Pandas Datareader Library is Down

Pandas Datareader library is down since December 16, 2022. I use it to get stock market historical data. This has happened before when it was my first attempt to download historical data. I figured to leave the task and go on with the next task and came after a few days and it went back to normal that is why this recent event is not a surprise for me. 

I like using this library because it is very useful and very simple to use but the only downside is the downtime is quite long, the developers are not able to fix it quickly. It has been 3 days and it still unuseable so I have to try its twin library(YFinance) if it still works. The output and features of this library is very much identical to Pandas Datareader but with a few other options like the ticker info(which is also currently down). And speaking of the ticker info method, this feature of yfinace is very slow. Way... way too slow.

In my trading platform program, I currently use yfinace ticker info to display some information about the ticker(obviously) but it has no use for me so probably that feature is needed by institutional investors who must have to analyze the ticker(company) from ground up which leads me to decide to just remove it from my program since I am not an institutional investor. And I also use the panda data reader to get historical data of the ticker which is currently down and maybe who knows when will it be up. So it is a huge problem for me because both yfnance ticker info is slow and currently down (not really, I noticed that it still works but a lot of the fields that I am using had disappeared and I do not know why) and the pandas data reader is down.

As my first option, I should to try if yfinace(before exploring other alternatives) function or methods to get the historical data are still working and I am glad that with the result of my experiments, it still works. So for those who have downloaded/copied my program, I am posting the latest version of my program and for those who want to try, this is the latest version:

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import sys from PyQt6.QtWidgets import QApplication, QWidget, QPushButton from PyQt6.QtWidgets import QApplication, QWidget, QDateEdit, QLabel, QComboBox, QTableWidget, QStyledItemDelegate, QTableWidgetItem, QHeaderView from PyQt6.QtCore import QDate, QEvent, QObject, Qt from PyQt6.QtGui import QPainter, QColor, QPen, QFont from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.figure import Figure import matplotlib.pyplot as plt from matplotlib.dates import DateFormatter import pandas_datareader as web import pandas as pd import datetime as dt import yfinance as yf import pandas_ta as ta import numpy as np from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, LSTM data = [] combo1 ='' ticker_info = {} class Delegate(QStyledItemDelegate): def createEditor(self, parent, option, index): if index.data() == "100": return super(Delegate, self).createEditor(parent, option, index) class Window(QWidget): def __init__(self): super(Window, self).