dsyoon/DeepStock
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

init

Browse Source
This commit is contained in:
dosang.yoon
2022-06-26 23:58:57 +09:00
parent 5c1be31d03
commit c48bde70c8
6 changed files with 231 additions and 1472 deletions
Download Patch File Download Diff File Expand all files Collapse all files

194
hts/BuySellChecker.py
View File

@@ -2,6 +2,7 @@ import pandas as pd
from stockpredictor.analysis.Common import Common
from stockpredictor.analysis.Stochastic import Stochastic
from stockpredictor.analysis.RSI import RSI
from stockpredictor.analysis.MACD import MACD
class BuySellChecker:
@@ -13,6 +14,7 @@ class BuySellChecker:
self.common = Common()
self.stochastic = Stochastic()
self.rsi = RSI()
self.macd = MACD()
return
@@ -20,7 +22,7 @@ class BuySellChecker:
status = set()
# 정배열 체크
temp_status = self.common.check_RightArrange(STOCK, last_index)
temp_status = self.common.check_RightArrange(STOCK)
if temp_status != "":
status.add(temp_status)
@@ -183,60 +185,52 @@ class BuySellChecker:
def getPriceAndWeight1(self, data, i):
buy, weight, sell = -1, -1, -1
################
### sell 분석 ###
################
# 1. 볼린져밴드 상단이 최고와 종가 사이 아래에 있는 경우 매도한다.
if (data["high"][i] - data["close"][i]) / 2 + data["close"][i] > data["upper"][i]:
sell = data["high"][i]
"""
# 2. slow_k가 90이 넘으면 매도한다.
if data["slow_k"][i] >= 90:
sell = data["high"][i]
"""
if data["slow_k"][i] >= 85:
if data["slow_d"][i-1] < data["slow_k"][i-1] and data["slow_k"][i] < data["slow_d"][i]:
if i >= 3:
################
### sell 분석 ###
################
# 1. 볼린져밴드 상단이 최고와 종가 사이 아래에 있는 경우 매도한다.
#if (data["high"][i] - data["close"][i]) / 2 + data["close"][i] > data["upper"][i]:
# sell = data["high"][i]
# 2. slow_k가 90이 넘으면 매도한다.
if data["slow_k"][i] > 90:
sell = data["high"][i]
# 3. 2시 이후에는 최고가가 볼린져밴드 상단 위에 있으면 매도한다.
if i > 300 and data["high"][i] > data["upper"][i]:
sell = data["high"][i]
#if data["slow_k"][i] >= 85:
# if data["slow_d"][i-1] < data["slow_k"][i-1] and data["slow_k"][i] < data["slow_d"][i]:
# sell = data["high"][i]
##########################
### STOCHASTIC buy 분석 ###
##########################
if i < 40:
if data["low"][i] < data["lower"][i]+5:
if data["slow_k"][i-1] < 50 and data["slow_k"][i] < 55:
if data["slow_k"][i-1] < data["slow_d"][i-1] and data["slow_d"][i] < data["slow_k"][i] and data["slow_k"][i-1] < data["slow_k"][i]:
buy = data["low"][i]
else:
if data["low"][i] < data["lower"][i] + 5:
# 3. 2시 이후에는 최고가가 볼린져밴드 상단 위에 있으면 매도한다.
if i > 300 and data["high"][i] > data["upper"][i]:
sell = data["high"][i]
##########################
### buy 분석 ###
##########################
if data["low"][i] < data["lower"][i] + 5 and data["open"][i] <= data["close"][i]:
if data["slow_k"][i-1] < 30 and data["slow_k"][i] < 30:
if data["slow_k"][i-1] < data["slow_k"][i]:
buy = data["low"][i]
#############################
### STOCHASTIC weight 분석 ###
#############################
if data["slow_k"][i] in (0, 1, 2, 3):
weight = 1
if data["slow_k"][i] in (4, 5, 6, 7, 8):
weight = 1
elif data["slow_k"][i] in (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20):
weight = 1
elif data["slow_k"][i] in (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35):
weight = 1
if data["rsi"][i] < 25:
if data["rsi"][i - 2] < data["rsis"][i - 2] and data["rsi"][i - 1] < data["rsis"][i - 1] and data["rsis"][i] < data["rsi"][i]:
if data["close"][i] < data["avg5"][i]:
buy = data["close"][i]
else:
buy = data["low"][i]
weight = 1
###################
### RSI buy 분석 ###
###################
if data["rsi"][i] < 25:
if data["rsi"][i - 2] < data["rsis"][i - 2] and data["rsi"][i - 1] < data["rsis"][i - 1] and data["rsis"][i] < data["rsi"][i]:
if data["close"][i] < data["avg5"][i]:
buy = data["close"][i]
else:
buy = data["low"][i]
#############################
### STOCHASTIC weight 분석 ###
#############################
if data["slow_k"][i] in (0, 1, 2, 3):
weight = 1
if data["slow_k"][i] in (4, 5, 6, 7, 8):
weight = 1
elif data["slow_k"][i] in (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20):
weight = 1
elif data["slow_k"][i] in (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35):
weight = 1
return buy, weight, sell
@@ -301,63 +295,14 @@ class BuySellChecker:
return buy, weight, sell
def getPriceAndWeight_1minute(self, data, i):
def getPriceAndWeight3(self, data, i):
