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zamburak

This repository contains implementation of bandit algorithms in OCaml and their application to trading.

zamburak

Zamburak (Persian: زمبورک), literally meaning wasp, was a specialized form of self-propelled artillery from the early modern period, featuring small cannons fired from swivel-mounts on camels. [1]

Installation

To get the same plots as are shown in this readme you might need to do

opam pin --dev-repo matplotlib
git clone https://github.com/MichaelKonobeev/zamburak.git

otherwise just pin to this repository to use zamburak package

opam pin https://github.com/MichaelKonobeev/zamburak.git

Stochastic & Adversarial k-armed bandits

The following figures show results of running UCB algorithm on stochastic bandit (left) and UCB, Exp3, Exp3-IX algorithms on adversarial bandit (right). The results could be reproduced by running make ucb.exe and make exp3.exe respectively. Note that since adversarial bandit is specifically designed against UCB algorithm, this algorithm has linear regret on it.

ucb exp3

Stock trading

k-armed bandit algorithms and their modifications were applied (make trade.exe) to stock trading problem to buy for the duration of a single day $1 worth of stock of one of several companies. The companies are either from the set of fortune 500 companies, or from some randomly selected set of companies. The results are averaged over 1000 runs and are the following.

For fortune 500:

Random UCB Stock-UCB Exp3 Stock-Exp3
Regret 2.9190±0.68 3.7203 0.3138 2.8982±0.70 0.1786±0.07
Payoff 1.1057±0.68 0.3044 3.7109 1.1265±0.70 3.8460±0.07

For random companies:

Random UCB Stock-UCB Exp3 Stock-Exp3
Regret 2.1743±1.02 4.3192 0.4174 2.1776±1.09 0.9386±0.34
Payoff -0.2621±1.02 -2.4070 1.4948 -0.2654±1.09 0.9736±0.34

References

  1. https://en.wikipedia.org/wiki/Zamburak
  2. Lattimore, Tor, and Csaba Szepesvári. Bandit algorithms. Cambridge University Press, 2020.
  3. https://jeremykun.com/2013/12/09/bandits-and-stocks/