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针对函数进行优化，同时给大部分 DataFrame 添加代表股票、基金、期货代码的列
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@Micro-sheep
Micro-sheep committed Jun 15, 2021
1 parent 1749764 commit c0e2113
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Showing 9 changed files with 117 additions and 214 deletions.
4 changes: 2 additions & 2 deletions efinance/fund/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,2 +1,2 @@
from efinance.fund.getter import (get_base_info, get_fund_codes, get_increase_rate, get_inverst_postion,
get_rank, get_types_persentage, get_period_change, get_public_dates, get_industry_distributing, get_history)
from .getter import (get_base_info, get_fund_codes, get_increase_rate, get_inverst_postion,
get_types_persentage, get_period_change, get_public_dates, get_industry_distributing, get_k_history)
200 changes: 62 additions & 138 deletions efinance/fund/getter.py
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Original file line number Diff line number Diff line change
@@ -1,14 +1,11 @@

from queue import Queue
import re
from typing import List, Union
import pandas as pd
import requests
from .utils import threadmethod
from .config import EastmoneyFundHeaders


def get_history(fund_code: str, pz: int = 40000) -> pd.DataFrame:
def get_k_history(fund_code: str, pz: int = 40000) -> pd.DataFrame:
'''
根据基金代码和要获取的页码抓取基金净值信息
Expand Down Expand Up @@ -67,97 +64,7 @@ def get_history(fund_code: str, pz: int = 40000) -> pd.DataFrame:
return df


def _get_rank(pidx: int) -> pd.DataFrame:

rows = []
columns = ['排名', '代码', '名称', '规模(亿元)']
params = (
('FundType', '0'),
('SortColumn', 'SYL_Y'),
('Sort', 'desc'),
('pageIndex', f'{pidx}'),
('pageSize', '30'),
('BUY', 'true'),
('CompanyId', ''),
('LevelOne', ''),
('LevelTwo', ''),
('ISABNORMAL', 'true'),
('DISCOUNT', ''),
('RISKLEVEL', ''),
('ENDNAV', ''),
('RLEVEL_SZ', ''),
('ESTABDATE', ''),
('TOPICAL', ''),
('CLTYPE', ''),
('DataConstraintType', '0'),
('deviceid', '3EA024C2-7F22-408B-95E4-383D38160FB3'),
('plat', 'Iphone'),
('product', 'EFund'),
('version', '6.2.8'),
('GTOKEN', '98B423068C1F4DEF9842F82ADF08C5db'),
)
url = 'https://fundmobapi.eastmoney.com/FundMNewApi/FundMNRank'
json_response = requests.get(
url, headers=EastmoneyFundHeaders, params=params).json()
if json_response is None:
return pd.DataFrame(rows, columns=columns)
data = json_response['Datas']

for index, stock in enumerate(data):
code = stock['FCODE']
name = stock['SHORTNAME']
size = stock['ENDNAV']
rank = 30*(pidx-1)+index+1
_size = '-'
try:
_size = f'{float(size)/100000000}'
except:
pass
rows.append([rank, code, name, _size])
df = pd.DataFrame(rows, columns=columns)
df.index = df['排名'].to_numpy()
return df


def get_rank(start=1, end=100) -> pd.DataFrame:
'''
获取排行榜信息，默认 1到 100
Parameters
----------
start : 可选的
开始排名，默认为 1
end : 可选的
结束排名，默认为 100
Return
------
DataFrame : 包含基金排名信息
'''
s = start
df = pd.DataFrame()
q = Queue()
_temp = []
while s < end:
q.put(s//30+1)
s += 30

@threadmethod
def _get_multi():

while not q.empty():
pidx = q.get()
_df = _get_rank(pidx)
if _df is None:
break
_temp.append(_df)

if end > s:
end = s
_get_multi()
df = pd.concat(_temp)
df.drop_duplicates(inplace=True)
df.sort_values(by=['排名'], inplace=True)
return df.iloc[start-1:end, :]


def get_increase_rate(fund_codes: Union[List[str], str]) -> pd.DataFrame:
Expand Down Expand Up @@ -193,7 +100,7 @@ def get_increase_rate(fund_codes: Union[List[str], str]) -> pd.DataFrame:
'https://fundmobapi.eastmoney.com/FundMNewApi/FundMNFInfo', headers=EastmoneyFundHeaders, data=data).json()
data_list = json_response['Datas']

columns = ['代码', '名称', '估算涨跌幅', '估算时间']
columns = ['基金代码', '名称', '估算涨跌幅', '估算时间']
rows = []
for fund in data_list:
code = fund['FCODE']
Expand Down Expand Up @@ -253,7 +160,7 @@ def get_fund_codes(ft=None) -> pd.DataFrame:
return df


def get_inverst_postion(fund_code: str, date=None) -> pd.DataFrame:
def get_inverst_postion(fund_code: str, dates: Union[str, List[str]] = None) -> pd.DataFrame:
'''
获取基金持仓占比信息
Expand All @@ -268,7 +175,18 @@ def get_inverst_postion(fund_code: str, date=None) -> pd.DataFrame:
------
DataFrame : 包含指定基金特定日期的公开持仓信息
'''
params = [
columns = {
'GPDM': '股票代码',
'GPJC': '股票简称',
'JZBL': '持仓占比',
'PCTNVCHG': '较上期变化',
}
df = pd.DataFrame(columns=columns.values())
if not isinstance(dates,List):
dates = [dates]

