779 lines
34 KiB
Python
779 lines
34 KiB
Python
import os
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import time
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import requests
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import json
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import ccxt
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import pybithumb
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import pandas as pd
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from math import nan
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import plotly.io as po
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from plotly import subplots
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import plotly.graph_objects as go
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from datetime import datetime, timedelta
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from sklearn.linear_model import LinearRegression
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from sklearn.preprocessing import StandardScaler, MinMaxScaler
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from stock.analysis.AnalyzerSqlite import AnalyzerSqlite
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from hts.BuySellChecker import BuySellChecker
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from hts.HTS import HTS
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from stock.util.SlackBot import SlackBot
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class Bithumb_minute(HTS):
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RESOURCE_PATH = None
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buySellChecker = None
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analyzerSqlite = None
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bithumb = None
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binance = None
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TODAY = None
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slackBot = None
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stock_code = None
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def __init__(self, RESOURCE_PATH, today):
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super().__init__(RESOURCE_PATH)
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self.slackBot = SlackBot()
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self.stock_code = {"XRP": "리플"}
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self.RESOURCE_PATH = RESOURCE_PATH
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con_key = "946dd0b0e6f8ad411144cd33f09518d3" # 본인의 Connect Key를 입력한다.
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sec_key = "56b2a3cdd9fe3a82aa3f38c97c161125" # 본인의 Secret Key를 입력한다.
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self.buySellChecker = BuySellChecker()
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self.analyzerSqlite = AnalyzerSqlite()
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# bithumb api에 연결한 클라스 객체를 선언한다.
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self.bithumb = pybithumb.Bithumb(con_key, sec_key)
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self.binance = ccxt.binance()
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self.TODAY = today
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return
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def bull_market(self, df, ticker):
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m5 = df['close'].rolling(5).mean()
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last_m5 = m5[-2]
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price = pybithumb.get_current_price(ticker)
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if price > last_m5:
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return True
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return False
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def append(self, df, stock):
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for i in range(len(df)):
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stock['PRICE'].append(
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{
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"ymd": df.index[i],
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"close": df['close'][i],
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"diff": 0,
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"open": df['open'][i],
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"high": df['high'][i],
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"low": df['low'][i],
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"volume": df['volume'][i],
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"avg3": -1,
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"avg4": -1,
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"avg5": -1,
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"avg6": -1,
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"avg10": -1,
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"avg12": -1,
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"avg20": -1,
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"avg36": -1,
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"avg40": -1,
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"avg48": -1,
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"avg60": -1,
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"avg120": -1,
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"avg200": -1,
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"avg240": -1,
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"avg300": -1,
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"disparity_avg5": -1,
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"disparity_avg10": -1,
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"disparity_avg20": -1,
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"disparity_avg60": -1,
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"disparity_avg120": -1,
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"bolingerband_upper": -1,
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"bolingerband_lower": -1,
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"bolingerband_middle": -1,
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"envelope_upper": -1,
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"envelope_lower": -1,
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"envelope_middle": -1,
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"ichimokucloud_changeLine": -1,
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"ichimokucloud_baseLine": -1,
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"ichimokucloud_leadingSpan1": -1,
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"ichimokucloud_leadingSpan2": -1,
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"stochastic_fast_k": -1,
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"stochastic_slow_k": -1,
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"stochastic_slow_d": -1,
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"rsi": -1,
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"rsis": -1,
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"macd": -1,
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"macds": -1,
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"macdo": -1,
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})
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return
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def analyze(self, stock, days=120):
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stock["PRICE"] = sorted(stock["PRICE"], key=lambda x: x['ymd'])
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self.analyzerSqlite.get_moving_average(stock["PRICE"])
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# 이동 평균을 이용한 이격도 계산
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self.analyzerSqlite.get_disparity(stock["PRICE"])
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self.analyzerSqlite.ichimokuCloud.analyze(stock)
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self.analyzerSqlite.stochastic.analyze(stock)
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self.analyzerSqlite.bolingerBand.analyze(stock)
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self.analyzerSqlite.envelope.analyze(stock)
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self.analyzerSqlite.rsi.analyze(stock)
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self.analyzerSqlite.macd.analyze(stock)
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result = {
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"ymd": [],
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"open": [],
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"close": [],
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"high": [],
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"low": [],
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"avg3": [],
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"avg4": [],
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"avg5": [],
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"avg6": [],
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"avg10": [],
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"avg12": [],
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"avg20": [],
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"avg36": [],
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"avg40": [],
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"avg48": [],
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"avg60": [],
