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"""用来创建公用的方法，方便程序进行调用"""
# from stockquant.quant import *
import pandas as pd
import baostock as bs
import datetime
import time
import requests
import json
import warnings
# 忽略pandas的FutureWarning警告（来自mootdx库）
warnings.filterwarnings('ignore', category=FutureWarning, module='mootdx')
warnings.filterwarnings('ignore', message='.*fillna.*method.*deprecated.*')
warnings.filterwarnings('ignore', message='.*Downcasting object dtype arrays.*')
from MyTT import *
from mootdx.quotes import Quotes
def backInDays(nday):
"""用来获得n天前的日期，用于从数据接口请求股票数据，避免一次要求过多数据影响程序效率"""
"""建议：30m数据，取值60，即回溯2个月的数据，约40个交易日，320个数据点，最多用于计算MA250"""
"""同理：60m数据，取值120; 日线数据，取值480; 周线数据，取值2400"""
# 获取当前时间并减去n天
n_days_back = datetime.datetime.now() - datetime.timedelta(days=float(nday))
# 将时间转换为字符串格式
n_days_back_str = n_days_back.strftime("%Y-%m-%d")
return n_days_back_str
# 对code列进行处理, 在调用baostock接口前添加前缀
def add_bs_prefix(code):
if code.startswith(('600', '601', '603', '688', '510', '511', '512', '513', '515', '113', '110', '118', '501')):
return 'sh.' + code
elif code.startswith(('0', '3')):
return 'sz.' + code
else:
return code
# 对code列进行处理, 在调用xtquant接口前添加后缀
def add_xt_suffix(stock='600031.SH'):
'''
调整代码
'''
if stock[-2:]=='SH' or stock[-2:]=='SZ' or stock[-2:]=='sh' or stock[-2:]=='sz':
stock=stock.upper()
else:
if stock[:3] in ['600','601','603','688','510','511','512','513','515','113','110','118','501'] or stock[:2] in ['11']:
stock=stock+'.SH'
else:
stock=stock+'.SZ'
return stock
# 对code列进行分类, 调用xtquant接口
def select_data_type( stock='600031'):
'''
选择数据类型
'''
if stock[:3] in ['110','113','123','127','128','111','118'] or stock[:2] in ['11','12']:
return 'bond'
elif stock[:3] in ['510','511','512','513','514','515','516','517','518','588','159','501','164'] or stock[:2] in ['16']:
return 'fund'
else:
return 'stock'
# 股票数据请求，用Baostock或者mootdx
# Baostock方式：
# res = getStockData('600519', fields="date,open,high,low,close,preclose,volume,amount", start_date=Methods.backInDays(500), freq='d', adjustflag='2')
# mootdx方式：
# res = Methods.getStockData('600519', offset=800, freq=9, adjustflag='qfq')
# res['datetime'] = pd.to_datetime(res['datetime']).dt.date
# res = res.rename(columns={'datetime': 'date'})
# res = res.reindex(columns=['date', 'open', 'high', 'low', 'close', 'preclose', 'volume', 'amount'])
# res = res.reset_index(drop=True)
def getStockData(code,
fields="date,code,open,high,low,close,volume,amount,adjustflag",
start_date=None, end_date=None,
offset=100,
freq='d', adjustflag='2'):
# 长周期K线数据如日线、周线、月线用Baostock接口，有换手率，PE等数据
# 日k线；d=日k线、w=周、m=月、5=5分钟、15=15分钟、30=30分钟、60=60分钟k线数据，不区分大小写；
# 指数没有分钟线数据；周线每周最后一个交易日才可以获取，月线每月最后一个交易日才可以获取
if freq=='d' or freq=='w' or freq=='m':
code = add_bs_prefix(code)
lg = bs.login()
result = bs.query_history_k_data_plus(code, fields, start_date, end_date, freq, adjustflag)
df = pd.DataFrame(result.get_data(), columns=result.fields)
return df
# 其它数据用mootdx接口,默认取100根K线数据，，没有换手率，PE等数据
# frequency -> K线种类 0 => 5分钟K线 => 5m 1 => 15分钟K线 => 15m 2 => 30分钟K线 => 30m 3 => 小时K线 => 1h
# 4 => 日K线 (小数点x100) => days 5 => 周K线 => week 6 => 月K线 => mon
# 7 => 1分钟K线(好像一样) => 1m 8 => 1分钟K线(好像一样) => 1m
# 9 => 日K线 => day 10 => 季K线 => 3mon 11 => 年K线 => year
elif freq>=0 and freq<=11:
if code.startswith(("sh.", "sz.")):
code = code.split('.')[1]
client = Quotes.factory('std') # 使用标准版通达信数据
df = client.bars(symbol=code, frequency=freq, offset=offset, adjust=adjustflag)
return df
else:
return None
def IsMarketGoingUp():
# 指数代码
indices = {
'sh.000001': '上证指数', # 上证指数
'sz.399001': '深证成指', # 深证成指
'sz.399005': '中小板指' # 中小板指
}
# 登录到Baostock
lg = bs.login()
# 遍历每个指数
for code, name in indices.items():
# 获取30天K线数据
fields = "date,code,open,high,low,close"
start_date = backInDays(30)
end_date = datetime.datetime.now().strftime("%Y-%m-%d") # 当前日期
res = bs.query_history_k_data_plus(code, fields, start_date, end_date, frequency='d', adjustflag='3')
df = pd.DataFrame(res.get_data(), columns=res.fields)
# 计算MA5
if len(df) >= 5:
df['close'] = df['close'].astype(float)
df['MA5'] = df['close'].rolling(window=5).mean()
# 检查MA5是否呈上升趋势
if df['MA5'].iloc[-1] > df['MA5'].iloc[-2] and df['MA5'].iloc[-2] > df['MA5'].iloc[-3]:
print(f"{name} 的MA5呈现上升趋势。")
return True
# 如果没有任何一个指数的MA5呈上升趋势
print("所有检查的指数的MA5都没有呈现上升趋势。")
return False
def calmacd(df):
df2 = df
if len(df2) > 33:
dif, dea, hist = MACD(df2['close'].astype(float).values, fastperiod=12, slowperiod=26, signalperiod=9)
df3 = pd.DataFrame({'dif': dif[33:], 'dea': dea[33:], 'hist': hist[33:]}, index=df2['date'][33:], columns=['dif', 'dea', 'hist'])
return df3
def WX_send(msg):
token = "65a7ae6c776c4881899e36aace47d491"
title = "Stockquant"
# 在pushplus推送加微信公众号-功能-个人中心-渠道配置-新增-webhook编码为“stockquant”， 请求地址为企微机器人的webhook地址
# webhook = "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=xxxxxxxxxxxxxxxxxxxxx"
url = "http://www.pushplus.plus/send"
headers = {"Content-Type": "application/json"}
data = {
"token": token,
"title": title,
"content": msg,
"channel": "webhook",
"webhook": "stockquant"
}
response = requests.post(url, headers=headers, data=json.dumps(data))
if response.status_code == 200:
return response.json()
else:
return None
# def sendTradeMsg(msg):
# try:
# DingTalk.markdown("python交易提醒："+msg)
# except Exception as e:
# print(e)
# try:
# WX_send("Stockquant："+msg)
# except Exception as e:
# print(e)
if __name__ == '__main__':
IsMarketGoingUp()