Wednesday, November 30, 2016

Cuenta De Práctica De Corretaje De Opciones Binarias

Por qué abrir una cuenta de práctica de opciones binarias?


Acaban de oír hablar de opciones binarias y no saben nada acerca de cómo hacer un comercio? Entonces antes de empezar a gastar dinero real apuestas en acciones que necesita para conseguir un poco de práctica. Para ello tendrá que abrir una cuenta de práctica de opciones binarias, o también conocida como una cuenta de demostración. Están disponibles en casi todas las plataformas de negociación por ahí y tendrán todas las características de una cuenta de dinero real regular tendría.


Cuánto tiempo se puede utilizar una cuenta de práctica de opciones binarias


El tiempo que debe pasar con las cuentas de práctica depende de cuánto tiempo le lleva a aprender a ejecutar operaciones de una manera rentable. No tendría sentido hacer un depósito si no sabes cómo obtener ganancias. A menos que usted sólo quería apostar unos dólares por la diversión de la misma. No hay nada malo en eso.


Para aquellos de ustedes que no pueden llegar a una estrategia ganadora, no importa cuánto tiempo tratan de buscar la orientación de los profesionales. Ni siquiera las plataformas de comercio de opciones binarias más fáciles de usar por ahí pueden ayudarle a ser rentable si su estrategia no funciona.


Abrir más de una cuenta de práctica de opciones binarias


Además de llegar a una estrategia de una cuenta de la práctica de opciones binarias también le permite ver lo que una plataforma de comercio será como antes de comprometerse a ella. Puede probar todas las características con el máximo detalle que desee. Esto le da la oportunidad de comparar las diferentes plataformas de negociación con el fin de ver cuál le gusta usar más.


Para una amplia selección de opciones binarias plataformas de negociación consulte nuestro sitio web. Lea algunas reseñas y descargue el software para que sea su opción de negociación de opciones binarias.


Tuesday, November 29, 2016

Forex Brunei

Forex Cargo Brunei


Forex Cargo Brunei, una empresa local de transporte de mercancías está ahora en su 10 º año de servicio a la comunidad filipina en Brunei. La compañía ahora está ofreciendo un coste de envío reducido y Bulilit libre o caja pequeña como nuestro gesto de agradecer a nuestros clientes leales para su ayuda continua.


Por cada caja de 1 Mega o Jumbo enviada por un remitente, 1 caja de Bulilit (17x17x9 pulgadas) es gratis. Por cada 2 Mega, 2 Jumbo o 1 Mega y Jumbo enviados por un remitente, una caja pequeña (24x24x10) es gratuita. Las dos cajas deben enviarse juntas en el mismo envío, pero pueden enviarse a diferentes destinatarios. Se cobra una tarifa de producción de caja por cada caja Buliliit o caja pequeña.


No hay límite de peso, garantizamos la entrega puerta a puerta a las Filipinas, y proporcionamos recibos de reconocimiento y fotografías de todas las entregas.


Dedicamos un contenedor a todo el envío. No co-carga. Limpiaremos nuestro propio contenedor.


Forex ha estado en el negocio de puerta a puerta por más de 27 años. Lo que comenzó como una empresa de remesas en Washington DC, EE. UU., ha crecido hasta convertirse en la empresa de carga puerta a puerta líder en todo el mundo.


En todo el mundo, Forex ha mantenido una reputación como una empresa rápida, eficiente y confiable.


Comerciante forex brunei


Supongo que el sueño de la mayoría de los comerciantes es convertirse en un millonario trading forex. Pero es realmente posible? Por supuesto. ¡Absolutamente! Sucede todos los días. Aunque, si quieres ser los porcentajes, me gustaría para cada nuevo comerciante de divisas que se convirtió en millonario hoy, tienes otras 10 personas que se han estrellado sus cuentas. Entonces, por qué hay una gran diferencia?


Bueno, para tener éxito en el comercio de divisas, usted tiene que ser capaz de pensar fuera de la caja. Eso es un problema para la mayoría.


Por ejemplo la mayoría de los comerciantes aman trucos. No quiero decir esto como una bajada, pero lo hacen. Les encanta cosas como robots forex. Para los que no saben, los robots forex son básicamente automatizados sistemas de divisas que el comercio sin ninguna interacción humana. La mayoría de los comerciantes también aman estos indicadores que les dicen exactamente cuándo comprar y vender.


Esto no es lo que el comercio se supone que debe ser.


En pocas palabras, eso es lo que separa a los millonarios de la divisa de los comerciantes que bloquean sus cuentas. Los comerciantes que están bloqueando su cuenta están buscando los atajos. Están buscando algo o alguien más para decirles lo que el mercado está haciendo. Los ricos comerciantes simplemente saben dónde va el precio. No tiene nada que ver con atajos. Simplemente entienden los mercados.


La mejor manera que puedo describir es, los comerciantes pueden mirar un gráfico simple de precios sin indicadores, y sólo tiene sentido para ellos. Mientras que los comerciantes luchando han inundado completamente sus cartas con cada juguete a su disposición.


John Templeton ha sido un exitoso comerciante de divisas después de aprender a cambiar la acción del precio. Una vez que entendió que todo lo que necesitaba para el comercio de divisas estaba en un gráfico simple sin indicadores, sus ganancias se dispararon.


Pronto voy a estar actualizando en mi análisis de forex y voy a mostrar cómo i cambió en mi cuenta diaria y también se agregó una lección valiosa de forex que probablemente tendría que aprender para su diario de comercio de divisas para quedarse música gente


sobre mi


Pip Drifter He estado involucrado activamente con la industria financiera y particularmente los mercados de Forex desde hace varios años y gané más y más experiencia y conocimiento cada día. He encontrado una fascinación para el mercado de la divisa siempre dinámico. El mercado Forex es el más grande de todos los mercados financieros con volúmenes diarios con un promedio de más de USD 3 billones. Cualquier analista o comerciante de seguimiento de los mercados financieros siempre tiene algo que aprender de los mercados. Creo que nadie podría haber dominado los mercados. Soy uno de esos estudiantes que analiza las principales divisas cruzadas. Solía ​​ser un comerciante robótico utilizando mi EA personal que he creado yo, pero luego pasar a la negociación manual en la actualidad ahora uso una mezcla de patrones de onda Elliott, patrones de gráfico clásico y métodos clásicos de análisis técnico para proyectar mis puntos de vista en los mercados . No todavía un millonario aunque hehe pero todavía hizo un syukran del ingreso de 5 dígitos a ALLAH el Todopoderoso para bendecirme con todo el conocimiento de la divisa. Lihat perfil lengkap saya


Forex Trading Curso en línea Brunei


Forex Trading en Brunei comienza en el año 2000, con un montón de configuración de corredor en línea. Brunei es un mercado en desarrollo con el 95% de los comerciantes pierden dinero y el 95% de Hedge fondos perdieron dinero. Sin embargo, la generación "Y" es el sector de crecimiento clave en el mercado de divisas, ya que necesitan tener Forex Trading como carrera de la vida, así como el costo de vida en Brunei está recogiendo.


De nuestra observación, un montón de comerciante aprender de EE. UU. y tratar de vender el curso que no están maduras y bien probado habilidades y mucho dinero de los comerciantes perdieron especialmente los jóvenes que sólo para probar su "suerte". Un montón de "Gurú" en Brunei es auto proclamar como experto y las tácticas utilizadas fue anticuado y un montón de intentar un error en Brunei en este momento.


El servicio de gestión de fondos de Forex está en la demanda caliente porque una gran cantidad de fondos de Hedge estalló que sin una adecuada gestión de riesgos y más de exponer al mercado en la crisis de subprime en 2008. Nuestro retorno sobre la gestión de fondos de Forex es alrededor de 5% -10% por mes.


Mucha Generación "Y" y ama de casa participan en Forex Trading y pero realmente necesitan un buen maestro para guiarlos paso a paso.


Somos fuertes en la educación y en busca de un socio de empresa conjunta en Brunei para que podamos llevar a cabo los programas de educación adecuada y trabajar con ellos en Forex Business y crecer en cada esquina en Asia Continente.


Contáctenos si intenta encontrar un socio fuerte, sincero y viable en Brunei.


Agregame en facebook & amp; Skype para forex ...


"El spread de pip más bajo en Forex World"


Andeerson Wong Director de ASIA Forex Fund Management Team Master IB en Asia. Móvil. + 6013-3194134. Identificación del skype. Andersonwongkl Wechat, Whatsapp & amp; Viber. Andeerson Wong +60133194134 Twitter. AndeersonForex Google Talk & amp; Email. Andersonw3@gmail. com Añádeme en Facebook. Http://www. facebook. com/Forexautopilot Sitio web. http://forexcoursemalaysia. wix. com/forexcourse-malaysia


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Bandas De Bollinger Amazonas

Kundenrezensionen


Las Bandas de Bollinger son, sin duda, uno de los indicadores más utilizados hoy en día. Debido a que su fórmula de cálculo tiene en cuenta la volatilidad que son capaces de adaptarse a las condiciones cambiantes del mercado y proporcionar al inversor con altos y bajos relativos.


El libro comienza con una rápida visión general de los fundamentos de la negociación - que uno tiene que ser creativo en el desarrollo de un enfoque comercial que se ajuste a sus propias necesidades y ser disciplinado lo suficiente como para seguir esas reglas. Si bien ciertamente no es una explicación exhaustiva sobre la psicología comercial es un buen punto de partida y muestra que el autor sabe lo que está escribiendo.


La segunda parte cubre lo básico sobre las bandas. Su historia y cómo se calculan, así como una palabra rápida sobre las estadísticas generales y por qué no todos estos conceptos son aplicables a los mercados. Para mí esta fue la parte más interesante del libro. La historia de las bandas comerciales y cómo el Sr. Bollinger llegó a inventar sus propias bandas le da al lector una clara comprensión de lo que son las bandas de Bollinger y lo que no son.


La tercera parte muestra cómo utilizar las bandas de Bollinger en conjunto con la formación de gráficos del análisis técnico clásico como W-bottoms y M-tops. No encontré las estrategias reales presentadas en el libro muy útil. El autor trata los patrones de gráficos técnicos como si fueran algo como hechos incontenibles, mientras que en realidad son bastante vagos y no tienen nada como una definición clara. No había mucho de sus métodos comerciales que podría incorporar en mi propio comercio.


El libro es un poco demasiado caro para lo que tiene que ofrecer, pero es sin embargo interesante para todos los que utilizan las bandas de Bollinger.


Lesen Sie weiter


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Una introducción muy simple a las bandas de Bollinger


Si usted ha oído hablar de bandas de Bollinger, pero no saben lo que son, Bollinger en bandas de Bollinger será una buena introducción simple.


Por otro lado, es probable que no necesite saber qué son las Bandas de Bollinger a menos que planee negociar acciones o productos en lugar de tener dinero en efectivo, bonos o fondos comunes de inversión indexados. Incluso entonces, el tema es opcional. El libro hace referencia a un sitio Web al que puede acudir para obtener ideas de inversión basadas en este tipo de análisis técnico.


Básicamente, hay tres Bandas Bollinger simultáneas. El medio es generalmente una media móvil de 20 meses (hora, día, semana o mes) del precio de la acción (u otro índice o mercancía que está siguiendo). La superior está separada del medio por algo entre 1,5 y 2,5 desviaciones estándar. La inferior está separada por la cantidad equivalente. La diferencia relativa se basa en el número de períodos que está utilizando. Las ecuaciones clave involucradas se imprimen en dos hojas de cartulina que se pueden extraer del libro para utilizarlas separadamente del libro o como marcas de libro.


Utiliza Bollinger Bands para crear una medida relativa del precio de las acciones. John Bollinger le advierte que usted tiene que usar algún arte en la construcción de las bandas para ajustarse al conjunto de datos que está utilizando. Una vez en su lugar, Bollinger Bands puede utilizarse para ayudar a detectar cambios en tendencias, identificar tendencias que probablemente continúen y generar ideas para posibles inversiones.


Le recomendamos que utilice Bollinger Bands junto con una amplia gama de otros datos técnicos y análisis fundamentales. La discusión de cuales otros datos técnicos a utilizar es absolutamente buena. La discusión del análisis fundamental es en su mayoría ausente.


Aquellos que estén interesados ​​en una historia de análisis de inversión técnica encontrarán en este libro una referencia amigable al mejor trabajo sobre "sobres" en torno a los precios durante las últimas tres décadas. El texto se complementa con un gran número de gráficos para apoyar los puntos clave. También hay un extenso glosario.


Si usted rápidamente comprender el análisis técnico, puede saltar a las secciones cerca del final donde se proponen tres métodos de inversión. Si haces eso, el libro parecerá un artículo largo para ti.


Después de leer el libro, miré todas las acciones de mi cartera usando Bollinger Bands, y encontré que Bollinger Bands no habría sido muy útil para detectar oportunidades de compra en los últimos dos años. Bollinger Bands habría señalado el riesgo de pérdida en tres situaciones. Otras herramientas técnicas habrían obtenido más puntos de compra / venta en estas acciones que Bollinger Bands. No estoy seguro de que los usaré después de leer el libro.


Dándose cuenta de que se puede superar el 90 por ciento de los gerentes de dinero profesional en el rendimiento total, simplemente por la celebración de fondos de inversión indexados en los próximos diez años, cuánto esfuerzo quiere poner en el aprendizaje de más herramientas técnicas?


Añade conocimientos que te hacen más sabio, en lugar de simplemente más ocupado!


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Técnicas para dominar las bandas de Bollinger: Trading Bollinger Bands like the Pros (Ingles) Copertina flessibile & ndash; Stampa grande, 8 mar 2013


Tabla de Contenidos Introducción Día Bollinger Bandas Bollinger Bandas con RSI Soporte y Resistencia con Bollinger Bands Bandas de Tendencia con Bollinger Bands Bandas de Bollinger en una Cartera Siguiendo el Plan de Trading Risk & Money Management Ejemplo Hipotético de una Cartera Introducción John Bollinger desarrolló el concepto de bandas de Bollinger en los 1980. Las bandas de Bollinger son bandas de volatilidad colocadas por encima y por debajo de un promedio móvil con un múltiplo de un estándar de desviación. La longitud de este promedio móvil puede variar dependiendo del tiempo del comerciante y de la sensibilidad. El estándar para el dimensionamiento de la posición (barras diarias) es generalmente 20 períodos. Sin embargo, para el día de comercio se sugiere que sea mucho menos. Puede ser tan poco como 10 períodos. Esto es algo que el comerciante individual puede probar dependiendo de sus propias preferencias personales. La volatilidad se basa en la desviación estándar, que cambia a medida que la volatilidad aumenta y disminuye. Las bandas se ensanchan automáticamente cuando la volatilidad aumenta y se estrecha cuando la volatilidad disminuye. La robustez de las bandas de Bollinger hace que este indicador sea aplicable a todos los marcos de tiempo y todos los mercados. El endurecimiento de las bandas es a menudo utilizado por los comerciantes como una indicación temprana de que la volatilidad está a punto de aumentar fuertemente. Cuanto más se acercan los precios a la banda superior, más sobrecompra el mercado se hace, y cuanto más cerca los precios se mueven a la banda inferior, más oversold el mercado se convierte. Sin embargo los mercados pueden permanecer overbought y oversold por períodos de tiempo largos. En este breve ebook vamos a discutir varias maneras con el fin de utilizar las bandas de Bollinger en la medida de 1. Day Trading con Bollinger Bands 2. Sobreventa y Overbought con reversiones RSI 3. Soporte y resistencia 4. Potenciales de ruptura 5. Cómo construir una tendencia siguiente portafolio


Leggi di più Leggi meno


Descrizione prodotto


L'autore


Andrew Abraham es un asesor comercial de materias primas en la empresa Abraham Investment Management, así como el autor del libro The Bible of Trend Following - Los comerciantes profesionales compuesto de dinero y gestionar los riesgos. Se especializa en seguimiento sistemático y mecánico de tendencias, utilizando técnicas rigurosas de gestión de riesgos para limitar pérdidas y capturar un pequeño número de tendencias principales para generar retornos. Su sitio web es TrendFollowingMentor. com. Abraham ha estado negociando su cuenta propietaria desde 1994, así como invertir con otros asesores de comercio de materias primas desde mediados de los años noventa. Ha sido citado en numerosas publicaciones comerciales, así como ha escrito para el análisis técnico de acciones y materias primas, revista de asesor de inversiones, la revista Futures y muchos otros. Abraham ha hablado en los eventos de Bloomberg, presentado en la Expo Traders, Emerging Managers Expo, CTA Expo y otras conferencias de la industria y ha hecho webinars para Reuters Metastock.


