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complex_prediction.py
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complex_prediction.py
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import yfinance as yf
import tkinter.ttk as ttk
import tkinter
from tkinter import *
import pandas as pd
from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import r2_score
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
from sklearn.linear_model import Ridge
from datetime import datetime
import datetime as dt
from tkinter import messagebox
#import plotly.graph_objects as go
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
#import matplotlib.finance
import mpl_finance
from mpl_finance import candlestick_ohlc
knn = KNeighborsClassifier(n_neighbors=1)
#importing pairs from .txt file
PAIRS_FILE_NAME = 'pairs.txt'
pairs_file = open(PAIRS_FILE_NAME)
pairs = [line.strip() for line in pairs_file.readlines()]
pairs_file.close()
scale = StandardScaler()
normalList = list()
tomorrowList = list()
tomorrowList_result = list()
tkinter.max = 0
tkinter.timeframe = ""
interval = ""#### 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d
def dataDownload(pair):
pair = pair[:3] + pair[4:] + "=X"
print("pair: ", pair)
bt = yf.Ticker(pair)
#return yf.download(pair, period = "60d",interval= "1h", group_by = "ticker")
interval = tkinter.timeframe
if interval != "1d":
return bt.history(period="60d", interval= interval)
else:
return bt.history(period="max", interval=interval)
def Analysis(pair):
tomorrow_datetime = ""
open_total = list()
close_total = list()
high_total = list()
low_total = list()
tkinter.pair = variable.get()
q = dataDownload(pair)
df = pd.DataFrame(q)
print(df)
if tkinter.timeframe != "1d":
date = df.reset_index()['Datetime']
open_prediction = predict_open(df)
close_prediction = predict_close(df)
high_prediction = predict_high(df)
low_prediction = predict_low(df)
else:
date = df.reset_index()['Date']
#print(len(date))
open_prediction = predict_open_1d(df)
close_prediction = predict_close_1d(df)
high_prediction = predict_high_1d(df)
low_prediction = predict_low_1d(df)
#print(open_prediction)
#print(close_prediction)
#print(high_prediction)
#print(low_prediction)
openn = df[['Open']]
close = df[['Close']]
high = df[['High']]
low = df[['Low']]
normalList = calc_normal()
today_year = date[len(date) - 1].strftime("%Y")
#print(today_year)
today_month = date[len(date) - 1].strftime("%m")
today_day = date[len(date) - 1].strftime("%d")
length = len(date)
for i in range(7):
if int(date[length - i -1].strftime("%Y")) < int(today_year):
break
tomorrowList_result.append(date[length - i * tkinter.max -1])
open_total.append(openn.iloc[length - i * tkinter.max -1][0])
close_total.append(close.iloc[length - i * tkinter.max -1][0])
high_total.append(high.iloc[length - i * tkinter.max -1][0])
low_total.append(low.iloc[length - i * tkinter.max-1][0])
mean_open = 0
mean_close = 0
mean_high = 0
mean_low = 0
for i in range(5):
tomorrow = calc_tomorrow(int(today_year),int(today_month),int(today_day))
open_total.append(open_prediction[i][0])
close_total.append(close_prediction[i][0])
high_total.append(high_prediction[i][0])
low_total.append(low_prediction[i][0])
today_year = tomorrow[0]
today_month = tomorrow[1]
today_day = tomorrow[2]
tomorrow_datetime_str = today_year+ "-" +today_month +"-"+today_day+ " " + "00:00:00"
tomorrow_datetime = datetime.strptime(tomorrow_datetime_str,"%Y-%m-%d %H:%M:%S")
tomorrowList_result.append(tomorrow_datetime)
with open('prediction_result.txt', 'w+') as filehandle:
Big_openList = list()
Big_closeList = list()
Big_highList = list()
Big_lowList = list()
for j in range(5):
Big_openList = Big_openList + open_prediction[j]
Big_closeList = Big_closeList + close_prediction[j]
Big_highList = Big_highList + high_prediction[j]
Big_lowList = Big_lowList + low_prediction[j]
print(Big_openList)
filehandle.write('DateTime\t')
filehandle.write('Open\t')
filehandle.write('Close\t')
filehandle.write('High\t')
filehandle.write('Low\n')
for j in range(len(tomorrowList)):
filehandle.write('%s\t' % str(tomorrowList[j]))
filehandle.write('%s\t' % str(Big_openList[j]))
filehandle.write('%s\t' % str(Big_closeList[j]))
