[update] 股票:复权因子计算，盘后自动除权更新仓位,委托/成交显示中文名

prod/jobs/daily_stock_adjust_grids.py

@@ -0,0 +1,134 @@
+# flake8: noqa
+# encoding: UTF-8
+
+# 功能:
+# 每日夜盘扫描一次
+# 根据账号所在目录下得cta_stock_setting.json，检查所有运行中网格策略实例的持仓合约，
+# 如果当日存在除权除息情况，就进行计算更新
+
+# AUTHOR:李来佳
+# WeChat/QQ: 28888502
+# 广东华富资产管理
+
+import sys, os, copy, csv, json
+
+import sys, os, platform
+from datetime import datetime, timedelta
+import pandas as pd
+import traceback
+import matplotlib
+import json
+from pymongo import *
+
+from datetime import datetime
+
+VNPY_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
+if VNPY_ROOT not in sys.path:
+    print(f'append {VNPY_ROOT} into sys.path')
+    sys.path.append(VNPY_ROOT)
+
+os.environ["VNPY_TESTING"] = "1"
+from vnpy.trader.utility import load_json, save_json, append_data
+from vnpy.data.stock.adjust_factor import get_adjust_factor, get_stock_base
+from vnpy.trader.util_wechat import send_wx_msg
+
+if __name__ == "__main__":
+
+    if len(sys.argv) <= 1:
+        print(u'请输入:{}目录下的子目录作为参数1'.format(os.path.abspath(os.path.join(VNPY_ROOT, 'prod'))))
+        exit()
+
+    # 进行报告的账号目录
+    account_folder = sys.argv[1]
+    account_folder = os.path.abspath(os.path.join(VNPY_ROOT, 'prod', account_folder))
+
+    # 策略实例配置文件
+    cta_setting_file = os.path.abspath(os.path.join(account_folder, 'cta_stock_setting.json'))
+    # 除权调整记录文件
+    adj_record_file = os.path.abspath(os.path.join(account_folder, 'data', 'adj_records.csv'))
+    field_names = ['date', 'strategy_name', 'vt_symbol', 'name', 'pre_volume', 'new_volume', 'rate', 'pre_back_adj',
+                   'last_back_adj']
+    print('开始扫描:{}'.format(cta_setting_file))
+    if not os.path.exists(cta_setting_file):
+        print(u'不存在策略实例配置文件{}'.format(cta_setting_file), file=sys.stderr)
+        exit()
+
+    # 获取所有股票基本信息
+    all_symbols = get_stock_base()
+
+    # 读取策略
+    cta_settings = []
+    with open(cta_setting_file, encoding='UTF-8') as f:
+        cta_settings = json.load(f)
+
+    # 逐一策略扫描
+    all_margin_usage = 0
+    for strategy_name in cta_settings.keys():
+        # 获取策略实例配置
+        strategy_setting = cta_settings.get(strategy_name)
+        # 策略类
+        strategy_class = strategy_setting.get("class_name", "")
+
+        setting = strategy_setting.get('setting', {})
+
+        grids = load_json(os.path.abspath(os.path.join(account_folder, 'data', f'{strategy_name}_Grids.json')),
+                          auto_save=False)
+
+        changed = False
+
+        margin_usage = 0
+        for grid in grids['dn_grids']:
+            if not grid['open_status']:
+                continue
+
+            vt_symbol = grid['vt_symbol']
+            info = all_symbols.get(vt_symbol,{})
+            name = info.get('name',vt_symbol)
+            factor = get_adjust_factor(vt_symbol)
+            if factor is None or len(factor) < 2:
+                print(f'没有找到{vt_symbol}的除权因子')
+                continue
+
+            # 检查除权日子的最后日期
+            last_data = factor[-1]
+            pre_data = factor[-2]
+
+            print(f'{vt_symbol}[{name}]复权因子:\n{pre_data} => \n{last_data}')
+            dividOperateDate = last_data['dividOperateDate']
+            foreAdjustFactor = float(last_data['foreAdjustFactor'])
+            adjusted_date = grid['snapshot'].get('adjusted_date', "")
+            yd_date = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')
+
+            # 记录的除权执行日期，跟昨日或今日不一样，除权日发生在昨日或今日
+            if adjusted_date != dividOperateDate and dividOperateDate >= yd_date and foreAdjustFactor == 1.0:
+                adj_rate = last_data.get('backAdjustFactor') / pre_data.get('backAdjustFactor')
+
+                # 当前持仓
+                cur_volume = grid['volume']
+                # 除权后的股票新数量
+                adj_volume = int(cur_volume * adj_rate)
+                # 更新数量
+                grid['volume'] = adj_volume
+
+                # 更新执行日期
+                grid['snapshot'].update({'adjusted_date': dividOperateDate})
+                msg = f'{strategy_name}:{vt_symbol}[{name}]发生除权调整:{cur_volume}=>{adj_volume}'
+                send_wx_msg(msg)
+                print(msg)
+                append_data(adj_record_file, dict_data={
+                    'date': dividOperateDate,
+                    'strategy_name': strategy_name,
+                    'vt_symbol': vt_symbol,
+                    'name': name,
+                    'pre_volume': cur_volume,
+                    'adj_volume': adj_volume,
+                    'rate': adj_rate,
+                    'pre_back_adj': pre_data.get('backAdjustFactor'),
+                    'last_back_adj': last_data.get('backAdjustFactor')
+                })
+                changed = True
+
+
+        if changed:
+            print('保存更新后的Grids.json文件')
+            save_json(os.path.abspath(os.path.join(account_folder, 'data', f'{strategy_name}_Grids.json')), grids)

