# encoding=utf-8
import pymysql
from sqlalchemy import create_engine
from my_config.log import MyLog
logger = MyLog('database_api').logger
class DatabaseApi:
def __init__(self):
pass
@staticmethod
def gen_db_conn(db_info):
"""
建立数据库连接
:param db_info:
包含 ip, port, user, passwd, db_name 这些字段
:return:
"""
return pymysql.connect(host=db_info['ip'],
port=db_info['port'],
user=db_info['user'],
passwd=db_info['passwd'],
db=db_info['db_name'],
charset='utf8')
@staticmethod
def gen_db_engine(db_info):
"""
# encoding=utf-8
import json
import joblib
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from data_source.Data_Sub import add_stk_index_to_df, Index
from data_source.auth_info import jq_login
from data_source.stk_data_class import StkData
from my_config.GlobalSetting import root_path
from my_config.log import MyLog
from sdk.pic_plot.plot_opt_sub import add_axis
from pylab import *
logger = MyLog('rf_class').logger
mpl.rcParams['font.sans-serif'] = ['SimHei']
matplotlib.rcParams['axes.unicode_minus'] = False
"""
使用随机森林对stk进行预测
本文件存储“数据预处理”相关的类
"""
class DataProForStudy(StkData):
"""
为随机森林模型提供“数据预处理”的类
"""
def __init__(self, stk_code, count=400, freq='1d', window=20):
super().__init__(stk_code, freq=freq)
# encoding=utf-8
"""
trade 时间判断
"""
import datetime
from data_source.futures_data import FuturesData
from my_config.global_setting import root_path
from sdk.pickle_save_sub import load_p
from my_config.log import MyLog
logger = MyLog('trade_timespan_judge').logger
class TradeTimeSpanJudge:
def __init__(self):
self.trade_timespan = load_p(load_location=root_path +
'/data_source/LocalData/local_data_save/',
file_name='futures_trade_timespan')
def futures_trade_timespan_judege(self, stk_code):
# 获取时间段
kind = FuturesData.get_futures_kind(stk_code)
t_s_f = list(filter(lambda x: x['kind'] == kind, self.trade_timespan))
if len(t_s_f) == 0:
logger.debug('%s品种未能查询到td时段，默认不在td中！' % str(stk_code))
return False
else:
t_s = t_s_f[0]
# encoding=utf-8
"""
bias相关的类
"""
import logging
import time
from data_source.Data_Sub import get_k_data_jq
from data_source.auth_info import jq_login
from data_source.stk_data_class import StkData
from my_config.log import MyLog
from pylab import plt
from sdk.MyTimeOPT import get_current_datetime_str
import jqdatasdk as jq
logger_eml = MyLog('bias_eml', console_level=logging.INFO).logger
logger = MyLog('bias', file_level=logging.WARNING,
console_level=logging.INFO).logger
import json
import os
import numpy as np
class Bias(StkData):
def __init__(self,
stk_code,
freq,
hist_count=2000,
span_q=3,
span_s=15,
local_data_dir='./'):
super().__init__(stk_code, freq)
# encoding=utf-8
import tensorflow as tf
import tushare as ts
import numpy as np
import pandas as pd
import os
from pylab import plt
from my_config.log import MyLog
from research.lstm_predict.demo1.lstm_data_process import GenLstmTrainData
logger_eml = MyLog('lstm_predict').logger
"""
"""
class NewTf:
def __init__(self):
self.tf_model = None
self.x_train = None
self.y_train = None
self.x_test = None
self.y_test = None
self.model_name = None
self.save_dir = None
self.model_save_url = None
import talib
import tushare as ts
import jqdatasdk as jq
import numpy as np
import pandas as pd
import math
import my_config.future_global_value as fgv
from data_source.Data_Sub import get_k_data_jq, add_stk_index_to_df, Index
from data_source.local_data.update_local_data import LocalData
from sdk.DataPro import relative_rank
from sdk.MyTimeOPT import get_current_date_str, get_current_datetime_str
from data_source.auth_info import jq_login
from my_config.log import MyLog
logger = MyLog('stk_data').logger
logger_eml = MyLog('stk_data_eml').logger
class StkData:
"""
stk数据基础类，用来准备一些基本的数据
数据预处理所用函数皆在于此
"""
def __init__(self, stk_code, freq='1d'):
self.freq = freq
# 用此参数请确保为分钟数据
self.freq_d = int(self.freq.replace('m', '').replace('d', ''))
self.stk_code = stk_code
# encoding=utf-8
"""
仿真
"""
import time
from Server.futures.ctp.my_ctp_api.ctp_class_no_use import MyCtp
from Server.futures.futures_account_info.account_info import ctp_login_info_sim
