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Master Thesis: Limit order placement with Reinforcement Learning

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mjuchli/ctc-executioner

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CTC-Executioner is a tool that provides an on-demand execution/placement strategy for limit orders on crypto currency markets using Reinforcement Learning techniques. The underlying framework provides functionalities which allow to analyse order book data and derive features thereof. Those findings can then be used in order to dynamically update the decision making process of the execution strategy.

The methods being used are based on a research project (master thesis) currently proceeding at TU Delft.

Documentation

Comprehensive documentation and concepts explained in theacademic report

For hands-on documentation and examples seeWiki

Usage

Load orderbooks

orderbook=Orderbook()orderbook.loadFromEvents('data/example-ob-train.tsv')orderbook.summary()orderbook.plot(show_bidask=True)orderbook_test=Orderbook()orderbook_test.loadFromEvents('data/example-ob-test.tsv')orderbook_test.summary()

Create and configure environments

importgym_ctc_executionerenv=gym.make("ctc-executioner-v0")env.setOrderbook(orderbook)env_test=gym.make("ctc-executioner-v0")env_test.setOrderbook(orderbook_test)

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