Quote from: illiki23 on November 04, 2017, 03:40:36 PM

Quote from: EugeneMcKey on October 14, 2017, 02:31:33 PM

Hi! Thank you for interesting in our platform. We are preparing a whitepaper and other promo-materials it will be available in a week.
To be short. We'll use different types of machine learning: artificial neural networks, deep learning, data mining, etc. We will analyse not only history of price movement, but also a hole bunch of other parameters for fundamental analysis.

If you like you can check our site: www.data-trading.com
It's in development now, in a week it will be updated with whitepaper and other materials.

Deep learning is really hot right now and has a lot of potential though has its downfalls such as model interpretability given the complexity of the resulting models and takes a lot of training data before you get decent classification and prediction accuracy.

We agree that you will definitely need to analyze more than the history of price movement. Algorithmic traders moved beyond price history analysis decades ago and to keep up you will have to as well. The efficient market hypothesis is somewhat relevant. Feature generation is one of the most critical parts of the data mining process as it leads to more information (you can have an awesome machine learning algorithm which fails to produce good results if given the wrong features). Lately I have seen people on the crypto scene using GitHub indicators (for example if a project posts an update), detecting trends in sentiment on Twitter and in forum threads, and so on. To get an advantage you want to be a first mover and use features others aren't or use them in a new way. One critical feature involves 'shilling detection' as the detection of shilling would make some features irrelevant.

For classification (such as classifying a price increase as a pump or natural growth or classifying coins into 'winners' and 'losers') I would really like to see 'gradient boosted decision tree induction' as one of the available classification algorithms. It is really neat and has replaced random forests as my favorite. It deals with things like overfitting rather well though has a number of benefits which will help when classifying new unknowns. As for numeric time series prediction make sure you give due diligence to recurrent networks. They 'remember' thus can find patterns within long sequences of temporal data.

blahblahblah. Just really like data mining. Good luck and I hope you are not scammers. While most projects like this are sure to fail we gain a lot from the attempts. I will be checking in on you guys to make sure there is nothing shady going on.

I see you address some of these things in the whitepaper. LSTM cells would be great! I do have a few concerns about the scope and feasibility of the project. As it is machine learning and AI is used extensively in algorithmic trading and adapts rather fast. You need to construct a platform which allows for quick adaptation.

On a side note - it would be really neat to incentivize hand labeling and tagging of data points. One problems with machine learning is a lack of labeled training data. I like unsupervised and semi supervised learning but supervised learning is just so powerful. Tokens could be provided to people though you would want to establish some sort of expert ranking based on how accurate they are.