vnpy/app/cta_stock/back_testing.py

@@ -63,7 +63,7 @@ from vnpy.trader.utility import (
     get_stock_exchange
 )
 from vnpy.data.stock.adjust_factor import get_all_adjust_factor
-
+from vnpy.data.common import stock_to_adj
 from vnpy.trader.util_logger import setup_logger
 from vnpy.data.mongo.mongo_data import MongoData
 from uuid import uuid1
@@ -584,41 +584,6 @@ class BackTestingEngine(object):
                 margin_rate=margin_rate
             )
 
-    def stock_to_adj(self, raw_data, adj_data, adj_type):
-        """
-        股票数据复权转换
-        :param raw_data: 不复权数据
-        :param adj_data:  复权记录 ( 从barstock下载的复权记录列表=》df）
-        :param adj_type: 复权类型: fore 前复权, 其他：后复权
-        :return:
-        """
-
-        if adj_type == 'fore':
-            adj_factor = adj_data["foreAdjustFactor"]
-            adj_factor = adj_factor / adj_factor.iloc[-1]  # 保证最后一个复权因子是1
-        else:
-            adj_factor = adj_data["backAdjustFactor"]
-            adj_factor = adj_factor / adj_factor.iloc[0]  # 保证第一个复权因子是1
-
-        # 把raw_data的第一个日期，插入复权因子df，使用后填充
-        if adj_factor.index[0] != raw_data.index[0]:
-            adj_factor.loc[raw_data.index[0]] = np.nan
-        adj_factor.sort_index(inplace=True)
-        adj_factor = adj_factor.ffill()
-
-        adj_factor = adj_factor.reindex(index=raw_data.index)  # 按价格dataframe的日期索引来扩展索引
-        adj_factor = adj_factor.ffill()  # 向前（向未来）填充扩展后的空单元格
-
-        # 把复权因子，作为adj字段，补充到raw_data中
-        raw_data['adj'] = adj_factor
-
-        # 逐一复权高低开平和成交量
-        for col in ['open', 'high', 'low', 'close']:
-            raw_data[col] = raw_data[col] * raw_data['adj']  # 价格乘上复权系数
-        raw_data['volume'] = raw_data['volume'] / raw_data['adj']  # 成交量除以复权系数
-
-        return raw_data
-
     def new_tick(self, tick):
         """新得tick"""
         self.last_tick.update({tick.vt_symbol: tick})

vnpy/app/cta_stock/engine.py

@@ -72,6 +72,7 @@ from vnpy.data.mongo.mongo_data import MongoData
 from vnpy.trader.setting import SETTINGS
 from vnpy.data.stock.adjust_factor import get_all_adjust_factor
 from vnpy.data.stock.stock_base import get_stock_base
+from vnpy.data.common import stock_to_adj
 from .base import (
     APP_NAME,
     EVENT_CTA_LOG,
@@ -1169,7 +1170,7 @@ class CtaEngine(BaseEngine):
                     adj_data["dividOperateDate"] = pd.to_datetime(adj_data["dividOperateDate"], format="%Y-%m-%d %H:%M:%S")
                     adj_data = adj_data.set_index("dividOperateDate")
                     # 调用转换方法，对open,high,low,close, volume进行复权, fore, 前复权， 其他，后复权
-                    symbol_df = self.stock_to_adj(symbol_df, adj_data, adj_type='fore')
+                    symbol_df = stock_to_adj(symbol_df, adj_data, adj_type='fore')
 