__init__() self.initUI() def initUI(self): global data, combo1, ticker_info self.combo_box = QComboBox(self) self.combo_box.setGeometry(125, 15, 125, 20) self.combo_box.addItem("AAPL") self.combo_box.addItem("TSLA") self.combo_box.addItem("AMZN") self.combo_box.addItem("META") self.combo_box.addItem("GOOG") self.combo_box.addItem("NFLX") self.combo_box.addItem("TWTR") self.combo_box.addItem("BTC") #ticker_info = yf.Ticker("AAPL") self.company_name = QLabel(self) self.company_name.setGeometry(400, 15, 807, 20) yf.pdr_override() #print((ticker_info.info)) #self.company_name.setText(" Company Name: " + ticker_info.info['longName'] + " Sector: " + ticker_info.info['sector'] + " Employees: " + str(ticker_info.info['fullTimeEmployees']) + " Cash On Hand: " + str(ticker_info.info['totalCash']) + " Debt: " + str(ticker_info.info['totalDebt'])) self.ML_button = QPushButton('Show Tommorrows Trend', self) self.ML_button.setGeometry(1250, 15, 150, 30) self.ML_button.clicked.connect(self.onClick_ML_button) self.ML_result = QLabel(self) self.ML_result.setGeometry(1265, 54, 110, 35) self.ML_result.setFont(QFont("Arial", 24)) self.ML_result.setAlignment(Qt.AlignmentFlag.AlignCenter) self.ML_hist = QLabel(self) self.ML_hist.setGeometry(1265, 100, 110, 20) self.ML_hist.setAlignment(Qt.AlignmentFlag.AlignCenter) self.combo_box.activated.connect(self.on_combobox_changed) self.combo_box_label = QLabel(self) self.combo_box_label.setGeometry(25, 15, 100, 20) self.combo_box_label.setText("Select Ticker:") self.combo_box_label_s = QLabel('AAPL',self) self.combo_box_label_s.setGeometry(255, 15, 100, 20) sdate= '01-01-2021' date_str = "1-Jan-2021" qdate = QDate.fromString(date_str, "d-MMM-yyyy") self.date_edit = QDateEdit(self, date=qdate, calendarPopup=True) self.date_edit.setGeometry(125, 45, 100, 20) self.date_edit.dateChanged.connect(self.update) self.date_label = QLabel('Set Date', self) self.date_label.setGeometry(25, 45, 150, 20) self.result_label = QLabel('to', self) self.result_label.setGeometry(235, 45, 20, 20) self.date_edit1 = QDateEdit(self, date=QDate.currentDate(), calendarPopup=True) self.date_edit1.setGeometry(265, 45, 100, 20) self.date_edit1.dateChanged.connect(self.update) company = 'AAPL' start = dt.datetime(2021, 1, 1) end = dt.datetime.now() combo1 = self.combo_box.currentText() data = yf.download("AAPL", start=start, end=end) #data = pd.read_csv("tsla.csv") #yf.pdr_override() #data = web.get_data_yahoo(symbols=[company], start=start, end=end) #data = web.DataReader(company, 'yahoo', start, end) m = PlotCanvas(self, width=9, height=2) m.move(35,145) m1 = PlotCanvas1(self, width=9, height=2) m1.move(965,145) m2 = PlotCanvas2(self, width=9, height=2) m2.move(965,360) m3 = PlotCanvas3(self, width=9, height=2) m3.move(965,580 ) m4 = PlotCanvas4(self, width=9, height=2) m4.move(965,790) m5 = PlotCanvas5(self, width=9, height=2) m5.move(35,360) m6 = PlotCanvas6(self, width=9, height=2) m6.move(35,580) m7 = PlotCanvas7(self, width=9, height=2) m7.move(35,790) #self.createTable() self.setGeometry(10, 35, 1900, 1000) self.setWindowTitle('My Trading Platform') self.show() def onClick_ML_button(self): company = self.combo_box.currentText() start = dt.datetime(2021, 1, 1) end = dt.datetime.now() ml_data = yf.download(company, start=start, end=end) #ml_data = web.DataReader(company, 'yahoo', start, end) #ml_data = pd.read_csv("tsla.csv") scaler = MinMaxScaler(feature_range=(0,1)) scaled_data = scaler.fit_transform(ml_data["Close"].values.reshape(-1,1)) prediction_days = 60 x_train = [] y_train = [] for x in range(prediction_days, len(scaled_data)): x_train.append(scaled_data[x-prediction_days:x,0]) y_train.append(scaled_data[x, 0]) x_train, y_train = np.array(x_train), np.array(y_train) x_train = np.reshape(x_train,(x_train.shape[0], x_train.shape[1],1)) model = Sequential() model.add(LSTM(units=50, return_sequences='TRUE', input_shape=(x_train.shape[1], 1))) model.add(Dropout(0.2)) model.add(LSTM(units=50, return_sequences='TRUE')) model.add(Dropout(0.2)) model.add(LSTM(units=50)) model.add(Dropout(0.2)) model.add(Dense(units=1)) model.compile(optimizer='adam', loss='mean_squared_error') model.fit(x_train, y_train, epochs=128, batch_size=128) test_start = dt.datetime(2021, 6, 1) test_end = dt.datetime.now() test_data = web.DataReader(company, 'yahoo', test_start, test_end) actual_prices = test_data['Close'].values total_dataset = pd.concat((data['Close'], test_data['Close']), axis=0) model_inputs = total_dataset[len(total_dataset) - len(test_data) - prediction_days:].values model_inputs = model_inputs.reshape(-1, 1) model_inputs = scaler.transform(model_inputs) x_test = [] for x in range(prediction_days, len(model_inputs)): x_test.append(model_inputs[x-prediction_days:x, 0]) x_test = np.array(x_test) x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1)) predicted_prices = model.predict(x_test) predicted_prices = scaler.inverse_transform(predicted_prices) real_data = [model_inputs[len(model_inputs) - prediction_days:len(model_inputs+1), 0]] real_data = np.array(real_data) real_data = np.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1)) prediction = model.predict(real_data) prediction = scaler.inverse_transform(prediction) dast1 = data[-1:]['Close'] v = float(dast1 - float(prediction)) if v>1: self.ML_result.setText("BUY") else: self.ML_result.setText("SELL") print(company) self.ML_hist.setText("ML Accuracy: 100%") def createTable(self): global ticker_info self.tableWidget = QTableWidget(self) self.tableWidget.viewport().installEventFilter(self) #self.tableWidget.installEventFilter(self) #self.tableWidget.setEditTriggers(QTreeView.NoEditTriggers) self.tableWidget.setRowCount(2) self.tableWidget.setColumnCount(8) self.tableWidget.setFixedSize(820, 80) self.tableWidget.move(400, 45) delegate = Delegate(self.tableWidget) self.tableWidget.setItemDelegate(delegate) #print(dir(QHeaderView)) self.tableWidget.setColumnWidth(0, 150) text = 'averageDailyVolume(10D)' it = QTableWidgetItem(text) self.tableWidget.setItem(0, 0, it) text = str(ticker_info.info['averageDailyVolume10Day']) it = QTableWidgetItem(text) self.tableWidget.setItem(1, 0, it) self.tableWidget.setColumnWidth(1, 150) text = 'averageVolume(10D)' it = QTableWidgetItem(text) self.tableWidget.setItem(0, 1, it) text = str(ticker_info.info['averageVolume10days']) it = QTableWidgetItem(text) self.tableWidget.setItem(1, 1, it) self.tableWidget.setColumnWidth(2, 150) text = 'marketCap' it = QTableWidgetItem(text) self.tableWidget.setItem(0, 2, it) text = str(ticker_info.info['marketCap']) it = QTableWidgetItem(text) self.tableWidget.setItem(1, 2, it) self.tableWidget.setColumnWidth(3, 150) text = 'twoHundredDayAverage' it = QTableWidgetItem(text) self.tableWidget.setItem(0, 3, it) text = str(ticker_info.info['twoHundredDayAverage']) it = QTableWidgetItem(text) self.tableWidget.setItem(1, 3, it) self.tableWidget.setColumnWidth(4, 100) text = 'pegRatio' it = QTableWidgetItem(text) self.tableWidget.setItem(0, 4, it) text = str(ticker_info.info['pegRatio']) it = QTableWidgetItem(text) self.tableWidget.setItem(1, 4, it) self.tableWidget.setColumnWidth(5, 150) text = 'volume' it = QTableWidgetItem(text) self.tableWidget.setItem(0, 5, it) text = str(ticker_info.info['volume']) it = QTableWidgetItem(text) self.tableWidget.setItem(1, 5, it) self.tableWidget.verticalHeader().hide() self.tableWidget.horizontalHeader().hide() def eventFilter(self, source, event): #if self.tableWidget.selectedIndexes() != []: if event.type() == QEvent.Type.MouseButtonRelease: row = self.tableWidget.currentRow() col = self.tableWidget.currentColumn() if self.tableWidget.item(row, col) is not None: print(str(row) + " " + str(col) + " " + self.tableWidget.item(row, col).text()) else: print(str(row) + " " + str(col)) return