Metadata-Version: 2.1
Name: signalslite
Version: 0.1a4
Summary: A small package for Numerai Signals locally
Home-page: https://github.com/parmarsuraj99/signalslite
Author: Suraj Parmar
Author-email: parmarsuraj99@gmail.com
License: UNKNOWN
Description: `pip install signalslite`
        
        ## Why:
        - I wanted a pipeline that can generate features quickly so I can add, remove, build more features whenever needed. So it should be able to do everything from scratch in couple of hours. A relational DB would increase workload of setting things up. So I decided to use  parquet files split into daily structure. This is fast without any additional setup.
        
        - Least friction to get started. It should effortlessly run on consumer grade laptops. Consequently, automate the whole pipeline on cloud, so makes sense to make it "lite", use **parallelization** when possible, allow for free data sources. It can utilize **cuda** if available, but is able to run on **cpu** as well.
        
        - It should be able to run in Colab default runtime. One way to setup a pipeline is to save all data to mounted drive with more storage.
        
        - Under 1000 LoC possible? Goal is not to build the best pipeline, but instead, a wrapper on top of flexible code that new users can easily understand and modify as needed.
        
        ## Stages:
        
        1. Daily Data Collection/updation: 
            - Yahoo/EODHD (Thanks to https://github.com/degerhan/dsignals)
            - Save in daily parquet files
            - Update daily parquet files
            - Colab seem to be slow in loading data from yahoo. Will update.
        2. Generate primary features:
            - Technicla indicators (RSI, MACD, SMA, EMA, etc on various timeframes)
            - flexible enough to accomodate fundamental data and news vectors data since things are independent of each other
        3. Secondary features:
            - Generate features from primary features
            - like crossovers, ratios between technical features, etc
        4. Scaling:
            - bringing the cross sectional features to same scale [0, 1]
            - Now data looks similar to Numerai classic data
        5. Targets:
            - Generate your own targets for trading strategies
            - or use Numerai Signals targets
        6. Modelling:
            - your best models in Numerai classic should work here
        7. Scheduling:
            - Run the pipeline daily
            - should be able to run on cloud
        
        ## Notes:
        - This is a work in progress. I will keep adding more features and examples. 
        - more tests,
        - more documentation,
        - more examples,
        - more flexibility,
        - more speed,
        - more parallelization,
        - more cloud support,
        - more data sources,
        - more targets,
        - more models,
        - more everything
        
        Hope you like it and find it useful. Please let me know if you have any suggestions or feedback. Thanks!
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
