yivash / bitpred

Time-series prediction scaffolding library. Sample example is with bitcoin prediction.

bitpred

Open source bitcoin price movement prediction engine.

V0.01 - Features

• Is it "going down" - tomorrow ( within 24hours) api backend classifier
• Get data from public sources (yahoo)
• Combine data with relevant influencers data ( Twitter for ) now
• Create a complete e2e backend classifier and ML pipeline

data

Currently using live streams from : Yahoo

How to run training

Install MLFlow locally

$ pip install mlflow

From mlflow

Locally: $ mlflow run .

From github: $ mlflow run https://github.com/nlauchande/bitpred/

How to run a listening prediction api

Using the id of the model and the model name you can run the following commnad :

$ mlflow models serve -m ./mlruns/0/b9ee36e80a934cef9cac3a0513db515c/artifacts/model_random_forest/

How to run a prediction

curl http://127.0.0.1:5000/invocations -H 'Content-Type: application/json' -d '{"data":[[1,1,1,1,0,1,1,1,0,1,1,1,0,0]]}' [1]%

How to monitor experiments with MLFlow

Run the server

How to add a new algorithm

You can modify the train.py file .

======= Currently using live streams from Yahoo.

GitHub Issues

Features

We also use the issue tracker to track features. If you have an idea for a feature, or think you can help kops become even more awesome follow the steps below.

• Open a new issue.
• Remember users might be searching for your issue in the future, so please give it a meaningful title to help others.
• Clearly define the use case, using concrete examples. EG: I type this and kops does that.
• Some of our larger features will require some design. If you would like to include a technical design for your feature please include it in the issue.
• After the new feature is well understood, and the design agreed upon we can start coding the feature. We would love for you to code it. So please open up a WIP (work in progress) pull request, and happy coding.