هندسة نظام تجارة الطاقة

أنظمة وحلول تجارة الطاقة.
في قطاع الطاقة سريع الخطى، يحتاج التجار إلى تصور السوق بسرعة وبدقة.
لدينا خبرة في بناء ذات جودة عالية، جذابة بصريا وبديهية منصات تداول الطاقة التي توفر مجموعة واسعة من خيارات التصور البيانات، من وجهات النظر السوق المخصصة وتحليل الاتجاهات لتسعير المعلومات لمجموعة من السلع وعلى منصات متعددة.
يستخدم العديد من عملائنا مسرعات التصور البيانات لدائرة الرقابة الداخلية، والتي توفر:
الدعم الكامل لبيانات السوق التاريخية والداخلية. نظم التداول الشمعدان، وحجم والأسعار المؤامرات. تحليل الاتجاه باستخدام البولنجر باند، مؤشر القوة النسبية، المتوسطات المتحركة وأكثر من ذلك.
بنية مرنة.
وتعتبر الخدمات الخلفية الفعالة والمتكاملة ضرورية للتطهير التجاري الدقيق وفي الوقت المناسب، مما يقلل من الخطأ والتكلفة. نحن نساعد العملاء على تطوير أبنية مرنة وحدات، وربط منصات التداول إلى المقاصة، ولديهم خبرة واسعة في تنفيذ ستب (من خلال المعالجة المباشرة) لتجارة السلع باستخدام بروتوكول فيكس.
مما يتيح لك الحافة في الأسواق العصيبة.
إن سجلنا في القطاع المالي يعطينا فهما شاملا للمسائل التقنية التي ينطوي عليها تقديم حلول تداول السلع وإدارة المخاطر. ومع نمو الطلب على الحلول عبر المنصات، يزداد اهتمام عملائنا بتجربتنا مع HTML5 وجافا سكريبت لتزويدهم بأنظمة وحلول تجارية قابلة للتكيف وسريعة، قادرة على التعامل مع كميات هائلة من البيانات. وتستكمل هذه المهارات من خلال خبرتنا الواسعة مع التقنيات التقليدية مثل جافا. لدينا تجربة المستخدم تصميم الممارسة يمكن أن تساعد أيضا في تطوير واجهة المستخدم الأكثر فعالية وجميلة لتعطيك ميزة في الأسواق المضطربة.
شعبنا.
رئيس التنمية & # 8211؛ ادنبره.
& # 8220؛ التعقيد المتزايد وعولمة سوق الطاقة يجعل من الضروري أن التجار قادرون على التعرف بسرعة على الاتجاهات والأنماط. للسماح بإعطاء هذا الاعتبار الدقيق للعرض الديناميكي لبيانات السوق. & # 8221؛
تحويل تجربة العملاء في أسواق الطاقة.
كيف ساعد نهج بقيادة أوكس سوق الطاقة الرائدة في أوروبا لتغيير اللعبة في تجربة العملاء عبر الإنترنت.
إنيرجي تجارة نظام المقاصة.
اقترب عميلنا من سكوت المنطق لتحل محل نظام المقاصة الشيخوخة المحلية مع حل استضافت الحديثة.
الرسم البياني لتجارب الطاقة.
وقد اقترب سكوت المنطق من قبل مزود أنظمة التداول السلع لإضافة الرسم البياني عالية الجودة لمنصة التداول الطاقة.
معرفة المزيد عن خبرة سكوت المنطق في تجارة الطاقة وحلول المقاصة.
القطاعات.
وظائف.
حقوق الطبع والنشر © 2018 سكوت المنطق المحدودة جميع الحقوق محفوظة.

بنية نظام تداول الطاقة
تقدم إترم سيستمز، ليك حلول وخدمات مبتكرة تتطابق مع استراتيجياتك التجارية والتجارية وإدارة المخاطر.
حلول تلبي احتياجاتك.
صناعة الخبرة الرائدة.
الخدمات المهنية الشاملة التي تعرف في جميع أنحاء صناعة الطاقة مجموعات إترم أنظمة، ليك فوق بقية.
حلول عالمية ناجحة لتداول الطاقة.
استراتيجيات السلع المتعددة.
تقدم حلول التداول متعددة السلع الفعالة من خلال التصميم والخدمات المهنية استثنائية.
وظيفة التداول المتطورة.
دولة ما توصلت إليه التكنولوجيا.
نهج مبتكرة وخدمات مهنية استثنائية لصناعة الطاقة.
والهندسة المعمارية التجارية وتكامل النظام.
بدأت نظم إترم، ليك في عام 2008.
نحن نقدم خدمات استشارية من ذوي الخبرة ل.
لماذا تختار إترم سيستمز، ليك لحلول إدارة المخاطر الخاصة بك؟
حظ، عن، ال التعريف، سوق الطاقة، إندستريز.
آخر الأخبار في مجال الطاقة من الولايات المتحدة وخارجها.
نظم إترم، ليك يوفر فرصا كبيرة لتطوير و.
نظم إترم، ليك فخور لرعاية والتطوع في العديد من المبادرات.
اتصل بنا.
لمزيد من المعلومات، يرجى الاتصال بنا على.
إترم سيستمز، ليك | حلول تكنولوجيا المعلومات لتداول الطاقة وإدارة المخاطر.
بالنسبه لشركتنا.
إترم، وإدارة المخاطر، وإدارة مخاطر تداول الطاقة، وتداول الطاقة، استشارات تداول الطاقة، وتصميم برامج الطاقة.
آخر التحديثات.
نظم إترم المختارة من قبل هورايزون ترادس تكنولوجيز لمنصة التداول متعددة السلع العالمية. اقرأ أكثر. إترم سيستمز، ليك تعلن عن إطلاق موقعها الجديد! إترم سيستمز، ليك لديها عنوان مكتب جديد في ممر الطاقة في هيوستن، تكساس!
روابط مفيدة.
معلومات الاتصال.
1001 S. الألبان أشفورد أردي.
هيوستن، تكساس 77077.
حقوق الطبع والنشر 2018. نظم إترم، ليك. كل الحقوق محفوظة.
حقوق الطبع والنشر 2018. نظم إترم، ليك. كل الحقوق محفوظة.

ويبور هو منصة تداول الطاقة الخضراء القائمة على بلوكشين.
وهي تمكن منتجي الطاقة الخضراء من زيادة رأس المال بإصدار رموز الطاقة القابلة للتداول.
ما هو ويبور؟
والناس ينتظرون بالفعل ل ويبور بيع رمزية.
أيضا الانضمام إلينا:
بدعم من الشركاء العالميين.
شركاء الطاقة.
الشركاء الاستراتيجيين.
كما شوهد على.
ويبور هو جعل تلك السلطة قابلة للتداول ويمكن الوصول إليها لأي شخص. و أنها تعطي الناس المزيد من السيطرة.
هذا هو المكان بلوكشين يمكن أن تؤثر الصناعات ولكن أيضا رفاه كوكبنا.
ويبور هو أنه يربط المستهلكين مباشرة إلى منتجي الطاقة الخضراء.
سهولة بيع وشراء الرموز تسمح للتجارة العالمية للطاقة الموقرة على بلوكشين.
وهذا سوف تصبح إتيريوم القائم على محطة الطاقة الافتراضية.
وسوف تنمو القيمة الحقيقية لرموز ور عندما توسع ويبور وبدأ المزيد من منتجي الطاقة المتجددة باستخدام المنصة.
نموذج الرمز الثوري.
أو شاهد الفيديو أدناه.
تقدم ور رمزية الاستخدامات المتعددة والفوائد.
مزاد الطاقة الخضراء.
أصحاب ور رمزية الحصول على الأولوية إلى مزادات بيع الطاقة الجديدة رمزية على منصة ويبور. أكثر ور الرموز لديك، وتخصيص أكبر من الطاقة التي تحصل عليها.
الأولوية المزاد يسمح شراء الطاقة بأفضل الأسعار. كما أنه يزيد من قيمة التداول ور ورمز بسبب الطلب الإضافي من المشترين الطاقة الكبيرة.
بركة تبرع للطاقة الخضراء.
ويعلن مطور مشروع الطاقة الخضراء عن بيع مصنع مميز على منصة ويبور وينشر رموز الطاقة.
يتم وضع 0.9٪ من رموز الطاقة هذه تلقائيا في برك المساهمين. فقط ور أصحاب رمزية الوصول إلى هذا التجمع.
وبمجرد أن يتم بناء المصنع ويتم إنتاج الطاقة ور أصحاب رمزية تماما مثل أصحاب الطاقة رمزية يمكن استخدام الطاقة، والنقدية أو إعادة الاستثمار في تزايد تجمع المساهمات.
التبرعات تنمو مع منصة.
هنا هو نمو حجم مساهمة الطاقة الخضراء لأول 6 سنوات على أساس توقعاتنا. يمكنك أن ترى بوضوح أن نمو تجمع المساهمة يرتبط ارتباطا مباشرا بنمو المنصة. ومكافأتك في الطاقة الخضراء من شأنه أن يكسب حتى في 3 سنوات من القيمة الدفترية ومن ثم تسريع نموها إلى أبعد من ذلك.
يرجى ملاحظة أن حجم بركة التبرع يعتمد على عدد من المشاريع الخضراء على استعداد لاستخدام منصة ويبور. الجدول المقدم هو مجرد إسقاط.
الانضمام إلى مجتمعنا في برقية.
أهداف جمع التبرعات.
منصة الطاقة.
ويبور تمكن منتجي الطاقة المتجددة لرفع رأس المال عن طريق إصدار رموز الطاقة الخاصة بهم. هذه الرموز تمثل الطاقة التي تلتزم لإنتاج وتسليم. تسهل عملية توصيف الطاقة تبسيط وفتح النظام البيئي الحالي للاستثمار في الطاقة حاليا. ونتيجة لذلك، يمكن لمنتجي الطاقة أن يتاجروا مباشرة مع مشترين الطاقة الخضراء (المستهلكين والمستثمرين) وأن يرفع رأس المال عن طريق بيع الطاقة مقدما، بأسعار أقل من السوق. ويضمن توكين الطاقة السيولة ويوسع فرص الحصول على رأس المال. يتم التعرف على الحل بلوكشين ويبور حاليا من قبل إليرينغ، واحدة من مشغلي نظام النقل الأكثر ابتكارا في أوروبا.
ترميز الطاقة وبيع الرموز الطاقة.
استكشاف المستقبل القريب نفسك!
الانضمام إلى الثورة الخضراء.
فريقنا.
نيك لديه 10 عاما من الخبرة في العمل في قطاع الطاقة. قام نيك ببناء محطات الطاقة الشمسية وشراء / بيع الطاقة الخضراء والشهادات الخضراء. وعلاوة على ذلك، شارك في استيراد / تصدير الطاقة بين البلدان.
ArtD «راس هو ممارسة القانون في واحدة من أكبر شركات المحاماة في دول البلطيق، حيث انه مسؤول عن جميع فينتيش بلوكشين والتشفير العملات ذات الصلة واللوائح. وهو أيضا رئيس جمعية ليتوانيا فينتيش ومرتين المعترف بها باعتبارها الراعي ليتوانيا تمويل الجماعي من قبل مفوضية الاتحاد الأوروبي.
كان كاسبار رائدة في قطاع الخدمات الأوروبية و دسو على مدى السنوات العشر الماضية. كمتحدث، استراتيجي التكنولوجيا والمشكلة معقدة حلالا كان نشطا جدا ليس فقط في وطنه استونيا، حيث كان مؤخرا كتو من إلكتريليفي & # 8211؛ الاستونيين أكبر دسو، ولكن في جميع أنحاء أوروبا. وكان عقد منصب كتو تقدما طبيعيا نظرا لمواقفه وواجباته السابقة داخل إليكتريليفي. وكان مسؤولا عن تطوير وتنفيذ الاستراتيجية.
خطة وخارطة طريق التكنولوجيا، فضلا عن إدارة العمارة الشاملة لتكنولوجيا المعلومات للبنية التحتية والتطبيقات. وقبل هذا الدور، كان يقود عملية دمج التكنولوجيا الرقمية مع البنية التحتية لتوزيع الكهرباء كرئيس للإدارة. وكان مسؤولا عن رؤية التنمية إلكتريليفي الذكية الشبكة، منهجية التنفيذ، والبنية التقنية والأمن السيبراني. وساعد في بناء قسم تكنولوجيا الشبكة الرقمية باعتباره ورشة عمل متعددة التخصصات هندسة الطاقة والعمليات. الجانب العمليات من قسم التعامل مع عمليات متر الذكية، وأنظمة دعم أوت وتكنولوجيا المعلومات الاستعانة بمصادر خارجية التنسيق.
جون ماتونيس هو المدير المؤسس لمؤسسة بيتكوين ورئيس غلوبيتكس، منصة تشفير العملات. وشملت حياته المهنية مناصب رفيعة المؤثرة في فيسا الدولية، فيريسين، بنك سوميتومو، و هوشميل.
