动量研究¶

前言

考虑做动量研究，是因为看到某篇HMM的研究中用1日、5日涨幅数据效果出奇的好，当我尝试自己更改输入feature，更换成收盘价、成交量等其他平时我们更加关心的数据时，得到的预测结果异常差！这让我突然意识到1日、5日涨幅其实就是动量。
于是，我计划采用动量参数从简单出发，试一试各个算法的预测效果

作为输入1日、5日、10日、20日、30日、50日、120日、250日涨幅
预测后面1日、5日涨幅
预测中使用60%数据进行训练，使用40%数据测试
结果评估采用 np.sqrt(np.square(np.abs(r_y51 - y51)).mean())

一、获取数据¶

from sklearn.preprocessing import MinMaxScaler
import datetime
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import cm
from matplotlib import pyplot#获取沪深300原始数据
startdate = '2005-06-01'
enddate = '2019-03-7'
df = get_price(['000300.XSHG'], start_date=startdate, end_date=enddate, frequency='daily', 
               fields=['close'])
close = df['close']['000300.XSHG']
close.shape

/opt/conda/lib/python3.6/site-packages/jqresearch/api.py:86: FutureWarning: 
Panel is deprecated and will be removed in a future version.
The recommended way to represent these types of 3-dimensional data are with a MultiIndex on a DataFrame, via the Panel.to_frame() method
Alternatively, you can use the xarray package http://xarray.pydata.org/en/stable/.
Pandas provides a `.to_xarray()` method to help automate this conversion.

  pre_factor_ref_date=_get_today())

(3350,)

#1-250日涨幅,输入数据
r1 = (np.log(np.array(close[1:]))-np.log(np.array(close[:-1])))[(250-1):-5]
r5 = (np.log(np.array(close[5:]))-np.log(np.array(close[:-5])))[(250-5):-5] 
r10 = (np.log(np.array(close[10:]))-np.log(np.array(close[:-10])))[(250-10):-5]
r20 = (np.log(np.array(close[20:]))-np.log(np.array(close[:-20])))[(250-20):-5]
r30 = (np.log(np.array(close[30:]))-np.log(np.array(close[:-30])))[(250-30):-5]
r50 = (np.log(np.array(close[50:]))-np.log(np.array(close[:-50])))[(250-50):-5]
r120 = (np.log(np.array(close[120:]))-np.log(np.array(close[:-120])))[(250-120):-5]
r250 = (np.log(np.array(close[250:]))-np.log(np.array(close[:-250])))[:-5]

# 1-5日涨幅，输出数据
y1 = (np.log(np.array(close[1:]))-np.log(np.array(close[:-1])))[(250-1+1):(-5+1)]
y5 = (np.log(np.array(close[5:]))-np.log(np.array(close[:-5])))[(250-5+5):]

X = np.column_stack([r1,r5,r10,r20,r30,r50,r120,r250])
#X = np.column_stack([r5,r20,r50,r120,r250])


#参数归一化
mms = MinMaxScaler()
tt = mms.fit_transform(X)
X = tt
#训练、测试数据分开
k = np.int(X.shape[0] * 0.9)
X1 = X[:k]
X2 = X[k:]
y11 = 10*y1[:k]
y12 = 10*y1[k:]
y51 = 10*y5[:k]
y52 = 10*y5[k:]

record = pd.DataFrame(columns=(['算法','训练误差','测试误差']))

数据格式为8维非线性数据，所以一般的线性回归算法不适合

二、预测¶

SVR¶

from sklearn import svm
svrr = svm.SVR(kernel='rbf', C=1e1)
svrr.fit(X1,y51)
r_y52 = svrr.predict(X2)
r_y51 = svrr.predict(X1)
train_e = np.sqrt(np.square(np.abs(r_y51 - y51)).mean())
test_e = np.sqrt(np.square(np.abs(r_y52 - y52)).mean())
print('训练数据的平均误差', train_e)
print('测试数据的平均误差', test_e)
record.loc[len(record),:] =('SVR',train_e,test_e) 
plt.plot(y52.cumsum(),'r-')
plt.plot(r_y52.cumsum(),'b-')

