Data Types - kScript Core Concepts

Notice!

This is documentation for kScript v1.0, which may not include the latest features. For up-to-date documentation, see the latest version.

Basic Data Types

kScript provides several fundamental data types that form the building blocks of your trading algorithms.

numberPrimitive

Represents numerical values including integers and floating-point numbers.

Examples:let price = 45000.50;
let volume = 1000;

stringPrimitive

Text data used for labels, symbols, and configuration values.

Examples:let symbol = "BTCUSDT";
let exchange = "BINANCE";

booleanPrimitive

True/false values used for conditional logic and flags.

Examples:let isUptrend = true;
let showSignals = false;

colorVisual

Color values for chart visualization and plotting.

Examples:let lineColor = "#FF6B35";
let fillColor = "#008080";

ShapeTypeVisual

Defines the type of shape to render when using plotShape function for marking signals and annotations.

Values:"circle" - Circular markers
Additional shapes may be available

Complex Data Types

Advanced types for handling time-series data and market information.

TimeSeriesCore

The fundamental data structure for time-aligned market data. All price data and indicators are TimeSeries.

Examples:timeseries ohlcv = source("ohlcv", currentSymbol, currentExchange);
timeseries sma20 = sma(ohlcv, 20, 4);

Structure:

Timestamp - Unix timestamp for each data point
Values - Array of numerical data (OHLCV)
Indexing - Access individual price components

DataSourceInput

Identifiers for different types of market data available in the platform.

Examples:"ohlcv" - Price and volume data
"funding_rate" - Futures funding rates
"liquidations" - Liquidation events

PositionConfiguration

Determines where your indicator appears on the chart interface.

Values:"onchart" - On the main price chart
"offchart" - In a separate panel below

Array Types

kScript supports arrays for handling multiple values of the same type.

Color Arrays

Used for multi-line plots with different colors.

Number Arrays

For storing multiple numerical values or calculations.

Type Conversion & Indexing

How to work with different data types and extract values from TimeSeries.

TimeSeries Indexing

Access specific price components from OHLCV data:

0Timestamp

1Open

2High

3Low

4Close

5Volume

Creating TimeSeries

Extract specific data from multi-dimensional TimeSeries:

Best Practices

✅

Use Descriptive Names

Choose clear variable names that indicate the data type and purpose.

timeseries close_prices = timeseries(ohlcv, 4);

⚡

Minimize Type Conversions

Work with native TimeSeries types when possible for better performance.

// Good: Direct TimeSeries operations
timeseries ma = sma(ohlcv, 20, 4);

🎯

Use Appropriate Types

Choose the right data type for your use case to ensure type safety.

// Use boolean for flags
boolean showMA = input("showMA", true);