├── .gitignore ├── .idea └── inspectionProfiles │ ├── Project_Default.xml │ └── profiles_settings.xml ├── README.md ├── docs ├── zipline_arch.graphml └── zipline_arch.png ├── setup.py ├── test ├── test_cn_squant2_run.py └── test_cn_squant2_run_text.py └── zipline_cn_databundle ├── __init__.py ├── all_stocks.py ├── index_list └── __init__.py ├── loader.py ├── squant_source.py ├── tdx ├── __init__.py └── reader.py ├── tushare_source.py └── yahoo.py /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | # Created by https://www.gitignore.io/api/python,pycharm 3 | 4 | ### Python ### 5 | # Byte-compiled / optimized / DLL files 6 | __pycache__/ 7 | *.py[cod] 8 | *$py.class 9 | 10 | # C extensions 11 | *.so 12 | 13 | # Distribution / packaging 14 | .Python 15 | env/ 16 | build/ 17 | develop-eggs/ 18 | dist/ 19 | downloads/ 20 | eggs/ 21 | .eggs/ 22 | lib/ 23 | lib64/ 24 | parts/ 25 | sdist/ 26 | var/ 27 | *.egg-info/ 28 | .installed.cfg 29 | 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mpeltonen/sbt-idea plugin 131 | .idea_modules/ 132 | 133 | # JIRA plugin 134 | atlassian-ide-plugin.xml 135 | 136 | # Crashlytics plugin (for Android Studio and IntelliJ) 137 | com_crashlytics_export_strings.xml 138 | crashlytics.properties 139 | crashlytics-build.properties 140 | fabric.properties 141 | 142 | ### PyCharm Patch ### 143 | # Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721 144 | 145 | # *.iml 146 | # modules.xml 147 | # .idea/misc.xml 148 | # *.ipr 149 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 15 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Equtity Data bundle of chinese exchange market for Zipline 2 | ======= 3 | 4 | 5 | Installation 6 | ----- 7 | 8 | ``` 9 | pip install zipline-cn-databundle 10 | ``` 11 | or 12 | 13 | ``` 14 | python setup.py install 15 | ``` 16 | 17 | Usage 18 | ---- 19 | 20 | We need Re-Implement All Features instead of old ways 21 | 22 | Working in progress!!!! 23 | 24 | ### Others 25 | 26 | #### Get All Stocks info in Chinese Market 27 | 28 | ``` 29 | 30 | get_all_stocks() 31 | 32 | ``` 33 | 34 | returns infomations in pandas format 35 | 36 | ``` 37 | name industry area pe outstanding totals totalAssets \ 38 | code 39 | 601997 N贵银 银行 贵州 9.11 50000.00 229859.19 28492688.00 40 | 000972 中基健康 食品 新疆 0.00 77128.35 77128.35 229892.13 41 | 601011 宝泰隆 焦炭加工 黑龙江 1240.98 130874.56 136750.00 794420.81 42 | .... 43 | ``` -------------------------------------------------------------------------------- /docs/zipline_arch.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rainx/zipline_cn_databundle/92ba9258ebdf836180651cea1365ebcbd585e60d/docs/zipline_arch.png -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | setup( 4 | name = "zipline-cn-databundle", 5 | version = "0.5", 6 | author= 'RainX', 7 | description='ingest zipline databundle source for chinese market', 8 | packages = find_packages(), 9 | install_requires=[ 10 | 'pandas-datareader', 11 | ], 12 | entry_points={ 13 | 'console_scripts': [ 14 | 'zipline-cn-databundle-update=zipline_cn_databundle:zipline_cn_databundle_update', 15 | 'gen-all-index-benchmark-data=zipline_cn_databundle.index_list.__init__:gen_data', 16 | ] 17 | } 18 | ) 19 | 20 | -------------------------------------------------------------------------------- /test/test_cn_squant2_run.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | from zipline.data.bundles import register 3 | from zipline_cn_databundle.squant_source import squant_bundle 4 | import pandas as pd 5 | import os 6 | 7 | from zipline.api import ( 8 | schedule_function, 9 | symbol, 10 | order_target_percent, 11 | date_rules, 12 | record 13 | ) 14 | import re 15 | from zipline.algorithm import TradingAlgorithm 16 | from zipline.finance.trading import TradingEnvironment 17 | from zipline.utils.calendars import get_calendar, register_calendar 18 | from zipline.finance import trading 19 | from zipline.utils.factory import create_simulation_parameters 20 | from zipline.data.bundles.core import load 21 | from zipline.data.data_portal import DataPortal 22 | 23 | from zipline_cn_databundle.loader import load_market_data 24 | 25 | # register SHSZ 26 | 27 | from cn_stock_holidays.zipline.default_calendar import shsz_calendar 28 | 29 | 30 | bundle = 'cn_squant' 31 | 32 | start_session_str = '2011-01-05' 33 | 34 | register( 35 | bundle, 36 | squant_bundle, 37 | "SHSZ", 38 | pd.Timestamp(start_session_str, tz='utc'), 39 | pd.Timestamp('2016-10-31', tz='utc') 40 | ) 41 | 42 | 43 | bundle_data = load( 44 | bundle, 45 | os.environ, 46 | None, 47 | ) 48 | 49 | prefix, connstr = re.split( 50 | r'sqlite:///', 51 | str(bundle_data.asset_finder.engine.url), 52 | maxsplit=1, 53 | ) 54 | 55 | env = trading.environment = TradingEnvironment(asset_db_path=connstr, 56 | trading_calendar=shsz_calendar, 57 | bm_symbol='000001.SS', 58 | load=load_market_data) 59 | 60 | 61 | first_trading_day = \ 62 | bundle_data.equity_minute_bar_reader.first_trading_day 63 | data = DataPortal( 64 | env.asset_finder, shsz_calendar, 65 | first_trading_day=first_trading_day, 66 | equity_minute_reader=bundle_data.equity_minute_bar_reader, 67 | equity_daily_reader=bundle_data.equity_daily_bar_reader, 68 | adjustment_reader=bundle_data.adjustment_reader, 69 | ) 70 | 71 | 72 | def initialize(context): 73 | schedule_function(handle_daily_data, date_rules.every_day()) 74 | 75 | def handle_daily_data(context, data): 76 | sym = symbol('000001.SZ') 77 | 78 | # 计算均线 79 | short_mavg = data.history(sym, 'close', 5, '1d').mean() 80 | long_mavg = data.history(sym, 'close', 10, '1d').mean() 81 | 82 | # 交易逻辑 83 | if short_mavg > long_mavg: 84 | # 满仓 85 | order_target_percent(sym, 1) 86 | elif short_mavg < long_mavg: 87 | # 清仓 88 | order_target_percent(sym, 0) 89 | 90 | # Save values for later inspection 91 | record(价格=data.current(sym, 'price'), 92 | short_mavg=short_mavg, 93 | long_mavg=long_mavg) 94 | 95 | def analyze(context, perf): 96 | fig = plt.figure(figsize=(14,13)) 97 | ax1 = fig.add_subplot(211) 98 | perf.portfolio_value.plot(ax=ax1, grid=True) 99 | ax1.set_ylabel('portfolio value in $') 100 | 101 | ax2 = fig.add_subplot(212) 102 | perf['GOOG'].plot(ax=ax2) 103 | perf[['short_mavg', 'long_mavg']].plot(ax=ax2) 104 | 105 | perf_trans = perf.ix[[t != [] for t in perf.transactions]] 106 | buys = perf_trans.ix[[t[0]['amount'] > 0 for t in perf_trans.transactions]] 107 | sells = perf_trans.ix[ 108 | [t[0]['amount'] < 0 for t in perf_trans.transactions]] 109 | ax2.plot(buys.index, perf.short_mavg.ix[buys.index], 110 | '^', markersize=4, color='m') 111 | ax2.plot(sells.index, perf.short_mavg.ix[sells.index], 112 | 'v', markersize=4, color='k') 113 | ax2.set_ylabel('price in $') 114 | plt.legend(loc=0) 115 | plt.show() 116 | plt.imsave("output.png") 117 | 118 | 119 | if __name__ == '__main__': 120 | sim_params = create_simulation_parameters( 121 | start=pd.to_datetime(start_session_str + " 00:00:00").tz_localize("Asia/Shanghai"), 122 | end=pd.to_datetime("2012-01-01 00:00:00").tz_localize("Asia/Shanghai"), 123 | data_frequency="daily", emission_rate="daily", trading_calendar=shsz_calendar) 124 | 125 | algor_obj = TradingAlgorithm(initialize=initialize, 126 | handle_data=None, 127 | sim_params=sim_params, 128 | env=trading.environment, 129 | trading_calendar=shsz_calendar) 130 | # not use run method of TradingAlgorithm 131 | #perf_manual = algor_obj.run(data) 132 | #perf_manual.to_pickle('/tmp/perf.pickle') 133 | 134 | algor_obj.data_portal = data 135 | algor_obj._assets_from_source = \ 136 | algor_obj.trading_environment.asset_finder.retrieve_all( 137 | algor_obj.trading_environment.asset_finder.sids 138 | ) 139 | algor_obj.perf_tracker = None 140 | import demjson 141 | import pickle 142 | import pprint 143 | try: 144 | perfs = [] 145 | for perf in algor_obj.get_generator(): 146 | perfs.append(perf) 147 | print('-' * 40) 148 | try: 149 | print(demjson.encode(perf)) 150 | except Exception as e: 151 | pprint.pprint(e) 152 | print('-' * 40) 153 | 154 | pickle.dump(perfs, open('/tmp/raw_perfs.pickle', 'wb')) 155 | daily_stats = algor_obj._create_daily_stats(perfs) 156 | print('*' * 40) 157 | print('daily_stats') 158 | print('*' * 40) 159 | print(daily_stats) 160 | daily_stats.to_pickle('/tmp/perf.pickle') 161 | finally: 162 | algor_obj.data_portal = None 163 | -------------------------------------------------------------------------------- /test/test_cn_squant2_run_text.