训练数据的平均误差 0.40973969978302655
测试数据的平均误差 0.3046188105020312

[<matplotlib.lines.Line2D at 0x7effbc764e10>]

神经网络¶

from sklearn.neural_network import MLPRegressor  # 多层线性回归
from sklearn.metrics import accuracy_score
# solver='lbfgs',  MLP的求解方法：L-BFGS 在小数据上表现较好，Adam 较为鲁棒，SGD在参数调整较优时会有最佳表现（分类效果与迭代次数）；SGD标识随机梯度下降。
# alpha:L2的参数：MLP是可以支持正则化的，默认为L2，具体参数需要调整
# hidden_layer_sizes=(x, x) 
clf = MLPRegressor(solver='adam', alpha=1e-5,hidden_layer_sizes=(124, 124), random_state=1)
clf.fit(X1, y51)
r_y51 = clf.predict(X1)
r_y52 = clf.predict(X2)
train_e = np.sqrt(np.square(np.abs(r_y51 - y51)).mean())
test_e = np.sqrt(np.square(np.abs(r_y52 - y52)).mean())
print('训练数据的平均误差', train_e)
print('测试数据的平均误差', test_e)
record.loc[len(record),:] =('神经网络',train_e,test_e) 
plt.plot(y52.cumsum(),'r-')
plt.plot(r_y52.cumsum(),'b-')

训练数据的平均误差 0.40413568589060633
测试数据的平均误差 0.30510380584932517

[<matplotlib.lines.Line2D at 0x7effbc689438>]

线性回归¶

from sklearn import linear_model        
clf = linear_model.LinearRegression()
clf.fit(X1, y51)
r_y51 = clf.predict(X1)
r_y52 = clf.predict(X2)
train_e = np.sqrt(np.square(np.abs(r_y51 - y51)).mean())
test_e = np.sqrt(np.square(np.abs(r_y52 - y52)).mean())
print('训练数据的平均误差', train_e)
print('测试数据的平均误差', test_e)
record.loc[len(record),:] =('线性回归',train_e,test_e) 
plt.plot(y52.cumsum(),'r-')
plt.plot(r_y52.cumsum(),'b-')

训练数据的平均误差 0.41411735301396474
测试数据的平均误差 0.30582235268289204

[<matplotlib.lines.Line2D at 0x7effbc64b518>]

决策树¶

from sklearn import tree
clf = tree.DecisionTreeRegressor(max_depth=25)
clf.fit(X1, y51)
r_y51 = clf.predict(X1)
r_y52 = clf.predict(X2)
train_e = np.sqrt(np.square(np.abs(r_y51 - y51)).mean())
test_e = np.sqrt(np.square(np.abs(r_y52 - y52)).mean())
print('训练数据的平均误差', train_e)
print('测试数据的平均误差', test_e)
record.loc[len(record),:] =('决策树',train_e,test_e) 
plt.plot(y52.cumsum(),'r-')
plt.plot(r_y52.cumsum(),'b-')

训练数据的平均误差 0.13403025976182179
测试数据的平均误差 0.4323796945683956

[<matplotlib.lines.Line2D at 0x7effbc629a58>]

随机森林¶

from sklearn.ensemble import RandomForestRegressor
clf = RandomForestRegressor()
clf.fit(X1, y51)
r_y51 = clf.predict(X1)
r_y52 = clf.predict(X2)
train_e = np.sqrt(np.square(np.abs(r_y51 - y51)).mean())
test_e = np.sqrt(np.square(np.abs(r_y52 - y52)).mean())
print('训练数据的平均误差', train_e)
print('测试数据的平均误差', test_e)
record.loc[len(record),:] =('随机森林',train_e,test_e) 
plt.plot(y52.cumsum(),'r-')
plt.plot(r_y52.cumsum(),'b-')

训练数据的平均误差 0.1364986899768852
测试数据的平均误差 0.33043807910741707

[<matplotlib.lines.Line2D at 0x7effbc5804e0>]