buy, weight, sell = -1, -1, -1
################
### sell 분석 ###
################
# 1. 볼린져밴드 상단이 최고와 종가 사이 아래에 있는 경우 매도한다.
if (data["high"][i] - data["close"][i]) / 2 + data["close"][i] > data["upper"][i]:
sell = data["high"][i]
if data["slow_k"][i] >= 85:
if data["slow_d"][i - 1] < data["slow_k"][i - 1] and data["slow_k"][i] < data["slow_d"][i]:
sell = data["high"][i]
# 3. 2시 이후에는 최고가가 볼린져밴드 상단 위에 있으면 매도한다.
if i > 300 and data["high"][i] > data["upper"][i]:
sell = data["high"][i]
##########################
### STOCHASTIC buy 분석 ###
##########################
if i < 40:
pre_slow = data["slow_k"][i - 1] / data["slow_d"][i - 1] - 1
now_slow = data["slow_k"][i] / data["slow_d"][i] - 1
if pre_slow < 0 and 0 < now_slow:
if data["slow_k"][i] <= 35:
if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.35:
if data["slow_k"][i - 1] < data["slow_d"][i - 1] and data["slow_d"][i] < data["slow_k"][i]:
if data['avg3'][i] <= data['avg2'][i]:
if data["open"][i] < data["close"][i]:
buy = data["close"][i]
else:
buy = data["low"][i]
else:
pre_slow = data["slow_k"][i - 1] / data["slow_d"][i - 1] - 1
now_slow = data["slow_k"][i] / data["slow_d"][i] - 1
if pre_slow < 0 and pre_slow < now_slow and -0.15 < now_slow:
if data["slow_k"][i] <= 10:
if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.35:
if data["slow_k"][i - 1] < data["slow_d"][i - 1] and data["slow_d"][i] < data["slow_k"][i]:
if data['avg3'][i] <= data['avg2'][i]:
if data["close"][i] < data["avg5"][i]:
buy = data["close"][i]
else:
buy = data["low"][i]
#############################
### STOCHASTIC weight 분석 ###
#############################
if data["slow_k"][i] in (0, 1, 2, 3):
weight = 1
if data["slow_k"][i] in (4, 5, 6, 7, 8):
weight = 1
elif data["slow_k"][i] in (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20):
weight = 1
elif data["slow_k"][i] in (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35):
weight = 1
# 381: 어제 날짜 데이터 개수
if i >= 381 + 5:
if data["macdo"][i] < 0 and data["macd"][i] < -5:
if data["macd"][i-3] > data["macd"][i-2] and data["macd"][i-2] > data["macd"][i-1] and data["macd"][i-1] < data["macd"][i]:
buy = data["close"][i]
return buy, weight, sell
@@ -370,8 +315,8 @@ class BuySellChecker:
vol = result["vol"]
close_df = pd.DataFrame(close)
avg2_list = close_df.rolling(window=2).mean().fillna(close[0]).values.tolist()
avg2 = [item[0] for item in avg2_list]
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()
@@ -382,8 +327,8 @@ class BuySellChecker:
avg60 = [item[0] for item in avg60_list]
df = pd.DataFrame(close)
max20 = df.rolling(window=10).mean()
stddev20 = df.rolling(window=10).std()
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
@@ -401,7 +346,7 @@ class BuySellChecker:
STOCK = []
for i in range(len(open)):
STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i],
'avg2': avg2[i], 'avg5': avg5[i],'avg10': avg10[i],'avg30': avg30[i],'avg60': avg60[i]})
'avg3': avg3[i], 'avg5': avg5[i],'avg10': avg10[i],'avg30': avg30[i],'avg60': avg60[i]})
# stochastic 계산
stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5)
@@ -410,6 +355,13 @@ class BuySellChecker:
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)
@@ -418,10 +370,32 @@ class BuySellChecker:
