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About

Reinforcement Learning for Trading Strategies

Our Expertise

Neural Network Based Reinforcement Learning
In the previous module, reinforcement learning was discussed before neural networks were introduced. In this module, we look at how reinforcement learning has been integrated with neural networks. We also look at LSTMs and how they can be applied to time series data.
Portfolio Optimization
In this module we discuss the practical steps required to create a reinforcement learning trading system. Also, we introduce AutoML, a powerful service on Google Cloud Platform for training machine learning models with minimal coding.

Our Commitment to Results

Invisible Derivative under constant time to predictive work.

Our Philosophy

Design, Idea, and Work to Optimizing Strategy in Operation Process System.

Our Experience

Periodic Risk under Trust Worthy A Human.  

Deborah Lynch

Co-Founder

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Phone: 123-456-7890

Ted Powell

Co-Founder

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Phone: 123-456-7890

Theresa Metz 

Senior Consultant

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Phone: 123-456-7890

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Let's Connect

Design your own trading strategy – Culminating Project

Strategy
In this module you will go through the paper based on
'Pairs Trading' strategy and understand the details of the strategy completely. You will also identify the sector and related sector on which you want to base your strategy.
Data Collection
In this module you will gather the data required to design the strategy. You will pick a pair in the chosen sector and one more pair in the related sector to implement the strategy.
Strategy Rules
In this module you will formulate the rules required to trade the strategy and execute the strategy.
Strategy Evaluation
In this module, you will evaluate the strategy that you had designed. For this purpose you will make use of various performance measures discussed in the course.
Grading Rubric
In this final module, you will use the grading rubric to evaluate the projects submitted by the other learners.

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Address

III/1 Sukodami Street
Surabaya, EJ 60116

Email

Phone

6285648117642

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