在本文，我们仅有50Python代码开发一个股价预测模型，利用scikit-learn
基于历史数据，我们是否能够预测股价呢？可以！利用机器学习即可。 这听起来只是一个科学问题，由于市场有效性，股票市场充满随机与不确定性。
我们将开发三种不同的预测模型来预测立讯精密的股价，然后画图比较结果。
步骤： 1.安装库文件
import numpy as np
from sklearn.svm import SVR
import matplotlib.pyplot as plt
import pandas as pd # 首先导入库
df = D.history_data(instruments = ['002475.SZA'],
start_date = '2010-09-15',
end_date = '2017-08-18',
fields = ['close']
) # 以立讯精密为例，导入某一时间段历史数据
df = df[['date','close']] # 运行结果根据需要进行列调整
dates = df['date'].map(lambda x:x.day)
prices = df['close']
assert len(dates) == len(prices)
dates = [i for i in dates]
prices = [p for p in prices]
def predict_prices(dates, prices, x):
dates = np.reshape(dates, (len(dates), 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)
svr_lin.fit(dates, prices)
svr_poly.fit(dates, prices)
svr_rbf.fit(dates, prices)
plt.scatter(dates,
prices,
color="black",
label="Data")
plt.plot(dates,
svr_rbf.predict(dates),
color="red",
label="RBF Model")
plt.plot(dates,
svr_lin.predict(dates),
color="green",
label='linear Model')
plt.plot(dates,
svr_poly.predict(dates),
color="blue",
label="Ploynomial Model")
plt.xlabel('Dates')
plt.ylabel('Price')
plt.title('Support Vector Reg')
plt.legend()
plt.show()
return svr_rbf.predict(x)[0], svr_lin.predict(x)[0], svr_poly.predict(x)[0]
predicted_prices = predict_prices(dates, prices, 29)
print(predicted_prices)