             for dt, bar_data in symbol_df.iterrows():
                 bar_datetime = dt #- timedelta(seconds=bar_interval_seconds)
@@ -1209,40 +1210,6 @@ class CtaEngine(BaseEngine):
 
         return bars
 
-    def stock_to_adj(self, raw_data, adj_data, adj_type):
-        """
-        股票数据复权转换
-        :param raw_data: 不复权数据
-        :param adj_data:  复权记录 ( 从barstock下载的复权记录列表=》df）
-        :param adj_type: 复权类型
-        :return:
-        """
-
-        if adj_type == 'fore':
-            adj_factor = adj_data["foreAdjustFactor"]
-            adj_factor = adj_factor / adj_factor.iloc[-1]  # 保证最后一个复权因子是1
-        else:
-            adj_factor = adj_data["backAdjustFactor"]
-            adj_factor = adj_factor / adj_factor.iloc[0]  # 保证第一个复权因子是1
-
-        # 把raw_data的第一个日期，插入复权因子df，使用后填充
-        if adj_factor.index[0] != raw_data.index[0]:
-            adj_factor.loc[raw_data.index[0]] = np.nan
-        adj_factor.sort_index(inplace=True)
-        adj_factor = adj_factor.ffill()
-
-        adj_factor = adj_factor.reindex(index=raw_data.index)  # 按价格dataframe的日期索引来扩展索引
-        adj_factor = adj_factor.ffill()  # 向前（向未来）填充扩展后的空单元格
-
-        # 把复权因子，作为adj字段，补充到raw_data中
-        raw_data['adj'] = adj_factor
-
-        # 逐一复权高低开平和成交量
-        for col in ['open', 'high', 'low', 'close']:
-            raw_data[col] = raw_data[col] * raw_data['adj']  # 价格乘上复权系数
-        raw_data['volume'] = raw_data['volume'] / raw_data['adj']  # 成交量除以复权系数
-
-        return raw_data
 
     def resample_bars(self, df, x_min=None, x_hour=None, to_day=False):
         """

vnpy/app/cta_stock/portfolio_testing.py

@@ -35,7 +35,7 @@ from vnpy.trader.utility import (
     get_csv_last_dt
 )
 
-from .back_testing import BackTestingEngine
+from .back_testing import BackTestingEngine, stock_to_adj
 
 
 class PortfolioTestingEngine(BackTestingEngine):
@@ -146,7 +146,7 @@ class PortfolioTestingEngine(BackTestingEngine):
                                                                   format="%Y-%m-%d %H:%M:%S")
                     adj_data = adj_data.set_index("dividOperateDate")
                     # 调用转换方法，对open,high,low,close, volume进行复权, fore, 前复权， 其他，后复权
-                    symbol_df = self.stock_to_adj(symbol_df, adj_data, adj_type='fore' if qfq else "")
+                    symbol_df = stock_to_adj(symbol_df, adj_data, adj_type='fore' if qfq else "")
 
                     if auto_generate_fq:
                         self.write_log(f'加载数据[{vt_symbol}] ,缓存{fq_name}文件=>{fq_bar_file}')

vnpy/app/cta_stock/template.py

@@ -283,7 +283,8 @@ class CtaTemplate(ABC):
             }
             if grid:
                 d.update({'grid': grid})
-                grid.order_ids.append(vt_orderid)
+                if len(vt_orderid) > 0:
+                    grid.order_ids.append(vt_orderid)
                 grid.order_time = order_time
             self.active_orders.update({vt_orderid: d})
         if direction == Direction.LONG:

