Is it possible... to recreat this RBF Model.. to predict price in Quantopian...?

Still relatively new here.. in Quantopian not really familiar what command lines to use.. any help from the community... is greatly appreciated thanks... https://www.youtube.com/watch?v=SSu00IRRraY&t=304s

import csv
import numpy as np
from sklearn.svm import SVR
import matplotlib.pyplot as plt

plt.switch_backend('newbackend')

dates = []
prices = []

def get_data(filename):
with open(filename, 'r') as csvfile:
dates.append(int(row[0].split('-')[0]))
prices.append(float(row[1]))
return

def predict_price(dates, prices, x):
dates = np.reshape(dates,(len(dates), 1)) # converting to matrix of n X 1

svr_lin = SVR(kernel= 'linear', C= 1e3)
svr_poly = SVR(kernel= 'poly', C= 1e3, degree= 2)
svr_rbf = SVR(kernel= 'rbf', C= 1e3, gamma= 0.1) # defining the support vector regression models
svr_rbf.fit(dates, prices) # fitting the data points in the models
svr_lin.fit(dates, prices)
svr_poly.fit(dates, prices)

plt.scatter(dates, prices, color= 'black', label= 'Data') # plotting the initial datapoints
plt.plot(dates, svr_rbf.predict(dates), color= 'red', label= 'RBF model') # plotting the line made by the RBF kernel
plt.plot(dates,svr_lin.predict(dates), color= 'green', label= 'Linear model') # plotting the line made by linear kernel
plt.plot(dates,svr_poly.predict(dates), color= 'blue', label= 'Polynomial model') # plotting the line made by polynomial kernel
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Support Vector Regression')
plt.legend()
plt.show()

return svr_rbf.predict(x)[0], svr_lin.predict(x)[0], svr_poly.predict(x)[0]


get_data('aapl.csv') # calling get_data method by passing the csv file to it

# print "Prices- ", prices

predicted_price = predict_price(dates, prices, 29)

1 response

It looks like you should be able to recreate that example using Quantopian.

First off, you want to do this in the research environment. Do the "Introduction to Research" lecture.

Then, you want to replace that get_data function with the Quantopian get_pricing function.

From there, it looks like it should mostly work. If you get stuck, share your work so far as an attached notebook.

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