temp = {"date": point_temp,
"open": open, "high": high, "low": low, "close": close, "volume": vol, "upper": upper, "lower": lower,
"avg2": avg2, "avg5": avg5, "avg10": avg10, "avg30": avg30, "avg60": avg60,
"fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d, "rsi": rsi, "rsis": rsis}
"avg3": avg3, "avg5": avg5, "avg10": avg10, "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
return data
return data
def checkTransaction(self, data, stock_code):
size = len(data["close"])
bsLine = {}
bsLine['buy'] = [-1 for i in range(size)]
bsLine['weight'] = [-1 for i in range(size)]
bsLine['sell'] = [-1 for i in range(size)]
for i in range(size):
if stock_code == "252670":
buy, weight, sell = self.getPriceAndWeight3(data, i)
else:
buy, weight, sell = self.getPriceAndWeight3(data, i)
bsLine['buy'][i] = buy
bsLine['weight'][i] = weight
bsLine['sell'][i] = sell
return bsLine

58
hts/HTS.py
View File

@@ -1,5 +1,7 @@
import win32com.client
import csv
import time
from datetime import datetime
from hts.OrderItem import OrderItem
class HTS:
@@ -371,7 +373,7 @@ class HTS:
return
# 주식 현재가 조회
def writeStockData(self, stock_code, given_day):
def writeStockData(self, stock_code, today):
objCpCybos = win32com.client.Dispatch("CpUtil.CpCybos")
bConnect = objCpCybos.IsConnect
if (bConnect == 0):
@@ -381,11 +383,11 @@ class HTS:
# 차트 객체 구하기
objStockChart = win32com.client.Dispatch("CpSysDib.StockChart")
outfp = open("./data/"+stock_code+"_"+given_day+".csv", mode="w", encoding="utf-8")
outfp = open("./data/"+stock_code+"_"+today+".csv", mode="w", encoding="utf-8")
objStockChart.SetInputValue(0, 'A' + stock_code) # 종목 코드
objStockChart.SetInputValue(1, ord('1')) # 1: 기간으로 조회, 2: 개수로 조회
objStockChart.SetInputValue(2, given_day) # 기간 조회 시, 시작일
objStockChart.SetInputValue(3, given_day) # 기간 조회 시, 종료일
objStockChart.SetInputValue(2, today) # 기간 조회 시, 시작일
objStockChart.SetInputValue(3, today) # 기간 조회 시, 종료일
objStockChart.SetInputValue(4, 400) # 조회 시 가져오는 Line 개수
objStockChart.SetInputValue(5, [0, 1, 2, 3, 4, 5, 8]) # 날짜,시간,시가,고가,저가,종가,거래량
objStockChart.SetInputValue(6, ord('m')) # '차트 주가 - 월(M), 주(W), 일(D), 시(H), 분(m), 초(S) 차트 요청
@@ -428,9 +430,47 @@ class HTS:
outFp.close()
return
def getCSV(self, fileName, given_day, result):
with open(fileName, 'r') as infp:
reader = csv.reader(infp)
next(reader)
for rows in reader:
days = rows[0] # data.날짜
time = rows[1] # data.시간
open_v = rows[2] # data.시가
high = rows[3] # data.고가
low = rows[4] # data.저가
close = rows[5] # data.종가
vol = rows[6] # data.거래량
start_time = datetime.strptime(given_day + " 090000", '%Y%m%d %H%M%S')
temp = datetime.strptime(str(days) + " " + str(time).zfill(4) + "00", '%Y%m%d %H%M%S')
if temp < start_time:
continue
result["time"].append(temp)
result["open"].append(int(open_v))
result["close"].append(int(close))
result["high"].append(int(high))
result["low"].append(int(low))
result["vol"].append(int(vol))
return
# 주식 현재가 조회
def getRealTime(self, stock_code, given_day, result):
int_given_day = int(given_day)
def getRealTime(self, stock_code, lastday, today):
result = {"check": set(),
"time": [],
"open": [],
"close": [],
"high": [],
"low": [],
"vol": []}
self.getCSV("./data/" + stock_code + "_" + lastday + ".csv", today, result)
