def loadDataFcn(self):
self.fname, self.__var__ = QFileDialog.getOpenFileName(self, 'Open file', '{}'.format(curDir), "CSV Files (*.csv)")
self.f = rc(self.fname, delimiter = ',')
self.runTime = self.f.iloc[:, 0].values
self.sens1 = self.f.iloc[:, 1].values
self.sens2 = self.f.iloc[:, 2].values
self.sens3 = self.f.iloc[:, 3].values
# self.sens4 = self.f.iloc[:, 0].values
self.tempVal = self.f.iloc[:, 4].values
self.pressVal = self.f.iloc[:, 5].values
self.humVal = self.f.iloc[:, 6].values
self.oxVal = self.f.iloc[:, 7].values
self.p1 = self.graph.plot(self.runTime, self.sens1, pen='r', name='{}'.format(s1n))
self.p2 = self.graph.plot(self.runTime, self.sens2, pen='g', name='{}'.format(s2n))
self.p3 = self.graph.plot(self.runTime, self.sens3, pen='b', name='{}'.format(s3n))
# self.graph.plot(self.runTime, self.sens4, pen='k', name='{}'.format(s4n))
self.graph.setYRange(0, 5)
self.graph.setXRange(0, self.totTime)
self.tempVal_last = mean(self.tempVal)
self.pressVal_last = mean(self.pressVal)
self.humVal_last = mean(self.humVal)
self.oxVal_last = mean(self.oxVal)
self.vecL.setText('Temp: {:d}℃ \n\nHumidity: {:d}% \n\nPressure: {:d}kPa \n\nOxygen: {:d}%'.format(int(self.tempVal_last), int(self.humVal_last), int(self.pressVal_last), int(self.oxVal_last)))
def loadData(self):
fname,something = QFileDialog.getOpenFileName(self, 'Open file','{}'.format(curDir),"CSV Files (*.csv)")
print(fname)
f = rc(fname,delimiter=',',)
global p3Time
global p3sens1
global p3sens2
global p3sens3
global p3sens4
global p3temp
global p3hum
global p3press
global p3ox
p3Time = f.iloc[:,0].values
p3sens1 = f.iloc[:,1].values
p3sens2 = f.iloc[:,2].values
p3sens3 = f.iloc[:,3].values
p3sens4 = f.iloc[:,4].values
p3temp = f.iloc[:,5].values
p3hum = f.iloc[:,6].values
p3press = f.iloc[:,7].values
p3ox = f.iloc[:,8].values
p3Graph.addLegend(offset=(0,10))
p3sens1plot = p3Graph.plot(p3Time,p3sens1,pen='r',name='{}'.format(sensor1name))
p3sens2plot = p3Graph.plot(p3Time,p3sens2,pen='g',name='{}'.format(sensor2name))
p3sens3plot = p3Graph.plot(p3Time,p3sens3,pen='b',name='{}'.format(sensor3name))
p3sens4plot = p3Graph.plot(p3Time,p3sens4,pen='k',name='{}'.format(sensor4name))
p3temp = mean(p3temp)
p3hum = mean(p3hum)
p3press = mean(p3press)
p3ox = mean(p3ox)
p3vecLabel.setText("Temp: {}℃ \n\nHumidity: {}% \n\nPressure: {}kPa \n\nOxygen: {}%".format(int(p3temp),int(p3hum),int(p3press),int(p3ox)))
p3Graph.setYRange(0,5)
p3Graph.setXRange(0,50)
def x(csv, column):
listX = [float(x) for x in rc(csv)[column].to_list()]
listXMean, listXMedian, listXMode, listXStd = sts.mean(listX), sts.median(
listX), sts.mode(listX), sts.stdev(listX)
x1s, x1e = listXMean - listXStd, listXMean + listXStd
x2s, x2e = listXMean - (2 * listXStd), listXMean + (2 * listXStd)
x3s, x3e = listXMean - (3 * listXStd), listXMean + (3 * listXStd)
y1 = [
x for x in listX
if x > listXMean - listXStd and x < listXMean + listXStd
]
y2 = [
x for x in listX
if x > listXMean - (2 * listXStd) and x < listXMean + (2 * listXStd)
]
y3 = [
x for x in listX
if x > listXMean - (3 * listXStd) and x < listXMean + (3 * listXStd)
]
return [listX, listXMean, listXMedian, listXMode, listXStd, y1, y2, y3]
y=str(pindex[0])[5:tl]
timearray.append(y)
i0=0
indexarrayL=[i0]
for x in pindex:
if y!=str(x)[5:tl]:
timearray.append(str(x)[5:tl]) # change to 16 to 19 to make it in seconds
indexarrayL.append(i0)
y=str(x)[5:tl]
i0+=1
return [indexarrayL,timearray]
tl = 19 # use 19 for seconds, 16 is for minutes
pl=rc('p_luno_large_2.csv')
