681 lines
28 KiB
Python
681 lines
28 KiB
Python
import csv
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import os
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import time
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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
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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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class Bithumb_daily(HTS):
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RESOURCE_PATH = None
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buySellChecker = None
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analyzerSqlite = None
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def __init__(self, RESOURCE_PATH):
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super().__init__(RESOURCE_PATH)
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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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# bithumb api에 연결한 클라스 객체를 선언한다.
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self.bithumb = pybithumb.Bithumb(con_key, sec_key)
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self.buySellChecker = BuySellChecker()
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self.analyzerSqlite = AnalyzerSqlite()
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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].strftime('%Y.%m.%d'),
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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(max(item['disparity_avg5'], item['disparity_avg20'], item['disparity_avg60']) - min(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': 'int',
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'high': 'int',
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'low': 'int',
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'close': 'int',
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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': 'int',
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'sell': 'int',
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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, today):
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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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envelope_upper = go.Scatter(x=data['ymd'], y=data["envelope_upper"], name="upper", line_color='#000000')
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envelope_middle = go.Scatter(x=data['ymd'], y=data["envelope_middle"], name="middle", line_color='#927786')
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envelope_lower = go.Scatter(x=data['ymd'], y=data["envelope_lower"], name="lower", line_color='#000000')
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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, envelope_upper, envelope_middle, envelope_lower, 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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fileName = "%s/%s_%s.html" % (dirName, ticker, today)
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po.write_html(fig, file=fileName, auto_open=False)
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return
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def getBalance(self, ticker):
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tmp = self.bithumb.get_balance(ticker)
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return tmp[2]
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def exist_buy(self, ticker, log_filename):
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if os.path.exists(log_filename):
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log_file = open(log_filename, 'r', )
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reader = csv.reader(log_file)
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for line in reader:
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if line[2] == ticker:
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log_file.close()
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return True
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log_file.close()
|
|
return False
|
|
|
|
def notBuy(self, data, i):
|
|
if i > 5:
|
|
check = True
|
|
for l in range(i-4, i+1):
|
|
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, data, analyzed_day=120, isRealTime=False):
|
|
|
|
bsLine = {}
|
|
size = len(data["close"])
|
|
|
|
bsLine['buy'] = [-1 for i in range(size)]
|
|
bsLine['buy_weight'] = [-1 for i in range(size)]
|
|
bsLine['sell'] = [-1 for i in range(size)]
|
|
bsLine['sell_weight'] = [-1 for i in range(size)]
|
|
|
|
gap_interval = analyzed_day
|
|
gap_state = False
|
|
for i in range(size):
|
|
if isRealTime:
|
|
if i < size - 1:
|
|
continue
|
|
|
|
if i > 10:
|
|
# 만약 전일 저가와 오늘 종의 차이가 1만원이 넘으면 향후 60일은 분석하지 않는다.
|
|
if data['high'][i] < int(data['low'][i - 1] * 0.7):
|
|
gap_state = True
|
|
gap_interval -= 1
|
|
continue
|
|
if gap_state:
|
|
if gap_interval <= 0:
|
|
gap_state = False
|
|
gap_interval = 60
|
|
else:
|
|
gap_interval -= 1
|
|
continue
|
|
|
|
if data['disparity'][i] < 2:
|
|
check = True
|
|
for l in range(i - 3, i):
|
|
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] or
|
|
data['disparity_avg5'][l - 1] > data['disparity_avg5'][l] or
|
|
data['disparity'][l - 1] < data['disparity'][l]
|
|
):
|
|
check = False
|
|
break
|
|
if check and 99 < sum(data['disparity_avg5'][i - 4:i + 1]) / 5 < 100 and 99 < sum(
|
|
data['disparity_avg60'][i - 4:i + 1]) / 5 < 100:
|
|
buy = data['low'][i]
|
|
data['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 10
|
|
|
|
check = True
|
|
for l in range(i - 2, i):
|
|
if (
|
|
data['gradients_avg60'][l - 1] > data['gradients_avg60'][l] or
|
|
data['gradients_low'][l - 1] > data['gradients_low'][l]
|
|
):
|
|
check = False
|
|
break
|
|
if (
|
|
check and
|
|
-0.0011 < data['gradients_low'][i] < 0 and -0.007 < data['gradients_avg5'][i] < 0.001 and
|
|
-0.0012 < data['gradients_avg60'][i] < 0 and
|
|
98.90 < data['disparity_avg5'][i] < 101
|
|
):
|
|
buy = data['low'][i]
|
|
data['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 10
|
|
|
|
check = True
|
|
for l in range(i - 6, i):
|
|
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] or
|
|
-0.039 < data['gradients_low'][l - 1] < -0.35 or
|
|
-0.05 < data['gradients_avg20'][l - 1] < -0.30 or
|
|
-0.40 < data['gradients_avg60'][l - 1] < -0.30
|
|
):
|
|
check = False
|
|
break
|
|
if check and 99 < min(data['disparity_avg5'][i - 6:i]) < max(data['disparity_avg5'][i - 6:i]) < 101:
|
|
buy = data['low'][i]
|
|
data['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 10
|
|
|
|
check = True
|
|
for l in range(i - 3, i):
|
|
if (
|
|
data['gradients_low'][l - 1] < data['gradients_low'][l] or
|
|
data['gradients_avg60'][l - 1] < data['gradients_avg60'][l] or
|
|
data['gradients_avg20'][l - 1] < data['gradients_avg20'][l] or
|
|
0.01 < data['gradients_low'][l - 1] < 0.21 or
|
|
-0.09 < data['gradients_avg20'][l - 1] < -0.002 or
|
|
0.01 < data['gradients_avg60'][l - 1] < 0.021
|
|
):
|
|
check = False
|
|
break
|
|
if check:
|
|
buy = data['low'][i]
|
|
data['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 10
|
|
|
|
if (data['disparity'][i] < 5 and 99.0 < data['disparity_avg60'][i] < 99.1 and
|
|
-0.009 < data['gradients_avg60'][i] < -0.008 and 0.015 < data['gradients_avg20'][i] < 0.016 and
|
|
-0.006 < data['gradients_avg5'][i] < -0.005 and -0.009 < data['gradients_low'][i] < -0.008):
|
|
check = True
|
|
for l in range(i - 5, i):
|
|
if (
|
|
data['gradients_avg60'][l - 1] > data['gradients_avg60'][l] or
|
|
data['gradients_low'][l - 1] > data['gradients_low'][l] or
|
|
data['disparity'][l - 1] < data['disparity'][l]
|
|
):
|
|
check = False
|
|
break
|
|
if check:
|
|
buy = data['low'][i]
|
|
data['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 10
|
|
|
|
if data['macd'][i] < -4000:
|
|
if data['macd'][i - 1] < data['macd'][i]:
|
|
if not self.notBuy(data, i) and data['slow_k'][i] < 30:
|
|
buy = data['low'][i]
|
|
data['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 10
|
|
|
|
# macd 이전에 없던 바닥인 경우 상승할 찰나 매수
|
|
if data['macds'][i - 1] < min(data['macds'][:i - 1]):
|
|
if data['macds'][i - 1] < data['macds'][i]:
|
|
if not self.notBuy(data, i) and data['slow_k'][i] < 30:
|
|
buy = data['low'][i]
|
|
data['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 10
|
|
|
|
if (
|
|
98 < data['disparity_avg5'][i] < 100 and data['disparity_avg20'][i] < 93.5 and
|
|
data['disparity_avg60'][i] < 89 and
|
|
-0.014 < data['gradients_avg60'][i] < -0.013 and -0.03 < data['gradients_avg20'][
|
|
i] < -0.02 and -0.014 < data['gradients_low'][i] < -0.013 and
|
|
data['slow_k'][i] < 11
|
|
):
|
|
if not self.notBuy(data, i):
|
|
buy = data['low'][i]
|
|
data['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 15
|
|
|
|
if data['slow_k'][i] < 20 and data['slow_k'][i - 1] < data['slow_d'][i - 1] and data['slow_d'][i] < data['slow_k'][i]:
|
|
buy = data['low'][i]
|
|
data['buy'][i] = buy
|
|
bsLine['buy'][i] = buy
|
|
bsLine['buy_weight'][i] = 15
|
|
|
|
return bsLine, data
|
|
|
|
def buyRealTime(self, ticker, analyzed_day=120, isRealTime=False):
|
|
|
|
stock = {"CODE": ticker, "NAME": ticker, "PRICE": []}
|
|
df = pybithumb.get_ohlcv(ticker)
|
|
if df is None:
|
|
return
|
|
|
|
close = pybithumb.get_current_price(ticker)
|
|
|
|
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)
|
|
|
|
bsLine, data = self.checkWithEnvelope(data, analyzed_day, isRealTime=isRealTime)
|
|
print(ticker, "/", datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "/", data['close'][len(data['close'])-1], "/", data['slow_k'][len(data['slow_k'])-1])
|
|
|
|
# 그래프를 그린다.
|
|
if len(data.index) > 10:
|
|
today = datetime.today().strftime('%Y%m%d')
|
|
log_filename = os.path.join(RESOURCE_PATH, 'analysis', '', today + '.log')
|
|
|
|
# realtime이고, 5분이내 매수 기록이 없다면
|
|
if isRealTime and not self.exist_buy(ticker, log_filename):
|
|
if max(bsLine['buy'][len(bsLine['buy']) - 2:]) > 100:
|
|
balance = self.getBalance(ticker)
|
|
count = round((balance * (bsLine['buy_weight'][len(bsLine['buy_weight'])-1]/100)) / bsLine['buy'][len(bsLine['buy'])-1], 2)
|
|
order = self.bithumb.buy_limit_order(ticker, bsLine['buy'][len(bsLine['buy'])-1], count)
|
|
# order: ('bid', 'BTC', 'C0101000000322993432', 'KRW')
|
|
|
|
with open(log_filename, 'a', newline='', encoding='utf-8') as log_file:
|
|
wr = csv.writer(log_file)
|
|
wr.writerow([datetime.now().strftime('%Y-%m-%d %H:%M:%S'), order[0], order[1], order[2], order[3]])
|
|
|
|
dirName = os.path.join(RESOURCE_PATH, 'analysis', '')
|
|
self.writeFile(dirName, ticker, data, bsLine, datetime.now().strftime('%Y%m%d %H%M%S'), 'buy')
|
|
else:
|
|
dirName = os.path.join(RESOURCE_PATH, 'analysis', '')
|
|
self.writeFile(dirName, ticker, data, bsLine, datetime.now().strftime('%Y%m%d %H%M%S'))
|
|
|
|
return bsLine, data
|
|
|
|
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', '')):
|
|
os.mkdir(os.path.join(RESOURCE_PATH, 'analysis', ''))
|
|
dirName = os.path.join(RESOURCE_PATH, 'analysis', '')
|
|
if not os.path.exists(dirName):
|
|
os.mkdir(dirName)
|
|
|
|
bithumb = Bithumb_daily(RESOURCE_PATH)
|
|
|
|
tickers = ['XRP', 'BTC', 'SOL']
|
|
analyzed_day = 120
|
|
isRealTime = False
|
|
if isRealTime:
|
|
while True:
|
|
try:
|
|
for ticker in tickers:
|
|
bithumb.buyRealTime(ticker, analyzed_day, isRealTime)
|
|
except:
|
|
time.sleep(30)
|
|
continue
|
|
time.sleep(10)
|
|
else:
|
|
for ticker in tickers:
|
|
bithumb.buyRealTime(ticker, analyzed_day, isRealTime)
|