int_given_day = int(today)
objCpCybos = win32com.client.Dispatch("CpUtil.CpCybos")
bConnect = objCpCybos.IsConnect
if (bConnect == 0):
@@ -442,8 +482,8 @@ class HTS:
objStockChart.SetInputValue(0, 'A'+stock_code) # 종목 코드
objStockChart.SetInputValue(1, ord('1')) # 1: 기간으로 조회, 2: 개수로 조회
objStockChart.SetInputValue(2, given_day) # 기간 조회 시, 시작일
objStockChart.SetInputValue(3, given_day) # 기간 조회 시, 종료일
objStockChart.SetInputValue(2, today) # 기간 조회 시, 시작일
objStockChart.SetInputValue(3, today) # 기간 조회 시, 종료일
objStockChart.SetInputValue(4, 400) # 조회 시 가져오는 Line 개수
objStockChart.SetInputValue(5, [0, 1, 2, 3, 4, 5, 8]) # 날짜,시간,시가,고가,저가,종가,거래량
objStockChart.SetInputValue(6, ord('m')) # '차트 주가 - 월(M), 주(W), 일(D), 시(H), 분(m), 초(S) 차트 요청
@@ -454,7 +494,7 @@ class HTS:
size = objStockChart.GetHeaderValue(3)
#print("날짜", "시간", "시가", "고가", "저가", "종가", "거래량")
start_time = datetime.strptime(given_day + " 090000", '%Y%m%d %H%M%S')
start_time = datetime.strptime(today + " 090000", '%Y%m%d %H%M%S')
for i in range(size-1, -1, -1):
int_day = objStockChart.GetDataValue(0, i)
int_time = objStockChart.GetDataValue(1, i)

180
hts/HTS_122630.py
View File

@@ -1,180 +0,0 @@
import time
import os
from datetime import datetime
import pandas as pd
from hts.HTS import HTS
from hts.OrderType import OrderType
from hts.BuySellChecker import BuySellChecker
from hts.OrderChecker import OrderChecker
class HTS_122630 (HTS):
buySellChecker = None
stock_code = None
buy_count = None
def __init__(self, stock_code, buy_count):
super().__init__()
self.buySellChecker = BuySellChecker()
self.stock_code = stock_code
self.buy_count = buy_count
return
def checkTransaction(self, data):
size = len(data["close"])
bsLine = {}
bsLine['buy'] = [-1 for i in range(size)]
bsLine['weight'] = [-1 for i in range(size)]
bsLine['sell'] = [-1 for i in range(size)]
i = size - 1
if i < 5:
return -1, -1, -1
buy, weight, sell = self.buySellChecker.getPriceAndWeight2(data, i)
return buy, weight, sell
def getSellingPrice(self, final_price):
# 만약 잔고가 있으면 장부가보다 5원 높게 매도한다.
jangoDic = self.requstJango()
if jangoDic and len(jangoDic.keys()) > 0:
for code in jangoDic:
if jangoDic[code]['매도가능'] > 0:
if final_price >= jangoDic[code]['장부가'] + 5:
return jangoDic[code]['매도가능'], final_price
else:
# 장부가 가격의 마지막 자리를 0으로 만든다. (2090 -> 2090, 2092 -> 2090, 2098 -> 2090)
sell_price = int(jangoDic[code]['장부가'] / 10) * 10
# 장부가의 마지막 자리수를 가져온다.
last_number = int(jangoDic[code]['장부가']) % 10
if last_number in [0, 1, 2, 3]:
# 장부가의 마지막 자리수가 0,1,2,3 이라면 (2090, 2091, 2092 -> 2095 에 매도)
return jangoDic[code]['매도가능'], sell_price + 5
elif last_number in [4, 5, 6, 7]:
# 장부가의 마지막 자리수가 4,5,6,7 이라면 (2093, 2094, 2095, 2096 -> 2100 에 매도)
return jangoDic[code]['매도가능'], sell_price + 10
else:
# 장부가의 마지막 자리수가 8,9 라면 (2098, 2099 -> 2105 에 매도)
return jangoDic[code]['매도가능'], sell_price + 15
return 0, 0
def buyRealTime(self, GIVEN_DAY):
orderChecker = OrderChecker(self.stock_code)
timecheckList = pd.read_csv("timecheck.csv").values.tolist()
timecheck = {GIVEN_DAY + " " + str(second).zfill(6):False for second, check in timecheckList}
result = {"check": set(),
"time": [],
"open": [],
"close": [],
"high": [],
"low": [],
"vol": []}
final_price = 0
print ("START...")