شركة رائدة في مجال التكنولوجيا تدعمها المشاريع لأكثر من 20 عاما. مؤسسة مؤسس ميتاكاف، أسرع موقع في العالم لتقاسم الفيديو الذي يصل إلى أكثر من 50 مليون قطعة في ذروتها. في السابق، تأسست إيال شبكات الاتصال، واحدة من الشبكات الاجتماعية الأولى في عام 1999. إيال ديه كان زعيم الفكر الصريح على كريبتوكيرنسي في إسرائيل وهو البيانو موهوب وموسيقي باس.
ديفيد أ. كوهين هو مؤسس ورئيس مجلس إدارة دسنترال، شركة الأمن السيبراني القائم على بلوكشين. وهو معروف عالميا لعمله الرائد على منصات الأنظمة الذكية البرمجيات. في عام 2018، كان اسمه ديفيد واحدة من أفضل 100 المحركون والهزازات في سمارتغريد من قبل جرينتيش وسائل الإعلام. وكان ديفيد المؤسس والرئيس التنفيذي لشركة إنفوتيليتي حيث كان رائدا في الشبكة Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ Ђќ.......... ديفيد هو عضو مؤسس في مجلس الهندسة غريدويز (غواك) الذي كان له دور أساسي في إطلاق رؤية لصناعة سمارتغريد. وكان ديفيد عضوا في مؤسسة يوتا و إوتا رمزية كريبتوكيرنسي.
بنية النظام المعقدة. هيكي هو المستشار الرئيسي لفريق الخدمات الاستشارية المنجنيق Lab†™ ق. في هذا الدور يقود النشاط العام لدينا ثلاثة عروض الخدمات الاستشارية؛ تكامل النظم، دعم تقييم المخاطر بعد الأمن وتحليل الأعمال & # 038؛ دعم حوكمة تقنية المعلومات. وقبل العمل في مختبرات كاتابولت، عمل هيكي في شركة إلكتريليفي، أكبر مشغل لنظام التوزيع في إستونيا، حيث شغل أدوارا مختلفة على مدى 10 سنوات. بدأ كمهندس سكادا، والمشاركة في وقيادة مشاريع مختلفة مثل الانتقال إلى شبكات سكادا القائمة على بروتوكول الإنترنت ورفع مستوى نظام سكادا Elektrilevi†™ ق. وفي وقت لاحق كان مسؤولا أيضا عن تحديد حالات استخدام مركز التحكم في جميع مشاريع تكنولوجيا المعلومات الرئيسية وتنفيذ المشاريع في إلكتريليفي، بما في ذلك نظام معلومات العملاء، والعديد من أنظمة إدارة الأصول وأنظمة القياس الذكية. في حين كان مهندس الشبكة الذكية، استشارة هيكي مع الزملاء في تحديد حلول جديدة مثل التحليلات المتقدمة، استجابة جانب الطلب ومنصات إدارة الجيل الموزعة.
ليراز سيري هو مهنيا وايتهات الهاكر بجنون العظمة، في وقت مبكر بيتكوين مؤيد، والمؤسس المشارك ل تورنكي لينكس التي القوى & أمب؛ يحمي 100،000+ ملقمات في جميع أنحاء العالم. في 18 قام بمسح الإنترنت بأكمله عن نقاط الضعف. وفي وقت لاحق، في الجيش، شارك في تأسيس وحدة إلكترونية إسرائيلية. اليوم، انه يبشر المصدر المفتوح، تشفير & أمب؛ اللامركزية، مع تسويق الأمن المتسامح مع الفشل في التطبيقات ذات المخاطر العالية (و / 2007/066333).
يقدم ستيفن الاستشارات، والمعاملات التجارية والتجارية لصناديق الاستثمار، وتخصصات الطاقة والتجار المستقلين في أسواق الطاقة العالمية، وتشمل المشاريع الأخيرة في مجالات دعم الاستثمار / المعاملات واستراتيجية التداول.
براد هو رجل أعمال، مستثمر، معلمه ومستشار الذي بدأ وتمهيد العديد من الشركات من البداية إلى مرحلة النضج على مدى 20 عاما. براد هو المؤسس المشارك حاليا & # 038؛ الشريك الإداري لشركة كرود منتور، وهي شركة استشارية للتمويل الجماعي الاستراتيجي تركز على المكاتب القطرية، و كريبتوكيرنسيز، بلوكشين، و توكين تعمل بالطاقة المنظمات.
آرون بيكلر هو المهنية لعبة البوكر السابق، الذي تم استخدام معرفته العميقة من نظرية اللعبة وإدارة المخاطر بنجاح في السوق كريبتوكيرنسي لعدة سنوات.
وقد أسس اثنين من وكالات التسويق الرقمي المزدهرة مع العملاء الدوليين الرئيسيين بما في ذلك ثروة 500 شركة.
صابر أريا هو الرئيس التنفيذي ومؤسس اثنين من وكالات التسويق الرقمي البارزة، مع كل محفظة متنوعة من العملاء بما في ذلك العديد من الشركات فورتشن 500.
صابر لديه شغف للبحث عن ومساعدة الناشئة الناشئة كمستشار ومستثمر. ب هو يركز مجلسه ليس فقط على الأفكار التجارية الرائعة، ولكن بنفس القدر من الأهمية، والفرق وراء كل project. В В SaberlЂ ™ التسويق و وقد أدت الخبرة الاستشارية له أن يكون متحدثا رئيسيا في العديد من الأحداث مثل أفيليات العالم آسيا حيث ألهم الحشد مع فريقه “0 إلى 7 أرقام في سنة واحدة Ђќ.
المؤسس المشارك والرئيس التنفيذي لشركة سيمبلكس وعضو مجلس إدارة جمعية بيتكوين الإسرائيلية. سيمبلكس هو فينتيش & أمب؛ شركة الأمن السيبراني إدخال التجار إلى عالم دون احتيال.
خبير سابق في غوغل و بوسينيس جيك و ستارتوب و ريادة الأعمال وتنمية الصادرات (سيد) يؤدي إلى الأسواق الأوروبية الجديدة. على مر السنين عمل مع مجموعة من مشاريع التحول الرقمي داخل وخارج جوجل.
العمل في وكالة الأداء الرقمي الرائدة إيبروسبيكت، جيتيس يقود اكتساب المستخدم الرقمي وتطوير استراتيجيات المبيعات من العلامات التجارية العالمية والمحلية الأكثر شهرة بما في ذلك إيربالتيك، أدميرال الأسواق وغيرها.
مؤسس هواندجا، منطقة أوروبا الوسطى والشرقية & # 8217؛ أول منصة تمويل الجماعي غير ربحية مع أكثر من 2 مليون يورو رفعت للمشاريع الإبداعية والمنظمات غير الحكومية. أكثر من عقد من الخبرة الرائدة برامج ومشاريع تطوير الشبكة، بما في ذلك مبادرات مثل Arenguidee. ee و Rahvakogu. ee. عضو مجلس إدارة الصندوق الإستوني للطبيعة. عضو مجلس الفكر الأستوني ورئيس المجلس. المؤسس المشارك ل ليت & # 8217؛ ق القيام به! المبادرة العالمية التي نسقت 50،000 شخص لتنظيف ما يصل إلى 10،000 طن من النفايات غير القانونية في يوم واحد. بحلول عام 2017 أكثر من 10 مليون شخص شاركوا في Let†™ ق تفعل ذلك! إجراءات التنظيف في 100+ البلدان.
جيفري هو راوي دولي والتسويق & # 038؛ الاتصالات المهنية مقرها في جنوب فرنسا. مع أكثر من 20 عاما من الخبرة في العمل مع B - إلى - B والعلامات التجارية الاستهلاكية، وقال انه يساعد الشركات التي ترغب في القيام بأعمال تجارية على المستوى الدولي تحقيق أهدافهم ماركوم. لديه خبرة واسعة في مجال الاتصالات السلكية واللاسلكية، تقنيات عمليات، المدن الذكية، الشبكة الذكية / القياس، الطاقة والعمل مع دسو الأوروبي & # 8217؛ ق على قضايا الأمن السيبراني.
داريوس روجيفيسيوس من ذوي الخبرة في بناء وتزايد الأعمال الناجحة القائمة على التكنولوجيا، بعد أن باعت اثنين من له المبتدئة السابقة في السنوات ال 4 الماضية وحدها.
باستخدام مهاراته لتنفيذ استراتيجيات النمو الفعال، وتنفيذ التنفيذ، والوفاء بالمواعيد النهائية، وقال انه ساعد الشركات العاملة في بلوكشين، فنلندا التكنولوجيا، والروبوتات والتكنولوجيا الحيوية القطاعات. في العام الماضي عملت داريوس مع العديد من المشاريع إيكو، ومساعدتهم على وضع استراتيجية ناجحة إيكو، خطة التسويق والنموذج الرمزية.
شريك في استشارات العلامة التجارية والاستراتيجية بي & أمب؛ دو. عاش وعملت في 20+ بلدا كمدير الإبداعي، استراتيجي، منظم، والاستشاري. زبائنه ميزة أي صناعة يمكن تخيلها، وتمتد من أبطال المحليين للشركات الكبيرة متعددة الجنسيات.
الدكتور تاداس جوسيكاس هو المسؤول عن تحليلات البيانات والتعلم الآلي داخل المنصة. وهو مؤسس والرئيس التنفيذي لشركة الجنس أي وهي شركة الذكاء الاصطناعي تمكين الشركات للتفاعل مع عملائها بطريقة ذكية عاطفيا.
في السابق قامت تاداس ببناء وإدارة فرق علوم البيانات في القطاعين الخاص والعام ورائدة في تطبيق خوارزميات التعلم الآلي على مجموعات بيانات متنوعة، مثل بيانات وسائل الاعلام الاجتماعية غير المهيكلة، وبيانات سلسلة التوريد أو بيانات العمليات الحكومية.
أكمل الدكتوراه. في علم الأعصاب الحسابي في جامعة كامبريدج مع نتائج البحوث في المجلات العلمية الرائدة في العالم ™ مثل الطبيعة و يناس.
بلوكشين ومطور العقد الذكية والاستشاري. لديه مصلحة شخصية في بناء منتجات وأنظمة جديدة مدعومة بتقنية بلوكشين، ويساعد الفريق في تقديم المشورة بشأن كيفية تطوير الشبكات اللامركزية.
10+ سنوات من الخبرة الملونة جلب العلامات التجارية والعملاء أقرب إلى بعضها البعض، وتلميع هويات ™ العلامات التجارية والأصوات، وتمكين الشركات على تطوير الحوار مع عملائها.
7 سنوات من التدريب العملي والممارسة الأكاديمية في مجال الاتصالات، مع التركيز على وسائل الاعلام الاجتماعية وإدارة السمعة على الانترنت. أستا لديها خبرة في العمل مع العلامات التجارية المحلية والعالمية الرائدة، والنحت الرسالة الصحيحة، والاستماع باهتمام إلى العملاء وخلق حوار مفيد لكلا الجزأين.
جاوة العليا و مونغودب شهادة مهنية مع المعرفة واجهة المستخدم قوية والمهارات. ومن ذوي الخبرة في العمل في الفرق والبيئات الموزعة دوليا، شارك بنجاح في تطوير أنظمة العمارة الموزعة واسعة النطاق مع مجموعة متنوعة من التكنولوجيات.

تجارة الأرض الهندسة المعمارية.
اللغات المتوفرة.
خيارات التنزيل.
عرض مع أدوبي ريدر على مجموعة متنوعة من الأجهزة.
جدول المحتويات.
تجارة الأرض الهندسة المعمارية.
نظرة عامة التنفيذية.
زيادة المنافسة، وارتفاع حجم بيانات السوق، والطلبات التنظيمية الجديدة هي بعض القوى الدافعة وراء التغيرات في الصناعة. تحاول الشركات الحفاظ على قدرتها التنافسية من خلال تغيير استراتيجياتها التجارية باستمرار وزيادة سرعة التداول.
يجب أن تتضمن الهندسة المعمارية القابلة للحياة أحدث التقنيات من مجالات الشبكات والتطبيقات. يجب أن تكون وحدات لتوفير مسار يمكن إدارته لتطوير كل مكون مع الحد الأدنى من تعطيل للنظام العام. ولذلك فإن العمارة المقترحة من قبل هذه الورقة تستند إلى إطار الخدمات. نحن نفحص الخدمات مثل الترا-- منخفضة الكمون الرسائل، رصد الكمون، البث المتعدد، والحوسبة، والتخزين والبيانات والتطبيق الظاهري، ومرونة التداول، والتداول التنقل، والعميل رقيقة.
يجب أن يتم بناء الحل للمتطلبات المعقدة من منصة التداول من الجيل التالي مع عقلية شاملة، عبور حدود الصوامع التقليدية مثل الأعمال والتكنولوجيا أو التطبيقات والشبكات.
الهدف الرئيسي لهذه الوثيقة هو توفير مبادئ توجيهية لبناء منصة تداول الكمون منخفضة للغاية مع تحسين الإنتاجية الخام ومعدل الرسالة لكل من بيانات السوق وأوامر التداول فيكس.
