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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 4,867
  2. jaungiers/Random-Portfolio-vs-Benchmark-Strategy

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

    Python 21
  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 18
  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 3
  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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