@doit2022 高手，求带。

``` # example # # 1 - 获取20180301上证50指数（000016.XSHG）成分股 # t = get_index_stocks('000016.XSHG') # # 上证50包含的第一支股票代码 # print(t[0]) # 600000.XSHG # # 上证50包含的所有股票代码，以列表的形式返回 # print(t) ''' ['600000.XSHG', '600016.XSHG', '600019.XSHG', '600028.XSHG', '600029.XSHG', '600030.XSHG', '600036.XSHG', '600048.XSHG', '600050.XSHG', '600104.XSHG', '600111.XSHG', '600309.XSHG', '600340.XSHG', '600518.XSHG', '600519.XSHG', '600547.XSHG', '600606.XSHG', '600837.XSHG', '600887.XSHG', '600919.XSHG', '600958.XSHG', '600999.XSHG', '601006.XSHG', '601088.XSHG', '601166.XSHG', '601169.XSHG', '601186.XSHG', '601211.XSHG', '601229.XSHG', '601288.XSHG', '601318.XSHG', '601328.XSHG', '601336.XSHG', '601390.XSHG', '601398.XSHG', '601601.XSHG', '601628.XSHG', '601668.XSHG', '601669.XSHG', '601688.XSHG', '601766.XSHG', '601800.XSHG', '601818.XSHG', '601857.XSHG', '601878.XSHG', '601881.XSHG', '601985.XSHG', '601988.XSHG', '601989.XSHG', '603993.XSHG'] ''' # 2 - 获取股票行情数据 # history() # w = history(count=3, field='money', security_list=['000001.XSHE', '000002.XSHE']) # print('------------------------------------') # print(w) ''' 000001.XSHE 000002.XSHE 2018-02-26 1.318960e+09 1.832518e+09 2018-02-27 1.590018e+09 1.683178e+09 2018-02-28 1.463314e+09 1.621988e+09 ''' # w = history(count=3, field='money', security_list=['000001.XSHE', '000002.XSHE'], df=False) # print(w) ''' {'000001.XSHE': array([1318960463.96, 1590018492.14, 1463313702.75]), '000002.XSHE': array([1832518319.86, 1683177664.9, 1621987601.86])} ''' # attribute_history() # w = attribute_history(security='000001.XSHE', count=3, fields=['money', 'high']) # print(w) ''' money high 2018-02-26 1.318960e+09 12.00 2018-02-27 1.590018e+09 11.86 2018-02-28 1.463314e+09 11.38 ''' # pandas.DataFrame # w = attribute_history(security='000001.XSHE', count=3, fields=['money', 'high', 'open']) # print(w) ''' money high open 2018-02-26 1.318960e+09 12.00 11.93 2018-02-27 1.590018e+09 11.86 11.80 2018-02-28 1.463314e+09 11.38 11.30 ''' # 获取index # print(w.index) ''' DatetimeIndex(['2018-02-26', '2018-02-27', '2018-02-28'], dtype='datetime64[ns]', freq=None) ''' # 获取column # print(w.columns) ''' Index(['money', 'high', 'open'], dtype='object') ''' # print(list(w.columns)) ''' ['money', 'high', 'open'] ''' # print(w.values) ''' [[1318960463.96 12.0 11.93] [1590018492.14 11.86 11.8] [1463313702.75 11.38 11.3]] ''' # print(w.loc[:, ['open', 'high']]) # 按标签获取 ''' open high 2018-02-26 11.93 12.00 2018-02-27 11.80 11.86 2018-02-28 11.30 11.38 ''' # print(w.iloc[:, [0, 2]]) # 按位置获取 ''' money open 2018-02-26 1.318960e+09 11.93 2018-02-27 1.590018e+09 11.80 2018-02-28 1.463314e+09 11.30 ''' # print(w.iloc[:, :]) # print(w.loc[:, :]) ''' money high open 2018-02-26 1.318960e+09 12.00 11.93 2018-02-27 1.590018e+09 11.86 11.80 2018-02-28 1.463314e+09 11.38 11.30 ''' # print(w.iloc[:, [0, 2]].values) ''' [[1318960463.96 11.93] [1590018492.14 11.8] [1463313702.75 11.3]] ''' # print(w.iloc[[0, 