ولتحقيق ذلك، نقترح تقنيات الحد من الكمون التالية:
• سرعة عالية الاتصال بين إنفينيباند أو 10 جيجابايت في الثانية الاتصال لمجموعة التداول.
• ناقل الرسائل عالية السرعة.
• تسريع التطبيق عبر ردما دون إعادة تطبيق التطبيق.
• في الوقت الحقيقي رصد الكمون وإعادة توجيه حركة التداول إلى المسار مع الحد الأدنى من الكمون.
اتجاهات الصناعة والتحديات.
الجيل التالي من أبنية التداول يجب أن تستجيب لزيادة الطلب على السرعة والحجم والكفاءة. على سبيل المثال، من المتوقع أن يتضاعف حجم بيانات سوق الخيارات بعد إدخال خيارات التداول بيني في عام 2007. وهناك أيضا متطلبات تنظيمية لتنفيذ أفضل، والتي تتطلب معالجة الأسعار التحديثات بمعدلات التي تقترب من 1M مسغ / ثانية. للتبادلات. كما أنها تتطلب رؤية في نضارة البيانات وإثبات أن العميل حصلت على أفضل تنفيذ ممكن.
على المدى القصير، سرعة التداول والابتكار هي عوامل التفريق الرئيسية. يتم التعامل مع عدد متزايد من الصفقات من خلال تطبيقات التداول الحسابية وضعت في أقرب وقت ممكن إلى مكان تنفيذ التجارة. وهناك تحد مع هذه & كوت؛ الصندوق الأسود & كوت؛ ومحركات التداول هي أنها تزيد من حجم الزيادة بإصدار أوامر فقط لإلغائها وإعادة تقديمها. سبب هذا السلوك هو عدم وجود رؤية في أي مكان يقدم أفضل تنفيذ. التاجر البشري هو الآن & كوت؛ مهندس مالي، & كوت؛ a & كوت؛ كوانت & كوت؛ (المحلل الكمي) مع مهارات البرمجة، الذين يمكن ضبط نماذج التداول على الطاير. وتطور الشرکات أدوات مالیة جدیدة مثل المشتقات المناخیة أو الصفقات الصناعیة عبر الأصول، وتحتاج إلی نشر التطبیقات الجدیدة بسرعة وبطریقة قابلة للتطویر.
على المدى الطويل، ينبغي أن يأتي التمايز التنافسي من التحليل وليس المعرفة فقط. تجار نجمة الغد يتحملون المخاطر، ويحققون رؤية العميل الحقيقية، ويضربون باستمرار السوق (المصدر عب: www-935.ibm/services/us/imc/pdf/ge510-6270-trader. pdf).
وقد كانت مرونة الأعمال أحد الشواغل الرئيسية للشركات التجارية منذ 11 سبتمبر 2001. وتتراوح الحلول في هذا المجال من مراكز بيانات زائدة تقع في مناطق جغرافية مختلفة ومتصلة بأماكن تجارية متعددة لحلول التاجر الافتراضية التي تقدم لمتداولي الطاقة معظم وظائف أرضية التداول في مكان بعيد.
صناعة الخدمات المالية هي واحدة من الأكثر تطلبا من حيث متطلبات تكنولوجيا المعلومات. وتشهد هذه الصناعة تحولا معماريا نحو العمارة الموجهة نحو الخدمات (سوا)، وخدمات الويب، والمحاكاة الافتراضية لموارد تكنولوجيا المعلومات. سوا يستفيد من الزيادة في سرعة الشبكة لتمكين ديناميكية ملزمة والافتراضية مكونات البرمجيات. وهذا يسمح بإنشاء تطبيقات جديدة دون أن تفقد الاستثمار في النظم والبنية التحتية القائمة. وللمفهوم القدرة على إحداث ثورة في الطريقة التي يتم بها التكامل، مما يتيح تخفيضات كبيرة في تعقيد وتكلفة هذا التكامل (جيغاسباسز / دونلواد / MerrilLynchGigaSpacesWP. pdf).
وهناك اتجاه آخر يتمثل في توطيد الخوادم في مزارع ملقمات مراكز البيانات، في حين أن مكاتب التاجر لديها ملحقات كفم وعملاء رقيقة جدا (مثل حلول سونراي و هب للنصل). تتيح شبكات منطقة المترو عالية السرعة لبيانات السوق أن تكون متعددة الإرسال بين مواقع مختلفة، مما يتيح التمثيل الافتراضي لقاعة التداول.
الهندسة المعمارية عالية المستوى.
ويصور الشكل 1 البنية عالية المستوى لبيئة تجارية. يقع مصنع السهم ومحركات التداول الخوارزمية في مجموعة التداول عالية الأداء في مركز بيانات الشركة أو في البورصة. يقع التجار البشر في منطقة تطبيقات المستخدم النهائي.
وظيفيا هناك نوعان من مكونات التطبيق في بيئة تجارية المؤسسة والناشرين والمشتركين. يوفر ناقل الرسائل مسار الاتصال بين الناشرين والمشتركين.
هناك نوعان من حركة المرور الخاصة ببيئة التداول:
• بيانات السوق - يحمل معلومات التسعير للأدوات المالية، والأخبار، وغيرها من المعلومات ذات القيمة المضافة مثل التحليلات. وهو أحادي الاتجاه والكمون جدا حساسة، تسليم عادة عبر أودب الإرسال المتعدد. ويقاس في التحديثات / ثانية. و مبس. وتتدفق بيانات السوق من تغذية واحدة أو عدة خلاصات خارجية، تأتي من مزودي بيانات السوق مثل البورصات ومجمعات البيانات و إنز. كل مزود لديه شكل بيانات السوق الخاصة بها. يتم تلقي البيانات من قبل معالجات الأعلاف، والتطبيقات المتخصصة التي تطبيع وتنظيف البيانات ومن ثم إرسالها إلى المستهلكين البيانات، مثل محركات التسعير، وتطبيقات التداول حسابي، أو التجار البشر. كما تقوم الشركات البيعية بإرسال بيانات السوق إلى عملائها وشركات الشراء مثل صناديق الاستثمار وصناديق التحوط ومديري الأصول الأخرى. وقد تختار بعض شركات الشراء الحصول على تغذية مباشرة من البورصات، مما يقلل من الكمون.
الشكل 1 تجارة العمارة لجانب شراء / بيع شركة جانبية.
لا يوجد معيار الصناعة لتنسيقات بيانات السوق. كل تبادل لها شكل الملكية. مزودي المحتوى المالي مثل رويترز وبلومبرغ تجميع مصادر مختلفة من بيانات السوق، وتطبيعه، وإضافة الأخبار أو التحليلات. ومن الأمثلة على الأعلاف الموحدة هي ردف (خلاصة بيانات رويترز)، و روف (ريوترز وير فورمات)، وبيانات بلومبرغ للخدمات المهنية.
ولتقديم بيانات أقل عن بيانات زمن الاستجابة، قام كل من البائعين بإصدار خلاصات بيانات السوق في الوقت الفعلي والتي تكون أقل معالجتها ولديها تحليلات أقل:
- بلومبرغ B - الأنابيب مع B - الأنابيب، بلومبرغ دي الأزواج تغذية بيانات السوق الخاصة بهم من منصة التوزيع لأن محطة بلومبرغ غير مطلوب للحصول على B - الأنابيب. وقد أعلنت وومبات ورويترز أعلاف معالجات دعم B - الأنابيب.
قد تقرر الشركة تلقي الخلاصات مباشرة من التبادل لتقليل وقت الاستجابة. يمكن أن تكون المكاسب في سرعة الإرسال بين 150 ميلي ثانية إلى 500 ميلي ثانية. هذه الأعلاف هي أكثر تعقيدا وأكثر تكلفة، وعلى الشركة أن تبني وتحافظ على النباتات الخاصة بهم شريط (فينانسيتيش / مميزة / showArticle. jhtml؟ أرتيكليد = 60404306).
• أوامر التداول - هذا النوع من حركة المرور يحمل الصفقات الفعلية. فمن ثنائية الاتجاه والكمون جدا حساسة. ويقاس في الرسائل / ثانية. و مبس. أوامر تنشأ من جانب شراء أو بيع الجانب شركة ويتم إرسالها إلى أماكن التداول مثل إكسهانج أو إن للتنفيذ. الشكل الأكثر شيوعا لنقل النظام هو فيكس (فينانسيال إنفورماتيون إكسهانج-فيكسبروتوكول /). وتسمى التطبيقات التي تعالج رسائل فيكس محركات فيكس وهي واجهة مع أنظمة إدارة النظام (أومز).
يسمى التحسين إلى فيكس فاست (فيكس أدابتد فور سترامينغ)، والذي يستخدم مخطط ضغط لتقليل طول الرسالة، وفي الواقع، تقليل زمن الاستجابة. ويستهدف فاست أكثر إلى تسليم بيانات السوق ولها القدرة على أن تصبح معيارا. فاست يمكن أن تستخدم أيضا كمخطط ضغط لتنسيقات بيانات السوق الملكية.
للحد من وقت الاستجابة، قد تختار الشركات إنشاء الوصول المباشر إلى الأسواق (دما).
دما هي العملية الآلية لتوجيه نظام الأوراق المالية مباشرة إلى مكان التنفيذ، وبالتالي تجنب تدخل طرف ثالث (تاورغروب / ريزارتش / كونتنت / glossary. jsp؟ بادج = 1 & أمب؛ غلوسارييد = 383). دما يتطلب اتصال مباشر إلى مكان التنفيذ.
ناقل الرسائل هو برامج الوسيطة من البائعين مثل تيبكو، 29West، رويترز رمدس، أو منصة مفتوحة المصدر مثل أمكب. يستخدم ناقل الرسائل آلية موثوقة لتقديم الرسائل. ويمكن أن يتم النقل عبر تكب / إب (تيبكومز، 29West، رمدس، و أمكب) أو أودب / الإرسال المتعدد (تيبكورف، 29West، و رمدس). أحد المفاهيم المهمة في توزيع الرسائل هو & كوت؛ ساحة المشاركات، & كوت؛ وهي مجموعة فرعية من بيانات السوق التي تحددها معايير مثل رمز المؤشر أو الصناعة أو سلة معينة من الأدوات المالية. ينضم المشتركون إلى مجموعات الموضوعات التي تم تعيينها لموضوع واحد أو عدة مواضيع فرعية من أجل الحصول على المعلومات ذات الصلة فقط. في الماضي، تلقى جميع التجار جميع بيانات السوق. في الأحجام الحالية من حركة المرور، وهذا سيكون دون المستوى الأمثل.
تلعب الشبكة دورا حاسما في البيئة التجارية. يتم نقل بيانات السوق إلى الطابق التجاري حيث يقع التجار البشري عبر شبكة عالية السرعة الحرم الجامعي أو مترو منطقة. كما أن توافرها العالي ووقت الاستجابة المنخفض، فضلا عن الإنتاجية العالية، هما أهم المقاييس.
بيئة التداول عالية الأداء لديها معظم مكوناتها في مركز خدمة مركز البيانات. لتقليل وقت الاستجابة، تحتاج محركات التداول الخوارزمية إلى تحديد موقعها بالقرب من معالجات التغذية ومحركات فيكس وأنظمة إدارة الطلبات. نموذج النشر البديل لديه أنظمة التداول الحسابية الموجودة في تبادل أو مزود خدمة مع اتصال سريع لتبادل متعددة.
نماذج النشر.
هناك نوعان من نماذج النشر لمنصة تداول عالية الأداء. قد تختار الشركات أن يكون لها مزيج من الاثنين:
• مركز بيانات الشركة التجارية) الشكل 2 (- هذا هو النموذج التقليدي، حيث يتم تطوير منصة تداول كاملة والحفاظ عليها من قبل الشركة مع روابط اتصال لجميع أماكن التداول. الكمون يختلف مع سرعة الروابط وعدد من القفزات بين الشركة والأماكن.
الشكل 2 نموذج النشر التقليدي.
• املوقع املشرتك يف مكان التداول) البورصات، مقدمي اخلدمات املالية) فسب (() السكل 3
تقوم الشركة التجارية بنشر منصة التداول الآلية في أقرب وقت ممكن إلى أماكن التنفيذ لتقليل وقت الاستجابة.
الشكل 3 نموذج النشر المستضاف.
خدمات المنحى تجارة العمارة.
نحن نقترح إطارا موجها نحو الخدمات لبناء بنية تجارية من الجيل التالي. ويوفر هذا النهج إطارا مفاهيميا ومسارا للتنفيذ يستند إلى نمطية التقليل من التبعيات وتقليلها إلى أدنى حد.
ويوفر هذا الإطار للمنشآت منهجية للقيام بما يلي:
• تقييم حالتها الراهنة من حيث الخدمات.
• إعطاء األولوية للخدمات استنادا إلى قيمتها بالنسبة لألعمال.
• تطوير منصة التداول إلى الدولة المطلوب باستخدام نهج وحدات.
تعتمد بنية التداول عالية األداء على الخدمات التالية، كما هو محدد في إطار بنية الخدمات الممثلة في الشكل 4.
الشكل 4 إطار عمل الخدمة للتداول عالي الأداء.
