Data Types - kScript Core Concepts

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);