polyreg.py

# -*- coding: utf-8 -*-
"""PolyReg.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1q4b1o3iFGk4vqJM4KLE7T2ClDhJo_QC8
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import requests
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
import warnings
warnings.filterwarnings("ignore")



# Fetching data from the server
url = "https://web-api.coinmarketcap.com/v1/cryptocurrency/ohlcv/historical"
param = {"convert":"USD","slug":"bitcoin","time_end":"1601510400","time_start":"1367107200"}
content = requests.get(url=url, params=param).json()
df = pd.json_normalize(content['data']['quotes'])

# Extracting and renaming the important variables
df['Date']=pd.to_datetime(df['quote.USD.timestamp']).dt.tz_localize(None)
df['Low'] = df['quote.USD.low']
df['High'] = df['quote.USD.high']
df['Open'] = df['quote.USD.open']
df['Close'] = df['quote.USD.close']
df['Volume'] = df['quote.USD.volume']

# Drop original and redundant columns
df=df.drop(columns=['time_open','time_close','time_high','time_low', 'quote.USD.low', 'quote.USD.high', 'quote.USD.open', 'quote.USD.close', 'quote.USD.volume', 'quote.USD.market_cap', 'quote.USD.timestamp'])

# Creating a new feature for better representing day-wise values
df['Mean'] = (df['Low'] + df['High'])/2

# Cleaning the data for any NaN or Null fields
df = df.dropna()



# Creating a copy for making small changes
dataset_for_prediction = df.copy()
dataset_for_prediction['Actual']=dataset_for_prediction['Mean'].shift()
dataset_for_prediction=dataset_for_prediction.dropna()

# date time typecast
dataset_for_prediction['Date'] =pd.to_datetime(dataset_for_prediction['Date'])
dataset_for_prediction.index= dataset_for_prediction['Date']

from sklearn.preprocessing import PolynomialFeatures 
from sklearn.linear_model import LinearRegression
N=2411
#test size
X=[]
for i in range(2411):
    X.append([i])


#make numpy array
X=np.array(X)

#make train data
Xtrain=X[:N]
Xtest=X[-272:]
Y=df["Mean"]
Y=np.array(Y,dtype='float32')
ytrain=Y[:N]

#make test data
ytest=Y[-272:]
arr=ytest
#plot actual values
plt.plot(arr,label='actual')

# grid serach for optimal polynomial degree
for j in [2,3,5]:
    #make polynomial features
    poly = PolynomialFeatures(degree = j)
    X_poly = poly.fit_transform(Xtrain.reshape((2411,1))) 


    poly.fit(X_poly, ytrain) 
    reg = LinearRegression() 
    reg.fit(X_poly, ytrain) 

    ypred=reg.predict(poly.fit_transform(Xtest.reshape((272,1))))
    ytest=ytest.reshape((272,1))

    #plot the same
    plt.plot(ypred,label='predicted with degree'+str(j))
    plt.legend()
    #plt.show()

    print("POLYNOMIAL REGRESSION")

    c=0
    for i in range(272):
        c+=(ypred[i]-ytest[i])**2
    c/=272
    
    print("Degree=",j,"        RMSE:",c**0.5)

print()
print("POLYNOMIAL REGRESSION, depending upon no of days")