الجدول 1 وصف الخدمات وتكنولوجياتها.
الترابط منخفضة جدا الرسائل.
الأجهزة والأجهزة، وكلاء البرمجيات، ووحدات التوجيه.
نظام التشغيل و I / O الافتراضية، الوصول عن بعد الوصول المباشر (ردما)، محركات تفب حمولة (تو)
الوسيطة التي تتوازى معالجة التطبيق.
الوسيطة التي تسرع الوصول إلى البيانات للتطبيقات، على سبيل المثال، التخزين المؤقت في الذاكرة.
النسخ المتماثل متعدد الإرسال بمساعدة الأجهزة من خلال الشبكة؛ متعدد الطبقات 2 والطبقة 3 التحسينات.
المحاكاة الافتراضية لأجهزة التخزين (فسانز)، النسخ المتماثل للبيانات، النسخ الاحتياطي عن بعد، والمحاكاة الافتراضية للملف.
مرونة التداول والتنقل.
موازنة تحميل المحلية وموقع وارتفاع شبكات الحرم الجامعي توافر.
منطقة واسعة خدمات التطبيقات.
تسريع التطبيقات عبر اتصال وان للتجار المقيمين خارج الحرم الجامعي.
خدمة العميل رقيقة.
إزالة اقتران موارد الحوسبة من المطاريف التي تواجه المستعمل النهائي.
خدمة التراسل المنخفض جدا.
يتم توفير هذه الخدمة من قبل حافلة الرسائل، وهو نظام البرمجيات التي يحل مشكلة ربط العديد من التطبيقات لكثير. ويتكون النظام من:
• مجموعة من مخططات الرسائل المحددة مسبقا.
• مجموعة من رسائل الأوامر المشتركة.
• بنية تحتية مشتركة للتطبيق لإرسال الرسائل إلى المستلمين. يمكن أن تقوم البنية التحتية المشتركة على وسيط رسالة أو على نموذج نشر / اشتراك.
المتطلبات الرئيسية لحافلة الرسائل من الجيل التالي هي (المصدر 29West):
• أقل وقت ممكن ممكن (على سبيل المثال، أقل من 100 ميكروثانية)
• الاستقرار تحت الحمل الثقيل (على سبيل المثال، أكثر من 1.4 مليون مسغ / ثانية)
• التحكم والمرونة (مراقبة معدل وشبكات قابلة للتكوين)
هناك جهود في هذه الصناعة لتوحيد ناقل الرسائل. بروتوكول المتقدم قائمة انتظار الرسائل (أمكب) هو مثال على معيار مفتوح بطل من قبل J. P. مورغان تشيس وبدعم من مجموعة من البائعين مثل سيسكو، إنفوي تكنولوجيز، ريد هات، تويست ابتكارات العملية، ايونا، 29West، و إماتيكس. اثنين من الأهداف الرئيسية هي توفير مسار أكثر بساطة إلى قابلية التشغيل للتطبيقات المكتوبة على منصات مختلفة ونمطية بحيث الوسيطة يمكن أن تتطور بسهولة.
وبعبارات عامة جدا، يكون خادم أمكب مشابها لخادم البريد الإلكتروني حيث يعمل كل تبادل كعميل لنقل الرسائل وكل طابور رسالة كعلبة بريد. تحدد الارتباطات جداول التوجيه في كل عامل نقل. يرسل الناشرون رسائل إلى وكلاء النقل الفرديين، ثم يقومون بتوجيه الرسائل إلى صناديق البريد. المستهلكون يأخذون رسائل من علب البريد، مما يخلق نموذجا قويا ومرنا بسيطا (المصدر: أمكب / تيكيويكي / تيكي-index. php؟ بادج = أوبينابرواش # Why_AMQP_).
خدمة رصد الكمون.
المتطلبات الرئيسية لهذه الخدمة هي:
• دقة دقيقة ملي ثانية من القياسات.
• الرؤية في الوقت الحقيقي تقريبا دون إضافة الكمون لحركة التداول.
• القدرة على التفريق بين الكمون معالجة التطبيق من الكمون عبور الشبكة.
• القدرة على التعامل مع معدلات رسالة عالية.
• توفير واجهة برنامجية لتداول التطبيقات لتلقي البيانات الكمون، وبالتالي تمكين محركات التداول خوارزمية للتكيف مع الظروف المتغيرة.
• ربط أحداث الشبكة مع أحداث التطبيق لأغراض استكشاف الأخطاء وإصلاحها.
وميكن تعريف الكمون على أنه الفاصل الزمني بني وقت إرسال أمر جتاري وعندما يسلم الطرف املستلم بنفس األمر ويبت فيه.
Addressing the latency issue is a complex problem, requiring a holistic approach that identifies all sources of latency and applies different technologies at different layers of the system.
Figure 5 depicts the variety of components that can introduce latency at each layer of the OSI stack. It also maps each source of latency with a possible solution and a monitoring solution. This layered approach can give firms a more structured way of attacking the latency issue, whereby each component can be thought of as a service and treated consistently across the firm.
Maintaining an accurate measure of the dynamic state of this time interval across alternative routes and destinations can be of great assistance in tactical trading decisions. The ability to identify the exact location of delays, whether in the customer's edge network, the central processing hub, or the transaction application level, significantly determines the ability of service providers to meet their trading service-level agreements (SLAs). For buy-side and sell-side forms, as well as for market-data syndicators, the quick identification and removal of bottlenecks translates directly into enhanced trade opportunities and revenue.
Figure 5 Latency Management Architecture.
Cisco Low-Latency Monitoring Tools.
Traditional network monitoring tools operate with minutes or seconds granularity. Next-generation trading platforms, especially those supporting algorithmic trading, require latencies less than 5 ms and extremely low levels of packet loss. On a Gigabit LAN, a 100 ms microburst can cause 10,000 transactions to be lost or excessively delayed.
Cisco offers its customers a choice of tools to measure latency in a trading environment:
• Bandwidth Quality Manager (BQM) (OEM from Corvil)
• Cisco AON-based Financial Services Latency Monitoring Solution (FSMS)
Bandwidth Quality Manager.
Bandwidth Quality Manager (BQM) 4.0 is a next-generation network application performance management product that enables customers to monitor and provision their network for controlled levels of latency and loss performance. While BQM is not exclusively targeted at trading networks, its microsecond visibility combined with intelligent bandwidth provisioning features make it ideal for these demanding environments.
Cisco BQM 4.0 implements a broad set of patented and patent-pending traffic measurement and network analysis technologies that give the user unprecedented visibility and understanding of how to optimize the network for maximum application performance.
Cisco BQM is now supported on the product family of Cisco Application Deployment Engine (ADE). The Cisco ADE product family is the platform of choice for Cisco network management applications.
BQM Benefits.
Cisco BQM micro-visibility is the ability to detect, measure, and analyze latency, jitter, and loss inducing traffic events down to microsecond levels of granularity with per packet resolution. This enables Cisco BQM to detect and determine the impact of traffic events on network latency, jitter, and loss. Critical for trading environments is that BQM can support latency, loss, and jitter measurements one-way for both TCP and UDP (multicast) traffic. This means it reports seamlessly for both trading traffic and market data feeds.
BQM allows the user to specify a comprehensive set of thresholds (against microburst activity, latency, loss, jitter, utilization, etc.) on all interfaces. BQM then operates a background rolling packet capture. Whenever a threshold violation or other potential performance degradation event occurs, it triggers Cisco BQM to store the packet capture to disk for later analysis. This allows the user to examine in full detail both the application traffic that was affected by performance degradation ("the victims") and the traffic that caused the performance degradation ("the culprits"). This can significantly reduce the time spent diagnosing and resolving network performance issues.
BQM is also able to provide detailed bandwidth and quality of service (QoS) policy provisioning recommendations, which the user can directly apply to achieve desired network performance.
BQM Measurements Illustrated.
To understand the difference between some of the more conventional measurement techniques and the visibility provided by BQM, we can look at some comparison graphs. In the first set of graphs (Figure 6 and Figure 7), we see the difference between the latency measured by BQM's Passive Network Quality Monitor (PNQM) and the latency measured by injecting ping packets every 1 second into the traffic stream.
In Figure 6, we see the latency reported by 1-second ICMP ping packets for real network traffic (it is divided by 2 to give an estimate for the one-way delay). It shows the delay comfortably below about 5ms for almost all of the time.
Figure 6 Latency Reported by 1-Second ICMP Ping Packets for Real Network Traffic.
In Figure 7, we see the latency reported by PNQM for the same traffic at the same time. Here we see that by measuring the one-way latency of the actual application packets, we get a radically different picture. Here the latency is seen to be hovering around 20 ms, with occasional bursts far higher. The explanation is that because ping is sending packets only every second, it is completely missing most of the application traffic latency. In fact, ping results typically only indicate round trip propagation delay rather than realistic application latency across the network.
Figure 7 Latency Reported by PNQM for Real Network Traffic.
In the second example (Figure 8), we see the difference in reported link load or saturation levels between a 5-minute average view and a 5 ms microburst view (BQM can report on microbursts down to about 10-100 nanosecond accuracy). The green line shows the average utilization at 5-minute averages to be low, maybe up to 5 Mbits/s. The dark blue plot shows the 5ms microburst activity reaching between 75 Mbits/s and 100 Mbits/s, the LAN speed effectively. BQM shows this level of granularity for all applications and it also gives clear provisioning rules to enable the user to control or neutralize these microbursts.
Figure 8 Difference in Reported Link Load Between a 5-Minute Average View and a 5 ms Microburst View.
BQM Deployment in the Trading Network.
Figure 9 shows a typical BQM deployment in a trading network.
Figure 9 Typical BQM Deployment in a Trading Network.
BQM can then be used to answer these types of questions:
• Are any of my Gigabit LAN core links saturated for more than X milliseconds? Is this causing loss? Which links would most benefit from an upgrade to Etherchannel or 10 Gigabit speeds?
• What application traffic is causing the saturation of my 1 Gigabit links?
• Is any of the market data experiencing end-to-end loss?
• How much additional latency does the failover data center experience? Is this link sized correctly to deal with microbursts?
• Are my traders getting low latency updates from the market data distribution layer? Are they seeing any delays greater than X milliseconds?
Being able to answer these questions simply and effectively saves time and money in running the trading network.
BQM is an essential tool for gaining visibility in market data and trading environments. It provides granular end-to-end latency measurements in complex infrastructures that experience high-volume data movement. Effectively detecting microbursts in sub-millisecond levels and receiving expert analysis on a particular event is invaluable to trading floor architects. Smart bandwidth provisioning recommendations, such as sizing and what-if analysis, provide greater agility to respond to volatile market conditions. As the explosion of algorithmic trading and increasing message rates continues, BQM, combined with its QoS tool, provides the capability of implementing QoS policies that can protect critical trading applications.
Cisco Financial Services Latency Monitoring Solution.
Cisco and Trading Metrics have collaborated on latency monitoring solutions for FIX order flow and market data monitoring. Cisco AON technology is the foundation for a new class of network-embedded products and solutions that help merge intelligent networks with application infrastructure, based on either service-oriented or traditional architectures. Trading Metrics is a leading provider of analytics software for network infrastructure and application latency monitoring purposes (tradingmetrics/).
The Cisco AON Financial Services Latency Monitoring Solution (FSMS) correlated two kinds of events at the point of observation:
• Network events correlated directly with coincident application message handling.
• Trade order flow and matching market update events.
Using time stamps asserted at the point of capture in the network, real-time analysis of these correlated data streams permits precise identification of bottlenecks across the infrastructure while a trade is being executed or market data is being distributed. By monitoring and measuring latency early in the cycle, financial companies can make better decisions about which network service—and which intermediary, market, or counterparty—to select for routing trade orders. Likewise, this knowledge allows more streamlined access to updated market data (stock quotes, economic news, etc.), which is an important basis for initiating, withdrawing from, or pursuing market opportunities.
The components of the solution are:
• AON hardware in three form factors:
– AON Network Module for Cisco 2600/2800/3700/3800 routers.
– AON Blade for the Cisco Catalyst 6500 series.
– AON 8340 Appliance.
• Trading Metrics M&A 2.0 software, which provides the monitoring and alerting application, displays latency graphs on a dashboard, and issues alerts when slowdowns occur (tradingmetrics/TM_brochure. pdf).
Figure 10 AON-Based FIX Latency Monitoring.
Cisco IP SLA.
Cisco IP SLA is an embedded network management tool in Cisco IOS which allows routers and switches to generate synthetic traffic streams which can be measured for latency, jitter, packet loss, and other criteria (cisco/go/ipsla).
Two key concepts are the source of the generated traffic and the target. Both of these run an IP SLA "responder," which has the responsibility to timestamp the control traffic before it is sourced and returned by the target (for a round trip measurement). Various traffic types can be sourced within IP SLA and they are aimed at different metrics and target different services and applications. The UDP jitter operation is used to measure one-way and round-trip delay and report variations. As the traffic is time stamped on both sending and target devices using the responder capability, the round trip delay is characterized as the delta between the two timestamps.
