您的代码使用此 DataFrame 作为 X 来生成预测：
df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
这意味着，如果您想预测五年后的价格，您需要这些 ['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume'] 数据点来获取未来值，以便继续预测更远的距离。
请注意，您图像中的预测是根据在此处作为测试集分离的历史数据创建的：X_lately = X[-forecast_out:]。所以它预测的每个点都使用历史数据来预测未来的某个点。
如果您真的想使用此模型预测 5 年的未来，您首先需要预测/计算所有这些变量：predicted_X = ['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']，并在内部使用clf.predict(predicted_X) 继续运行一些循环。
我相信这个Machine Learning Course for Trading at Udacity 对您来说可能是一个很好的资源，它将为您提供更好的框架和思维方式来解决此类问题。
希望我的回答清晰明了，对你有帮助，如果不只是让我知道，我会澄清或回答其他问题。
按照我所说的更新您的模型：
import quandl
import numpy as np
from sklearn import preprocessing, model_selection
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from matplotlib import style
import datetime
style.use('ggplot')
df = quandl.get("WIKI/GOOGL")
df = df[['Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume']]
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. Close'] * 100.0
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0
df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
forecast_col = 'Adj. Close'
df.fillna(value=-99999, inplace=True)
forecast_out = 1
df['label'] = df[forecast_col].shift(-forecast_out)
X = np.array(df.drop(['label'], 1))
X = preprocessing.scale(X)
X_lately = X[-forecast_out:]
X = X[:-forecast_out]
df.dropna(inplace=True)
y = np.array(df['label'])
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2)
# Instantiate regressors
reg_close = LinearRegression(n_jobs=-1)
reg_close.fit(X_train, y_train)
reg_hl = LinearRegression(n_jobs=-1)
reg_hl.fit(X_train, y_train)
reg_pct = LinearRegression(n_jobs=-1)
reg_pct.fit(X_train, y_train)
reg_vol = LinearRegression(n_jobs=-1)
reg_vol.fit(X_train, y_train)
# Prepare variables for loop
last_close = df['Adj. Close'][-1]
last_date = df.iloc[-1].name.timestamp()
df['Forecast'] = np.nan
predictions_arr = X_lately
for i in range(100):
# Predict next point in time
last_close_prediction = reg_close.predict(predictions_arr)
last_hl_prediction = reg_hl.predict(predictions_arr)
last_pct_prediction = reg_pct.predict(predictions_arr)
last_vol_prediction = reg_vol.predict(predictions_arr)
# Create np.Array of current predictions to serve as input for future predictions
predictions_arr = np.array((last_close_prediction, last_hl_prediction, last_pct_prediction, last_vol_prediction)).T
next_date = datetime.datetime.fromtimestamp(last_date)
last_date += 86400
# Outputs data into DataFrame to enable plotting
df.loc[next_date] = [np.nan, np.nan, np.nan, np.nan, np.nan, float(last_close_prediction)]
df['Adj. Close'].plot()
df['Forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()
这个模型不是很有用，因为它很快就会向上爆发，但是在它的实现中存在一些有趣和不寻常的事情。
为了更真实地预测未来价格，您还需要实施某种随机游走。
您也可以使用不同的模型来代替 LinearRegression，例如 RandomForestRegressor，这会产生非常不同的结果。
from sklearn.ensemble import RandomForestRegressor
clf_close = RandomForestRegressor(n_jobs=-1)
clf_close.fit(X_train, y_train)
clf_hl = RandomForestRegressor(n_jobs=-1)
clf_hl.fit(X_train, y_train)
clf_pct = RandomForestRegressor(n_jobs=-1)
clf_pct.fit(X_train, y_train)
clf_vol = RandomForestRegressor(n_jobs=-1)
clf_vol.fit(X_train, y_train)
在给定特定入场参数和出场参数的情况下，预测特定头寸（买入或卖出）是否有利可图通常是一种更好的方法，而不是预测价格。 Udacity course 涵盖了这种方法。
随机游走模型：
import quandl
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
import datetime
import random
style.use('ggplot')
df = quandl.get("WIKI/GOOGL")
df = df[['Adj. Close']]
df.dropna(inplace=True)
# Prepare variables for loop
last_close = df['Adj. Close'][-1]
last_date = df.iloc[-1].name.timestamp()
df['Forecast'] = np.nan
for i in range(1000):
# Create np.Array of current predictions to serve as input for future predictions
modifier = random.randint(-100, 105) / 10000 + 1
last_close *= modifier
next_date = datetime.datetime.fromtimestamp(last_date)
last_date += 86400
# Outputs data into DataFrame to enable plotting
df.loc[next_date] = [np.nan, last_close]
df['Adj. Close'].plot()
df['Forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()
随机游走输出图像