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How Token Metrics Uses AI

Guide on How Token Metrics uses Machine Learning

by Sandra Ramos
2022/08/09
in Help, Token Metrics Machine Learning
Reading Time: 2 mins read
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At Token Metrics, our machine learning models take inspiration from genetics and, more specifically, evolutionary natural selection.

To better understand how it works, consider this example:

You want to breed giraffes with long necks.

You start by taking a random sample of 100 giraffes, score them and rank them based on how long their necks are.

You then take the best two giraffes from your sample and let them mate to create a new generation of 100 giraffes.

Repeat this process for 500 generations, and after that, you will most likely have giraffes with very long necks.

 

That’s what we do with our models for crypto tokens!

Let’s get more specific:

Our expert team does in-depth research to grade over 20 fundamental analysis components like scarcity, liquidity, team, and others.

To get the total fundamental grade, we have to assign weights to each component, and that’s where our machine learning models factor in.

We simulate 100 different weighting schemes, back-test each one, then we pick the best performing model in terms of accuracy, to then create new generations.

So with each generation, our models increase in accuracy.

We repeat this process over 500 times, resulting in picking the best performing fundamental analysis grade model from 50,000 different models.

We do the same process for our Technology grade, Technical Analysis grade, and the Overall grade, leading to a total of over 20 Million models PER DAY.

For example, consider the grading scheme of ETH for a day trader:

 

Our machine learning algorithms provide the optimal weighting scheme in calculating the overall grade.

And since no grade could reach 100%, we normalize the overall grade by taking a percentile grade.

For long-term value investors, we give more importance to our human expertise, so we calculate the overall grade by equally weighting the fundaments, technology, and technical analysis grades.

In addition to that, we also provide 30-day price predictions through our Multi-layer Perceptron Model (MLP), also known as a feed-forward artificial neural network.

Although the models will never be perfect, they get more accurate with more data, and we are gathering a massive amount of data daily.

At Token Metrics, you can see the accuracy of our predictive models and use the 30-day pricing predictions to craft your trades.

So with Token Metrics, you get the benefits of quant trading and the constant improvement of machine learning, all in one platform.

Without having to be a developer!

Sandra Ramos

Sandra Ramos

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DISCLAIMER

Token Metrics Media LLC is a regular publication of information, analysis, and commentary focused especially on blockchain technology and business, cryptocurrency, blockchain-based tokens, market trends, and trading strategies.

Token Metrics Media LLC does not provide individually tailored investment advice and does not take a subscriber’s or anyone’s personal circumstances into consideration when discussing investments; nor is Token Metrics Advisers LLC registered as an investment adviser or broker-dealer in any jurisdiction.

Information contained herein is not an offer or solicitation to buy, hold, or sell any security. The Token Metrics team has advised and invested in many blockchain companies. A complete list of their advisory roles and current holdings can be viewed here: https://tokenmetrics.com/disclosures/

Token Metrics Media LLC relies on information from various sources believed to be reliable, including clients and third parties, but cannot guarantee the accuracy and completeness of that information. Additionally, Token Metrics Media LLC does not provide tax advice, and investors are encouraged to consult with their personal tax advisors.

All investing involves risk, including the possible loss of money you invest, and past performance does not guarantee future performance. Ratings and price predictions are provided for informational and illustrative purposes, and may not reflect actual future performance.

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