PyMLT/simulation_daily_trade.py at master · didw/PyMLT · 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
155
156
157
158
159
160
161
162
# -*- encoding: utf-8 -*-
import pandas as pd
import numpy as np
import sqlite3
from sklearn.externals import joblib
from sklearn.preprocessing import StandardScaler
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='3'
import tensorflow as tf
import datetime


class Simulation:
    def __init__(self):
        self.len_past = 30
        init_op = tf.global_variables_initializer()
        self.config = tf.ConfigProto()
        self.config.gpu_options.allow_growth = True
        self.init_op = tf.global_variables_initializer()
        self.s_date = "20120101_20160330"
        self.model_dir = '../model/tf/regression/%s/' % self.s_date

        # define variable
        self.W1 = tf.Variable(tf.random_normal([690, 200], stddev=0.35), name="W1")
        self.b1 = tf.Variable(tf.zeros([200]), name="b1")
        self.W2 = tf.Variable(tf.random_normal([200, 1], stddev=0.35), name="W2")
        self.b2 = tf.Variable(tf.zeros([1]), name="b2")

        # design graph
        self.scalarInput =  tf.placeholder(shape=[None,690],dtype=tf.float32)
        self.out1 = tf.matmul(self.scalarInput, self.W1) + self.b1
        self.stream1 = tf.layers.dropout(tf.nn.relu(self.out1), rate=0.5)
        #self.output = tf.layers.dense(self.stream1, 1)
        self.output = tf.matmul(self.stream1, self.W2) + self.b2

        self.saver = tf.train.Saver([self.W1, self.b1, self.W2, self.b2])

    def load_scaler(self):
        model_name = "../model/scaler_%s.pkl" % self.s_date
        self.scaler = joblib.load(model_name)

    def make_x(self, data, code):
        data_x = []
        days = []
        for col in data.columns:
            try:
                data.loc[:, col] = data.loc[:, col].str.replace('--', '-')
                data.loc[:, col] = data.loc[:, col].str.replace('+', '')
            except AttributeError as e:
                pass
        days = data.index[:]
        try:
            data.loc[:, 'month'] = data.index[:].str[4:6]
        except AttributeError as e:
            pass
        data = data.drop(['체결강도'], axis=1)

        # normalization
        data = np.array(data)
        if len(data) <= 0 :
            return np.array([]), np.array([])

        self.load_scaler()
        if code not in self.scaler:
            print("code %s is not exist in scaler" % code)
            return np.array([]), np.array([])
        else:
            data = self.scaler[code].transform(data)

        for i in range(self.len_past, len(data)):
            data_x.extend(np.array(data[i-self.len_past:i, :]))
        np_x = np.array(data_x).reshape(-1, 23*30)
        return np_x, days[self.len_past:]


    def load_data(self, code, begin_date, end_date):
        con = sqlite3.connect('../data/stock.db')
        df = pd.read_sql("SELECT * from '%s'" % code, con, index_col='일자').sort_index()
        data = df.loc[df.index > str(begin_date)]
        data = data.loc[data.index < str(end_date)]
        data_x, days = self.make_x(data, code)
        assert len(data_x) == len(days)
        return data_x, days

    def predict(self, X_data):
        with tf.Session(config=self.config) as sess:
            sess.run(self.init_op)
            ckpt = tf.train.get_checkpoint_state(self.model_dir)
            self.saver.restore(sess, ckpt.model_checkpoint_path)
            return sess.run(self.output, feed_dict={self.scalarInput: X_data})

    def simulation_daily_trade(self, code, start_date, end_date):
        X_data, days = self.load_data(code[0], start_date, end_date)
        if len(X_data) == 0: return 0
        MONEY = 100000
        qty = 0
        account_balance = 0
        day_last = 0
        pred_list = self.predict(X_data)
        for idx in range(len(X_data)-1):
            pred = pred_list[idx][0]
            cur_price = X_data[idx][29*23]
            buying_price = X_data[idx+1][29*23+3]
            #print("buying_price: %f" % buying_price)
            pred_transform = self.scaler[code[0]].inverse_transform([pred] + [0]*22)[0]
            cur_real_price = self.scaler[code[0]].inverse_transform([cur_price] + [0]*22)[0]
            #print([0]*3 + [buying_price] + [0]*19)
            buying_real_price = self.scaler[code[0]].inverse_transform([0]*3 + [buying_price] + [0]*19)[3]
            #print(pred, cur_price)
            day_last += 1
            if pred_transform > 2*cur_real_price and qty == 0:
                day_last = 0
                qty += (MONEY / buying_real_price + 1)
                account_balance -= buying_real_price * (MONEY / buying_real_price + 1)
                #print("pred: %.2f, %d, cur: %.2f, %d" % (pred, pred_transform, cur_price, cur_real_price))
                print("[BUY] balance: %d, price: %d qty: %d" % (account_balance, buying_real_price, qty))
            elif day_last >= 5 and qty > 0 and False:
                account_balance += 0.995 * buying_real_price * qty
                qty = 0
                print("[SELL] balance: %d, price: %d, qty: %d" % (account_balance, buying_real_price, qty))
            elif pred < cur_price and qty > 0:
                account_balance += 0.995 * buying_real_price * qty
                qty = 0
                print("[SELL] balance: %d, price: %d, qty: %d" % (account_balance, buying_real_price, qty))
        if qty > 0:
            account_balance += 0.995 * buying_real_price * qty
            print("[L SELL] balance: %d, price: %d, qty: %d" % (account_balance, buying_real_price, qty))
        return account_balance

    def simulation_monthly_daily_trade(self, start_date, end_date):
        con = sqlite3.connect('../data/stock.db')
        code_list = con.execute("SELECT name FROM sqlite_master WHERE type='table'").fetchall()
        account_balance = 0
        idx = 0
        trade = 0
        for code in code_list:
            res = self.simulation_daily_trade(code, start_date.strftime("%Y%m%d"), end_date.strftime("%Y%m%d"))
            idx += 1
            if res != 0:
                trade += 1
                account_balance += res
                print("[%d/%d] balance: %d" % (trade, idx, account_balance))
        return account_balance

    def simulation_all(self):
        begin_month = 201501
        res = 0
        while begin_month <= 201701:
            self.model_dir = '../model/tf/regression/%d01_%d01/' % (begin_month-500, begin_month)
            print(self.model_dir)
            begin_date = datetime.date(begin_month/100, begin_month%100, 1) - datetime.timedelta(days=40)
            end_date = datetime.date(begin_month/100, begin_month%100, 1) + datetime.timedelta(days=40)
            res += self.simulation_monthly_daily_trade(begin_date, end_date)
            print("[%d]total res: %d" % (begin_month, res))
            begin_month += 1
            if begin_month%100 == 13:
                begin_month += 88


if __name__ == '__main__':
    sm = Simulation()
    sm.simulation_all()