El endpoint "Obtener Tarifas" devuelve datos del mercado de valores en tiempo real, incluidos el último precio de negociación, la apertura diaria, el máximo, el mínimo, el cierre anterior y el volumen de negociación para los símbolos de acciones especificados.

Los campos clave en la respuesta incluyen "símbolo," "apertura," "máximo_del_día," "mínimo_del_día," "cierre_anterior," "precio_de_última_negociación," "volumen," y "cambio_del_día." Cada campo proporciona información esencial sobre el rendimiento de la acción.

Los datos de respuesta están estructurados en formato JSON, con pares clave-valor que representan varios métricas de acciones. Por ejemplo, una respuesta podría incluir "último_precio_de_venta": 898.8 y "máximo_del_día": 914.95, lo que facilita su análisis y utilización.

El endpoint "Get Rates" proporciona información como el último precio de negociación, el precio de apertura diario, los precios altos y bajos, el cierre anterior, el volumen de negociación y los porcentajes de cambio de precio, lo que permite un análisis integral de las acciones.

El parámetro principal para el endpoint "Get Rates" es el símbolo de acción (código). Los usuarios pueden personalizar sus solicitudes especificando diferentes símbolos de acción para recuperar datos de varias acciones.

La precisión de los datos se mantiene a través de actualizaciones en tiempo real de la Bolsa Nacional de Valores de India (NSE) y la Bolsa de Valores de Bombay (BSE). Los controles de calidad regulares aseguran que los datos reflejen las últimas condiciones del mercado.

Los casos de uso típicos incluyen la creación de paneles financieros, el desarrollo de aplicaciones de seguimiento de acciones y la creación de herramientas de análisis de inversiones. La API permite a los usuarios monitorear el rendimiento de las acciones y las tendencias del mercado de manera efectiva.

Los usuarios pueden analizar los datos devueltos para rastrear el rendimiento de las acciones, identificar tendencias y tomar decisiones de inversión informadas. Por ejemplo, comparar "day_high" y "day_low" puede ayudar a evaluar la volatilidad y el sentimiento del mercado.

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