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Tools · Finance
Test investment ideas before money moves.
Turn “just doing something” into an analytical, repeatable investment process.
01
Problem
Working daily with financial data and investors, the same thing stands out again and again: many invest on gut feeling instead of analytically.
Good tools are expensive or pure black boxes. Testing hypotheses, truly understanding companies, planning risk and return and staying disciplined — all of it is laborious, so it often doesn't happen.
02
Hypotheses
- Better decisions come from data and reflection, not gut feeling.
- Investment hypotheses can be generated, tested and mapped to matching companies systematically.
- What a company really does lives in text — not in sector/industry buckets (e.g. “invests in Alzheimer” or “in humanoid robots”).
- A journal with bot feedback measurably improves trade quality.
03
Target metrics
- Hit rate of tested hypotheses
- Quality of hypothesis → company matching
- Discipline score from the investment journal
- Plan vs. actual on the target return path (win rate, risk-to-reward)
04
KPIs
- Hypothesis generation and backtesting
- Theme screening via text/AI beyond classic sectors
- Position sizing: how much, when, at what risk and expected profit
- Investment calendar: required monthly return for a target sum at a given win rate and risk-to-reward
- Investment journal with hints from a journal bot
05
Outcome
“Just doing something” becomes an analytical, repeatable process: sharper ideas, better-matched companies, planned risk and more disciplined trades.
06
Output
- Hypothesis engine (generation, backtest, company matching)
- Theme screener based on description texts and AI
- Position and risk calculators plus an investment calendar
- Investment journal with bot feedback — primarily for personal use