KNN¶

from sklearn import neighbors
clf = neighbors.KNeighborsRegressor()
clf.fit(X1, y51)
r_y51 = clf.predict(X1)
r_y52 = clf.predict(X2)
train_e = np.sqrt(np.square(np.abs(r_y51 - y51)).mean())
test_e = np.sqrt(np.square(np.abs(r_y52 - y52)).mean())
print('训练数据的平均误差', train_e)
print('测试数据的平均误差', test_e)
record.loc[len(record),:] =('KNN',train_e,test_e) 
plt.plot(y52.cumsum(),'r-')
plt.plot(r_y52.cumsum(),'b-')

训练数据的平均误差 0.27278574027555313
测试数据的平均误差 0.34086055925114783

[<matplotlib.lines.Line2D at 0x7effbc580128>]

GBRT回归¶

from sklearn import ensemble
clf = ensemble.GradientBoostingRegressor(n_estimators=300)
clf.fit(X1, y51)
r_y51 = clf.predict(X1)
r_y52 = clf.predict(X2)
train_e = np.sqrt(np.square(np.abs(r_y51 - y51)).mean())
test_e = np.sqrt(np.square(np.abs(r_y52 - y52)).mean())
print('训练数据的平均误差', train_e)
print('测试数据的平均误差', test_e)
record.loc[len(record),:] =('GBRT回归',train_e,test_e) 
plt.plot(y52.cumsum(),'r-')
plt.plot(r_y52.cumsum(),'b-')

训练数据的平均误差 0.24569913706146893
测试数据的平均误差 0.31092995595911593

[<matplotlib.lines.Line2D at 0x7effbc47ed68>]

Bagging回归¶

from sklearn.ensemble import BaggingRegressor
clf = BaggingRegressor()
clf.fit(X1, y51)
r_y51 = clf.predict(X1)
r_y52 = clf.predict(X2)
train_e = np.sqrt(np.square(np.abs(r_y51 - y51)).mean())
test_e = np.sqrt(np.square(np.abs(r_y52 - y52)).mean())
print('训练数据的平均误差', train_e)
print('测试数据的平均误差', test_e)
record.loc[len(record),:] =('Bagging回归',train_e,test_e) 
plt.plot(y52.cumsum(),'r-')
plt.plot(r_y52.cumsum(),'b-')

训练数据的平均误差 0.14380879115660758
测试数据的平均误差 0.34497932345187565

[<matplotlib.lines.Line2D at 0x7effbc47e898>]

极端随机森林¶

from sklearn.tree import ExtraTreeRegressor
clf = ExtraTreeRegressor()
clf.fit(X1, y51)
r_y51 = clf.predict(X1)
r_y52 = clf.predict(X2)
train_e = np.sqrt(np.square(np.abs(r_y51 - y51)).mean())
test_e = np.sqrt(np.square(np.abs(r_y52 - y52)).mean())
print('训练数据的平均误差', train_e)
print('测试数据的平均误差', test_e)
record.loc[len(record),:] =('极端随机森林',train_e,test_e) 
plt.plot(y52.cumsum(),'r-')
plt.plot(r_y52.cumsum(),'b-')

训练数据的平均误差 1.7095137609083828e-05
测试数据的平均误差 0.4812990001394652

[<matplotlib.lines.Line2D at 0x7effbc47ecc0>]

Adaboost回归¶

from sklearn import ensemble
clf = ensemble.AdaBoostRegressor(n_estimators=50)
clf.fit(X1, y51)
r_y51 = clf.predict(X1)
r_y52 = clf.predict(X2)
train_e = np.sqrt(np.square(np.abs(r_y51 - y51)).mean())
test_e = np.sqrt(np.square(np.abs(r_y52 - y52)).mean())
print('训练数据的平均误差', train_e)
print('测试数据的平均误差', test_e)
record.loc[len(record),:] =('Adaboost回归',train_e,test_e) 
plt.plot(y52.cumsum(),'r-')
plt.plot(r_y52.cumsum(),'b-')

训练数据的平均误差 0.40497653122445065
测试数据的平均误差 0.32104811458688914

[<matplotlib.lines.Line2D at 0x7effbc384550>]

基于keras的LSTM¶

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM

X1.shape[1]