QObject.event(source, event) def on_combobox_changed(self, value): global ticker_info self.combo_box_label_s.setText(self.combo_box.currentText()) #if self.combo_box.currentText() != 'BTC': # ticker_info = yf.Ticker(self.combo_box.currentText()) # self.company_name.setText(" Company Name: " + ticker_info.info['longName'] + " Sector: " + ticker_info.info['sector'] + " Employees: " + str(ticker_info.info['fullTimeEmployees']) + " Cash On Hand: " + str(ticker_info.info['totalCash']) + " Debt: " + str(ticker_info.info['totalDebt'])) self.update_chart() def update(self): value = self.date_edit.date() #self.result_label.setText(str(value.toPyDate())) self.update_chart() def update_chart(self): global data, combo1 combo1 = self.combo_box.currentText() value = self.date_edit.date() value = (str(value.toPyDate())) company = self.combo_box.currentText() start = dt.datetime(int(value[:4]), int(value[5:7]), int(value[-2] + value[-1])) value = self.date_edit1.date() value = (str(value.toPyDate())) #print(value) #print((value[:4]), (value[5:7]), (value[-2] + value[-1])) end = dt.datetime(int(value[:4]), int(value[5:7]), int(value[-2] + value[-1])) #start = dt.datetime(2021, 1, 1) #end = dt.datetime.now() #data = web.DataReader(company, 'yahoo', start, end) data = yf.download(company, start=start, end=end) m = PlotCanvas(self, width=9, height=2) m.move(35,145) m.show() m1 = PlotCanvas1(self, width=9, height=2) m1.move(965,145) m1.show() m2 = PlotCanvas2(self, width=9, height=2) m2.move(965,360) m2.show() m3 = PlotCanvas3(self, width=9, height=2) m3.move(965,580 ) m3.show() m4 = PlotCanvas4(self, width=9, height=2) m4.move(965,790) m4.show() m5 = PlotCanvas5(self, width=9, height=2) m5.move(35,360) m5.show() m6 = PlotCanvas6(self, width=9, height=2) m6.move(35,580) m6.show() m7 = PlotCanvas7(self, width=9, height=2) m7.move(35,790) m7.show() class PlotCanvas(FigureCanvas): def __init__(self, parent=None, width=15, height=2, dpi=100): global combo1 fig = Figure(figsize=(width, height), dpi=dpi) fig.suptitle(f'{combo1} Price Chart', fontsize=10) #fig.supxlabel('X title', fontsize=10) #fig.supylabel('Y title', fontsize=10) FigureCanvas.__init__(self, fig) self.setParent(parent) FigureCanvas.updateGeometry(self) self.plot() def plot(self): global data x = data.index y = data['Close'] ax = self.figure.add_subplot(111) #ax.set_ylim(ymax=450) #ax.set_ylim(bottom=100) #print(dir(ax)) #ax.set_xlabel('Test Cases') #ax.set_ylabel('fault courage') ax.plot(x,y) #ax.plot(y,z) #ax.plot(y,a) self.draw() self.flush_events() class PlotCanvas1(FigureCanvas): def __init__(self, parent=None, width=15, height=2, dpi=100): fig = Figure(figsize=(width, height), dpi=dpi) fig.suptitle('MACD', fontsize=10) #fig.supxlabel('X title', fontsize=10) #fig.supylabel('Y title', fontsize=10) FigureCanvas.__init__(self, fig) self.setParent(parent) FigureCanvas.updateGeometry(self) self.plot1() def plot1(self): global data macd = data['Close'] exp1 = macd.ewm(span=12).mean() exp2 = macd.ewm(span=26).mean() comp_macd = exp1 - exp2 signal = comp_macd.ewm(span=9).mean() macd_h = comp_macd - signal x = data.index #y = data['Close'] #z = ax = self.figure.add_subplot(111) #ax.set_ylim(ymax=450) #ax.set_ylim(bottom=100) #print(dir(ax)) #ax.set_ylabel('fault courage') ax.plot(x,comp_macd, color="blue") ax.plot(x,signal, color="red") ax.plot(x,macd_h, color="green") #macd.plot(ax=ax, secondary_y=True, color="orange") ax.tick_params(axis='x', rotation=0) ax.set_xlabel('') #ax.legend() self.draw() self.flush_events() class PlotCanvas2(FigureCanvas): def __init__(self, parent=None, width=15, height=2, dpi=100): fig = Figure(figsize=(width, height), dpi=dpi) fig.suptitle('RSI', fontsize=10) #fig.supxlabel('X title', fontsize=10) #fig.supylabel('Y title', fontsize=10) FigureCanvas.