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@jaungiers

Jakob Aungiers

jaungiers
Global

My open source work builds on creating frameworks, tutorials and knowledge in the area of applied machine learning in investment finance. My past backgrounds in buy-side asset management followed by high-frequency trading funds has allowed me to contribute to some of the world-leading research in the field, and through this research I am able to offer up my knowledge in the field.

Featured work

  1. jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction

    LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

    Python 4,820
  2. jaungiers/Random-Portfolio-vs-Benchmark-Strategy

    Python Monte Carlo Simulation to model returns from randomly generated portfolios against a benchmark index.

    Python 20
  3. jaungiers/Yahoo-Finance-Stocklist-Scraper

    Converts the full Yahoo Finance stocks list into a .csv file with ticker symbol, company name, exchange and country mapped.

    Python 17
  4. jaungiers/TensorFlow-Intro

    Introduction to TensorFlow. Basic operators, linear and logistic regression and Tensorboard

    Python 21
  5. jaungiers/R-Datastream

    Simple framework library for communicating between Thompson Reuters Datastream service API and R

    R 2
  6. jaungiers/Code-Line-Counter

    Python script for iterating directory and outputting total line counts for all file types

    Python 3

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A general thank you for enjoying my work <3

$2,500 a month

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Retainer for specific investment finance advice. Allows you dedicate up to 24 hours per month in advice from me on issues ranging from market dynamics/microstructure, strategy auditing, ML model auditing, architecture development, code and mathematical advice etc.