> For the complete documentation index, see [llms.txt](https://gorilli.gitbook.io/gorillionaire/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gorilli.gitbook.io/gorillionaire/signals/introduction-to-signals.md).

# Introduction to Signals

A guide to better understand signals and the signal generation process behind Gorillionaire's API.

In financial markets, a signal refers to a data-driven indicator or insight that suggests a potential trading opportunity, typically highlighting when to buy, sell, or stay out of a position. Effective signals are accurate and rooted in quantitative or behavioral patterns.

At a high level, signals are generated using an indicator-based algorithm. Indicators are applied to the time-series data provided by the CODEX API, which is resampled to a specific timeframe if necessary. Depending on the given indicators, the data used to calculate the signals includes 'Open,' 'High,' 'Low,' 'Close,' and 'Volume,' which together represent the OHLCV candles typically displayed on trading charts.

<figure><img src="https://3991155293-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-MT_kyAFF1_TCEvQbT0Q%2Fuploads%2FLrWGQHc8NgnfHtD6AqKj%2FIntroduction%20to%20signals.svg?alt=media&amp;token=439a5de5-9afb-4dbd-8e80-af7fe0046a5e" alt="" width="563"><figcaption></figcaption></figure>

In this section, we will dive deep into each step of the signal generation process to better understand the path that transforms raw price data into actionable trading signals.

Keep in mind that a signal has 3 properties, which uniquely identify it:

* **TOKEN**: the token (or pool of tokens) on which the indicator is calculated
* **INDICATOR**: the indicator that determines the conditions to be met
* **TIMEFRAME**: the timeframe of the price data; different timeframes generate different time-series data, and therefore different indicator values and signals.