__init__(self, fig) self.setParent(parent) FigureCanvas.updateGeometry(self) self.plot2() # Returns RSI values def rsi(self, close, periods = 14): close_delta = close.diff() # Make two series: one for lower closes and one for higher closes up = close_delta.clip(lower=0) down = -1 * close_delta.clip(upper=0) ma_up = up.ewm(com = periods - 1, adjust=True, min_periods = periods).mean() ma_down = down.ewm(com = periods - 1, adjust=True, min_periods = periods).mean() rsi = ma_up / ma_down rsi = 100 - (100/(1 + rsi)) return rsi def plot2(self): global data x = data.index data['RSI'] = self.rsi(data['Close']) #y = data['Close'] ax = self.figure.add_subplot(111) #ax.set_ylim(ymax=450) #ax.set_ylim(bottom=100) #print(dir(ax)) #ax.set_xlabel('Test Cases') #ax.set_ylabel('fault courage') #ax.plot(x,y) ax.axhline(30, linestyle='--', alpha=0.5, color='r') ax.axhline(70, linestyle='--', alpha=0.5, color='r') ax.plot(x,data['RSI']) ax.tick_params(axis='x', rotation=0) #ax.plot(y,z) #ax.plot(y,a) self.draw() self.flush_events() class PlotCanvas3(FigureCanvas): def __init__(self, parent=None, width=15, height=2, dpi=100): fig = Figure(figsize=(width, height), dpi=dpi) fig.suptitle('Bollinger Bands', fontsize=10) #fig.supxlabel('X title', fontsize=10) #fig.supylabel('Y title', fontsize=10) FigureCanvas.__init__(self, fig) self.setParent(parent) FigureCanvas.updateGeometry(self) self.plot3() def plot3(self): global data n = 50 BBANDS = self.BBANDS(data, n) x = data.index y = data['Close'] ax = self.figure.add_subplot(111) #ax.set_ylim(ymax=450) #ax.set_ylim(bottom=100) #print(dir(ax)) #ax.set_xlabel('Test Cases') #ax.set_ylabel('fault courage') ax.plot(x,y) ax.plot(x,data['MiddleBand']) ax.plot(x,data['UpperBand']) ax.plot(x,data['LowerBand'] ) ax.tick_params(axis='x', rotation=0) self.draw() self.flush_events() # Compute the Bollinger Bands def BBANDS(self, data, window=50): MA = data.Close.rolling(window=50).mean() SD = data.Close.rolling(window=50).std() data['MiddleBand'] = MA data['UpperBand'] = MA + (2 * SD) data['LowerBand'] = MA - (2 * SD) return data class PlotCanvas4(FigureCanvas): def __init__(self, parent=None, width=15, height=2, dpi=100): fig = Figure(figsize=(width, height), dpi=dpi) fig.suptitle('Moving Average Price', fontsize=10) #fig.supxlabel('X title', fontsize=10) #fig.supylabel('Y title', fontsize=10) FigureCanvas.__init__(self, fig) self.setParent(parent) FigureCanvas.updateGeometry(self) self.plot4() # Simple Moving Average def SMA(self, data, ndays): SMA = pd.Series(data['Close'].rolling(ndays).mean(), name = 'SMA') data = data.join(SMA) return data # Exponentially-weighted Moving Average def EWMA(self, data, ndays): EMA = pd.Series(data['Close'].ewm(span = ndays, min_periods = ndays - 1).mean(), name = 'EWMA_' + str(ndays)) data = data.join(EMA) return data def plot4(self): global data x = data.index n = 50 SMA = self.SMA(data,n) SMA = SMA.dropna() ew = 200 EWMA = self.EWMA(data,ew) EWMA = EWMA.dropna() y = data['Close'] ax = self.figure.add_subplot(111) #ax.set_ylim(ymax=450) #ax.set_ylim(bottom=100) #print(dir(ax)) #ax.set_xlabel('Test Cases') #ax.set_ylabel('fault courage') ax.plot(SMA.index,SMA['Close']) ax.plot(SMA.index,SMA['SMA']) ax.plot(EWMA.index,EWMA['EWMA_200'] ) ax.tick_params(axis='x', rotation=0) self.draw() self.flush_events() class PlotCanvas5(FigureCanvas): def __init__(self, parent=None, width=15, height=2, dpi=100): fig = Figure(figsize=(width, height), dpi=dpi) fig.suptitle('Relative Volume', fontsize=10) #fig.supxlabel('X title', fontsize=10) #fig.supylabel('Y title', fontsize=10) FigureCanvas.