Comments (14)
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
l_t has its method in the paper. but a \alpha I do not know how to set it. I set several a \alpha by man-made. the results of accuracy is far away from them in your paper. How can I set a \alpha? Must I loop through all possible values of a \alpha to see which one has the best results?
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
what you mean 'have a balanced training set' is let the proportion(1,0,-1) to be 1:1:1?
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
Great, thank you. higher thresholds can help Trading Strategy get more profit. I have try some higher thresholds, and it will make the class aacount(0) higher, such as 20%:60%:20%. The model can learn the accuracy more than 60%. but the accuracy is mainly for the class aacount(0). it is very low for the class aacounts(-1,1). it is get a bad profit for Trading Strategy, only class aacounts(-1,1) making trade. So i think the imbalance of class aacounts will let the model always select 0 for classification and get worse result comparing with mean proportion 33%. it is confuse me how to make the model get a better result by higher thresholds. Can you give me some suggestion?
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
Thanks for you help. I really need a higher threshold, as the transaction cost is not free.
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
I have read your paper 'BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books'. The solution with Bayesian networks is so wonderful. I do not find code for Bayesian network in your project.
'we add a we apply a dropout layer with rate 0.2 after the Inception Module, decided using grid-search methods'. Is it the code as following?
use the MC dropout here
conv_reshape = Reshape((int(convsecond_output.shape[1]), int(convsecond_output.shape[3])))(convsecond_output)
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
I add a dropout layer with rate 0.2 after the Inception Module and applied Concrete Dropout to
convolutional layers in the Inception Module, by import the class ConcreteDropout(Wrapper) in the paper Concrete Dropout, as the paper introduce. The code is as following, but it ovefit after several epochs. Can you give me some suggestion about my implement code of your paper?
build the inception module
convsecond_1 = ConcreteDropout(Conv2D(64, (1, 1), padding='same'))(conv_first1)
convsecond_1 = keras.layers.LeakyReLU(alpha=0.01)(convsecond_1)
convsecond_1 = ConcreteDropout(Conv2D(64, (3, 1), padding='same'))(convsecond_1)
convsecond_1 = keras.layers.LeakyReLU(alpha=0.01)(convsecond_1)
convsecond_2 = ConcreteDropout(Conv2D(64, (1, 1), padding='same'))(conv_first1)
convsecond_2 = keras.layers.LeakyReLU(alpha=0.01)(convsecond_2)
convsecond_2 = ConcreteDropout(Conv2D(64, (5, 1), padding='same'))(convsecond_2)
convsecond_2 = keras.layers.LeakyReLU(alpha=0.01)(convsecond_2)
convsecond_3 = MaxPooling2D((3, 1), strides=(1, 1), padding='same')(conv_first1)
convsecond_3 = ConcreteDropout(Conv2D(64, (1, 1), padding='same'))(convsecond_3)
convsecond_3 = keras.layers.LeakyReLU(alpha=0.01)(convsecond_3)
convsecond_output = keras.layers.concatenate([convsecond_1, convsecond_2, convsecond_3], axis=3)
# use the MC dropout here
convsecond_output_drop = Dropout(0.2)(convsecond_output)
conv_reshape = Reshape((int(convsecond_output.shape[1]), int(convsecond_output.shape[3])))(convsecond_output_drop)
# build the last LSTM layer
conv_lstm = CuDNNLSTM(number_of_lstm)(conv_reshape)
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
Great, thank you. higher thresholds can help Trading Strategy get more profit. I have try some higher thresholds, and it will make the class aacount(0) higher, such as 20%:60%:20%. The model can learn the accuracy more than 60%. but the accuracy is mainly for the class aacount(0). it is very low for the class aacounts(-1,1). it is get a bad profit for Trading Strategy, only class aacounts(-1,1) making trade. So i think the imbalance of class aacounts will let the model always select 0 for classification and get worse result comparing with mean proportion 33%. it is confuse me how to make the model get a better result by higher thresholds. Can you give me some suggestion?
I have the same problem, do you try to downsample class 0 to make balanced data?
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
from deeplob-deep-convolutional-neural-networks-for-limit-order-books.
Related Issues (20)
- Question about the label in code HOT 2
- The label Extraction HOT 11
- Why only use 40 features on the benchmark dataset?
- acc很高, 老哥label分布是咋样的 HOT 2
- Query on recreation issue - Macro F1 or weighted F1 usesd? How skewness is handled? HOT 4
- How do you implement the z-score normalization in the pre-processing progress?
- about mc-dropout HOT 2
- ❗ Target leakage in your method HOT 9
- Why the final accuracy become larger when the prediction horizon increase? HOT 1
- CNN Kernel Size HOT 1
- Unable to replicate dataset's labaling HOT 4
- the accuracy is very low when applying to 1 second ES futures orderbook data HOT 5
- Why the normlization method is different to the paper? HOT 1
- LSE是等事件宽度截面还是等时间宽度的截面 HOT 1
- small model size
- LSE data avaliable? HOT 2
- Original Dataset HOT 3
- Unable to replicate result HOT 1
- another question regarding alpha paramtres for dataset labeling HOT 1
- A question regarding threshold for the percentage change HOT 4
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