filehandle.write('%s\t' % str(Big_highList[j]))
filehandle.write('%s\n' % str(Big_lowList[j]))
print(tomorrowList_result)
print(open_total)
print(close_total)
print(high_total)
print(low_total)
df1 = pd.DataFrame(list(zip(tomorrowList_result, open_total, close_total, high_total, low_total)),
columns=['DateTime', 'Open', 'Close', 'High', 'Low'])
graph_data(df1)
#print(open_prediction)
#print(close_prediction)
#print(high_prediction)
#print(low_prediction)
def calc_tomorrow(year,month,day):
tomorrowList = list()
if day == 31:
if month == 12:
month = 1
day = 1
year = year+1
tomorrowList.append(str(year))
tomorrowList.append(str(month))
tomorrowList.append(str(day))
return tomorrowList
else:
month = month + 1
day = 1
tomorrowList.append(str(year))
tomorrowList.append(str(month))
tomorrowList.append(str(day))
return tomorrowList
if day == 30:
if month == 4 or month ==6 or month ==9 or month == 11:
month =month+1
day = 1
tomorrowList.append(str(year))
tomorrowList.append(str(month))
tomorrowList.append(str(day))
return tomorrowList
day = day +1
tomorrowList.append(str(year))
tomorrowList.append(str(month))
tomorrowList.append(str(day))
return tomorrowList
def graph_data(df):
plt.style.use('ggplot')
#df['DateTime'] = df['DateTime'].map(mdates.date2num)
ohlc = df.loc[:, ['DateTime', 'Open', 'High', 'Low', 'Close']]
ohlc['DateTime'] = pd.to_datetime(ohlc['DateTime'], utc=True)
ohlc['DateTime'] = ohlc['DateTime'].apply(mdates.date2num)
ohlc = ohlc.astype(float)
# Creating Subplots
fig, ax = plt.subplots()
candlestick_ohlc(ax, ohlc.values, width=0.6, colorup='green', colordown='red', alpha=0.8);
# Customize graph.
##########################
plt.xlabel('Date')
plt.ylabel('Price')
plt.title(tkinter.pair)
# Format time.
ax.xaxis_date()
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d %H:%M:%S"))
ax.xaxis.set_major_locator(mticker.MaxNLocator(15))
ax.grid(True)
fig.autofmt_xdate()
fig.tight_layout()
plt.gcf().autofmt_xdate() # Beautify the x-labels
plt.autoscale(tight=True)
plt.legend()
plt.subplots_adjust(left=0.09, bottom=0.20, right=0.94, top=0.90, wspace=0.2, hspace=0)
plt.show()
# Save graph to file.
plt.savefig('mpl_finance-prediction.png')
def calc_normal():
if tkinter.timeframe == "1m":
frame = 1
elif tkinter.timeframe == "2m":
frame = 2
elif tkinter.timeframe == "5m":
frame = 5
elif tkinter.timeframe == "15m":
frame = 15
elif tkinter.timeframe == "30m":
frame = 30
elif tkinter.timeframe == "60m":
frame = 60
elif tkinter.timeframe == "1d":
frame = 24*60
normal_min = 0
normal_hour = 0
normalList = list()
tkinter.max = int(24*60/frame)
for i in range(tkinter.max):
if normal_min >= 60:
normal_hour += 1
normal_min = 0
if normal_min < 10:
normal_minstr = "0" + str(normal_min)
else:
normal_minstr = str(normal_min)
if normal_hour < 10:
normal_hourstr = "0" + str(normal_hour)
else:
normal_hourstr = str(normal_hour)
normal = normal_hourstr + normal_minstr
normalList.append(normal)
normal_min = normal_min + frame
return normalList
def predict_open(df):
result_open = list()
timeList = list()
dateList = list()
openList = list()
open1List = list()
open2List = list()
open3List = list()
open4List = list()
open5List = list()
totalList = list()
open_prediction1 = list()
open_prediction2 = list()
open_prediction3 = list()
open_prediction4 = list()
open_prediction5 = list()
totalvalueList = list()
date_index = 0
time_index = 0
timecountList = list()
open = df[['Open']]
date = df.reset_index()['Datetime']
real_date = ""
first_md = date[0].strftime("%m") + date[0].strftime("%d")
#print(first_md)
###making dateList(md:HM)
for i in range(len(date)):
dateDict = dict()
openList.append(open.iloc[i][0])
date_md = date[i].strftime("%m") + date[i].strftime("%d")
date_time = date[i].strftime("%H") + date[i].strftime("%M")
#print(date_time)
if first_md == date_md:
timeList.append(date_time)
if i == len(date) -1:
timecountList.append(len(timeList))
dateDict[first_md] = timeList
dateList.append(dateDict)
else:
#print(len(timeList))
timecountList.append(len(timeList))
dateDict[first_md] = timeList
first_md = date_md
dateList.append(dateDict)
timeList.clear()
timeList.append(date_time)
#print(dateList)
###get same data in the past
for i in range(len(date)):
