None,
                  main_data: NormalData = None,
+                 factor_data: NormalData = None,
                  sub_data: NormalData = None,
                  annotation_df: pd.DataFrame = None) -> None:
         # 主图数据
         if main_data is None:
             main_data = NormalData(main_df)
         self.main_data: NormalData = main_data
 
+        # 主图因子
+        if factor_data is None:
+            factor_data = NormalData(factor_df)
+        self.factor_data: NormalData = factor_data
+
         # 副图数据
         if sub_data is None:
             sub_data = NormalData(sub_df)
@@ -36,7 +44,7 @@ def __init__(self,
     def _draw(self,
               main_chart='kline',
               sub_chart='bar',
-              mode='markers',
+              mode='lines',
               width=None,
               height=None,
               title=None,
@@ -66,6 +74,16 @@ def _draw(self,
                     ydata = df[col].values.tolist()
                     traces.append(go.Scatter(x=df.index, y=ydata, mode=mode, name=trace_name, **kwargs))
 
+            # 绘制幅图
+            factor_df = self.factor_data.entity_map_df.get(entity_id)
+            if pd_is_not_null(factor_df):
+                for col in factor_df.columns:
+                    trace_name = '{}_{}'.format(code, col)
+                    ydata = factor_df[col].values.tolist()
+
+                    line = go.Scatter(x=df.index, y=ydata, mode=mode, name=trace_name, **kwargs)
+                    traces.append(line)
+
             if subplot:
                 # 绘制幅图
                 sub_df = self.sub_data.entity_map_df.get(entity_id)
@@ -256,9 +274,10 @@ def to_annotations(annotation_df: pd.DataFrame):
 
 
 if __name__ == '__main__':
-    df = get_data(data_schema=Stock1dKdata, provider='joinquant', entity_ids=['stock_sz_000001', 'stock_sz_000002'])
-    df1 = get_data(data_schema=Stock1dMaStateStats, provider='zvt', entity_ids=['stock_sz_000001', 'stock_sz_000002'],
-                   columns=['current_count'])
+    data_reader1 = DataReader(codes=['002223'], data_schema=Stock1dKdata, entity_schema=Stock)
+    data_reader2 = DataReader(codes=['002223'], data_schema=Stock1dMaStateStats, entity_schema=Stock,
+                              columns=['ma5', 'ma10', 'current_count', 'current_pct'])
 
-    drawer = Drawer(df, df1[['current_count']])
+    drawer = Drawer(main_df=data_reader1.data_df, factor_df=data_reader2.data_df[['ma5', 'ma10']],
+                    sub_df=data_reader2.data_df[['current_pct']])
     drawer.draw_kline()

zvt/factors/ma/ma_stats.py

@@ -6,7 +6,6 @@
 
 from zvdata import IntervalLevel, EntityMixin
 from zvdata.utils.pd_utils import pd_is_not_null
-from zvdata.utils.time_utils import now_pd_timestamp
 from zvt.api import get_entities, Stock
 from zvt.api.common import get_ma_state_stats_schema
 from zvt.factors.algorithm import MaTransformer
@@ -88,13 +87,22 @@ def acc(self, input_df, acc_df) -> pd.DataFrame:
                                 # up
                                 if acc_col_current > 0 and (current_state == 'up'):
                                     count = acc_col_current + 1
+
+                                    pct = acc_df.loc[(entity_id,)].iloc[-1]["current_pct"]
+                                    pct = (1 + pct) * (1 + df['change_pct'][index]) - 1
+
                                 # down
                                 elif acc_col_current < 0 and (current_state == 'down'):
                                     count = acc_col_current - 1
+
+                                    pct = acc_df.loc[(entity_id,)].iloc[-1]["current_pct"]
+                                    pct = (1 + pct) * (1 + df['change_pct'][index]) - 1
+
                                 # state has changed
                                 else:
                                     pre_timestamp = acc_df.loc[(entity_id,), 'timestamp'][-1]
                                     acc_df.loc[(entity_id, pre_timestamp), self.total_col] = acc_col_current
+
                         check_acc = True
 
                 # 设置目前状态
@@ -148,8 +156,8 @@ def __init__(self, entity_schema: EntityMixin = Stock, provider: str = None, ent
     parser = argparse.ArgumentParser()
     parser.add_argument('--level', help='trading level', default='1d',
                         choices=[item.value for item in IntervalLevel])
-    parser.add_argument('--start', help='start code', default='000338')
-    parser.add_argument('--end', help='end code', default='000339')
+    parser.add_argument('--start', help='start code', default='002223')
+    parser.add_argument('--end', help='end code', default='002224')
 
     args = parser.parse_args()
 
@@ -163,6 +171,9 @@ def __init__(self, entity_schema: EntityMixin = Stock, provider: str = None, ent
     codes = entities.index.to_list()
 
     factor = MaStateStatsFactor(codes=codes, start_timestamp='2005-01-01',
-                                end_timestamp=now_pd_timestamp(), persist_factor=False,
+                                end_timestamp='2020-04-01', persist_factor=True,
                                 level=level)
     print(factor.factor_df)
+
+    for entity_id, df in factor.factor_df.groupby(level=0):
+        print(df['current_pct'].max)

zvt/trader/trader.py

@@ -307,7 +307,7 @@ def on_finish(self):
             import plotly.io as pio
             pio.renderers.default = "browser"
             reader = AccountReader(trader_names=[self.trader_name])
-            df = reader.data_df.reset_index()
+            df = reader.data_df
             drawer = Drawer(main_data=NormalData(df.copy()[['trader_name', 'timestamp', 'all_value']],
                                                  category_field='trader_name'))
             drawer.draw_line()