How It Works

1

Define Your Goal

What are you trying to predict? Be specific. A stock price, a sales figure, a trend.

2

Choose Variables

Pick the inputs you believe influence your outcome. Use what you know.

3

Set Weights

How much does each variable matter? Assign relative importance to each one.

4

Run & Refine

Test against historical data. Find the pattern. Extrapolate to the future.

// Step 1 — Define Your Goal

What are you predicting?Be specific about the outcome
Time horizonWhen do you want the answer?
Your baselineWhat are you comparing against?

// Step 2 — Choose Your Input Variables

01
02
03
04
05
06
07
08

// Step 3 — Assign Weight to Each Variable

How much does each variable influence your outcome? 1 = low influence, 5 = high influence.

Variable 01
Variable 02
Variable 03
Variable 04
Variable 05
Variable 06
Variable 07
Variable 08

// Step 4 — Your Result

Predicted outcome
Confidence level
Notes & observations

// Full calculation engine coming soon. For now — record your inputs, run the numbers manually or in Excel, and log your results here. The pattern will emerge.

A few tips before you start.

Start with what you know

Don't try to include every possible variable. Start with 3-4 that you genuinely understand and trust. You can add more once you see how the model behaves.

Run it multiple times

One result tells you nothing. Run the same model across 10 or 20 historical data points and look for where it was right and where it was wrong.

The pattern is the point

After several runs a pattern will start to emerge in your graph. That pattern is your algorithm finding its shape. Once you see it you can project it forward.

Refine relentlessly

Your first model won't be your best. Every refinement — adding a variable, adjusting a weight — makes the output more reliable. This is an iterative process.

Want to understand the theory before you build?

← Back to the Basics