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Data Package Entity

The data package is the entity that controls what the AI can see during a trading cycle.

A data package can define:

  • one or more timeframes
  • candle depth per timeframe
  • indicator inputs
  • screenshot capture rules
  • economic-calendar context
  • account information
  • risk and performance context
  • trade history and supporting data

Timeframes decide which chart views the AI receives.

Use them to give the AI the right balance of:

  • execution detail
  • structural context
  • higher-timeframe direction

Candle depth controls how much historical data is sent for a given timeframe.

More bars can help with structure, but too many bars can also increase prompt weight and reduce clarity.

Indicators add calculated market context on top of raw candles.

Use them when the strategy truly depends on them. Avoid adding indicators only because they are familiar.

Screenshots attach chart images for selected timeframes.

They are most useful when visual structure matters, such as:

  • trend shape
  • support and resistance zones
  • pattern recognition
  • context that is easier to see than describe numerically

These toggles widen the context beyond pure chart data.

They help when the AI should consider:

  • event risk
  • current account state
  • platform risk state
  • recent trading behavior

In practical terms, screenshots are configured per timeframe.

  • A timeframe can have screenshots enabled or disabled.
  • Screenshot collection is most useful on the timeframes where chart structure matters.
  • If screenshots are enabled, Cortiq can include the relevant chart image alongside the rest of the market payload.
  • Screenshots work best when paired with the indicators that matter on that same timeframe.

This means screenshots should be treated as selective visual context, not as something to enable everywhere by default.

Screenshots can improve understanding, but they are not free.

They can increase prompt weight and operational complexity, so the best professional setups usually:

  • enable screenshots only on the most meaningful timeframes
  • avoid visual duplication across too many charts
  • use screenshots when visual confirmation genuinely improves decision quality

The data package is not the strategy. It is the information scope.

This distinction is important:

  • playbooks tell the AI what to look for
  • data packages tell the AI what it is allowed to use while looking for it

Use lean data packages for:

  • focused intraday setups
  • narrow strategy logic
  • faster review and less noise

Use broader packages for:

  • multi-timeframe swing workflows
  • more context-heavy strategies
  • workflows where screenshots and broader account context genuinely help

Data packages usually work together with:

Keep the package tight enough that every included input has a reason to be there.