Machine learning cryptocurrency reddit

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That way anyone who follows along with our study can reproduce the results. This will be done using a script that is provided at the bottom of this post. The top ten assets with the highest predicted increases will be included in the portfolio for that week. Only assets in the top by market cap will be considered. Stablecoins will not be considered unless there are assets in the top 10 that have negative percent predictions. This image depicts the top assets by market cap as calculated by Nomics.

LSTM is used in the field of deep learning to process, classify, and make predictions based on time series data. Learn more about long short-term memory. Our team would also like to keep the methodology for how we test the Nomics ML predictions as easy as possible. Based on the predictions for the week, we will sort the assets in descending order based on the highest to lowest predicted percent price increase. The asset that has the highest predicted percent increase will be listed first, the one with the lowest predicted percent increase will be listed last.

DeFi is an absolute monster of an asset class, and has posted some impressive numbers now that institutional investment firms have taken notice and gotten their feet wet. Smart Contracts are one of the more interesting uses of blockchain technology with a wide range of industries that can be affected positively by this new asset class.

The following settings will be used for the Smart Contracts Index:. The Shrimpy Portfolio Management application will be used for the allocation of portfolios, rebalancing, and performance tracking. Each of these functionalities is core to the Shrimpy platform and makes it the ideal service for running this study.

Portfolio Rebalancing for Cryptocurrency. Portfolio Rebalancing Algorithms in Crypto. To simplify the process of updating our portfolio with the latest price forecasts, we can automate the collection of the data through scripts that collect data from the API. We can do this automation with Python. In the following sections, we will outline the process for setting up your Python environment and running your first script. There are a few things we will need to set up for our Python environment before we can start coding.

First, start by installing the Shrimpy Python Library. If you are using Python2, please update your version of Python. Sign up for an account. If you have not yet enabled 2FA for your account, you will first need to go through the process of setting up 2FA.

Enter your 6-digit verification code and account password. Once you have verified your account, Shrimpy will send you an email that will require you to confirm the creation of the API key. Confirm your email by clicking on the link in the verification email. After confirming the creation of the API key in your email, you can then see a card that represents your developer API key. The public key will be displayed by default. This can only be done one time, so securely store the secret key once it has been shown.

The private key will not be shown by default and can only be viewed ONE time. That means after you view your private key, Shrimpy will never show you the key again. Copy both the public and private secret keys to secure locations. Do not ever share this API key with anyone.

We will use all of the settings for this tutorial guide, however, you can reconfigure your setup once you are ready to deploy your production version of your trading bot. Note: You can create multiple API keys. We don't need to buy any credits to test Shrimpy, but you can purchase credits at any time on the "Payment" tab. This will look something like the screenshot below. Purchase credits when ready.

Before credits can be purchased, we first require you to link a payment method. After linking a payment method, you can enter the value of the credits you wish to purchase. With these predictions, we can allocate our portfolio each week and track performance. Over the course of the coming weeks, we might extend this script to include other functionality such as automatically updating our Shrimpy portfolio.

That way the entire process can be automated. Follow us on Youtube , Twitter and Facebook for updates, ask any questions to our amazing, active community on Discord or reach out to our Support staff via the blue Support button found in the bottom left corner of your Dashboard.

The Content is for informational purposes only, you should not construe any such information or other material as legal, tax, investment, financial, or other advice. Nothing contained on our Site constitutes a solicitation, recommendation, endorsement, or offer by Shrimpy or any third party service provider to buy or sell any securities or other financial instruments in this or in any other jurisdiction in which such solicitation or offer would be unlawful under the securities laws of such jurisdiction.

All Content on this site is information of a general nature and does not address the circumstances of any particular individual or entity. Finding the right subreddit to submit your post can be tricky, especially for people new to Reddit. There are thousands of active subreddits with overlapping content.

In this article, I share how to build an end-to-end machine learning pipeline and an actual data product that suggests subreddits for a post. You get access to the data, code, model, an API endpoint and a user interface to try it yourself. I exported the data to a CSV available for download on S3.

This dataset is far from perfect in terms of data quality. A previous approach selected a subset of 1k subreddits that are more coherent topic-wise. Instead of spending a lot of time early on data transformations, I prefer to directly build an end-to-end baseline as fast and as simple as possible. Once I have got the first results, I can run version controlled experiments to see the impact of each transformation. Proceeding otherwise, you may end up with a more complex baseline and ignoring the impact of each transformation.

As a Data Scientist, I often overestimate the impact of a data transformation. By letting the final user interact with the model early on, you can learn and iterate faster. In a previous article, I built a generic ML pipeline for text classification using fastText. In multi-label text classification, each post is assigned to each subreddit by a probability.

Released by Facebook, fastText is a neural network with two layers. The first layer trains word vectors and the second layer trains a classifier. As noted in the original paper , fastText works well with a high number of labels. The collected dataset contains M words, enough to train word vectors from scratch with Reddit data.

On top of that, fastText creates vectors for subwords controlled by two parameters, minn and maxn, to set the minimum and maximum character spans to split a word into subwords. On Reddit, typos are common and specific terms may be out of vocabulary if not using subwords. The machine learning pipeline consists of 5 executions that exchange data through Valohai pipelines. Each execution is a Python CLI and you can find the code of each one on Github and more details about how to create a pipeline that runs on the cloud in the previous article.

End-to-end ML pipeline generated from dependencies between data artifacts, executions and API endpoints. The text features are concatenated, transformed to lowercase and punctuation is removed. I set the autotune command to run for 24 hours on a cloud machine with 16 cores to find the best parameters on the validation dataset. Finally, the model is retrained on all the data and the final metrics are reported on the test dataset.

For each execution, Valohai takes care of launching and stopping a cloud machine with the proper environment, code, data and parameters. Classification tasks can be evaluated with classic metrics such as precision, recall and f1-score. The autotune execution logs the best parameters and reports a f1-score of 0.

The autotune execution smartly chose 9 different sets of parameters to decide on a final model that trained for epochs, word vectors of 92 dimensions, n-grams of up to 3 words and subwords from 2 to 5 characters. That results on a vocabulary size of 3M words including subwords and a model that trains on 7 hours and weighs 2 GB. Below, we can see the precision and recall on the test dataset for different k values.

R k goes from 0. Naturally, metrics vary between subreddits. There is a positive correlation between the f1-score and P 1, the probability of the first prediction given by the model. Still, p 1 lacks behind the f1-score on the test dataset. For example, when the model says that the first suggestion has a probability of 0. Metrics are important but they should not stop you from looking at the data. Metrics tell you exactly where to look.

One last example.