A new feature was introduced in IOS 12.3(14)T, IP SLA Sub Millisecond Reporting, which allows for timestamps to be displayed with a resolution in microseconds, thus providing a level of granularity not previously available. This new feature has now made IP SLA relevant to campus networks where network latency is typically in the range of 300-800 microseconds and the ability to detect trends and spikes (brief trends) based on microsecond granularity counters is a requirement for customers engaged in time-sensitive electronic trading environments.
As a result, IP SLA is now being considered by significant numbers of financial organizations as they are all faced with requirements to:
• Report baseline latency to their users.
• Trend baseline latency over time.
• Respond quickly to traffic bursts that cause changes in the reported latency.
Sub-millisecond reporting is necessary for these customers, since many campus and backbones are currently delivering under a second of latency across several switch hops. Electronic trading environments have generally worked to eliminate or minimize all areas of device and network latency to deliver rapid order fulfillment to the business. Reporting that network response times are "just under one millisecond" is no longer sufficient; the granularity of latency measurements reported across a network segment or backbone need to be closer to 300-800 micro-seconds with a degree of resolution of 100 ì seconds.
IP SLA recently added support for IP multicast test streams, which can measure market data latency.
A typical network topology is shown in Figure 11 with the IP SLA shadow routers, sources, and responders.
Figure 11 IP SLA Deployment.
Computing Services.
Computing services cover a wide range of technologies with the goal of eliminating memory and CPU bottlenecks created by the processing of network packets. Trading applications consume high volumes of market data and the servers have to dedicate resources to processing network traffic instead of application processing.
• Transport processing—At high speeds, network packet processing can consume a significant amount of server CPU cycles and memory. An established rule of thumb states that 1Gbps of network bandwidth requires 1 GHz of processor capacity (source Intel white paper on I/O acceleration intel/technology/ioacceleration/306517.pdf).
• Intermediate buffer copying—In a conventional network stack implementation, data needs to be copied by the CPU between network buffers and application buffers. This overhead is worsened by the fact that memory speeds have not kept up with increases in CPU speeds. For example, processors like the Intel Xeon are approaching 4 GHz, while RAM chips hover around 400MHz (for DDR 3200 memory) (source Intel intel/technology/ioacceleration/306517.pdf).
• Context switching—Every time an individual packet needs to be processed, the CPU performs a context switch from application context to network traffic context. This overhead could be reduced if the switch would occur only when the whole application buffer is complete.
Figure 12 Sources of Overhead in Data Center Servers.
• TCP Offload Engine (TOE)—Offloads transport processor cycles to the NIC. Moves TCP/IP protocol stack buffer copies from system memory to NIC memory.
• Remote Direct Memory Access (RDMA)—Enables a network adapter to transfer data directly from application to application without involving the operating system. Eliminates intermediate and application buffer copies (memory bandwidth consumption).
• Kernel bypass — Direct user-level access to hardware. Dramatically reduces application context switches.
Figure 13 RDMA and Kernel Bypass.
InfiniBand is a point-to-point (switched fabric) bidirectional serial communication link which implements RDMA, among other features. Cisco offers an InfiniBand switch, the Server Fabric Switch (SFS): cisco/application/pdf/en/us/guest/netsol/ns500/c643/cdccont_0900aecd804c35cb. pdf.
Figure 14 Typical SFS Deployment.
Trading applications benefit from the reduction in latency and latency variability, as proved by a test performed with the Cisco SFS and Wombat Feed Handlers by Stac Research:
Application Virtualization Service.
De-coupling the application from the underlying OS and server hardware enables them to run as network services. One application can be run in parallel on multiple servers, or multiple applications can be run on the same server, as the best resource allocation dictates. This decoupling enables better load balancing and disaster recovery for business continuance strategies. The process of re-allocating computing resources to an application is dynamic. Using an application virtualization system like Data Synapse's GridServer, applications can migrate, using pre-configured policies, to under-utilized servers in a supply-matches-demand process (networkworld/supp/2005/ndc1/022105virtual. html? page=2).
There are many business advantages for financial firms who adopt application virtualization:
• Faster time to market for new products and services.
• Faster integration of firms following merger and acquisition activity.
• Increased application availability.
• Better workload distribution, which creates more "head room" for processing spikes in trading volume.
• Operational efficiency and control.
• Reduction in IT complexity.
Currently, application virtualization is not used in the trading front-office. One use-case is risk modeling, like Monte Carlo simulations. As the technology evolves, it is conceivable that some the trading platforms will adopt it.
Data Virtualization Service.
To effectively share resources across distributed enterprise applications, firms must be able to leverage data across multiple sources in real-time while ensuring data integrity. With solutions from data virtualization software vendors such as Gemstone or Tangosol (now Oracle), financial firms can access heterogeneous sources of data as a single system image that enables connectivity between business processes and unrestrained application access to distributed caching. The net result is that all users have instant access to these data resources across a distributed network (gridtoday/03/0210/101061.html).
This is called a data grid and is the first step in the process of creating what Gartner calls Extreme Transaction Processing (XTP) (gartner/DisplayDocument? ref=g_search&id=500947). Technologies such as data and applications virtualization enable financial firms to perform real-time complex analytics, event-driven applications, and dynamic resource allocation.
One example of data virtualization in action is a global order book application. An order book is the repository of active orders that is published by the exchange or other market makers. A global order book aggregates orders from around the world from markets that operate independently. The biggest challenge for the application is scalability over WAN connectivity because it has to maintain state. Today's data grids are localized in data centers connected by Metro Area Networks (MAN). This is mainly because the applications themselves have limits—they have been developed without the WAN in mind.
Figure 15 GemStone GemFire Distributed Caching.
Before data virtualization, applications used database clustering for failover and scalability. This solution is limited by the performance of the underlying database. Failover is slower because the data is committed to disc. With data grids, the data which is part of the active state is cached in memory, which reduces drastically the failover time. Scaling the data grid means just adding more distributed resources, providing a more deterministic performance compared to a database cluster.
Multicast Service.
Market data delivery is a perfect example of an application that needs to deliver the same data stream to hundreds and potentially thousands of end users. Market data services have been implemented with TCP or UDP broadcast as the network layer, but those implementations have limited scalability. Using TCP requires a separate socket and sliding window on the server for each recipient. UDP broadcast requires a separate copy of the stream for each destination subnet. Both of these methods exhaust the resources of the servers and the network. The server side must transmit and service each of the streams individually, which requires larger and larger server farms. On the network side, the required bandwidth for the application increases in a linear fashion. For example, to send a 1 Mbps stream to 1000recipients using TCP requires 1 Gbps of bandwidth.
IP multicast is the only way to scale market data delivery. To deliver a 1 Mbps stream to 1000 recipients, IP multicast would require 1 Mbps. The stream can be delivered by as few as two servers—one primary and one backup for redundancy.
There are two main phases of market data delivery to the end user. In the first phase, the data stream must be brought from the exchange into the brokerage's network. Typically the feeds are terminated in a data center on the customer premise. The feeds are then processed by a feed handler, which may normalize the data stream into a common format and then republish into the application messaging servers in the data center.
The second phase involves injecting the data stream into the application messaging bus which feeds the core infrastructure of the trading applications. The large brokerage houses have thousands of applications that use the market data streams for various purposes, such as live trades, long term trending, arbitrage, etc. Many of these applications listen to the feeds and then republish their own analytical and derivative information. For example, a brokerage may compare the prices of CSCO to the option prices of CSCO on another exchange and then publish ratings which a different application may monitor to determine how much they are out of synchronization.
Figure 16 Market Data Distribution Players.
The delivery of these data streams is typically over a reliable multicast transport protocol, traditionally Tibco Rendezvous. Tibco RV operates in a publish and subscribe environment. Each financial instrument is given a subject name, such as CSCO. last. Each application server can request the individual instruments of interest by their subject name and receive just a that subset of the information. This is called subject-based forwarding or filtering. Subject-based filtering is patented by Tibco.
A distinction should be made between the first and second phases of market data delivery. The delivery of market data from the exchange to the brokerage is mostly a one-to-many application. The only exception to the unidirectional nature of market data may be retransmission requests, which are usually sent using unicast. The trading applications, however, are definitely many-to-many applications and may interact with the exchanges to place orders.
Figure 17 Market Data Architecture.
Design Issues.
Number of Groups/Channels to Use.
Many application developers consider using thousand of multicast groups to give them the ability to divide up products or instruments into small buckets. Normally these applications send many small messages as part of their information bus. Usually several messages are sent in each packet that are received by many users. Sending fewer messages in each packet increases the overhead necessary for each message.
In the extreme case, sending only one message in each packet quickly reaches the point of diminishing returns—there is more overhead sent than actual data. Application developers must find a reasonable compromise between the number of groups and breaking up their products into logical buckets.
Consider, for example, the Nasdaq Quotation Dissemination Service (NQDS). The instruments are broken up alphabetically:
Another example is the Nasdaq Totalview service, broken up this way:
This approach allows for straight forward network/application management, but does not necessarily allow for optimized bandwidth utilization for most users. A user of NQDS that is interested in technology stocks, and would like to subscribe to just CSCO and INTL, would have to pull down all the data for the first two groups of NQDS. Understanding the way users pull down the data and then organize it into appropriate logical groups optimizes the bandwidth for each user.
In many market data applications, optimizing the data organization would be of limited value. Typically customers bring in all data into a few machines and filter the instruments. Using more groups is just more overhead for the stack and does not help the customers conserve bandwidth. Another approach might be to keep the groups down to a minimum level and use UDP port numbers to further differentiate if necessary. The other extreme would be to use just one multicast group for the entire application and then have the end user filter the data. In some situations this may be sufficient.
Intermittent Sources.
A common issue with market data applications are servers that send data to a multicast group and then go silent for more than 3.5 minutes. These intermittent sources may cause trashing of state on the network and can introduce packet loss during the window of time when soft state and then hardware shorts are being created.
PIM-Bidir or PIM-SSM.
The first and best solution for intermittent sources is to use PIM-Bidir for many-to-many applications and PIM-SSM for one-to-many applications.
Both of these optimizations of the PIM protocol do not have any data-driven events in creating forwarding state. That means that as long as the receivers are subscribed to the streams, the network has the forwarding state created in the hardware switching path.
Intermittent sources are not an issue with PIM-Bidir and PIM-SSM.
Null Packets.
In PIM-SM environments a common method to make sure forwarding state is created is to send a burst of null packets to the multicast group before the actual data stream. The application must efficiently ignore these null data packets to ensure it does not affect performance. The sources must only send the burst of packets if they have been silent for more than 3 minutes. A good practice is to send the burst if the source is silent for more than a minute. Many financials send out an initial burst of traffic in the morning and then all well-behaved sources do not have problems.
Periodic Keepalives or Heartbeats.
An alternative approach for PIM-SM environments is for sources to send periodic heartbeat messages to the multicast groups. This is a similar approach to the null packets, but the packets can be sent on a regular timer so that the forwarding state never expires.
S, G Expiry Timer.
Finally, Cisco has made a modification to the operation of the S, G expiry timer in IOS. There is now a CLI knob to allow the state for a S, G to stay alive for hours without any traffic being sent. The (S, G) expiry timer is configurable. This approach should be considered a workaround until PIM-Bidir or PIM-SSM is deployed or the application is fixed.
RTCP Feedback.
A common issue with real time voice and video applications that use RTP is the use of RTCP feedback traffic. Unnecessary use of the feedback option can create excessive multicast state in the network. If the RTCP traffic is not required by the application it should be avoided.
Fast Producers and Slow Consumers.
Today many servers providing market data are attached at Gigabit speeds, while the receivers are attached at different speeds, usually 100Mbps. This creates the potential for receivers to drop packets and request re-transmissions, which creates more traffic that the slowest consumers cannot handle, continuing the vicious circle.
The solution needs to be some type of access control in the application that limits the amount of data that one host can request. QoS and other network functions can mitigate the problem, but ultimately the subscriptions need to be managed in the application.
Tibco Heartbeats.
TibcoRV has had the ability to use IP multicast for the heartbeat between the TICs for many years. However, there are some brokerage houses that are still using very old versions of TibcoRV that use UDP broadcast support for the resiliency. This limitation is often cited as a reason to maintain a Layer 2 infrastructure between TICs located in different data centers. These older versions of TibcoRV should be phased out in favor of the IP multicast supported versions.
Multicast Forwarding Options.
PIM Sparse Mode.
The standard IP multicast forwarding protocol used today for market data delivery is PIM Sparse Mode. It is supported on all Cisco routers and switches and is well understood. PIM-SM can be used in all the network components from the exchange, FSP, and brokerage.
There are, however, some long-standing issues and unnecessary complexity associated with a PIM-SM deployment that could be avoided by using PIM-Bidir and PIM-SSM. These are covered in the next sections.
The main components of the PIM-SM implementation are:
• PIM Sparse Mode v2.
• Shared Tree (spt-threshold infinity)
A design option in the brokerage or in the exchange.
Details of Anycast RP can be found in:
The classic high availability design for Tibco in the brokerage network is documented in:
Bidirectional PIM.