No es necesario tener un Kindle. Scarica una delle app Kindle Kindle por Kindle y Kindle.


Para obtener más información, escriba el e-mail o el numero de celulare.


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ETF que negocia con las vendas de Bollinger & reg; (Connors Research Trading Strategy Series) (Versión en inglés) Formato Kindle


Usted negocia ETFs con las vendas de Bollinger?


Si lo hace, usted estará interesado en esta nueva investigación de la serie Connors Research Trading Strategy. Aplicamos las rigurosas reglas de la prueba histórica cuantificada de Connors y las superamos con Bollinger Bands & # xae; & ETF trading data.


Los resultados incluyen estrategias múltiples con un porcentaje excepcionalmente alto. La mayoría de estas estrategias muestran porcentajes históricos ganadores superiores al 80% y algunos superan el 90%.


Bollinger Bands son utilizados por cientos de miles de comerciantes de todo el mundo. Cuando se negocian correctamente, Bollinger Bands puede ser una de las estrategias más consistentes disponibles para su comercio. Y, por supuesto, los ETF también son considerados como vehículos altamente consistentes por los comerciantes. ETFs + Bollinger Bands + Quantified Back-testing de Connors Research ha creado una combinación ganadora para los comerciantes que buscan mayores niveles de precisión.


En la nueva Guía de la Serie de Estrategias de Negociación, ETF Trading con Bandas de Bollinger, Connors Research está poniendo a disposición del público un enfoque completamente sistemático y cuantificado para negociar ETFs con Bollinger Bands.


Resultados de negociación altamente consistentes


En ETF Trading con Bandas de Bollinger & # xae; Obtendrá docenas de variaciones de estrategia de Bollinger Bands que han sido correctas más del 80% del tiempo de enero de 2006 a agosto de 2012.


Y, usted verá 11 variaciones de la estrategia - utilizando órdenes de límite - con una tasa de triunfo más del 90%! (Del 92,16% al 97,67% para ser exactos).


Como usted puede saber, el comercio con bandas de Bollinger puede ser muy subjetivo. Hemos eliminado la subjetividad y cuantificado para usted docenas de exacta Bollinger Bands ETF comercio set-ups que han visto ganancias extremadamente confiables con un alto porcentaje de las operaciones con éxito.


Top 10 Bollinger Bands ETFs Resultados de la Prueba Clasificados por P / L (sin límite de entradas)


Enero 2006 - Agosto 2012 Variación Promedio. % Ganancia / Pérdida Prom. \ Vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip 5 2,18% 1,56 93,06% 6 2,17% 2,19 90,28% 7 2,01% 4,66 83,53% 8 1,96% 3,15 88,89% 9 1,75% 4,21 83,06% 10 1,68% 2,50 84,26%


Las órdenes límite se usan a menudo para mejorar la precisión. Lograr una tasa de victoria del 100% no es posible, pero aquí están las 5 más consistentes variaciones de estrategia en ETF Trading con Bollinger Bands - todos con un% de más de 95%.


Bollinger Bands ETFs Resultados de las pruebas Clasificadas por% Correcto (con entradas límite) Enero 2006 - Ago. 2012 Variación Promedio. % Ganancia / Pérdida Prom. \ Vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \


Esto es lo que recibirás.


- Las reglas comerciales exactas. Esto no es una caja negra. La divulgación completa de las reglas se le da a usted - Cómo identificar los mejores Bollinger Bands ETFs Trading set - ups --Cómo seleccionar los mejores niveles de entrada que se ajusten a su estilo de negociación --Dónde exactamente hacer sus pedidos cada día --Where Y cuándo exactamente salir de sus pedidos


Para operadores de opciones


ETF Trading con bandas de Bollinger es adecuado para usted si las opciones de comercio. Las rentabilidades históricas han sido fuertes y los comerciantes profesionales entienden el poder de aplicar opciones a su comercio de acciones. En esta guía usted podrá hacer lo mismo al combinar las Bandas de Bollinger con la negociación de opciones.


Si está buscando negociar las estrategias basadas en reglas cuantificadas más consistentes disponibles para los comerciantes de hoy, descargue "ETF Trading with Bollinger Bands & # x201d; A su Kindle.


Entendiendo las Bandas de Bollinger Paperback & ndash; Junio ​​1994


Bandas de Bollinger han sido un método popular de análisis técnico durante varios años, sin embargo, nunca ha habido una referencia completa sobre el tema disponible. La discusión y el tratamiento de las bandas de Bollinger ha sido o "omitido" en los libros recientes, que ofrecen orientación en la interpretación y uso de historias e indicadores técnicos, como "Trading for a Living" de Elder y " En el impulso del mercado "(omitido). Las Bandas de Bollinger están incluidas en muchos packabes de software diferentes, pero los manuales dan poca guía en profundidad sobre el uso y la interpretación.


El autor trabajó estrechamente con John Bollinger para poder ayudar al lector a aumentar su conocimiento de Bollinger Bands, y su uso efectivo y adecuado.


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Por un cliente el 9 de diciembre de 1998


Este es un libro muy conciso sobre las Bandas de Bollinger, que a menudo no se interpretan completamente en los libros de Análisis Técnico y aquí están bastante bien explicados. Este libro está construido con entrevistas y artículos de revistas sobre bandas de Bollinger. De todos modos, este es el único libro que sé sobre ellos.


esta opinion es util para usted? Sí No Envío de comentarios.


Gracias por tus comentarios.


Lo sentimos, no pudimos registrar su voto. Vuelve a intentarlo.


Por un cliente el 1 de julio de 2003


Esta es la mayor pérdida de dinero de todos los tiempos. Por lo que se refiere a la ausencia de este folleto. No es un libro y es mi propia culpa por comprarlo sin comprobar el número de páginas. Comprar "Bollinger on Bollinger bands & quot; En su lugar - ahora que es un buen libro.


esta opinion es util para usted? Sí No Envío de comentarios.


Gracias por tus comentarios.


Lo sentimos, no pudimos registrar su voto. Vuelve a intentarlo.


Por un cliente el 1 de julio de 2003


Esta es la mayor pérdida de dinero de todos los tiempos. Por lo que se refiere a la ausencia de este folleto. No es un libro y es mi propia culpa por comprarlo sin comprobar el número de páginas. Comprar "Bollinger on Bollinger bands & quot; En su lugar - ahora que es un buen libro.


esta opinion es util para usted? Sí No Envío de comentarios.


Gracias por tus comentarios.


Lo sentimos, no pudimos registrar su voto. Vuelve a intentarlo.


Toda la información de este pequeño folleto está disponible gratuitamente en el sitio web de Bollinger. Casi palabra por palabra.


esta opinion es util para usted? Sí No Envío de comentarios.


Gracias por tus comentarios.


Lo sentimos, no hemos podido registrar su voto. Vuelve a intentarlo.


Comentarios de los clientes más útiles sobre Amazon. com (beta)


Amazon. com: HASH (0xa8c472c4) de 5 estrellas 10 opiniones


41 de 42 personas encontraron la siguiente reseña útil


HASH (0xa8c81294) de 5 estrellas un desperdicio de dinero Sept. 8 2001


Por John Matott - Publicado en Amazon. com


Toda la información de este pequeño folleto está disponible gratuitamente en el sitio web de Bollinger. Casi palabra por palabra.


19 de 20 personas encontraron la siguiente reseña útil


HASH (0xa9305b04) de 5 estrellas No compre 1 de julio de 2003


Por un cliente - Publicado en Amazon. com


Esta es la mayor pérdida de dinero de todos los tiempos. Por lo que se refiere a la ausencia de este folleto. No es un libro y es mi propia culpa por comprarlo sin comprobar el número de páginas. Comprar "Bollinger on Bollinger bands & quot; En lugar de eso - ahora que es un buen libro.


9 de 10 personas encontraron la siguiente reseña útil


HASH (0xa8c814a4) de 5 estrellas No compre 1 de julio de 2003


Por un cliente - Publicado en Amazon. com


Esta es la mayor pérdida de dinero de todos los tiempos. Por lo que se refiere a la ausencia de este folleto. No es un libro y es mi propia culpa por comprarlo sin comprobar el número de páginas. Comprar "Bollinger on Bollinger bands & quot; En su lugar - ahora que es un buen libro.


5 de 5 personas encontraron la siguiente reseña útil


HASH (0xa8c815a0) de 5 estrellas Información obsoleta sobre Bollinger Bands 28 de marzo 2007


Por DKB - Publicado en Amazon. com


John Bollinger ha pedido a la editorial que retire este folleto de la distribución. Fue publicado en 1994 y es obsoleto e inútil. Cuando John hizo la investigación para su libro de 2001 "Bollinger on Bollinger Bands" (disponible en Amazon), desarrolló nuevas directrices para el uso de sus Bandas que hacen este folleto de 28 páginas obsoleto. Si usted está interesado en Bollinger Bands leer John Bollinger libro de 288 páginas. Es una explicación completa y completa de Bollinger Bands. Incluye tres enfoques comerciales basados ​​en Bollinger Bands y qué indicadores utilizar con las bandas para la confirmación. Bollinger en Bollinger Bands


21 de 30 personas encontraron la siguiente reseña útil


HASH (0xa8c81738) de 5 estrellas Eficiente pero muy conciso Dic 9 1998


Por un cliente - Publicado en Amazon. com


Este es un libro muy conciso sobre las Bandas de Bollinger, que a menudo no se interpretan completamente en los libros de Análisis Técnico y aquí están bastante bien explicados. Este libro está construido con entrevistas y artículos de revistas sobre bandas de Bollinger. De todos modos, este es el único libro que sé sobre ellos.


Amazon. com, Inc. (NASDAQ: AMZN) a $ 500


Amazon. com, Inc. (NASDAQ: AMZN) a $ 500


AMAZON COM cerró -57,253 a 578,097. El volumen era 39% sobre el promedio (neutral) y las vendas de Bollinger eran 31% más anchas que el normal.


Abierto Alto Bajo Cerrar Volumen___ 571.980 593.000 570.000 578.097 6.543.524


Perspectiva Técnica Corto Plazo: Neutro Plazo Medio: Bajo Largo Plazo: Alcista


Promedios móviles: 10 períodos 50 períodos 200 períodos Cerrar: 588,24 642,70 533,29 Volatilidad: 86 52 44 Volumen: 6,450,374 4,979,305 4,132,808


Los comerciantes a corto plazo deben prestar más atención a las flechas de compra / venta, mientras que los intermediarios / a largo plazo los comerciantes deben poner mayor énfasis en la tendencia alcista o bajista se refleja en la cinta inferior.


AMAZON COM cayó hoy (bajista) en volumen normal. Posibilidad de una brecha Runaway que normalmente significa una continuación de la tendencia. Existen cuatro tipos de diferencias de precios. Común, Breakaway, Fugitivo y Agotamiento. Las lagunas actúan como soporte / resistencia.


AMAZON COM está actualmente 8.4% por encima de su promedio móvil de 200 períodos y está en una tendencia a la baja. La volatilidad es extremadamente alta en comparación con la volatilidad promedio de los últimos 10 períodos. Existe una buena posibilidad de que la volatilidad disminuya y los precios se estabilicen en el corto plazo. Nuestros indicadores de volumen reflejan flujos moderados de volumen de AMZN. O (ligeramente bajista). Nuestros osciladores de pronóstico de tendencia son actualmente bajistas en AMZN. O y han tenido esta perspectiva para los últimos 34 períodos.


Momentum es un término general utilizado para describir la velocidad a la que los precios se mueven durante un período de tiempo determinado. En general, los cambios en el momento tienden a provocar cambios en los precios. Este experto muestra los valores actuales de cuatro indicadores de impulso populares.


Un método de interpretación del Oscilador Estocástico está buscando áreas de sobrecompra (por encima de 80) y áreas de sobreventa (por debajo de 20). El Oscilador Estocástico es 46.4622. Esto no es una lectura de sobrecompra o sobrevenda. La última señal fue una venta de 2 período (s).


Índice de Fuerza Relativa (RSI)


El RSI muestra áreas de sobrecompra (sobre 70) y sobreventa (por debajo de 30). El valor actual del RSI es 41.38. Esto no es un área de topping o bottoming. Una señal de compra o venta se genera cuando el RSI se mueve fuera de un área de sobrecompra / sobreventa. La última señal fue una compra de 8 período (s).


Índice de canales de productos básicos (CCI)


La CCI muestra áreas de sobrecompra (sobre 100) y sobreventa (por debajo de -100). El valor actual del CCI es -60. Esto no es un área de topping o bottoming. La última señal fue una venta de 0 período (s).


El indicador de Convergencia / Divergencia de Media Móvil (MACD) da señales cuando cruza su línea de señal de 9 periodos. La última señal fue una compra 3 período (s).


Amazon. com, Inc. (Amazon. com), incorporada el 28 de mayo de 1996, es una empresa de comercio electrónico. La empresa ofrece una gama de productos y servicios a través de sus sitios web. Los productos de la Compañía, ofrecidos a través de Sitios Web orientados al consumidor, incluyen mercancía y contenido que la Compañía compra para la reventa de vendedores y aquellos ofrecidos por vendedores de terceros. La Compañía ofrece sus propios productos, así como productos de terceros a través de diversas categorías, a través de sus sitios web minoristas ya través de sus sitios web y aplicaciones móviles. También fabrica y vende dispositivos electrónicos, incluyendo Kindle e-readers, Fire comprimidos, Fire TV, Echo y teléfonos Fire.


La Compañía ofrece programas que permiten a los vendedores vender sus productos y cumplir con los pedidos a través de los sitios web de la compañía y los propios sitios Web de los vendedores. Sirve a desarrolladores y empresas de diversos tamaños a través de Amazon Web Services (AWS), que ofrece un conjunto de servicios globales de computación, almacenamiento, base de datos, análisis, aplicaciones y despliegue. Sirve a autores y editores independientes con Kindle Direct Publishing, una plataforma en línea que permite a autores y editores independientes elegir una opción de regalías del 70% y hacer que sus libros estén disponibles en Kindle Store, junto con Amazon Publishing. También ofrece programas que permiten a los autores, músicos, cineastas, desarrolladores de aplicaciones y otros publicar y comercializar contenido.


Amazon. com opera en dos segmentos: América del Norte e Internacional. El segmento de América del Norte de la Compañía se centra en las ventas minoristas obtenidas a través de sitios web enfocados en Norteamérica. El segmento internacional se centra en las operaciones de la Compañía realizadas a través de sus sitios web internacionales. Los sitios web minoristas de la Compañía incluyen www. amazon. com, www. amazon. co. uk, www. amazon. de, www. amazon. co. jp, www. amazon. fr, www. amazon. ca, www. amazon. cn , Www. amazon. it, www. amazon. es, www. amazon. com. br y www. amazon. mx. La Compañía a través de sus sitios web ofrece productos bajo diversas categorías, incluyendo videos instantáneos, música digital, Appstore para Android, Amazon Cloud Drive, Kindle E-readers y libros, tabletas FIRE, FIRE TV, teléfono FIRE, libros y audibles, películas, música y Juguetes, niños y bebés, ropa, zapatos y joyería, deportes y actividades al aire libre, automotriz e industrial, y productos de crédito y pago.


