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How many times have you had a trading
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idea and thought, "I wish I could test
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this quickly without spending hours of
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coding and debugging." If you're into
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algorithmic trading or strategy
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development, you already know how
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timeconuming and frustrating it can be
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to convert a trading idea into a working
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Python code and then practiced it
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properly. Let's be honest, most back
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testing tutorials out there aren't
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helping. I've taken what I've learned in
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the last few years and I've created a
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learning road map that I believe will
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completely change the way you back test
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training strategies. That's exactly what
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I've captured in a simple course that
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will set out to change the way you have
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looked at strategy back testing. Even
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better, you don't even have to be an
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expert in Python to do this. So, basic
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Python knowledge is all you need. Hi, my
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name is Vive, an algorithmic trader,
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Python programmer, and a passionate
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advocate of fire movement, the financial
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independence and retire early. I reached
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financial independence at the age of 45
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and now I spend my time helping others
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build the tools, skills they need to
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take control of their financial future.
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Over the years, I've tested dozens of
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strategies and I've also tested dozens
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of AI prompts across chart GP, Gemini,
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and Claude. After lots of trial and
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error, I finally built something that I
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now rely on every day, a super prompt.
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This super prompt is the core of this
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course. It is a highly tuned, structured
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prompt that allows you to input your
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trading strategy in plain language and
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get back clean, modular, and accurate
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Python code that can practice your logic
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right away. No clunky scripts, no
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blackbox tools, just a powerful way to
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use AI as a coding assistant to rapidly
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validate and evolve your trading ideas.
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In this course, you'll not only learn
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how to use super effectively, but also
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how to tweak and adapt it across various
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strategies, market conditions, and
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trading styles. We'll walk through a few
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real world strategies and go hands-on in
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testing them using AI generated code.
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But back testing is only part of the
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story. Once you've tested your idea, you
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need to visualize the performance. And
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that's why the second pillar of this
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course comes in the strategy analytics
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dashboard built in stream uh using
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Python. This dashboard transform your
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raw trade book into an insighter rich
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you know visual report. You'll see your
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equity curve benchmark comparison draw
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downs underwater plots trade locks and a
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detailed performance metrics from win
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ratio to sharp sautino and more. This
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tool is what takes you from data to
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decision giving you the confidence to
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know whether your strategy is worth
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pursuing or needs more work. We won't
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just stop there. You'll also learn how
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to optimize your strategy, reduce draw
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downs, apply filters, and even refine
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your logic. By the end of the course,
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you won't just have learned to back test
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strategies. You'll have a repeatable AI
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powered workflow for turning ideas into
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practice and back test into actionable
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insights. This course is ideal for
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Python programmers looking to explore
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trading, aspiring algo traders who want
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to accelerate strategy validation, and
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even experienced market participants who
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wants to bring AI into their research
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process. So if you are ready to level up
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from a trial and error coding to a smart
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AI assistant strategy development and
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want a dashboard that brings the
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performance to life, this course is just
