• AI in Finance & Algo Trading 04 (Virtual)

    Online event

    Dear All.

    This is our 3rd virtual cross-Meetup group event.

    Please register under:



    Dr. Yves Hilpisch (TPQ | The AI Machine): Introduction

    TALK 1:
    How to Create a Sustainable and Risk Optimized Portfolio | Dr. Jennifer Rasch and Dr. Caroline Löbhard

    Combining a Markowitz Model with Monte Carlo Simulations and different risk measures applied on a sustainable stocks universe, we show you how we created a portfolio.

    TALK 2:
    Forecasting Stock Market Data with AI (The YUCE-8 Approach) | Torsten Langer

    The nature of stock market data is determined by ups and downs. Today you have access to Terabytes of data in real time. But the human brain is not capable of processing this amount of information. Whilst the human brain gets overrun by the amount of data that is produced per second another technology is waiting for its deployment: AI. AI is able to learn from the past in order to predict the future. But financial data is difficult to predict.

    In this talk Torsten will talk about the journey he went on in order to create an API based set of TensorFlow based AI models and the promising results that this library, called YUCE-8, delivers today – 1.5 years later. The demo will be run in Python (Google Colab).

    Closing Remarks

    See you all online on Wednesday, 22nd July 2020!

    Yves & Jason

  • AI in Finance & Algo Trading 03 (Virtual)

    Online event

    Dear All.

    This is our second Cross-Meetup-Group Virtual Event.

    Please register under:



    Jason Ramchandani (Refinitiv): Introduction

    TALK 1
    Accelerate financial modelling using IPUs and Poplar via standard ML frameworks like TensorFlow
    Alexander Tsyplikhin, Senior AI Engineer at Graphcore

    In the finance sector, the need for new hardware and software to run complex machine learning models for both training and inference is significant. This talk will outline how Graphcore’s IPU architecture and Poplar® Software Stack powers incredible breakthroughs in Machine Intelligence – and what this means for the future of finance and trading. It will also highlight how to run advanced financial models up to 15x faster using TensorFlow, example use cases and performance benchmarks.

    TALK 2
    Introduction to Bayesian Modelling using COVID-19 Data
    Dr. Thomas Wiecki, VP of Data Science, Head of Research at Quantopian

    In this talk, Thomas will demonstrate the benefits of Bayesian statistics using COVID-19 as an example. Specifically, he will show how important uncertainty quantification is, the benefits of hierarchical modelling, and the model development and refinement process, going from a simple exponential model, to a logistic model, to an SIR model.

    Closing Remarks

    See you all online on Thursday, 14th May 2020!


  • AI in Finance & Algo Trading 02 (Virtual)

    Online event

    Dear All.

    Welcome to our 2nd Meetup event. Due to the current situation, we have decided to kick-off a series of virtual Meetup events. The great thing is we hope to be able to reach more of the group than we could with an in-person event. So if you haven't seen us for a while it would be great to see you at this virtual event and future ones over the coming months.

    Places are limited. Register in time under:


    The agenda for the first virtual Meetup event is as follows:

    Jason Ramchandani (Refinitiv): Introduction

    TALK 1:
    Dr. Richard L. Peterson & Anthony Luciani (MarketPsych Indices): Creating Market Forecasts with News and Social Media Data using Jupyter Notebooks

    In this talk Anthony Luciani and Richard Peterson will help developers investigate the predictive (and reactive) nature of news and social media information flow during crises like COVID-19. The Refinitiv MarketPsych Indices are aggregated sentiment, emotional, and thematic indices derived from the top 2,000 global business news and 800 social media sites. Using this data Luciani and Peterson will also demo the free MarketPsych Data Eikon App for visual exploration and Jupyter notebooks for predictive analytics on this dataset.

    TALK 2:
    Dr. Yves Hilpisch (The Python Quants | The AI Machine):
    Reinforcement Learning: From Playing Games to Trading Stocks

    This talk introduces Q-learning as a successful algorithm in reinforcement learning. It illustrates the application of a DQL agent to a game from the OpenAI Gym environment. It also illustrates how the same DQL agent can learn to trade financial instruments. The examples are based on self-contained Python code.

    Book Raffle, Closing Remarks

    See you all online on 23. April 2020!


  • AI in Finance & Algo Trading 01

    3 Times Square

    Dear All,

    We are excited to announce our first Meetup group event in New York.

    We also want to thank our corporate sponsors and speakers from Refinitiv and Beacon Platform.

    We have planned for the following agenda:

    I. Welcome

    II. Talk

    Dr. Mark Higgins (Beacon): "Deep Hedging using Neural Networks in Beacon"

    Abstract: The JPMorgan QR team invented "deep hedging": training neural networks to give optimal hedges for derivatives portfolios in the presence of transaction costs and unhedgeable risks. We applied that technique to hedging of natural gas storage facilities, implemented through Google TensorFlow in Beacon's Python-based quant platform. This talk describes the results as well as how we did the implementation.

    III. Talks

    Dr. Hossein Adeli Jalodar (Refinitiv): "Short-Term Price Prediction using Tick History Level 2 Order Book Data"

    Dr. Joel Sebold (Refinitiv): "Imaging and Classification of Limit Order Book
    in HPC environment using a RNN"

    IV. Break

    V. Lightning Talk:

    http://techtrader.ai: Autonomous Trading with Tech Trader - Experiences with both building a system that has been trading hands-off for years as well as launching a fund around it.

    V. Talk

    Dr. Yves Hilpisch (TPQ | TAIM): "AI-Powered Algorithmic Trading"

    Abstract: The talk explores approaches to use AI, Machine Learning & Deep Learning for market prediction and algorithmic trading. It also discusses the importance of strategy deployment and the application of execution rules to optimize P&L. A demo of The AI Machine platform is given.

    VI. Book Raffle & Signing (Python for Finance, 2nd ed., O'Reilly)

    VII. Networking

    I hope to see you in person in New York.



    P.S. Security rules at the venue require that you provide your full name (given & last name) via the Meetup page if you RSVP.