THIS_TIME = datetime.now()
while datetime.strptime(GIVEN_DAY + " 070000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(GIVEN_DAY + " 15200", '%Y%m%d %H%M%S'):
if datetime.strptime(GIVEN_DAY + " 090100", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(GIVEN_DAY + " 151500", '%Y%m%d %H%M%S'):
if THIS_TIME.strftime('%Y%m%d %H%M%S') in timecheck and not timecheck[THIS_TIME.strftime('%Y%m%d %H%M%S')]:
# 데이터를 가지고 온다.
self.getRealTime(self.stock_code, GIVEN_DAY, result)
# 분석을 통해서 볼린저밴드 상/하단을 계산한다.
data = self.buySellChecker.analyze(result)
# 사야 할 시점/가격과 팔아야 할 시점/가격을 체크한다.
bs_buy_price, bs_weight, bs_sell_price = self.checkTransaction(data)
data_size = len(data["close"])
final_price = data["close"][data_size-1]
if bs_buy_price > 0:
# 기본 100 주에 가중치를 추가해서 매수한다.
#BUY_COUNT = int(self.buy_count * bs_weight)
BUY_COUNT = int(self.buy_count * 1)
# 매수를 주문한다.
orderNum = self.requestOrder(OrderType.buy, self.stock_code, BUY_COUNT , bs_buy_price)
# 미체결 기록을 가져온다.
ORDER_LIST = self.requestOrderList()
# 매수 주문을 기록한다.
orderListToCancel = orderChecker.add(self.stock_code, OrderType.buy, orderNum, BUY_COUNT, bs_buy_price, ORDER_LIST)
# 두 시간 이전 미체결을 모두 취소한다.
self.cancelOrderList(orderListToCancel)
# 로그 출력
print("BUY", THIS_TIME.strftime('%Y%m%d %H%M%S'), BUY_COUNT, bs_buy_price, len(orderListToCancel), len(ORDER_LIST))
if bs_sell_price > 0:
# 미체결 기록을 가져온다.
ORDER_LIST = self.requestOrderList()
# 매도 주문을 기록을 가져온다.
orderListToCancel = orderChecker.remove(self.stock_code, OrderType.sell, ORDER_LIST)
# 매도 미체결을 모두 취소한다.
self.cancelOrderList(orderListToCancel)
# 매도 가격을 가져온다.
selling_count, selling_price = self.getSellingPrice(final_price)
# 분석된 가격으로 매도 요청한다.
if selling_count != 0 and selling_price != 0:
# 매도를 요청한다.
orderNum = self.requestOrder(OrderType.sell, self.stock_code, selling_count, selling_price)
# 로그 출력
print("SELL", THIS_TIME.strftime('%Y%m%d %H%M%S'), selling_count, selling_price, len(orderListToCancel), len(ORDER_LIST))
# 로그 출력
print("TIMECHECK", THIS_TIME, final_price, data["low"][data_size-1], data["slow_k"][data_size-1], data["slow_d"][data_size-1])
timecheck[THIS_TIME] = True
if datetime.strptime(GIVEN_DAY + " 151530", '%Y%m%d %H%M%S') < THIS_TIME:
####
# 손해 보지 않는 가격에 매도한다.
####
# 주문 리스트를 가져온다.
orderList = self.requestOrderList()
# 15:10:00 이후라면 모든 미체결 취소한다.
self.cancelOrderList(orderList)
# 매도 가격을 가져온다.
selling_count, selling_price = self.getSellingPrice(final_price)
# 분석된 가격으로 매도 요청한다.
if selling_count != 0 and selling_price != 0:
orderNum = self.requestOrder(OrderType.sell, self.stock_code, selling_count, selling_price)
# 로그 출력
print("SELL", THIS_TIME, selling_count, selling_price)
break
time.sleep(0.9)
THIS_TIME = datetime.now()
return
if __name__ == "__main__":
today = datetime.today()
PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__))))
RESOURCE_DIR = PROJECT_HOME + "/resources/analysis/"+today.strftime("%Y%m%d")
# KODEX 인버스 * 2
stock_code = "122630"
buy_count = 120
hts = HTS_122630(stock_code, buy_count)
given_day = datetime.today().strftime('%Y%m%d')
hts.writeStockData(stock_code, "20220620")
#hts.buyRealTime(given_day)
print ("done...")