total_index = 0
date_md = date[i].strftime("%m") + date[i].strftime("%d")
date_time = date[i].strftime("%H") + date[i].strftime("%M")
#print(date_md)
#print(date_time)
for j in range(len(dateList)):
for key in dateList[j].keys():
#print(key)
real_date = key
if date_md == real_date:
#print(real_date)
#print(j)
date_index = j
break
#print(date_index)
if date_index <= 0:
open1List.append(0)
else:
for value in dateList[date_index - 1].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-1):
total_index = total_index + timecountList[t] - 1
total_index += time_index
open1List.append(open.iloc[total_index][0])
total_index = 0
else:
open1List.append(0)
if date_index <= 1:
open2List.append(0)
else:
for value in dateList[date_index - 2].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-2):
total_index = total_index + timecountList[t] - 1
total_index += time_index
open2List.append(open.iloc[total_index][0])
total_index = 0
else:
open2List.append(0)
if date_index <= 2:
open3List.append(0)
else:
for value in dateList[date_index - 3].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-3):
total_index = total_index + timecountList[t] - 1
total_index += time_index
open3List.append(open.iloc[total_index][0])
total_index = 0
else:
open3List.append(0)
if date_index <= 3:
open4List.append(0)
else:
for value in dateList[date_index - 4].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-4):
total_index = total_index + timecountList[t] -1
total_index += time_index
open4List.append(open.iloc[total_index][0])
total_index = 0
else:
open4List.append(0)
if date_index <= 4:
open5List.append(0)
else:
for value in dateList[date_index - 5].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-5):
total_index = total_index + timecountList[t] - 1
total_index += time_index
open5List.append(open.iloc[total_index][0])
total_index = 0
else:
open5List.append(0)
#print(openList)
#print(open1List)
#print(open2List)
#print(open3List)
#print(open4List)
#print(open5List)
#making dataframe
df1 = pd.DataFrame(list(zip(openList, open1List, open2List, open3List, open4List, open5List)),
columns=['Open', 'Open1', 'Open2', 'Open3', 'Open4', 'Open5'])
#making test data
test_open1 = list()
test_open2 = list()
test_open3 = list()
test_open4 = list()
test_open5 = list()
open1_dateindex = len(dateList) - 1
index = 0
for i in range(len(dateList) - 1):
index = index + timecountList[i]
normalList = calc_normal()
print(normalList)
#print(dateList[len(timecountList) - 1])
for i in range(len(normalList)):
for value in dateList[len(timecountList) - 1].values():
if normalList[i] in value:
open1_index = value.index(normalList[i])
for j in range(len(timecountList) - 1):
total_index = total_index + timecountList[j] -1
total_index += open1_index
#print(total_index)
test_open1.append(open.iloc[total_index][0])
total_index = 0
else:
test_open1.append(0)
for value in dateList[len(timecountList) - 2].values():
if normalList[i] in value:
open2_index = value.index(normalList[i])
for j in range(len(timecountList) - 2):
total_index = total_index + timecountList[j] - 1
total_index += open2_index
test_open2.append(open.iloc[total_index][0])
total_index = 0
else:
test_open2.append(0)
for value in dateList[len(timecountList) - 3].values():
if normalList[i] in value:
open3_index = value.index(normalList[i])
for j in range(len(timecountList) - 3):
total_index = total_index + timecountList[j] -1
total_index += open3_index
test_open3.append(open.iloc[total_index][0])
total_index = 0
else:
test_open3.append(0)
for value in dateList[len(timecountList) - 4].values():
if normalList[i] in value:
open4_index = value.index(normalList[i])
for j in range(len(timecountList) - 4):
total_index = total_index + timecountList[j] - 1
total_index += open4_index
test_open4.append(open.iloc[total_index][0])
total_index = 0
else:
test_open4.append(0)
for value in dateList[len(timecountList) - 5].values():
if normalList[i] in value:
open5_index = value.index(normalList[i])
for j in range(len(timecountList) - 5):
total_index = total_index + timecountList[j] -1
total_index += open5_index
test_open5.append(open.iloc[total_index][0])
total_index = 0
else:
test_open5.append(0)
#print(test_open1)
#print(test_open2)
#print(test_open3)