from my_config.log import MyLog
ml = MyLog('ctp_sim')
logger = ml.logger
if __name__ == '__main__':
mctp = MyCtp(ctp_login_info=ctp_login_info_sim)
mctp.login()
while True:
if mctp.ctp_spi.login_success:
# mctp.req_order_field_insert(
# instrument_id='m2009',
# price=2700,
# volume=1,
# offset='0',
# direction='b')
# mctp.ctp_spi.login_success = False
# 撤
# mctp.req_order_action(mctp.ctp_spi.ccl.cmd_dict[list(mctp.ctp_spi.ccl.cmd_dict.keys())[0]].cmd_id)
# 查
# encoding=utf-8
"""
本脚本实现检测日志功能
原理：
监控文件修改情况，检测到有修改，
便直接将该文件邮件发送给指定联系人
"""
import chardet
from my_config.GlobalSetting import root_path
from my_config.log import MyLog
from sdk.auto_email.email_class import MyEmail
logger = MyLog('file_monitor').logger
import os
import time
class GetFileContext:
def __init__(self, amount=1000):
"""
检测到日志改动时，将最后指定数量的数据取出，放在邮件正文，方便查看
"""
self.amount = amount
@staticmethod
def judge_file_code_format(f_url):
with open(f_url, 'rb') as f:
data = f.read()
# encoding=utf-8
"""
bias相关的类
"""
import time
from data_source.Data_Sub import get_k_data_jq, get_rt_price
from data_source.auth_info import jq_login
from data_source.stk_data_class import StkData
from my_config.log import MyLog
from pylab import plt
from sdk.MyTimeOPT import get_current_datetime_str
logger_eml = MyLog('bias_eml').logger
logger = MyLog('bias').logger
import json
import os
import numpy as np
class Bias(StkData):
def __init__(self,
stk_code,
freq,
hist_count=2000,
span_q=3,
span_s=15,
local_data_dir='./'):
super().__init__(stk_code, freq)
self.hist_count = hist_count
self.span_s = span_s
from email.header import decode_header
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from email.mime.text import MIMEText
from email.mime.image import MIMEImage
from email.encoders import encode_base64
from email.parser import Parser
from email.utils import parseaddr
import time
from my_config.GlobalSetting import root_path
from my_config.log import MyLog
from sdk.MyTimeOPT import get_current_datetime_str
logger = MyLog('email_class').logger
logger_eml = MyLog('email_class_eml').logger
class MyEmail:
def __init__(self, sender_info_json_url, modeng=False):
self.sender_info_json_url = sender_info_json_url
self.modeng = modeng
self.sender = ''
self.passwd = ''
self.smtp_domain = '' # 'smtp.163.com'
self.config_sender_info_by_json()
def config_sender_info_by_json(self):
"""
import talib
import tushare as ts
import jqdatasdk as jq
import numpy as np
import pandas as pd
import math
from Server.futures.futures_sub import future_global_value as fgv
from Server.futures.futures_sub.future_global_value import debug_date
from data_source.Data_Sub import get_k_data_jq, add_stk_index_to_df, Index
from data_source.LocalData.update_local_data import LocalData
from sdk.DataPro import relative_rank
from sdk.MyTimeOPT import get_current_date_str, add_date_str, get_current_datetime_str
from data_source.auth_info import jq_login
from my_config.log import MyLog
logger = MyLog('stk_data', console_level=logging.INFO).logger
logger_eml = MyLog('stk_data_eml').logger
class StkData:
"""
stk数据基础类，用来准备一些基本的数据
数据预处理所用函数皆在于此
"""
def __init__(self, stk_code, freq='1d'):
self.freq = freq
# 用此参数请确保为分钟数据
self.freq_d = int(self.freq.replace('m', '').replace('d', ''))
self.stk_code = stk_code
# encoding=utf-8
"""
根据标准差来计算格子
"""
import numpy as np
import pandas as pd
import copy
from server.futures.futures_sub.future_global_value import debug_date
from data_source.Data_Sub import get_k_data_jq
from data_source.auth_info import jq_login
from sdk.MyTimeOPT import add_date_str, get_current_date_str
from data_source.stk_data_class import StkData
from my_config.log import MyLog
import logging
logger_debug = MyLog('reseau_cal').logger
logger_eml = MyLog('reseau_cal_eml', file_level=logging.DEBUG).logger
"""---------------------- 定义全局变量 ------------------------"""
today_df_update_date = ''
today_df = pd.DataFrame()
class Reseau(StkData):
"""
动态网格策略底层实现类
"""
def __init__(self, stk_code):
# 将一天用的网格信息记录下来，
# 避免每次重复计算，实现一天只计算一次
super().__init__(stk_code)
self.stk_code = stk_code
def cal_rt_rsv(self):