PIM-Bidir is an optimization of PIM Sparse Mode for many-to-many applications. It has several key advantages over a PIM-SM deployment:
• Better support for intermittent sources.
• No data-triggered events.
One of the weaknesses of PIM-SM is that the network continually needs to react to active data flows. This can cause non-deterministic behavior that may be hard to troubleshoot. PIM-Bidir has the following major protocol differences over PIM-SM:
– No source registration.
Source traffic is automatically sent to the RP and then down to the interested receivers. There is no unicast encapsulation, PIM joins from the RP to the first hop router and then registration stop messages.
All PIM-Bidir traffic is forwarded on a *,G forwarding entry. The router does not have to monitor the traffic flow on a *,G and then send joins when the traffic passes a threshold.
– No need for an actual RP.
The RP does not have an actual protocol function in PIM-Bidir. The RP acts as a routing vector in which all the traffic converges. The RP can be configured as an address that is not assigned to any particular device. This is called a Phantom RP.
– No need for MSDP.
MSDP provides source information between RPs in a PIM-SM network. PIM-Bidir does not use the active source information for any forwarding decisions and therefore MSDP is not required.
Bidirectional PIM is ideally suited for the brokerage network in the data center of the exchange. In this environment there are many sources sending to a relatively few set of groups in a many-to-many traffic pattern.
The key components of the PIM-Bidir implementation are:
Further details about Phantom RP and basic PIM-Bidir design are documented in:
Source Specific Multicast.
PIM-SSM is an optimization of PIM Sparse Mode for one-to-many applications. In certain environments it can offer several distinct advantages over PIM-SM. Like PIM-Bidir, PIM-SSM does not rely on any data-triggered events. Furthermore, PIM-SSM does not require an RP at all—there is no such concept in PIM-SSM. The forwarding information in the network is completely controlled by the interest of the receivers.
Source Specific Multicast is ideally suited for market data delivery in the financial service provider. The FSP can receive the feeds from the exchanges and then route them to the end of their network.
Many FSPs are also implementing MPLS and Multicast VPNs in their core. PIM-SSM is the preferred method for transporting traffic in VRFs.
When PIM-SSM is deployed all the way to the end user, the receiver indicates his interest in a particular S, G with IGMPv3. Even though IGMPv3 was defined by RFC 2236 back in October, 2002, it still has not been implemented by all edge devices. This creates a challenge for deploying an end-to-end PIM-SSM service. A transitional solution has been developed by Cisco to enable an edge device that supports IGMPv2 to participate in an PIM-SSM service. This feature is called SSM Mapping and is documented in:
Storage Services.
The service provides storage capabilities into the market data and trading environments. Trading applications access backend storage to connect to different databases and other repositories consisting of portfolios, trade settlements, compliance data, management applications, Enterprise Service Bus (ESB), and other critical applications where reliability and security is critical to the success of the business. The main requirements for the service are:
Storage virtualization is an enabling technology that simplifies management of complex infrastructures, enables non-disruptive operations, and facilitates critical elements of a proactive information lifecycle management (ILM) strategy. EMC Invista running on the Cisco MDS 9000 enables heterogeneous storage pooling and dynamic storage provisioning, allowing allocation of any storage to any application. High availability is increased with seamless data migration. Appropriate class of storage is allocated to point-in-time copies (clones). Storage virtualization is also leveraged through the use of Virtual Storage Area Networks (VSANs), which enable the consolidation of multiple isolated SANs onto a single physical SAN infrastructure, while still partitioning them as completely separate logical entities. VSANs provide all the security and fabric services of traditional SANs, yet give organizations the flexibility to easily move resources from one VSAN to another. This results in increased disk and network utilization while driving down the cost of management. Integrated Inter VSAN Routing (IVR) enables sharing of common resources across VSANs.
Figure 18 High Performance Computing Storage.
Replication of data to a secondary and tertiary data center is crucial for business continuance. Replication offsite over Fiber Channel over IP (FCIP) coupled with write acceleration and tape acceleration provides improved performance over long distance. Continuous Data Replication (CDP) is another mechanism which is gaining popularity in the industry. It refers to backup of computer data by automatically saving a copy of every change made to that data, essentially capturing every version of the data that the user saves. It allows the user or administrator to restore data to any point in time. Solutions from EMC and Incipient utilize the SANTap protocol on the Storage Services Module (SSM) in the MDS platform to provide CDP functionality. The SSM uses the SANTap service to intercept and redirect a copy of a write between a given initiator and target. The appliance does not reside in the data path—it is completely passive. The CDP solutions typically leverage a history journal that tracks all changes and bookmarks that identify application-specific events. This ensures that data at any point in time is fully self-consistent and is recoverable instantly in the event of a site failure.
Backup procedure reliability and performance are extremely important when storing critical financial data to a SAN. The use of expensive media servers to move data from disk to tape devices can be cumbersome. Network-accelerated serverless backup (NASB) helps you back up increased amounts of data in shorter backup time frames by shifting the data movement from multiple backup servers to Cisco MDS 9000 Series multilayer switches. This technology decreases impact on application servers because the MDS offloads the application and backup servers. It also reduces the number of backup and media servers required, thus reducing CAPEX and OPEX. The flexibility of the backup environment increases because storage and tape drives can reside anywhere on the SAN.
Trading Resilience and Mobility.
The main requirements for this service are to provide the virtual trader:
• Fully scalable and redundant campus trading environment.
• Resilient server load balancing and high availability in analytic server farms.
• Global site load balancing that provide the capability to continue participating in the market venues of closest proximity.
A highly-available campus environment is capable of sustaining multiple failures (i. e., links, switches, modules, etc.), which provides non-disruptive access to trading systems for traders and market data feeds. Fine-tuned routing protocol timers, in conjunction with mechanisms such as NSF/SSO, provide subsecond recovery from any failure.
The high-speed interconnect between data centers can be DWDM/dark fiber, which provides business continuance in case of a site failure. Each site is 100km-200km apart, allowing synchronous data replication. Usually the distance for synchronous data replication is 100km, but with Read/Write Acceleration it can stretch to 200km. A tertiary data center can be greater than 200km away, which would replicate data in an asynchronous fashion.
Figure 19 Trading Resilience.
A robust server load balancing solution is required for order routing, algorithmic trading, risk analysis, and other services to offer continuous access to clients regardless of a server failure. Multiple servers encompass a "farm" and these hosts can added/removed without disruption since they reside behind a virtual IP (VIP) address which is announced in the network.
A global site load balancing solution provides remote traders the resiliency to access trading environments which are closer to their location. This minimizes latency for execution times since requests are always routed to the nearest venue.
Figure 20 Virtualization of Trading Environment.
A trading environment can be virtualized to provide segmentation and resiliency in complex architectures. Figure 20 illustrates a high-level topology depicting multiple market data feeds entering the environment, whereby each vendor is assigned its own Virtual Routing and Forwarding (VRF) instance. The market data is transferred to a high-speed InfiniBand low-latency compute fabric where feed handlers, order routing systems, and algorithmic trading systems reside. All storage is accessed via a SAN and is also virtualized with VSANs, allowing further security and segmentation. The normalized data from the compute fabric is transferred to the campus trading environment where the trading desks reside.
Wide Area Application Services.
This service provides application acceleration and optimization capabilities for traders who are located outside of the core trading floor facility/data center and working from a remote office. To consolidate servers and increase security in remote offices, file servers, NAS filers, storage arrays, and tape drives are moved to a corporate data center to increase security and regulatory compliance and facilitate centralized storage and archival management. As the traditional trading floor is becoming more virtual, wide area application services technology is being utilized to provide a "LAN-like" experience to remote traders when they access resources at the corporate site. Traders often utilize Microsoft Office applications, especially Excel in addition to Sharepoint and Exchange. Excel is used heavily for modeling and permutations where sometime only small portions of the file are changed. CIFS protocol is notoriously known to be "chatty," where several message normally traverse the WAN for a simple file operation and it is addressed by Wide Area Application Service (WAAS) technology. Bloomberg and Reuters applications are also very popular financial tools which access a centralized SAN or NAS filer to retrieve critical data which is fused together before represented to a trader's screen.
Figure 21 Wide Area Optimization.
A pair of Wide Area Application Engines (WAEs) that reside in the remote office and the data center provide local object caching to increase application performance. The remote office WAEs can be a module in the ISR router or a stand-alone appliance. The data center WAE devices are load balanced behind an Application Control Engine module installed in a pair of Catalyst 6500 series switches at the aggregation layer. The WAE appliance farm is represented by a virtual IP address. The local router in each site utilizes Web Cache Communication Protocol version 2 (WCCP v2) to redirect traffic to the WAE that intercepts the traffic and determines if there is a cache hit or miss. The content is served locally from the engine if it resides in cache; otherwise the request is sent across the WAN the initial time to retrieve the object. This methodology optimizes the trader experience by removing application latency and shielding the individual from any congestion in the WAN.
WAAS uses the following technologies to provide application acceleration:
• Data Redundancy Elimination (DRE) is an advanced form of network compression which allows the WAE to maintain a history of previously-seen TCP message traffic for the purposes of reducing redundancy found in network traffic. This combined with the Lempel-Ziv (LZ) compression algorithm reduces the number of redundant packets that traverse the WAN, which improves application transaction performance and conserves bandwidth.
• Transport Flow Optimization (TFO) employs a robust TCP proxy to safely optimize TCP at the WAE device by applying TCP-compliant optimizations to shield the clients and servers from poor TCP behavior because of WAN conditions. By running a TCP proxy between the devices and leveraging an optimized TCP stack between the devices, many of the problems that occur in the WAN are completely blocked from propagating back to trader desktops. The traders experience LAN-like TCP response times and behavior because the WAE is terminating TCP locally. TFO improves reliability and throughput through increases in TCP window scaling and sizing enhancements in addition to superior congestion management.
Thin Client Service.
This service provides a "thin" advanced trading desktop which delivers significant advantages to demanding trading floor environments requiring continuous growth in compute power. As financial institutions race to provide the best trade executions for their clients, traders are utilizing several simultaneous critical applications that facilitate complex transactions. It is not uncommon to find three or more workstations and monitors at a trader's desk which provide visibility into market liquidity, trading venues, news, analysis of complex portfolio simulations, and other financial tools. In addition, market dynamics continue to evolve with Direct Market Access (DMA), ECNs, alternative trading volumes, and upcoming regulation changes with Regulation National Market System (RegNMS) in the US and Markets in Financial Instruments Directive (MiFID) in Europe. At the same time, business seeks greater control, improved ROI, and additional flexibility, which creates greater demands on trading floor infrastructures.
Traders no longer require multiple workstations at their desk. Thin clients consist of keyboard, mouse, and multi-displays which provide a total trader desktop solution without compromising security. Hewlett Packard, Citrix, Desktone, Wyse, and other vendors provide thin client solutions to capitalize on the virtual desktop paradigm. Thin clients de-couple the user-facing hardware from the processing hardware, thus enabling IT to grow the processing power without changing anything on the end user side. The workstation computing power is stored in the data center on blade workstations, which provide greater scalability, increased data security, improved business continuance across multiple sites, and reduction in OPEX by removing the need to manage individual workstations on the trading floor. One blade workstation can be dedicated to a trader or shared among multiple traders depending on the requirements for computer power.
The "thin client" solution is optimized to work in a campus LAN environment, but can also extend the benefits to traders in remote locations. Latency is always a concern when there is a WAN interconnecting the blade workstation and thin client devices. The network connection needs to be sized accordingly so traffic is not dropped if saturation points exist in the WAN topology. WAN Quality of Service (QoS) should prioritize sensitive traffic. There are some guidelines which should be followed to allow for an optimized user experience. A typical highly-interactive desktop experience requires a client-to-blade round trip latency of <20ms for a 2Kb packet size. There may be a slight lag in display if network latency is between 20ms to 40ms. A typical trader desk with a four multi-display terminal requires 2-3Mbps bandwidth consumption with seamless communication with blade workstation(s) in the data center. Streaming video (800x600 at 24fps/full color) requires 9 Mbps bandwidth usage.
Figure 22 Thin Client Architecture.
Management of a large thin client environment is simplified since a centralized IT staff manages all of the blade workstations dispersed across multiple data centers. A trader is redirected to the most available environment in the enterprise in the event of a particular site failure. High availability is a key concern in critical financial environments and the Blade Workstation design provides rapid provisioning of another blade workstation in the data center. This resiliency provides greater uptime, increases in productivity, and OpEx reduction.
Advanced Encryption Standard.
Advanced Message Queueing Protocol.
Application Oriented Networking.
The Archipelago® Integrated Web book gives investors the unique opportunity to view the entire ArcaEx and ArcaEdge books in addition to books made available by other market participants.
ECN Order Book feed available via NASDAQ.
Chicago Board of Trade.
Class-Based Weighted Fair Queueing.
Continuous Data Replication.
Chicago Mercantile Exchange is engaged in trading of futures contracts and derivatives.
Central Processing Unit.
Distributed Defect Tracking System.
Direct Market Access.
Data Redundancy Elimination.