for date in dates:
params = [
('FCODE', fund_code),
('MobileKey', '3EA024C2-7F22-408B-95E4-383D38160FB3'),
('OSVersion', '14.3'),
Expand All @@ -283,25 +201,21 @@ def get_inverst_postion(fund_code: str, date=None) -> pd.DataFrame:
('userId', 'f8d95b2330d84d9e804e7f28a802d809'),
('version', '6.2.8'),
]
if date is not None:
params.append(('DATE', date))
params = tuple(params)

response = requests.get('https://fundmobapi.eastmoney.com/FundMNewApi/FundMNInverstPosition',
headers=EastmoneyFundHeaders, params=params)
rows = []
stocks = response.json()['Datas']['fundStocks']
columns = {
'GPDM': '股票代码',
'GPJC': '股票简称',
'JZBL': '持仓占比',
'PCTNVCHG': '较上期变化',
}
if stocks is None:
return pd.DataFrame(rows, columns=columns.values())

df = pd.DataFrame(stocks)
df = df[list(columns.keys())].rename(columns=columns)
if date is not None:
params.append(('DATE',date))
params = tuple(params)
response = requests.get('https://fundmobapi.eastmoney.com/FundMNewApi/FundMNInverstPosition',
headers=EastmoneyFundHeaders, params=params)
stocks = response.json()['Datas']['fundStocks']

if stocks is None or len(stocks) == 0:
continue

_df = pd.DataFrame(stocks)
_df = _df[list(columns.keys())].rename(columns=columns)
_df['公开日期'] = [date for _ in range(len(_df))]
df = pd.concat([df,_df],axis=0)
df.insert(0,'基金代码',[fund_code for _ in range(len(df))])
return df


Expand Down Expand Up @@ -357,6 +271,7 @@ def get_period_change(fund_code: str) -> pd.DataFrame:

df = df[list(columns.keys())].rename(columns=columns)
df['时间段'] = titles.values()
df.insert(0,'基金代码',[fund_code for _ in range(len(df))])
return df


Expand Down Expand Up @@ -396,7 +311,7 @@ def get_public_dates(fund_code: str) -> List[str]:
return json_response['Datas']


def get_types_persentage(fund_code: str, date=None) -> pd.DataFrame:
def get_types_persentage(fund_code: str, dates:Union[List[str],str,None]=None) -> pd.DataFrame:
'''
获取指定基金不同类型占比信息
Expand All @@ -406,13 +321,25 @@ def get_types_persentage(fund_code: str, date=None) -> pd.DataFrame:
date : 可选的
None : 最新公开持仓数据的日期
2020-09-30 指定日期数据
'2020-09-30' 指定日期数据
Return
------
DataFrame : 指定基金的在不同日期的不同类型持仓占比信息
'''
params = [
columns = {
'GP': '股票比重',
'ZQ': '债券比重',
'HB': '现金比重',
'JZC': '总规模(亿元)',
'QT': '其他比重'
}
df = pd.DataFrame(columns = columns.values())
if not isinstance(dates,List):
dates = [dates]
for date in dates:
params = [
('FCODE', fund_code),
('MobileKey', '3EA024C2-7F22-408B-95E4-383D38160FB3'),
('OSVersion', '14.3'),
Expand All @@ -427,22 +354,18 @@ def get_types_persentage(fund_code: str, date=None) -> pd.DataFrame:
('userId', 'f8d95b2330d84d9e804e7f28a802d809'),
('version', '6.3.8'),
]
if date is not None:
params.append(('DATE', date))
params = tuple(params)
json_response = requests.get(
'https://fundmobapi.eastmoney.com/FundMNewApi/FundMNAssetAllocationNew', headers=EastmoneyFundHeaders, params=params).json()
columns = {
'GP': '股票比重',
'ZQ': '债券比重',
'HB': '现金比重',
'JZC': '总规模(亿元)',
'QT': '其他比重'
}
if len(json_response['Datas']) == 0:
return pd.DataFrame(columns=columns.values())
df = pd.DataFrame(json_response['Datas'])
df = df[list(columns.keys())].rename(columns=columns)
if date is not None:
params.append(('DATE', date))
params = tuple(params)
json_response = requests.get(
'https://fundmobapi.eastmoney.com/FundMNewApi/FundMNAssetAllocationNew', headers=EastmoneyFundHeaders, params=params).json()

if len(json_response['Datas']) == 0:
continue
_df = pd.DataFrame(json_response['Datas'])
_df = df[list(columns.keys())].rename(columns=columns)
df = pd.concat([df,_df],axis=0)
df.insert(0,'基金代码',[fund_code for _ in range(len(df))])
return df


Expand Down Expand Up @@ -537,10 +460,11 @@ def get_industry_distributing(fund_code: str, dates: Union[str, List[str]] = Non
params.append(('DATE', date))
params = tuple(params)
response = requests.get('https://fundmobapi.eastmoney.com/FundMNewApi/FundMNSectorAllocation',
headers=EastmoneyFundHeaders, params=params, verify=False)
headers=EastmoneyFundHeaders, params=params)
datas = response.json()['Datas']
_df = pd.DataFrame(datas)
_df = df.rename(columns=columns)
pd.concat([df, _df])
_df = _df.rename(columns=columns)
df = pd.concat([df, _df],axis=0)
df.insert(0,'基金代码',[fund_code for _ in range(len(df))])
df = df.drop_duplicates()
return df
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