El segmento de América del Norte de la Compañía se dedica a la venta al por menor de productos de consumo, incluso de vendedores. Amazon. com también ofrece suscripciones de contenido digital a través de sitios web enfocados en Norteamérica, tales como www. amazon. com y www. amazon. ca. El segmento también incluye las operaciones de AWS. El segmento de América del Norte incluye las ventas de exportación de www. amazon. com y www. amazon. ca.


El segmento internacional de la Compañía se dedica a la venta al por menor de productos de consumo y suscripciones de contenido digital a través de sitios web focalizados internacionalmente. El segmento internacional incluye las ventas de exportación de estos sitios web internacionales (incluyendo las ventas de exportación de estos sitios a clientes en Estados Unidos y Canadá).


Shayne Heffernan Gerente de Fondos de HEFFX tiene un Ph. D. En economía y trae consigo más de 25 años de experiencia comercial en Asia y experiencia práctica en Venture Capital, ha participado en varias empresas de nueva creación que han visto capitalización de mercado de más de 500 millones de dólares y 1 que alcanzan un máximo de mercado de 15 dólares. Ha gestionado y supervisado las nuevas empresas en Minería, Transporte, Tecnología y Servicios Financieros.


Bollinger en bandas de Bollinger


Durante las últimas dos décadas, miles de comerciantes veteranos han llegado a ver a Bollinger Bands como la herramienta más representativa y confiable para evaluar la acción de precios esperada. Ahora, en el tan esperado Bollinger en Bollinger Bands. El propio John Bollinger explica cómo utilizar esta extraordinaria técnica para comparar efectivamente los movimientos de precios y indicadores.


Los comerciantes pueden mirar a este libro orientado a las técnicas para cientos de ideas valiosas, incluyendo:


Análisis de los indicadores primarios derivados de las bandas de Bollinger% b y anchura de banda


Cómo los comerciantes pueden utilizar las bandas de Bollinger para trabajar en vez de contra los patrones de comercio encontrados con frecuencia


Uso estratégico de Bollinger Bands en programas comerciales de corto, mediano y largo plazo


Tres sistemas comerciales basados ​​en Bandas de Bollinger


La esencia de una inversión exitosa es determinar cuándo el precio de una acción es demasiado alto o demasiado bajo y luego actuar en consecuencia. Mientras que John Bollinger sería el primero en argumentar que no existen técnicas para determinar definitivamente estos niveles, Bollinger en Bollinger Bands presenta el examen de un miembro de la única herramienta que su popularidad generalizada es un juez preciso de su efectividad se acerca más que cualquier otra cosa. Conciso pero comprensivo, es una de las guías de inversión verdaderamente indispensables de hoy.


Bollinger on Bollinger Bands (Tapa dura)


Copyright y copia; 1995-2012 TOWER. COM INC Tower, Tower Records, Tower Tower. com, Tower. com, Torre de la estación de escucha de CD, Tower Outlet, Tower Records Videos Libros, Tower Essentials, Pulse! Y los logotipos y marcas comerciales asociados son marcas registradas de Tower. com Inc. en los Estados Unidos y otros países. Copyright 2012. Para uso personal no comercial solamente. Tower. com es un participante en el Programa de Asociados de Amazon Services LLC, un programa de publicidad de afiliados diseñado para proporcionar un medio para que los sitios ganen comisiones publicitarias mediante la publicidad y la vinculación a amazon. com.


Para enlazar directamente a esta página: http://www. tower. com/bollinger-on-bands-john-hardcover/wapi/100196930


Torre 2.0 Versión 6.0.0


ETF que negocia con las vendas de Bollinger & reg; (Serie de Estrategia de Negociación de Investigación de Connors) (Versión en Inglés) Versión Kindle


Usted negocia ETFs con las vendas de Bollinger?


Si lo hace, usted estará interesado en esta nueva investigación de la serie Connors Research Trading Strategy. Aplicamos las rigurosas reglas de la prueba histórica cuantificada de Connors y las superamos con Bollinger Bands & # xae; & ETF trading data.


Los resultados incluyen estrategias múltiples con un porcentaje excepcionalmente alto. La mayoría de estas estrategias muestran porcentajes históricos ganadores superiores al 80% y algunos superan el 90%.


Bollinger Bands son utilizados por cientos de miles de comerciantes de todo el mundo. Cuando se negocian correctamente, Bollinger Bands puede ser una de las estrategias más consistentes disponibles para su comercio. Y, por supuesto, los ETF también son considerados como vehículos altamente consistentes por los comerciantes. ETFs + Bollinger Bands + Quantified Back-testing de Connors Research ha creado una combinación ganadora para los comerciantes que buscan mayores niveles de precisión.


En la nueva Guía de la Serie de Estrategias de Negociación, ETF Trading con Bandas de Bollinger, Connors Research está poniendo a disposición del público un enfoque completamente sistemático y cuantificado para negociar ETFs con Bollinger Bands.


Resultados de negociación altamente consistentes


En ETF Trading con Bandas de Bollinger & # xae; Obtendrá docenas de variaciones de estrategia de Bollinger Bands que han sido correctas más del 80% del tiempo de enero de 2006 a agosto de 2012.


Y, usted verá 11 variaciones de la estrategia - utilizando órdenes de límite - con una tasa de triunfo más del 90%! (Del 92,16% al 97,67% para ser exactos).


Como usted puede saber, el comercio con bandas de Bollinger puede ser muy subjetivo. Hemos eliminado la subjetividad y cuantificado para usted docenas de exacta Bollinger Bands ETF comercio set-ups que han visto ganancias extremadamente confiables con un alto porcentaje de las operaciones con éxito.


Top 10 Bollinger Bands ETFs Resultados de la Prueba Clasificados por P / L (sin límite de entradas)


Enero 2006 - Agosto 2012 Variación Promedio. % Ganancia / Pérdida Prom. \ Vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip 5 2,18% 1,56 93,06% 6 2,17% 2,19 90,28% 7 2,01% 4,66 83,53% 8 1,96% 3,15 88,89% 9 1,75% 4,21 83,06% 10 1,68% 2,50 84,26%


Las órdenes límite se usan a menudo para mejorar la precisión. Lograr una tasa de victoria del 100% no es posible, pero aquí están las 5 más consistentes variaciones de estrategia en ETF Trading con Bollinger Bands - todos con un% de más de 95%.


Bollinger Bands ETFs Resultados de las pruebas Clasificadas por% Correcto (con entradas límite) Enero 2006 - Ago. 2012 Variación Promedio. % Ganancia / Pérdida Prom. \ Vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \ baselineskip \ vskip1.000000 \


Esto es lo que recibirás.


- Las reglas comerciales exactas. Esto no es una caja negra. La divulgación completa de las reglas se le da a usted - Cómo identificar los mejores Bollinger Bands ETFs Trading set - ups --Cómo seleccionar los mejores niveles de entrada que se ajusten a su estilo de negociación --Dónde exactamente hacer sus pedidos cada día --Where Y cuándo exactamente salir de sus pedidos


Para operadores de opciones


ETF Trading con bandas de Bollinger es adecuado para usted si las opciones de comercio. Las rentabilidades históricas han sido fuertes y los comerciantes profesionales entienden el poder de aplicar opciones a su comercio de acciones. En esta guía usted podrá hacer lo mismo al combinar las Bandas de Bollinger con la negociación de opciones.


Si está buscando negociar las estrategias basadas en reglas cuantificadas más consistentes disponibles para los comerciantes de hoy, descargue "ETF Trading with Bollinger Bands & # x201d; A su Kindle.


Bandas de Bollinger: Simple Bollinger Band Trading Estrategias para Consistentes Beneficios (Edición en Inglés)


Bandas de Bollinger: Simple Bollinger Band Trading Estrategias para Consistentes Beneficios (Edición en Inglés)


Formato Kindle € 3,17


Estrategias Simples, Probadas Bollinger Band Trading Para Ganancias Consistentes


Gráficos detallados de velas y diagramas incluidos


Las vendas de Bollinger se nombran después de Juan Bollinger, que los desarrolló como método de identificar la acción del precio en cualquier momento dado en tiempo. Aunque las bandas de Bollinger son uno de los indicadores técnicos más utilizados, la mayoría de los comerciantes los usan incorrectamente.


En este libro, usted aprenderá qué bandas de Bollinger son, cómo trabajan, y la manera correcta de usarlas. Además, aprenderá varias estrategias de negociación de bandas específicas de Bollinger para ayudarle a obtener ganancias consistentes.


Éstos son algunos de los detalles que aprenderá.


- Qué bandas de Bollinger son y el común (y costoso!) Error que la mayoría de los comerciantes hacen con ellos - Capítulo 1


- Cómo bandas de Bollinger son utilizados por los comerciantes de swing, los comerciantes impulso, y los inversores a largo plazo - Capítulo 1


- Exactamente cómo funcionan las bandas de Bollinger y cómo se construyen, incluyendo diagramas y ejemplos detallados.


- Aquí está cómo usar bandas de Bollinger incorrectamente podría costar grandes y qué hacer de manera diferente (no quieres perder esto!) - Capítulo 3


- Múltiples estrategias específicas de Bollinger para la venta de bandas, incluyendo diagramas detallados de velas y ejemplos - Capítulo 4


- Cómo utilizar las bandas de Bollinger para identificar reversiones de tendencias, períodos de alta volatilidad y mucho más-- Capítulo 4


- Cómo la divergencia de precios lejos de una banda de Bollinger puede marcar el final de una tendencia (y cómo sacar provecho de esta situación!) - Capítulo 4


- Directrices y técnicas para los inversores a largo plazo - Capítulo 5


- Recomendaciones y advertencias para la exitosa banda de Bollinger - Capítulo 7


- ¡Y mucho, mucho más!


Descargue su copia hoy mismo!


Dettagli prodotto


Por Corey Rosenbloom, fundador de AfraidtoTrade. com y autor de The Complete Trading Course: patrones de precios, estrategias, configuraciones y tácticas de ejecución


Para los comerciantes intradía, especialmente los comerciantes de futuros de índices de acciones o ETFs relacionados, la adaptación al entorno de volatilidad intradía puede ser un factor importante en un día de negociación exitosa. Diferentes días requieren diferentes tácticas, ya que ciertas configuraciones comerciales e indicadores funcionan mejor ya sea en un rango de baja volatilidad o en una sesión de tendencias de alta volatilidad. Vamos a centrarnos en los "tres grandes" indicadores que se combinan para disparar las mejores operaciones en una sesión de baja volatilidad "Día de la gama".


Los días de la gama se desarrollan a menudo cuando no hay noticias de la noche a la mañana, ningún informe o anuncio económico previo al mercado, y ninguna brecha de apertura importante por encima o por debajo del cierre de la sesión anterior. Si no hay nada fuera de lo común a medida que comenzamos el día de negociación actual, podemos esperar que el resto de la sesión se convierta en una sesión de baja volatilidad limitada por rangos.


Además de anotar el precio de la sesión anterior alto y bajo junto con cualquier línea de tendencia de precios intradía que se desarrollen, centraremos nuestra atención en tres indicadores principales para crear oficios:


Bandas de Bollinger (traza dos desviaciones estándar arriba y debajo de una media móvil de 20 períodos)


Velas de reversión (doji clásico, martillo / estrella fugaz, tapas giratorias, engrosamiento alcista / alcista)


Divergencias Internas de Mercado o Momentum (una divergencia negativa se produce cuando el precio registra un nuevo swing o alta intradía mientras el indicador registra una baja visual más baja)


Mediante la combinación de estos indicadores, el perfecto "Range Day Fade Trade" se desarrolla cuando el precio declina a la Banda de Bollinger inferior (o línea de tendencia anterior de soporte de precios) en un gráfico intradía de 5 minutos como una vela de inversión alcista. Si también vemos la gráfica intradía de 1 minuto, podemos ver claramente un momento visual positivo (usando el Indicador de Cambio o 3/10 MACD) o la divergencia interna del mercado (TICK o Breadth). La misma lógica se aplicaría a un comercio bajista o de venta corta con un nivel de resistencia superior.


Gráfico 1: Carta de SPY 5-min el 9 de julio de 2012. Después de una venta de la mañana, una gama visual se desarrolló entonces. "Fade" Trades 1 - 3 destacan las velas de reversión en Bollinger Band extremos. (Creado con TradeStation)


Figura 2: Diagrama de SPY 1-min para el 9 de julio de 2012. Dejando caer al gráfico de 1 minuto, vemos divergencias positivas o negativas utilizando los indicadores de tasa de cambio y NYSE TICK (Market Internal). Combine los 5 minutos "Bollinger Reversal Candles" con 1-min correspondiente indicador divergencias, todo lo cual aumenta las probabilidades de un comercio exitoso. (Creado con TradeStation).


Para las tácticas de entrada y gestión, un comerciante se vería a comprar como ruptura de precios por encima de la alta de la vela de reversión en un gráfico de 5 minutos, colocando una parada cómodamente por debajo de la oscilación del precio esperado bajo o apoyar trendline, A la banda de Bollinger superior o línea de tendencia de resistencia para un objetivo de salida. Un comerciante entonces evaluaría si jugar un comercio de fade de venta corta si una nueva vela de reversión bajista o una divergencia negativa desarrolla y maneja el comercio similarmente (apuntando a la Banda de Bollinger inferior). Los comerciantes abandonarían esta estrategia basada en el rango de "desvanecimiento" en el caso de que el precio fuera a la ruptura del rango intradía establecido.


Las actividades de inversión exitosas de Thomas Bulkowski le permitieron jubilarse a los 36 años. Es un autor y comerciante internacionalmente conocido con más de 30 años de experiencia en el mercado de valores y ampliamente considerado como un experto líder en patrones gráficos. Puede ser contactado en


¡Apoye este sitio! Al hacer clic en los vínculos (a continuación) le lleva a Amazon. com. Si usted compra NADA, pagan por la referencia.


Indicadores de Bulkowski


Escrito por y copyright & copy; 2005-2016 de Thomas N. Bulkowski. Todos los derechos reservados. Descargo de responsabilidad: Usted es el único responsable de sus decisiones de inversión. Ver Privacidad / Exención de responsabilidad para más información.


Esta página sirve como puerta de entrada a varios indicadores que he probado. A continuación se muestra la lista. Sí, es corto porque no uso muchos indicadores. Al considerar un comercio, repasaré el índice del canal de la materia (CCI) y el índice relativo de la fuerza de Wilder (RSI) más bandas de Bollinger.


Con CCI, da señales de comercio a muy corto plazo y divergencia. RSI también muestra divergencia y señales de sobrecompra / sobrevendido. Estos pueden ser muy oportunos. Las bandas de Bollinger, cuando son estrechas, sugieren un gran precio en movimiento. Eso significa una mayor volatilidad. Los movimientos entre bandas también proporcionan información, lo que significa que cuando el precio toca la banda superior, puede rebotar y tocar la inferior, y viceversa.


El indicador de patrón de gráfico, o CPI, proporciona información de dirección de mercado. Lo mejor es negociar con la tendencia y el CPI ayuda con eso. Si señala verde, voy largo. Si señala rojo, evito tomar nuevas posiciones largas y puede tomar beneficios si el indicador de patrón de gráfico acaba de pasar de comprar (verde) a vender (rojo).


Indicador de patrón de gráfico. Inventado por Thomas Bulkowski, el indicador hace un muy buen trabajo al predecir las vueltas del mercado, pero tiene varios días de retraso. Por lo tanto, el indicador se utiliza mejor como un dispositivo de señalización semanal solamente. La lectura actual del indicador se puede encontrar en la página de inicio. Actualizaciones semanales. Cerca de la parte superior izquierda de la página (ver la imagen, como "Market outlook.") O en la mayoría de las páginas de otros sitios web (como 'CPI.').


Índice de fuerza relativa de Wilder. La página discute el RSI y ajusta los parámetros para las existencias. Los mejores resultados vienen de ajustes de umbral de 20 y 80 con 16 días de vista atrás para las compras y 14 días para las ventas. Revise las pruebas y elija su propia configuración.