8

clf = Sequential()
tX1 = X1.reshape(X1.shape[0],1,X1.shape[1])
tX2 = X2.reshape(X2.shape[0],1,X2.shape[1])
clf.add(LSTM(50, input_shape=(tX1.shape[1],tX1.shape[2])))
clf.add(Dense(1))
clf.compile(loss='mae', optimizer='adam')
clf.fit(tX1,y51,epochs=50, batch_size=250)
r_y51 = clf.predict(tX1)
r_y52 = clf.predict(tX2)

Epoch 1/50
2785/2785 [==============================] - 1s 538us/step - loss: 0.3103
Epoch 2/50
2785/2785 [==============================] - 0s 107us/step - loss: 0.3084
Epoch 3/50
2785/2785 [==============================] - 0s 77us/step - loss: 0.3069
Epoch 4/50
2785/2785 [==============================] - 0s 109us/step - loss: 0.3062
Epoch 5/50
2785/2785 [==============================] - 0s 110us/step - loss: 0.3062
Epoch 6/50
2785/2785 [==============================] - 0s 108us/step - loss: 0.3056
Epoch 7/50
2785/2785 [==============================] - 0s 109us/step - loss: 0.3053
Epoch 8/50
2785/2785 [==============================] - 0s 138us/step - loss: 0.3051
Epoch 9/50
2785/2785 [==============================] - 0s 110us/step - loss: 0.3048
Epoch 10/50
2785/2785 [==============================] - 0s 111us/step - loss: 0.3046
Epoch 11/50
2785/2785 [==============================] - 0s 104us/step - loss: 0.3044
Epoch 12/50
2785/2785 [==============================] - 0s 108us/step - loss: 0.3042
Epoch 13/50
2785/2785 [==============================] - 0s 140us/step - loss: 0.3042
Epoch 14/50
2785/2785 [==============================] - 0s 110us/step - loss: 0.3037
Epoch 15/50
2785/2785 [==============================] - 0s 108us/step - loss: 0.3041
Epoch 16/50
2785/2785 [==============================] - 0s 107us/step - loss: 0.3035
Epoch 17/50
2785/2785 [==============================] - 0s 107us/step - loss: 0.3034
Epoch 18/50
2785/2785 [==============================] - 0s 105us/step - loss: 0.3035
Epoch 19/50
2785/2785 [==============================] - 0s 106us/step - loss: 0.3034
Epoch 20/50
2785/2785 [==============================] - 0s 76us/step - loss: 0.3032
Epoch 21/50
2785/2785 [==============================] - 0s 105us/step - loss: 0.3033
Epoch 22/50
2785/2785 [==============================] - 0s 105us/step - loss: 0.3035
Epoch 23/50
2785/2785 [==============================] - 0s 104us/step - loss: 0.3033
Epoch 24/50
2785/2785 [==============================] - 0s 78us/step - loss: 0.3031
Epoch 25/50
2785/2785 [==============================] - 0s 104us/step - loss: 0.3032
Epoch 26/50
2785/2785 [==============================] - 0s 75us/step - loss: 0.3031
Epoch 27/50
2785/2785 [==============================] - 0s 104us/step - loss: 0.3029
Epoch 28/50
2785/2785 [==============================] - 0s 75us/step - loss: 0.3028
Epoch 29/50
2785/2785 [==============================] - 0s 104us/step - loss: 0.3028
Epoch 30/50
2785/2785 [==============================] - 0s 75us/step - loss: 0.3038
Epoch 31/50
2785/2785 [==============================] - 0s 75us/step - loss: 0.3036
Epoch 32/50
2785/2785 [==============================] - 0s 103us/step - loss: 0.3026
Epoch 33/50
2785/2785 [==============================] - 0s 75us/step - loss: 0.3030
Epoch 34/50
2785/2785 [==============================] - 0s 74us/step - loss: 0.3028
Epoch 35/50
2785/2785 [==============================] - 0s 103us/step - loss: 0.3026
Epoch 36/50
2785/2785 [==============================] - 0s 76us/step - loss: 0.3026
Epoch 37/50
2785/2785 [==============================] - 0s 104us/step - loss: 0.3027
Epoch 38/50
2785/2785 [==============================] - 0s 75us/step - loss: 0.3031
Epoch 39/50
2785/2785 [==============================] - 0s 76us/step - loss: 0.3024
Epoch 40/50
2785/2785 [==============================] - 0s 108us/step - loss: 0.3025
Epoch 41/50
2785/2785 [==============================] - 0s 103us/step - loss: 0.3025
Epoch 42/50
2785/2785 [==============================] - 0s 75us/step - loss: 0.3029
Epoch 43/50
2785/2785 [==============================] - 0s 105us/step - loss: 0.3028
Epoch 44/50
2785/2785 [==============================] - 0s 77us/step - loss: 0.3034
Epoch 45/50
2785/2785 [==============================] - 0s 105us/step - loss: 0.3031
Epoch 46/50
2785/2785 [==============================] - 0s 74us/step - loss: 0.3024
Epoch 47/50
2785/2785 [==============================] - 0s 103us/step - loss: 0.3024
Epoch 48/50
2785/2785 [==============================] - 0s 76us/step - loss: 0.3024
Epoch 49/50
2785/2785 [==============================] - 0s 77us/step - loss: 0.3022
Epoch 50/50
2785/2785 [==============================] - 0s 103us/step - loss: 0.3022

