Sep-24-2021, 01:38 PM
(This post was last modified: Sep-24-2021, 02:05 PM by Larz60+.
Edit Reason: fixed error tag
)
Good afternoon to the community 
.
I am using Google Colaboratory.
I want to predict the price of cryptocurrencies.
But the console shows me this error:
.I am using Google Colaboratory.
I want to predict the price of cryptocurrencies.
But the console shows me this error:
Error:RemoteDataError Traceback (most recent call last)
<ipython-input-21-66861f1bbce0> in <module>()
14 end = dt.datetime.now()
15
---> 16 data = web.DataReader(f'{crypto_currency}-{against_currency}', 'yahoo', start, end)
17
18 #Prepare Data
4 frames
/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
197 else:
198 kwargs[new_arg_name] = new_arg_value
--> 199 return func(*args, **kwargs)
200
201 return cast(F, wrapper)
/usr/local/lib/python3.7/dist-packages/pandas_datareader/data.py in DataReader(name, data_source, start, end, retry_count, pause, session, api_key)
382 retry_count=retry_count,
383 pause=pause,
--> 384 session=session,
385 ).read()
386
/usr/local/lib/python3.7/dist-packages/pandas_datareader/base.py in read(self)
251 # If a single symbol, (e.g., 'GOOG')
252 if isinstance(self.symbols, (string_types, int)):
--> 253 df = self._read_one_data(self.url, params=self._get_params(self.symbols))
254 # Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])
255 elif isinstance(self.symbols, DataFrame):
/usr/local/lib/python3.7/dist-packages/pandas_datareader/yahoo/daily.py in _read_one_data(self, url, params)
151 url = url.format(symbol)
152
--> 153 resp = self._get_response(url, params=params)
154 ptrn = r"root\.App\.main = (.*?);\n}\(this\)\);"
155 try:
/usr/local/lib/python3.7/dist-packages/pandas_datareader/base.py in _get_response(self, url, params, headers)
179 msg += "\nResponse Text:\n{0}".format(last_response_text)
180
--> 181 raise RemoteDataError(msg)
182
183 def _get_crumb(self, *args):
RemoteDataError: Unable to read URL: https://finance.yahoo.com/quote/ETH-USD/history?period1=1434340800&period2=1632542399&interval=1d&frequency=1d&filter=history
Response Text:
b'<!DOCTYPE html>\n <html lang="en-us"><head>\n <meta http-equiv="content-type" content="text/html; charset=UTF-8">\n <meta charset="utf-8">\n <title>Yahoo</title>\n <meta name="viewport" content="width=device-width,initial-scale=1,minimal-ui">\n <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">\n <style>\n html {\n height: 100%;\n }\n body {\n background: #fafafc url(https://s.yimg.com/nn/img/sad-panda-201402200631.png) 50% 50%;\n background-size: cover;\n height: 100%;\n text-align: center;\n font: 300 18px "helvetica neue", helvetica, verdana, tahoma, arial, sans-serif;\n }\n table {\n height: 100%;\n width: 100%;\n table-layout: fixed;\n border-collapse: collapse;\n border-spacing: 0;\n border: none;\n }\n h1 {\n font-size: 42px;\n font-weight: 400;\n color: #400090;\n }\n p {\n color: #1A1A1A;\n }\n #message-1 {\n font-weight: bold;\n margin: 0;\n }\n #message-2 {\n display: inline-block;\n *display: inline;\n zoom: 1;\n max-width: 17em;\n _width: 17em;\n }\n </style>\n <script>\n document.write(\'<img src="//geo.yahoo.com/b?s=1197757129&t=\'+new Date().getTime()+\'&src=aws&err_url=\'+encodeURIComponent(document.URL)+\'&err=%<pssc>&test=\'+encodeURIComponent(\'%<{Bucket}cqh[:200]>\')+\'" width="0px" height="0px"/>\');var beacon = new Image();beacon.src="//bcn.fp.yahoo.com/p?s=1197757129&t="+ne...import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pandas_datareader as web
import datetime as dt
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import Dense, Dropout, LSTM
from tensorflow.keras.models import Sequential
crypto_currency = 'ETH'
against_currency = 'USD'
start = dt.datetime (2015,6,15)
end = dt.datetime.now()
data = web.DataReader(f'{crypto_currency}-{against_currency}', 'yahoo', start, end)
#Prepare Data
scaler = MinMaxScaler(feature_range(0,1))
scaled_data = scaler.fit_transform(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))
#Create neural network
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=25, batch_size=32 )
#Testing the model
test_start = dt.datetime(2020,1,1)
test_end = dt.datetime.now()
test_data = web.DataReader(f'{crypto_currency}-{against_currency}', 'yahoo', test_tart, 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.fit_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.reshape[0], x_test.shape[1], 1))
prediction_prices = model.predict(x_test)
prediction_prices = scaler.inverse_transform(prediction_prices)
plt.plot(actual_prices, color = 'black', label='Actual Prices')
plt.plot(prediction_prices, color='green', label ='Predicted Prices')
plt.title(f'{crypto_currency}price_prediction')
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend(loc='upper left')
plt.show()Thank your for your help.