Indicadores Técnicos Avanzados - Bollinger Bands


Si sigue nuestro blog, entonces definitivamente está familiarizado con el comerciante Larry Levin, Presidente de Trading Advantage LLC. Hemos obtenido una respuesta tan grande de algunos de sus posts anteriores que ha acordado compartir uno más de sus consejos comerciales favoritos como un regalo especial para nuestros espectadores. Determinar la dirección del mercado puede ser complicado y simplemente confuso a veces, pero la opinión experta de Larry lo mantiene sencillo. Si te gusta este artículo, Larry también accedió a darte acceso libre a su técnica favorita.


Echemos un vistazo a una herramienta técnica más avanzada: Bandas de Bollinger. Estos fueron desarrollados por John Bollinger en los años ochenta. En términos simples, utilizan una media móvil sencilla y desviaciones estándar para dar una perspectiva diferente sobre los potenciales altos y bajos.


Las bandas de Bollinger tienen una banda media y dos bandas externas.


La banda media que se muestra en este indicador es un promedio móvil, generalmente un promedio móvil simple (vea la Sugerencia # 29 para más sobre ésos) aunque algunos comerciantes usan los promedios móviles exponenciales. Los cálculos de la desviación estándar para las bandas externas se pueden calcular como en este ejemplo:


* Banda Media = promedio móvil simple de 20 días (SMA)


* Banda superior = 20 días de SMA + (desviación estándar de 20 días del precio x 2)


* Banda Inferior = SMA de 20 días - (desviación estándar de 20 días del precio x 2)


Los valores reales utilizados pueden depender de la preferencia del usuario. El uso y la interpretación también pueden variar.


Esta herramienta técnica es una forma en que algunos comerciantes tratan de definir y observar patrones potenciales. No pretendo ser un experto en éstos, pero hay algunos fundamentos comunes sobre los cuales los analistas están de acuerdo. Volatilidad es el nombre del juego para la banda superior e inferior. Since they are based on standard deviations from the middle band they move closer to the middle when volatility contracts, and further out when volatility expands. Based on this level of volatility, the relationship between those lines and prices can be used to signal potential market conditions. Some analysts might see an overbought market where prices touch the upper band. Conversely, an oversold market might exist when prices are edging towards the lower band.


El rendimiento pasado no es necesariamente indicativa de resultados futuros. Chart courtesy of Gecko Software.


Other subtle patterns can be seen with Bollinger Bands on a chart.


The way the prices interact with the bands can lead to different kinds of patterns that technical analysts might interpret for trade designs. They have names like W-bottom or M-top or walking the bands. If you like playing with these statistical measures, you might enjoy reading more about them. Generally speaking, the visual cues regarding volatility are the main feature for this kind of chart overlay. They can also be used in conjunction with other analysis or observations as a way of complementing other signals or patterns. Play with Bollinger Bands and see how they might work with your trading tools to confirm or sharpen your market observations.


Best Trades to you,


Larry Levin Founder & President - Trading Advantage larry@tradingadvantage. com


Disclaimer: Trading in futures and options involves a substantial degree of a risk of loss and is not suitable for all investors. El rendimiento pasado no es necesariamente indicativa de resultados futuros.


Possible ‘M’ Reversal setup on Amazon


The ‘M’ Reversal needs one peak to be formed outside the upper Bollinger Band and the subsequent peak to be formed within the band;


The stop loss will generally be placed above the high of the second peak;


The trader may choose to move their stop as the price moves in their favour using either an x bar trailing stop or alternatively place the stop above new peaks as they form.


Scouring through hundreds of charts a day means that you need to try and keep your mind flexible to different opportunities that exist which can be tricky when you consider the mass of setup types that you may see. One that is relatively easy to spot is the ‘M’ Reversal because of the specific way the price needs to form up in relation to the Bollinger Bands.


The more subjective element is the angle at which the price initially is moving when it breaks out of the bands. By this I mean that this is the type of formation that needs to be accompanied by a fairly aggressive price rise in the lead up to the pattern. If you look through the blog at some of the other ‘M’s (and its long side cousin – the ‘W’) you can see what we are typically looking for.


Whilst this type of trade will always be seen as counter-trending it does potentially allow the trader to place their stop quite close to the entry point which may provide a good risk/reward setup – which is one of the key attractions for reversal trading.


Please let us know any comments, thoughts or questions that you have. Ric and I can both be followed on twitter at RicCharts and DaveCharts respectively.


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Amazon is Putting In a Parabolic Short


The post earnings run in AMZN has been tremendous. After a month of consolidation, the stock is nearing 400 after a solid 1 week run.


However, on top of this run, the stock is putting in a parabolic short pattern.


I look for two things with this pattern:


1. A close above the 2nd standard deviation Bollinger Band


2. A gap higher above that close


These two conditions suggest that buying pressure is reaching extreme levels, and that the odds of further stock movement continue to decrease.


We are seeing that setup right now in AMZN:


Now does this mean that a long term top is going to happen right now? Probably not. You've gotta respect the momentum and how 400 is a big psychological magnet.


But I am looking at some short delta opportunities. Not necessarily long puts, but something that will profit if the stock pulls back and retraces the past week's action, but not much more.


Steven Place is the founder and head trader at investingwithoptions. com/


Abstract


The Bollinger Band is a widely used technical indicator to measure and display the volatility of securities by showing whether prices are high with the use of an upper band and whether they are low with the use of a lower band. The bands are based on the volatility (standard deviation) of the past price data. This indicator can aid in rigorous pattern recognition and is useful in comparing current price action to possible buy and sell signals, helping to arrive at a self contained systematic trading decision. However, due to its inherent lagging characteristics, the indicator can provide false signals during trading in some markets. The paper proposed a modified model, combining neural networks with the Bollinger Band technical indicator, to predict and trade on the security trend. The benefit of the combined system is that the neural network will help to p overcome the lagging aspects of the Bollinger Band indicator by providing a next day forecast, allowing the trader to make the correct trading decisions. The profitability of the model will be tested using data from the S&P 500, Nasdaq composite, and Russell 2000, IBM, Amazon, Pfizer, and General Electric, among others.


Appears In


Intelligent Engineering Systems Through Artificial Neural Networks (ANNIE '05)


Publication Date


Publisher


American Society of Mechanical Engineers


Source Publication Title


Intelligent Engineering Systems Through Artificial Neural Networks (ANNIE '05)


Recommended Citation


Dong, Yanqiong; Dagli, Cihan H.; and Enke, David Lee, "A modified trading strategy model combining neural networks with the Bollinger Bands technical indicator" (2005). Faculty Research & Creative Works. Paper 3345. http://scholarsmine. mst. edu/faculty_work/3345


Rights


© 2005 American Society of Mechanical Engineers, All rights reserved.


Rubber Band Stocks: A Simple Strategy for Trading Stocks Kindle Edition


Tired of losing money?


Ever wish that there was a simple road map that you could follow to learn how to trade profitably?


"Rubber Band Stocks" is a simple but powerful way of trading stocks.


This strategy is easy to understand and easy to trade.


Warren Buffett is famous for saying, "You need to be fearful when others are greedy, and greedy when others are fearful."


This sounds easy in principle, but how exactly do you do it in real life?


This book will will show you how, step-by-step, in clear and easy-to-understand language.


Are you a complete newbie?


Or a trader who has blown up his account and is ready to start again?


Or just someone who is curious how to make money from home, using just a laptop computer?


If so, this book is for you.


Amazon best-selling author . Matthew Kratter will introduce you to all of the tools that you need in order to profit from market oscillations.


You will learn everything you need to know about:


Buy signals


Profit targets


Position sizing


Stop losses


Get started today:


Scroll to the top of the page and select the "Buy Now" botón.


HASH(0xa5e64120) out of 5 stars Excellent 10 March 2016


By Davidd - Published on Amazon. com


Format: Kindle Edition Verified Purchase


Excellent concept Have the chance to read this book as a FREE preview from Matt prior availability on Amazon, thought is a worthwhile addition to my investing library and reference data bank. Decided to buy the title for couple of reasons, first only the Kindle edition gives me the ability to highlight and make notes as well as I understand Matt does not rely on the income producing publishing books but nevertheless I like him to feel "recognized" for his dedication to share. One basic yardstick I use is,"this book, article, technique, system, adviser etc makes me money?." With Matt the answer has been yes with his Covered calls book that been a MUST to have. This offering gave me the chance to revisit the use of Bollinger bands, I edited the parameters to bring it in line with his 80 (standard/default in my charting is lower) and can see why he likes it. I really appreciate his willingness on making himself available via Email, I contacted him recently and he answered promptly. In summary, the addition of this tool plus the gems offered on his Covered calls prior book provide a very useful must do set of steps before clicking the buy on the broker site. .


HASH(0xa5e640f0) out of 5 stars Good Value 9 March 2016


By Amazon Customer - Published on Amazon. com


Format: Kindle Edition


A straightforward strategy that Matt explains clearly, it uses Bollinger Bands with 80-day look back and 2 standard deviations in combination with bearish trader sentiment (a subjective evaluation) to identify stocks that have reached capitulation and are ready to bounce back. He then recommends getting out when price rises above the midpoint of the Bollinger Band. Also included are recommendations on when to exit a losing trade. Short, concise, and well written. I would have liked to see some metrics on his ROI with this strategy to make a convincing case for using it. Overall a good value at $2.99 and worth reading. Although I don't plan to use the strategy itself, the concepts can be applied for entry/exit points in many other trades. [I received an advance copy in exchange for posting an objective review]


HASH(0xa5e64b34) out of 5 stars A book that is set apart from the herd 9 March 2016


By Mark R. Anderson - Published on Amazon. com


Format: Kindle Edition


Matthew kindly sent me an advanced copy which I found to be extremely readable, as he cuts through all of the 'noise' and presents succinctly, only what one needs to know to implement this strategy. Not only are there a few charts to help cement strategy procedures, there are also some links which will undoubtedly prove invaluable to the trader. Personally, what sets this book apart for me, is that Matthew doesn't simply just cite a number of past examples, but offers practical advice as how to get started in finding potential trade setups. So often many books simply cite old trades but don't assist the reader in how to go about finding the trades of today and tomorrow. All in all an excellent simplified book. I intend to paper trade this strategy and prove it for myself.


HASH(0xa5e649fc) out of 5 stars This is a excellent simple strategy that Matt Kratter has used over the. 9 March 2016


By tzsub - Published on Amazon. com


Format: Kindle Edition


This is a excellent simple strategy that Matt Kratter has used over the years to help pay his bills. He focuses on price to enter a trade, when to take profits and where to place your stop/loss. Matt tells you how to use the Bollinger bands to identify the potential stocks you may want to trade. The book is short and sweet and right to the point. He gives you examples and recommendation to help you in your trading. A excellent book that doesn't take long to read and a must have if you are new to trading. As Matt is given you a strategy that he has learned over the years, that you will be able to use in you own trading account. Another good book by Matt that I will use in my trading education and a job well done. Thank you Matt you are a good mentor and teacher. Tom Z


HASH(0xa5e64654) out of 5 stars I can use thinkorswim platform to find stocks which meets the Bollinger band criteria for the type of stocks I would like to inv 11 March 2016


By Rom - Published on Amazon. com


Format: Kindle Edition


I had received an advanced review copy of the book. I am new in trading and doing it for supplemental income. Even after reading beginner's book such as Trading for Dummies, I lacked some very basic and concrete principles in strategies. This book (and others) by Matthew Kratter offers the direct practical and to the point approach I have been looking for. The Rubber Band Stocks strategy adds to my repertoire. I can use thinkorswim platform to find stocks which meets the Bollinger band criteria for the type of stocks I would like to invest in and from there I can proceed forward and execute upon the other criteria and principles mentioned in this book. For newbies, I would reiterate to paper trade this until a sense of success and probability of failure is attained.


Put Selling, Covered Calls and Bollinger Band Strategy For Walmart Stock


Renuncia


Comercio bajo su propio riesgo. There are risks involved in all investment strategies and investors can and do lose money. Stocks, options and investing are risky and can result in considerable losses. None of the strategies, stocks or information discussed and presented are financial or trading advice or recommendations. Everything presented and discussed are the author's ideas and opinions only and the author assumes no liability for topics, ideas, errors, omissions, content and external links. Fullyinformed. com is a private website. By using this site, you agree to be bound by its terms of use. If you do not agree to the terms of use, do not use this site. Any corporate insignia used are registered trademarks of their respective company or corporation and are used for identification purposes only. All rights are reserved. Reproduction in whole or in part without written permission is prohibited. All material copyrighted by FullyInformed. com © 2006 -2015


Synopsis: Bollinger bands are one of the most widely used technical indicators--and for good reason: they powerfully and clearly capture price action and reveal the market trend. Now, Sylvain Vervoort, best-selling author of Capturing Profit with Technical Analysis has harnessed the power of Bollinger bands in his brand-new money-making machine—The Band Break System. Traded automatically or manually, the Band Break System, or “BBS” for short, is a trend-following trading system designed specifically for trading today’s stock and Forex markets for major profits.


The concept behind is system is simple—that there exists a minimum reversal amount before you can assume a change in trend. With extensive backtesting, Vervoort has determined that exact amount and used it to develop signals to capitalize on short-term price reactions in all time frames. In this four-hour video course, Vervoort personally explains the ins and outs of his system to you, including:


• An exclusive chart template complete with the proprietary indicators needed to extract sizable, consistent profits from any market using BBS


• The complete trading manual with true test results and REAL examples of trades made with BBS


• BBS-specific guidelines for stock selection to make the most money possible with this system; plus risk and money management techniques to hold on to all your profits!


PLUS, all of the formulas to trade BBS automatically on your trading platform are included—this is a tremendous value available only through this course! Imagine setting up a few parameters and simply watching your trading account multiply—it is possible with the Band Break System!


Jacket Description: About the Author Sylvain Vervoort started his professional career with a degree in electronics and has been studying and using technical analysis since 1978 with the birth of the European Option Exchange (EOE) in Amsterdam. After training to become an investment and credit advisor, he has since conducted many courses and presentations about technical analysis. His first book (Dutch-language only) on technical analysis was published in 2000. He is also the author of Capturing Profit with Technical Analysis. Since his retirement, he has been publishing articles in Stocks & Commodities magazine, trading short-term on stocks, E-mini futures, and Forex, and maintaining his website Stocata. org about trading based on the application of technical analysis.


Bollinger bands - Maximise Profits By Mastering Bollinger Bands


In the following video the presenter presents techniques for mastering Bollinger bands for maximum profit in currency (forex) and stocks trading. He presents 5 Bollinger bands set-ups and their variations that you must know if you want to use Bollinger bands effectively.


According to the presenter Bollinger bands are about the best indicator you will ever use to help identify high probability trades. Bollinger bands measure a standard deviation from the mean or middle. Usually the "mean" or middle is a 21 day moving averade of closing price.


So you would lay down a 21 day moving average and then a 2.0 standard deviation set of Bollinger bands and when price closed outside of either band it is said to have closed outside a 2 standard deviation band. 2 standard deviations from the mean.


To profit in currency or forex trading you need to master the techniques and choose the techniques that are working for you when practising them on a practice account. The rule is, don't trade real money before proving your skills on a practice account. I would recommend trading on a practice account for at least a month before opening a real money account.


Watch the video to learn more about profiting from forex trading by using bollinger bands. To learn more buy the book by the inventor of bollinger bands himself John A Bollinger, Bollinger on Bollinger Bands


Tagged with bollinger bands strategy


Jueves 2 de Enero de 2014 por Michael Freeman


The Fence Trading Strategy, also known as ‘Double Profit’, allows traders to increase the ITM payout and minimize the potential OTM loss. The ideal situation for Fence Trading is when the asset’s price bounces up and down within a reasonable price range allowing us to enter into two opposite trades and as close as possible to the highest and lowest prices on the asset’s chart. By creating a wide enough range, we can increase the probability to win both trades and get double the ITM payout, hence the term ‘Double Profit’.