Dense Wavelength Division Multiplexing.
Electronic Communication Network.
Enterprise Service Bus.
Enterprise Solutions Engineering.
FIX Adapted for Streaming.
Fibre Channel over IP.
Financial Information Exchange.
Financial Services Latency Monitoring Solution.
Financial Service Provider.
Information Lifecycle Management.
Instinet Island Book.
Internetworking Operating System.
Keyboard Video Mouse.
Low Latency Queueing.
Metro Area Network.
Multilayer Director Switch.
Markets in Financial Instruments Directive.
Message Passing Interface is an industry standard specifying a library of functions to enable the passing of messages between nodes within a parallel computing environment.
Network Attached Storage.
Network Accelerated Serverless Backup.
Network Interface Card.
Nasdaq Quotation Dissemination Service.
Order Management System.
Open Systems Interconnection.
Protocol Independent Multicast.
PIM-Source Specific Multicast.
Quality of Service.
Random Access Memory.
Reuters Data Feed.
Reuters Data Feed Direct.
Remote Direct Memory Access.
Regulation National Market System.
Remote Graphics Software.
Reuters Market Data System.
RTP Control Protocol.
Real Time Protocol.
Reuters Wire Format.
Storage Area Network.
Small Computer System Interface.
Sockets Direct Protocol—Given that many modern applications are written using the sockets API, SDP can intercept the sockets at the kernel level and map these socket calls to an InfiniBand transport service that uses RDMA operations to offload data movement from the CPU to the HCA hardware.
Server Fabric Switch.
Secure Financial Transaction Infrastructure network developed to provide firms with excellent communication paths to NYSE Group, AMEX, Chicago Stock Exchange, NASDAQ, and other exchanges. It is often used for order routing.

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By Michael Halls-Moore on July 26th, 2018.
One of the most frequent questions I receive in the QS mailbag is "What is the best programming language for algorithmic trading?". The short answer is that there is no "best" language. Strategy parameters, performance, modularity, development, resiliency and cost must all be considered. This article will outline the necessary components of an algorithmic trading system architecture and how decisions regarding implementation affect the choice of language.
Firstly, the major components of an algorithmic trading system will be considered, such as the research tools, portfolio optimiser, risk manager and execution engine. Subsequently, different trading strategies will be examined and how they affect the design of the system. In particular the frequency of trading and the likely trading volume will both be discussed.
Once the trading strategy has been selected, it is necessary to architect the entire system. This includes choice of hardware, the operating system(s) and system resiliency against rare, potentially catastrophic events. While the architecture is being considered, due regard must be paid to performance - both to the research tools as well as the live execution environment.
What Is The Trading System Trying To Do?
Before deciding on the "best" language with which to write an automated trading system it is necessary to define the requirements. Is the system going to be purely execution based? Will the system require a risk management or portfolio construction module? Will the system require a high-performance backtester? For most strategies the trading system can be partitioned into two categories: Research and signal generation.
Research is concerned with evaluation of a strategy performance over historical data. The process of evaluating a trading strategy over prior market data is known as backtesting . The data size and algorithmic complexity will have a big impact on the computational intensity of the backtester. CPU speed and concurrency are often the limiting factors in optimising research execution speed.
Signal generation is concerned with generating a set of trading signals from an algorithm and sending such orders to the market, usually via a brokerage. For certain strategies a high level of performance is required. I/O issues such as network bandwidth and latency are often the limiting factor in optimising execution systems. Thus the choice of languages for each component of your entire system may be quite different.
Type, Frequency and Volume of Strategy.
The type of algorithmic strategy employed will have a substantial impact on the design of the system. It will be necessary to consider the markets being traded, the connectivity to external data vendors, the frequency and volume of the strategy, the trade-off between ease of development and performance optimisation, as well as any custom hardware, including co-located custom servers, GPUs or FPGAs that might be necessary.
The technology choices for a low-frequency US equities strategy will be vastly different from those of a high-frequency statistical arbitrage strategy trading on the futures market. Prior to the choice of language many data vendors must be evaluated that pertain to a the strategy at hand.
It will be necessary to consider connectivity to the vendor, structure of any APIs, timeliness of the data, storage requirements and resiliency in the face of a vendor going offline. It is also wise to possess rapid access to multiple vendors! Various instruments all have their own storage quirks, examples of which include multiple ticker symbols for equities and expiration dates for futures (not to mention any specific OTC data). This needs to be factored in to the platform design.
Frequency of strategy is likely to be one of the biggest drivers of how the technology stack will be defined. Strategies employing data more frequently than minutely or secondly bars require significant consideration with regards to performance.
A strategy exceeding secondly bars (i. e. tick data) leads to a performance driven design as the primary requirement. For high frequency strategies a substantial amount of market data will need to be stored and evaluated. Software such as HDF5 or kdb+ are commonly used for these roles.
In order to process the extensive volumes of data needed for HFT applications, an extensively optimised backtester and execution system must be used. C/C++ (possibly with some assembler) is likely to the strongest language candidate. Ultra-high frequency strategies will almost certainly require custom hardware such as FPGAs, exchange co-location and kernal/network interface tuning.
Research Systems.
Research systems typically involve a mixture of interactive development and automated scripting. The former often takes place within an IDE such as Visual Studio, MatLab or R Studio. The latter involves extensive numerical calculations over numerous parameters and data points. This leads to a language choice providing a straightforward environment to test code, but also provides sufficient performance to evaluate strategies over multiple parameter dimensions.
Typical IDEs in this space include Microsoft Visual C++/C#, which contains extensive debugging utilities, code completion capabilities (via "Intellisense") and straightforward overviews of the entire project stack (via the database ORM, LINQ); MatLab, which is designed for extensive numerical linear algebra and vectorised operations, but in an interactive console manner; R Studio, which wraps the R statistical language console in a fully-fledged IDE; Eclipse IDE for Linux Java and C++; and semi-proprietary IDEs such as Enthought Canopy for Python, which include data analysis libraries such as NumPy, SciPy, scikit-learn and pandas in a single interactive (console) environment.
For numerical backtesting, all of the above languages are suitable, although it is not necessary to utilise a GUI/IDE as the code will be executed "in the background". The prime consideration at this stage is that of execution speed. A compiled language (such as C++) is often useful if the backtesting parameter dimensions are large. Remember that it is necessary to be wary of such systems if that is the case!
Interpreted languages such as Python often make use of high-performance libraries such as NumPy/pandas for the backtesting step, in order to maintain a reasonable degree of competitiveness with compiled equivalents. Ultimately the language chosen for the backtesting will be determined by specific algorithmic needs as well as the range of libraries available in the language (more on that below). However, the language used for the backtester and research environments can be completely independent of those used in the portfolio construction, risk management and execution components, as will be seen.
Portfolio Construction and Risk Management.
The portfolio construction and risk management components are often overlooked by retail algorithmic traders. This is almost always a mistake. These tools provide the mechanism by which capital will be preserved. They not only attempt to alleviate the number of "risky" bets, but also minimise churn of the trades themselves, reducing transaction costs.
Sophisticated versions of these components can have a significant effect on the quality and consistentcy of profitability. It is straightforward to create a stable of strategies as the portfolio construction mechanism and risk manager can easily be modified to handle multiple systems. Thus they should be considered essential components at the outset of the design of an algorithmic trading system.
The job of the portfolio construction system is to take a set of desired trades and produce the set of actual trades that minimise churn, maintain exposures to various factors (such as sectors, asset classes, volatility etc) and optimise the allocation of capital to various strategies in a portfolio.
Portfolio construction often reduces to a linear algebra problem (such as a matrix factorisation) and hence performance is highly dependent upon the effectiveness of the numerical linear algebra implementation available. Common libraries include uBLAS, LAPACK and NAG for C++. MatLab also possesses extensively optimised matrix operations. Python utilises NumPy/SciPy for such computations. A frequently rebalanced portfolio will require a compiled (and well optimised!) matrix library to carry this step out, so as not to bottleneck the trading system.
Risk management is another extremely important part of an algorithmic trading system. Risk can come in many forms: Increased volatility (although this may be seen as desirable for certain strategies!), increased correlations between asset classes, counter-party default, server outages, "black swan" events and undetected bugs in the trading code, to name a few.
Risk management components try and anticipate the effects of excessive volatility and correlation between asset classes and their subsequent effect(s) on trading capital. Often this reduces to a set of statistical computations such as Monte Carlo "stress tests". This is very similar to the computational needs of a derivatives pricing engine and as such will be CPU-bound. These simulations are highly parallelisable (see below) and, to a certain degree, it is possible to "throw hardware at the problem".
Execution Systems.
The job of the execution system is to receive filtered trading signals from the portfolio construction and risk management components and send them on to a brokerage or other means of market access. For the majority of retail algorithmic trading strategies this involves an API or FIX connection to a brokerage such as Interactive Brokers. The primary considerations when deciding upon a language include quality of the API, language-wrapper availability for an API, execution frequency and the anticipated slippage.
The "quality" of the API refers to how well documented it is, what sort of performance it provides, whether it needs standalone software to be accessed or whether a gateway can be established in a headless fashion (i. e. no GUI). In the case of Interactive Brokers, the Trader WorkStation tool needs to be running in a GUI environment in order to access their API. I once had to install a Desktop Ubuntu edition onto an Amazon cloud server to access Interactive Brokers remotely, purely for this reason!
Most APIs will provide a C++ and/or Java interface. It is usually up to the community to develop language-specific wrappers for C#, Python, R, Excel and MatLab. Note that with every additional plugin utilised (especially API wrappers) there is scope for bugs to creep into the system. Always test plugins of this sort and ensure they are actively maintained. A worthwhile gauge is to see how many new updates to a codebase have been made in recent months.
Execution frequency is of the utmost importance in the execution algorithm. Note that hundreds of orders may be sent every minute and as such performance is critical. Slippage will be incurred through a badly-performing execution system and this will have a dramatic impact on profitability.
Statically-typed languages (see below) such as C++/Java are generally optimal for execution but there is a trade-off in development time, testing and ease of maintenance. Dynamically-typed languages, such as Python and Perl are now generally "fast enough". Always make sure the components are designed in a modular fashion (see below) so that they can be "swapped out" out as the system scales.
Architectural Planning and Development Process.
The components of a trading system, its frequency and volume requirements have been discussed above, but system infrastructure has yet to be covered. Those acting as a retail trader or working in a small fund will likely be "wearing many hats". It will be necessary to be covering the alpha model, risk management and execution parameters, and also the final implementation of the system. Before delving into specific languages the design of an optimal system architecture will be discussed.
Separation of Concerns.
One of the most important decisions that must be made at the outset is how to "separate the concerns" of a trading system. In software development, this essentially means how to break up the different aspects of the trading system into separate modular components.
By exposing interfaces at each of the components it is easy to swap out parts of the system for other versions that aid performance, reliability or maintenance, without modifying any external dependency code. This is the "best practice" for such systems. For strategies at lower frequencies such practices are advised. For ultra high frequency trading the rulebook might have to be ignored at the expense of tweaking the system for even more performance. A more tightly coupled system may be desirable.
Creating a component map of an algorithmic trading system is worth an article in itself. However, an optimal approach is to make sure there are separate components for the historical and real-time market data inputs, data storage, data access API, backtester, strategy parameters, portfolio construction, risk management and automated execution systems.
For instance, if the data store being used is currently underperforming, even at significant levels of optimisation, it can be swapped out with minimal rewrites to the data ingestion or data access API. As far the as the backtester and subsequent components are concerned, there is no difference.
Another benefit of separated components is that it allows a variety of programming languages to be used in the overall system. There is no need to be restricted to a single language if the communication method of the components is language independent. This will be the case if they are communicating via TCP/IP, ZeroMQ or some other language-independent protocol.
As a concrete example, consider the case of a backtesting system being written in C++ for "number crunching" performance, while the portfolio manager and execution systems are written in Python using SciPy and IBPy.
Performance Considerations.
Performance is a significant consideration for most trading strategies. For higher frequency strategies it is the most important factor. "Performance" covers a wide range of issues, such as algorithmic execution speed, network latency, bandwidth, data I/O, concurrency/parallelism and scaling. Each of these areas are individually covered by large textbooks, so this article will only scratch the surface of each topic. Architecture and language choice will now be discussed in terms of their effects on performance.
The prevailing wisdom as stated by Donald Knuth, one of the fathers of Computer Science, is that "premature optimisation is the root of all evil". This is almost always the case - except when building a high frequency trading algorithm! For those who are interested in lower frequency strategies, a common approach is to build a system in the simplest way possible and only optimise as bottlenecks begin to appear.
Profiling tools are used to determine where bottlenecks arise. Profiles can be made for all of the factors listed above, either in a MS Windows or Linux environment. There are many operating system and language tools available to do so, as well as third party utilities. Language choice will now be discussed in the context of performance.
C++, Java, Python, R and MatLab all contain high-performance libraries (either as part of their standard or externally) for basic data structure and algorithmic work. C++ ships with the Standard Template Library, while Python contains NumPy/SciPy. Common mathematical tasks are to be found in these libraries and it is rarely beneficial to write a new implementation.
