Playbooks and Data Packages

Why These Two Concepts Matter

Playbooks and data packages are two of the most important Cortiq concepts.

Playbooks define what the AI is supposed to look for and how it should reason about the setup.
Data packages define what information the AI is allowed to see while making that decision.

Together they turn Cortiq into a controlled operating environment instead of an open-ended prompt playground.

Where To Find This In The App

The related UI areas are split across the sidebar:

Playbooks -> My Playbooks for strategy design
Tools -> Data Packages for AI payload design
Tools -> Indicators for indicator definitions and supporting signal inputs
Preparation -> Instrument Profiles, Prep Packages, and Sentiment for reusable supporting context

Playbooks

A playbook is the strategy layer.

Typical playbook content includes:

Market conditions the setup requires
Entry logic and filters
Invalidations and no-trade conditions
Exit or management expectations
Priority relative to other linked playbooks

The important public takeaway is simple: Cortiq executes inside the framework you define. It does not replace the need for a trading plan.

What Each Playbook Section Is For

Customers usually get better results when they think of a playbook as a structured document rather than one big paragraph.

Section	What It Is Used For
Market bias	Gives the AI the broad strategy type or directional context, such as trend following or mean reversion
Primary timeframe	Tells the AI where the main structural reading should happen
Entry timeframe	Tells the AI where the actual trigger should be confirmed
Setup conditions	Defines what the market must look like before the setup is considered valid
Entry conditions	Defines what must happen before the trade is allowed to trigger
Risk rules	Defines stop placement, target logic, risk boundaries, or minimum reward-to-risk requirements
Trade-management rules	Defines what the AI should do after entry, such as trailing, scaling out, or moving to break-even
Invalidation conditions	Defines when the idea is no longer valid and should be ignored
Preferred symbols and sessions	Helps the user keep a playbook aligned with the markets and trading windows where it makes the most sense

Read Playbook Design Guide for a professional breakdown of each section.

Data Packages

A data package controls what the session gathers and sends to the AI.

This can include:

Historical candles across one or more timeframes
Technical indicators
Chart screenshots
Economic calendar context
Account information
Risk settings and performance context
Trade history
Cross-reference symbols

This matters because the quality of AI decisions depends heavily on both signal quality and prompt discipline.

How Screenshots Work In Data Packages

Screenshots are optional chart images captured for selected timeframes in the data package.

They are useful when:

chart structure matters more than raw numbers alone
the user wants the AI to visually confirm levels, patterns, or trend shape
a setup depends on something that is easier to interpret visually than in a table of candles

They should be used selectively because they add payload weight and are most valuable when visual context truly improves the decision.

Read Data Package Design Guide for a detailed explanation of timeframes, screenshots, tiers, and payload discipline.

If you want the deeper customer-facing reference pages, read Data Packages, Playbooks, and Supporting Context.

Indicators And Custom Inputs

Cortiq can incorporate indicator data from MT5, including custom indicator workflows where the MT5 environment exposes them correctly.

That allows advanced users to make the AI decision layer aware of the same signal stack they already use manually.

What These Functions Can Do For You

Used well, this part of Cortiq helps you:

stop relying on vague prompts and loose AI instructions
keep the AI focused on the signals and structure that actually matter to your strategy
separate reusable strategy logic from one-off active trading ideas
improve consistency by deciding in advance what the AI can see and how it should reason

Good Operating Practice

Use these principles when building your first serious playbook set:

Start with narrow playbooks rather than vague multi-market logic.
Keep the data package focused on what the strategy truly needs.
Add symbols, indicators, and screenshots only when they improve decisions.
Review journals and session output before widening the scope.

Professional Design Advice

The most common quality difference between weak and strong Cortiq setups is not the AI provider. It is documentation quality inside the configuration.

Professional setups usually have:

playbooks where each section has one clear job
data packages where each timeframe and indicator has a reason to exist
screenshots only where visual confirmation adds real value
enough structure that the AI can stay disciplined without becoming overloaded

What This Feature Solves

Without playbooks and data packages, AI trading workflows often fail for one of two reasons:

The AI has too little structure and improvises too much.
The AI has too much noisy context and loses signal quality.

Cortiq uses playbooks and data packages to reduce both problems.