At the highest price level within the ‘Fence’ we enter a PUT prediction while at the lowest price level we enter a CALL prediction and our goal is to ensure that the asset’s closing/expiry price is between the price levels of the upper and lower trades.


Ejemplo de estrategia de comercio de vallas


Let’s say that the price of Asset A is currently $25, yielding a %75 ITM return and a %0 OTM return. Decidimos invertir $ 100 que predice un aumento de precio sobre la caducidad que se fija en 60 minutos. 20 minutes later the price of asset A reaches $30, based on the neutral trend we assume that the price will close below $30 and we invest another $100, predicting a price decrease. En este punto, hemos introducido una llamada y un Put predicciones la creación de un & # 8220; Fence & # 8221; Entre $ 25 y $ 30. If the final asset’s price(expiry) falls between the the lowest and highest price levels, we will get the highest return on the investment formulated in the first example below.


Activo A expiración = $ 25- $ 30 ($ 100 + $ 75) + ($ 100 + $ 75).


Inversión Inicial = $ 200, Saldo Final = $ 350, Ganancia = $ 150.00


Activo A expiración & gt; $ 30 ($ 100 + $ 75) + ($ 100 - $ 100).


Inversión Inicial = $ 200, Saldo Final = $ 175, Pérdida = - $ 25.00


Activo A expiración & lt; $ 25 ($ 100- $ 100) + ($ 100 + $ 75).


Inversión Inicial = $ 200, Saldo Final = $ 175, Pérdida = - $ 25.00


La conclusión obvia es que con la estrategia de comercio de vallas podemos reducir significativamente la pérdida en las operaciones de OTM, mientras que duplicar el beneficio potencial! La ganancia en los oficios ganadores es 6 veces mayor que la pérdida potencial ($ 150 vs - $ 25), lo que significa que incluso si perdemos unos cuantos negocios consecutivos, seguiremos recuperando más ganancias sobre un volumen de operaciones. ¡Ésta es definitivamente una estrategia binaria de las opciones que gana!


Fence Trading y Bollinger Bands®


Now that we understand the great potential behind Fence Trading, it’s important to learn how we can properly identify a neutral trend while spotting the ‘right amount’ of price volatility to indicate a safe entry point so we can safely utilize this binary options strategy. Las Bandas de Bollinger se utilizan como un indicador de análisis técnico que nos permite medir la volatilidad de precios actuales de cualquier activo subyacente. The volatility bands are positioned above/below the moving average line, forming a range that is constantly adjusted depending on the market volatility, making it an ‘ideal indicator’ for Fence Trading. The visual representation of the Bollinger Bands is used to predict the ideal ‘lowest and highest’ entry points and confirm the presence of a neutral trend for the current time period displayed on the asset’s chart.


Fence Trading combinado con Bollinger Bands conforman una estrategia muy sólida. It’s also suggested that you set the expiry for one hour, giving yourself enough ‘response time’ to enter the 2nd trade. Remember that binary options involves high-risk, therefore any strategy that attempts to minimize the potential loss will ultimately minimize the risk and allow you generate more profit! Before you ‘jump in the water’ head first with $100 trades. Asegúrese de poner la estrategia de comercio de vallas a la prueba con menores cantidades de comercio o en una cuenta de demostración. Para los corredores que ofrecen montos bajos de operaciones y una cuenta demo visite las revisiones de corredores de opciones binarias recomendadas.


$ 20 a $ 100 Corredores de depósito mínimo!


How to trade options using bollinger bands – The Best Binary Options Trading Platform. www. ryfab. se


How to trade options using bollinger bands – short term trading strategies that work amazon


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John Bollinger Net Worth


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What is a 'bollinger band®' a bollinger band® is a band plotted two standard deviations away from a simple moving average, developed by famous technical trader john. In the 1980s, john bollinger, a long-time technician of the markets, developed the technique of using a moving average with two trading bands above and below it. Net worth: us$6.5 billion (2009) [1] religion: columbia university's president lee c. bollinger announced a $400 million pledge from kluge, "john kluge.


What is a 'bollinger band®' a bollinger band® is a band plotted two standard deviations away from a simple moving average, developed by famous technical trader john. In the 1980s, john bollinger, a long-time technician of the markets, developed the technique of using a moving average with two trading bands above and below it. Net worth: us$6.5 billion (2009) [1] religion: columbia university's president lee c. bollinger announced a $400 million pledge from kluge, "john kluge.


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QUOTE (mishael1977 @ Aug 17 2009, 10:32 AM)


I use bollinger bands and it has been extremely useful to me. I have a near perfect understanding of trading using bollinger bands. The default setting of 20 periods and deviation 2 would work just fine on the daily and hourly chart though I strongly suggest using 24 and 22 periods on the hourly and daily chart respectively. You may also wish to add any oscillator of your choice to help you determine OB & OS levels. Google "John Bollinger" he is the 'inventor' of the bollinger band. He's got a book on it that sells for just a little over $50. I suggest that you purchase his book. Remember, if you don't learn, you wouldn't earn.


Thanks so much for the advice! I agree learning is the most important aspect of succeeding in Forex, along with gaining real time market experience. It seems to me like the bb bands are a great tool but best used with other analysis tools.


Start Making Money in 10min!


Cloud computing is a computing paradigm that constitutes an advanced computing environment that evolved from utility and grid computing. The infrastructure of cloud computing typically includes a collection of interconnected and virtualized computers from parallel and distributed systems. The virtual computers are dynamically provided to consumers as one or more unified computing resources, based on service level agreements (SLA) established through negotiation between the service provider and consumers [ 1 – 5 ]. Typically, cloud computing services provide a high level of scalability of computing resources combined with Internet technology to multiple customers [ 6 ]. Currently, there are several commercial cloud systems in service such as Amazon EC2 [ 7 ], GoGrid [ 8 ], and FlexiScale [ 9 ].


In most of these cloud services, there is a notion of an instance to provide users with resources in a cost-efficient manner. An instance means the virtual machine (VM) that serves for the user's need. In general, instances are classified into two types: on-demand instances and spot instances. On-demand instances are charged for the compute capacity on an hourly basis without the long-term commitment. This frees users from the costs and complexities of planning, purchasing, and maintaining hardware and transforms commonly large fixed costs into much smaller variable costs [ 7 ]. On the other hand, spot instances allow users to bid on unused compute capacity and utilize those instances for as long as the current spot price is below their bid. The spot price is changing periodically based on supply and demand. When users' bids meet or exceed the price, they gain access to the available spot instances. If users are flexible as to when applications should run, spot instances can significantly decrease the cost as reported in [ 7 ]. Nevertheless, there is a risk of task failures, which occurs when the spot price of the instance becomes higher than the bid price.


To efficiently handle this problem, the checkpointing schemes have been proposed in the research community [ 10. 11 ]. The checkpointing saves the execution status of tasks if a certain condition is met and then recovers the task status from the last saved point upon a failure. It allows a reduction in the execution time and cost in an unreliable computing environment. On a legal side, the SLA is typically used for alleviating the uncertainty by specifying service details such as price and task execution time. SLA specifies the resource allocation and rental terms to consumers in agreement with providers.


In this paper, we propose the estimated interval-based checkpointing (EIC), which improves the efficiency over our previous study [ 12 ]. The key idea is adopting the weighted moving average (WMA) and Bollinger Bands. The moving average is a history-based prediction scheme. The WMA sets a different weight for each time interval in the past and calculates the average of the weights. With these weights, the failure occurrence probability is obtained in each interval. The threshold for checkpointing is calculated based on the average failure probability. We apply two thresholds of price and time in EIC. In addition, we use the Bollinger Bands to inform users of estimated execution time and cost. In the stock market, the Bollinger Bands is a well-known analysis method. It is used to measure the high and low value level of the previous trading data. This method is used to predict the price bid in the stock market. We use the Bollinger Bands to calculate both the estimated execution time and the cost.


We have measured the number of checkpoint trials and total cost per spot instance for a user bid. Simulation results show that the EIC outperforms the existing schemes, hour-boundary checkpointing (HBC) [ 13 ] and rising edge-driven checkpointing (REC), [ 11 ] in terms of the number of checkpoints. Consequently, the EIC minimizes the execution time of applications and the time wasted by task failures.


The rest of this paper is organized as follows. Section 2 briefly describes related work on resource allocation, SLA, fault tolerance, moving average, and Bollinger Bands in cloud computing. Section 3 presents our system architecture. Section 4 presents our SLA, estimation, and checkpoint algorithms based on the price history of spot instances. Section 5 presents performance evaluations with simulations. Finally, Section 6 concludes the paper.


Many researchers and companies have recently studied fault-tolerance techniques in two different environments of cloud computing: reliable environments, with on-demand instances [ 14. 15 ], and unreliable environments, with spot instances [ 11. 13. 16. 17 ]. The fault-tolerance techniques are more required in unreliable environments. Our study was performed in the latter category of the environments to provide the cost-effectiveness of task execution.


Spot instances are typically used in unreliable environments, and studies on spot instances focus on performing tasks at low monetary costs. The spot instances in the Amazon Elastic Compute Cloud (EC2) offer lower price at the expense of the reduced reliability [ 18 ]. Cloud exchange [ 19 ] supports the actual price history of EC2 spot instances. In the spot instances environment, there are numerous studies on resource allocation [ 16. 17 ], SLA [ 6. 20. 21 ], fault tolerance [ 10. 11. 13. 16 ], moving average [ 22. 23 ], and Bollinger Bands [ 24. 25 ].


On the resource allocation side, Voorsluys and Buyya [ 16 ] solve the problem of running computation-intensive tasks on a pool of intermittent VMs. To mitigate potential unavailability periods, the study proposed a multifaceted fault-aware resource provisioning policy. Their solution employs price and runtime estimation mechanisms. The proposed strategy achieves cost savings and stricter adherence to deadlines. Zhang et al. [ 17 ] introduced a solution of how best to match customer demand in terms of both supply and price and to maximize the provider's revenue and the customer's satisfaction in terms of VM scheduling. The proposed model is designed to solve the problem of discrete-time optimal control. This model achieves higher revenues than static allocation strategies and minimizes the average request waiting time. Our work differs from [ 16. 17 ] in that we focus on reducing the rollback time after a task failure, achieving the cost savings and reducing the total execution time.


On the SLA side, Andrzejak et al. [ 20 ] proposed a probabilistic decision model to help users decide a minimum cost according to an SLA between users and Amazon's EC2. The scheme is based on a probabilistic model for the optimization of cost, performance, and reliability. It improves the reliability of service by changing conditions dynamically to satisfy user requirements. Due to the dynamic nature of cloud computing, continuous monitoring of the quality of service (QoS) attributes is necessary to enforce SLAs. Two similar studies [ 6. 21 ] focus on cloud resource management in the reliable cloud environment. One is based on SLA monitoring and enforcement in a service-oriented architecture (SOA) [ 21 ], whereas the other focuses more on the resource management. The resource manager optimizes a global utility function that integrates both the SLA fulfillment degree and the computational costs [ 6 ]. Our paper differs from [ 6. 21 ] in that we deal with the resource management in the unreliable cloud environment.


On the fault tolerance side, two similar studies (HBC [ 13 ] and REC [ 11 ]) proposed enforcing fault tolerance in cloud computing with spot instances. Based on the actual price history of EC2 spot instances, they compared several adaptive checkpointing schemes in terms of monetary costs and job execution time. Goiri et al. [ 10 ] evaluated three fault tolerance schemes, checkpointing, migration, and job duplication, assuming that the communication cost is fixed. The migration-based scheme shows a better performance than the checkpointing or the job duplication-based scheme. Voorsluys and Buyya [ 16 ] also analyzed and evaluated the impact of checkpointing and migration on fault tolerance using spot instances. Our paper differs from [ 10. 11. 13. 16 ] in that we utilize double thresholds for fault tolerance.


On the moving average and Bollinger Bands side, the moving average takes the next observation data using the data in the past [ 22. 23 ]. Reference [ 22 ] introduced the simple moving average (SMA) and WMA. Reference [ 23 ] used the average data to apply weight according to each interval. It evaluates the average of price depending on the weight change. Our paper also adopts WMA to estimate price, execution time, and thresholds based on price history. However, we found that the estimation is not accurate enough. We overcome this shortcoming by applying Bollinger Bands to estimate the execution time and the price ranges. The Bollinger Bands, proposed by Bollinger [ 24 ], is a technical analysis method used in the stock market. It analyzes previous trades and determines the standard deviation. Daytrader [ 25 ] introduced a method to predict the range of Bollinger Bands. This prediction requires the selection of length of the moving average around which the Bollinger Bands are plotted, and standard deviations to calculate from this moving average. Our paper differs from [ 24. 25 ] in that we apply Bollinger Bands to predict both cost and execution time ranges in the unreliable cloud environment.


In our previous paper [ 12 ], we proposed a checkpoint scheme based on SLA to satisfy user requirements. Our previous study performs a checkpointing operation based on two thresholds: price and time. The estimated execution time is predicted using the price history of an instance only for the same amount of time in task execution in the past. This paper differs in that the Bollinger Bands was adopted to improve the accuracy of cost and execution time estimations with utilizing numerous estimation intervals of the past.


3. System Architecture


Figure 1 shows the cloud computing environment assumed in this paper, which basically consists of four entities: a cloud server, storage servers, cluster servers, and cloud users. The cloud server is connected to cluster servers and storage servers. The cluster server is composed of many nodes. Cloud users can access the cloud server via the cloud portal to utilize the nodes in the cluster servers as resources. Therefore, the cloud server takes responsibility of finding resources and spawning virtual machines to satisfy the user's requirements in terms of the SLA and QoS. The coordinator in the cloud server manages tasks and is responsible for the SLA management. We focus on the coordinator and the VM, which play important roles in our checkpointing scheme.


Cloud computing environment.


3.1. Layer Structure


Figure 2 shows the structure of the coordinator in the cloud server, which is composed of Scheduler, Estimation Predictor, VM Information Manager, History Manager, SLA Manager, QoS Manager, and VM Information Collector. In the coordinator, the four managers are responsible for generating and maintaining a list of available VMs, based on the information collected from VM Information Collector. The VM Information Collector collects VM information and provides it to VM information Manager. The VM Information Manager generates a list of CPU utilization, available memory and storage space, network bandwidth, and so on. The History Manager manages the history data, in which the past bid and execution time of spot instances are accumulated. SLA Manager and QoS Manager manage the SLA requirements and the QoS requirements, respectively. Estimation Predictor analyzes data taken from the other managers and calculates the range of estimation completion time and total prices. When a cloud user requests job execution, the Scheduler allocates the requested job to the selected VM.


Cloud computing environment.


Figure 3 shows the structure of the VM. In this figure, VM Status Collector collects the status information of the VM, such as CPU utilization and memory space. VM Information Provider extracts resource information needed for job execution using the VM status Collector and delivers the resource information to VM Manager. Job execution Manager executes a requested job received from the coordinator and returns a job result to VM Manager, and VM Manager then delivers the result to the coordinator. Checkpoint Manager manages checkpointing status and the data checkpointed by the Checkpoint Manager are stored in Checkpoint Storage.


The structure of virtual machine.


3.2. Instances Types


The difference between the two instance types is as follows. In on-demand instances, a failure does not occur during task execution, but the cost is comparatively high. In contrast, the cost of spot instances is lower than that of on-demand instances. However, there is an inevitable risk of task failures encountered when the price of the instances becomes higher than the user bid.


Amazon allows users to bid on unused EC2 capacity among 42 types of spot instances [ 18 ]. Their prices, which are referred to as spot prices, are changing dynamically based on supply and demand. Figure 4 shows a spot price fluctuations example during seven days in December 2010 for c1-xlarge (High-CPU Spot Instances—Extra Large) [ 19 ]. Our proposed system model is based on the characteristics of Amazon EC2's spot instances.


(i)


The system provides a spot instance when a user bid is higher than the current price.


The system immediately stops the spot instance without any notice when a user bid becomes less than or equal to the current price. We refer to it as an out-of-bid event or a failure.


The system does not charge for the last partial hour when the system stops the spot instance.