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"avg120": [],
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"avg200": [],
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"avg240": [],
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"avg300": [],
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"disparity_avg5": [],
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"disparity_avg20": [],
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"disparity_avg60": [],
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"disparity_avg120": [],
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"disparity": [],
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"disparity_type": [],
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"envelope_upper": [],
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"envelope_lower": [],
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"envelope_middle": [],
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"rsi": [],
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"rsis": [],
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"macd": [],
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"macds": [],
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"slow_k": [],
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"slow_d": [],
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"buy": [],
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"sell": [],
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}
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for item in stock['PRICE']:
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result["ymd"].append(item['ymd'])
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result["open"].append(item['open'])
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result["close"].append(item['close'])
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result["high"].append(item['high'])
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result["low"].append(item['low'])
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result["avg3"].append(item['avg3'])
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result["avg4"].append(item['avg4'])
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result["avg5"].append(item['avg5'])
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result["avg6"].append(item['avg6'])
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result["avg10"].append(item['avg10'])
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result["avg12"].append(item['avg12'])
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result["avg20"].append(item['avg20'])
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result["avg36"].append(item['avg36'])
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result["avg40"].append(item['avg40'])
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result["avg48"].append(item['avg48'])
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result["avg60"].append(item['avg60'])
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result["avg120"].append(item['avg120'])
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result["avg200"].append(item['avg200'])
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result["avg240"].append(item['avg240'])
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result["avg300"].append(item['avg300'])
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result["disparity_avg5"].append(item['disparity_avg5'])
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result["disparity_avg20"].append(item['disparity_avg20'])
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result["disparity_avg60"].append(item['disparity_avg60'])
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result["disparity_avg120"].append(item['disparity_avg120'])
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result['disparity'].append(
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max(item['disparity_avg5'], item['disparity_avg20'], item['disparity_avg60']) - min(
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item['disparity_avg5'], item['disparity_avg20'], item['disparity_avg60']))
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if item['disparity_avg60'] < item['disparity_avg20'] < item['disparity_avg5']:
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result['disparity_type'].append(1)
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elif item['disparity_avg5'] < item['disparity_avg20'] < item['disparity_avg60']:
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result['disparity_type'].append(-1)
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else:
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result['disparity_type'].append(0)
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result["envelope_upper"].append(item['envelope_upper'])
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result["envelope_lower"].append(item['envelope_lower'])
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result["envelope_middle"].append(item['envelope_middle'])
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result["rsi"].append(item['rsi'])
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result["rsis"].append(item['rsis'])
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result["macd"].append(item['macd'])
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result["macds"].append(item['macds'])
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result["slow_k"].append(item['stochastic_slow_k'])
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result["slow_d"].append(item['stochastic_slow_d'])
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result["buy"].append(-1)
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result["sell"].append(-1)
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data = pd.DataFrame(result)
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df_final_time = pd.DatetimeIndex(result['ymd'])
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data.index = df_final_time
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data = data.astype(
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{
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'open': 'float',
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'high': 'float',
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'low': 'float',
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'close': 'float',
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'avg3': 'float',
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'avg4': 'float',
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'avg5': 'float',
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'avg6': 'float',
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'avg10': 'float',
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'avg12': 'float',
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'avg20': 'float',
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'avg36': 'float',
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'avg40': 'float',
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'avg48': 'float',
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'avg60': 'float',
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'avg120': 'float',
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'avg200': 'float',
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'avg240': 'float',
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'avg300': 'float',
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'disparity_avg5': 'float',
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'disparity_avg20': 'float',
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'disparity_avg60': 'float',
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'disparity_avg120': 'float',
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'buy': 'float',
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'sell': 'float',
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'slow_k': 'float',
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'slow_d': 'float',
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'macd': 'float',
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'macds': 'float',
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'envelope_upper': 'float',
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'envelope_lower': 'float',
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'envelope_middle': 'float',
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'rsi': 'float',
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'rsis': 'float'
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}
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)
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scaler = StandardScaler()
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low_df = pd.DataFrame(data['low'])
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low_df.index = [c for c in range(len(low_df))]
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low_std = scaler.fit_transform(data['low'].values.reshape(-1, 1))
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low_std = pd.DataFrame(low_std, columns=['low_std'])
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min_df = pd.DataFrame({'open': data['open'].to_list(), 'close': data['close'].to_list()})
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min_df['min_std'] = min_df.min(axis=1)
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min_df.index = [c for c in range(len(min_df))]
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min_std = scaler.fit_transform(min_df['min_std'].values.reshape(-1, 1))
