Lesson #6 of 7:

The secret to multiplying your success to the Nth degree

From: Dan Murphy

Corona Del Mar, CA

(Missed some of the prior Lessons? Click the links below to get caught up.)

Lesson #1
Lesson #2
Lesson #3
Lesson #4
Lesson #5

Our saga continues…

And with this epic training beginning to wind down, I think it’s time …

If you're ready for the most impressive piece of the process, let's take a look.

This was the thing that made me realize the exponential improvement possible with this kind of system.

So, of course, I'm totally pumped to show you a blow-by-blow of how it’s done.

Let’s fire up Portfolio Boss and see what I’m talking about …

What's special about this?” you might wonder.

Especially if you’ve spent any amount of time in Portfolio Boss. Building strategies or playing around with Meta MLs.

But here’s the thing of it …

It’s not the process we’re going to learn today that’s so revolutionary. It’s the context.

For anyone who hasn’t spent as much time with Portfolio Boss or just needs a refresher …

Here’s the rundown.

The Boss AI supercomputer makes winning strategies for us to use.

I’ll get more into this in our final session, where we talk about how to supercharge The Boss with weird data and an AI research assistant.

For now, all you need to know is that it works.

If you’re reading this, I’m sure you already know that’s true.

The part I want to focus on today is how the Meta MLs interact with the strategies The Boss creates.

The keys to the golden castle.

Don’t get me wrong, before we had Meta MLs I still loved The Boss.

But applying a Meta ML to the high power strategies The Boss was coming up with was like finding the keys to the golden castle.

Essentially a Meta ML is an AI-powered strategy for how to use your AI-powered strategies.

The Meta ML looks at all of the strategies in your portfolio. 

It looks for subtle cycles in the markets that the human eye would never think to look for or — in many cases — be able to detect.

It uses those cycles to choose WHICH STRATEGIES to use.

Finding the ones most likely to give the desired result.

Whether that’s higher earnings, lower drawdown, or a lack of volatility. 

That means that the exact same collection of strategies might have wildly different results. Depending on which Meta ML you choose.

This is where things got truly interesting.

A portfolio divided into “buckets” like we learned in lesson two.

Like a farmer planning his crops.

Simplistically, applying a Meta ML squares the effectiveness of your portfolio. 

Pretend you’re a farmer. The Boss strategies are planting plans that are twice as effective as normal methods.

(Like relying on the Farmer’s Almanac.)

Instead of yielding the 1 ton of corn per acre, you’re growing two.

When you apply the Meta ML, it’s like you have accurate forecasting for the first time.

You know when a frost is going to come … 

When the weather will be hot … 

When it will be time to harvest.

Suddenly your yields are multiplied by themselves again! Instead of 2 tons per acre, you’re looking at 4!

Now, when we apply the bucket technique, each Meta ML treats each bucket like a different portfolio.

Choosing only the best strategies within that bucket.

So here it is… the big trick.

The revolutionary thing that raised the power of these strategies, Meta MLs and Portfolios to the Nth power.

It actually seemed so simple I was astounded I hadn’t thought of it already.

Instead of splitting the portfolio up into 4 buckets and telling the Meta ML to look at them separately.

Why not just use a separate Meta ML for every bucket?

That way, instead of a broad set of general rules for everything …

Each bucket could have its own little customized AI control center.

Instead of the farmer applying the forecast evenly to his whole farm, he has a custom report for every field.

He’s optimizing every single aspect of his farm.

Just like we’re optimizing every single improvement we’ve applied to our trading strategy.

Massive improvement from this one “minor” seeming tweak.

Christmas Eve, when we come up to wrap up this training, I’m going to explain how you find your own patches of ‘weird data.’

Giving you the opportunity to push every strategy, bucket, and Meta ML to its absolute limit.

You’ll also discover the enormous prompt I stole from NASA. This prompt turned Chat GPT into my super-powered AI research assistant.

It was almost like I didn’t have to think at all.

Chat GPT was doing the heavy lifting for me.

And I was getting better results than ever.

Till next time …