#print(test_open4)
#print(test_open5)
#prediction
open_prediction1 = predict_opennextday(df1,test_open1,test_open2,test_open3,test_open4,test_open5)
result_open.append(open_prediction1)
#print(open_prediction1)
open_prediction2 = predict_opennextday(df1,open_prediction1,test_open1,test_open2,test_open3,test_open4)
result_open.append(open_prediction2)
#print(open_prediction2)
open_prediction3 = predict_opennextday(df1, open_prediction1,open_prediction2,test_open1, test_open2, test_open3)
result_open.append(open_prediction3)
#print(open_prediction3)
open_prediction4 = predict_opennextday(df1, open_prediction1, open_prediction2, open_prediction3 , test_open1, test_open2)
result_open.append(open_prediction4)
#print(open_prediction4)
open_prediction5 = predict_opennextday(df1, open_prediction1, open_prediction2, open_prediction3, open_prediction4, test_open1)
result_open.append(open_prediction5)
#print(open_prediction5)
return result_open
def predict_opennextday(df1,test_open1,test_open2,test_open3,test_open4,test_open5):
open_result = list()
X = df1[["Open1","Open2","Open3","Open4","Open5"]]
y = df1[["Open"]]
regr = linear_model.LinearRegression()
regr.fit(X, y)
for i in range(len(test_open1)):
test_data = [[test_open1[i],test_open2[i],test_open3[i],test_open4[i],test_open5[i]]]
prediction = regr.predict(test_data)
open_result.append(np.reshape(prediction, 1)[0])
print(open_result)
return open_result
def predict_close(df):
result_open = list()
timeList = list()
dateList = list()
openList = list()
open1List = list()
open2List = list()
open3List = list()
open4List = list()
open5List = list()
totalList = list()
open_prediction1 = list()
open_prediction2 = list()
open_prediction3 = list()
open_prediction4 = list()
open_prediction5 = list()
totalvalueList = list()
date_index = 0
time_index = 0
timecountList = list()
open = df[['Close']]
date = df.reset_index()['Datetime']
real_date = ""
first_md = date[0].strftime("%m") + date[0].strftime("%d")
#print(first_md)
for i in range(len(date)):
dateDict = dict()
openList.append(open.iloc[i][0])
date_md = date[i].strftime("%m") + date[i].strftime("%d")
date_time = date[i].strftime("%H") + date[i].strftime("%M")
if first_md == date_md:
timeList.append(date_time)
if i == len(date) -1:
timecountList.append(len(timeList))
dateDict[first_md] = timeList
dateList.append(dateDict)
else:
#print(len(timeList))
timecountList.append(len(timeList))
dateDict[first_md] = timeList
first_md = date_md
dateList.append(dateDict)
timeList.clear()
timeList.append(date_time)
#print(dateList)
for i in range(len(date)):
total_index = 0
date_md = date[i].strftime("%m") + date[i].strftime("%d")
date_time = date[i].strftime("%H") + date[i].strftime("%M")
#print(date_md)
#print(date_time)
for j in range(len(dateList)):
for key in dateList[j].keys():
#print(key)
real_date = key
if date_md == real_date:
#print(real_date)
#print(j)
date_index = j
break
#print(date_index)
if date_index <= 0:
open1List.append(0)
else:
for value in dateList[date_index - 1].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-1):
total_index = total_index + timecountList[t]
total_index += time_index
open1List.append(open.iloc[total_index][0])
total_index = 0
else:
open1List.append(0)
if date_index <= 1:
open2List.append(0)
else:
for value in dateList[date_index - 2].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-2):
total_index = total_index + timecountList[t]
total_index += time_index
open2List.append(open.iloc[total_index][0])
total_index = 0
else:
open2List.append(0)
if date_index <= 2:
open3List.append(0)
else:
for value in dateList[date_index - 3].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-3):
total_index = total_index + timecountList[t]
total_index += time_index
open3List.append(open.iloc[total_index][0])
total_index = 0
else:
open3List.append(0)
if date_index <= 3:
open4List.append(0)
else:
for value in dateList[date_index - 4].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-4):
total_index = total_index + timecountList[t]
total_index += time_index
open4List.append(open.iloc[total_index][0])
total_index = 0
else:
open4List.append(0)
if date_index <= 4:
open5List.append(0)
else:
