Merge pull request #817 from yutiansut/master

一些代码更新 DataStruct的generator考虑修改模式

Documents/backendapi.md

@@ -239,7 +239,7 @@ http://[ip]:[port]/marketdata/future/min?code=[]&start=[]&end=[]
 
 
 ```
-http://[ip]:[port]/accounts
+http://[ip]:[port]/accounts/all
 ```
 
 #### 4.5.2 单个账户查询

QUANTAXIS/QAARP/QAAccount.py

@@ -152,7 +152,7 @@ def __init__(self, strategy_name=None, user_cookie=None, portfolio_cookie=None,
         self.frequence = frequence
         self.running_environment = running_environment
         ########################################################################
-        self.market_data = None
+        self._market_data = None
         self._currenttime = None
         self.commission_coeff = commission_coeff
         self.tax_coeff = tax_coeff
@@ -295,6 +295,10 @@ def end_date(self):
             raise RuntimeWarning(
                 'QAACCOUNT: THIS ACCOUNT DOESNOT HAVE ANY TRADE')
 
+    @property
+    def market_data(self):
+        return self._market_data
+
     @property
     def trade_range(self):
         return QA_util_get_trade_range(self.start_date, self.end_date)
@@ -930,13 +934,17 @@ def run(self, event):
             """update the market_data
             1. update the inside market_data struct
             2. tell the on_bar methods
+
+            # 这样有点慢
+
+            
             """
 
             self._currenttime = event.market_data.datetime[0]
-            if self.market_data is None:
-                self.market_data = event.market_data
+            if self._market_data is None:
+                self._market_data = event.market_data
             else:
-                self.market_data = self.market_data + event.market_data
+                self._market_data = self._market_data + event.market_data
             self.on_bar(event)
 
             if event.callback:

QUANTAXIS/QAAnalysis/QAAnalysis_block.py

@@ -43,7 +43,7 @@ def __repr__(self):
     @lru_cache()
     def market_data(self):
         return QA_quotation(self.code, self.start, self.end, self.frequence,
-                            market=MARKET_TYPE.STOCK_CN, source=DATASOURCE.MONGO, output=OUTPUT_FORMAT.DATASTRUCT)
+                            market=MARKET_TYPE.STOCK_CN, source=DATASOURCE.MONGO, output=OUTPUT_FORMAT.DATASTRUCT).to_qfq()
 
     @property
     @lru_cache()

QUANTAXIS/QAData/QADataStruct.py

@@ -47,8 +47,6 @@
 from QUANTAXIS.QAData.base_datastruct import _quotation_base
 from QUANTAXIS.QAData.data_fq import QA_data_stock_to_fq
 from QUANTAXIS.QAData.data_resample import QA_data_tick_resample, QA_data_day_resample, QA_data_min_resample
-from QUANTAXIS.QAData.proto import stock_day_pb2  # protobuf import
-from QUANTAXIS.QAData.proto import stock_min_pb2
 from QUANTAXIS.QAIndicator import EMA, HHV, LLV, SMA
 from QUANTAXIS.QAUtil import (DATABASE, QA_util_log_info,
                               QA_util_random_with_topic,

QUANTAXIS/QAData/dsmethods.py

@@ -26,11 +26,14 @@
 """DataStruct的方法
 """
 import pandas as pd
-
+import numpy as np
 from QUANTAXIS.QAData.QADataStruct import (QA_DataStruct_Index_day,
                                            QA_DataStruct_Index_min,
+                                           QA_DataStruct_Future_day,
+                                           QA_DataStruct_Future_min,
                                            QA_DataStruct_Stock_day,
                                            QA_DataStruct_Stock_min)
+from QUANTAXIS.QAUtil.QAParameter import FREQUENCE, MARKET_TYPE
 
 
 def concat(lists):
@@ -48,6 +51,66 @@ def concat(lists):
     return lists[0].new(pd.concat([lists.data for lists in lists]).drop_duplicates())
 
