AI is often asked to make trading decisions when the more useful role is helping people follow a consistent process. This toolkit takes that less glamorous approach: it organizes research, risk gates, planning, and post-trade review while leaving decisions and execution to the investor.
What Sets It Apart
- Workflows connect individual skills into repeatable routines, from daily market checks to monthly performance reviews. That makes the handoffs and decision gates visible instead of presenting isolated AI-generated opinions.
- Risk management comes before candidate discovery in the swing-trading path: circuit breakers and discipline checks can constrain whether new risk is allowed. This is designed to make “do nothing” a valid outcome.
- A no-paid-data-API starter path uses public CSVs, chart screenshots, and local files for selected tasks. More data-intensive workflows may require services such as FMP, FINVIZ, or Alpaca.
- Journaling and postmortems treat trading as a feedback loop. The aim is to examine process and outcomes over time, not to imply that an AI analysis can predict profits.
Who It’s For—and the Trade-offs
Great fit for individual investors who want structured market reviews, portfolio routines, and disciplined swing-trade planning while retaining control of every decision. Look elsewhere for fully automated execution, short-term scalping, guaranteed signals, or a substitute for financial advice. Data coverage depends on the selected workflow and its inputs, and the project explicitly warns that AI analysis and backtests do not guarantee future results.