vnpy/component/cta_line_bar.py

@@ -6512,7 +6512,7 @@ class CtaLineBar(object):
                 'type': 'line'
             }
             indicators.update({indicator.get('name'): copy.copy(indicator)})
-        if isinstance(self.para_ema4_len, int) and self.para_ema4_len > 0:
+        if getattr(self,'para_ema4_len',0) > 0: #isinstance(self.para_ema4_len, int) and self.para_ema4_len > 0:
             indicator = {
                 'name': 'EMA{}'.format(self.para_ema4_len),
                 'attr_name': 'line_ema4',
@@ -6520,7 +6520,7 @@ class CtaLineBar(object):
                 'type': 'line'
             }
             indicators.update({indicator.get('name'): copy.copy(indicator)})
-        if isinstance(self.para_ema5_len, int) and self.para_ema5_len > 0:
+        if getattr(self, 'para_ema5_len',0) > 0: #isinstance(self.para_ema5_len, int) and self.para_ema5_len > 0:
             indicator = {
                 'name': 'EMA{}'.format(self.para_ema5_len),
                 'attr_name': 'line_ema5',
@@ -7036,8 +7036,8 @@ class CtaMinuteBar(CtaLineBar):
 
         bar_len = len(self.line_bar)
 
-        minutes_passed = (tick.datetime - datetime.strptime(tick.datetime.strftime('%Y-%m-%d'),
+        minutes_passed = tick.datetime.hour * 60 + tick.datetime.minute
-                                                            '%Y-%m-%d')).total_seconds() / 60
+
         if self.underly_symbol in MARKET_ZJ:
             if int(tick.datetime.strftime('%H%M')) > 1130 and int(tick.datetime.strftime('%H%M')) < 1600:
                 # 扣除11:30到13:00的中场休息的90分钟

vnpy/component/cta_utility.py

@@ -1299,7 +1299,7 @@ def check_zs_3rd(big_kline,
     direction = 1 if signal_direction == Direction.LONG else -1
 
     if not big_kline.pre_duan or not big_kline.cur_bi_zs:
-        return
+        return False
 
     # 排除，须满足：当前段的方向 == 信号方向， 当前笔的方向 != 信号方向
     if big_kline.cur_duan.direction != direction or big_kline.cur_bi.direction == direction:

vnpy/data/common.py

@@ -153,14 +153,16 @@ def stock_to_adj(raw_data: pd.DataFrame,
         adj_factor = adj_data["backAdjustFactor"]
         adj_factor = adj_factor / adj_factor.iloc[0]  # 保证第一个复权因子是1
 
-    # 把raw_data的第一个日期，插入复权因子df，使用后填充
-    if adj_factor.index[0] != raw_data.index[0]:
-        adj_factor.loc[raw_data.index[0]] = np.nan
     adj_factor.sort_index(inplace=True)
-    adj_factor = adj_factor.ffill()
 
-    adj_factor = adj_factor.reindex(index=raw_data.index)  # 按价格dataframe的日期索引来扩展索引
+    # 按价格dataframe的日期索引来扩展索引
+    adj_factor2 = adj_factor.reindex(index=raw_data.index)  # 得到dataframe的日期索引
+
+    adj_factor = adj_factor2.append(adj_factor)   # 加入复权因子的日期索引
+    adj_factor = adj_factor.sort_index()
     adj_factor = adj_factor.ffill()  # 向前（向未来）填充扩展后的空单元格
+    adj_factor = adj_factor.loc[raw_data.index]  # 提取需要的日期索
+    adj_factor = adj_factor[~adj_factor.index.duplicated(keep='first')]  # 去重
 
     # 把复权因子，作为adj字段，补充到raw_data中
     raw_data['adj'] = adj_factor

vnpy/trader/util_monitor.py

@@ -96,6 +96,7 @@ class TradeMonitor(BasicMonitor):
         "tradeid": {"display": "成交号 ", "update": False},
         "orderid": {"display": "委托号", "update": False},
         "symbol": {"display": "代码", "update": False},
+        "name": {"display": "名称", "update": False},
         "exchange": {"display": "交易所", "update": False},
         "direction": {"display": "方向", "update": False},
         "offset": {"display": "开平", "update": False},
@@ -121,6 +122,7 @@ class OrderMonitor(BasicMonitor):
     headers: Dict[str, dict] = {
         "orderid": {"display": "委托号", "update": False},
         "symbol": {"display": "代码", "update": False},
+        "name": {"display": "名称", "update": False},
         "exchange": {"display": "交易所", "update": False},
         "type": {"display": "类型", "update": False},
         "direction": {"display": "方向", "update": False},
@@ -148,6 +150,7 @@ class PositionMonitor(BasicMonitor):
 
     headers = {
         "symbol": {"display": "代码", "update": False},
+        "name": {"display": "名称", "update": False},
         "exchange": {"display": "交易所", "update": False},
         "direction": {"display": "方向", "update": False},
         "volume": {"display": "数量", "update": True},