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| import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import csv
import copy
import numpy
from PyQt5 import QtCore, QtGui, QtWidgets, uic
from PyQt5 import Qt
from PyQt5.QtCore import QObject, pyqtSignal, QDate, QDateTime
from PyQt5.QtChart import QChart, QChartView, QBarSet, QPercentBarSeries, QCandlestickSeries, QValueAxis, QLineSeries, QCandlestickSet, QBarCategoryAxis
from PyQt5.QtWidgets import QScrollBar
from PyQt5.QtGui import QColor
class Container:
date = []
open = []
max = []
min = []
close = []
ema = []
tensorFlowLine = []
stepEma = 10
size = int
def __init__(self):
self.readFile()
def readFile(self):
self.date = []
self.open = []
self.max = []
self.min = []
self.close = []
# Date,Open,Max,Min,Close,"Trading Volume, USD","Market Capitalization, USD",
with open('RTSI-dailyhistory.csv', newline='') as csvfile:
spamreader = csv.reader(csvfile, delimiter=' ', quotechar='|')
for row in spamreader:
rowArray = row[0].split(",")
self.date.append(rowArray[0])
self.open.append(rowArray[1])
self.max.append(rowArray[2])
self.min.append(rowArray[3])
self.close.append(rowArray[4])
self.size = len(self.close)
# ema
k = 2 / (self.stepEma + 1)
lastSumm = 0
i = 0
while i < self.stepEma:
self.ema.append(None)
self.tensorFlowLine.append(None)
lastSumm + float(self.close[i])
i += 1
firstclose = float(self.close[0])
lastEma = lastSumm / self.stepEma
while i < self.size:
lastSumm = lastSumm - firstclose
firstclose = float(self.close[i - self.stepEma - 1])
ema = (float(self.close[i]) * k) + (lastEma * (1 - k))
lastEma = copy.deepcopy(ema)
self.ema.append(ema)
self.tensorFlowLine.append(ema + 300)
i += 1
class MainWindow(QObject):
lastScrollBarValue = 0
def __init__(self):
super(MainWindow, self).__init__()
self.ui = uic.loadUi("mainwindow.ui")
self.ui.testButton.clicked.connect(self.onClickedTestButton)
self.ui.pushButtonMinus.clicked.connect(self.onClickedMinus)
self.ui.pushButtonPlus.clicked.connect(self.onClickedPlus)
self.ui.pushButtonOriginalSize.clicked.connect(self.onClickedOriginalSize)
self.ui.pushButtonToRight.clicked.connect(self.onClickedToRight)
self.ui.pushButtonToLeft.clicked.connect(self.onClickedToLeft)
self.chartView = QChartView(self.ui)
# self.chartView.addScrollBarWidget()
self.chartView.setRubberBand(QChartView.HorizontalRubberBand)
self.ui.verticalLayoutForChart.addWidget(self.chartView)
# self.ui.scrollAreaForChart.setWidget(self.chartView)
self.scrollBar = QScrollBar(QtCore.Qt.Horizontal)
self.scrollBar.valueChanged.connect(self.scrollBarValueChanged)
self.ui.verticalLayoutForChart.addWidget(self.scrollBar)
self.container = Container()
self.drawChart()
self.model = tf.keras.models.Sequential()
# units=1 - этот параметр определяет количество нейронов в слое
# input_dim - в первом слое вы должны указать размерность входных данных
# здесь, это векторы длинной 30.
self.model.add(keras.layers.Dense(units=30, activation='relu', input_dim=30))
# убирает переобучаемость переодическим отключением указанной части нейронов
self.model.add(keras.layers.Dropout(0.5))
# добавляю 2-й слой
self.model.add(keras.layers.Dense(300, activation='relu'))
self.model.add(keras.layers.Dropout(0.5))
# наверно выходной слой
self.model.add(keras.layers.Dense(10, activation='softmax'))
sgd = keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
self. model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
# тренировка модели
# arrayInputNumPy = numpy.array([]) #, dtype=float
# arrayOutputNumPy = numpy.array([])
arrayInputAll = []
arrayOutputAll = []
arrayInputAllTest = []
arrayOutputAllTest = []
q = 50
while q < self.container.size:
arrayInput = []
i = q - 40
size1 = i + 30
while i < size1:
arrayInput.append(self.container.ema[i])
i += 1
arrayOutput = []
size2 = i + 10
while i < size2:
arrayOutput.append(self.container.ema[i])
i += 1
if (q / self.container.size) < 0.83:
arrayInputAll.append(arrayInput)
arrayOutputAll.append(arrayOutput)
else:
arrayInputAllTest.append(arrayInput)
arrayOutputAllTest.append(arrayOutput)
# print(len(arrayInput), len(arrayOutput))
q += 1
arrayInputNumPy = numpy.array(arrayInputAll, dtype=float)
arrayOutputNumPy = numpy.array(arrayOutputAll, dtype=float)
arrayInputNumPyTest = numpy.array(arrayInputAllTest, dtype=float)
arrayOutputNumPyTest = numpy.array(arrayOutputAllTest, dtype=float)
# fit model
self.fitTensorFlowModel(arrayInputNumPy, arrayOutputNumPy, arrayInputAllTest, arrayOutputAllTest)