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min_std = pd.DataFrame(min_std, columns=['min_std'])
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line_fitter = LinearRegression()
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size = len(data["close"])
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gradients_low = []
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gradients_avg5 = []
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gradients_avg20 = []
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gradients_avg60 = []
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for i in range(size):
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coef_low = -999
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coef_avg5 = -999
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coef_avg20 = -999
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coef_avg60 = -999
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if i > 0:
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l = days if i >= days else i
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x = pd.DataFrame([c for c in range(i - l, i + 1)])
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y = pd.DataFrame(low_std.values.tolist()[i - l:i + 1])
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line_fitter.fit(x.values.reshape(-1, 1), y)
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coef_low = line_fitter.coef_[0][0]
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l = 5 if i >= 5 else i
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x = pd.DataFrame([c for c in range(i - l, i + 1)])
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y = pd.DataFrame(min_std.values.tolist()[i - l:i + 1])
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line_fitter.fit(x.values.reshape(-1, 1), y)
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coef_avg5 = line_fitter.coef_[0][0]
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l = 20 if i >= 20 else i
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x = pd.DataFrame([c for c in range(i - l, i + 1)])
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y = pd.DataFrame(min_std.values.tolist()[i - l:i + 1])
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line_fitter.fit(x.values.reshape(-1, 1), y)
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coef_avg20 = line_fitter.coef_[0][0]
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l = 60 if i >= 60 else i
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x = pd.DataFrame([c for c in range(i - l, i + 1)])
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y = pd.DataFrame(min_std.values.tolist()[i - l:i + 1])
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line_fitter.fit(x.values.reshape(-1, 1), y)
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coef_avg60 = line_fitter.coef_[0][0]
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gradients_low.append(coef_low)
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gradients_avg5.append(coef_avg5)
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gradients_avg20.append(coef_avg20)
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gradients_avg60.append(coef_avg60)
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gradients_low_df = pd.DataFrame(gradients_low, columns=['gradients_low'])
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gradients_avg5_df = pd.DataFrame(gradients_avg5, columns=['gradients_avg5'])
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gradients_avg20_df = pd.DataFrame(gradients_avg20, columns=['gradients_avg20'])
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gradients_avg60_df = pd.DataFrame(gradients_avg60, columns=['gradients_avg60'])
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gradients_low_df.index = df_final_time
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gradients_avg5_df.index = df_final_time
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gradients_avg20_df.index = df_final_time
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gradients_avg60_df.index = df_final_time
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data = data.merge(gradients_low_df, left_index=True, right_index=True)
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data = data.merge(gradients_avg5_df, left_index=True, right_index=True)
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data = data.merge(gradients_avg20_df, left_index=True, right_index=True)
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data = data.merge(gradients_avg60_df, left_index=True, right_index=True)
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return data
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def writeFile(self, dirName, ticker, data, bsLine, type=None):
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if bsLine is None:
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return
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# 어제 데이터는 지운다.
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buy_line = bsLine['buy']
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buy_weight_line = bsLine['buy_weight']
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sell_line = bsLine['sell']
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buy_size = []
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buy_colors = []
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for i in range(len(buy_line)):
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if buy_line[i] < 0:
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buy_colors.append("#ffffff")
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buy_line[i] = nan
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buy_size.append(0)
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else:
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buy_colors.append("#B2028C")
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buy_size.append(10 + (0.1 * buy_weight_line[i]))
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sell_colors = []
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for i in range(len(sell_line)):
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if sell_line[i] < 0:
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sell_colors.append("#ffffff")
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sell_line[i] = nan
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else:
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sell_colors.append("#00ced1")
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# 그래프를 설정한다.
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buy_check = go.Scatter(x=data['ymd'], y=buy_line, mode='markers', name="buy", marker=dict(size=buy_size, color=buy_colors, line_width=0))
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sell_check = go.Scatter(x=data['ymd'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0))
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avg5 = go.Scatter(x=data['ymd'], y=data["avg5"], name="avg5", line_color='#6C2507')
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avg20 = go.Scatter(x=data['ymd'], y=data["avg20"], name="avg20", line_color='#f84c43')
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avg60 = go.Scatter(x=data['ymd'], y=data["avg60"], name="avg60", line_color='#f89543')
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candle_stick = go.Candlestick(x=data['ymd'], open=data['open'], high=data['high'], low=data['low'], close=data['close'], increasing_line_color='red', decreasing_line_color='blue', showlegend=False)
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macd_line = go.Scatter(x=data['ymd'], y=data["macd"], line=dict(color='red', width=2), name='macd')
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macd_s_line = go.Scatter(x=data['ymd'], y=data["macds"], line=dict(dash='dashdot', color='black', width=2), name='macds')
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# fast_k_line = go.Scatter(x=hts['date'], y=hts["fast_k"], mode='lines', name='fast_k')
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slow_k_line = go.Scatter(x=data['ymd'], y=data["slow_k"], line=dict(color='red', width=2), name='slow_k')
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slow_d_line = go.Scatter(x=data['ymd'], y=data["slow_d"], line=dict(dash='dashdot', color='black', width=2), name='slow_d')
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rsi_line = go.Scatter(x=data['ymd'], y=data["rsi"], line=dict(color='red', width=2), name='rsi')
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rsis_line = go.Scatter(x=data['ymd'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), name='rsis')
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disparity_avg5 = go.Scatter(x=data['ymd'], y=data["disparity_avg5"], name="disparity_avg5", line_color='#8F8203')
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disparity_avg20 = go.Scatter(x=data['ymd'], y=data["disparity_avg20"], name="disparity_avg20", line_color='#ff00ff')
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disparity_avg60 = go.Scatter(x=data['ymd'], y=data["disparity_avg60"], name="disparity_avg60", line_color='#1469F4')
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candle_data = [candle_stick, avg5, avg20, avg60, buy_check, sell_check]
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disparity_data = [disparity_avg5, disparity_avg20, disparity_avg60]
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macd_data = [macd_line, macd_s_line]
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stochastic_data = [slow_k_line, slow_d_line]
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rsi_data = [rsi_line, rsis_line]
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# 그래프를 그린다.
