stock/work_flow.py at master · Robin021/stock · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
# -*- encoding: UTF-8 -*-

import data_fetcher
import utils
import strategy.enter as enter
from strategy import turtle_trade
from strategy import backtrace_ma250
from strategy import breakthrough_platform
from strategy import parking_apron
from strategy import low_backtrace_increase
from strategy import keep_increasing
from strategy import gordon_trade
import tushare as ts
import push
import logging
import db
import time
import datetime
import urllib
import settings
import pandas as pd
import numpy as np


def process():
    logging.info("************************ process start ***************************************")
    try:
        sts = []
        cys = []
        kcs = []
        all_data = ts.get_today_all()
        subset = all_data[['code', 'name', 'nmc','mktcap']]

        #剔除出创业板的股票
        cy_data = subset[subset.code.str.contains('^30')]
        cys = cy_data['code'].values.tolist()
        for cy in cys:
            subset = subset.drop(
                index=(subset.loc[(subset['code'] == cy)].index))

        # 剔除出科创板的股票
        kc_data = subset[subset.code.str.contains('^68')]
        kcs = kc_data['code'].values.tolist()
        for kc in kcs:
            subset = subset.drop(
                index=(subset.loc[(subset['code'] == kc)].index))

        subset.to_csv(settings.config['stocks_file'], index=None, header=True)
        stocks = [tuple(x) for x in subset.values]
        # statistics(all_data, stocks)
    except urllib.error.URLError as e:
        subset = pd.read_csv(settings.config['stocks_file'])
        subset['code'] = subset['code'].astype(str)
        stocks = [tuple(x) for x in subset.values]

    if utils.need_update_data():
        utils.prepare()
        data_fetcher.run(stocks)
        check_exit()

    strategies = {
        # '海龟交易法则': turtle_trade.check_enter,
        # '放量上涨': enter.check_volume,
        '高登法则': gordon_trade.check,
        # '突破平台': breakthrough_platform.check,
        # '均线多头': keep_increasing.check,
        # '无大幅回撤': low_backtrace_increase.check,
        # '停机坪': parking_apron.check,
        # '回踩年线': backtrace_ma250.check,
    }

    # if datetime.datetime.now().weekday() == 0:
    #     strategies['均线多头'] = keep_increasing.check

    for strategy, strategy_func in strategies.items():
        check(stocks, strategy, strategy_func)
        time.sleep(2)

    logging.info("************************ process   end ***************************************")


def check(stocks, strategy, strategy_func):
    # end = '2021-09-23'
    end = None

    m_filter = check_enter(end_date=end, strategy_fun=strategy_func)
    results = list(filter(m_filter, stocks))
    a = np.array(results)
    results_filter = a[:,1].tolist()
    # print (len(results))
    maxp5 = []
    for x in range(len(results)):
        # results[x].append(gordon_trade.results_maxp5(
        #     results[x], utils.read_data(results[x]), end_date=None, threshold=5))
        maxp5.append(gordon_trade.results_maxp5(
            results[x], utils.read_data(results[x]), end_date=end, threshold=settings.config['max_price_day']))
    print (len(maxp5))

    results_pd = pd.DataFrame(results, dtype=str, columns=[
                              'code', 'name', 'nmc','mktcap'])
    results_pd['maxp5'] = maxp5
    results_pd.to_csv(
        settings.config['selected_file'], index=None, header=True)
    push.strategy(
        '**************"{0}"**************\n{1}\n**************"{0}"**************\n'.format(
            strategy, results_filter))

def check_enter(end_date=None, strategy_fun=enter.check_volume):
    def end_date_filter(code_name):
        data = utils.read_data(code_name)
        if data is None:
            return False
        else:
            return strategy_fun(code_name, data, end_date=end_date)
        # if result:
        #     message = turtle_trade.calculate(code_name, data)
        #     push.strategy("{0} {1}".format(code_name, message))

    return end_date_filter


# 统计数据
def statistics(all_data, stocks):
    limitup = len(all_data.loc[(all_data['changepercent'] >= 9.5)])
    limitdown = len(all_data.loc[(all_data['changepercent'] <= -9.5)])

    up5 = len(all_data.loc[(all_data['changepercent'] >= 5)])
    down5 = len(all_data.loc[(all_data['changepercent'] <= -5)])

    def ma250(stock):
        stock_data = utils.read_data(stock)
        return enter.check_ma(stock, stock_data)

    ma250_count = len(list(filter(ma250, stocks)))

    msg = "涨停数：{}   跌停数：{}\n涨幅大于5%数：{}  跌幅大于5%数：{}\n年线以上个股数量：    {}"\
        .format(limitup, limitdown, up5, down5, ma250_count)
    push.statistics(msg)


def check_exit():
    t_shelve = db.ShelvePersistence()
    file = t_shelve.open()
    for key in file:
        code_name = file[key]['code_name']
        data = utils.read_data(code_name)
        if turtle_trade.check_exit(code_name, data):
            push.strategy("{0} 达到退出条件".format(code_name))
            del file[key]
        elif turtle_trade.check_stop(code_name, data, file[key]):
            push.strategy("{0} 达到止损条件".format(code_name))
            del file[key]

    file.close()