train_e = np.sqrt(np.square(np.abs(r_y51 - y51)).mean())
test_e = np.sqrt(np.square(np.abs(r_y52 - y52)).mean())
print('训练数据的平均误差', train_e)
print('测试数据的平均误差', test_e)
record.loc[len(record),:] =('LSTM',train_e,test_e) 
plt.plot(y52.cumsum(),'r-')
plt.plot(r_y52.cumsum(),'b-')

训练数据的平均误差 0.4283344503276845
测试数据的平均误差 0.3079035993630009

[<matplotlib.lines.Line2D at 0x7effbc3e69e8>]

record

讨论：¶

1、很明显，SVR、神经网络、线性回归、LSTM的测试误差均在0.30-0.31以内；
2、30%以上级别的误差几乎等于预测失败。
3、算法复杂度来说，LSTM、线性回归两种算法有着天壤之别，然而他们的结果居然差不多；

结论¶

1、使用动量预测效果有限；
2、DeepLearning在本研究中没有逆袭……

	算法	训练误差	测试误差
0	SVR	0.40974	0.304619
1	神经网络	0.404136	0.305104
2	线性回归	0.414117	0.305822
3	决策树	0.13403	0.43238
4	随机森林	0.136499	0.330438
5	KNN	0.272786	0.340861
6	GBRT回归	0.245699	0.31093
7	Bagging回归	0.143809	0.344979
8	极端随机森林	1.70951e-05	0.481299
9	Adaboost回归	0.404977	0.321048
10	LSTM	0.428334	0.307904

量化交易吧 / 数理科学 帖子：3353575 新帖：2

各种ML算法的动量研究说明，交易没有圣杯？

美联储发表于：5 月 9 日 20：58回复(1)

动量研究¶

一、获取数据¶

二、预测¶

SVR¶

神经网络¶

线性回归¶

决策树¶

随机森林¶

KNN¶

GBRT回归¶

Bagging回归¶

极端随机森林¶

Adaboost回归¶

基于keras的LSTM¶

讨论：¶

结论¶

全部回复

0/140

粉丝:913

帖子数:0

粉丝:733

帖子数:0

粉丝:675

帖子数:391

量化课程

热门标签

删除回复

确认要删除这篇文章么？

举报用户

信息提示

该文章已删除

设置置顶

完成设置【置顶】！

设置置顶

已取消设置【置顶】！

设置精华

完成设置【精华】！

设置精华

已取消设置【精华】！

审核信息

该文章已审核通过

审核信息

您已设置该文章审核不通过

举报成功

您已举报成功

用户登录

移动帖子

创建私信

屏蔽提示

确认要屏蔽该用户么？

屏蔽回复

您已对该用户实现屏蔽

信息回复

已发送成功

量化交易吧 / 数理科学帖子：3353575 新帖：2