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"""
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fig = go.Figure(data=candle_data)
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fig.update_layout(title=stock_code + "_" + given_day)
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fig.show()
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"""
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fig = subplots.make_subplots(
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rows=5, cols=1,
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subplot_titles=("MACD", "RSI", "스토캐스틱", '이격도', '캔들'),
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# specs=[[{}], [{}], [{}], [{}], [{}], [{}]],
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shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01,
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row_heights=[200, 200, 200, 200, 750]
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)
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for trace in macd_data:
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fig.append_trace(trace, 1, 1)
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for trace in rsi_data:
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fig.append_trace(trace, 2, 1)
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for trace in stochastic_data:
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fig.append_trace(trace, 3, 1)
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for trace in disparity_data:
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fig.append_trace(trace, 4, 1)
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for trace in candle_data:
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fig.append_trace(trace, 5, 1)
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df = pd.DataFrame(bsLine)
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df = df.fillna(-1)
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buy_count = len(df.loc[df["buy"] > 0])
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sell_count = len(df.loc[df["sell"] > 0])
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fig.update_layout(height=1700, title="_" + str(buy_count) + "," + str(sell_count))
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fig['layout'].update()
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if type is None:
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fileName = "%s/%s_%s.html" % (dirName, ticker, self.TODAY)
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po.write_html(fig, file=fileName, auto_open=False)
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else:
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fileName = "%s/%s_%s_%s.html" % (dirName, type, ticker, self.TODAY)
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po.write_html(fig, file=fileName, auto_open=False)
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return
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def notBuy(self, data, i):
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if i > 5:
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check = True
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for l in range(i - 4, i + 1):
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if (
|
|
data['gradients_avg60'][l - 1] > data['gradients_avg60'][l] or
|
|
data['gradients_avg20'][l - 1] > data['gradients_avg20'][l] or
|
|
data['gradients_low'][l - 1] > data['gradients_low'][l]
|
|
):
|
|
check = False
|
|
break
|
|
if not check:
|
|
return False
|
|
return True
|
|
|
|
def checkWithEnvelope(self, data1, data2=None, isRealTime=False):