1066
hts/HTS_5min.py
View File

File diff suppressed because it is too large Load Diff

41
hts/HTS_252670.py → hts/HTS_ETF.py
View File

@@ -1,6 +1,6 @@
import time
import os
from datetime import datetime
from datetime import datetime, timedelta
import pandas as pd
from hts.HTS import HTS
@@ -32,11 +32,8 @@ class HTS_252670 (HTS):
bsLine['weight'] = [-1 for i in range(size)]
bsLine['sell'] = [-1 for i in range(size)]
i = size - 1
if i < 5:
return -1, -1, -1
buy, weight, sell = self.buySellChecker.getPriceAndWeight1(data, i)
last_index = size - 1
buy, weight, sell = self.buySellChecker.getPriceAndWeight1(data, last_index)
return buy, weight, sell
def getSellingPrice(self, final_price):
@@ -64,32 +61,25 @@ class HTS_252670 (HTS):
return 0, 0
def buyRealTime(self, GIVEN_DAY):
def buyRealTime(self, lastday, today):
orderChecker = OrderChecker(self.stock_code)
timecheckList = pd.read_csv("timecheck.csv").values.tolist()
timecheck = {GIVEN_DAY + " " + str(second).zfill(6):False for second, check in timecheckList}
result = {"check": set(),
"time": [],
"open": [],
"close": [],
"high": [],
"low": [],
"vol": []}
timecheck = {today + " " + str(second).zfill(6):False for second, check in timecheckList}
print ("START...")
THIS_TIME = datetime.now()
while datetime.strptime(GIVEN_DAY + " 070000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(GIVEN_DAY + " 15200", '%Y%m%d %H%M%S'):
while datetime.strptime(today + " 070000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 15200", '%Y%m%d %H%M%S'):
if datetime.strptime(GIVEN_DAY + " 090100", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(GIVEN_DAY + " 151500", '%Y%m%d %H%M%S'):
if datetime.strptime(today + " 090100", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 151500", '%Y%m%d %H%M%S'):
if THIS_TIME.strftime('%Y%m%d %H%M%S') in timecheck and not timecheck[THIS_TIME.strftime('%Y%m%d %H%M%S')]:
# 데이터를 가지고 온다.
self.getRealTime(self.stock_code, GIVEN_DAY, result)
result = self.getRealTime(self.stock_code, lastday, today)
# 분석을 통해서 볼린저밴드 상/하단을 계산한다.
data = self.buySellChecker.analyze(result)
# 사야 할 시점/가격과 팔아야 할 시점/가격을 체크한다.
bs_buy_price, bs_weight, bs_sell_price = self.checkTransaction(data)
data_size = len(data["close"])
@@ -140,7 +130,7 @@ class HTS_252670 (HTS):
data["rsi"][data_size-1], data["rsis"][data_size-1]))
timecheck[THIS_TIME] = True
if datetime.strptime(GIVEN_DAY + " 151530", '%Y%m%d %H%M%S') < THIS_TIME:
if datetime.strptime(today + " 151530", '%Y%m%d %H%M%S') < THIS_TIME:
####
# 손해 보지 않는 가격에 매도한다.
####
@@ -177,9 +167,14 @@ if __name__ == "__main__":
buy_count = 120
hts = HTS_252670(stock_code, buy_count)
given_day = datetime.today().strftime('%Y%m%d')
today_str = today.strftime('%Y%m%d')
lastday_str = ""
for i in range(1, 10):
lastday_str = (today - timedelta(i)).strftime('%Y%m%d')
if os.path.isfile("./data/" + stock_code + "_" + lastday_str + ".csv"):
break
hts.buyRealTime(given_day)
hts.writeStockData(stock_code, "20220620")
hts.buyRealTime(lastday_str, today_str)
hts.writeStockData(stock_code, today_str)
print ("done...")

164
hts/Simulation.py
View File

@@ -1,5 +1,6 @@
import os
from math import nan
from datetime import datetime, timedelta
import csv
import pandas as pd
import plotly.graph_objects as go
from plotly import subplots
@@ -19,110 +20,102 @@ class Simulation:
return
def getCSV(self, fileName, given_day, result):
data = pd.read_csv(fileName)
with open(fileName, 'r') as infp:
reader = csv.reader(infp)
next(reader)
days = data.날짜
time = data.시간
open = data.시가
close = data.종가
high = data.고가
low = data.저가
vol = data.거래량
start_time = datetime.strptime(given_day + " 090000", '%Y%m%d %H%M%S')
for rows in reader:
days = rows[0] # data.날짜
time = rows[1] # data.시간
open_v = rows[2] # data.시가
high = rows[3] # data.고가
low = rows[4] # data.저가
close = rows[5] # data.종가
vol = rows[6] # data.거래량
start_time = datetime.strptime(given_day + " 090000", '%Y%m%d %H%M%S')
for i in range(len(data)):
temp = datetime.strptime(str(days[i]) + " " + str(time[i]).zfill(4)+"00", '%Y%m%d %H%M%S')
if temp < start_time:
continue
temp = datetime.strptime(str(days) + " " + str(time).zfill(4)+"00", '%Y%m%d %H%M%S')
if temp < start_time:
continue
if temp not in result["check"]:
result["check"].add(temp)
result["time"].append(temp)
result["open"].append(open[i])
result["close"].append(close[i])
result["high"].append(high[i])
result["low"].append(low[i])
result["vol"].append(vol[i])
result["open"].append(int(open_v))
result["close"].append(int(close))
result["high"].append(int(high))
result["low"].append(int(low))
result["vol"].append(int(vol))
return
def checkTransaction(self, data):
size = len(data["close"])
bsLine = {}
bsLine['buy'] = [-1 for i in range(size)]
bsLine['weight'] = [-1 for i in range(size)]
bsLine['sell'] = [-1 for i in range(size)]
for i in range(6, size-5):
if self.stock_code == "252670":
buy, weight, sell = self.buySellChecker.getPriceAndWeight1(data, i)
else:
buy, weight, sell = self.buySellChecker.getPriceAndWeight2(data, i)
bsLine['buy'][i] = buy
bsLine['weight'][i] = weight
bsLine['sell'][i] = sell
return bsLine
def draw(self, stock_code, given_day, data, bsLine):
buy_line = bsLine['buy']
sell_line = bsLine['sell']