The system does charges for the last partial hour when the user voluntarily terminates the spot instance.


The system provides the spot price history.


Price history of EC2's spot instances for c1-xlarge.


4. Estimated Interval-Based Checkpointing


In this section, we detail an estimated interval-based checkpointing for spot instances that includes the SLA, the moving average, Bollinger Bands, and the fault tolerance.


4.1. Price History-Based SLA


Figure 5 shows the SLA process between a user and an instance. A user determines an instance type and the bid price to begin tasks on the instance. The coordinator calculates the task execution time based on the user's decision. Then, the coordinator sends a request message to the selected instance to investigate the performance of the instance and calculates the expected execution time, the expected failure time, and the expected cost. Then, the coordinator sends a user the expected execution time and cost. When a task is completed on the selected instance, the coordinator receives the outcome from the instance and sends it to the user. As shown in Figure 5. the prediction function in the coordinator plays an important role in our SLA process because it performs the estimation using price history.


4.2. Estimation Using Moving Average and Bollinger Bands


In EIC, the checkpointing operation is performed by analyzing the price variation at certain time intervals in the past. We use the moving average which estimates a job execution time and a cost from the analyzed data. The estimations are combined with the failure probability to calculate the thresholds for the checkpointing operation. The proper estimation of the execution time and cost is crucial for the credibility of service providers to customers. For the probable estimation information, we use Bollinger Bands. It suggests the upper and lower bounds of the execution time and the cost. We show in Section 5 that the actual execution time and the cost fall within the bounds.


In this paper, we introduce a terminology referred to as estimated interval (EI). Figure 6 shows an illustrative definition of the EI. The detailed definitions are as follows.


(i)


Pure task time: the time to execute a task on a selected instance when there are no failures.


Past pure task time: a sum of time durations taken for task execution on the selected instance in the past, excluding failure durations. It is extracted from the price history.


Past failure time: a sum of failure durations in the past to execute a task. A failure occurs when the current user bid is below the past spot price.


Estimated interval (EI): the sum of the past pure task time and the past failure time.


Moving average EI: the average of EIs computed using moving average.


Expected cost: the average of costs charged for task execution in EIs.


Moving average relation.


Pure task time and past pure task time


Moving average estimated interval


Based on the simple moving average (SMA), we calculate an estimated time SMA ET and an estimated price SMA Ep by the average of EIs in the price history, as shown below: (1) SM A ET ( n ) = E T 1 + E T 2 + E T 3 + ⋯ + E T n n. SM A EP ⁡ ( n ) = E P 1 + E P 2 + E P 3 + ⋯ + E P n n. where ET i is the estimated time in an interval i. EP ⁡ i is the estimated price in an interval i. and n is the number of intervals, as depicted in Figure 6 .


Based on the weighted moving average (WMA), WMA ET and WMA EP are averages of the estimated time and the estimated price from the price history with a weight using SMA. They are calculated by (2) WM A ET = ∑ i = 1 n α i E T n - i + 1 ∑ i = 1 n α yo.     WM A EP ⁡ = ∑ i = 1 n α i E P n - i + 1 ∑ i = 1 n α yo. where α is a weight. The α i is assigned the highest for the most recent EI 1 . and it is decreased from the most recent EI 1 to the last EI N. The weight α i is calculated by (3) α i = n + 1 - i ∑ i = 1 n i. where i and n are the interval number and the last interval number, respectively. By adjusting the weight, we empirically reduce the gap between the estimation and actual data from real execution. The Bollinger Bands presents the range of estimation using a moving average and a standard deviation. Generally, the Bollinger Bands itself adopts a moving average as the middle value. We use WMA as the middle value of the Bollinger Bands because the near past is more likely to be influencing the near future. The upper and lower bounds of the Bollinger Bands are defined as


(i)


Middle Bollinger Band = WMA


Lower Bollinger Band = Middle Bollinger Band − 2 σ


Upper Bollinger Band = Middle Bollinger Band + 2 σ


where σ is the standard deviation of EIs. Figure 7 illustrates the range of Bollinger Bands using training data that consist of each estimation value in EIs. The training data are obtained from (an) N-zone EIs.


Bollinger Bands acquisition.


4.3. Fault Tolerance Mechanisms Using Checkpoints


On a spot instance, a task failure occurs when the user’s bid is below the current spot price. This problem has been solved by using the checkpointing, one of fault tolerance mechanism [ 9 ]. In this section, we detail the existing checkpointing methods and our proposed scheme.


Figure 8 illustrates the hour-boundary checkpointing (HBC). In this scheme, the checkpointing operation is performed in the hour boundary, and a user pays the biding price on an hourly basis. Upon the task failure, the task is restarted from the position of the last checkpoint.


Figure 9 illustrates the rising edge-driven checkpointing (REC). In this scheme, the checkpointing operation is performed when both the price of the spot instance is raised (i. e. rising-edge) and the price is less than the user bid. It increases the number of checkpoints when the spot price fluctuates frequently. The critical problem in REC is that the rollback time becomes long when a rising edge does not appear for a long period of time after a checkpoint is taken. This could lead to a longer time for the task completion than HBC.


Rising edge-driven checkpointing.


Figure 10 illustrates checkpointing operation in EIC. It is basically performed using two thresholds, price and time, based on the expected execution time according to the price history. Now, let t start and t end denote a start point and an end point, respectively, in the total of EIs. Based on t start and t end. we obtain the price threshold (PriceTh) and the time threshold ( TimeT h P i ), which are used as thresholds in EIC.


Estimated interval-based checkpointing.


The price threshold, PriceTh, can be calculated by (4) PriceTh = W P min ⁡ + Use r bid 2. where Use r bid represents the user bid and W P min ⁡ represents an available minimum price using a moving average in the time duration between t start and t end .


First, the P min ⁡ E I i represents the minimum price in the time duration between T start and T end in EI i (5) P min ⁡ E I i = PriceMin ( t start. t end ). Second, the W P min ⁡ is the average of the product of P min ⁡ E I i and sum of the weighted value α yo. (6) W P min ⁡ = ∑ i = 1 n α i × P min ⁡ E I n - i + 1 ∑ i = 1 n α yo. The time threshold of price P i. TimeT h P i. is calculated by (7) TimeT h P i = ∑ j = 1 n α j × TimeT h P i E I n - j + 1 ∑ j = 1 n α j. In each EI, the time threshold of price P i. TimeT h P i E I j. is calculated by (8) TimeT h P i E I j = AvgTim e P i E I j ( t start. t end ) × ( 1 - F P i E I j ). where F P i E I j is the failure probability of price P i and AvgTim e P i E I j ( t start. t end ) represents the average execution time of P i in an interval between T start and T end in E I j. The failure occurrence probability F P i E I j is calculated by (9) F P i E I j = ∑ ex t k ∈ E I j. P i ex t k after    failure ∑ ex t k ∈ E I j. P i ( ex t k after    failure + ex t k after    non-failure ). where ex t k is the execution task time to invoke an interval k when a price p i is in E I j. The after failure function is calculated when the current spot price is above or equal to the user bid. The after non-failure function is calculated when the current spot price is below the user bid.


Using these two thresholds, our scheme performs checkpointing operations in two cases. First, a checkpointing is performed when there is a rising edge in the actual price variation, and the actual price falls in between the user bid and the price threshold; second, the checkpointing is based on the time threshold, which is computed with the failure probability and the average execution time as in ( 7 ). It is performed if the current execution time exceeds the time threshold computed at the same past price as the current one.


Algorithm 1 shows the checkpointing and recovery algorithm used in EIC. The flag represents the occurrence of a task failure, and it is initially set to false. The checkpointing process repeats until all tasks are completed. When the task execution is normal (i. e. the flag is false), the scheduler performs a checkpoint operation to cope with the potential job failure (lines 5–25). The scheduler estimates the execution time before the initial task starts (lines 6–9). The recovery process is performed when the flag is true (lines 11–14). The checkpoints are performed in two cases (lines 15–20). If the rising spot price falls in between the user bid and the price threshold, the scheduler performs a checkpointing operation (lines 15–17). If the execution time exceeds the time threshold, the scheduler also performs a checkpointing operation (lines 18–12). When a task failure occurs, the flag is set to true (lines 22–24). Lines 26–29 show the detailed process of time estimation. Lines 30–33 and 34–37 show the detailed process of checkpointing and recovery, respectively.


Algorithm 1: Checkpointing and recovery algorithm.


(1)   // Input: user’s requested task and bid


(2)   // Output: total task execution time and total cost


(3)  Boolean   F _flag = false // a flag representing occurrence of a task failure


(4)   Boolean EI_flag = true // a EI_flag representing a task start


(5)   while (! Task execution finishes) do


We also extracted the failure probability with the current bid price according to each spot price in the past (12-15-2010–12-19-2010), as drawn in Figure 4. The probability was used to determine the time threshold in EIC. Figure 11 shows the failure occurrence probability for the c1.xlarge instance. The X - axis and Y - axis denote the spot price and the failure probability for a given user bid, respectively.


Failure occurrence probability.


Figure 11 states that the failure occurrence probability changes according to the user bid. As anticipated, if the bid price is low, the failure probability is high across all spot prices. If the bid price is high, the failure probability is low. Thus, it is reasonable to predict that the task execution time will be longer if the failure probability is high because both the total failure time and total rollback time increase.


Figure 12 estimated execution time, cost, and Bollinger Bands of each EI zone computed with the past price history. Figures 12(a) and 12(b) show the execution time and the cost according to the user bid respectively. Estimated interval (EI) with the weighted moving average which is calculated by using the past spot price history, is necessary for the user bid. Figure 12 also shows the Bollinger Bands (Lower_BB, Middle_BB, and Upper_BB) according to the user bid.


Estimated execution time, cost, and Bollinger Bands of each EI zone computed with the past price history.


Estimated Bollinger Bands of execution time


Estimated Bollinger Bands of costs


Figure 13 shows the task execution time, the cost, and Bollinger Bands when the number of EI zones is increased. For example, 2 in x - axis means that two zones (EI 1 and EI 2 ) are included in the simulation.


Estimated execution time, cost, and Bollinger Bands of expanded EI zones computed with the past price history.


Estimated Bollinger Bands of execution time


Estimated Bollinger Bands of costs


Estimated Bollinger Bands of product


Figure 14 shows the rollback times of EIC, HBC, and REC. The rollback time is calculated from a failure point in time to the last checkpointed time. The EIC lessens the average rollback time by 72.46% over HBC and 88.49% over REC.


Comparison of rollback time according to user bid.


Figure 15 shows the performance comparison of EIC, HBC, and REC. The EIC reduces the number of checkpoints on average by 35.97% and 37.92%, compared to HBC and REC, respectively. Consequently, the EIC shortens the task execution time by 35.53% over the HBC and 40.40% over REC.


Performance comparison of checkpointing schemes according to user bid.


Number of failures and checkpoints


Total task execution time and total failure time


Figure 16 shows the total costs according to the user bid. The EIC reduces the cost on average by 36.26% and 38.52% over HBC and REC, respectively.


Comparison of total costs.


Figure 17 shows the combined performance metric, the product of the total execution time, and cost. According to the user bid, the EIC shows marginal variation due to the lowest amount of rollback time among the compared schemes. The EIC achieves the relative benefits in the combined metric on average by 55.73% and 60.95% when compared to HBC and REC, respectively.


Comparison of combined metrics (total task execution time and costs).


Figure 18 shows how well the actual execution time and cost are predicted with EIC according to the user bid. The actual execution time and cost are located between the lower and upper bounds of the Bollinger Band. Figures 18(a) and 18(b) show that they are close to the middle point of the Bollinger Band. The experiments show that the adoption of the Bollinger Band would provide reliable estimations to Cloud users.


Comparison of actual EIC outputs (execution time and cost) and estimations according to the user bid.


Total task execution time


5.2. Task Time Impact on Performance


In this section, we analyzed the performance of computing-type instances according to the task time. Table 3 shows the simulation parameters. Note that the execution time in simulations varies from minimum time to maximum time at the granularity of the time interval.


Simulation parameters and values.


Figure 19 shows the rollback time of EIC, HBC, and REC according to the task time. The increase rate of rollback time in EIC is small compared to HBC and REC. The rollback times are increased by 5.25 times, 15.84 times, and 12.41 times for EIC, HBC, and REC, respectively, when the task times are increased from the minimum to the maximum times. EIC lessens the rollback time on average by 80.61% and 84.36% over HBC and REC, respectively.


Comparison of rollback times according to the task time.


Figure 20 shows the performance comparison of EIC, HBC, and REC. Figures 20(a) and 20(b) show the numbers of failures and checkpoints, and total task execution time and total failure time according to the task time. The EIC reduces the number of checkpoints on average by 31.97% and 32.93% compared to HBC and REC, respectively. Thus, the EIC achieves performance improvements in the task execution time on average by 43.79% and 48.25% over HBC and REC, respectively.


Performance comparison according to the task time.


Number of failures and checkpoints


Total task execution time and total failure time


Figure 21 shows the total cost according to the task time. The EIC reduces the cost on average by 39.38% and 40.08% compared to HBC and REC, respectively.


Comparison of total costs.


Figure 22 shows the combined performance metric, the product of the total task execution time, and cost. The rate of increase in the product in EIC is lowest among the compared schemes. The EIC achieves a performance improvement on average by 65.36% and 68.51% when compared to HBC and REC, respectively.


Comparison of combined metrics (product of total execution time and cost).


Figure 23 shows the estimation accuracies according to the task time. The actual execution time and cost are located between the lower and upper bounds of the Bollinger Band. Figures 23(a) and 23(b) prove that the actual execution time and cost are close to the middle point of the Bollinger Band. They state that the EIC would be able to offer approximate ranges of total costs and task execution time to Cloud users.


Comparison of actual EIC outputs (execution time and cost) and estimations according to the task time.


Total task execution time


Overall, the EIC significantly reduces the number of checkpoint trials compared to the existing checkpointing schemes. Furthermore, the rollback time is much lesser because the EIC adaptively performs the checkpointing operation according to the execution time and price. Simulation results showed that our scheme achieved the cost-efficiency by reducing rollback time regardless of the resource types of spot instances.


Analyzing history to compute the estimated interval requires overheads such as CPU time. However, computations only involve failure probability, execution time and cost estimations, and a range of the Bollinger Band. Considering the advancement of modern computers, we strongly believe it would take the minimal amount of overheads for computations.


In this paper, we proposed the estimated interval-based checkpointing (EIC) in the unreliable cloud computing environment. The weighted moving average estimates the execution time and cost using the price history of spot instances to improve the performance and stability of task processing. The EIC performs the checkpointing operation, based on price and time thresholds. The thresholds are determined based on the moving average and the failure probability. They are used to determine the checkpointing position to recover from the potential failures of spot instances arising from the price fluctuation. The Bollinger Bands determines the lower and upper bounds of the estimated execution time and cost. The ranges are informed to users as guidance for their decision. The simulation results reveal that, compared to the hour-boundary checkpointing (HBC) and rising edge-driven checkpointing (REC), the EIC reduces the number of checkpoints by 35.97% and 37.92%, respectively, on average according to the user bid. It also reduces the rollback time by 72.46% and 88.49% on average. Consequently, the task execution time is decreased with ETC by 35.53% over HBC and 40.40% over REC. The EIC also provides the benefit of the cost reduction by 36.26% over HBC and 38.52% over REC, on average.


John Bollinger - Bollinger on Bollinger Bands 2001 | ISBN: 0071373683 | Español | 288 pages | PDF | 21 MB


John Bollinger is a giant in today's trading community. His Bollinger Bands sharpen the sensitivity of fixed indicators, allowing them to more precisely reflect a market's volatility. By more accurately indicating the existing market environment, they are seen by many as today's standard-and most reliable-tool for Descriptionting expected price action.


Now, in Bollinger on Bollinger Bands, Bollinger himself explains how to use this extraordinary technique to compare price and indicator action and make sound, sensible, and profitable trading decisions.


