cyh0BgFq

1from binance.client import Client
2from datetime import datetime
3import time
4import numpy
5import pandas
6from talib import abstract
7from stockstats import StockDataFrame
8import math
9
10class Trade:
11    def __init__(self, publicKey, secretKey, interval, symbol):
12        self.startingTime = int(time.time() - 60) * 1000
13        self.interval = interval
14        self.symbol = symbol
15        self.client = Client(publicKey, secretKey)
16        self.initializeHistoricalKlines()
17
18    def getCurrentKlines(self):
19        return self.client.get_klines(symbol=self.symbol, interval=self.interval, startTime=self.startingTime)
20
21    def initializeHistoricalKlines(self):
22        self.historicalKlines = self.client.get_klines(symbol=self.symbol, interval=self.interval, endTime=self.startingTime)
23
24    def getCurrentDataFrame(self):
25        currentKlines = self.getCurrentKlines()
26        allKlines = numpy.concatenate((self.historicalKlines, currentKlines), axis=0)
27
28        allKlines = numpy.delete(allKlines, [6,7,8,9,10,11], axis=1)
29        dataframe = pandas.DataFrame(allKlines)
30        dataframe.transpose()
31        dataframe.columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume']
32        dataframe['datetime'] = dataframe.timestamp.apply(
33            lambda x: pandas.to_datetime(datetime.fromtimestamp(float(x) / 1000).strftime('%c'))
34        )
35        dataframe.set_index('datetime', inplace=True, drop=True)
36        dataframe.drop('timestamp', axis=1, inplace=True)
37
38        return dataframe.astype('float64')
39
40    def analyze_macd(self, hot_thresh, cold_thresh, all_data=False):
41        dataframe = self.getCurrentDataFrame()
42        macd_values = abstract.MACD(dataframe).iloc[:, 2]
43        macd_result_data = []
44
45        for i in range (0, macd_values.size):
46            if math.isnan(macd_values[i]):
47                continue
48
49            is_hot = False
50            if hot_thresh is not None:
51                is_hot = macd_values[i] > hot_thresh
52
53            is_cold = False
54            if cold_thresh is not None:
55                is_cold = macd_values[i] < cold_thresh
56
57            data_point_result = {
58                'date': macd_values.keys()[i],
59                'values': macd_values[i],
60                'is_cold': is_cold,
61                'is_hot': is_hot
62            }
63
64            macd_result_data.append(data_point_result)
65
66        if all_data:
67            return macd_result_data
68        else:
69            if macd_result_data:
70                return macd_result_data[-1]
71            else:
72                return macd_result_data
73
74    def run(self, hot_thresh, cold_thresh, buy):
75        lastMacd = 0
76        while(True):
77            macd = self.analyze_macd(hot_thresh=hot_thresh, cold_thresh=cold_thresh)
78            difference = math.fabs(macd['values'] - lastMacd)
79            if macd['is_hot'] and buy and difference > 1:
80                print(datetime.now(), end=' ')
81                print("Buy: ", end='')
82                print(self.client.get_orderbook_ticker(symbol=self.symbol)['askPrice'])
83                buy = False
84                lastMacd = macd['values']
85            elif macd['is_cold'] and buy == False and difference > 1:
86                print(datetime.now(), end=' ')
87                print("Sell: ", end='')
88                print(self.client.get_orderbook_ticker(symbol=self.symbol)['bidPrice'])
89                buy = True
90                lastMacd = macd['values']
91            time.sleep(1)
92
93
94trade = Trade(publicKey = '',
95              secretKey = '',
96              interval = "1m",
97              symbol = "BTCUSDT")
98print(trade.run(hot_thresh = 0, cold_thresh = 0, buy=True))