# 어제 데이터는 지운다.
data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])]
buy_line = bsLine['buy'][381:]
sell_line = bsLine['sell'][381:]
#buy_line = bsLine['buy']
#sell_line = bsLine['sell']
# 그래프 설정을 위한 변수를 생성한다.
data['open'] = pd.to_numeric(data['open'])
data['high'] = pd.to_numeric(data['high'])
data['low'] = pd.to_numeric(data['low'])
data['close'] = pd.to_numeric(data['close'])
data['volume'] = pd.to_numeric(data['volume'])
data['avg2'] = pd.to_numeric(data['avg2'])
data['avg5'] = pd.to_numeric(data['avg5'])
data['avg10'] = pd.to_numeric(data['avg10'])
data['avg30'] = pd.to_numeric(data['avg30'])
data['avg60'] = pd.to_numeric(data['avg60'])
data["fast_k"] = pd.to_numeric(data['fast_k'])
data["slow_k"] = pd.to_numeric(data['slow_k'])
data["slow_d"] = pd.to_numeric(data['slow_d'])
data["rsi"] = pd.to_numeric(data['rsi'])
data["rsis"] = pd.to_numeric(data['rsis'])
data = data.astype({'open': 'int',
'high': 'int',
'low': 'int',
'close': 'int',
'volume': 'int',
'avg3': 'float',
'avg5': 'float',
'avg10': 'float',
'avg30': 'float',
'avg60': 'float',
'fast_k': 'float',
'slow_k': 'float',
'slow_d': 'float',
'rsi': 'float',
'rsis': 'float'
})
buy_colors = []
for i in range(len(buy_line)):
if buy_line[i] < 0:
buy_colors.append("#ffffff")
buy_line[i] = data["lower"][0]
buy_line[i] = nan
else:
buy_colors.append("#ff00ff")
sell_colors = []
for i in range(len(sell_line)):
if sell_line[i] < 0:
sell_colors.append("#ffffff")
sell_line[i] = data["lower"][0]
sell_line[i] = nan
else:
sell_colors.append("#00ced1")