|
|
|
|
bsLine = {}
|
|
size = len(data1["close"])
|
|
|
|
bsLine['buy'] = [-1.0 for i in range(size)]
|
|
bsLine['buy_weight'] = [-1.0 for i in range(size)]
|
|
bsLine['sell'] = [-1.0 for i in range(size)]
|
|
bsLine['sell_weight'] = [-1.0 for i in range(size)]
|
|
|
|
for i in range(size):
|
|
if isRealTime:
|
|
if i < size - 1:
|
|
continue
|
|
|
|
if i > 10:
|
|
|
|
if data1['slow_k'][i] < 20:
|
|
if data1['slow_k'][i - 1] < data1['slow_d'][i - 1] and data1['slow_d'][i] < data1['slow_k'][i]:
|
|
buy = data1['low'][i]
|
|
data1['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 0.3
|
|
|
|
if data2['slow_k'][i] < 30 and data1['slow_k'][i] < 30:
|
|
if data1['slow_k'][i-1] < data1['slow_d'][i-1] and data1['slow_d'][i] < data2['slow_k'][i]:
|
|
buy = data1['close'][i]
|
|
data1['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 0.3
|
|
|
|
|
|
if data2['slow_k'][i] < 30:
|
|
if data1['slow_k'][i] < 30:
|
|
if data1['avg5'][i] < data1['close'][i]:
|
|
buy = data1['close'][i]
|
|
data1['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 0.2
|
|
|
|
if data1['slow_k'][i-1] < data1['slow_d'][i-1] and data1['slow_d'][i] < data2['slow_k'][i]:
|
|
buy = data1['close'][i]
|
|
data1['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 0.3
|
|
|
|
if data2['slow_k'][i] > 90:
|
|
if (data1['slow_d'][i-1] < data1['slow_k'][i-1] and data1['slow_k'][i] < data1['slow_d'][i]):
|
|
sell = data1['close'][i]
|
|
data1['sell'][i] = sell
|
|
bsLine['sell'][i] = sell
|
|
bsLine['sell_weight'][i] = 100
|
|
|
|
if data1['slow_k'][i] > 95 and data1['slow_k'][i] < data1['slow_d'][i]:
|
|
sell = data1['close'][i]
|
|
data1['sell'][i] = sell
|
|
bsLine['sell'][i] = sell
|
|
bsLine['sell_weight'][i] = 100
|
|
|
|
if data2['slow_k'][i] > 98 and data1['slow_k'][i] > 98:
|
|
sell = data1['close'][i]
|
|
data1['sell'][i] = sell
|
|
bsLine['sell'][i] = sell
|
|
bsLine['sell_weight'][i] = 100
|
|
|
|
return bsLine
|
|
|
|
def get_ohlcv(self, ticker, minute=5):
|
|
url = "https://api.upbit.com/v1/candles/minutes/"+str(minute)
|
|
querystring = {"market": "KRW-"+ticker, "count": "300"}
|
|
response = requests.request("GET", url, params=querystring)
|
|
json_response = json.loads(response.text)
|
|
|
|
btc_ohlcv = []
|
|
for json_data in json_response:
|
|
btc_ohlcv.append({'datetime': datetime.strptime(json_data['candle_date_time_kst'], '%Y-%m-%dT%H:%M:%S'), 'open': json_data['opening_price'], 'high': json_data['high_price'], 'low': json_data['low_price'], 'close': json_data['trade_price'], 'volume': json_data['candle_acc_trade_volume']})
|
|
btc_ohlcv = sorted(btc_ohlcv, key=lambda item: (item['datetime']))
|
|
|
|
df = pd.DataFrame(btc_ohlcv, columns=['datetime', 'open', 'high', 'low', 'close', 'volume'])
|
|
df['datetime'] = pd.to_datetime(df['datetime'], unit='ms')
|
|
df.set_index('datetime', inplace=True)
|
|
df.insert(1, "datetime", df.index)
|
|
return df
|
|
|
|
def cancel_order(self, log_df, log_filename, min=5):
|
|
|
|
now = datetime.now() - timedelta(minutes=min)
|
|
# min 분 이상 된 시간인 내용을 가지고 옴
|
|
df = log_df.loc[(log_df.index <= now) ]
|
|
# 취소가 되지 않은 것만 가지고 옴 (0: 취소 되지 않음, 1: 취소함)
|
|
df = df.loc[(log_df["canceled"] == 0)]
|
|
|
|
if df is not None:
|
|
for i in range(len(df)):
|
|
order = (df['order0'][i], df['order1'][i], df['order2'][i], df['order3'][i])
|
|
cancel = self.bithumb.cancel_order(order)
|
|
# slackbot에 메시지를 보냄
|
|
#self.slackBot.post_to_slack(ticker, self.stock_code[ticker], "CANCEL", -1, -1)
|
|
|
|
for i in range(len(df)):
|
|
log_df.loc[(log_df.index == df.index[i]), 'canceled'] = 1
|
|
|
|
log_df.to_csv(log_filename, index=False)
|
|
|
|
return log_df
|
|
|
|
