Standalone module within Stocks & Crypto AI Toolkit

Stock Research Flow

The third stage of the process: after niche discovery and candidate selection comes detailed review of selected stocks and ETFs.

In the full workflow, Investment Niche Lab first identifies niches and themes worth further research. Then AI Investment Lab narrows one niche into a shortlist of instruments and candidate scenarios. This module shows the final stage: deep-dive review of selected stocks and ETFs with fundamentals, technical context, SWOT, risks, scenario tracking and strategic web research. It is structured research support for human-reviewed conclusions, not a source of automatic buy or sell guidance.

See the flow Back to the case study

What this module delivers

The user does not get a trading chat. They get the final, most detailed stage of the workflow: from a selected niche and shortlisted candidates to a decision-support record saved as structured data for later review.

Screening

Start with the wider market

Configuring market scope, horizon, risk tolerance, setup types, exclusions, minimum risk-reward and candidate volume.

Candidates

Then narrow to the top set

AI-assisted analysis classifies instruments, explains the selection, drafts first plans and stores candidates as system objects.

Deep dive

Finish with the detailed review

Fundamental data, technical snapshots, SWOT, strategic map, bull/base/bear scenarios and monitoring points.

One flow from screening to decision support

Within the full toolkit, this module closes the broader path: Niche Lab -> AI Investment Lab -> Stocks.

1

Niche Lab

Broad discovery of niches, themes and segments worth further exploration.

2

AI Investment Lab

Reviewing one niche through instrument screening, classification and candidate selection.

3

Top candidates

Selecting the strongest instruments, drafting an early thesis, key risks and watch conditions.

4

Checklist

Final review of risk factors, entry conditions, invalidation levels and the reasonableness of the risk-reward setup.

5

Stocks deep dive

Detailed analysis of the selected instrument using structured data, web research, SWOT and scenario tracking.

What goes into the detailed research bundle

The deep dive does not start from a blank prompt. The system assembles an input bundle from instrument data, history, technical context, fundamentals and earlier analysis outputs.

Instrument data Identification, market, sector, instrument type, quote, price, logo and the basic profile.
Fundamentals Multiples, valuation snapshot, growth, profitability, key risks and finance data depending on the available sources.
Technical context Daily, weekly and monthly snapshots, trend, momentum, levels and invalidation conditions.
Strategic map Moat, growth optionality, catalysts, monitoring points, red flags and scenario structure.
Web research OpenAI with web search extends the 3-12 month context, sources, risks and executive summary.
JSON as a contract The AI response is constrained by schema and stored as a candidate, plan, decision record or audit log.

Shared building blocks across modules

Some mechanisms are worth showing here and on the crypto page as well, because they represent the real technical value of the overall system.

PromptLog

History of prompts, responses, statuses, errors, models, tokens and costs.

JSON schema

A constrained response format with parsing and storage in system models.

Cache and compression

Shortened time series, summaries of earlier analyses and context-length control.

Decision audit trail

Returning to earlier analyses, conditions, sources, costs and rationale.

Presentation scope: the module can technically support crypto as well, but this standalone page is intentionally framed around stocks and ETFs. Crypto has its own separate page, and this module is presented as financial research support and scenario review, not automated trading advice.

Stock Research Flow screens

These views show the final part of the process: shortlisted candidates, instrument detail and an AI Investment Lab run that feeds further thesis review.

Stock watchlist with scoring

A watchlist of stocks and ETFs with filtering and scoring across overall score, business quality, valuation attractiveness, technical setup and risk.

Stock instrument detail

An instrument view combining core data, fundamentals, qualitative analysis and the AI-supported conclusion for further human review.

AI Investment Lab run

A run detail showing workflow input, parameters, shortlisted candidates, decisions and the resulting analysis path.

The same pattern works beyond financial markets

Screening, classification, deep dive, checklists and AI audit trails can also support lead scoring, supplier review, document evaluation, competitor analysis, due diligence or management reporting workflows.

Contact info@elistar.pl