pk=rc('p_kraken_large_2.csv')
pl = pl.set_index('timestamp_rounded')
pk = pk.set_index('timestamp_rounded')
# pk=clean_prices(pkraken)
# pl=clean_prices(pluno)
# it was already cleaned - just the lower boundary was 0 instead of 100, but that should not matter
indexarrayL, timearrayL = makearray(pl.index, tl)
indexarrayK, timearrayK = makearray(pk.index, tl)
mergeddf=df(columns=['Time', 'kBid', 'kAsk','lBid','lAsk'])
num_iters = len(indexarrayL)
print('Number of iterations: ' + str(num_iters))
from datetime import datetime
import matplotlib.pyplot as plt
import os
import pandas as pd
import sqlite3
from pandas import DataFrame as df
import numpy as np
from pandas import read_csv as rc
df_general=rc('Copy of merged_large_2_min.csv')
#Housekeeping
df_general = df_general.ffill()
signalmatrix = df(df_general['lBid']-df_general['kBid'])
signalmatrix = signalmatrix.join(df(df_general['lAsk']-df_general['kAsk']),rsuffix='r')
(signalmatrix).columns=('BidDiff','AskDiff')
timeindf, RoT = trading_plugin(buy_and_sell_entries(20,10))
#In case you fancy
timeindf['RoT']=RoT
def buy_and_sell_entries(ADhandle,BDhandle,df_general=None,signalmatrix=None):
# df_general is Best Bid Best Ask for both exchanges
# Our entry and exits
buyentry=[];sellentry=[]
# Enter the first buy (Index is zero indexed)
open_signal = signalmatrix['AskDiff']< -ADhandle
first_entry_idx = df_general[open_signal].head(1).index.values
buyentry.append(first_entry_idx)
import numpy as np
import os
import sys
from PyQt5 import QtWidgets, QtGui, QtCore
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from pandas import read_csv as rc
import adminGui
import usrGui
global bg_color
bg_color = '#484848'
w = 320
h = 240
prm_list = rc('parameters.csv', delimiter=',', header='infer')
sVersion = prm_list['Software Version'].values
dVersion = prm_list['Device Version'].values
dID = prm_list['Device ID'].values
cwd = os.getcwd()
class Window(QWidget):
class usrButton(QPushButton):
def __init__(self, parent=None):
super(Window.usrButton, self).__init__()
self.setText('User GUI')
class adminButton(QPushButton):
def __init__(self, parent=None):
from pandas import read_csv as rc
from random import randint as ri
import statistics as sts
import plotly.figure_factory as ff
p = "data.csv"
def rx(x, y):
return sts.mean([y[ri(0, len(y) - 1)] for i in range(x)])
x = [rx(30, rc(p)['temp']) for i in range(100)]
print("Mean of population: {}\nMean of sample: {}".format(
sts.mean(rc(p)["temp"]), sts.mean(x)))
ff.create_distplot([x], [""], show_hist=False).show()
i0=0
indexarrayL=[i0]
for x in pindex:
if y!=str(x)[5:tl]:
timearray.append(str(x)[5:tl]) # change to 16 to 19 to make it in seconds
indexarrayL.append(i0)
y=str(x)[5:tl]
i0+=1
return [indexarrayL,timearray]
tl = 19 # use 19 for seconds, 16 is for minutes
relevant_cols = ['timestamp_rounded', 'timestamp', 'best_bid', 'best_ask']
# BTC/EUR
pl=rc('p_luno_btc_eur_v1.csv', usecols=relevant_cols)
pk=rc('p_kraken_btc_eur_v1.csv', usecols=relevant_cols)
pb=rc('p_bitstamp_btc_eur_v1.csv', usecols=relevant_cols)
pl = pl.set_index('timestamp_rounded')
pk = pk.set_index('timestamp_rounded')
pb = pb.set_index('timestamp_rounded')
# the datasets were already cleaned, so we do not need to run clean_prices
indexarrayL, timearrayL = makearray(pl.index, tl)
indexarrayK, timearrayK = makearray(pk.index, tl)
indexarrayB, timearrayB = makearray(pb.index, tl)
mergeddf=df(columns=['Time', 'kBid', 'kAsk', 'bBid', 'bAsk', 'lBid','lAsk'])
num_iters = len(indexarrayL)