通过预测未来比特币价格,我陷入了以下困境:
我只能通过以下方式预测y标签(例如,开盘价):
所有X功能
我曾经训练过我的模特。
以下是我的数据片段(6个要素列,1个标签):
Open High Low HL-PCT PCT-change \
2016-01-01 00:00:00 430.89 432.58 429.82 0.642129 -0.030161
2016-01-01 01:00:00 431.51 432.01 429.08 0.682856 0.348829
2016-01-01 02:00:00 430.00 431.69 430.00 0.393023 -0.132383
2016-01-01 03:00:00 430.50 433.37 430.03 0.776690 -0.662252
2016-01-01 04:00:00 433.34 435.72 432.55 0.732863 -0.406794
2016-01-01 05:00:00 435.11 436.00 434.47 0.352153 -0.066605
2016-01-01 06:00:00 435.44 435.44 430.08 1.246280 0.440569
2016-01-01 07:00:00 434.71 436.00 433.50 0.576701 0.126681
2016-01-01 08:00:00 433.82 434.19 431.00 0.740139 -0.059897
2016-01-01 09:00:00 433.99 433.99 431.23 0.640030 0.460648
Volume (BTC) Label
2016-01-01 00:00:00 41.32 434.87
2016-01-01 01:00:00 31.21 434.44
2016-01-01 02:00:00 12.25 433.47
2016-01-01 03:00:00 74.98 431.80
2016-01-01 04:00:00 870.80 433.28
2016-01-01 05:00:00 78.53 433.31
2016-01-01 06:00:00 177.11 433.39
2016-01-01 08:00:00 210.59 432.80
2016-01-01 09:00:00 129.68 432.17
这是我的代码:
#First get my own data
symbols = ["bitstamp_hourly_2016"]
timestamp = pd.date_range(start='2016-01-01 00:00', end='2016-12-23 09:00',
freq='1h', periods=None)
df_all = bf.get_data2(symbols, timestamp)
#Feature Slicing
df = df_all[['Open', 'High', 'Low', 'Close', 'Volume (BTC)']]
df.loc[:,'HL-PCT'] = (df['High'] - df['Low'])/df['Low']*100.0
df.loc[:,'PCT-change'] = (df['Open'] - df['Close'])/df['Close']*100.0
#only relevant features
df= df[['Open','High', 'Low', 'HL-PCT', 'PCT-change', 'Volume (BTC)']]
df.fillna(-99999, inplace=True)
#cut off the last 24 hours
forecast_out = int(math.ceil(0.0027*len(df)))
forecast_col = 'Open'
df['Label'] = df[forecast_col].shift(-forecast_out)
#X Features and y Label
X = np.array(df.drop(['Label'],1))
X = preprocessing.scale(X)
#Last 24 hours
X_lately = X[-forecast_out:]
X = X[:-forecast_out]
y = np.array(df['Label'])
y = y[:-forecast_out]
#Train and Test set
test_size= int(math.ceil(0.3*len(df)))
X_train, y_train = X[:-test_size], y[:-test_size]
X_test, y_test= X[-test_size:], y[-test_size:]
#use linear regression
clf = LinearRegression(n_jobs=-1)
clf.fit(X_train, y_train)
#BIG QUESTION: WHAT TO INSERT HERE TO GET THE REAL FUTURE VALUES
prediction = clf.predict(X_lately)
# The coefficients
print('Coefficients: \n', clf.coef_)
# The mean squared error
print("Mean squared error: %.4f"
% np.mean((clf.predict(X_test) - y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.4f' % clf.score(X_test, y_test))
结果:
How many Hours were predicted: 24
Coefficients: [ 5.30676009e+00 1.05641430e+02 1.44632212e+01 1.47255264e+00
-1.52247332e+00 -6.26777634e-03]
Mean squared error: 133.4017
Variance score: 0.9717
我想做的是:
只给出一个新的日期,使用经过训练的模型及其过去的知识,给我一个合理的结果
比如说接下来的24小时(实际的未来,我没有数据)。
到目前为止,我只能使用clf.predict()上的过去数据。
这应该是可能的回归线,但如何?我也可以使用日期作为我的X数据帧,但这不会使我的模型无用吗?
谢谢
