State the outcome first
A user should know in one sentence whether this is the lesson they need.
How-Tos / Academy
One task. Clear steps. The concept behind it.
Use How-Tos when you want to complete or interpret a specific task. Each lesson combines written steps, explanations, optional video, common mistakes, and links to deeper reference.
Start with the written steps, use video when it helps, and open Docs when you need field-level product detail.
One lesson / six useful parts
01Goalstate the outcome
02Stepswritten first
03Understandlink the method
04Videoclick to load
05Recoverymistakes + fixes
06Referenceexact Docs handoff
Lesson structure
Every How-To is organized the same way.
Start with the written steps
Short steps with expected outcomes and screenshots where they help.
Link the underlying method
The lesson explains why the step exists by linking to Concepts and Methods.
Click to load
Video is optional and lazy-loaded so it does not add tracking or bandwidth until requested.
Common mistakes and troubleshooting
Failure states belong next to the workflow, not hidden in a separate support maze.
Open the reference
When you need field-by-field detail, open the matching Docs page.
Published written guides
Interpret the evidence before you act on the verdict.
Use these task guides to interpret research evidence step by step. Videos are optional.
Read a Monte Carlo result
Identify the simulation rule, preserved information, distribution, and interpretation limits.
Open the How-To →Read a staged validation chain
Move through acceptance, retest, time slices, regimes, path stress, holdout evidence, and record keeping without treating stage count as proof.
Open the How-To →Read result metrics together
Keep sample, units, costs, trade count, path risk, and selection context attached to every headline statistic.
Open the How-To →Read chart evidence
Keep the data source, interval, units, profile construction, event timing, and replay boundaries attached to visual interpretation.
Open the How-To →
Published lesson / video optional
Watch the concept without making the video the documentation.
This public lesson uses an existing TraderCockpit research video as an optional companion to the written Monte Carlo explanation. No third-party player is contacted until you choose to load it.
Research & Validation
Monte Carlo Block Bootstrap
Use the video for visual intuition about randomized path resampling, then return to the written method page for the assumptions and interpretation limits.
Loading the player connects your browser to YouTube. The written explanation remains available without loading the video.
Third-party player not loaded.Choose “Load video from YouTube” to initialize the privacy-enhanced embed.
Academy path 01 / research validation
One question, six connected layers.
Move from intuition to evidence without losing the context that explains why each layer exists.
Academy path 02 / path risk & uncertainty
Move from the path you saw to the distribution you did not.
This path connects drawdown geometry, distribution shape, and Monte Carlo interpretation so one realized path does not become the entire risk story.
Academy path 03 / staged validation
Seven checks, seven different questions.
Follow the staged-validation lesson when you need to understand how evidence changes from an initial rule through the final record.
Academy path 04 / result interpretation
Move from headline numbers back to the evidence.
Read scale, gain/loss balance, trade average, path risk, temporal sequence, and sample scope before using a result statistic in a conclusion.
Academy path 05 / chart evidence
Read source, path, distribution, and timing as one evidence stack.
Move from the data source and interval to the price path, profile structure, replay boundary, and final synthesis without turning visual prominence into prediction.
Learning collections
Start HereOrientation, terminology, and first workflows.
Research & ValidationTask lessons connected to holdout, walk-forward, Monte Carlo, and selection concepts.
Charts & Visual AnalysisHow to inspect visual evidence and move between research views.
Models & Advanced ResearchEvaluation-focused learning for models and advanced research.