You choose the subject.
Build the guides and skills to excel.

Stop stacking courses to go deep on one topic. QuantBot generates study guides and runnable skills around what you want to learn, and updates them as you go.

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Two tracks

Two ways in. Same workflow.

Pick the path that matches how you study and build.

The Agentic Teacher

For builders and engineers who want depth

Name a hard subject. QuantBot builds a curriculum, pulls sources in parallel, and checks whether you actually understand each module.

  • Curriculum from your topic
  • Source checks on each module
  • Notes that stay searchable

The Quant Teacher

For quants, traders, and fintech people

Study alphas, market cycles, crashes, and regulation. Every lesson can run on live data in Python.

  • Tests on live market data
  • Runnable Python on every topic
  • From concept to backtest
Two modes

How QuantBot Works

A searchable knowledge base, research adapters, and a Python runtime. Pick learn or build.

quant-teacher

Teacher Mode

Ask for a quant concept. QuantBot searches the knowledge base, pulls research, and writes a study guide you can keep.

Example prompt:

"Explain Black-Scholes and its assumptions"

Process Flow

KB Lookup

Checks what you already know

Research Adapters

Pulls papers, docs, and market data

Synthesis

Writes the study guide

Save to KB

Saves it for next time

Learn
quant-implementer

Implementer Mode

Ask for a tool or backtest. QuantBot writes the Python, saves it as a skill, and runs it against live data.

Example prompt:

"Build a momentum backtester for equities"

Process Flow

Read Topic

Reads the topic brief

Scaffold Skill

Writes the Python

Register

Registers as a skill

Test Live

Runs on live data

Build
Under the hood

What runs underneath

One stack for learning quant finance and shipping the code.

Knowledge Base

A dictionary-style store for every topic you study or build. Past work stays searchable.

persistentsearchable

Research Adapters

Pulls market data, papers, and docs at the same time. No single-source summaries.

parallelmulti-source

Python Runtime

Run backtests, portfolio checks, and strategy tests on live data without leaving the workflow.

executionlive data

Skill Registry

Every build becomes a registered skill. Reuse it on the next topic.

reusableregistered

Study Guides

Built from several sources. Each one includes examples and checkpoints, not one flat summary.

multi-sourceworked examples

Live Testing

Run new code against live market data before you trust the output.

live datachecks