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Robot Wealth - Trade Like a Quant Bootcamp

Original price was: $597.00.Current price is: $99.00.

Price in official website: 597$

We offer in just : 99$

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Description

​LEARN TO TRADE a portfolio of low-touch, rules-based, quant trading strategies, in 6 weeks.

Successful trading is HARD.

Every trader starts with a lack of respect for how hard it is to make consistent trading profits.

They jump from idea to idea. They overcomplicate things. They pass over high-probability edges in favor of heroic ideas that are unlikely to pay out.

In this 6 week course, you’ll learn a simple, high-probability, quantitative approach to trading that can work for you, the non-professional trader.

Your trainers, James and Kris, will guide you step-by-step through the process of setting up a portfolio of 3 low-touch, rules-based trading strategies. 

Your enrollment includes:

  • 6 Week Interactive Workshop including video lessons, written content and custom tools
  • 6 Weekly LIVE Q&A Webinars
  • 3 low-touch, rules-based strategies that you can implement and manage as a part-time retail trader
  • Quantitative approach to portfolio construction and management
  • Vibrant Slack Channel for real-time discussion with your Instructors and peers
  • Lifetime access to all training material

 + these BONUSES included FREE

  • Embrace the Mayhem video course
  • A simple gold trading strategy

Customer Reviews

5 out of 5
Based on 2 reviews
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  1. Max Paramet (Verified Purchase)

    Superb Quality

    Great quality and reliable. Definitely recommend!!

  2. Juan Manuel Canar (Verified Purchase)

    Great Course

    Excellent Course to learn algorithmic trading

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Customer Reviews

5 out of 5
Based on 2 reviews
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0
0
0
0
Write a product review

Your email address will not be published. Required fields are marked *

  1. Max Paramet (Verified Purchase)

    Superb Quality

    Great quality and reliable. Definitely recommend!!

  2. Juan Manuel Canar (Verified Purchase)

    Great Course

    Excellent Course to learn algorithmic trading

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