2], :]) ''' money high open 2018-02-26 1.318960e+09 12.00 11.93 2018-02-28 1.463314e+09 11.38 11.30 ''' # print(w.T) ''' 2018-02-26 2018-02-27 2018-02-28 money 1.318960e+09 1.590018e+09 1.463314e+09 high 1.200000e+01 1.186000e+01 1.138000e+01 open 1.193000e+01 1.180000e+01 1.130000e+01 ''' # q = query(valuation.code, balance.retained_profit # ).filter(valuation.market_cap > 100, valuation.pe_ratio < 100 # ).order_by(valuation.market_cap.desc() # ).limit(10) # w = get_fundamentals(q) # print(w) ''' code retained_profit 0 601398.XSHG 1.084375e+12 1 601939.XSHG 8.842860e+11 2 601857.XSHG 7.039340e+11 3 601288.XSHG 5.666660e+11 4 601988.XSHG 6.485020e+11 5 601318.XSHG 2.222000e+11 6 600519.XSHG 7.327905e+10 7 600028.XSHG 2.808460e+11 8 600036.XSHG 2.390510e+11 9 601628.XSHG 1.404090e+11 ''' # 练习 # 获取任一股票最近5个交易日最高价的平均价 # w = history(count=5, unit='1d', field='high', security_list=['000001.XSHE'], df=True, skip_paused=False) # print(w) # print(w.mean()) # 方法1 # print(sum(w) / len(w)) # 方法2 ''' 000001.XSHE 2022-07-29 12.90 2022-08-01 12.66 2022-08-02 12.34 2022-08-03 12.27 2022-08-04 12.17 000001.XSHE 12.468 dtype: float64 000001.XSHE 12.468 dtype: float64 ''' # 生成一个list，list中为上证指数成分股中流通市值最大的5个股票的股票代码 cmc_max5 = [] q = query(valuation.code, valuation.circulating_market_cap ).order_by(valuation.circulating_market_cap.desc()).limit(5) cmc_max5 = get_fundamentals(q) print(cmc_max5) ''' code circulating_market_cap 0 600519.XSHG 24068.8750 1 601398.XSHG 11782.0537 2 300750.XSHE 10211.6309 3 601288.XSHG 8559.5381 4 601857.XSHG 8209.4492 ''' # 在本地环境安装使用jqdatasdk，获取当天的实时行情数据 # from jqdatasdk import * # import jqdatasdk as jq # jq.auth('你的手机号', '你的密码') # df = jq.get_price('000001.XSHE', start_date='2022-08-05', end_date='2022-08-11') # print(df) ''' open close high low volume money 2022-08-05 12.13 12.31 12.33 11.98 76529787.0 9.308364e+08 2022-08-08 12.25 12.20 12.36 12.16 55199969.0 6.749509e+08 2022-08-09 12.21 12.12 12.22 12.08 55844757.0 6.770986e+08 2022-08-10 12.12 12.06 12.24 12.02 68302321.0 8.258060e+08 2022-08-11 12.08 12.35 12.39 12.04 139930361.0 1.710719e+09 ''' ```

先打个卡

@cqg 怎么就学习那么好

打卡学习

@Lookan 一起加油，一起学习

打卡

@雨荷的爹 用get_price函数

这边解决下两个可能大家会遇到的问题： 1. 没有history或者attitude_history模块的问题：这两个函数是http里面的，本地化JQDATA是没有的，需要用get_price. 具体可以翻阅本地化文档 2. 本地化数据只能使用2022年之后的数据，所以日期注意，不然只会返回一个空的dataframe

#生成一个list，list中为上证指数成分股中流通市值最大的5个股票的股票代码。 #创建一个query对象 #获取股票代码，流通市值数据 q=query(valuation.code,valuation.circulating_market_cap #筛选条件 ).filter(valuation.code.in_(get_index_stocks('000001.XSHG')) #降序排列 ).order_by(valuation.circulating_market_cap.desc() ).limit(5) w=get_fundamentals(q,'2022-11-11') print(w) #获取股票代码并输出 code_list=w.loc[:,'code'].tolist() print('\n',code_list) # code circulating_market_cap # 0 600519.XSHG 19196.5859 # 1 601398.XSHG 11188.9072 # 2 601288.XSHG 8350.0391 # 3 601857.XSHG 8177.0649 # 4 601628.XSHG 6534.4238 # ['600519.XSHG', '601398.XSHG', '601288.XSHG', '601857.XSHG', '601628.XSHG']

