import quandl

import numpy as np

import pandas as pd

from sklearn.linear_model import LinearRegression

from sklearn.svm import SVR

from sklearn.model_selection import train_test_split

# Get Data from Quandl

df = quandl.get("BITFINEX/BTCUSD", authtoken="-aBPy-8nsYvmnQ3sasXS")

# Isolate High and Low Columns

dfh = df[['High']]

dfl = df[['Low']]

# Predicting into the Future

forecast_out = 30 #'n=30' days

# Create more Columns (the target or dependent variable) shifted 'n' units up

dfh['Prediction'] = df[['High']].shift(-forecast_out)

dfl['Prediction'] = df[['Low']].shift(-forecast_out)

# Independent Data Set

X_h = np.array(dfh.drop(['Prediction'],1))

X_l = np.array(dfl.drop(['Prediction'],1))

# Remove the last 'n' rows

X_h = X_h[:-forecast_out]

X_l = X_l[:-forecast_out]

# Dependent Data Set

y_h = np.array(dfh['Prediction'])

y_l = np.array(dfl['Prediction'])

# Remove the last 'n' rows

y_h = y_h[:-forecast_out]

y_l = y_l[:-forecast_out]

# Split and Train

x_train_h, x_test_h, y_train_h, y_test_h = train_test_split(X_h, y_h, test_size=0.1)

x_train_l, x_test_l, y_train_l, y_test_l = train_test_split(X_l, y_l, test_size=0.1)

# LinearRegression (lr)

lr_h = LinearRegression()

lr_l = LinearRegression()

# Train the model

lr_h.fit(x_train_h, y_train_h)

lr_l.fit(x_train_l, y_train_l)

lr_confidence_h = lr_h.score(x_test_h, y_test_h)

print("lr confidence HIGH: ", lr_confidence_h)

lr_confidence_l = lr_l.score(x_test_l, y_test_l)

print("lr confidence LOW: ", lr_confidence_l)