# 그래프를 설정한다.
buy_check = go.Scatter(x=data['date'], y=buy_line, mode='markers', name="buy", marker=dict(size=14, color=buy_colors, line_width=0))
sell_check = go.Scatter(x=data['date'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0))
bolinger_upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#8B4513')
bolinger_lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#8B4513')
avg2 = go.Scatter(x=data['date'], y=data["avg2"], name="avg2", line_color='#800080')
avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#800080')
upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#000000')
lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#000000')
avg3 = go.Scatter(x=data['date'], y=data["avg3"], name="avg3", line_color='#000000')
avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#00FF00')
avg10 = go.Scatter(x=data['date'], y=data["avg10"], name="avg10", line_color='#ff00ff')
avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#00ffff')
avg60 = go.Scatter(x=data['date'], y=data["avg60"], name="avg60", line_color='#008000')
avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#FFA500')
#avg60 = go.Scatter(x=data['date'], y=data["avg60"], name="avg60", line_color='#008000')
candle_stick = go.Candlestick(x=data['date'], open=data['open'], high=data['high'], low=data['low'], close=data['close'], increasing_line_color='red', decreasing_line_color='blue')
volume_line = go.Scatter(x=data['date'], y=data["volume"], mode='lines', name='volume')
fast_k_line = go.Scatter(x=data['date'], y=data["fast_k"], mode='lines', name='fast_k')
#fast_k_line = go.Scatter(x=data['date'], y=data["fast_k"], mode='lines', name='fast_k')
macd_line = go.Scatter(x=data['date'], y=data["macd"], mode='lines', name='macd')
macd_s_line = go.Scatter(x=data['date'], y=data["macds"], mode='lines', name='macds')
macd_o_line = go.Scatter(x=data['date'], y=data["macdo"], mode='lines', name='macdo')
slow_k_line = go.Scatter(x=data['date'], y=data["slow_k"], mode='lines', name='slow_k')
slow_d_line = go.Scatter(x=data['date'], y=data["slow_d"], mode='lines', name='slow_d')
rsi_line = go.Scatter(x=data['date'], y=data["rsi"], mode='lines', name='rsi')
rsis_line = go.Scatter(x=data['date'], y=data["rsis"], mode='lines', name='rsis')
#candle_data = [candle_stick, bolinger_upper, bolinger_lower, buy_check, sell_check, avg1, avg2, avg5, avg10, avg20, avg30, avg40, avg50, avg60]
candle_data = [candle_stick, bolinger_upper, bolinger_lower, avg2, avg5, avg10, avg30, avg60, buy_check, sell_check]
candle_data = [candle_stick, upper, lower, avg3, avg5, avg10, avg30, buy_check, sell_check]
volume_data = [volume_line]
stochastic_data = [fast_k_line, slow_k_line, slow_d_line]
macd_data = [macd_line, macd_s_line, macd_o_line]
stochastic_data = [slow_k_line, slow_d_line]
rsi_data = [rsi_line, rsis_line]
# 그래프를 그린다.
@@ -132,15 +125,17 @@ class Simulation:
fig.show()
"""
fig = subplots.make_subplots(rows=4, cols=1, subplot_titles=('캔들', "거래량", "스토캐스틱", "RSI"))
fig = subplots.make_subplots(rows=5, cols=1, subplot_titles=('캔들', "거래량", "MACD", "스토캐스틱", "RSI"))
for trace in candle_data:
fig.append_trace(trace, 1, 1)
for trace in volume_data:
fig.append_trace(trace, 2, 1)
for trace in stochastic_data:
for trace in macd_data:
fig.append_trace(trace, 3, 1)
for trace in rsi_data:
for trace in stochastic_data:
fig.append_trace(trace, 4, 1)
for trace in rsi_data:
fig.append_trace(trace, 5, 1)
#fig.update_xaxes(nticks=5)
#fig.update_layout(height=1800, title=stock_code + "_" + given_day, xaxis_rangeslider_visible=False)
@@ -149,12 +144,12 @@ class Simulation:
buy_count = len(df.loc[df["buy"] > 0])
sell_count = len(df.loc[df["sell"] > 0])
fig.update_layout(height=1800, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
fig.update_layout(height=5000, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
fig.show()
return
def simulate(self, GIVEN_DAY):
def simulate(self, days):
result = {"check": set(),
"time": [],
"open": [],
@@ -163,30 +158,31 @@ class Simulation:
"low": [],
"vol": []}
last_day = days[0]
today = days[1]
# 데이터를 가지고 온다.
self.getCSV("./data/"+self.stock_code+"_"+GIVEN_DAY+".csv", GIVEN_DAY, result)
self.getCSV("./data/" + self.stock_code + "_" + last_day + ".csv", last_day, result)
self.getCSV("./data/" + self.stock_code + "_" + today + ".csv", today, result)
# 분석을 통해서 볼린저밴드 상/하단을 계산한다.
data = self.buySellChecker.analyze(result)
# 사야 할 시점과 팔아야 할 시점을 체크한다.
bsLine = self.checkTransaction(data)
bsLine = self.buySellChecker.checkTransaction(data, self.stock_code)
# 그래프를 그린다.
self.draw(self.stock_code, GIVEN_DAY, data, bsLine)
self.draw(self.stock_code, today, data, bsLine)
return
if __name__ == "__main__":
today = datetime.today()
PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__))))
RESOURCE_DIR = PROJECT_HOME + "/resources/analysis/"+today.strftime("%Y%m%d")
stock_codes = {
"252670": ['20220520', '20220128', '20220121', '20220120', '20211026'],
"122630": ['20211026', '20211025', '20211022', '20211021', '20211020']
# "252670": ['20220620', '20220621', '20220622', '20220623', '20220624'],
# "122630": ['20220620', '20220621', '20220622', '20220623', '20220624']
"252670": [('20220620', '20220621')],
"122630": [('20220620', '20220621')]
}
for stock_code in stock_codes: