-La arquitectura de Benzinga ofrece opciones de despliegue flexibles para satisfacer las diversas necesidades de sus clientes:
+Cada despliegue de servicio incluye la configuración de Horizontal Pod Autoscaler:
+
+```yaml
+# Ejemplo de configuración de HPA
+apiVersion: autoscaling/v2
+kind: HorizontalPodAutoscaler
+spec:
+ minReplicas: 3
+ maxReplicas: 50
+ metrics:
+ - type: Resource
+ resource:
+ name: cpu
+ target:
+ type: Utilization
+ averageUtilization: 70
+```
-- **Servicio en la nube gestionado**: Para los clientes que buscan una experiencia fluida, Benzinga ofrece un servicio en la nube totalmente gestionado, que incluye escalado automático y mantenimiento.
-- **Opción autoalojada**: Para quienes necesitan más control, la arquitectura de Benzinga puede adaptarse para despliegues en las instalaciones (on-premises) o en nubes privadas, garantizando plena propiedad de los datos y cumplimiento de las regulaciones locales.
+**Condiciones de escalado:**
-
- ### Alcance global con edge caching
+* Uso de CPU > 70%
+* Uso de memoria > 80%
+* Métricas personalizadas (profundidad de la cola de solicitudes, percentiles de latencia)
+
+
+
+ ### Escalado a nivel de nodo (Karpenter)
-Para mejorar aún más el rendimiento, Benzinga emplea **edge caching** para garantizar que los datos, incluidos los flujos en tiempo real y el contenido estático, lleguen a los usuarios con una latencia mínima. Al ubicar estratégicamente Puntos de Presencia (PoPs) en todo el mundo, Benzinga asegura una alta disponibilidad y baja latencia para clientes en todos los continentes.
+**Karpenter** gestiona la capacidad del clúster mediante:
-Esta arquitectura distribuida permite a Benzinga proporcionar datos confiables en tiempo real a los clientes, independientemente de su ubicación geográfica, satisfaciendo las necesidades de usuarios tanto locales como globales.
+- Aprovisiona nodos de tamaño óptimo en menos de 60 segundos
+- Consolida nodos infrautilizados para reducir costos
+- Admite instancias spot para cargas de trabajo no críticas
+- Respeta la topología de pods y las restricciones de zona de disponibilidad
---
-Esta arquitectura combina tecnologías modernas y escalables para ofrecer datos e insights financieros rápidos y confiables, respaldando el compromiso de Benzinga con la calidad y el rendimiento. Para obtener más detalles técnicos o para comenzar a usar nuestras API, comunícate con el equipo de soporte de Benzinga o visita nuestra documentación.
\ No newline at end of file
+
+ ## Observabilidad y monitorización a nivel de producción
+
+
+Nuestra infraestructura emplea **monitorización a nivel empresarial** con múltiples capas de observabilidad, lo que garantiza una visibilidad total del estado, el rendimiento y la fiabilidad del sistema. Cada componente se monitoriza de forma continua con alertas automatizadas y protocolos de respuesta a incidentes.
+
+
+ ### Stack de monitoreo integral
+
+
+
+
+ **Trazas distribuidas y registros (logging)**
+ - Agregación de registros en tiempo real de todos los servicios
+ - Trazas distribuidas a través de microservicios
+ - Monitoreo de rendimiento de aplicaciones (APM)
+ - Seguimiento de solicitudes de extremo a extremo con IDs de correlación
+ - Reconocimiento de patrones en los registros y detección de anomalías
+ - Paneles personalizados para métricas de negocio
+
+
+ **Alertas y monitoreo sintético**
+ - Pruebas continuas 24/7 de endpoints de API
+ - Monitoreo sintético multirregión
+ - Seguimiento de tiempo de respuesta y disponibilidad
+ - Alertas automatizadas con enrutamiento inteligente
+ - Seguimiento de indicadores de nivel de servicio (SLI)
+ - Detección de regresiones de rendimiento
+
+
+
+
+ ### Coralogix: Trazas y registros
+
+
+Coralogix proporciona **observabilidad completa** en nuestra capa de aplicación:
+
+
+
+ Todos los registros de las aplicaciones de cada servicio, pod y contenedor se agregan en tiempo real, lo que proporciona acceso instantáneo a la información de depuración en toda la infraestructura.
+
+
+ Cada solicitud de API se rastrea de extremo a extremo a través de microservicios, balanceadores de carga, bases de datos y servicios externos. Esto permite un análisis rápido de la causa raíz de problemas de rendimiento o errores.
+
+
+ Detección automática de errores con trazas de pila, información contextual y número de usuarios afectados. Los errores se categorizan por gravedad e impacto.
+
+
+ Métricas en tiempo real sobre tiempos de respuesta de la API, rendimiento, tasas de error y utilización de recursos en todos los servicios.
+
+
+
+**Características clave de Coralogix en producción:**
+
+- **Política de retención**: 30 días de almacenamiento en caliente para acceso inmediato, 90 días de archivo para cumplimiento normativo
+- **Rendimiento de consultas**: Consultas en menos de un segundo sobre miles de millones de entradas de registro
+- **Integración de alertas**: Enrutamiento automatizado a canales de Slack e ingenieros de guardia
+- **Paneles personalizados**: Métricas específicas del negocio visibles para las partes interesadas en tiempo real
+
+
+ ### Datadog: Alertas y pruebas sintéticas
+
+
+Datadog proporciona **monitorización proactiva** y validación continua:
+
+
+
+ Las pruebas automatizadas se ejecutan cada 60 segundos desde múltiples regiones geográficas, validando la disponibilidad de la API, los tiempos de respuesta y la precisión de los datos antes de que los clientes se vean afectados.
+
+
+ La detección de anomalías basada en aprendizaje automático identifica patrones inusuales en las métricas, activando alertas antes de que los problemas afecten a los clientes.
+
+
+ Seguimiento en tiempo real de los objetivos de nivel de servicio (SLO) con generación de informes automatizados sobre objetivos de disponibilidad del 99,9 %.
+
+
+ Monitorización continua de los percentiles de latencia p50, p95 y p99 para garantizar un rendimiento constante.
+
+
+
+**Las pruebas sintéticas de Datadog incluyen:**
+
+| Tipo de prueba | Frecuencia | Regiones | Métricas supervisadas |
+|----------------|------------|----------|------------------------|
+| **Comprobaciones de estado de API** | Cada 60 s | 5 regiones globales | Disponibilidad, tiempo de respuesta, códigos de estado |
+| **Pruebas de precisión de datos** | Cada 5 min | 3 regiones | Frescura de los datos, validación de esquema, integridad |
+| **Pruebas de rendimiento** | Cada 60 s | 5 regiones | Latencia (p50/p95/p99), throughput, tasas de error |
+| **Pruebas de autenticación** | Cada 5 min | 2 regiones | Validación de clave de API, rate limiting, flujos OAuth |
+
+
+ ### Integración con Slack y gestión de incidentes
+
+
+Todos los sistemas de monitorización se integran con **canales dedicados de Slack** para visibilidad inmediata y respuesta rápida:
+
+
+
+ **Alertas críticas**
+
+ * Incidentes P1/P2 que requieren acción inmediata
+ * Alertado automático del ingeniero de guardia
+ * Métricas en tiempo real y enlaces a runbooks
+ * Asignación de un responsable del incidente
+
+
+
+ **Información de rendimiento**
+
+ * Resúmenes diarios del estado
+ * Alertas de planificación de capacidad
+ * Notificaciones de tendencias de rendimiento
+ * Advertencias de detección de anomalías
+
+
+
+**Flujo de alertas de Slack:**
+
+```mermaid
+flowchart LR
+ A[Alerta Activada] --> B{Nivel de Severidad}
+ B -->|P1/P2| C[#alerts-production]
+ B -->|P3/P4| D[#monitoring-insights]
+ C --> E[Ingeniero de Guardia Notificado]
+ C --> F[Canal de Incidente Creado]
+ F --> G[Desarrollador Asignado]
+ G --> H[Investigación de Causa Raíz]
+ H --> I[Corrección Implementada]
+ I --> J[Informe Post-Mortem]
+```
+
+
+
+ ### Alertas e incidentes
+
+
+**Proceso de asignación de desarrolladores:**
+
+1. **Alerta activada** → Notificación automática en Slack con contexto y métricas
+2. **Ingeniero de guardia realiza el triage** → Se evalúa la gravedad y se crea el canal de incidentes
+3. **Desarrollador asignado** → Se etiqueta a la persona experta en el tema según el servicio afectado
+4. **Investigación** → Análisis de causa raíz usando trazas de Coralogix y métricas de Datadog
+5. **Resolución** → Corrección desplegada mediante el pipeline estándar de GitOps
+6. **Análisis post mortem** → Incidente documentado con medidas preventivas
+
+
+ Todos los incidentes P1/P2 activan un **paging automatizado inmediato** a los ingenieros de guardia con cobertura 24/5.
+
+
+
+ Nuestros sistemas de monitoreo han detectado y resuelto **el 95% de los problemas potenciales antes de que afecten a los clientes** mediante alertas proactivas y remediación automatizada.
+
+
+---
+
+
+ ## Seguridad y cumplimiento normativo
+
+
+
+ ### Seguridad de red
+
+
+- **Aislamiento de VPC** — Segmentación completa de la red respecto de la Internet pública
+- **Security Groups** — Reglas estrictas de entrada/salida, con denegación por defecto
+- **TLS en todas partes** — Todo el tráfico interno y externo cifrado
+- **Gestión de secretos** — AWS Secrets Manager para credenciales confidenciales
+
+
+ ### Control de acceso
+
+
+- **RBAC** — Control de acceso basado en roles (RBAC) de Kubernetes para todas las operaciones
+- **Integración SSO** — Integración con el proveedor de identidad empresarial (SSO)
+- **Registros de auditoría** — Registros de acceso completos conservados para fines de cumplimiento normativo
+
+---
+
+
+ ## Recuperación ante desastres
+
+
+
+ ### Objetivos de recuperación
+
+
+| Métrica | Objetivo | Actual |
+|--------|----------|--------|
+| **RTO** (Recovery Time Objective) | < 15 minutos | ~5 minutos |
+| **RPO** (Recovery Point Objective) | < 1 minuto | Replicación en tiempo real |
+
+
+ ### Características de resiliencia
+
+
+- **Replicación Multi-AZ** — Datos replicados entre zonas de disponibilidad
+- **Conmutación por error automatizada** — Las comprobaciones de estado de Route 53 activan la conmutación por error de DNS
+- **Despliegues progresivos** — Despliegues sin tiempo de inactividad con reversión automática
+- **Copia de seguridad y restauración** — Copias de seguridad diarias automatizadas con recuperación a un punto en el tiempo
+
+---
+
+
+ ## Garantías de confiabilidad a nivel de producción
+
+
+
+ ### Por qué nuestra infraestructura es a prueba de fallos
+
+
+La infraestructura de Benzinga está **probada en producción a gran escala**, gestionando millones de solicitudes diarias con fiabilidad demostrada:
+
+
+
+ **Estadísticas de producción**
+ - Más de 10 millones de solicitudes de API procesadas diariamente
+ - Tiempo de respuesta medio inferior a 100 ms
+ - 99,9 % de disponibilidad histórica
+ - Sin pérdida de datos en más de 3 años
+
+
+ **Excelencia operativa**
+ - Cobertura de ingeniería on-call 24/5/365
+ - Conmutación por error automatizada y autorrecuperación
+ - Redundancia multirregión
+
+
+
+
+ ### Excelencia en monitoreo y observabilidad
+
+
+Nuestro **monitoreo integral** garantiza que los problemas se detecten y se resuelvan antes de que afecten su negocio:
+
+
+
+ Cada solicitud, entrada de log y métrica se sigue de extremo a extremo usando trazas distribuidas de Coralogix y registros centralizados
+
+
+ El monitoreo sintético de Datadog prueba las APIs cada 60 segundos desde múltiples regiones, y alerta sobre problemas antes de que afecten a los clientes
+
+
+ La integración automatizada con Slack enruta las alertas a canales dedicados con asignación inmediata de desarrolladores y seguimiento de la resolución
+
+
+ El análisis postmortem de todos los incidentes garantiza que los problemas no vuelvan a ocurrir, con medidas preventivas automatizadas
+
+
+
+
+ ### Confianza de los clientes: qué significa esto para usted
+
+
+Cuando integra con las API de Benzinga, se conecta a una **infraestructura de nivel de producción** respaldada por:
+
+| Feature | Client Benefit |
+|---------|----------------|
+| **Multi-AZ Redundancy** | Su aplicación se mantiene en línea incluso durante caídas en zonas de disponibilidad de AWS |
+| **Automated Scaling** | Sus solicitudes se gestionan sin problemas durante picos de tráfico, sin aplicar limitación de tasa |
+| **24/7 Monitoring** | Los ingenieros detectan y resuelven los problemas antes de que usted note cualquier degradación |
+| **Zero-Downtime Deployments** | Nuestras actualizaciones nunca interrumpen la disponibilidad de su servicio |
+| **Complete Audit Trail** | Cada implementación se registra, se revisa y puede revertirse al instante |
+| **Proactive Alerting** | El 95% de los posibles problemas se resuelven antes de que afecten al cliente |
+
+
+ **Listo para producción**: Nuestra infraestructura ha procesado más de **mil millones de solicitudes de API** con un 99,9% de disponibilidad y mantiene una latencia inferior a 100 ms para la entrega de datos financieros en tiempo real.
+
+
+---
+
+
+ ## Resumen
+
+
+La infraestructura de Benzinga ofrece **fiabilidad a nivel empresarial** mediante:
+
+
+
+ AWS EKS con implementación multi-AZ y plano de control gestionado que garantiza el máximo tiempo de actividad
+
+
+ Despliegues gestionados con ArgoCD, con registro de auditoría completo y capacidades de reversión inmediata
+
+
+ Karpenter + HPA para una gestión de capacidad transparente que maneja automáticamente los picos de tráfico
+
+
+ Trazado/registro con Coralogix + alertas/pruebas sintéticas con Datadog e integración con Slack para una respuesta rápida ante incidentes
+
+
+ Ingenieros dedicados de guardia
+
+
+ Defensa en profundidad con cifrado, RBAC y aislamiento de red para proteger sus datos
+
+
+
+
+ **Su éxito es nuestra prioridad**: Si tiene preguntas sobre nuestra infraestructura, las garantías de SLA o desea hablar de sus requisitos específicos de fiabilidad, póngase en contacto con su representante de cuenta o envíe un correo electrónico a [support@benzinga.com](mailto:support@benzinga.com).
+
\ No newline at end of file
diff --git a/es/openapi/calendar_api.spec.yml b/es/openapi/calendar_api.spec.yml
index 59775b44..ea62fd61 100644
--- a/es/openapi/calendar_api.spec.yml
+++ b/es/openapi/calendar_api.spec.yml
@@ -2974,14 +2974,19 @@ components:
calendarapi.AggregateRating:
properties:
buy:
+ example: 10
type: integer
hold:
+ example: 3
type: integer
sell:
+ example: 1
type: integer
strong_buy:
+ example: 5
type: integer
strong_sell:
+ example: 0
type: integer
type: object
calendarapi.AnalystInsightsJSON:
@@ -3021,22 +3026,31 @@ components:
aggregate_ratings:
$ref: "#/components/schemas/calendarapi.AggregateRating"
aggregate_type:
+ example: 90D
type: string
consensus_price_target:
+ example: 195.5
type: number
consensus_rating:
+ example: Buy
type: string
consensus_rating_val:
+ example: 4.2
type: number
high_price_target:
+ example: 225
type: number
low_price_target:
+ example: 150
type: number
total_analyst_count:
+ example: 19
type: integer
unique_analyst_count:
+ example: 15
type: integer
updated_at:
+ example: 2024-01-09T16:00:00Z
type: string
type: object
calendarapi.DividendJSON:
@@ -3122,40 +3136,53 @@ components:
calendarapi.InsiderTransaction:
properties:
accession_number:
+ example: 0001127602-24-000123
type: string
company_cik:
+ example: 320193
type: string
company_name:
+ example: Apple Inc.
type: string
company_symbol:
+ example: AAPL
type: string
filing_date:
+ example: 2024-01-09T00:00:00Z
type: string
footnotes:
items:
$ref: "#/components/schemas/calendarapi.SECFootnotes"
type: array
form_type:
+ example: "4"
type: string
html_url:
+ example: https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=0000320193
type: string
id:
+ example: 64a1b2c3d4e5f6789012345
type: string
is_10b5:
+ example: true
type: boolean
owner:
$ref: "#/components/schemas/calendarapi.Owner"
remaining_shares:
+ example: "125000"
type: string
remaining_underlying_shares:
+ example: "50000"
type: string
traded_percentage:
+ example: "15.5"
type: string
transactions:
items:
$ref: "#/components/schemas/calendarapi.Transaction"
type: array
updated:
+ example: 1704758400
type: integer
type: object
calendarapi.InsiderTransactionFilingDoc:
@@ -3192,18 +3219,25 @@ components:
calendarapi.Owner:
properties:
insider_cik:
+ example: "0001234567"
type: string
insider_name:
+ example: Tim Cook
type: string
insider_title:
+ example: Chief Executive Officer
type: string
is_director:
+ example: true
type: boolean
is_officer:
+ example: true
type: boolean
is_ten_percent_owner:
+ example: false
type: boolean
raw_signature:
+ example: /s/ Tim Cook
type: string
type: object
calendarapi.RatingJSON:
@@ -3249,8 +3283,10 @@ components:
calendarapi.SECFootnotes:
properties:
id:
+ example: F1
type: string
text:
+ example: Shares were sold pursuant to a Rule 10b5-1 trading plan
type: string
type: object
calendarapi.SplitJSON:
@@ -3264,40 +3300,58 @@ components:
calendarapi.Transaction:
properties:
acquired_or_disposed:
+ example: D
type: string
conversion_exercise_price_derivative:
+ example: "150.25"
type: string
date_deemed_execution:
+ example: 2024-01-09T00:00:00Z
type: string
date_exercisable:
+ example: 2024-01-09T00:00:00Z
type: string
date_expiration:
+ example: 2025-01-09T00:00:00Z
type: string
date_transaction:
+ example: 2024-01-09T00:00:00Z
type: string
is_derivative:
+ example: false
type: boolean
ownership:
+ example: Direct
type: string
post_transaction_quantity:
+ example: "250000"
type: string
price_per_share:
+ example: "150.25"
type: string
remaining_underlying_shares:
+ example: "50000"
type: string
security_title:
+ example: Common Stock
type: string
shares:
+ example: "10000"
type: string
transaction_code:
+ example: S
type: string
transaction_id:
+ example: TXN-20240109-001
type: string
underlying_security_title:
+ example: Common Stock
type: string
underlying_shares:
+ example: "10000"
type: string
voluntarily_reported:
+ example: false
type: boolean
type: object
servers:
diff --git a/es/openapi/logo-api_api.spec.yml b/es/openapi/logo-api_api.spec.yml
index 54f8eb35..8c956185 100644
--- a/es/openapi/logo-api_api.spec.yml
+++ b/es/openapi/logo-api_api.spec.yml
@@ -157,6 +157,13 @@ paths:
name: max_width
schema:
type: string
+ - description: Si es true, agrega el parámetro de consulta flutter_compatible=true
+ a las URL SVG para garantizar la compatibilidad con la biblioteca
+ flutter_svg.
+ in: query
+ name: flutter_compatible
+ schema:
+ type: boolean
responses:
"200":
description: Respuesta exitosa con un array de objetos LogoClientResponse en el
diff --git a/es/openapi/newsquantified-api_api.spec.yml b/es/openapi/newsquantified-api_api.spec.yml
index 9c4285c7..c9c713b7 100644
--- a/es/openapi/newsquantified-api_api.spec.yml
+++ b/es/openapi/newsquantified-api_api.spec.yml
@@ -121,8 +121,6 @@ components:
type: object
bzhttp.ErrorCode:
enum:
- - unsupported_message_type
- - database_query_error
- json_unmarshaling_error
- no_data_found
- no_matching_endpoint
@@ -134,10 +132,10 @@ components:
- failed_dependency
- service_unavailable
- upstream_api_error
+ - unsupported_message_type
+ - database_query_error
type: string
x-enum-varnames:
- - ErrorUnsupportedMessageType
- - ErrorDBQueryError
- ErrorJSONUnmarshaling
- ErrorNoDataFound
- ErrorNoMatchingEndpoint
@@ -149,6 +147,8 @@ components:
- ErrorFailedDependency
- ErrorServiceUnavailable
- ErrorUpstreamAPIError
+ - ErrorUnsupportedMessageType
+ - ErrorDBQueryError
bzhttp.Resp:
properties:
data:
diff --git a/gt-lock.json b/gt-lock.json
index 81eb663a..465d75f0 100644
--- a/gt-lock.json
+++ b/gt-lock.json
@@ -45,19 +45,19 @@
},
"ee9d261f48c27ab14e838f7f43c24b328cf08468f1e9be291fe2d51df7db3f56": {
"es": {
- "updatedAt": "2026-01-16T09:12:32.840Z",
+ "updatedAt": "2026-01-29T10:33:18.820Z",
"postProcessHash": "a54a24a91226e02411065b7800d4ab13029836319cad2456677076f88629aa99"
},
"ja": {
- "updatedAt": "2026-01-16T09:12:32.843Z",
+ "updatedAt": "2026-01-29T10:33:18.824Z",
"postProcessHash": "6fd5617957de38bf025330c33db225310431dc5792a4278cbe517f932da7ba52"
},
"ko": {
- "updatedAt": "2026-01-16T09:12:32.932Z",
+ "updatedAt": "2026-01-29T10:33:18.919Z",
"postProcessHash": "c6f2ec7a1b3c137cf21b59dcb8fe928238fc905a696bfa6c079129b86d9dc4ff"
},
"ar": {
- "updatedAt": "2026-01-16T09:12:32.954Z",
+ "updatedAt": "2026-01-29T10:33:18.942Z",
"postProcessHash": "0014a34b977bea17d8f1bc5fa363550f612384f9b076c95bab01b9d8c6e2ac82"
}
}
@@ -105,19 +105,19 @@
},
"4206787938097b3f7cf80ae91a7a2432655b9d24e8ff01a6691a3240b69dc573": {
"es": {
- "updatedAt": "2026-01-16T09:12:32.457Z",
+ "updatedAt": "2026-01-29T10:33:18.611Z",
"postProcessHash": "03ca70cf659879498aeae3eb522c6a1a283f5be9473e494ea4e84d9d747d4385"
},
"ja": {
- "updatedAt": "2026-01-16T09:12:32.458Z",
+ "updatedAt": "2026-01-29T10:33:18.613Z",
"postProcessHash": "0eb4679f1c31253d8c49c9e8e5f035b4942c17cd61d95ccbe6680f9ddfbf34af"
},
"ko": {
- "updatedAt": "2026-01-16T09:12:32.633Z",
+ "updatedAt": "2026-01-29T10:33:18.752Z",
"postProcessHash": "76c50a7b81e3fe72c1b6f4dd11b2db5fc585cc5abbb0acc3edb6ae3a8532eff8"
},
"ar": {
- "updatedAt": "2026-01-16T09:12:32.672Z",
+ "updatedAt": "2026-01-29T10:33:18.778Z",
"postProcessHash": "1a0fe0c73feeff622cd8329aa4e86f092ec06d954fb76d0fdeb70fba9e2415c2"
}
}
@@ -167,19 +167,19 @@
},
"969d424d653fae1ac27c787d554ba799b678a6763b43436657baa1e98165f479": {
"es": {
- "updatedAt": "2026-01-16T09:12:32.401Z",
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diff --git a/ja/api-reference/guides/consensus-ratings-explained.mdx b/ja/api-reference/guides/consensus-ratings-explained.mdx
new file mode 100644
index 00000000..7cb7fc0f
--- /dev/null
+++ b/ja/api-reference/guides/consensus-ratings-explained.mdx
@@ -0,0 +1,320 @@
+---
+title: "コンセンサスレーティングの仕組み"
+description: "個々のアナリスト推奨から Benzinga がコンセンサスレーティングをどのように算出するかを解説します"
+---
+
+
+ ## 概要
+
+
+コンセンサスレーティングは、個々のアナリストのレーティングを集約し、特定銘柄に対するアナリストの見方を単一の分かりやすい指標として示すものです。本ガイドでは、コンセンサスレーティングをどのように算出しているかを説明し、適切な投資判断に役立てることを目的としています。
+
+
+ ## コンセンサス・レーティングの算出方法
+
+
+
+ ### ステップ 1: アナリスト・レーティングの収集
+
+
+特定の銘柄について、利用可能なすべてのアナリスト・レーティングを収集します。各レーティングには以下が含まれます:
+
+* **アナリスト名** と **所属機関名**
+* **レーティング**(例:Strong Buy、Buy、Hold、Sell、Strong Sell)
+* **目標株価** - アナリストが予測する株価
+* **更新日** - レーティングが発行された日
+
+
+ ### ステップ 2: 一意のアナリストごとのフィルタリング
+
+
+精度を確保し二重計上を防ぐため、各アナリストと証券会社の一意な組み合わせごとに、**最新のレーティングのみ**を集計対象とします。
+
+**例:** Morgan Stanley のアナリスト John Doe が同じ銘柄を複数回レーティングしている場合でも、計算に含まれるのは最新のレーティングのみです。
+
+
+ 有効な目標株価が設定されていないレーティングは分析対象から除外されます。
+
+
+
+ ### ステップ3: レーティングの分類
+
+
+アナリスト・レーティングは、次の5つの標準的なカテゴリに分類されます。
+
+| カテゴリ | 数値 |
+|----------|---------------|
+| **強い売り推奨** | 1 |
+| **売り** | 2 |
+| **ホールド** | 3 |
+| **買い** | 4 |
+| **強い買い推奨** | 5 |
+
+
+ ### ステップ 4: レーティングの集計
+
+
+各カテゴリーごとのアナリスト数を数えます。
+
+**集計例:**
+
+* Strong Buy: アナリスト 8人
+* Buy: アナリスト 12人
+* Hold: アナリスト 5人
+* Sell: アナリスト 2人
+* Strong Sell: アナリスト 1人
+
+**合計:** 重複を除くアナリスト 28人
+
+
+ ### ステップ5: コンセンサスレーティング値の計算
+
+
+コンセンサスレーティング値は、加重平均で計算されます。
+
+```
+Consensus Value = (Strong Buy × 5 + Buy × 4 + Hold × 3 + Sell × 2 + Strong Sell × 1) / Total Analysts
+```
+
+**先ほどの例を用いると:**
+
+```
+Consensus Value = (8×5 + 12×4 + 5×3 + 2×2 + 1×1) / 28
+Consensus Value = (40 + 48 + 15 + 4 + 1) / 28
+Consensus Value = 108 / 28
+Consensus Value = 3.86
+```
+
+
+ ### ステップ 6: コンセンサス評価の判定
+
+
+コンセンサス値は最も近い整数に四捨五入され、以下の評価にマッピングされます:
+
+| コンセンサス値 | 標準評価 | 簡易評価 |
+|----------------|----------|----------|
+| **1** | 強い売り | 売り |
+| **2** | 売り | 売り |
+| **3** | ホールド | ホールド |
+| **4** | 買い | 買い |
+| **5** | 強い買い | 買い |
+
+**この例では:** 3.86 は **4** に四捨五入され、コンセンサス評価は **「買い」** になります。
+
+***
+
+
+ ## 表示設定
+
+
+
+ ### 標準レーティングと簡略レーティング
+
+
+表示形式は次の 2 種類があります。
+
+**標準(5 区分):**
+
+* Strong Sell、Sell、Hold、Buy、Strong Buy
+
+**簡略(3 区分):**
+
+* Sell(Strong Sell と Sell をまとめたもの)
+* Hold
+* Buy(Buy と Strong Buy をまとめたもの)
+
+
+ ### 件数表示と割合表示
+
+
+**件数表示:**
+各カテゴリに属するアナリストの実人数を表示します。
+
+**割合表示:**
+各カテゴリに属するアナリストの比率を、合計が100%になるように整数(パーセント)に丸めて表示します。
+
+**例:**
+
+* Buy: 71%(アナリスト20人)
+* Hold: 18%(アナリスト5人)
+* Sell: 11%(アナリスト3人)
+
+***
+
+
+ ## 目標株価の計算
+
+
+
+ ### コンセンサス目標株価
+
+
+全アナリストの目標株価の平均値:
+
+```
+Consensus PT = Sum of all Price Targets / Number of Analysts
+```
+
+
+ ### 最低および最高目標株価
+
+
+* **最低目標株価:** すべてのアナリストの中で最も低い目標株価
+* **最高目標株価:** すべてのアナリストの中で最も高い目標株価
+
+これらにより、アナリスト予想のレンジ(幅)を把握できます。
+
+***
+
+
+ ## 指標の概要
+
+
+
+ ### アナリスト総数 vs. ユニークアナリスト数
+
+
+* **アナリスト総数:** 収集されたすべてのレーティング(同一アナリストからの複数レーティングを含む場合があります)
+* **ユニークアナリスト数:** アナリストとファームの一意な組み合わせ数(計算に使用)
+
+ユニーク数を用いることで、各アナリストの意見が一度だけカウントされ、より正確なコンセンサスが得られます。
+
+***
+
+
+ ## 重要な考慮事項
+
+
+
+ ### レーティングの最新性
+
+
+コンセンサス・レーティングは、最新のアナリストの見解を反映しています。**Updated At** タイムスタンプは、コンセンサスが最後に算出された時刻を示します。
+
+
+ ### カバレッジの深さ
+
+
+コンセンサスレーティングは、より多くの異なるアナリストによる評価に基づいているほど信頼性が高くなります。担当アナリストが 3~5 名に満たない銘柄は、コンセンサスレーティングが相対的に不安定になる可能性があります。
+
+
+ ### レーティング分布
+
+
+コンセンサスだけでなく、レーティングの分布にも目を向けてください。半数が Buy、半数が Sell の銘柄はコンセンサスとしては「Hold」になりますが、これはアナリスト間に大きな見解の相違があることを覆い隠してしまいます。
+
+***
+
+
+ ## 計算例のウォークスルー
+
+
+**XYZ Corporation** を題材に、計算の一連の手順を順を追って見ていきます。
+
+
+ ### 元データ(アナリスト10名)
+
+
+| アナリスト | 会社 | レーティング | 目標株価 |
+|---------|------|--------|--------------|
+| Analyst A | Goldman Sachs | 買い | $150 |
+| Analyst B | JP Morgan | 強い買い | $165 |
+| Analyst C | Morgan Stanley | 買い | $155 |
+| Analyst D | Bank of America | 中立 | $140 |
+| Analyst E | Citigroup | 強い買い | $170 |
+| Analyst F | Wells Fargo | 買い | $152 |
+| Analyst G | Deutsche Bank | 中立 | $145 |
+| Analyst H | Barclays | 売り | $130 |
+| Analyst I | UBS | 買い | $158 |
+| Analyst J | Credit Suisse | 強い買い | $168 |
+
+
+ ### ステップごとの計算
+
+
+**1. アナリストを一意なものに絞り込み:** 10件すべてのレーティングがユニーク(10人のアナリストがそれぞれ1件ずつ評価)
+
+**2. 件数を集計:**
+
+* Strong Buy: 3
+* Buy: 4
+* Hold: 2
+* Sell: 1
+* Strong Sell: 0
+
+**3. コンセンサス値を算出:**
+
+```
+(3×5 + 4×4 + 2×3 + 1×2 + 0×1) / 10 = (15 + 16 + 6 + 2 + 0) / 10 = 3.9
+```
+
+**4. コンセンサス評価を算出:** 3.9 を四捨五入すると **4 = 「Buy(買い)」**
+
+**5. 目標株価:**
+
+* コンセンサス目標株価: (150+165+155+140+170+152+145+130+158+168) / 10 = **$153.30**
+* 最低目標株価: **$130**
+* 最高目標株価: **$170**
+
+**6. 割合の内訳:**
+
+* Buy(買い): 70%(Strong Buy(強い買い)30% + Buy(買い)40%)
+* Hold(中立): 20%
+* Sell(売り): 10%
+
+
+ ### 最終コンセンサスレーティングの出力
+
+
+```json
+{
+ "consensusRating": "BUY",
+ "consensusRatingValue": 3.9,
+ "consensusPriceTarget": 153.30,
+ "priceTargetLow": 130,
+ "priceTargetHigh": 170,
+ "totalAnalysts": 10,
+ "uniqueAnalysts": 10,
+ "lastUpdated": "2026-01-16T10:30:00Z"
+}
+```
+
+***
+
+
+ ## よくある質問
+
+
+
+
+ 同じ証券会社のアナリストがレーティングを複数回更新した場合、合計数にはそのすべての更新が含まれますが、計算には最新のもののみが使用されます(ユニーク数に反映されます)。
+
+
+
+ コンセンサスレーティングは、新しいアナリストレーティングが公開されるか、既存のレーティングが更新されるたびに再計算されます。
+
+
+
+ アナリストによるカバレッジがない場合、コンセンサスレーティングは算出できません。その銘柄にはコンセンサスデータは表示されません。
+
+
+
+ いいえ。コンセンサスは、アナリストが新しいレーティングを出すか、既存のレーティングを更新したときにのみ変化します。
+
+
+
+***
+
+
+ ## 関連エンドポイント
+
+
+
+
+ 株式のコンセンサスレーティングデータを取得します
+
+
+
+ 個々のアナリストによるレーティングを取得します
+
+
\ No newline at end of file
diff --git a/ja/changelog/roadmap.mdx b/ja/changelog/roadmap.mdx
new file mode 100644
index 00000000..50192015
--- /dev/null
+++ b/ja/changelog/roadmap.mdx
@@ -0,0 +1,120 @@
+---
+title: "ロードマップ"
+description: "今後の開発予定をご確認ください。"
+---
+
+
+ {/* 作業中 */}
+
+
+
+
+
+
+
トランスクリプトの PDF 版
+
カンファレンスコールのトランスクリプトを、ダウンロード可能な PDF 形式で提供します。
+
+
+
+
トランスクリプトのヒストリカルデータ拡充
+
カンファレンスコールのトランスクリプトについて、ヒストリカルデータの提供範囲を拡大します。
+
+
+
+
+ {/* 計画中 */}
+
+
+
+
+
+
+
監視ダッシュボードの拡張
+
すべてのサービス提供内容を監視するクライアント向けダッシュボードです。
+
+
+
+
翻訳エンジンの最適化
+
翻訳サービスのパフォーマンスおよび品質を向上させます。
+
+
+
+
ニュース向け Security Master 連携
+
社内の Security Master を統合し、クエリ機能を強化します。
+
+
+
+
+ {/* バックログ */}
+
+
+
+
+
+
+
Go SDK
+
公式の Go クライアントライブラリです。
+
+
+
+
すべての calendar 用ウェブソケット
+
すべての calendar エンドポイント向けにリアルタイムストリーミングを提供します。
+
+
+
+
+ {/* 完了 */}
+
+
+
+
+
+
+
自社セキュリティマスター
+
データ整合性を高めるための集中管理型セキュリティマスター。
+
+
+
+
TCP ストリーミング
+
リアルタイムデータ向けの低遅延 TCP 接続。
+
+
+
+
カンファレンスコールトランスクリプト
+
話者情報付きの完全なトランスクリプト。
+
+
+
+
Webhook エンジン
+
プッシュ型のデータ配信。
+
+
+
+
Java SDK
+
公式の Java クライアントライブラリ。
+
+
+
+
+
+---
+
+
+新機能のご要望はありませんか?[GitHub で issue を作成](https://github.com/Benzinga/benzinga-docs/issues/new)するか、[こちらからお問い合わせ](mailto:licensing@benzinga.com)ください。
+
\ No newline at end of file
diff --git a/ja/introduction/architecture.mdx b/ja/introduction/architecture.mdx
index dc7ca66d..6fe0cc60 100644
--- a/ja/introduction/architecture.mdx
+++ b/ja/introduction/architecture.mdx
@@ -1,97 +1,518 @@
---
-title: アーキテクチャ
-description: スケーラブルなリアルタイム金融データ基盤
-icon: database
+title: "インフラアーキテクチャ"
+description: "Benzinga の 99.9% API 可用性を支えるエンタープライズグレードのインフラと、世界水準の監視・信頼性"
+icon: microchip
---
-Benzinga は、世界中のアプリケーションやサービスにリアルタイムの金融データを配信するために設計された、堅牢で高いスケーラビリティを備えた API 基盤を提供しています。速度と可用性を重視した Benzinga のアーキテクチャは、ユーザーが必要なときに常に信頼性が高く低レイテンシなデータを受け取れるよう、**Go (Golang)**、**Kafka**、**ウェブソケット**、**Elasticsearch**、**マイクロサービス** などの最新技術を活用しています。
+
+ Benzinga のインフラは **99.9% の可用性** を実現するよう設計されており、アプリケーションが常時、信頼性の高いリアルタイム金融データを受信できるようになっています。本番環境は実運用で実証されており、全面的に監視され、平日24時間(24/5)のオンコールエンジニアリングサポートによって支えられています。
+
-