for value in dateList[date_index - 5].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-5):
total_index = total_index + timecountList[t]
total_index += time_index
open5List.append(open.iloc[total_index][0])
total_index = 0
else:
open5List.append(0)
#print(openList)
#print(open1List)
#print(open2List)
#print(open3List)
#print(open4List)
#print(open5List)
df1 = pd.DataFrame(list(zip(openList, open1List, open2List, open3List, open4List, open5List)),
columns=['Close', 'Close1', 'Close2', 'Close3', 'Close4', 'Close5'])
test_open1 = list()
test_open2 = list()
test_open3 = list()
test_open4 = list()
test_open5 = list()
open1_dateindex = len(dateList) - 1
index = 0
for i in range(len(dateList) - 1):
index = index + timecountList[i]
normalList = calc_normal()
#print(normalList)
#print(dateList[len(timecountList) - 1])
for i in range(len(normalList)):
for value in dateList[len(timecountList) - 1].values():
if normalList[i] in value:
open1_index = value.index(normalList[i])
for j in range(len(timecountList) - 1):
total_index = total_index + timecountList[j]
total_index += open1_index
#print(total_index)
test_open1.append(open.iloc[total_index][0])
total_index = 0
else:
test_open1.append(0)
for i in range(len(normalList)):
for value in dateList[len(timecountList) - 2].values():
if normalList[i] in value:
open2_index = value.index(normalList[i])
for j in range(len(timecountList) - 2):
total_index = total_index + timecountList[j]
total_index += open2_index
test_open2.append(open.iloc[total_index][0])
total_index = 0
else:
test_open2.append(0)
for i in range(len(normalList)):
for value in dateList[len(timecountList) - 3].values():
if normalList[i] in value:
open3_index = value.index(normalList[i])
for j in range(len(timecountList) - 3):
total_index = total_index + timecountList[j]
total_index += open3_index
test_open3.append(open.iloc[total_index][0])
total_index = 0
else:
test_open3.append(0)
for i in range(len(normalList)):
for value in dateList[len(timecountList) - 4].values():
if normalList[i] in value:
open4_index = value.index(normalList[i])
for j in range(len(timecountList) - 4):
total_index = total_index + timecountList[j]
total_index += open4_index
test_open4.append(open.iloc[total_index][0])
total_index = 0
else:
test_open4.append(0)
for i in range(len(normalList)):
for value in dateList[len(timecountList) - 5].values():
if normalList[i] in value:
open5_index = value.index(normalList[i])
for j in range(len(timecountList) - 5):
total_index = total_index + timecountList[j]
total_index += open5_index
test_open5.append(open.iloc[total_index][0])
total_index = 0
else:
test_open5.append(0)
#print(test_open1)
#print(test_open2)
#print(test_open3)
#print(test_open4)
#print(test_open5)
open_prediction1 = predict_closenextday(df1,test_open1,test_open2,test_open3,test_open4,test_open5)
result_open.append(open_prediction1)
#print(open_prediction1)
open_prediction2 = predict_closenextday(df1,open_prediction1,test_open1,test_open2,test_open3,test_open4)
result_open.append(open_prediction2)
#print(open_prediction2)
open_prediction3 = predict_closenextday(df1, open_prediction1,open_prediction2,test_open1, test_open2, test_open3)
result_open.append(open_prediction3)
#print(open_prediction3)
open_prediction4 = predict_closenextday(df1, open_prediction1, open_prediction2, open_prediction3 , test_open1, test_open2)
result_open.append(open_prediction4)
#print(open_prediction4)
open_prediction5 = predict_closenextday(df1, open_prediction1, open_prediction2, open_prediction3, open_prediction4, test_open1)
result_open.append(open_prediction5)
#print(open_prediction5)
return result_open
def predict_closenextday(df1,test_open1,test_open2,test_open3,test_open4,test_open5):
open_result = list()
X = df1[["Close1","Close2","Close3","Close4","Close5"]]
y = df1[["Close"]]
regr = linear_model.LinearRegression()
regr.fit(X, y)
for i in range(len(test_open1)):
test_data = [[test_open1[i],test_open2[i],test_open3[i],test_open4[i],test_open5[i]]]
prediction = regr.predict(test_data)
open_result.append(np.reshape(prediction, 1)[0])
return open_result
def predict_high(df):
result_open = list()
timeList = list()
dateList = list()
openList = list()
open1List = list()
open2List = list()
