Python机器学习之预测黄金价格

# LinearRegression 是一个用于线性回归的机器学习库  
from sklearn.linear_model import LinearRegression  
# pandas 和 numpy 用于数据操作  
import pandas as pd  
import numpy as np  
# matplotlib 和 seaborn 用于绘制图形  
import matplotlib.pyplot as plt  
%matplotlib inline  
plt.style.use('seaborn-darkgrid')  
# yahoo Finance用于获取数据  
import yfinance as yf 

Df = yf.download('GLD', '2008-01-01', '2020-6-22', auto_adjust=True)  
DfDf = Df[['Close']]  
DfDf = Df.dropna()  
Df.Close.plot(figsize=(10, 7),color='r')  
plt.ylabel("Gold ETF Prices")  
plt.title("Gold ETF Price Series")  
plt.show() 

Df['S_3'] = Df['Close'].rolling(window=3).mean()  
Df['S_9'] = Df['Close'].rolling(window=9).mean() 
Df['next_day_price'] = Df['Close'].shift(-1)  
DfDf = Df.dropna()  
X = Df[['S_3', 'S_9']]  
y = Df['next_day_price'] 

t = .8  
t = int(t*len(Df))  
XX_train = X[:t]  
yy_train = y[:t]  
XX_test = X[t:]  
yy_test = y[t:] 

Y = m1 * X1 + m2 * X2 + C  
Gold ETF price = m1 * 3 days moving average + m2 * 15 days moving average + c  

linear = LinearRegression().fit(X_train, y_train)  
print("Linear Regression model")  
print("Gold ETF Price (y) = %.2f * 3 Days Moving Average (x1) \  
+ %.2f * 9 Days Moving Average (x2) \  
+ %.2f (constant)" % (linear.coef_[0], linear.coef_[1], linear.intercept_)) 

predicted_price = linear.predict(X_test)  
predicted_price = pd.DataFrame(  
    predicted_price, index=y_test.index, columns=['price'])  
predicted_price.plot(figsize=(10, 7))  
y_test.plot()  
plt.legend(['predicted_price', 'actual_price'])  
plt.ylabel("Gold ETF Price")  
plt.show() 

r2_score = linear.score(X[t:], y[t:])*100  
float("{0:.2f}".format(r2_score)) 

gold = pd.DataFrame()  
gold['price'] = Df[t:]['Close']  
gold['predicted_price_next_day'] = predicted_price  
gold['actual_price_next_day'] = y_test  
gold['gold_returns'] = gold['price'].pct_change().shift(-1)  
gold['signal'] = np.where(gold.predicted_price_next_day.shift(1) < gold.predicted_price_next_day,1,0)  
gold['strategy_returns'] = gold.signal * gold['gold_returns']  
((gold['strategy_returns']+1).cumprod()).plot(figsize=(10,7),color='g')  
plt.ylabel('Cumulative Returns')  
plt.show() 

sharpe = gold['strategy_returns'].mean()/gold['strategy_returns'].std()*(252**0.5)  
'Sharpe Ratio %.2f' % (sharpe) 

import datetime as dt  
current_date = dt.datetime.now()  
data = yf.download('GLD', '2008-06-01', current_date, auto_adjust=True)  
data['S_3'] = data['Close'].rolling(window=3).mean()  
data['S_9'] = data['Close'].rolling(window=9).mean()  
datadata = data.dropna()  
data['predicted_gold_price'] = linear.predict(data[['S_3', 'S_9']])  
data['signal'] = np.where(data.predicted_gold_price.shift(1) < data.predicted_gold_price,"Buy","No Position")  
data.tail(1)[['signal','predicted_gold_price']].T