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dosangyoon
2022-07-30 16:08:37 +09:00
parent d230a51652
commit cddcdf4f35
6 changed files with 321 additions and 165 deletions
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stock/util/Stock2Vector.py
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@@ -1,15 +1,184 @@
import os
import sqlite3
from datetime import datetime, timedelta
import pandas as pd
from stock.analysis.Common import Common
from stock.analysis.Stochastic import Stochastic
from stock.analysis.RSI import RSI
from stock.analysis.MACD import MACD
from stock.analysis.IchimokuCloud import IchimokuCloud
class Stock2Vector:
def __init__(self):
RESOURCE_PATH = None
common = None
stochastic = None
rsi = None
macd = None
ichimokuCloud = None
def __init__(self, RESOURCE_PATH):
self.RESOURCE_PATH = RESOURCE_PATH
self.common = Common()
self.stochastic = Stochastic()
self.rsi = RSI()
self.macd = MACD()
self.ichimokuCloud = IchimokuCloud()
return
def read(self, fileName):
return None
def analyze(self, result):
open = result["open"]
close = result["close"]
high = result["high"]
low = result["low"]
vol = result["vol"]
def process(self, vector):
close_df = pd.DataFrame(close)
avg3_list = close_df.rolling(window=3).mean().fillna(close[0]).values.tolist()
avg3 = [item[0] for item in avg3_list]
avg5_list = close_df.rolling(window=5).mean().fillna(close[0]).values.tolist()
avg5 = [item[0] for item in avg5_list]
avg10_list = close_df.rolling(window=10).mean().fillna(close[0]).values.tolist()
avg10 = [item[0] for item in avg10_list]
avg20_list = close_df.rolling(window=20).mean().fillna(close[0]).values.tolist()
avg20 = [item[0] for item in avg20_list]
avg30_list = close_df.rolling(window=30).mean().fillna(close[0]).values.tolist()
avg30 = [item[0] for item in avg30_list]
avg60_list = close_df.rolling(window=60).mean().fillna(close[0]).values.tolist()
avg60 = [item[0] for item in avg60_list]
df = pd.DataFrame(close)
max20 = df.rolling(window=20).mean()
stddev20 = df.rolling(window=20).std()
upper_df = max20 + (stddev20 * 2) # 상단 볼린저 밴드
lower_df = max20 - (stddev20 * 2) # 하단 볼린저 밴드
upper, lower = [], []
for i in range(len(upper_df)):
if i < 10:
upper.append(upper_df.values[0][0])
lower.append(lower_df.values[0][0])
else:
upper.append(upper_df.values[i][0])
lower.append(lower_df.values[i][0])
point_temp = result["time"]
STOCK = []
for i in range(len(open)):
STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i],
'avg3': avg3[i], 'avg5': avg5[i],'avg10': avg10[i],'avg20': avg20[i],'avg30': avg30[i],'avg60': avg60[i]})
# stochastic 계산
stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5)
stochastic_df = stochastic_df.fillna(100)
fast_k = stochastic_df['fast_k'].values.tolist()
slow_k = stochastic_df['slow_k'].values.tolist()
slow_d = stochastic_df['slow_d'].values.tolist()
# macd 계산
macd_df = self.macd.apply(STOCK, short=12, long=26, t=9)
macd_df = macd_df.fillna(100)
macd = macd_df['macd'].values.tolist()
macds = macd_df['macds'].values.tolist()
macdo = macd_df['macdo'].values.tolist()
# rsi 계산
rsi_df = self.rsi.apply(STOCK, period=30, window=5)
rsi_df = rsi_df.fillna(100)
rsi = rsi_df['rsi'].values.tolist()
rsis = rsi_df['rsis'].values.tolist()
# ichimokuCloud 계산
# ichimokuCloud_df = self.ichimokuCloud.apply(STOCK, c=9, b=26, l=52)
# ichimokuCloud_df = rsi_df.fillna(100)
# changeLine = rsi_df['changeLine'].values.tolist()
# baseLine = rsi_df['baseLine'].values.tolist()
# leadingSpan1 = rsi_df['leadingSpan1'].values.tolist()
# leadingSpan2 = rsi_df['leadingSpan2'].values.tolist()
temp = {"date": point_temp,
"open": open, "high": high, "low": low, "close": close, "volume": vol, "upper": upper, "lower": lower,
"avg3": avg3, "avg5": avg5, "avg10": avg10, "avg20": avg20, "avg30": avg30, "avg60": avg60,
"macd": macd, "macds": macds, "macdo": macdo,
"fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d,
"rsi": rsi, "rsis": rsis}
data = pd.DataFrame(temp)
df_final_time = pd.DatetimeIndex(point_temp)
data.index = df_final_time
data.fillna(0)
return data
def getDBData(self, stock_code, lastday, result):
tableName = 'hts'
conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, "hts.db"))
cursor = conn.cursor()
cursor.execute('SELECT ymd, hms, open, high, low, close, volume FROM ' + tableName + ' WHERE CODE=? and ymd=? order by ymd, hms', (stock_code, lastday,))
db_result = cursor.fetchall()
for rows in db_result:
ymd = rows[0] # hts.날짜
hms = rows[1] # hts.시간
open = rows[2] # hts.시가
high = rows[3] # hts.고가
low = rows[4] # hts.저가
close = rows[5] # hts.종가
vol = rows[6] # hts.거래량
temp = datetime.strptime(str(ymd) + " " + str(hms).zfill(4) + "00", '%Y%m%d %H%M%S')
result["time"].append(temp)
result["open"].append(int(open))
result["close"].append(int(close))
result["high"].append(int(high))
result["low"].append(int(low))
result["vol"].append(int(vol))
return
def vectorize(self, stock_code, given_day):
result = {"check": set(),
"time": [],
"open": [],
"close": [],
"high": [],
"low": [],
"vol": []}
for i in range(1, 10):
last_day = (datetime.strptime(given_day, '%Y%m%d') - timedelta(i)).strftime('%Y%m%d')
self.getDBData(stock_code, last_day, result)
if len(result['time']) > 0:
break
self.getDBData(stock_code, given_day, result)
# 분석을 통해서 볼린저밴드 상/하단을 계산한다.
data = self.analyze(result)
return data
if __name__ == "__main__":
stock2Vector = Stock2Vector()
fileName = "./resources/hts/252670_20220726.csv"
PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__))))))
RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
stock2Vector = Stock2Vector(RESOURCE_PATH)
# to check bying
stock_codes = {
# 252670
# 122630
"122630": ['20220725'],
}
for stock_code in stock_codes:
for given_day in stock_codes[stock_code]:
stock2Vector.vectorize(stock_code, given_day)
print ("done...")