问如何使用机器学习预测金融符号(EURUSD)的收盘价？

EN

import pandas as pd
import numpy as np
#import matplotlib.pyplot as plt
#import pickle

# 1. Read the EURUSD csv data.
# 2. Process the DataFrame, using only the Open, High, Low, Close columns.
df = pd.read_csv( 'EURUSD1440.csv', index_col= 'Date' )
df = df[['Open','High','Low','Close']]
array = df.values

# Features consist of Open, High, Low column, and stored in x.
# Label is the Close column stored in y.
x = array[:,0:3]
y = array[:,3]


# Split Data into Test and Train.
# 60% Train and 40% Test.
from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split( x, y, test_size = 0.4 )


# 1. Train the Model using .fit method.
# 2. Predict the future Closing prices using the .predict method.
# 3. Know how Accurate the Model is using the .score method.
from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score

model = LinearRegression()
model.fit( x_train, y_train )
forecast = model.predict( x_test )
accuracy = model.score( x_test, y_test )

print( forecast, accuracy )