open3List = list()
open4List = list()
open5List = list()
totalList = list()
open_prediction1 = list()
open_prediction2 = list()
open_prediction3 = list()
open_prediction4 = list()
open_prediction5 = list()
totalvalueList = list()
date_index = 0
time_index = 0
timecountList = list()
open = df[['High']]
date = df.reset_index()['Datetime']
real_date = ""
first_md = date[0].strftime("%m") + date[0].strftime("%d")
#print(first_md)
for i in range(len(date)):
dateDict = dict()
openList.append(open.iloc[i][0])
date_md = date[i].strftime("%m") + date[i].strftime("%d")
date_time = date[i].strftime("%H") + date[i].strftime("%M")
if first_md == date_md:
timeList.append(date_time)
if i == len(date) -1:
timecountList.append(len(timeList))
dateDict[first_md] = timeList
dateList.append(dateDict)
else:
#print(len(timeList))
timecountList.append(len(timeList))
dateDict[first_md] = timeList
first_md = date_md
dateList.append(dateDict)
timeList.clear()
timeList.append(date_time)
#print(dateList)
for i in range(len(date)):
total_index = 0
date_md = date[i].strftime("%m") + date[i].strftime("%d")
date_time = date[i].strftime("%H") + date[i].strftime("%M")
#print(date_md)
#print(date_time)
for j in range(len(dateList)):
for key in dateList[j].keys():
#print(key)
real_date = key
if date_md == real_date:
#print(real_date)
#print(j)
date_index = j
break
#print(date_index)
if date_index <= 0:
open1List.append(0)
else:
for value in dateList[date_index - 1].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-1):
total_index = total_index + timecountList[t]
total_index += time_index
open1List.append(open.iloc[total_index][0])
total_index = 0
else:
open1List.append(0)
if date_index <= 1:
open2List.append(0)
else:
for value in dateList[date_index - 2].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-2):
total_index = total_index + timecountList[t]
total_index += time_index
open2List.append(open.iloc[total_index][0])
total_index = 0
else:
open2List.append(0)
if date_index <= 2:
open3List.append(0)
else:
for value in dateList[date_index - 3].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-3):
total_index = total_index + timecountList[t]
total_index += time_index
open3List.append(open.iloc[total_index][0])
total_index = 0
else:
open3List.append(0)
if date_index <= 3:
open4List.append(0)
else:
for value in dateList[date_index - 4].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-4):
total_index = total_index + timecountList[t]
total_index += time_index
open4List.append(open.iloc[total_index][0])
total_index = 0
else:
open4List.append(0)
if date_index <= 4:
open5List.append(0)
else:
for value in dateList[date_index - 5].values():
#print(value)
if date_time in value:
time_index = value.index(date_time)
for t in range(date_index-5):
total_index = total_index + timecountList[t]
total_index += time_index
open5List.append(open.iloc[total_index][0])
total_index = 0
else:
open5List.append(0)
#print(openList)
##print(open1List)
#print(open2List)
#print(open3List)
#print(open4List)
#print(open5List)
df1 = pd.DataFrame(list(zip(openList, open1List, open2List, open3List, open4List, open5List)),
columns=['High', 'High1', 'High2', 'High3', 'High4', 'High5'])
test_open1 = list()
test_open2 = list()
test_open3 = list()
test_open4 = list()
test_open5 = list()
open1_dateindex = len(dateList) - 1
index = 0
for i in range(len(dateList) - 1):
index = index + timecountList[i]
normalList = calc_normal()
#print(normalList)
#print(dateList[len(timecountList) - 1])
for i in range(len(normalList)):
for value in dateList[len(timecountList) - 1].values():
if normalList[i] in value:
open1_index = value.index(normalList[i])
for j in range(len(timecountList) - 1):
total_index = total_index + timecountList[j]
total_index += open1_index
#print(total_index)
test_open1.append(open.iloc[total_index][0])
total_index = 0
else:
test_open1.append(0)
for i in range(len(normalList)):
for value in dateList[len(timecountList) - 2].values():
if normalList[i] in value:
open2_index = value.index(normalList[i])
for j in range(len(timecountList) - 2):
total_index = total_index + timecountList[j]
total_index += open2_index
test_open2.append(open.iloc[total_index][0])
total_index = 0
else: