AI Agents Hackathon

Join us for hotdogs, networking and AI Talk in our Warsaw office! Check more info and adress here - https://bit.ly/WarsawHack

Opening Party in Warsaw

We will guide you through the roles and technolody for this Hackathon

Hackathon Kik-Off

Join us to work on the Hackathon in our Warsaw office. 
Solutions Rent (0 floor) 
plac Trzech Kryzy 10/14, 00-535,
Warsaw, Poland 

Warsaw Open Doors

Join us for the second day of the Hackathon in Warsaw.
Solutions Rent (0 floor) 
plac Trzech Kryzy 10/14, 00-535,
Warsaw, Poland 

Please, make sure to prepare video presentation of your project!

Submission Deadline

Creating innovative softwares and solutions for the modern world.

Cyber World

Michael G. Inso

A trading agent AI is an artificial intelligence system that uses computational intelligence methods such as machine learning and deep reinforcement learning to automatically discover, implement, and fine-tune strategies for autonomous adaptive automated trading in financial markets
This project implements a Stock Trading Bot, trained using Deep Reinforcement Learning, specifically Deep Q-learning. Implementation is kept simple and as close as possible to the algorithm discussed in the paper, for learning purposes.
Generally, Reinforcement Learning is a family of machine learning techniques that allow us to create intelligent agents that learn from the environment by interacting with it, as they learn an optimal policy by trial and error. This is especially useful in many real world tasks where supervised learning might not be the best approach due to various reasons like nature of task itself, lack of appropriate labelled data, etc.

The important idea here is that this technique can be applied to any real world task that can be described loosely as a Markovian process.
This work uses a Model-free Reinforcement Learning technique called Deep Q-Learning (neural variant of Q-Learning). At any given time (episode), an agent abserves it's current state (n-day window stock price representation), selects and performs an action (buy/sell/hold), observes a subsequent state, receives some reward signal (difference in portfolio position) and lastly adjusts it's parameters based on the gradient of the loss computed.

There have been several improvements to the Q-learning algorithm over the years, and a few have been implemented in this project:
 Vanilla DQN
 DQN with fixed target distribution
 Double DQN
 Prioritized Experience Replay
 Dueling Network Architectures
Trained on GOOG 2010-17 stock data, tested on 2019 with a profit of $1141.45 (validated on 2018 with profit of $863.41):


A trading agent AI is an artificial intelligence system that uses computational intelligence methods such as machine learning and deep reinforcement learning to automatically discover, implement, and fine-tune strategies for autonomous adaptive automated.

"Cyber World team on AI Agents Hackathon Hackathon"

Team Idea

Submission