 
+def datastruct_formater(data, frequence=FREQUENCE.DAY, market_type=MARKET_TYPE.STOCK_CN, default_header=[]):
+    """一个任意格式转化为DataStruct的方法
+    
+    Arguments:
+        data {[type]} -- [description]
+    
+    Keyword Arguments:
+        frequence {[type]} -- [description] (default: {FREQUENCE.DAY})
+        market_type {[type]} -- [description] (default: {MARKET_TYPE.STOCK_CN})
+        default_header {list} -- [description] (default: {[]})
+    
+    Returns:
+        [type] -- [description]
+    """
+
+    if isinstance(data, list):
+        try:
+            res = pd.DataFrame(data, columns=default_header)
+            if frequence is FREQUENCE.DAY:
+                if market_type is MARKET_TYPE.STOCK_CN:
+                    return QA_DataStruct_Stock_day(
+                        res.assign(date=pd.to_datetime(res.date)).set_index(
+                            ['date', 'code'], drop=False),
+                        dtype='stock_day')
+            elif frequence in [FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN, FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN]:
+                if market_type is MARKET_TYPE.STOCK_CN:
+                    return QA_DataStruct_Stock_min(
+                        res.assign(datetime=pd.to_datetime(res.datetime)).set_index(
+                            ['datetime', 'code'], drop=False),
+                        dtype='stock_min')
+        except:
+            pass
+    elif isinstance(data, np.ndarray):
+        try:
+            res = pd.DataFrame(data, columns=default_header)
+            if frequence is FREQUENCE.DAY:
+                if market_type is MARKET_TYPE.STOCK_CN:
+                    return QA_DataStruct_Stock_day(
+                        res.assign(date=pd.to_datetime(res.date)).set_index(
+                            ['date', 'code'], drop=False),
+                        dtype='stock_day')
+            elif frequence in [FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN, FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN]:
+                if market_type is MARKET_TYPE.STOCK_CN:
+                    return QA_DataStruct_Stock_min(
+                        res.assign(datetime=pd.to_datetime(res.datetime)).set_index(
+                            ['datetime', 'code'], drop=False),
+                        dtype='stock_min')
+        except:
+            pass
+
+    elif isinstance(data, pd.DataFrame):
+        index = data.index
+        if isinstance(index, pd.MultiIndex):
+            pass
+        elif isinstance(index, pd.DatetimeIndex):
+            pass
+        elif isinstance(index, pd.Index):
+            pass
+
+
 def from_tushare(dataframe, dtype='day'):
     """dataframe from tushare
 
@@ -71,7 +134,6 @@ def QDS_StockDayWarpper(func):
     def warpper(*args, **kwargs):
         data = func(*args, **kwargs)
 
-
         if isinstance(data.index, pd.MultiIndex):
 
             return QA_DataStruct_Stock_day(data)
@@ -94,6 +156,7 @@ def warpper(*args, **kwargs):
             return QA_DataStruct_Stock_min(data.assign(datetime=pd.to_datetime(data.datetime)).set_index(['datetime', 'code'], drop=False), dtype='stock_min')
     return warpper
 
+
 def QDS_IndexDayWarpper(func, *args, **kwargs):
     """
     指数日线QDS装饰器
@@ -124,9 +187,9 @@ def warpper(*args, **kwargs):
     return warpper
 
 
-if __name__ =='__main__':
+if __name__ == '__main__':
     """演示QDS装饰器
-    
+
     Returns:
         [type] -- [description]
     """
@@ -142,7 +205,7 @@ def warpper(*args, **kwargs):
     """
 
     import tushare as ts
-    print(from_tushare(ts.get_k_data('000001','2018-01-01','2018-06-26')))
+    print(from_tushare(ts.get_k_data('000001', '2018-01-01', '2018-06-26')))
 
     """[summary]
     """