Concise, straightforward, and filled with instructive charts and graphs, this remarkable book will be essential reading for all serious traders, regardless of market. Bollinger includes his simple system for implementation, and techniques for combining bands and indicators.


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Received 21 January 2014; Accepted 6 May 2014; Published 28 May 2014


Academic Editor: Young-Sik Jeong


Copyright © 2014 Daeyong Jung et al. This is an open access article distributed under the Creative Commons Attribution License. which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Abstract


In cloud computing, users can rent computing resources from service providers according to their demand. Spot instances are unreliable resources provided by cloud computing services at low monetary cost. When users perform tasks on spot instances, there is an inevitable risk of failures that causes the delay of task execution time, resulting in a serious deterioration of quality of service (QoS). To deal with the problem on spot instances, we propose an estimated interval-based checkpointing (EIC) using weighted moving average. Our scheme sets the thresholds of price and execution time based on history. Whenever the actual price and the execution time cross over the thresholds, the system saves the state of spot instances. The Bollinger Bands is adopted to inform the ranges of estimated cost and execution time for user's discretion. The simulation results reveal that, compared to the HBC and REC, the EIC reduces the number of checkpoints and the rollback time. Consequently, the task execution time is decreased with EIC by HBC and REC. The EIC also provides the benefit of the cost reduction by HBC and REC, on average. We also found that the actual cost and execution time fall within the estimated ranges suggested by the Bollinger Bands.


1. Introduction


Cloud computing is a computing paradigm that constitutes an advanced computing environment that evolved from utility and grid computing. The infrastructure of cloud computing typically includes a collection of interconnected and virtualized computers from parallel and distributed systems. The virtual computers are dynamically provided to consumers as one or more unified computing resources, based on service level agreements (SLA) established through negotiation between the service provider and consumers [1 –5 ]. Typically, cloud computing services provide a high level of scalability of computing resources combined with Internet technology to multiple customers [6 ]. Currently, there are several commercial cloud systems in service such as Amazon EC2 [7 ], GoGrid [8 ], and FlexiScale [9 ].


In most of these cloud services, there is a notion of an instance to provide users with resources in a cost-efficient manner. An instance means the virtual machine (VM) that serves for the user's need. In general, instances are classified into two types: on-demand instances and spot instances. On-demand instances are charged for the compute capacity on an hourly basis without the long-term commitment. This frees users from the costs and complexities of planning, purchasing, and maintaining hardware and transforms commonly large fixed costs into much smaller variable costs [7 ]. On the other hand, spot instances allow users to bid on unused compute capacity and utilize those instances for as long as the current spot price is below their bid. The spot price is changing periodically based on supply and demand. When users' bids meet or exceed the price, they gain access to the available spot instances. If users are flexible as to when applications should run, spot instances can significantly decrease the cost as reported in [7 ]. Nevertheless, there is a risk of task failures, which occurs when the spot price of the instance becomes higher than the bid price.


To efficiently handle this problem, the checkpointing schemes have been proposed in the research community [10. 11 ]. The checkpointing saves the execution status of tasks if a certain condition is met and then recovers the task status from the last saved point upon a failure. It allows a reduction in the execution time and cost in an unreliable computing environment. On a legal side, the SLA is typically used for alleviating the uncertainty by specifying service details such as price and task execution time. SLA specifies the resource allocation and rental terms to consumers in agreement with providers.


In this paper, we propose the estimated interval-based checkpointing (EIC), which improves the efficiency over our previous study [12 ]. The key idea is adopting the weighted moving average (WMA) and Bollinger Bands. The moving average is a history-based prediction scheme. The WMA sets a different weight for each time interval in the past and calculates the average of the weights. With these weights, the failure occurrence probability is obtained in each interval. The threshold for checkpointing is calculated based on the average failure probability. We apply two thresholds of price and time in EIC. In addition, we use the Bollinger Bands to inform users of estimated execution time and cost. In the stock market, the Bollinger Bands is a well-known analysis method. It is used to measure the high and low value level of the previous trading data. This method is used to predict the price bid in the stock market. We use the Bollinger Bands to calculate both the estimated execution time and the cost.


We have measured the number of checkpoint trials and total cost per spot instance for a user bid. Simulation results show that the EIC outperforms the existing schemes, hour-boundary checkpointing (HBC) [13 ] and rising edge-driven checkpointing (REC), [11 ] in terms of the number of checkpoints. Consequently, the EIC minimizes the execution time of applications and the time wasted by task failures.


The rest of this paper is organized as follows. Section 2 briefly describes related work on resource allocation, SLA, fault tolerance, moving average, and Bollinger Bands in cloud computing. Section 3 presents our system architecture. Section 4 presents our SLA, estimation, and checkpoint algorithms based on the price history of spot instances. Section 5 presents performance evaluations with simulations. Finally, Section 6 concludes the paper.


2. Related Work


Many researchers and companies have recently studied fault-tolerance techniques in two different environments of cloud computing: reliable environments, with on-demand instances [14. 15 ], and unreliable environments, with spot instances [11. 13. 16. 17 ]. The fault-tolerance techniques are more required in unreliable environments. Our study was performed in the latter category of the environments to provide the cost-effectiveness of task execution.


Spot instances are typically used in unreliable environments, and studies on spot instances focus on performing tasks at low monetary costs. The spot instances in the Amazon Elastic Compute Cloud (EC2) offer lower price at the expense of the reduced reliability [18 ]. Cloud exchange [19 ] supports the actual price history of EC2 spot instances. In the spot instances environment, there are numerous studies on resource allocation [16. 17 ], SLA [6. 20. 21 ], fault tolerance [10. 11. 13. 16 ], moving average [22. 23 ], and Bollinger Bands [24. 25 ].


On the resource allocation side, Voorsluys and Buyya [16 ] solve the problem of running computation-intensive tasks on a pool of intermittent VMs. To mitigate potential unavailability periods, the study proposed a multifaceted fault-aware resource provisioning policy. Their solution employs price and runtime estimation mechanisms. The proposed strategy achieves cost savings and stricter adherence to deadlines. Zhang et al. [17 ] introduced a solution of how best to match customer demand in terms of both supply and price and to maximize the provider's revenue and the customer's satisfaction in terms of VM scheduling. The proposed model is designed to solve the problem of discrete-time optimal control. This model achieves higher revenues than static allocation strategies and minimizes the average request waiting time. Our work differs from [16. 17 ] in that we focus on reducing the rollback time after a task failure, achieving the cost savings and reducing the total execution time.


On the SLA side, Andrzejak et al. [20 ] proposed a probabilistic decision model to help users decide a minimum cost according to an SLA between users and Amazon's EC2. The scheme is based on a probabilistic model for the optimization of cost, performance, and reliability. It improves the reliability of service by changing conditions dynamically to satisfy user requirements. Due to the dynamic nature of cloud computing, continuous monitoring of the quality of service (QoS) attributes is necessary to enforce SLAs. Two similar studies [6. 21 ] focus on cloud resource management in the reliable cloud environment. One is based on SLA monitoring and enforcement in a service-oriented architecture (SOA) [21 ], whereas the other focuses more on the resource management. The resource manager optimizes a global utility function that integrates both the SLA fulfillment degree and the computational costs [6 ]. Our paper differs from [6. 21 ] in that we deal with the resource management in the unreliable cloud environment.


On the fault tolerance side, two similar studies (HBC [13 ] and REC [11 ]) proposed enforcing fault tolerance in cloud computing with spot instances. Based on the actual price history of EC2 spot instances, they compared several adaptive checkpointing schemes in terms of monetary costs and job execution time. Goiri et al. [10 ] evaluated three fault tolerance schemes, checkpointing, migration, and job duplication, assuming that the communication cost is fixed. The migration-based scheme shows a better performance than the checkpointing or the job duplication-based scheme. Voorsluys and Buyya [16 ] also analyzed and evaluated the impact of checkpointing and migration on fault tolerance using spot instances. Our paper differs from [10. 11. 13. 16 ] in that we utilize double thresholds for fault tolerance.


On the moving average and Bollinger Bands side, the moving average takes the next observation data using the data in the past [22. 23 ]. Reference [22 ] introduced the simple moving average (SMA) and WMA. Reference [23 ] used the average data to apply weight according to each interval. It evaluates the average of price depending on the weight change. Our paper also adopts WMA to estimate price, execution time, and thresholds based on price history. However, we found that the estimation is not accurate enough. We overcome this shortcoming by applying Bollinger Bands to estimate the execution time and the price ranges. The Bollinger Bands, proposed by Bollinger [24 ], is a technical analysis method used in the stock market. It analyzes previous trades and determines the standard deviation. Daytrader [25 ] introduced a method to predict the range of Bollinger Bands. This prediction requires the selection of length of the moving average around which the Bollinger Bands are plotted, and standard deviations to calculate from this moving average. Our paper differs from [24. 25 ] in that we apply Bollinger Bands to predict both cost and execution time ranges in the unreliable cloud environment.


In our previous paper [12 ], we proposed a checkpoint scheme based on SLA to satisfy user requirements. Our previous study performs a checkpointing operation based on two thresholds: price and time. The estimated execution time is predicted using the price history of an instance only for the same amount of time in task execution in the past. This paper differs in that the Bollinger Bands was adopted to improve the accuracy of cost and execution time estimations with utilizing numerous estimation intervals of the past.


3. System Architecture


Figure 1 shows the cloud computing environment assumed in this paper, which basically consists of four entities: a cloud server, storage servers, cluster servers, and cloud users. The cloud server is connected to cluster servers and storage servers. The cluster server is composed of many nodes. Cloud users can access the cloud server via the cloud portal to utilize the nodes in the cluster servers as resources. Therefore, the cloud server takes responsibility of finding resources and spawning virtual machines to satisfy the user's requirements in terms of the SLA and QoS. The coordinator in the cloud server manages tasks and is responsible for the SLA management. We focus on the coordinator and the VM, which play important roles in our checkpointing scheme.


Figure 1: Cloud computing environment.


3.1. Layer Structure


Figure 2 shows the structure of the coordinator in the cloud server, which is composed of Scheduler, Estimation Predictor, VM Information Manager, History Manager, SLA Manager, QoS Manager, and VM Information Collector. In the coordinator, the four managers are responsible for generating and maintaining a list of available VMs, based on the information collected from VM Information Collector. The VM Information Collector collects VM information and provides it to VM information Manager. The VM Information Manager generates a list of CPU utilization, available memory and storage space, network bandwidth, and so on. The History Manager manages the history data, in which the past bid and execution time of spot instances are accumulated. SLA Manager and QoS Manager manage the SLA requirements and the QoS requirements, respectively. Estimation Predictor analyzes data taken from the other managers and calculates the range of estimation completion time and total prices. When a cloud user requests job execution, the Scheduler allocates the requested job to the selected VM.


Figure 2: Cloud computing environment.


Figure 3 shows the structure of the VM. In this figure, VM Status Collector collects the status information of the VM, such as CPU utilization and memory space. VM Information Provider extracts resource information needed for job execution using the VM status Collector and delivers the resource information to VM Manager. Job execution Manager executes a requested job received from the coordinator and returns a job result to VM Manager, and VM Manager then delivers the result to the coordinator. Checkpoint Manager manages checkpointing status and the data checkpointed by the Checkpoint Manager are stored in Checkpoint Storage.


Figure 3: The structure of virtual machine.


3.2. Instances Types


The difference between the two instance types is as follows. In on-demand instances, a failure does not occur during task execution, but the cost is comparatively high. In contrast, the cost of spot instances is lower than that of on-demand instances. However, there is an inevitable risk of task failures encountered when the price of the instances becomes higher than the user bid.


Amazon allows users to bid on unused EC2 capacity among 42 types of spot instances [18 ]. Their prices, which are referred to as spot prices, are changing dynamically based on supply and demand. Figure 4 shows a spot price fluctuations example during seven days in December 2010 for c1-xlarge (High-CPU Spot Instances—Extra Large) [19 ]. Our proposed system model is based on the characteristics of Amazon EC2's spot instances. (i) The system provides a spot instance when a user bid is higher than the current price. (ii) The system immediately stops the spot instance without any notice when a user bid becomes less than or equal to the current price. We refer to it as an out-of-bid event or a failure. (iii) The system does not charge for the last partial hour when the system stops the spot instance. (iv) The system does charges for the last partial hour when the user voluntarily terminates the spot instance. (v) The system provides the spot price history.


Figure 4: Price history of EC2's spot instances for c1-xlarge.


4. Estimated Interval-Based Checkpointing


In this section, we detail an estimated interval-based checkpointing for spot instances that includes the SLA, the moving average, Bollinger Bands, and the fault tolerance.


4.1. Price History-Based SLA


Figure 5 shows the SLA process between a user and an instance. A user determines an instance type and the bid price to begin tasks on the instance. The coordinator calculates the task execution time based on the user's decision. Then, the coordinator sends a request message to the selected instance to investigate the performance of the instance and calculates the expected execution time, the expected failure time, and the expected cost. Then, the coordinator sends a user the expected execution time and cost. When a task is completed on the selected instance, the coordinator receives the outcome from the instance and sends it to the user. As shown in Figure 5. the prediction function in the coordinator plays an important role in our SLA process because it performs the estimation using price history.


Figure 5: SLA processing.


4.2. Estimation Using Moving Average and Bollinger Bands


In EIC, the checkpointing operation is performed by analyzing the price variation at certain time intervals in the past. We use the moving average which estimates a job execution time and a cost from the analyzed data. The estimations are combined with the failure probability to calculate the thresholds for the checkpointing operation. The proper estimation of the execution time and cost is crucial for the credibility of service providers to customers. For the probable estimation information, we use Bollinger Bands. It suggests the upper and lower bounds of the execution time and the cost. We show in Section 5 that the actual execution time and the cost fall within the bounds.


In this paper, we introduce a terminology referred to as estimated interval (EI). Figure 6 shows an illustrative definition of the EI. The detailed definitions are as follows. (i) Pure task time: the time to execute a task on a selected instance when there are no failures. (ii) Past pure task time: a sum of time durations taken for task execution on the selected instance in the past, excluding failure durations. It is extracted from the price history. (iii) Past failure time: a sum of failure durations in the past to execute a task. A failure occurs when the current user bid is below the past spot price. (iv) Estimated interval (EI): the sum of the past pure task time and the past failure time. (v) Moving average EI: the average of EIs computed using moving average. (vi) Expected cost: the average of costs charged for task execution in EIs.


Figure 6: Moving average relation.


Based on the simple moving average (SMA), we calculate an estimated time SMA ET and an estimated price SMA Ep by the average of EIs in the price history, as shown below:


is the estimated price in an interval. and is the number of intervals, as depicted in Figure 6 .


Based on the weighted moving average (WMA), WMA ET and WMA EP are averages of the estimated time and the estimated price from the price history with a weight using SMA. They are calculated by


where is a weight. The is assigned the highest for the most recent EI 1 . and it is decreased from the most recent EI 1 to the last


The weight is calculated by


where and are the interval number and the last interval number, respectively. By adjusting the weight, we empirically reduce the gap between the estimation and actual data from real execution. The Bollinger Bands presents the range of estimation using a moving average and a standard deviation. Generally, the Bollinger Bands itself adopts a moving average as the middle value. We use WMA as the middle value of the Bollinger Bands because the near past is more likely to be influencing the near future. The upper and lower bounds of the Bollinger Bands are defined as (i) Middle Bollinger Band = WMA (ii) Lower Bollinger Band = Middle Bollinger Band − 2 σ (iii) Upper Bollinger Band = Middle Bollinger Band + 2 σ where σ is the standard deviation of EIs. Figure 7 illustrates the range of Bollinger Bands using training data that consist of each estimation value in EIs. The training data are obtained from (an) N-zone EIs.


Figure 7: Bollinger Bands acquisition.


4.3. Fault Tolerance Mechanisms Using Checkpoints


On a spot instance, a task failure occurs when the user’s bid is below the current spot price. This problem has been solved by using the checkpointing, one of fault tolerance mechanism [9 ]. In this section, we detail the existing checkpointing methods and our proposed scheme.