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | from zipline.data.bundles import register 3 | from zipline_cn_databundle.squant_source import squant_bundle 4 | import pandas as pd 5 | import os 6 | 7 | from zipline.api import ( 8 | schedule_function, 9 | symbol, 10 | order_target_percent, 11 | date_rules, 12 | record 13 | ) 14 | import re 15 | from zipline.algorithm import TradingAlgorithm 16 | from zipline.finance.trading import TradingEnvironment 17 | from zipline.utils.calendars import get_calendar, register_calendar 18 | from zipline.finance import trading 19 | from zipline.utils.factory import create_simulation_parameters 20 | from zipline.data.bundles.core import load 21 | from zipline.data.data_portal import DataPortal 22 | 23 | from zipline_cn_databundle.loader import load_market_data 24 | 25 | # register SHSZ 26 | 27 | from cn_stock_holidays.zipline.default_calendar import shsz_calendar 28 | 29 | 30 | bundle = 'cn_squant' 31 | 32 | start_session_str = '2011-01-05' 33 | 34 | register( 35 | bundle, 36 | squant_bundle, 37 | "SHSZ", 38 | pd.Timestamp(start_session_str, tz='utc'), 39 | pd.Timestamp('2016-10-31', tz='utc') 40 | ) 41 | 42 | 43 | bundle_data = load( 44 | bundle, 45 | os.environ, 46 | None, 47 | ) 48 | 49 | prefix, connstr = re.split( 50 | r'sqlite:///', 51 | str(bundle_data.asset_finder.engine.url), 52 | maxsplit=1, 53 | ) 54 | 55 | env = trading.environment = TradingEnvironment(asset_db_path=connstr, 56 | trading_calendar=shsz_calendar, 57 | bm_symbol='000001.SS', 58 | load=load_market_data) 59 | 60 | 61 | first_trading_day = \ 62 | bundle_data.equity_minute_bar_reader.first_trading_day 63 | data = DataPortal( 64 | env.asset_finder, shsz_calendar, 65 | first_trading_day=first_trading_day, 66 | equity_minute_reader=bundle_data.equity_minute_bar_reader, 67 | equity_daily_reader=bundle_data.equity_daily_bar_reader, 68 | adjustment_reader=bundle_data.adjustment_reader, 69 | ) 70 | 71 | 72 | strategy = """ 73 | from zipline.api import ( 74 | schedule_function, 75 | symbol, 76 | order_target_percent, 77 | date_rules, 78 | record 79 | ) 80 | 81 | def initialize(context): 82 | schedule_function(handle_daily_data, date_rules.every_day()) 83 | 84 | def handle_daily_data(context, data): 85 | sym = symbol('000001.SZ') 86 | 87 | # 计算均线 88 | short_mavg = data.history(sym, 'close', 5, '1d').mean() 89 | long_mavg = data.history(sym, 'close', 10, '1d').mean() 90 | 91 | # 交易逻辑 92 | if short_mavg > long_mavg: 93 | # 满仓 94 | order_target_percent(sym, 1) 95 | elif short_mavg < long_mavg: 96 | # 清仓 97 | order_target_percent(sym, 0) 98 | 99 | # Save values for later inspection 100 | record(价格=data.current(sym, 'price'), 101 | short_mavg=short_mavg, 102 | long_mavg=long_mavg) 103 | """ 104 | 105 | def analyze(context, perf): 106 | fig = plt.figure(figsize=(14,13)) 107 | ax1 = fig.add_subplot(211) 108 | perf.portfolio_value.plot(ax=ax1, grid=True) 109 | ax1.set_ylabel('portfolio value in $') 110 | 111 | ax2 = fig.add_subplot(212) 112 | perf['GOOG'].plot(ax=ax2) 113 | perf[['short_mavg', 'long_mavg']].plot(ax=ax2) 114 | 115 | perf_trans = perf.ix[[t != [] for t in perf.transactions]] 116 | buys = perf_trans.ix[[t[0]['amount'] > 0 for t in perf_trans.transactions]] 117 | sells = perf_trans.ix[ 118 | [t[0]['amount'] < 0 for t in perf_trans.transactions]] 119 | ax2.plot(buys.index, perf.short_mavg.ix[buys.index], 120 | '^', markersize=4, color='m') 121 | ax2.plot(sells.index, perf.short_mavg.ix[sells.index], 122 | 'v', markersize=4, color='k') 123 | ax2.set_ylabel('price in $') 124 | plt.legend(loc=0) 125 | plt.show() 126 | plt.imsave("output.png") 127 | 128 | 129 | if __name__ == '__main__': 130 | sim_params = create_simulation_parameters( 131 | start=pd.to_datetime(start_session_str + " 00:00:00").tz_localize("Asia/Shanghai"), 132 | end=pd.to_datetime("2012-01-01 00:00:00").tz_localize("Asia/Shanghai"), 133 | data_frequency="daily", emission_rate="daily", trading_calendar=shsz_calendar) 134 | 135 | algor_obj = TradingAlgorithm(script=strategy, 136 | sim_params=sim_params, 137 | env=trading.environment, 138 | trading_calendar=shsz_calendar) 139 | # not use run method of TradingAlgorithm 140 | #perf_manual = algor_obj.run(data) 141 | #perf_manual.to_pickle('/tmp/perf.pickle') 142 | 143 | algor_obj.data_portal = data 144 | algor_obj._assets_from_source = \ 145 | algor_obj.trading_environment.asset_finder.retrieve_all( 146 | algor_obj.trading_environment.asset_finder.sids 147 | ) 148 | algor_obj.perf_tracker = None 149 | import demjson 150 | import pickle 151 | import pprint 152 | try: 153 | perfs = [] 154 | for perf in algor_obj.get_generator(): 155 | perfs.append(perf) 156 | print('-' * 40) 157 | try: 158 | print(demjson.encode(perf)) 159 | except Exception as e: 160 | pprint.pprint(perf) 161 | print('-' * 40) 162 | 163 | pickle.dump(perfs, open('/tmp/raw_perfs.pickle', 'wb')) 164 | daily_stats = algor_obj._create_daily_stats(perfs) 165 | print('*' * 40) 166 | print('daily_stats') 167 | print('*' * 40) 168 | print(daily_stats) 169 | daily_stats.to_pickle('/tmp/perf.pickle') 170 | finally: 171 | algor_obj.data_portal = None 172 | -------------------------------------------------------------------------------- /zipline_cn_databundle/__init__.py: -------------------------------------------------------------------------------- 1 | from .yahoo import ( 2 | register_cn_bundle_from_yahoo, 3 | get_all_yahoo_stock_names, 4 | check_code, 5 | get_filtered_symbols, 6 | zipline_cn_databundle_update, 7 | ) 8 | 9 | from .all_stocks import ( 10 | get_all_stocks, 11 | ) -------------------------------------------------------------------------------- /zipline_cn_databundle/all_stocks.py: -------------------------------------------------------------------------------- 1 | """ 2 | Get all stock name 3 | """ 4 | 5 | import requests 6 | from io import StringIO 7 | import pandas as pd 8 | import os 9 | import shutil 10 | import sys 11 | 12 | ALL_STOCKS_URL = 'http://218.244.146.57/static/all.csv' 13 | 14 | 15 | def get_cache_dir(): 16 | home_path = os.path.expanduser('~') 17 | cache_dir = os.path.join(home_path, '.zipline_cn_databundle') 18 | 19 | if not os.path.isdir(cache_dir): 20 | os.makedirs(cache_dir) 21 | return cache_dir 22 | 23 | def get_cache_path(): 24 | cache_dir = get_cache_dir() 25 | return os.path.join(cache_dir, 'all_stocks.csv') 26 | 27 | def get_all_stocks(cache=True): 28 | """ 29 | used idea from tushare 30 | :return: data frame of stock list 31 | """ 32 | cache_path = get_cache_path() 33 | 34 | #use cache 35 | if cache and os.path.isfile(cache_path): 36 | df = pd.read_csv(cache_path, dtype={'code': 'object'}) 37 | df = df.set_index('code') 38 | return df 39 | 40 | response = requests.get(ALL_STOCKS_URL) 41 | text = response.content 42 | text = text.decode('GBK') 43 | text = text.replace('--', '') 44 | df = pd.read_csv(StringIO(text), dtype={'code': 'object'}) 45 | df = df.set_index('code') 46 | # write to cache 47 | with open(cache_path, 'wb') as f: 48 | f.write(text.encode('utf-8')) 49 | 50 | return df 51 | 52 | 53 | 54 | if __name__ == '__main__': 55 | print(get_all_stocks(True)) -------------------------------------------------------------------------------- /zipline_cn_databundle/index_list/__init__.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from io import StringIO 3 | import click 4 | import os 5 | import sys 6 | 7 | JSON_DATA = """ 8 | [ 9 | {"name": "上证指数", "symbol": "000001.XSHG"}, 10 | {"name": "A股指数", "symbol": "000002.XSHG"}, 11 | {"name": "B股指数", "symbol": "000003.XSHG"}, 12 | {"name": "工业指数", "symbol": "000004.XSHG"}, 13 | {"name": "商业指数", "symbol": "000005.XSHG"}, 14 | {"name": "地产指数", "symbol": "000006.XSHG"}, 15 | {"name": "公用指数", "symbol": "000007.XSHG"}, 16 | {"name": "综合指数", "symbol": "000008.XSHG"}, 17 | {"name": "上证380", "symbol": "000009.XSHG"}, 18 | {"name": "上证180", "symbol": "000010.XSHG"}, 19 | {"name": "基金指数", "symbol": "000011.XSHG"}, 20 | {"name": "国债指数", "symbol": "000012.XSHG"}, 21 | {"name": "上证企业债指数", "symbol": "000013.XSHG"}, 22 | {"name": "红利指数", "symbol": "000015.XSHG"}, 23 | {"name": "上证50", "symbol": "000016.XSHG"}, 24 | {"name": "新综指", "symbol": "000017.XSHG"}, 25 | {"name": "180金融", "symbol": "000018.XSHG"}, 26 | {"name": "治理指数", "symbol": "000019.XSHG"}, 27 | {"name": "中型综指", "symbol": "000020.XSHG"}, 28 | {"name": "180治理", "symbol": "000021.XSHG"}, 29 | {"name": "上证公司债指数", "symbol": "000022.XSHG"}, 30 | {"name": "180基建", "symbol": "000025.XSHG"}, 31 | {"name": "180资源", "symbol": "000026.XSHG"}, 32 | {"name": "180运输", "symbol": "000027.XSHG"}, 33 | {"name": "180成长", "symbol": "000028.XSHG"}, 34 | {"name": "180价值", "symbol": "000029.XSHG"}, 35 | {"name": "180R成长", "symbol": "000030.XSHG"}, 36 | {"name": "180R价值", "symbol": "000031.XSHG"}, 37 | {"name": "上证能源", "symbol": "000032.XSHG"}, 38 | {"name": "上证材料", "symbol": "000033.XSHG"}, 39 | {"name": "上证工业", "symbol": "000034.XSHG"}, 40 | {"name": "上证可选", "symbol": "000035.XSHG"}, 41 | {"name": "上证消费", "symbol": "000036.XSHG"}, 42 | {"name": "上证医药", "symbol": "000037.XSHG"}, 43 | {"name": "上证金融", "symbol": "000038.XSHG"}, 44 | {"name": "上证信息", "symbol": "000039.XSHG"}, 45 | {"name": "上证电信", "symbol": "000040.XSHG"}, 46 | {"name": "上证公用", "symbol": "000041.XSHG"}, 47 | {"name": "上证央企", "symbol": "000042.XSHG"}, 48 | {"name": "超大盘", "symbol": "000043.XSHG"}, 49 | {"name": "上证中盘", "symbol": "000044.XSHG"}, 50 | {"name": "上证小盘", "symbol": "000045.XSHG"}, 51 | {"name": "上证中小", "symbol": "000046.XSHG"}, 52 | {"name": "上证全指", "symbol": "000047.XSHG"}, 53 | {"name": "责任指数", "symbol": "000048.XSHG"}, 54 | {"name": "上证民企", "symbol": "000049.XSHG"}, 55 | {"name": "50等权", "symbol": "000050.XSHG"}, 56 | {"name": "180等权", "symbol": "000051.XSHG"}, 57 | {"name": "50基本", "symbol": "000052.XSHG"}, 58 | {"name": "180基本", "symbol": "000053.XSHG"}, 59 | {"name": "上证海外", "symbol": "000054.XSHG"}, 60 | {"name": "上证地企", "symbol": "000055.XSHG"}, 61 | {"name": "上证国企", "symbol": "000056.XSHG"}, 62 | {"name": "全指成长", "symbol": "000057.XSHG"}, 63 | {"name": "全指价值", "symbol": "000058.XSHG"}, 64 | {"name": "全R成长", "symbol": "000059.XSHG"}, 65 | {"name": "全R价值", "symbol": "000060.XSHG"}, 66 | {"name": "沪企债30", "symbol": "000061.XSHG"}, 67 | {"name": "上证沪企", "symbol": "000062.XSHG"}, 68 | {"name": "上证周期", "symbol": "000063.XSHG"}, 