arr = [254.20363636363635, 464.8720661157025, 635.7462359128475, 769.6960112014206, 877.8712818920714, 967.1419579116947, 1038.0961473822956, 1092.8804842218783, 1136.9822143633548, 1168.6309026609265, 1189.2380112680307, 1209.8183728556614, 1227.5132141546321, 1241.025357035608, 1249.406201210952, 1250.7523464453243, 1252.9228289098107, 1254.0114054716632, 1258.1456953859063, 1258.2028416793778, 1260.0950522831272, 1254.2195882316494, 1251.3523903713494, 1248.1628648492858, 1245.3823439675975, 1243.7310087007615, 1231.830825300623, 1220.7234025186915, 1200.2555111516567, 1176.4508727604464]
# print(len(arr))
arr2 = numpy.array(arr, dtype=float)
self.model.predict(arr2) #, batch_size=128
# # check my model
# q = 50
# while q < self.container.size:
# arrayInput = []
# i = q - 40
# size1 = i + 30
# while i < size1:
# arrayInput.append(self.container.ema[i])
# i += 1
#
# arrayOutput = []
# size2 = i + 10
# while i < size2:
# arrayOutput.append(self.container.ema[i])
# i += 1
#
# # check my model
# print(arrayInput)
# arrayForModel = numpy.array(arrayInput, dtype=float)
# # self.model.predict(arrayForModel)
# self.model.predict(arrayInput)
# # result = self.model.predict(arrayForModel)
# # print(result, arrayOutput)
#
# q += 1
def fitTensorFlowModel(self, arrayInput, arrayOutput, arrayInputTest, arrayOutputTest):
# тренировка модели
# epochs - кол-во эпох, повторов
# batch_size - видимо кол-во принимаемых порций данных за раз. Количество отсчетов на обновление градиента
# self. model.fit(a1, a2,
# a1 - входные данные
# a2 - желаемые данные на выходе
self.model.fit(arrayInput, arrayOutput,
epochs=30,
# verbose=False
batch_size=128
)
# self. model.evaluate(a1, a2,
# a1 - входные данные
# a2 - желаемые данные на выходе
# Возвращает значение потерь и метрики для модели в тестовом режиме
score = self.model.evaluate(arrayInputTest, arrayOutputTest, batch_size=128)
print("score: ", score)
def drawChart(self):
self.scrollBar.setRange(0, self.container.size)
# self.scrollBar.setRange(0, 10)
candlestickSeries = QCandlestickSeries()
candlestickSeries.setIncreasingColor(QColor(QtCore.Qt.green))
candlestickSeries.setDecreasingColor(QColor(QtCore.Qt.red))
# ema
seriesEma = QLineSeries()
# tensorFlowLine orange
tensorFlowLine = QLineSeries()
# tensorFlowLine.setColor(QColor("#f24713"))
for i in range(len(self.container.close)):
open = float(self.container.open[i])
max = float(self.container.max[i])
min = float(self.container.min[i])
close = float(self.container.close[i])
dateTime = QDateTime.fromString(self.container.date[i], "dd.MM.yyyy")
candlestickSet = QCandlestickSet(open, max, min, close, dateTime.toMSecsSinceEpoch())
# candlestickSet = QCandlestickSet(open, max, min, close, i*1000000)
candlestickSeries.append(candlestickSet)
# ema
if self.container.ema[i] != None:
ema = self.container.ema[i]
else:
ema = close
# tensorFlowLine
if self.container.tensorFlowLine[i] != None:
tensorFlowLineVal = self.container.tensorFlowLine[i]
else:
tensorFlowLineVal = close
seriesEma.append(i, ema)
tensorFlowLine.append(i, tensorFlowLineVal)
self.chart = QChart()
self.chart.addSeries(candlestickSeries)
self.chart.addSeries(seriesEma)
self.chart.addSeries(tensorFlowLine)
self.chart.setAnimationOptions(QChart.SeriesAnimations)
self.chart.createDefaultAxes()
self.axisX = QValueAxis()
self.axisX.rangeChanged.connect(self.axisXRangeChanged)
self.chart.addAxis(self.axisX, QtCore.Qt.AlignBottom);
self.chart.legend().setVisible(False)
candlestickSeries.attachAxis(self.axisX)
seriesEma.attachAxis(self.axisX)
tensorFlowLine.attachAxis(self.axisX)
self.chartView.setChart(self.chart)
def axisXRangeChanged(self, x1, x2):
# print(x1, x2)
# self.scrollBar.setRange(0, self.container.size)
self.scrollBar.setValue(x1)
def scrollBarValueChanged(self, value):
if self.lastScrollBarValue != value:
self.lastScrollBarValue = value
# print(value)
sizeRange = self.axisX.max() - self.axisX.min()
self.axisX.setRange(value, value + sizeRange)
def onClickedTestButton(self):
1+1
# self.chart.scroll(100, 0) #смещает вправо-влево на указанное число точек
self.axisX.setRange(0, 100) # указывает видимый диапазон
# self.scrollBar.setValue(1)
def onClickedMinus(self):
sizeRange = self.axisX.max() - self.axisX.min()
newSize = sizeRange / 1.2
min = self.axisX.min() - newSize/2
max = self.axisX.max() + newSize/2
self.axisX.setRange(min, max)
def onClickedPlus(self):
sizeRange = self.axisX.max() - self.axisX.min()
newSize = sizeRange / 1.2
min = self.axisX.min() + newSize / 2
max = self.axisX.max() - newSize / 2
self.axisX.setRange(min, max)
def onClickedOriginalSize(self):
self.scrollBar.setRange(0, self.container.size)
def onClickedToRight(self):
sizeRange = self.axisX.max() - self.axisX.min()
self.chart.scroll(sizeRange * 0.2, 0)
def onClickedToLeft(self):
sizeRange = self.axisX.max() - self.axisX.min()
self.chart.scroll(-1 * sizeRange * 0.2, 0)
if __name__ == '__main__':
app = Qt.QApplication([])
print('main')
mainWindow = MainWindow()
mainWindow.ui.show()
print('main2')
app.exec() |
(
