INNOVATION＆EXCHANGE

 

I&E AI utilizes computer vision technology to help understand and classify major directional changes in the cryptocurrency market. This is one of the key aspects of our decision making process in calculating and managing risks. With large amounts of training dataset I&E AI generalizes market patterns to classify directional changes by processing an image of a cryptocurrency chart. The key here is to not overcomplicate our overall decision-making process; we believe that there are 5 key directional changes or continuation that will support a self-sustaining and reliable ground truth classified data that can be utilize for I&E AI trading bot.

Fundamental Challenges

One of our biggest challenges in this project is to be able to provide a high relevance[1] training datasets to our model—as it is our bottleneck of achieving a state-of-the-art market trend classifier. Our market trend classifier must master the following areas:

• Vanishing gradient problem. 

  Many supervised learning models experience the vanishing gradient problem, which will ultimately limits the overall accuracy of the model.

• Labeled large datasets.

  Classifying a high-dimensional image with high precision requires large training datasets. Our datasets consists of cryptocurrency chart images that are labeled properly with implicit variance in colors and dimensions. This variance is crucial to our overall model performance to avoid over fitting and in the end generalize better.

• Precision.

  Managing large datasets can be difficult when it comes to optimizing for precision. In order to clean[2]our data, our overall training and validation datasets must have a high relevance score overall. In the end, images with low relevance might increase the overall false positives classified by our model; this might need further data cleaning processes in order to increase the overall precision.

Our Approach

We have discretized major trend directional changes into 5 different classes: ascending triangle, descending triangle, consolidation, inverse head and shoulder and head and shoulder. These 5 major patterns embody the characteristics of market directional changes or trend continuation. For instance a definitive ascending or descending triangle pattern in higher timeframes will generally have a stronger probability[3]of trend continuation. In contrast a market that has a definitive head and shoulder or inverse head and shoulder pattern will generally have a stronger probability of a change in market direction.

Data Accumulation

In order for us to accumulate large datasets we must be agile in retrieving high relevance images from many platforms. What better platform that stores a high relevance images than Google image? For starter, we accumulated data from Google image retrieving at least 1,000 images for each class that we are trying to classify. For this specific project, we need more than that, at least 10,000 images for each class with variance in colors and dimensions needed to generalize with a high precision overall. For most of our data, we ignored data that has angle transformations as most of the time we process flat angle screenshots of charts as our input to our model. Also, we have to ignore mirroring and rotation transformation of our images, as the direction of the candlestick is fundamental towards classifying features in the image.

To overcome the vanishing gradient problem, we decided to use FastAI’s ResNet34[4] architecture for our model. The sole purpose of using this architecture is for our model to be able to utilize higher parameters in building a very deep network without degrading the overall performance[5]

With a total of roughly a total of 40,000 data[6] in our set, we decided to segmentize 20/80% distribution between validation and training dataset. Our naïve approach was to train the model with the standard learning rates and use error rating as our overall metrics.

As we can clearly see that the model is not performing well at all. It has an error rate of roughly 44.59% by the end of its 5th training cycle. We will need to re-tune the learning rate in order to minimize the error rate.

With the help of FastAI library we can utilize theirlr_find()[7] model fitting method in order to expedite our process of finding the optimal hyper-parameter needed to train our model. By convention we will use the learning rate (x-axis) before the loss value (y-axis) flies of the roof which is at (1e-02), then use the learning rate 10x smaller (1e-03).

Loss vs. Learning Rate

After this hyper-parameter tuning, we can train our model again and see how well our model perform.

Error rate shrinks down to 27.41% after hyper-parameter tuning—suggesting an overall accuracy of 72.59%.

Transfer Learning and Data Processing

In order to achieve a higher accuracy in our model, we implemented a transfer learning approach into our classification problem. We trained our model with image sizes of 224x224 and through transfer learning approach; we will train our model using a smaller size of 128x128 images. The general theory in this approach is to have a pre-trained model (the first trained model) to extract general low-level features such as edges, patterns and gradients and then train this model with a smaller image size to identify more detailed features within the graphs such as candlestick trend patterns and trend direction. However, before we start this process we would need to do process our data from previous iterations results.

Using FastAI’s ClassificationInterpretation class, we can easily visualize our model’s overall performance and more importantly distinguishing the false positives[8]. In order to increase our overall precision, we must understand the causes of our model’s false positives. What we have found out is that there are many misclassified images in our datasets, images with high distortion, images with low relevancy and some are just wasn’t suppose to be there. In response to this, a data processing was conducted to reduce the overall false positive rate of our model. This data processing is crucial to our optimization approach to increase the accuracy before implementing transfer learning to our model.

If we look at the confusion matrix above, we can see the distribution of our model's false positives for each classes. High missclassifications are apparent between classifiying head and shoulder and the inverse of it, also there's a sligh high volume of false positives between classifying consolidation and descending triangle iamges. We focus more on these high missclasifications and pin point the characteristics of these data that might affect the model's prediction. As previously hypothesized, there are images that shows low relevancy with the class including images with high noises and distortion. These images will affet the overall performance of the model and should be cleaned from the training set.

Our approach was to use the same 40,000 datasets excluding the bad training data that was cleansed previously, and transformed it into smaller size image (128x128) with the same 20/80% validation and training distribution. Furthermore, we repeated the overall training method but this time we find the optimal hyper-parameter before training our model with the smaller size data. This time our metrics will be accuracy of the model itself instead of the error rate.

As hypothesized previously, the overall error rate of the model decreased significantly due to the fact that detailed features of the graphs are extracted a lot better after implementing transfer learning. Previously, when the model was trained with a bigger image size, it can only extract general macro features such as edges, gradients and shapes.

As shown in table 3, the model reached its maxima of roughly 95.1% overall accuracy. However, we can see an increase in training and validation error over 10 periods of iteration—proving that the current model may need another approach of optimization if we want to increase the overall accuracy. However, we don’t want to over fit our model, as the current 95.1% is sufficient enough for generalizing trend directions.