69 | {"name": "非周期", "symbol": "000064.XSHG"}, 70 | {"name": "上证龙头", "symbol": "000065.XSHG"}, 71 | {"name": "上证商品", "symbol": "000066.XSHG"}, 72 | {"name": "上证新兴", "symbol": "000067.XSHG"}, 73 | {"name": "上证资源", "symbol": "000068.XSHG"}, 74 | {"name": "消费80", "symbol": "000069.XSHG"}, 75 | {"name": "能源等权", "symbol": "000070.XSHG"}, 76 | {"name": "材料等权", "symbol": "000071.XSHG"}, 77 | {"name": "工业等权", "symbol": "000072.XSHG"}, 78 | {"name": "可选等权", "symbol": "000073.XSHG"}, 79 | {"name": "消费等权", "symbol": "000074.XSHG"}, 80 | {"name": "医药等权", "symbol": "000075.XSHG"}, 81 | {"name": "金融等权", "symbol": "000076.XSHG"}, 82 | {"name": "信息等权", "symbol": "000077.XSHG"}, 83 | {"name": "电信等权", "symbol": "000078.XSHG"}, 84 | {"name": "公用等权", "symbol": "000079.XSHG"}, 85 | {"name": "上证流通", "symbol": "000090.XSHG"}, 86 | {"name": "沪财中小", "symbol": "000091.XSHG"}, 87 | {"name": "资源50", "symbol": "000092.XSHG"}, 88 | {"name": "180分层", "symbol": "000093.XSHG"}, 89 | {"name": "上证上游", "symbol": "000094.XSHG"}, 90 | {"name": "上证中游", "symbol": "000095.XSHG"}, 91 | {"name": "上证下游", "symbol": "000096.XSHG"}, 92 | {"name": "高端装备", "symbol": "000097.XSHG"}, 93 | {"name": "上证F200", "symbol": "000098.XSHG"}, 94 | {"name": "上证F300", "symbol": "000099.XSHG"}, 95 | {"name": "上证F500", "symbol": "000100.XSHG"}, 96 | {"name": "5年信用", "symbol": "000101.XSHG"}, 97 | {"name": "沪投资品", "symbol": "000102.XSHG"}, 98 | {"name": "沪消费品", "symbol": "000103.XSHG"}, 99 | {"name": "380能源", "symbol": "000104.XSHG"}, 100 | {"name": "380材料", "symbol": "000105.XSHG"}, 101 | {"name": "380工业", "symbol": "000106.XSHG"}, 102 | {"name": "380可选", "symbol": "000107.XSHG"}, 103 | {"name": "380消费", "symbol": "000108.XSHG"}, 104 | {"name": "380医药", "symbol": "000109.XSHG"}, 105 | {"name": "380金融", "symbol": "000110.XSHG"}, 106 | {"name": "380信息", "symbol": "000111.XSHG"}, 107 | {"name": "380电信", "symbol": "000112.XSHG"}, 108 | {"name": "380公用", "symbol": "000113.XSHG"}, 109 | {"name": "持续产业", "symbol": "000114.XSHG"}, 110 | {"name": "380等权", "symbol": "000115.XSHG"}, 111 | {"name": "信用100", "symbol": "000116.XSHG"}, 112 | {"name": "380成长", "symbol": "000117.XSHG"}, 113 | {"name": "380价值", "symbol": "000118.XSHG"}, 114 | {"name": "380R成长", "symbol": "000119.XSHG"}, 115 | {"name": "380R价值", "symbol": "000120.XSHG"}, 116 | {"name": "医药主题", "symbol": "000121.XSHG"}, 117 | {"name": "农业主题", "symbol": "000122.XSHG"}, 118 | {"name": "180动态", "symbol": "000123.XSHG"}, 119 | {"name": "180稳定", "symbol": "000125.XSHG"}, 120 | {"name": "消费50", "symbol": "000126.XSHG"}, 121 | {"name": "380基本", "symbol": "000128.XSHG"}, 122 | {"name": "180波动", "symbol": "000129.XSHG"}, 123 | {"name": "380波动", "symbol": "000130.XSHG"}, 124 | {"name": "上证高新", "symbol": "000131.XSHG"}, 125 | {"name": "上证100", "symbol": "000132.XSHG"}, 126 | {"name": "上证150", "symbol": "000133.XSHG"}, 127 | {"name": "上证银行", "symbol": "000134.XSHG"}, 128 | {"name": "180高贝", "symbol": "000135.XSHG"}, 129 | {"name": "180低贝", "symbol": "000136.XSHG"}, 130 | {"name": "380高贝", "symbol": "000137.XSHG"}, 131 | {"name": "380低贝", "symbol": "000138.XSHG"}, 132 | {"name": "上证转债", "symbol": "000139.XSHG"}, 133 | {"name": "380动态", "symbol": "000141.XSHG"}, 134 | {"name": "380稳定", "symbol": "000142.XSHG"}, 135 | {"name": "优势资源", "symbol": "000145.XSHG"}, 136 | {"name": "优势制造", "symbol": "000146.XSHG"}, 137 | {"name": "优势消费", "symbol": "000147.XSHG"}, 138 | {"name": "消费领先", "symbol": "000148.XSHG"}, 139 | {"name": "180红利", "symbol": "000149.XSHG"}, 140 | {"name": "380红利", "symbol": "000150.XSHG"}, 141 | {"name": "上国红利", "symbol": "000151.XSHG"}, 142 | {"name": "上央红利", "symbol": "000152.XSHG"}, 143 | {"name": "上民红利", "symbol": "000153.XSHG"}, 144 | {"name": "市值百强", "symbol": "000155.XSHG"}, 145 | {"name": "上证环保", "symbol": "000158.XSHG"}, 146 | {"name": "上证沪股通指数", "symbol": "000159.XSHG"}, 147 | {"name": "上证一带一路主题指数", "symbol": "000160.XSHG"}, 148 | {"name": "上证中国制造2025主题指数", "symbol": "000161.XSHG"}, 149 | {"name": "上证互联网+主题指数", "symbol": "000162.XSHG"}, 150 | {"name": "沪深300", "symbol": "000300.XSHG"}, 151 | {"name": "资源80", "symbol": "000801.XSHG"}, 152 | {"name": "500沪市", "symbol": "000802.XSHG"}, 153 | {"name": "300波动", "symbol": "000803.XSHG"}, 154 | {"name": "500波动", "symbol": "000804.XSHG"}, 155 | {"name": "A股资源", "symbol": "000805.XSHG"}, 156 | {"name": "消费服务", "symbol": "000806.XSHG"}, 157 | {"name": "食品饮料", "symbol": "000807.XSHG"}, 158 | {"name": "医药生物", "symbol": "000808.XSHG"}, 159 | {"name": "细分农业", "symbol": "000809.XSHG"}, 160 | {"name": "细分能源", "symbol": "000810.XSHG"}, 161 | {"name": "细分有色", "symbol": "000811.XSHG"}, 162 | {"name": "细分机械", "symbol": "000812.XSHG"}, 163 | {"name": "细分化工", "symbol": "000813.XSHG"}, 164 | {"name": "细分医药", "symbol": "000814.XSHG"}, 165 | {"name": "细分食品", "symbol": "000815.XSHG"}, 166 | {"name": "细分地产", "symbol": "000816.XSHG"}, 167 | {"name": "兴证海峡", "symbol": "000817.XSHG"}, 168 | {"name": "细分金融", "symbol": "000818.XSHG"}, 169 | {"name": "有色金属", "symbol": "000819.XSHG"}, 170 | {"name": "煤炭指数", "symbol": "000820.XSHG"}, 171 | {"name": "300红利", "symbol": "000821.XSHG"}, 172 | {"name": "500红利", "symbol": "000822.XSHG"}, 173 | {"name": "中证800有色金属指数", "symbol": "000823.XSHG"}, 174 | {"name": "国企红利", "symbol": "000824.XSHG"}, 175 | {"name": "央企红利", "symbol": "000825.XSHG"}, 176 | {"name": "民企红利", "symbol": "000826.XSHG"}, 177 | {"name": "中证环保", "symbol": "000827.XSHG"}, 178 | {"name": "300高贝", "symbol": "000828.XSHG"}, 179 | {"name": "300低贝", "symbol": "000829.XSHG"}, 180 | {"name": "500高贝", "symbol": "000830.XSHG"}, 181 | {"name": "500低贝", "symbol": "000831.XSHG"}, 182 | {"name": "中证转债", "symbol": "000832.XSHG"}, 183 | {"name": "中高企债", "symbol": "000833.XSHG"}, 184 | {"name": "创业价值", "symbol": "000838.XSHG"}, 185 | {"name": "浙企综指", "symbol": "000839.XSHG"}, 186 | {"name": "浙江民企", "symbol": "000840.XSHG"}, 187 | {"name": "800医药", "symbol": "000841.XSHG"}, 188 | {"name": "800等权", "symbol": "000842.XSHG"}, 189 | {"name": "300动态", "symbol": "000843.XSHG"}, 190 | {"name": "300稳定", "symbol": "000844.XSHG"}, 191 | {"name": "ESG100", "symbol": "000846.XSHG"}, 192 | {"name": "中证腾安价值100指数", "symbol": "000847.XSHG"}, 193 | {"name": "沪深300非银行金融指数", "symbol": "000849.XSHG"}, 194 | {"name": "沪深300有色金属指数", "symbol": "000850.XSHG"}, 195 | {"name": "中证百度百发策略100指数", "symbol": "000851.XSHG"}, 196 | {"name": "中证1000指数", "symbol": "000852.XSHG"}, 197 | {"name": "中证申万一带一路主题投资指数", "symbol": "000853.XSHG"}, 198 | {"name": "央视财经500指数", "symbol": "000855.XSHG"}, 199 | {"name": "小康指数", "symbol": "000901.XSHG"}, 200 | {"name": "中证流通", "symbol": "000902.XSHG"}, 201 | {"name": "中证100", "symbol": "000903.XSHG"}, 202 | {"name": "中证200", "symbol": "000904.XSHG"}, 203 | {"name": "中证500", "symbol": "000905.XSHG"}, 204 | {"name": "中证800", "symbol": "000906.XSHG"}, 205 | {"name": "中证700", "symbol": "000907.XSHG"}, 206 | {"name": "300能源", "symbol": "000908.XSHG"}, 207 | {"name": "300材料", "symbol": "000909.XSHG"}, 208 | {"name": "300工业", "symbol": "000910.XSHG"}, 209 | {"name": "300可选", "symbol": "000911.XSHG"}, 210 | {"name": "300消费", "symbol": "000912.XSHG"}, 211 | {"name": "300医药", "symbol": "000913.XSHG"}, 212 | {"name": "300金融", "symbol": "000914.XSHG"}, 213 | {"name": "300信息", "symbol": "000915.XSHG"}, 214 | {"name": "300电信", "symbol": "000916.XSHG"}, 215 | {"name": "300公用", "symbol": "000917.XSHG"}, 216 | {"name": "300成长", "symbol": "000918.XSHG"}, 217 | {"name": "300价值", "symbol": "000919.XSHG"}, 218 | {"name": "300R成长", "symbol": "000920.XSHG"}, 219 | {"name": "300R价值", "symbol": "000921.XSHG"}, 220 | {"name": "中证红利", "symbol": "000922.XSHG"}, 221 | {"name": "公司债", "symbol": "000923.XSHG"}, 222 | {"name": "基本面50", "symbol": "000925.XSHG"}, 223 | {"name": "中证央企", "symbol": "000926.XSHG"}, 224 | {"name": "央企100", "symbol": "000927.XSHG"}, 225 | {"name": "中证能源", "symbol": "000928.XSHG"}, 226 | {"name": "中证材料", "symbol": "000929.XSHG"}, 227 | {"name": "中证工业", "symbol": "000930.XSHG"}, 228 | {"name": "中证可选", "symbol": "000931.XSHG"}, 229 | {"name": "中证消费", "symbol": "000932.XSHG"}, 230 | {"name": "中证医药", "symbol": "000933.XSHG"}, 231 | {"name": "中证金融", "symbol": "000934.XSHG"}, 232 | {"name": "中证信息", "symbol": "000935.XSHG"}, 233 | {"name": "中证电信", "symbol": "000936.XSHG"}, 234 | {"name": "中证公用", "symbol": "000937.XSHG"}, 235 | {"name": "中证民企", "symbol": "000938.XSHG"}, 236 | {"name": "民企200", "symbol": "000939.XSHG"}, 237 | {"name": "财富大盘", "symbol": "000940.XSHG"}, 238 | {"name": "新能源", "symbol": "000941.XSHG"}, 239 | {"name": "内地消费", "symbol": "000942.XSHG"}, 240 | {"name": "内地基建", "symbol": "000943.XSHG"}, 241 | {"name": "内地资源", "symbol": "000944.XSHG"}, 242 | {"name": "内地运输", "symbol": "000945.XSHG"}, 243 | {"name": "内地金融", "symbol": "000946.XSHG"}, 244 | {"name": "内地银行", "symbol": "000947.XSHG"}, 245 | {"name": "内地地产", "symbol": "000948.XSHG"}, 246 | {"name": "内地农业", "symbol": "000949.XSHG"}, 247 | {"name": "300基建", "symbol": "000950.XSHG"}, 248 | {"name": "300银行", "symbol": "000951.XSHG"}, 249 | {"name": "300地产", "symbol": "000952.XSHG"}, 250 | {"name": "中证地企", "symbol": "000953.XSHG"}, 251 | {"name": "地企100", "symbol": "000954.XSHG"}, 252 | {"name": "中证国企", "symbol": "000955.XSHG"}, 253 | {"name": "国企200", "symbol": "000956.XSHG"}, 254 | {"name": "300运输", "symbol": "000957.XSHG"}, 255 | {"name": "创业成长", "symbol": "000958.XSHG"}, 256 | {"name": "银河99", "symbol": "000959.XSHG"}, 