Figure 8 illustrates the hour-boundary checkpointing (HBC). In this scheme, the checkpointing operation is performed in the hour boundary, and a user pays the biding price on an hourly basis. Upon the task failure, the task is restarted from the position of the last checkpoint.


Figure 8: Hour-boundary checkpointing.


Figure 9 illustrates the rising edge-driven checkpointing (REC). In this scheme, the checkpointing operation is performed when both the price of the spot instance is raised (i. e. rising-edge) and the price is less than the user bid. It increases the number of checkpoints when the spot price fluctuates frequently. The critical problem in REC is that the rollback time becomes long when a rising edge does not appear for a long period of time after a checkpoint is taken. This could lead to a longer time for the task completion than HBC.


Figure 9: Rising edge-driven checkpointing.


Figure 10 illustrates checkpointing operation in EIC. It is basically performed using two thresholds, price and time, based on the expected execution time according to the price history. Now, let


denote a start point and an end point, respectively, in the total of EIs. Based on and. we obtain the price threshold (PriceTh) and the time threshold (


), which are used as thresholds in EIC.


Figure 10: Estimated interval-based checkpointing.


The price threshold, PriceTh, can be calculated by


where represents the user bid and represents an available minimum price using a moving average in the time duration between and .


represents the minimum price in the time duration between


Second, the is the average of the product of and sum of the weighted value.


where is the execution task time to invoke an interval


is in. The after failure function is calculated when the current spot price is above or equal to the user bid. The after non-failure function is calculated when the current spot price is below the user bid.


Using these two thresholds, our scheme performs checkpointing operations in two cases. First, a checkpointing is performed when there is a rising edge in the actual price variation, and the actual price falls in between the user bid and the price threshold; second, the checkpointing is based on the time threshold, which is computed with the failure probability and the average execution time as in (7 ). It is performed if the current execution time exceeds the time threshold computed at the same past price as the current one.


Algorithm 1 shows the checkpointing and recovery algorithm used in EIC. The flag represents the occurrence of a task failure, and it is initially set to false. The checkpointing process repeats until all tasks are completed. When the task execution is normal (i. e. the flag is false), the scheduler performs a checkpoint operation to cope with the potential job failure (lines 5–25). The scheduler estimates the execution time before the initial task starts (lines 6–9). The recovery process is performed when the flag is true (lines 11–14). The checkpoints are performed in two cases (lines 15–20). If the rising spot price falls in between the user bid and the price threshold, the scheduler performs a checkpointing operation (lines 15–17). If the execution time exceeds the time threshold, the scheduler also performs a checkpointing operation (lines 18–12). When a task failure occurs, the flag is set to true (lines 22–24). Lines 26–29 show the detailed process of time estimation. Lines 30–33 and 34–37 show the detailed process of checkpointing and recovery, respectively.


Algorithm 1: Checkpointing and recovery algorithm.


5. Performance Evaluation


Our simulations were conducted using the history data obtained from the Amazon EC2's spot instances [19 ], which was accumulated during a period from December 15, 2010 to December 22, 2010 as shown in Figure 4. The history data before December 20, 2010 were used to extract the expected execution time and failure probability for the proposed checkpointing scheme. The applicability of EIC was tested using the history data after December 20, 2010, which was also used in HBC and REC.


In the simulations, one type of spot instances, c1.xlarge, was applied to show the effect of the three checkpointing schemes on performance according to the user bid and the task time. Table 1 shows the applied resource type details used in Amazon EC2. The high-CPU instance offers more compute units than other resources (standard and high-memory instances) and is ideal for the compute-intensive applications. Under the simulation environments, we compare the performance of EIC with those of HBC and REC in terms of various metrics according to the user bid and task time.


Table 1: Resource type information.


5.1. User Bid Impact on Performance


Before analyzing the performance of EIC, we extracted the simulation specifics from the spot history presented in Figure 4. Table 2 shows the data used for simulation. The simulations were conducted with incrementing the user bid interval from minimum bid to maximum bid.


Table 2: Simulation parameters and values.


We also extracted the failure probability with the current bid price according to each spot price in the past (12-15-2010–12-19-2010), as drawn in Figure 4. The probability was used to determine the time threshold in EIC. Figure 11 shows the failure occurrence probability for the c1.xlarge instance. The X - axis and Y - axis denote the spot price and the failure probability for a given user bid, respectively.


Figure 11: Failure occurrence probability.


Figure 11 states that the failure occurrence probability changes according to the user bid. As anticipated, if the bid price is low, the failure probability is high across all spot prices. If the bid price is high, the failure probability is low. Thus, it is reasonable to predict that the task execution time will be longer if the failure probability is high because both the total failure time and total rollback time increase.


Figure 12 estimated execution time, cost, and Bollinger Bands of each EI zone computed with the past price history. Figures 12(a) and 12(b) show the execution time and the cost according to the user bid respectively. Estimated interval (EI) with the weighted moving average which is calculated by using the past spot price history, is necessary for the user bid. Figure 12 also shows the Bollinger Bands (Lower_BB, Middle_BB, and Upper_BB) according to the user bid.


Figure 12: Estimated execution time, cost, and Bollinger Bands of each EI zone computed with the past price history.


Figure 13 shows the task execution time, the cost, and Bollinger Bands when the number of EI zones is increased. For example, 2 in x - axis means that two zones (EI 1 and EI 2 ) are included in the simulation.


Figure 13: Estimated execution time, cost, and Bollinger Bands of expanded EI zones computed with the past price history.


Figure 14 shows the rollback times of EIC, HBC, and REC. The rollback time is calculated from a failure point in time to the last checkpointed time. The EIC lessens the average rollback time by 72.46% over HBC and 88.49% over REC.


Figure 14: Comparison of rollback time according to user bid.


Figure 15 shows the performance comparison of EIC, HBC, and REC. The EIC reduces the number of checkpoints on average by 35.97% and 37.92%, compared to HBC and REC, respectively. Consequently, the EIC shortens the task execution time by 35.53% over the HBC and 40.40% over REC.


Figure 15: Performance comparison of checkpointing schemes according to user bid.


Figure 16 shows the total costs according to the user bid. The EIC reduces the cost on average by 36.26% and 38.52% over HBC and REC, respectively.


Figure 16: Comparison of total costs.


Figure 17 shows the combined performance metric, the product of the total execution time, and cost. According to the user bid, the EIC shows marginal variation due to the lowest amount of rollback time among the compared schemes. The EIC achieves the relative benefits in the combined metric on average by 55.73% and 60.95% when compared to HBC and REC, respectively.


Figure 17: Comparison of combined metrics (total task execution time and costs).


Figure 18 shows how well the actual execution time and cost are predicted with EIC according to the user bid. The actual execution time and cost are located between the lower and upper bounds of the Bollinger Band. Figures 18(a) and 18(b) show that they are close to the middle point of the Bollinger Band. The experiments show that the adoption of the Bollinger Band would provide reliable estimations to Cloud users.


Figure 18: Comparison of actual EIC outputs (execution time and cost) and estimations according to the user bid.


5.2. Task Time Impact on Performance


In this section, we analyzed the performance of computing-type instances according to the task time. Table 3 shows the simulation parameters. Note that the execution time in simulations varies from minimum time to maximum time at the granularity of the time interval.


Table 3: Simulation parameters and values.


Figure 19 shows the rollback time of EIC, HBC, and REC according to the task time. The increase rate of rollback time in EIC is small compared to HBC and REC. The rollback times are increased by 5.25 times, 15.84 times, and 12.41 times for EIC, HBC, and REC, respectively, when the task times are increased from the minimum to the maximum times. EIC lessens the rollback time on average by 80.61% and 84.36% over HBC and REC, respectively.


Figure 19: Comparison of rollback times according to the task time.


Figure 20 shows the performance comparison of EIC, HBC, and REC. Figures 20(a) and 20(b) show the numbers of failures and checkpoints, and total task execution time and total failure time according to the task time. The EIC reduces the number of checkpoints on average by 31.97% and 32.93% compared to HBC and REC, respectively. Thus, the EIC achieves performance improvements in the task execution time on average by 43.79% and 48.25% over HBC and REC, respectively.


Figure 20: Performance comparison according to the task time.


Figure 21 shows the total cost according to the task time. The EIC reduces the cost on average by 39.38% and 40.08% compared to HBC and REC, respectively.


Figure 21: Comparison of total costs.


Figure 22 shows the combined performance metric, the product of the total task execution time, and cost. The rate of increase in the product in EIC is lowest among the compared schemes. The EIC achieves a performance improvement on average by 65.36% and 68.51% when compared to HBC and REC, respectively.


Figure 22: Comparison of combined metrics (product of total execution time and cost).


Figure 23 shows the estimation accuracies according to the task time. The actual execution time and cost are located between the lower and upper bounds of the Bollinger Band. Figures 23(a) and 23(b) prove that the actual execution time and cost are close to the middle point of the Bollinger Band. They state that the EIC would be able to offer approximate ranges of total costs and task execution time to Cloud users.


Figure 23: Comparison of actual EIC outputs (execution time and cost) and estimations according to the task time.


Overall, the EIC significantly reduces the number of checkpoint trials compared to the existing checkpointing schemes. Furthermore, the rollback time is much lesser because the EIC adaptively performs the checkpointing operation according to the execution time and price. Simulation results showed that our scheme achieved the cost-efficiency by reducing rollback time regardless of the resource types of spot instances.


Analyzing history to compute the estimated interval requires overheads such as CPU time. However, computations only involve failure probability, execution time and cost estimations, and a range of the Bollinger Band. Considering the advancement of modern computers, we strongly believe it would take the minimal amount of overheads for computations.


6. Conclusion


In this paper, we proposed the estimated interval-based checkpointing (EIC) in the unreliable cloud computing environment. The weighted moving average estimates the execution time and cost using the price history of spot instances to improve the performance and stability of task processing. The EIC performs the checkpointing operation, based on price and time thresholds. The thresholds are determined based on the moving average and the failure probability. They are used to determine the checkpointing position to recover from the potential failures of spot instances arising from the price fluctuation. The Bollinger Bands determines the lower and upper bounds of the estimated execution time and cost. The ranges are informed to users as guidance for their decision. The simulation results reveal that, compared to the hour-boundary checkpointing (HBC) and rising edge-driven checkpointing (REC), the EIC reduces the number of checkpoints by 35.97% and 37.92%, respectively, on average according to the user bid. It also reduces the rollback time by 72.46% and 88.49% on average. Consequently, the task execution time is decreased with ETC by 35.53% over HBC and 40.40% over REC. The EIC also provides the benefit of the cost reduction by 36.26% over HBC and 38.52% over REC, on average.


Conflict of Interests


The authors declare that there is no conflict of interests regarding the publication of this paper.


Reconocimiento


This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MEST) (NRF-2012R1A2A2A 02046684).


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Q. Zhang, E. Gürses, R. Boutaba, and J. Xiao, “Dynamic resource allocation for spot markets in clouds,” in Proceedings of the 11th USENIX Conference on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services (Hot-ICE '11) . pp. 1–6, 2011.


Amazon EC2 spot Instances, 2014, http://aws. amazon. com/ec2/spot-instances .


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Daytrader, “Bollinger bands as an entry technique,” 2000.


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Bollinger Bands Indicator - Binary Options Indicator


How to use the Bollinger Bands Indicator for Binary Options Trading? More videos: http://www. realbinaryoptionsreviews. com/indicators MORE INFO ABOUT BOLLINGER BANDS INDICATOR (READ MORE BELOW)


Bollinger Bands indicator is a volatility indicator that will help you determine the market volatility and price divergence in predetermined time frame. In simple words Bollinger Bands will tell you how volatile the market is and will measure the maximum divergence of a price according to historical movements taking in consideration the volatility of the market.


Bollinger Bands indicator is one of the most useful indicators for binary options trading that works best when trading the ranging markets with low volatility. However it can be also used to trade the highly volatile trending markets in a bit different manner.


Common use of the Bollinger Bands indicator is to search for the entry positions for the price to go down when the price breaks the upper Bollinger band and to search the entry positions for the price to go up when the price breaks the lower Bollinger band.


Expiry times for binary options trading based on the Bollinger Bands indicator can differ depending on the time frame chart being used. If a trader is trading on the 1 minute chart the recommended expiry times are from 5 to 10 minutes. If the trader is trading based on the 5 minute chart the recommended expiry times are from 5 to 30 minutes.


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Trading Trend along with self-discipline can be hugely hard. When the Trader utilizes higher influence she or he simply leaves hardly any space to become incorrect. Trading along with really restricted halts could lead to 10 as well as 20 consecutive cease outs prior to the Trader will find the Trade along with powerful impetus as well as directionality.


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Because of this, numerous Traders would rather Trade Range-bound methods. Please be aware that after We talk about ‘Range-bound Trading’ I’m not really talking about the actual traditional description from the term ‘Range’. Trading in this cost atmosphere entails separating foreign currencies which are Trading within stations, after which promoting towards the top of the actual funnel and purchasing at the end from the funnel. This is often a really useful technique, however, essentially, it’s nevertheless the Trend-based concept — although one which anticipates a good impending countertrend. Accurate Range Traders do not worry about path. The actual fundamental presumption associated with Range Trading is actually which whichever method the actual foreign currency moves, this will in all probability go back in order to it’s stage associated with source. Actually, Range Traders wager upon the chance that costs may Trade with the exact same amounts often, and also the Traders’ objective would be to crop individuals oscillations with regard to revenue again and again.


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One more question about moneystream..which one of them would it be. Is it the: MoneyFlow Index (MFI PCF – 10-days).


If you have any more links about metastock-analyses-video. Please write to me.


Best regards Jeru


To tell you the truth, I haven’t got a clue! “moneystream” is a proprietary indicator, and it is evident that Worden has no intention to make the formula public. All is not lost, though: Mark Chaikin and David Bostian are mentioned, this is important. Both Mark Chaikin’s “Money Flow” and Gene Quong and Avram Soudek’s “Money Flow Index” are already built in Metastock – just call them up. David Bostian isn’t – which is where I come in. The formula below is the “Intraday Intensity” oscillator version; you can find all this in John Bollinger’s “Bollinger on Bollinger Bands” text, ISBN 0-07-137368-3; check Amazon. com, you might get a deal. ____________________________________________ %b Intraday Intensity: ____________________________________________ var1 := (((2*C)-H)-L); var1 := If(var1=0,0.01,var1); var2 := (H-L); var2 := If(var2=0,0.01,var2); var3 := Sum((var1/var2)* VOLUME,21); var4 := (Sum(VOLUME,21)); var5 := (var3/var4); II := var5 *100;


II _____________________________________________ Use it as a proxy for Moneystream, as I think it is the closer you could probably find to it. Saludos.


Originally Posted by jensfrode


Im frustrated Eduardo:


im copy the formula for ms and bop and your last formula and i get a message :this is not a recognized name, constant or operator


What am i doing wrong.


And who do i get like a bar graf in tsv. Tsv working.


_____________________________________________ Use it as a proxy for Moneystream (what do you mean:-).


Dont give up on me :-)


Best wishes Jeru


I meant “instead of”, ie. use Intraday Intensity instead of Moneystream; this is because Moneystream is a proprietary indicator, ie. the guy who designed it will not tell anyone how he did it, but Intraday Intensity is probably the next best thing you’ll find. I’d like you to click here: http://www. equis. com/Customer/Resour. as/Primer. aspx and download the Formula Primer; print it; read it; read it again, and practice, because it contains a lot of info about developing your own indicators. Also, you could do worse than to subscribe to MetaStock Tips & Tools newsletter, the gold standard in Metastock training; click here and judge: http://www. metastocktips. co. nz/index. html As for your question about the presentation of an indicator, read my post to you in the Equis Forum.


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Once these factors are all accounted for, it instructs the expert advisor where to stop buying or selling. Having evolved from the concept of trading bands, Bollinger Bands are an indicator that allows users to compare volatility and relative price levels over a period time.


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