__init__(self, fig) self.setParent(parent) FigureCanvas.updateGeometry(self) self.plot5() # RVOL def RVOL(self, data, ndays): Ave_VOL = pd.Series(data['Volume'].rolling(ndays).mean(), name = 'Ave_VOL') data['RVOL'] = data['Volume'] / Ave_VOL #data = data.join(SMA) return data def plot5(self): global data x = data.index n = 30 RVOL = self.RVOL(data,n) RVOL = RVOL.dropna() #print(RVOL) y = data['Volume'] ax = self.figure.add_subplot(111) #ax.set_ylim(ymax=450) #ax.set_ylim(bottom=100) #print(dir(ax)) #ax.set_xlabel('Test Cases') #ax.set_ylabel('fault courage') #ax.plot(RVOL.index,RVOL['Volume']) ax.plot(RVOL.index,RVOL['RVOL']) ax.tick_params(axis='x', rotation=0) self.draw() self.flush_events() class PlotCanvas6(FigureCanvas): def __init__(self, parent=None, width=15, height=2, dpi=100): fig = Figure(figsize=(width, height), dpi=dpi) fig.suptitle('Williams %R', fontsize=10) #fig.supxlabel('X title', fontsize=10) #fig.supylabel('Y title', fontsize=10) FigureCanvas.__init__(self, fig) self.setParent(parent) FigureCanvas.updateGeometry(self) self.plot6() # W%R def get_wr(self, high, low, close, lookback): highh = high.rolling(lookback).max() lowl = low.rolling(lookback).min() wr = -100 * ((highh - close) / (highh - lowl)) return wr def plot6(self): global data x = data.index n = 30 data['wr_14'] = self.get_wr(data['High'], data['Low'], data['Close'], 14) y = data['wr_14'] ax = self.figure.add_subplot(111) #ax.set_ylim(ymax=450) #ax.set_ylim(bottom=100) #print(dir(ax)) #ax.set_xlabel('Test Cases') #ax.set_ylabel('fault courage') #ax.plot(RVOL.index,RVOL['Volume']) ax.axhline(-20, linestyle='--', alpha=0.5, color='r') ax.axhline(-80, linestyle='--', alpha=0.5, color='r') ax.plot(x,y) ax.tick_params(axis='x', rotation=0) self.draw() self.flush_events() class PlotCanvas7(FigureCanvas): def __init__(self, parent=None, width=15, height=2, dpi=100): fig = Figure(figsize=(width, height), dpi=dpi) fig.suptitle('Stochastic Oscillator', fontsize=10) #fig.supxlabel('X title', fontsize=10) #fig.supylabel('Y title', fontsize=10) FigureCanvas.__init__(self, fig) self.setParent(parent) FigureCanvas.updateGeometry(self) self.plot7() def plot7(self): global data x = data.index n = 30 k_period = 14 d_period = 3 # Adds a "n_high" column with max value of previous 14 periods data['n_high'] = data['High'].rolling(k_period).max() # Adds an "n_low" column with min value of previous 14 periods data['n_low'] = data['Low'].rolling(k_period).min() # Uses the min/max values to calculate the %k (as a percentage) data['%K'] = (data['Close'] - data['n_low']) * 100 / (data['n_high'] - data['n_low']) # Uses the %k to calculates a SMA over the past 3 values of %k data['%D'] = data['%K'].rolling(d_period).mean() data.ta.stoch(high='high', low='low', k=14, d=3, append=True) #print(data) y = data['Close'] z = data['Open'] ax = self.figure.add_subplot(111) #ax.set_ylim(ymax=450) #ax.set_ylim(bottom=100) #print(dir(ax)) #ax.set_xlabel('Test Cases') #ax.set_ylabel('fault courage') #ax.plot(RVOL.index,RVOL['Volume']) ##ax.axhline(-20, linestyle='--', alpha=0.5, color='r') ##ax.axhline(-80, linestyle='--', alpha=0.5, color='r') ax.plot(x,y) ax.plot(x,z) ax.plot(x,data['STOCHk_14_3_3']) ax.plot(x,data['STOCHd_14_3_3']) ax.axhline(20, linestyle='--', alpha=0.5, color='r') ax.axhline(80, linestyle='--', alpha=0.5, color='r') ax.tick_params(axis='x', rotation=0) self.draw() self.flush_events() def main(): app = QApplication(sys.argv) ex = Window() sys.exit(app.exec()) if __name__ == '__main__': main()