def check_buy_history(self, log_df, min=10):
|
|
# min 분 이상 된 시간인 내용을 가지고 옴
|
|
now = datetime.now() - timedelta(minutes=min)
|
|
# 취소가 되지 않은 것만 가지고 옴 (0: 취소 되지 않음, 1: 취소함)
|
|
log_df = log_df.loc[(now < log_df.index)]
|
|
|
|
if len(log_df) == 0:
|
|
return False
|
|
|
|
return True
|
|
|
|
def getStock(self, ticker, analyzed_day, minute=5, day=None):
|
|
stock = {"CODE": ticker, "NAME": ticker, "PRICE": []}
|
|
|
|
if day is None:
|
|
df = self.get_ohlcv(ticker, minute)
|
|
else:
|
|
df = pybithumb.get_ohlcv(ticker)
|
|
df["datetime"] = df.index
|
|
close = pybithumb.get_current_price(ticker)
|
|
if df is None or close is None:
|
|
return
|
|
|
|
size = len(df)
|
|
df['close'][size - 1] = close
|
|
if close < df['low'][size - 1]:
|
|
df['low'][size - 1] = close
|
|
if df['high'][size - 1] < close:
|
|
df['high'][size - 1] = close
|
|
self.append(df, stock)
|
|
|
|
data = self.analyze(stock, analyzed_day)
|
|
# 분석일 데이터만 활용한다 (이전 데이터는 제거)
|
|
data.drop(data.index[:len(data) - analyzed_day], inplace=True)
|
|
|
|
return data
|
|
|
|
def getXRPCount(self, price):
|
|
MAX_BUY_PRICE = 1000
|
|
if 870 < price: MAX_BUY_PRICE = 1000
|
|
elif 840 <= price < 870: MAX_BUY_PRICE = 2000
|
|
elif 810 <= price < 840: MAX_BUY_PRICE = 3000
|
|
elif 780 <= price < 810: MAX_BUY_PRICE = 4000
|
|
elif 750 <= price < 780: MAX_BUY_PRICE = 5000
|
|
elif 720 <= price < 750: MAX_BUY_PRICE = 6000
|
|
elif 690 <= price < 720: MAX_BUY_PRICE = 7000
|
|
elif 660 <= price < 690: MAX_BUY_PRICE = 8000
|
|
elif 630 <= price < 660: MAX_BUY_PRICE = 9000
|
|
elif 600 <= price < 630: MAX_BUY_PRICE = 10000
|
|
elif 570 <= price < 600: MAX_BUY_PRICE = 20000
|
|
elif 540 <= price < 570: MAX_BUY_PRICE = 30000
|
|
elif 510 <= price < 540: MAX_BUY_PRICE = 40000
|
|
elif 450 <= price < 480: MAX_BUY_PRICE = 50000
|
|
elif 420 <= price < 450: MAX_BUY_PRICE = 60000
|
|
elif 390 <= price < 420: MAX_BUY_PRICE = 70000
|
|
elif 360 <= price < 390: MAX_BUY_PRICE = 80000
|
|
elif 330 <= price < 360: MAX_BUY_PRICE = 90000
|
|
elif 300 <= price < 330: MAX_BUY_PRICE = 100000
|
|
elif 270 <= price < 300: MAX_BUY_PRICE = 110000
|
|
elif 240 <= price < 270: MAX_BUY_PRICE = 120000
|
|
elif 210 <= price < 240: MAX_BUY_PRICE = 130000
|
|
elif 180 <= price < 210: MAX_BUY_PRICE = 140000
|
|
elif 150 <= price < 180: MAX_BUY_PRICE = 150000
|
|
elif 120 <= price < 150: MAX_BUY_PRICE = 160000
|
|
elif 90 <= price < 120: MAX_BUY_PRICE = 170000
|
|
elif 60 <= price < 90: MAX_BUY_PRICE = 180000
|
|
elif 30 <= price < 60: MAX_BUY_PRICE = 190000
|
|
elif price < 30: MAX_BUY_PRICE = 200000
|
|
else: MAX_BUY_PRICE = 1000
|
|
|
|
count = int(MAX_BUY_PRICE / price)
|
|
return count
|
|
|
|
def buyRealTime(self, ticker, analyzed_day=120, isRealTime=False):
|
|
|
|
"""
|
|
# binance
|
|
btc_ohlcv = self.binance.fetch_ohlcv(ticker + "/BKRW")
|
|
df = pd.DataFrame(btc_ohlcv, columns=['datetime', 'open', 'high', 'low', 'close', 'volume'])
|
|
df['datetime'] = pd.to_datetime(df['datetime'], unit='ms')
|
|
df.set_index('datetime', inplace=True)
|
|
"""
|
|
|
|
"""
|
|
# bithumb
|
|
df_ = pybithumb.get_ohlcv(ticker)
|
|
"""
|
|
|
|
stock1 = self.getStock(ticker, analyzed_day, minute=5)
|
|
stock2 = self.getStock(ticker, analyzed_day, minute=30)
|
|
|
|
# 매수 매도 체크
|
|
bsLine = self.checkWithEnvelope(stock1, stock2, isRealTime=isRealTime)
|
|
print(ticker, "/", datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "/", stock1['close'][len(stock1['close'])-1], "/", "{:.2f}".format(stock2['slow_k'][len(stock1['slow_k'])-1]), "/", "{:.2f}".format(stock1['slow_k'][len(stock1['slow_k'])-1]), "/", "{:.2f}".format(stock2['macd'][len(stock2['macd']) - 1]), "/", "{:.2f}".format(stock1['macd'][len(stock1['macd']) - 1]))