257 | {"name": "中证龙头", "symbol": "000960.XSHG"}, 258 | {"name": "中证上游", "symbol": "000961.XSHG"}, 259 | {"name": "中证中游", "symbol": "000962.XSHG"}, 260 | {"name": "中证下游", "symbol": "000963.XSHG"}, 261 | {"name": "中证新兴", "symbol": "000964.XSHG"}, 262 | {"name": "基本200", "symbol": "000965.XSHG"}, 263 | {"name": "基本400", "symbol": "000966.XSHG"}, 264 | {"name": "基本600", "symbol": "000967.XSHG"}, 265 | {"name": "300周期", "symbol": "000968.XSHG"}, 266 | {"name": "300非周", "symbol": "000969.XSHG"}, 267 | {"name": "ESG40", "symbol": "000970.XSHG"}, 268 | {"name": "等权90", "symbol": "000971.XSHG"}, 269 | {"name": "300沪市", "symbol": "000972.XSHG"}, 270 | {"name": "技术领先", "symbol": "000973.XSHG"}, 271 | {"name": "中证800金融指数", "symbol": "000974.XSHG"}, 272 | {"name": "钱江30", "symbol": "000975.XSHG"}, 273 | {"name": "新华金牛", "symbol": "000976.XSHG"}, 274 | {"name": "内地低碳", "symbol": "000977.XSHG"}, 275 | {"name": "医药100", "symbol": "000978.XSHG"}, 276 | {"name": "大宗商品", "symbol": "000979.XSHG"}, 277 | {"name": "中证超大", "symbol": "000980.XSHG"}, 278 | {"name": "300分层", "symbol": "000981.XSHG"}, 279 | {"name": "500等权", "symbol": "000982.XSHG"}, 280 | {"name": "智能资产", "symbol": "000983.XSHG"}, 281 | {"name": "300等权", "symbol": "000984.XSHG"}, 282 | {"name": "中证全指", "symbol": "000985.XSHG"}, 283 | {"name": "全指能源", "symbol": "000986.XSHG"}, 284 | {"name": "全指材料", "symbol": "000987.XSHG"}, 285 | {"name": "全指工业", "symbol": "000988.XSHG"}, 286 | {"name": "全指可选", "symbol": "000989.XSHG"}, 287 | {"name": "全指消费", "symbol": "000990.XSHG"}, 288 | {"name": "全指医药", "symbol": "000991.XSHG"}, 289 | {"name": "全指金融", "symbol": "000992.XSHG"}, 290 | {"name": "全指信息", "symbol": "000993.XSHG"}, 291 | {"name": "全指电信", "symbol": "000994.XSHG"}, 292 | {"name": "全指公用", "symbol": "000995.XSHG"}, 293 | {"name": "领先行业", "symbol": "000996.XSHG"}, 294 | {"name": "大消费", "symbol": "000997.XSHG"}, 295 | {"name": "中证TMT", "symbol": "000998.XSHG"}, 296 | {"name": "证两岸三地500指数", "symbol": "000999.XSHG"}, 297 | {"name": "深证成指", "symbol": "399001.XSHE"}, 298 | {"name": "深成指R", "symbol": "399002.XSHE"}, 299 | {"name": "成份B指", "symbol": "399003.XSHE"}, 300 | {"name": "深证100R", "symbol": "399004.XSHE"}, 301 | {"name": "中小板指", "symbol": "399005.XSHE"}, 302 | {"name": "创业板指", "symbol": "399006.XSHE"}, 303 | {"name": "深证300", "symbol": "399007.XSHE"}, 304 | {"name": "中小300", "symbol": "399008.XSHE"}, 305 | {"name": "深证200", "symbol": "399009.XSHE"}, 306 | {"name": "深证700", "symbol": "399010.XSHE"}, 307 | {"name": "深证1000", "symbol": "399011.XSHE"}, 308 | {"name": "创业300", "symbol": "399012.XSHE"}, 309 | {"name": "深市精选", "symbol": "399013.XSHE"}, 310 | {"name": "深证中小创新指数", "symbol": "399015.XSHE"}, 311 | {"name": "新指数", "symbol": "399100.XSHE"}, 312 | {"name": "中小板综", "symbol": "399101.XSHE"}, 313 | {"name": "创业板综合指数", "symbol": "399102.XSHE"}, 314 | {"name": "乐富指数", "symbol": "399103.XSHE"}, 315 | {"name": "深证综指", "symbol": "399106.XSHE"}, 316 | {"name": "深证A指", "symbol": "399107.XSHE"}, 317 | {"name": "深证B指", "symbol": "399108.XSHE"}, 318 | {"name": "农林指数", "symbol": "399231.XSHE"}, 319 | {"name": "采矿指数", "symbol": "399232.XSHE"}, 320 | {"name": "制造指数", "symbol": "399233.XSHE"}, 321 | {"name": "水电指数", "symbol": "399234.XSHE"}, 322 | {"name": "建筑指数", "symbol": "399235.XSHE"}, 323 | {"name": "批零指数", "symbol": "399236.XSHE"}, 324 | {"name": "运输指数", "symbol": "399237.XSHE"}, 325 | {"name": "餐饮指数", "symbol": "399238.XSHE"}, 326 | {"name": "IT指数", "symbol": "399239.XSHE"}, 327 | {"name": "金融指数", "symbol": "399240.XSHE"}, 328 | {"name": "地产指数", "symbol": "399241.XSHE"}, 329 | {"name": "商务指数", "symbol": "399242.XSHE"}, 330 | {"name": "科研指数", "symbol": "399243.XSHE"}, 331 | {"name": "公共指数", "symbol": "399244.XSHE"}, 332 | {"name": "文化指数", "symbol": "399248.XSHE"}, 333 | {"name": "综企指数", "symbol": "399249.XSHE"}, 334 | {"name": "深证中高等级信用债指数", "symbol": "399298.XSHE"}, 335 | {"name": "深证中低等级信用债指数", "symbol": "399299.XSHE"}, 336 | {"name": "沪深300", "symbol": "399300.XSHE"}, 337 | {"name": "深信用债", "symbol": "399301.XSHE"}, 338 | {"name": "深公司债", "symbol": "399302.XSHE"}, 339 | {"name": "国证2000", "symbol": "399303.XSHE"}, 340 | {"name": "基金指数", "symbol": "399305.XSHE"}, 341 | {"name": "深证ETF", "symbol": "399306.XSHE"}, 342 | {"name": "深证转债", "symbol": "399307.XSHE"}, 343 | {"name": "国证50", "symbol": "399310.XSHE"}, 344 | {"name": "国证1000", "symbol": "399311.XSHE"}, 345 | {"name": "国证300", "symbol": "399312.XSHE"}, 346 | {"name": "巨潮100", "symbol": "399313.XSHE"}, 347 | {"name": "巨潮大盘", "symbol": "399314.XSHE"}, 348 | {"name": "巨潮中盘", "symbol": "399315.XSHE"}, 349 | {"name": "巨潮小盘", "symbol": "399316.XSHE"}, 350 | {"name": "国证A指", "symbol": "399317.XSHE"}, 351 | {"name": "巨潮B股指数", "symbol": "399318.XSHE"}, 352 | {"name": "资源优势", "symbol": "399319.XSHE"}, 353 | {"name": "国证服务", "symbol": "399320.XSHE"}, 354 | {"name": "国证红利", "symbol": "399321.XSHE"}, 355 | {"name": "国证治理", "symbol": "399322.XSHE"}, 356 | {"name": "深证红利", "symbol": "399324.XSHE"}, 357 | {"name": "成长40", "symbol": "399326.XSHE"}, 358 | {"name": "深证治理", "symbol": "399328.XSHE"}, 359 | {"name": "深证100", "symbol": "399330.XSHE"}, 360 | {"name": "深证创新", "symbol": "399332.XSHE"}, 361 | {"name": "中小板R", "symbol": "399333.XSHE"}, 362 | {"name": "深证央企", "symbol": "399335.XSHE"}, 363 | {"name": "深证民营", "symbol": "399337.XSHE"}, 364 | {"name": "深证科技", "symbol": "399339.XSHE"}, 365 | {"name": "深证责任", "symbol": "399341.XSHE"}, 366 | {"name": "深证300R", "symbol": "399344.XSHE"}, 367 | {"name": "深证成长", "symbol": "399346.XSHE"}, 368 | {"name": "深证价值", "symbol": "399348.XSHE"}, 369 | {"name": "皖江30", "symbol": "399350.XSHE"}, 370 | {"name": "深报指数", "symbol": "399351.XSHE"}, 371 | {"name": "深报综指", "symbol": "399352.XSHE"}, 372 | {"name": "国证物流", "symbol": "399353.XSHE"}, 373 | {"name": "长三角", "symbol": "399355.XSHE"}, 374 | {"name": "珠三角", "symbol": "399356.XSHE"}, 375 | {"name": "环渤海", "symbol": "399357.XSHE"}, 376 | {"name": "泰达指数", "symbol": "399358.XSHE"}, 377 | {"name": "国证基建", "symbol": "399359.XSHE"}, 378 | {"name": "国证装备", "symbol": "399360.XSHE"}, 379 | {"name": "国证商业", "symbol": "399361.XSHE"}, 380 | {"name": "国证民营", "symbol": "399362.XSHE"}, 381 | {"name": "计算机指", "symbol": "399363.XSHE"}, 382 | {"name": "中金消费", "symbol": "399364.XSHE"}, 383 | {"name": "国证农业", "symbol": "399365.XSHE"}, 384 | {"name": "国证大宗", "symbol": "399366.XSHE"}, 385 | {"name": "巨潮地产", "symbol": "399367.XSHE"}, 386 | {"name": "国证军工", "symbol": "399368.XSHE"}, 387 | {"name": "CBN-兴全", "symbol": "399369.XSHE"}, 388 | {"name": "国证成长", "symbol": "399370.XSHE"}, 389 | {"name": "国证价值", "symbol": "399371.XSHE"}, 390 | {"name": "大盘成长", "symbol": "399372.XSHE"}, 391 | {"name": "大盘价值", "symbol": "399373.XSHE"}, 392 | {"name": "中盘成长", "symbol": "399374.XSHE"}, 393 | {"name": "中盘价值", "symbol": "399375.XSHE"}, 394 | {"name": "小盘成长", "symbol": "399376.XSHE"}, 395 | {"name": "小盘价值", "symbol": "399377.XSHE"}, 396 | {"name": "南方低碳", "symbol": "399378.XSHE"}, 397 | {"name": "国证基金", "symbol": "399379.XSHE"}, 398 | {"name": "国证ETF", "symbol": "399380.XSHE"}, 399 | {"name": "1000能源", "symbol": "399381.XSHE"}, 400 | {"name": "1000材料", "symbol": "399382.XSHE"}, 401 | {"name": "1000工业", "symbol": "399383.XSHE"}, 402 | {"name": "1000可选", "symbol": "399384.XSHE"}, 403 | {"name": "1000消费", "symbol": "399385.XSHE"}, 404 | {"name": "1000医药", "symbol": "399386.XSHE"}, 405 | {"name": "1000金融", "symbol": "399387.XSHE"}, 406 | {"name": "1000信息", "symbol": "399388.XSHE"}, 407 | {"name": "国证通信", "symbol": "399389.XSHE"}, 408 | {"name": "1000公用", "symbol": "399390.XSHE"}, 409 | {"name": "投资时钟", "symbol": "399391.XSHE"}, 410 | {"name": "国证新兴", "symbol": "399392.XSHE"}, 411 | {"name": "国证地产", "symbol": "399393.XSHE"}, 412 | {"name": "国证医药", "symbol": "399394.XSHE"}, 413 | {"name": "国证有色", "symbol": "399395.XSHE"}, 414 | {"name": "国证食品", "symbol": "399396.XSHE"}, 415 | {"name": "OCT文化", "symbol": "399397.XSHE"}, 416 | {"name": "绩效指数", "symbol": "399398.XSHE"}, 417 | {"name": "中经GDP", "symbol": "399399.XSHE"}, 418 | {"name": "大中盘", "symbol": "399400.XSHE"}, 419 | {"name": "中小盘", "symbol": "399401.XSHE"}, 420 | {"name": "周期100", "symbol": "399402.XSHE"}, 421 | {"name": "防御100", "symbol": "399403.XSHE"}, 422 | {"name": "大盘低波", "symbol": "399404.XSHE"}, 423 | {"name": "大盘高贝", "symbol": "399405.XSHE"}, 424 | {"name": "中盘低波", "symbol": "399406.XSHE"}, 425 | {"name": "中盘高贝", "symbol": "399407.XSHE"}, 426 | {"name": "小盘低波", "symbol": "399408.XSHE"}, 427 | {"name": "小盘高贝", "symbol": "399409.XSHE"}, 428 | {"name": "苏州率先", "symbol": "399410.XSHE"}, 429 | {"name": "红利100", "symbol": "399411.XSHE"}, 430 | {"name": "国证新能", "symbol": "399412.XSHE"}, 431 | {"name": "国证转债", "symbol": "399413.XSHE"}, 432 | {"name": "I100", "symbol": "399415.XSHE"}, 433 | {"name": "I300", "symbol": "399416.XSHE"}, 434 | {"name": "国证新能源汽车指数", "symbol": "399417.XSHE"}, 435 | {"name": "国证国家安全指数", "symbol": "399418.XSHE"}, 436 | {"name": "国证高铁指数", "symbol": "399419.XSHE"}, 437 | {"name": "国证保险证券指数", "symbol": "399420.XSHE"}, 438 | {"name": "中关村50指数", "symbol": "399423.XSHE"}, 439 | {"name": "国证德高行专利领先指数", "symbol": "399427.XSHE"}, 440 | {"name": "国证定向增发指数", "symbol": "399428.XSHE"}, 441 | {"name": "新丝路指数", "symbol": "399429.XSHE"}, 442 | {"name": "国证银行行业指数", "symbol": "399431.XSHE"}, 443 | {"name": "国证汽车与汽车零配件行业指数", "symbol": "399432.XSHE"}, 444 | {"name": "国证交通运输行业指数", "symbol": "399433.XSHE"}, 445 | {"name": "国证传媒行业指数", "symbol": "399434.XSHE"}, 446 | {"name": "国证农牧渔产品行业指数", "symbol": "399435.XSHE"}, 447 | {"name": "国证煤炭行业指数", "symbol": "399436.XSHE"}, 448 | {"name": "国证证券行业指数", "symbol": "399437.XSHE"}, 449 | {"name": "国证电力公用事业行业指数", "symbol": "399438.XSHE"}, 450 | {"name": "国证石油天然气行业指数", "symbol": "399439.XSHE"}, 451 | {"name": "国证黑色金属行业指数", "symbol": "399440.XSHE"}, 452 | {"name": "国证生物医药指数", "symbol": "399441.XSHE"}, 453 | {"name": "企债指数", "symbol": "399481.XSHE"}, 454 | {"name": "央视50", "symbol": "399550.XSHE"}, 455 | {"name": "央视创新", "symbol": "399551.XSHE"}, 456 | {"name": "央视成长", "symbol": "399552.XSHE"}, 457 | {"name": "央视回报", "symbol": "399553.XSHE"}, 458 | {"name": "央视治理", "symbol": "399554.XSHE"}, 459 | {"name": "央视责任", "symbol": "399555.XSHE"}, 460 | {"name": "央视生态", "symbol": "399556.XSHE"}, 461 | {"name": "央视文化", "symbol": "399557.XSHE"}, 462 | {"name": "中小成长", "symbol": "399602.XSHE"}, 463 | {"name": "中小价值", "symbol": "399604.XSHE"}, 464 | {"name": "创业板R", "symbol": "399606.XSHE"}, 465 | {"name": "科技100", "symbol": "399608.XSHE"}, 466 | {"name": "TMT50", "symbol": "399610.XSHE"}, 467 | {"name": "中创100R", "symbol": "399611.XSHE"}, 468 | {"name": "中创100", "symbol": "399612.XSHE"}, 469 | {"name": "深证能源", "symbol": "399613.XSHE"}, 470 | {"name": "深证材料", "symbol": "399614.XSHE"}, 471 | {"name": "深证工业", "symbol": "399615.XSHE"}, 472 | {"name": "深证可选", "symbol": "399616.XSHE"}, 473 | {"name": "深证消费", "symbol": "399617.XSHE"}, 474 | {"name": "深证医药", "symbol": "399618.XSHE"}, 475 | {"name": "深证金融", "symbol": "399619.XSHE"}, 476 | {"name": "深证信息", "symbol": "399620.XSHE"}, 477 | {"name": "深证电信", "symbol": "399621.XSHE"}, 478 | {"name": "深证公用", "symbol": "399622.XSHE"}, 479 | {"name": "中小基础", "symbol": "399623.XSHE"}, 480 | {"name": "中创400", "symbol": "399624.XSHE"}, 481 | {"name": "中创500", "symbol": "399625.XSHE"}, 482 | {"name": "中创成长", "symbol": "399626.XSHE"}, 483 | {"name": "中创价值", "symbol": "399627.XSHE"}, 484 | {"name": "700成长", "symbol": "399628.XSHE"}, 485 | {"name": "700价值", "symbol": "399629.XSHE"}, 486 | {"name": "1000成长", "symbol": "399630.XSHE"}, 487 | {"name": "1000价值", "symbol": "399631.XSHE"}, 488 | {"name": "深100EW", "symbol": "399632.XSHE"}, 489 | {"name": "深300EW", "symbol": "399633.XSHE"}, 490 | {"name": "中小板EW", "symbol": "399634.XSHE"}, 491 | {"name": "创业板EW", "symbol": "399635.XSHE"}, 492 | {"name": "深证装备", "symbol": "399636.XSHE"}, 493 | {"name": "深证地产", "symbol": "399637.XSHE"}, 494 | {"name": "深证环保", "symbol": "399638.XSHE"}, 495 | {"name": "深证大宗", "symbol": "399639.XSHE"}, 496 | {"name": "创业基础", "symbol": "399640.XSHE"}, 497 | {"name": "深证新兴", "symbol": "399641.XSHE"}, 498 | {"name": "中小新兴", "symbol": "399642.XSHE"}, 499 | {"name": "创业新兴", "symbol": "399643.XSHE"}, 500 | {"name": "深证时钟", "symbol": "399644.XSHE"}, 501 | {"name": "100低波", "symbol": "399645.XSHE"}, 502 | {"name": "深消费50", "symbol": "399646.XSHE"}, 503 | {"name": "深医药50", "symbol": "399647.XSHE"}, 504 | {"name": "深证GDP", "symbol": "399648.XSHE"}, 505 | {"name": "中小红利", "symbol": "399649.XSHE"}, 506 | {"name": "中小治理", "symbol": "399650.XSHE"}, 507 | {"name": "中小责任", "symbol": "399651.XSHE"}, 508 | {"name": "中创高新", "symbol": "399652.XSHE"}, 509 | {"name": "深证龙头", "symbol": "399653.XSHE"}, 510 | {"name": "深证文化", "symbol": "399654.XSHE"}, 511 | {"name": "深证绩效", "symbol": "399655.XSHE"}, 512 | {"name": "100绩效", "symbol": "399656.XSHE"}, 513 | {"name": "300绩效", "symbol": "399657.XSHE"}, 514 | {"name": "中小绩效", "symbol": "399658.XSHE"}, 515 | {"name": "深成指EW", "symbol": "399659.XSHE"}, 516 | {"name": "中创EW", "symbol": "399660.XSHE"}, 517 | {"name": "深证低波", "symbol": "399661.XSHE"}, 518 | {"name": "深证高贝", "symbol": "399662.XSHE"}, 519 | {"name": "中小低波", "symbol": "399663.XSHE"}, 520 | {"name": "中小高贝", "symbol": "399664.XSHE"}, 521 | {"name": "中创低波", "symbol": "399665.XSHE"}, 522 | {"name": "中创高贝", "symbol": "399666.XSHE"}, 523 | {"name": "创业板G", "symbol": "399667.XSHE"}, 524 | {"name": "创业板V", "symbol": "399668.XSHE"}, 525 | {"name": "深证农业", "symbol": "399669.XSHE"}, 526 | {"name": "深周期50", "symbol": "399670.XSHE"}, 527 | {"name": "深防御50", "symbol": "399671.XSHE"}, 528 | {"name": "深红利50", "symbol": "399672.XSHE"}, 529 | {"name": "创业板50", "symbol": "399673.XSHE"}, 530 | {"name": "深A医药卫生指数", "symbol": "399674.XSHE"}, 531 | {"name": "深A软件与互联网指数", "symbol": "399675.XSHE"}, 532 | {"name": "深A医药卫生等权指数", "symbol": "399676.XSHE"}, 533 | {"name": "深A软件与互联网等权指数", "symbol": "399677.XSHE"}, 534 | {"name": "深证次新股指数", "symbol": "399678.XSHE"}, 535 | {"name": "深证200指数", "symbol": "399679.XSHE"}, 536 | {"name": "深成能源行业指数", "symbol": "399680.XSHE"}, 537 | {"name": "深成原材料行业指数", "symbol": "399681.XSHE"}, 538 | {"name": "深成工业行业指数", "symbol": "399682.XSHE"}, 539 | {"name": "深成可选消费行业指数", "symbol": "399683.XSHE"}, 540 | {"name": "深成主要消费行业指数", "symbol": "399684.XSHE"}, 541 | {"name": "深成医药卫生行业指数", "symbol": "399685.XSHE"}, 542 | {"name": "深成金融地产行业指数", "symbol": "399686.XSHE"}, 543 | {"name": "深成信息技术行业指数", "symbol": "399687.XSHE"}, 544 | {"name": "深成电信业务行业指数", "symbol": "399688.XSHE"}, 545 | {"name": "深成公用事业行业指数", "symbol": "399689.XSHE"}, 546 | {"name": "深证F60", "symbol": "399701.XSHE"}, 547 | {"name": "深证F120", "symbol": "399702.XSHE"}, 548 | {"name": "深证F200", "symbol": "399703.XSHE"}, 549 | {"name": "深证上游", "symbol": "399704.XSHE"}, 550 | {"name": "深证中游", "symbol": "399705.XSHE"}, 551 | {"name": "深证下游", "symbol": "399706.XSHE"}, 552 | {"name": "中证申万证券行业指数", "symbol": "399707.XSHE"}, 553 | {"name": "500深市", "symbol": "399802.XSHE"}, 554 | {"name": "中证工业4.0指数", "symbol": "399803.XSHE"}, 555 | {"name": "中证体育产业指数", "symbol": "399804.XSHE"}, 556 | {"name": "中证互联网金融指数", "symbol": "399805.XSHE"}, 557 | {"name": "中证环境治理指数", "symbol": "399806.XSHE"}, 558 | {"name": "中证高铁产业指数", "symbol": "399807.XSHE"}, 559 | {"name": "中证新能源指数", "symbol": "399808.XSHE"}, 560 | {"name": "中证方正富邦保险主题指数", "symbol": "399809.XSHE"}, 561 | {"name": "中证申万传媒行业投资指数", "symbol": "399810.XSHE"}, 562 | {"name": "中证申万电子行业投资指数", "symbol": "399811.XSHE"}, 563 | {"name": "中证养老产业指数", "symbol": "399812.XSHE"}, 564 | {"name": "中证国防安全指数", "symbol": "399813.XSHE"}, 565 | {"name": "中证大农业指数", "symbol": "399814.XSHE"}, 566 | {"name": "中证阿拉善生态主题100指数", "symbol": "399817.XSHE"}, 567 | {"name": "中证南方小康产业指数", "symbol": "399901.XSHE"}, 568 | {"name": "中证100指数", "symbol": "399903.XSHE"}, 569 | {"name": "中证中盘200指数", "symbol": "399904.XSHE"}, 570 | {"name": "中证500", "symbol": "399905.XSHE"}, 571 | {"name": "中证中小盘700指数", "symbol": "399907.XSHE"}, 572 | {"name": "沪深300能源指数", "symbol": "399908.XSHE"}, 573 | {"name": "沪深300原材料指数", "symbol": "399909.XSHE"}, 574 | {"name": "沪深300工业指数", "symbol": "399910.XSHE"}, 575 | {"name": "沪深300可选消费指数", "symbol": "399911.XSHE"}, 576 | {"name": "沪深300主要消费指数", "symbol": "399912.XSHE"}, 577 | {"name": "沪深300医药卫生指数", "symbol": "399913.XSHE"}, 578 | {"name": "沪深300金融地产指数", "symbol": "399914.XSHE"}, 579 | {"name": "沪深300信息技术指数", "symbol": "399915.XSHE"}, 580 | {"name": "沪深300电信业务指数", "symbol": "399916.XSHE"}, 581 | {"name": "沪深300公用事业指数", "symbol": "399917.XSHE"}, 582 | {"name": "沪深300成长指数", "symbol": "399918.XSHE"}, 583 | {"name": "沪深300价值指数", "symbol": "399919.XSHE"}, 584 | {"name": "沪深300相对成长指数", "symbol": "399920.XSHE"}, 585 | {"name": "中证红利指数", "symbol": "399922.XSHE"}, 586 | {"name": "中证锐联基本面50指数", "symbol": "399925.XSHE"}, 587 | {"name": "中证中央企业综合指数", "symbol": "399926.XSHE"}, 588 | {"name": "中证中央企业100指数", "symbol": "399927.XSHE"}, 589 | {"name": "中证能源指数", "symbol": "399928.XSHE"}, 590 | {"name": "中证原材料指数", "symbol": "399929.XSHE"}, 591 | {"name": "中证工业指数", "symbol": "399930.XSHE"}, 592 | {"name": "中证可选消费指数", "symbol": "399931.XSHE"}, 593 | {"name": "中证主要消费指数", "symbol": "399932.XSHE"}, 594 | {"name": "中证医药卫生指数", "symbol": "399933.XSHE"}, 595 | {"name": "中证金融地产指数", "symbol": "399934.XSHE"}, 596 | {"name": "中证信息技术指数", "symbol": "399935.XSHE"}, 597 | {"name": "中证电信业务指数", "symbol": "399936.XSHE"}, 598 | {"name": "中证公用事业指数", "symbol": "399937.XSHE"}, 599 | {"name": "中证民营企业综合指数", "symbol": "399938.XSHE"}, 600 | {"name": "中证民营企业200指数", "symbol": "399939.XSHE"}, 601 | {"name": "中证内地新能源主题指数", "symbol": "399941.XSHE"}, 602 | {"name": "中证内地消费主题指数", "symbol": "399942.XSHE"}, 603 | {"name": "中证内地基建主题指数", "symbol": "399943.XSHE"}, 604 | {"name": "中证内地资源主题指数", "symbol": "399944.XSHE"}, 605 | {"name": "中证内地运输主题指数", "symbol": "399945.XSHE"}, 606 | {"name": "中证内地金融主题指数", "symbol": "399946.XSHE"}, 607 | {"name": "中证内地银行主题指数", "symbol": "399947.XSHE"}, 608 | {"name": "中证内地地产主题指数", "symbol": "399948.XSHE"}, 609 | {"name": "中证内地农业主题指数", "symbol": "399949.XSHE"}, 610 | {"name": "沪深300基建主题指数", "symbol": "399950.XSHE"}, 611 | {"name": "沪深300银行指数", "symbol": "399951.XSHE"}, 612 | {"name": "沪深300地产指数", "symbol": "399952.XSHE"}, 613 | {"name": "中证地方国有企业综合指数", "symbol": "399953.XSHE"}, 614 | {"name": "中证地方国有企业100指数", "symbol": "399954.XSHE"}, 615 | {"name": "中证国有企业200指数", "symbol": "399956.XSHE"}, 616 | {"name": "沪深300运输指数", "symbol": "399957.XSHE"}, 617 | {"name": "中证创业成长指数", "symbol": "399958.XSHE"}, 618 | {"name": "军工指数", "symbol": "399959.XSHE"}, 619 | {"name": "中证龙头企业指数", "symbol": "399960.XSHE"}, 620 | {"name": "中证上游资源产业指数", "symbol": "399961.XSHE"}, 621 | {"name": "中证中游制造产业指数", "symbol": "399962.XSHE"}, 622 | {"name": "中证下游消费与服务产业指数", "symbol": "399963.XSHE"}, 623 | {"name": "中证新兴产业指数", "symbol": "399964.XSHE"}, 