``` code ```# 导入函数库 from jqdata import * # 初始化函数，设定基准等等 def initialize(context): # 开盘时或每分钟开始时运行 run_daily(market_open, time='every_bar') g.stocksnum = 5 #开盘时运行函数 def market_open(context): stocks = get_index_stocks("000001.XSHG") q=query(valuation.code#获取 市值表.股票代码 ).filter(valuation.code.in_(stocks)#筛选 当天股票列表 code是股票代码带后缀 ).order_by(valuation.market_cap.desc()#排序 按市值从小到大排 ).limit(g.stocksnum) #数量上限5 df = get_fundamentals(q)#查询财务数据 # 选取股票代码并转为list buylist=list(df['code']) log.info(buylist) #也可以直接用print（df）c

mark

# 获取上证指数成分股中，流通市值最大的5只股票 szzs = get_index_stocks("000001.XSHG") # 上证指数成分股股票代码 # print(szzs) q = query(valuation).filter( valuation.code.in_(szzs) ).order_by( valuation.circulating_market_cap.desc() ).limit(5) df = get_fundamentals(q) for ind, row in df.iterrows(): print("上证成分股代码: %s, 流通市值是：%f"%(row['code'], row['circulating_market_cap']))

# 导入聚宽函数库 from jqdata import * # 初始化函数，设定要操作的股票、基准等等 def initialize(context): # 设置要操作的股票为上证指数 context.index = '000001.XSHG' # 处理数据函数，用来进行股票交易前的数据处理 def handle_data(context, data): # 获取上证指数成分股信息 index_stocks = get_index_stocks(context.index) # 获取成分股流通市值数据 q = query(valuation.code, valuation.circulating_market_cap).filter( valuation.code.in_(index_stocks) ) df = get_fundamentals(q) # 按照流通市值从大到小排序，获取前5个股票代码 df = df.sort_values('circulating_market_cap', ascending=False)[:5] codes = df['code'].tolist() # 打印结果 log.info('上证指数成分股中流通市值最大的5个股票代码为{}'.format(codes))

@cqg 太厉害了

获取上证指数成分股中，流通市值最大的5只股票 q = query(valuation.code, valuation.circulating_market_cap ).filter(valuation.circulating_market_cap>100 ).order_by(valuation.circulating_market_cap.desc() ).limit(5) w=get_fundamentals(q) print(w)

获取上证指数成分股中，流通市值最大的5只股票 ``` # 导入函数库 from jqdata import * # 初始化函数，设定基准等等 def initialize(context): target_stocks = get_index_stocks("000001.XSHG") q = query( valuation.code, valuation.circulating_market_cap ).filter( valuation.code.in_(target_stocks) ).order_by( valuation.circulating_market_cap.desc() ).limit( 5 ) df = get_fundamentals(q) print(df) ```

获取任意一只股票近5日最高价的平均值 price3 = get_price('002304.XSHE', count=5, end_date=date.today(), frequency='1d', fields=['high']) print(price3) price3['high'].mean()

stocks = get_index_stocks('000001.XSHG', date=date.today()) q = query( valuation.code, valuation.circulating_market_cap ).filter( valuation.code.in_(stocks) ).order_by( valuation.circulating_market_cap.desc() ).limit(5) df = get_fundamentals(q) # 获取公司名称 stocks = list(df['code']) names = [] for stock in stocks: sec = get_security_info(stock) names.append(sec.display_name) df['name'] = names print("上证成分股流通市值最大的5只股票：") for ind, row in df.iterrows(): print("股票代码: %s %s,流通市值是：%f"%(row['code'],row['name'], row['circulating_market_cap']))