|
|
|
|
# 그래프를 그린다.
|
|
if len(stock1.index) > 10:
|
|
# 매수 요청 n분 이상된 주문은 취소하기 위함
|
|
order_log_filename = os.path.join(RESOURCE_PATH, 'order', "bithumb"+"_"+self.TODAY + '.log')
|
|
if os.path.exists(order_log_filename):
|
|
order_log_df = pd.read_csv(order_log_filename)
|
|
order_log_df.columns = ["type", "datetime", "order0", "order1", "order2", "order3", "canceled", "slow_k_30", "slow_k_5", "price", "count"]
|
|
order_log_df["datetime"] = pd.to_datetime(order_log_df["datetime"], format='%Y-%m-%d %H:%M:%S')
|
|
else:
|
|
order_log_df = pd.DataFrame(columns=["type", "datetime", "order0", "order1", "order2", "order3", "canceled", "slow_k_30", "slow_k_5", "price", "count"])
|
|
order_log_df['datetime'] = pd.to_datetime(order_log_df['datetime'], unit='s')
|
|
order_log_df.set_index('datetime', inplace=True)
|
|
order_log_df.insert(1, "datetime", order_log_df.index)
|
|
|
|
# 10분이 지난 미체결은 취소한다.
|
|
order_log_df = self.cancel_order(order_log_df, order_log_filename, min=10)
|
|
# 한번 매수 후 n분 이후 매수하기 위함
|
|
check_buy_history = self.check_buy_history(order_log_df, min=60)
|
|
|
|
if isRealTime and not check_buy_history:
|
|
# 매수 조건
|
|
if max(bsLine['buy'][len(bsLine['buy']) - 2:]) > 100:
|
|
|
|
#stock = self.getStock(ticker, analyzed_day, day=30)
|
|
#if stock["slow_k"][len(stock['close'])-1] > 40:
|
|
# print (datetime.now().strftime('%Y-%m-%d %H:%M:%S'), ",", round(stock["slow_k"][len(stock['close'])-3],2), ",", round(stock["slow_k"][len(stock['close'])-2],2), ",", round(stock["slow_k"][len(stock['close'])-1], ))
|
|
# return
|
|
|
|
# [잔고 확인하기]
|
|
# - 비트코인의 총 잔고
|
|
# - 거래 중인 비트코인의 수량
|
|
# - 보유 중인 총원화
|
|
# - 주문에 사용된 원화
|
|
# (4.978e-05, 0.0, 3438133.120299, 0)
|
|
tmp = self.bithumb.get_balance(ticker)
|
|
balance = tmp[2]
|
|
count = self.getXRPCount(bsLine['buy'][len(bsLine['buy']) - 1])
|
|
|
|
# 매수를 요청한다.
|
|
order = self.bithumb.buy_limit_order(ticker, bsLine['buy'][len(bsLine['buy']) - 1], count)
|
|
# slackbot에 메시지를 보냄
|
|
self.slackBot.post_to_slack(ticker, self.stock_code[ticker], "BUY", bsLine['buy'][len(bsLine['buy']) - 1], count)
|
|
|
|
# order: ('bid', 'BTC', 'C0101000000322993432', 'KRW')
|
|
if len(stock1['close']) > 0:
|
|
print(ticker, "/", datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "/", stock1['close'][len(stock1['close'])-1], "/", "{:.2f}".format(stock2['slow_k'][len(stock1['slow_k'])-1]), "/", "{:.2f}".format(stock1['slow_k'][len(stock1['slow_k'])-1]), "/", "{:.2f}".format(stock2['macd'][len(stock2['macd']) - 1]), "/", "{:.2f}".format(stock1['macd'][len(stock1['macd']) - 1]), "/", bsLine['buy'][len(bsLine['buy']) - 1], "/", count)
|
|
datetime_value = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
|
value = {"type": "BUY", "datetime": datetime_value, "order0": order[0], "order1": order[1], "order2": order[2], "order3": order[3], "canceled": 0, "slow_k_30": stock2['slow_k'][len(stock2['slow_k']) - 1], "slow_k_5": stock1['slow_k'][len(stock1['slow_k']) - 1], "price": bsLine['buy'][len(bsLine['buy']) - 1], "count": count}
|
|
value_df = pd.DataFrame(value, index=[datetime_value])
|
|
value_df["datetime"] = pd.to_datetime(value_df["datetime"], format='%Y-%m-%d %H:%M:%S')
|
|
indexes1 = order_log_df.index.tolist()
|
|
indexes1.append(datetime_value)
|
|
|
|
order_log_df = order_log_df.append(value_df, ignore_index = True)
|
|
order_log_df.index = indexes1
|
|
order_log_df['datetime'] = order_log_df.index
|
|
order_log_df.to_csv(order_log_filename, index=False)
|
|
|
|
# 파일에 매수 시점 그래프
|
|
#dirName = os.path.join(RESOURCE_PATH, 'analysis', 'bithumb')
|
|
#self.writeFile(dirName, ticker, stock1, bsLine, 'buy')
|
|
|
|
"""
|
|
if max(bsLine['sell'][len(bsLine['sell']) - 2:]) > 100:
|
|
# [잔고 확인하기]
|
|
# - 비트코인의 총 잔고
|
|
# - 거래 중인 비트코인의 수량
|
|
# - 보유 중인 총원화
|
|
# - 주문에 사용된 원화
|
|
# (4.978e-05, 0.0, 3438133.120299, 0)
|
|
tmp = self.bithumb.get_balance(ticker)
|
|
if tmp is None:
|
|
return
|
|
count = tmp[0] - tmp[1]