624 | {"name": "800地产", "symbol": "399965.XSHE"}, 625 | {"name": "800非银", "symbol": "399966.XSHE"}, 626 | {"name": "中证军工", "symbol": "399967.XSHE"}, 627 | {"name": "沪深300周期行业指数", "symbol": "399968.XSHE"}, 628 | {"name": "沪深300非周期行业指数", "symbol": "399969.XSHE"}, 629 | {"name": "中证移动互联网指数", "symbol": "399970.XSHE"}, 630 | {"name": "中证传媒指数", "symbol": "399971.XSHE"}, 631 | {"name": "300深市", "symbol": "399972.XSHE"}, 632 | {"name": "中证国防指数", "symbol": "399973.XSHE"}, 633 | {"name": "中证国有企业改革指数", "symbol": "399974.XSHE"}, 634 | {"name": "中证全指证券公司指数(四级行业)", "symbol": "399975.XSHE"}, 635 | {"name": "中证新能源汽车指数", "symbol": "399976.XSHE"}, 636 | {"name": "中证内地低碳经济主题指数", "symbol": "399977.XSHE"}, 637 | {"name": "中证医药100指数", "symbol": "399978.XSHE"}, 638 | {"name": "中证大宗商品股票指数", "symbol": "399979.XSHE"}, 639 | {"name": "中证超级大盘指数", "symbol": "399980.XSHE"}, 640 | {"name": "中证500等权重指数", "symbol": "399982.XSHE"}, 641 | {"name": "沪深300地产等权重指数", "symbol": "399983.XSHE"}, 642 | {"name": "中证银行指数", "symbol": "399986.XSHE"}, 643 | {"name": "中证酒指数", "symbol": "399987.XSHE"}, 644 | {"name": "中证医疗指数", "symbol": "399989.XSHE"}, 645 | {"name": "中证煤炭等权指数", "symbol": "399990.XSHE"}, 646 | {"name": "中证一带一路主题指数", "symbol": "399991.XSHE"}, 647 | {"name": "中证万得并购重组指数", "symbol": "399992.XSHE"}, 648 | {"name": "中证万得生物科技指数", "symbol": "399993.XSHE"}, 649 | {"name": "中证信息安全主题指数", "symbol": "399994.XSHE"}, 650 | {"name": "中证基建工程指数", "symbol": "399995.XSHE"}, 651 | {"name": "中证智能家居指数", "symbol": "399996.XSHE"}, 652 | {"name": "中证白酒指数", "symbol": "399997.XSHE"}, 653 | {"name": "中证煤炭指数", "symbol": "399998.XSHE"} 654 | ] 655 | """ 656 | 657 | 658 | def get_list(style='yahoo'): 659 | df = pd.read_json(StringIO(JSON_DATA)) 660 | 661 | if style == 'yahoo': 662 | df.symbol = df.symbol.apply(lambda x: str(x).replace('XSHG', 'SS').replace('XSHE', 'SZ')) 663 | 664 | return df 665 | 666 | @click.command() 667 | @click.argument("outputdir") 668 | def gen_data(outputdir): 669 | """ 670 | gen all ticker data result to output dir 671 | """ 672 | 673 | if not os.path.isdir(outputdir): 674 | raise Exception("Please Provide a valid output dir path") 675 | 676 | 677 | 678 | df = get_list('yahoo') 679 | for s in df.symbol: 680 | 681 | outputfile = os.path.join(outputdir, '%s_benchmark.csv' % str(s).upper()) 682 | 683 | """ 684 | zhikuang-squant is a private command of RainX 685 | """ 686 | click.echo("generate file or %s " % s) 687 | cmd = 'zhikuang-squant gen_zipline_benchmark_file -s %s -o %s' % (s, outputfile) 688 | # click.echo(cmd) 689 | os.system(cmd) 690 | click.echo("done! in path : %s" % outputfile) 691 | 692 | if __name__ == "__main__": 693 | gen_data() 694 | -------------------------------------------------------------------------------- /zipline_cn_databundle/loader.py: -------------------------------------------------------------------------------- 1 | # 2 | # Copyright 2016 Quantopian, Inc. 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | import os 16 | 17 | import logbook 18 | import pandas as pd 19 | import pytz 20 | from six import iteritems 21 | from .index_list import get_list 22 | 23 | from zipline.utils.paths import ( 24 | cache_root, 25 | data_root, 26 | ) 27 | import pandas as pd 28 | 29 | from cn_stock_holidays.zipline.default_calendar import shsz_calendar 30 | 31 | from cn_treasury_curve.data import get_zipline_format 32 | import requests 33 | import shutil 34 | 35 | logger = logbook.Logger('Loader') 36 | 37 | ONE_HOUR = pd.Timedelta(hours=1) 38 | 39 | 40 | def last_modified_time(path): 41 | """ 42 | Get the last modified time of path as a Timestamp. 43 | """ 44 | return pd.Timestamp(os.path.getmtime(path), unit='s', tz='UTC') 45 | 46 | 47 | def get_data_filepath(name): 48 | """ 49 | Returns a handle to data file. 50 | 51 | Creates containing directory, if needed. 52 | """ 53 | dr = data_root() 54 | 55 | if not os.path.exists(dr): 56 | os.makedirs(dr) 57 | 58 | return os.path.join(dr, name) 59 | 60 | 61 | def get_cache_filepath(name): 62 | cr = cache_root() 63 | if not os.path.exists(cr): 64 | os.makedirs(cr) 65 | 66 | return os.path.join(cr, name) 67 | 68 | 69 | def get_benchmark_filename(symbol): 70 | return "%s_benchmark.csv" % symbol 71 | 72 | 73 | def has_data_for_dates(series_or_df, first_date, last_date): 74 | """ 75 | Does `series_or_df` have data on or before first_date and on or after 76 | last_date? 77 | """ 78 | dts = series_or_df.index 79 | if not isinstance(dts, pd.DatetimeIndex): 80 | raise TypeError("Expected a DatetimeIndex, but got %s." % type(dts)) 81 | first, last = dts[[0, -1]] 82 | #ignore first date check 83 | return last >= last_date 84 | 85 | 86 | def load_market_data(trading_day=None, trading_days=None, bm_symbol='000001.SS', trading_day_before=2): 87 | """ 88 | Load benchmark returns and treasury yield curves for the given calendar and 89 | benchmark symbol. 90 | 91 | Benchmarks are downloaded as a Series from Yahoo Finance. Treasury curves 92 | are US Treasury Bond rates and are downloaded from 'www.federalreserve.gov' 93 | by default. For Canadian exchanges, a loader for Canadian bonds from the 94 | Bank of Canada is also available. 95 | 96 | Results downloaded from the internet are cached in 97 | ~/.zipline/data. Subsequent loads will attempt to read from the cached 98 | files before falling back to redownload. 99 | 100 | Parameters 101 | ---------- 102 | trading_day : pandas.CustomBusinessDay, optional 103 | A trading_day used to determine the latest day for which we 104 | expect to have data. Defaults to an NYSE trading day. 105 | trading_days : pd.DatetimeIndex, optional 106 | A calendar of trading days. Also used for determining what cached 107 | dates we should expect to have cached. Defaults to the NYSE calendar. 108 | bm_symbol : str, optional 109 | Symbol for the benchmark index to load. Defaults to '^GSPC', the Yahoo 110 | ticker for the S&P 500. 111 | 112 | trading_day_before : int, optional 113 | Trading day before is 2 default 114 | 115 | Returns 116 | ------- 117 | (benchmark_returns, treasury_curves) : (pd.Series, pd.DataFrame) 118 | 119 | Notes 120 | ----- 121 | 122 | Both return values are DatetimeIndexed with values dated to midnight in UTC 123 | of each stored date. The columns of `treasury_curves` are: 124 | 125 | '1month', '3month', '6month', 126 | '1year','2year','3year','5year','7year','10year','20year','30year' 127 | """ 128 | if trading_day is None: 129 | trading_day = shsz_calendar.trading_day 130 | if trading_days is None: 131 | trading_days = shsz_calendar.all_sessions 132 | 133 | first_date = trading_days[0] 134 | now = pd.Timestamp.utcnow() 135 | 136 | # We expect to have benchmark and treasury data that's current up until 137 | # **two** full trading days prior to the most recently completed trading 138 | # day. 139 | # Example: 140 | # On Thu Oct 22 2015, the previous completed trading day is Wed Oct 21. 141 | # However, data for Oct 21 doesn't become available until the early morning 142 | # hours of Oct 22. This means that there are times on the 22nd at which we 143 | # cannot reasonably expect to have data for the 21st available. To be 144 | # conservative, we instead expect that at any time on the 22nd, we can 145 | # download data for Tuesday the 20th, which is two full trading days prior 146 | # to the date on which we're running a test. 147 | 148 | # We'll attempt to download new data if the latest entry in our cache is 149 | # before this date. 150 | last_date = trading_days[trading_days.get_loc(now, method='ffill') - trading_day_before] 151 | 152 | br = ensure_benchmark_data( 153 | bm_symbol, 154 | first_date, 155 | last_date, 156 | now, 157 | # We need the trading_day to figure out the close prior to the first 158 | # date so that we can compute returns for the first date. 159 | trading_day, 160 | ) 161 | tc = ensure_treasury_data( 162 | bm_symbol, 163 | first_date, 164 | last_date, 165 | now, 166 | ) 167 | benchmark_returns = br[br.index.slice_indexer(first_date, last_date)] 168 | treasury_curves = tc[tc.index.slice_indexer(first_date, last_date)] 169 | return benchmark_returns, treasury_curves 170 | 171 | 172 | def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day): 173 | """ 174 | Ensure we have benchmark data for `symbol` from `first_date` to `last_date` 175 | 176 | Parameters 177 | ---------- 178 | symbol : str 179 | The symbol for the benchmark to load. 180 | first_date : pd.Timestamp 181 | First required date for the cache. 182 | last_date : pd.Timestamp 183 | Last required date for the cache. 184 | now : pd.Timestamp 185 | The current time. This is used to prevent repeated attempts to 186 | re-download data that isn't available due to scheduling quirks or other 187 | failures. 188 | trading_day : pd.CustomBusinessDay 189 | A trading day delta. Used to find the day before first_date so we can 190 | get the close of the day prior to first_date. 191 | 192 | We attempt to download data unless we already have data stored at the data 193 | cache for `symbol` whose first entry is before or on `first_date` and whose 194 | last entry is on or after `last_date`. 195 | 196 | If we perform a download and the cache criteria are not satisfied, we wait 197 | at least one hour before attempting a redownload. This is determined by 198 | comparing the current time to the result of os.path.getmtime on the cache 199 | path. 200 | """ 201 | path = get_data_filepath(get_benchmark_filename(symbol)) 202 | 203 | # If the path does not exist, it means the first download has not happened 204 | # yet, so don't try to read from 'path'. 