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Automate existing statistical arbitrage trading strategy using Deep Reinforcement Learning

Scope: Automate the buy/sell order execution (trade entry & exit) and minimize drawdowns* using MATLAB, IQFeed (real time price data feed) and Interactive Brokers (trade execution broker)

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Expectations:

1. Connect MATLAB to Interactive Brokers using existing paid and licensed infrastructure

2. Currently I have to execute each stock asset buy or sell order manually, the night before the market open at 6.30AM next day. It’s many assets traded daily and that is time consuming and not scalable. MATLAB needs to place all orders (AFTER I authorize them in MATLAB) automatically without me having to enter each asset ticker in Interactive Brokers manually the night before.

3. Implement TWO drawdown control systems to be connected to the “main” system:

a. “Learning to trade via Direct Reinforcement by John Moody and Matthew Saffell”. Attached paper

b. Your own deterministic/ other control algo, which would tell MATLAB to exit all positions when -x% loss has been met.

4. Exit the trades based on 3ª or 3b above (to be chosen by the trader i.e. me) decision control real time by MATLAB reading the P&L % from Interactive Brokers

Skills: Machine Learning (ML), Deep Learning, Matlab and Mathematica, Computer Vision, MATLAB

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About the Employer:
( 0 reviews ) North Bergen, United States

Project ID: #26402351

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