|
|
|
|
# 매도를 요청한다.
|
|
order = self.bithumb.sell_limit_order(ticker, bsLine['sell'][len(bsLine['sell'])-1], count)
|
|
# slackbot에 메시지를 보냄
|
|
self.slackBot.post_to_slack(ticker, self.stock_code[ticker], "SELL", bsLine['sell'][len(bsLine['sell']) - 1], count)
|
|
|
|
if len(order) > 2 and len(stock1['close']) > 0:
|
|
print(ticker, "/", datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "/", stock1['close'][len(stock1['close'])-1], "/", "{:.2f}".format(stock2['slow_k'][len(stock1['slow_k'])-1]), "/", "{:.2f}".format(stock1['slow_k'][len(stock1['slow_k'])-1]), "/", "{:.2f}".format(stock2['macd'][len(stock2['macd']) - 1]), "/", "{:.2f}".format(stock1['macd'][len(stock1['macd']) - 1]), "/", bsLine['sell'][len(bsLine['sell']) - 1], "/", count)
|
|
value = {"type": "SELL","order0": order[0], "order1": order[1], "order2": order[2], "order3": order[3], "canceled": 0, "slow_k_30": stock2['slow_k'][len(stock2['slow_k']) - 1], "slow_k_5": stock1['slow_k'][len(stock1['slow_k']) - 1], "price": bsLine['sell'][len(bsLine['sell']) - 1], "count": count}
|
|
datetime_value = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
|
value_df = pd.DataFrame(value, index=[datetime_value])
|
|
|
|
indexes = order_log_df.index.tolist()
|
|
indexes.append(datetime_value)
|
|
|
|
order_log_df = order_log_df.append(value_df, ignore_index=True)
|
|
order_log_df.index = indexes
|
|
order_log_df['datetime'] = order_log_df.index
|
|
order_log_df.to_csv(order_log_filename, index=False)
|
|
|
|
dirName = os.path.join(RESOURCE_PATH, 'analysis', 'bithumb')
|
|
self.writeFile(dirName, ticker, stock1, bsLine, 'sell')
|
|
"""
|
|
#else:
|
|
# dirName = os.path.join(RESOURCE_PATH, 'analysis', 'bithumb')
|
|
# self.writeFile(dirName, ticker, stock1, bsLine)
|
|
|
|
return
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
PROJECT_HOME = os.getcwd()
|
|
RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
|
|
|
|
if not os.path.exists(os.path.join(RESOURCE_PATH, 'analysis', 'bithumb')):
|
|
os.mkdir(os.path.join(RESOURCE_PATH, 'analysis', 'bithumb'))
|
|
dirName = os.path.join(RESOURCE_PATH, 'analysis', 'bithumb')
|
|
if not os.path.exists(dirName):
|
|
os.mkdir(dirName)
|
|
|
|
# bithumb_daily = Bithumb_daily(RESOURCE_PATH)
|
|
today = datetime.today().strftime('%Y%m%d')
|
|
bithumb = Bithumb_minute(RESOURCE_PATH, today)
|
|
|
|
tickers = ['XRP']
|
|
analyzed_day = 120
|
|
isRealTime = True
|
|
if isRealTime:
|
|
while True:
|
|
for ticker in tickers:
|
|
#data_daily = bithumb_daily.buyRealTime(ticker, analyzed_day)
|
|
#size = len(data_daily)
|
|
#if data_daily['slow_k'] < 30:
|
|
try:
|
|
time.sleep(30)
|
|
bithumb.buyRealTime(ticker, analyzed_day, isRealTime)
|
|
except:
|
|
continue
|
|
else:
|
|
for ticker in tickers:
|
|
bithumb.buyRealTime(ticker, analyzed_day, isRealTime)
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