205 | if os.path.exists(path): 206 | try: 207 | data = pd.Series.from_csv(path).tz_localize('UTC') 208 | if has_data_for_dates(data, first_date, last_date): 209 | return data 210 | 211 | # Don't re-download if we've successfully downloaded and written a 212 | # file in the last hour. 213 | last_download_time = last_modified_time(path) 214 | if (now - last_download_time) <= ONE_HOUR: 215 | logger.warn( 216 | "Refusing to download new benchmark data because a " 217 | "download succeeded at %s." % last_download_time 218 | ) 219 | return data 220 | 221 | except (OSError, IOError, ValueError) as e: 222 | # These can all be raised by various versions of pandas on various 223 | # classes of malformed input. Treat them all as cache misses. 224 | logger.info( 225 | "Loading data for {path} failed with error [{error}].".format( 226 | path=path, error=e, 227 | ) 228 | ) 229 | logger.info( 230 | "Cache at {path} does not have data from {start} to {end}.\n" 231 | "Downloading benchmark data for '{symbol}'.", 232 | start=first_date, 233 | end=last_date, 234 | symbol=symbol, 235 | path=path, 236 | ) 237 | 238 | try: 239 | 240 | symbol_list = get_list().symbol.values 241 | 242 | if str(symbol).upper() in symbol_list: 243 | get_url = 'https://raw.githubusercontent.com/rainx/cn_index_benchmark_for_zipline/master/data/%s_benchmark.csv' % str(symbol).upper() 244 | print("fetch data via url : %s " % get_url) 245 | response = requests.get(get_url) 246 | with open(path, 'wb') as fileobj: 247 | fileobj.write(response.content) 248 | print("length of response is : %s" % len(response.content)) 249 | data = pd.Series.from_csv(path).tz_localize('UTC') 250 | else: 251 | #logger.exception('your bm_symbol not in existing symbol list') 252 | raise Exception('your bm_symbol not in existing symbol list') 253 | except (OSError, IOError): 254 | logger.exception('failed to cache the new benchmark returns') 255 | raise 256 | if not has_data_for_dates(data, first_date, last_date): 257 | logger.warn("Still don't have expected data after redownload!") 258 | return data 259 | 260 | 261 | def ensure_treasury_data(bm_symbol, first_date, last_date, now): 262 | """ 263 | Ensure we have treasury data from treasury module associated with 264 | `bm_symbol`. 265 | 266 | Parameters 267 | ---------- 268 | bm_symbol : str 269 | Benchmark symbol for which we're loading associated treasury curves. 270 | first_date : pd.Timestamp 271 | First date required to be in the cache. 272 | last_date : pd.Timestamp 273 | Last date required to be in the cache. 274 | now : pd.Timestamp 275 | The current time. This is used to prevent repeated attempts to 276 | re-download data that isn't available due to scheduling quirks or other 277 | failures. 278 | 279 | We attempt to download data unless we already have data stored in the cache 280 | for `module_name` whose first entry is before or on `first_date` and whose 281 | last entry is on or after `last_date`. 282 | 283 | If we perform a download and the cache criteria are not satisfied, we wait 284 | at least one hour before attempting a redownload. This is determined by 285 | comparing the current time to the result of os.path.getmtime on the cache 286 | path. 287 | """ 288 | filename = "cn_treasury_curves.csv" 289 | 290 | path = get_data_filepath(filename) 291 | 292 | # If the path does not exist, it means the first download has not happened 293 | # yet, so don't try to read from 'path'. 294 | if os.path.exists(path): 295 | try: 296 | data = pd.DataFrame.from_csv(path).tz_localize('UTC') 297 | if has_data_for_dates(data, first_date, last_date): 298 | return data 299 | 300 | # Don't re-download if we've successfully downloaded and written a 301 | # file in the last hour. 302 | last_download_time = last_modified_time(path) 303 | if (now - last_download_time) <= ONE_HOUR: 304 | logger.warn( 305 | "Refusing to download new treasury data because a " 306 | "download succeeded at %s." % last_download_time 307 | ) 308 | return data 309 | 310 | except (OSError, IOError, ValueError) as e: 311 | # These can all be raised by various versions of pandas on various 312 | # classes of malformed input. Treat them all as cache misses. 313 | logger.info( 314 | "Loading data for {path} failed with error [{error}].".format( 315 | path=path, error=e, 316 | ) 317 | ) 318 | 319 | try: 320 | data = get_zipline_format() 321 | data.to_csv(path) 322 | #reload it and convert to UTC tz 323 | data = pd.DataFrame.from_csv(path).tz_localize('UTC') 324 | except (OSError, IOError): 325 | logger.exception('failed to cache treasury data') 326 | if not has_data_for_dates(data, first_date, last_date): 327 | logger.warn("Still don't have expected data after redownload!") 328 | return data -------------------------------------------------------------------------------- /zipline_cn_databundle/squant_source.py: -------------------------------------------------------------------------------- 1 | from squant.data.stock import file_parser 2 | from squant.zipline.datasource import get_symbol_list 3 | import os 4 | import datetime 5 | from .tdx.reader import TdxReader, TdxFileNotFoundException 6 | import pandas as pd 7 | 8 | """ 9 | Squant is a private library that parse the data from our private source 10 | 11 | That read data from binary files 12 | 13 | via RainX 14 | 15 | ipython 3 only 16 | """ 17 | 18 | 19 | # 需要设定沪深文件目录 20 | CQCX_SH = os.environ.get("CQCX_SH") 21 | CQCX_SZ = os.environ.get("CQCX_SZ") 22 | 23 | TDX_DIR = os.environ.get("TDX_DIR") 24 | 25 | if not CQCX_SH or not CQCX_SZ: 26 | raise Exception("need set cqcx file on CQCX_SH CQCX_SZ") 27 | 28 | if not os.path.isfile(CQCX_SH) \ 29 | or not os.path.isfile(CQCX_SZ): 30 | raise Exception("setting CQCX_SH, CQCX_SZ path is not correct") 31 | 32 | 33 | if not TDX_DIR: 34 | raise Exception("Please Setting TDX data dir") 35 | 36 | CQCX_LIST = (CQCX_SH, CQCX_SZ) 37 | 38 | def load_splits_and_dividends(): 39 | """ 40 | 获取所有除权出息的信息, 根据zipline平台的特点,忽略配股信息 41 | :return: 42 | """ 43 | 44 | splits = {} 45 | dividends = {} 46 | 47 | for CQCX in CQCX_LIST: 48 | cqcx_data = file_parser.get_cqcx(CQCX.encode("utf-8")) 49 | for row in cqcx_data: 50 | code = str(row['stock']).zfill(6) 51 | # sgVal 送股数，每1000股送股数 52 | if row['sgVal'] != 0: 53 | if code not in splits.keys(): 54 | splits[code] = [] 55 | splits[code].append({ 56 | 'effective_date' : int_to_date(row['date']), 57 | 'ratio' : 1000 / (1000 + row['sgVal']), 58 | }) 59 | 60 | if row['pxVal'] != 0: 61 | if code not in dividends.keys(): 62 | dividends[code] = [] 63 | dividends[code].append({ 64 | 'amount' : row['pxVal'] / 1000, 65 | 'ex_date' : int_to_date(row['date']), 66 | }) 67 | 68 | 69 | return splits, dividends 70 | 71 | 72 | def zipline_splits_and_dividends(symbol_map): 73 | raw_splits, raw_dividends = load_splits_and_dividends() 74 | splits = [] 75 | dividends = [] 76 | for sid, code in symbol_map.iteritems(): 77 | if code in raw_splits: 78 | split = pd.DataFrame(data=raw_splits[code]) 79 | split['sid'] = sid 80 | split.index = split['effective_date'] = pd.DatetimeIndex(split['effective_date']) 81 | splits.append(split) 82 | if code in raw_dividends: 83 | dividend = pd.DataFrame(data = raw_dividends[code]) 84 | dividend['sid'] = sid 85 | dividend['record_date'] = dividend['declared_date'] = dividend['pay_date'] = pd.NaT 86 | dividend.index = dividend['ex_date'] = pd.DatetimeIndex(dividend['ex_date']) 87 | dividends.append(dividend) 88 | return splits, dividends 89 | 90 | def int_to_date(d): 91 | d = str(d) 92 | return datetime.date(int(d[:4]), int(d[4:6]), int(d[6:])) 93 | 94 | 95 | def squant_bundle(environ, 96 | asset_db_writer, 97 | minute_bar_writer, 98 | daily_bar_writer, 99 | adjustment_writer, 100 | calendar, 101 | start_session, 102 | end_session, 103 | cache, 104 | show_progress, 105 | output_dir): 106 | 107 | tdx_reader = TdxReader(TDX_DIR) 108 | 109 | symbol_df = get_symbol_list() 110 | # 只保留未停牌的 111 | symbol_df = symbol_df[symbol_df['status'] == False] 112 | 113 | # 由于meta,split,dividend 和 行情数据源不同,所以有可能会不同,所以我们这里统一根据 114 | 115 | symbol_map = symbol_df.simplesymbol 116 | 117 | # 更新日期信息 118 | def update_start_and_end_date(s): 119 | start_date = start_session.replace(tzinfo=None) 120 | end_date = end_session.replace(tzinfo=None) 121 | if s.start_date < start_date: 122 | s.start_date = start_date 123 | if s.end_date == pd.Timestamp('1900-01-01') or s.end_date is pd.NaT: 124 | s.end_date = end_date 125 | return s 126 | symbol_df = symbol_df.apply(func=update_start_and_end_date, axis=1) 127 | 128 | 129 | # 写入基础信息 130 | asset_db_writer.write(symbol_df) 131 | # 写入数据文件 132 | daily_bar_writer.write(get_hist_data(symbol_df, symbol_map, tdx_reader, start_session, end_session, calendar), 133 | show_progress=show_progress) 134 | # split and diviends 135 | splits, dividends = zipline_splits_and_dividends(symbol_map) 136 | 137 | # hack for tdx data , for tdx source for shenzhen market, we can not get data before 1991-12-23 138 | splits_df = pd.concat(splits, ignore_index=True) 139 | dividends_df = pd.concat(dividends, ignore_index=True) 140 | 141 | splits_df= splits_df.loc[splits_df['effective_date'] > start_session] 