PyInvesting

Founder note

Why I built PyInvesting

A personal note about turning investment ideas into rules that can be examined before they are trusted.

Problem

I wanted a process I could inspect

Before PyInvesting was a product, it was a personal question: how could I invest in a way that was systematic enough to examine, practical enough to maintain, and still mine to control?

I was working toward greater financial security for my family. Depending only on active income did not feel sufficient, but making an investment decision from instinct or a persuasive story did not feel responsible either. I wanted to understand the rule, the trade-offs, and the periods in which it could fail.

I wanted a way to test the rules before relying on them.

Build

The first version solved my own workflow

I started with a small web application. It downloaded financial data, ran my investment rules over earlier market periods, and turned the results into a summary I could inspect. That made it possible to compare ideas without treating the latest chart or headline as evidence.

The difficult work happened behind a simple form. Instead of assembling data and calculations by hand, I could describe the rule, run a historical simulation, and spend my time reviewing what the result actually meant.

That personal tool became PyInvesting: a research workspace for individual investors who want the benefits of a systematic process without having to build the entire system themselves.

Method

Turn an investment idea into something testable

PyInvesting is organized around a straightforward research sequence. Each step makes the assumptions more visible and gives you something concrete to question.

  1. Define

    Write the investment rule

    Choose the universe, signals, allocation, and rebalancing logic that express the idea.

  2. Simulate

    Run it through history

    Observe how the same rule behaved across different periods instead of judging one isolated outcome.

  3. Review

    Examine the evidence

    Compare returns with drawdowns, assumptions, costs, and the practical demands of following the rule.

Purpose

Make the decision more deliberate

No two investors have the same objectives, constraints, or tolerance for uncertainty. PyInvesting does not need to make those choices for you. Its job is to help you describe an idea clearly, test it consistently, and understand the evidence before deciding what belongs in your own process.

That remains the reason I am building it: to give self-directed investors a more disciplined way to learn from market history while keeping ownership of the final decision.

Your next question

Start with a question. Test it before you trust it.

Define an investment rule, examine its historical behavior, and decide what the evidence means for you.