142 | dividends_df = dividends_df.loc[dividends_df['ex_date'] > start_session] 143 | adjustment_writer.write( 144 | splits=splits_df, 145 | dividends=dividends_df, 146 | ) 147 | 148 | def get_hist_data(symbol_df, symbol_map, tdx_reader, start_session, end_session, calendar): 149 | for sid, index in symbol_map.iteritems(): 150 | exchagne = '' 151 | if symbol_df.loc[sid]['exchange'] == 'SZSE': 152 | exchagne = 'sz' 153 | elif symbol_df.loc[sid]['exchange'] == 'SSE': 154 | exchagne = 'sh' 155 | 156 | try: 157 | history = tdx_reader.get_df(index, exchagne) 158 | 159 | #print('max-min for %s is %s : %s', (index, history.index[0], history.index[-1])) 160 | # 去除没有报价信息的内容 161 | if history.index[0] > pd.Timestamp((end_session.date())): 162 | continue 163 | except TdxFileNotFoundException as e: 164 | #print('symbol %s file no found, ignore it ' % index) 165 | continue 166 | # history.to_pickle('/tmp/debug.pickle') 167 | 168 | #reindex 169 | sessions = calendar.sessions_in_range(start_session, end_session) 170 | 171 | history = history.reindex( 172 | sessions.tz_localize(None), 173 | copy=False, 174 | ).fillna(0.0) 175 | 176 | yield sid, history.sort_index() 177 | pass 178 | 179 | if __name__ == '__main__': 180 | 181 | import tushare as ts 182 | import pandas as pd 183 | 184 | ts_symbols = ts.get_stock_basics() 185 | 186 | 187 | symbols = [] 188 | 189 | 190 | # 获取股票数据 191 | i = 0 192 | total = len(ts_symbols) 193 | for index, row in ts_symbols.iterrows(): 194 | i = i +1 195 | if i > 10: 196 | break 197 | 198 | srow = {} 199 | srow['t'] = 1 200 | srow['symbol'] = index 201 | srow['asset_name'] = row['name'] 202 | symbols.append(srow) 203 | 204 | df_symbols = pd.DataFrame(data=symbols).sort_values('symbol') 205 | symbol_map = pd.DataFrame.copy(df_symbols.symbol) 206 | 207 | raw_splits, raw_dividends = load_splits_and_dividends() 208 | 209 | splits = [] 210 | dividends = [] 211 | for sid, code in symbol_map.iteritems(): 212 | if code in raw_splits: 213 | split = pd.DataFrame(data=raw_splits[code]) 214 | split['sid'] = sid 215 | split.index = split['effective_date'] = pd.DatetimeIndex(split['effective_date']) 216 | splits.append(split) 217 | if code in raw_dividends: 218 | dividend = pd.DataFrame(data = raw_dividends[code]) 219 | dividend['sid'] = sid 220 | dividend['record_date'] = dividend['declared_date'] = dividend['pay_date'] = pd.NaT 221 | dividend.index = dividend['ex_date'] = pd.DatetimeIndex(dividend['ex_date']) 222 | dividends.append(dividend) 223 | 224 | print(pd.concat(splits, ignore_index=True)) 225 | print(pd.concat(dividends, ignore_index=True)) 226 | -------------------------------------------------------------------------------- /zipline_cn_databundle/tdx/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rainx/zipline_cn_databundle/92ba9258ebdf836180651cea1365ebcbd585e60d/zipline_cn_databundle/tdx/__init__.py -------------------------------------------------------------------------------- /zipline_cn_databundle/tdx/reader.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import os 3 | import numpy as np 4 | 5 | import struct 6 | """ 7 | 读取通达信数据 8 | """ 9 | 10 | 11 | class TdxFileNotFoundException(Exception): 12 | pass 13 | 14 | class TdxReader: 15 | 16 | def __init__(self, vipdoc_path): 17 | self.vipdoc_path = vipdoc_path 18 | 19 | def get_kline_by_code(self, code, exchange): 20 | fname = os.path.join(self.vipdoc_path, exchange) 21 | fname = os.path.join(fname, 'lday') 22 | fname = os.path.join(fname, '%s%s.day' % (exchange, code)) 23 | return self.parse_data_by_file(fname) 24 | 25 | def parse_data_by_file(self, fname): 26 | 27 | if not os.path.isfile(fname): 28 | raise TdxFileNotFoundException('no tdx kline data, pleaes check path %s', fname) 29 | 30 | with open(fname, 'rb') as f: 31 | content = f.read() 32 | return self.unpack_records(' 10: 71 | break 72 | 73 | srow = {} 74 | # 获取历史报价信息 75 | click.echo("正在获取代码%s(%s)的历史行情信息 (%d/%d)" % (index, row['name'], i, total)) 76 | histories[index] = ts.get_hist_data(index) 77 | srow['start_date'] = histories[index].index[-1] 78 | srow['end_date'] = histories[index].index[0] 79 | srow['symbol'] = index 80 | srow['asset_name'] = row['name'] 81 | symbols.append(srow) 82 | 83 | df_symbols = pd.DataFrame(data=symbols).sort_values('symbol') 84 | symbol_map = pd.DataFrame.copy(df_symbols.symbol) 85 | 86 | # fix the symbol exchange info 87 | df = df_symbols.apply(func=convert_symbol_series, axis=1) 88 | 89 | 90 | return df, histories, symbol_map 91 | 92 | 93 | def symbol_to_exchange(symbol): 94 | isymbol = int(symbol) 95 | if (isymbol>=600000): 96 | return symbol + ".SS", "SSE" 97 | else: 98 | return symbol + ".SZ", "SZSE" 99 | 100 | def convert_symbol_series(s): 101 | symbol, e = symbol_to_exchange(s['symbol']) 102 | s['symbol'] = symbol 103 | s['exchange'] = e 104 | return s 105 | 106 | def get_hist_data(symbol_map, histories, start_session, end_session): 107 | for sid, index in symbol_map.iteritems(): 108 | history = histories[index] 109 | 110 | """ 111 | writer needs format with 112 | [index], open, close, high, low, volume 113 | 114 | so we do not need to change the format from tushare 115 | 116 | but we need resort it 117 | """ 118 | yield sid, history.sort_index() 119 | 120 | if __name__ == '__main__': 121 | df_symbols, histories, symbol_map = get_basic_info() 122 | print(df_symbols) 123 | 124 | """ 125 | for h,df in histories.items(): 126 | print(df) 127 | """ 128 | -------------------------------------------------------------------------------- /zipline_cn_databundle/yahoo.py: -------------------------------------------------------------------------------- 1 | from zipline.data.bundles import register, yahoo_equities 2 | import requests 3 | import os 4 | from pandas_datareader.data import DataReader 5 | 6 | """ 7 | For ingest chinese history day bar from Yahoo 8 | 9 | From both Shenzhen And Shanghai Stock Exchange 10 | """ 11 | 12 | from .all_stocks import get_all_stocks, get_cache_dir 13 | 14 | 15 | def get_all_yahoo_stock_names(cache=True): 16 | 17 | if cache==False: 18 | print('Get All Stock List.....') 19 | all_stocks = get_all_stocks(cache=cache) 20 | 21 | return [full_code(code) for code in all_stocks.index] 22 | 23 | def full_code(code): 24 | if int(code[0]) >= 6: 25 | return "%s.SS" % code 26 | else: 27 | return '%s.SZ' % code 28 | 29 | def register_cn_bundle_from_yahoo(name, cache=True): 30 | """ 31 | register a new bundle of stocks from chinese market from yahoo 32 | :param name: the name of bundle 33 | :return: register result 34 | """ 35 | symbol_list = get_filtered_symbols(cache) 36 | 37 | return register( 38 | name, 39 | yahoo_equities(dict(list(zip(symbol_list, symbol_list)))), 40 | ) 41 | 42 | def check_code(code): 43 | """ 44 | XXX: used check_data_reader instead 45 | :param code: 46 | :return: 47 | """ 48 | checkurl = r'http://finance.yahoo.com/_finance_doubledown/api/resource/searchassist;gossipConfig=%7B%22url%22%3A%7B%22host%22%3A%22s.yimg.com%22%2C%22path%22%3A%22%2Fxb%2Fv6%2Ffinance%2Fautocomplete%22%2C%22query%22%3A%7B%22appid%22%3A%22yahoo.com%22%2C%22nresults%22%3A10%2C%22output%22%3A%22yjsonp%22%2C%22region%22%3A%22US%22%2C%22lang%22%3A%22en-US%22%7D%2C%22protocol%22%3A%22https%22%7D%2C%22isJSONP%22%3Atrue%2C%22queryKey%22%3A%22query%22%2C%22resultAccessor%22%3A%22ResultSet.Result%22%2C%22suggestionTitleAccessor%22%3A%22symbol%22%2C%22suggestionMeta%22%3A%5B%22symbol%22%2C%22name%22%2C%22exch%22%2C%22type%22%2C%22exchDisp%22%2C%22typeDisp%22%5D%7D;searchTerm={{CODE}}?bkt=3E0%2507canary&dev_info=0&device=desktop&intl=us&lang=en-US&partner=none®ion=US&site=finance&tz=America%2FLos_Angeles&ver=0.101.302&returnMeta=true' 49 | referer = 'http://finance.yahoo.com/' 50 | 51 | url = checkurl.replace('{{CODE}}', code) 52 | 53 | response = requests.get(url, headers={ 54 | 'Referer' : 'http://finance.yahoo.com/', 55 | 'User-Agent' : 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.103 Safari/537.36', 56 | }) 57 | try: 58 | data = response.json() 59 | items = data['data']['items'] 60 | 61 | if len(items) > 0: 62 | for item in items: 63 | if item['symbol'].upper() == code.upper(): 64 | print('.') 65 | return True 66 | 67 | except Exception as e: 68 | print(str(e)) 69 | return False 70 | 71 | print('x') 72 | return False 73 | 74 | def check_data_reader(code): 75 | try: 76 | DataReader(code, 'yahoo') 77 | except: 78 | print('%s ok!' % code) 79 | return False 80 | print('%s not ok!' % code) 81 | return True 82 | 83 | def get_filtered_symbols(cache=True): 84 | cache_dir = get_cache_dir() 85 | file_path = os.path.join(cache_dir, 'symbols.txt') 86 | 87 | if cache and os.path.isfile(file_path): 88 | with open(file_path, 'r') as f: 89 | content = f.read() 90 | if content: 91 | return content.split("\n") 92 | 93 | symbols = get_all_yahoo_stock_names(cache) 94 | 95 | if cache==False: 96 | print('Check availablity from Yahoo...') 97 | filtered_symbols = list(filter(check_data_reader, symbols)) 98 | print('cache output to %s' % file_path) 99 | with open(file_path, 'w') as f: 100 | f.write("\n".join(filtered_symbols)) 101 | print('done!') 102 | return filtered_symbols 103 | 104 | def zipline_cn_databundle_update(): 105 | print('Start to fetch data and update cache') 106 | get_filtered_symbols(cache=False) --------------------------------------------------------------------------------