One exception is if highly customised hardware architecture is required and an algorithm is making extensive use of proprietary extensions (such as custom caches). However, often "reinvention of the wheel" wastes time that could be better spent developing and optimising other parts of the trading infrastructure. Development time is extremely precious especially in the context of sole developers.
Latency is often an issue of the execution system as the research tools are usually situated on the same machine. For the former, latency can occur at multiple points along the execution path. Databases must be consulted (disk/network latency), signals must be generated (operating syste, kernal messaging latency), trade signals sent (NIC latency) and orders processed (exchange systems internal latency).
For higher frequency operations it is necessary to become intimately familiar with kernal optimisation as well as optimisation of network transmission. This is a deep area and is significantly beyond the scope of the article but if an UHFT algorithm is desired then be aware of the depth of knowledge required!
Caching is very useful in the toolkit of a quantitative trading developer. Caching refers to the concept of storing frequently accessed data in a manner which allows higher-performance access, at the expense of potential staleness of the data. A common use case occurs in web development when taking data from a disk-backed relational database and putting it into memory. Any subsequent requests for the data do not have to "hit the database" and so performance gains can be significant.
For trading situations caching can be extremely beneficial. For instance, the current state of a strategy portfolio can be stored in a cache until it is rebalanced, such that the list doesn't need to be regenerated upon each loop of the trading algorithm. Such regeneration is likely to be a high CPU or disk I/O operation.
However, caching is not without its own issues. Regeneration of cache data all at once, due to the volatilie nature of cache storage, can place significant demand on infrastructure. Another issue is dog-piling , where multiple generations of a new cache copy are carried out under extremely high load, which leads to cascade failure.
Dynamic memory allocation is an expensive operation in software execution. Thus it is imperative for higher performance trading applications to be well-aware how memory is being allocated and deallocated during program flow. Newer language standards such as Java, C# and Python all perform automatic garbage collection , which refers to deallocation of dynamically allocated memory when objects go out of scope .
Garbage collection is extremely useful during development as it reduces errors and aids readability. However, it is often sub-optimal for certain high frequency trading strategies. Custom garbage collection is often desired for these cases. In Java, for instance, by tuning the garbage collector and heap configuration, it is possible to obtain high performance for HFT strategies.
C++ doesn't provide a native garbage collector and so it is necessary to handle all memory allocation/deallocation as part of an object's implementation. While potentially error prone (potentially leading to dangling pointers) it is extremely useful to have fine-grained control of how objects appear on the heap for certain applications. When choosing a language make sure to study how the garbage collector works and whether it can be modified to optimise for a particular use case.
Many operations in algorithmic trading systems are amenable to parallelisation . This refers to the concept of carrying out multiple programmatic operations at the same time, i. e in "parallel". So-called "embarassingly parallel" algorithms include steps that can be computed fully independently of other steps. Certain statistical operations, such as Monte Carlo simulations, are a good example of embarassingly parallel algorithms as each random draw and subsequent path operation can be computed without knowledge of other paths.
Other algorithms are only partially parallelisable. Fluid dynamics simulations are such an example, where the domain of computation can be subdivided, but ultimately these domains must communicate with each other and thus the operations are partially sequential. Parallelisable algorithms are subject to Amdahl's Law, which provides a theoretical upper limit to the performance increase of a parallelised algorithm when subject to $N$ separate processes (e. g. on a CPU core or thread ).
Parallelisation has become increasingly important as a means of optimisation since processor clock-speeds have stagnated, as newer processors contain many cores with which to perform parallel calculations. The rise of consumer graphics hardware (predominently for video games) has lead to the development of Graphical Processing Units (GPUs), which contain hundreds of "cores" for highly concurrent operations. Such GPUs are now very affordable. High-level frameworks, such as Nvidia's CUDA have lead to widespread adoption in academia and finance.
Such GPU hardware is generally only suitable for the research aspect of quantitative finance, whereas other more specialised hardware (including Field-Programmable Gate Arrays - FPGAs) are used for (U)HFT. Nowadays, most modern langauges support a degree of concurrency/multithreading. Thus it is straightforward to optimise a backtester, since all calculations are generally independent of the others.
Scaling in software engineering and operations refers to the ability of the system to handle consistently increasing loads in the form of greater requests, higher processor usage and more memory allocation. In algorithmic trading a strategy is able to scale if it can accept larger quantities of capital and still produce consistent returns. The trading technology stack scales if it can endure larger trade volumes and increased latency, without bottlenecking .
While systems must be designed to scale, it is often hard to predict beforehand where a bottleneck will occur. Rigourous logging, testing, profiling and monitoring will aid greatly in allowing a system to scale. Languages themselves are often described as "unscalable". This is usually the result of misinformation, rather than hard fact. It is the total technology stack that should be ascertained for scalability, not the language. Clearly certain languages have greater performance than others in particular use cases, but one language is never "better" than another in every sense.
One means of managing scale is to separate concerns, as stated above. In order to further introduce the ability to handle "spikes" in the system (i. e. sudden volatility which triggers a raft of trades), it is useful to create a "message queuing architecture". This simply means placing a message queue system between components so that orders are "stacked up" if a certain component is unable to process many requests.
Rather than requests being lost they are simply kept in a stack until the message is handled. This is particularly useful for sending trades to an execution engine. If the engine is suffering under heavy latency then it will back up trades. A queue between the trade signal generator and the execution API will alleviate this issue at the expense of potential trade slippage. A well-respected open source message queue broker is RabbitMQ.
Hardware and Operating Systems.
The hardware running your strategy can have a significant impact on the profitability of your algorithm. This is not an issue restricted to high frequency traders either. A poor choice in hardware and operating system can lead to a machine crash or reboot at the most inopportune moment. Thus it is necessary to consider where your application will reside. The choice is generally between a personal desktop machine, a remote server, a "cloud" provider or an exchange co-located server.
Desktop machines are simple to install and administer, especially with newer user friendly operating systems such as Windows 7/8, Mac OSX and Ubuntu. Desktop systems do possess some significant drawbacks, however. The foremost is that the versions of operating systems designed for desktop machines are likely to require reboots/patching (and often at the worst of times!). They also use up more computational resources by the virtue of requiring a graphical user interface (GUI).
Utilising hardware in a home (or local office) environment can lead to internet connectivity and power uptime problems. The main benefit of a desktop system is that significant computational horsepower can be purchased for the fraction of the cost of a remote dedicated server (or cloud based system) of comparable speed.
A dedicated server or cloud-based machine, while often more expensive than a desktop option, allows for more significant redundancy infrastructure, such as automated data backups, the ability to more straightforwardly ensure uptime and remote monitoring. They are harder to administer since they require the ability to use remote login capabilities of the operating system.
In Windows this is generally via the GUI Remote Desktop Protocol (RDP). In Unix-based systems the command-line Secure SHell (SSH) is used. Unix-based server infrastructure is almost always command-line based which immediately renders GUI-based programming tools (such as MatLab or Excel) to be unusable.
A co-located server, as the phrase is used in the capital markets, is simply a dedicated server that resides within an exchange in order to reduce latency of the trading algorithm. This is absolutely necessary for certain high frequency trading strategies, which rely on low latency in order to generate alpha.
The final aspect to hardware choice and the choice of programming language is platform-independence. Is there a need for the code to run across multiple different operating systems? Is the code designed to be run on a particular type of processor architecture, such as the Intel x86/x64 or will it be possible to execute on RISC processors such as those manufactured by ARM? These issues will be highly dependent upon the frequency and type of strategy being implemented.
Resilience and Testing.
One of the best ways to lose a lot of money on algorithmic trading is to create a system with no resiliency . This refers to the durability of the sytem when subject to rare events, such as brokerage bankruptcies, sudden excess volatility, region-wide downtime for a cloud server provider or the accidental deletion of an entire trading database. Years of profits can be eliminated within seconds with a poorly-designed architecture. It is absolutely essential to consider issues such as debuggng, testing, logging, backups, high-availability and monitoring as core components of your system.
It is likely that in any reasonably complicated custom quantitative trading application at least 50% of development time will be spent on debugging, testing and maintenance.
Nearly all programming languages either ship with an associated debugger or possess well-respected third-party alternatives. In essence, a debugger allows execution of a program with insertion of arbitrary break points in the code path, which temporarily halt execution in order to investigate the state of the system. The main benefit of debugging is that it is possible to investigate the behaviour of code prior to a known crash point .
Debugging is an essential component in the toolbox for analysing programming errors. However, they are more widely used in compiled languages such as C++ or Java, as interpreted languages such as Python are often easier to debug due to fewer LOC and less verbose statements. Despite this tendency Python does ship with the pdb, which is a sophisticated debugging tool. The Microsoft Visual C++ IDE possesses extensive GUI debugging utilities, while for the command line Linux C++ programmer, the gdb debugger exists.
Testing in software development refers to the process of applying known parameters and results to specific functions, methods and objects within a codebase, in order to simulate behaviour and evaluate multiple code-paths, helping to ensure that a system behaves as it should. A more recent paradigm is known as Test Driven Development (TDD), where test code is developed against a specified interface with no implementation. Prior to the completion of the actual codebase all tests will fail. As code is written to "fill in the blanks", the tests will eventually all pass, at which point development should cease.
TDD requires extensive upfront specification design as well as a healthy degree of discipline in order to carry out successfully. In C++, Boost provides a unit testing framework. In Java, the JUnit library exists to fulfill the same purpose. Python also has the unittest module as part of the standard library. Many other languages possess unit testing frameworks and often there are multiple options.
In a production environment, sophisticated logging is absolutely essential. Logging refers to the process of outputting messages, with various degrees of severity, regarding execution behaviour of a system to a flat file or database. Logs are a "first line of attack" when hunting for unexpected program runtime behaviour. Unfortunately the shortcomings of a logging system tend only to be discovered after the fact! As with backups discussed below, a logging system should be given due consideration BEFORE a system is designed.
Both Microsoft Windows and Linux come with extensive system logging capability and programming languages tend to ship with standard logging libraries that cover most use cases. It is often wise to centralise logging information in order to analyse it at a later date, since it can often lead to ideas about improving performance or error reduction, which will almost certainly have a positive impact on your trading returns.
While logging of a system will provide information about what has transpired in the past, monitoring of an application will provide insight into what is happening right now . All aspects of the system should be considered for monitoring. System level metrics such as disk usage, available memory, network bandwidth and CPU usage provide basic load information.
Trading metrics such as abnormal prices/volume, sudden rapid drawdowns and account exposure for different sectors/markets should also be continuously monitored. Further, a threshold system should be instigated that provides notification when certain metrics are breached, elevating the notification method (email, SMS, automated phone call) depending upon the severity of the metric.
System monitoring is often the domain of the system administrator or operations manager. However, as a sole trading developer, these metrics must be established as part of the larger design. Many solutions for monitoring exist: proprietary, hosted and open source, which allow extensive customisation of metrics for a particular use case.
Backups and high availability should be prime concerns of a trading system. Consider the following two questions: 1) If an entire production database of market data and trading history was deleted (without backups) how would the research and execution algorithm be affected? 2) If the trading system suffers an outage for an extended period (with open positions) how would account equity and ongoing profitability be affected? The answers to both of these questions are often sobering!
It is imperative to put in place a system for backing up data and also for testing the restoration of such data. Many individuals do not test a restore strategy. If recovery from a crash has not been tested in a safe environment, what guarantees exist that restoration will be available at the worst possible moment?
Similarly, high availability needs to be "baked in from the start". Redundant infrastructure (even at additional expense) must always be considered, as the cost of downtime is likely to far outweigh the ongoing maintenance cost of such systems. I won't delve too deeply into this topic as it is a large area, but make sure it is one of the first considerations given to your trading system.
Choosing a Language.
Considerable detail has now been provided on the various factors that arise when developing a custom high-performance algorithmic trading system. The next stage is to discuss how programming languages are generally categorised.
Type Systems.
When choosing a language for a trading stack it is necessary to consider the type system . The languages which are of interest for algorithmic trading are either statically - or dynamically-typed . A statically-typed language performs checks of the types (e. g. integers, floats, custom classes etc) during the compilation process. Such languages include C++ and Java. A dynamically-typed language performs the majority of its type-checking at runtime. Such languages include Python, Perl and JavaScript.
For a highly numerical system such as an algorithmic trading engine, type-checking at compile time can be extremely beneficial, as it can eliminate many bugs that would otherwise lead to numerical errors. However, type-checking doesn't catch everything, and this is where exception handling comes in due to the necessity of having to handle unexpected operations. 'Dynamic' languages (i. e. those that are dynamically-typed) can often lead to run-time errors that would otherwise be caught with a compilation-time type-check. For this reason, the concept of TDD (see above) and unit testing arose which, when carried out correctly, often provides more safety than compile-time checking alone.
Another benefit of statically-typed languages is that the compiler is able to make many optimisations that are otherwise unavailable to the dynamically - typed language, simply because the type (and thus memory requirements) are known at compile-time. In fact, part of the inefficiency of many dynamically-typed languages stems from the fact that certain objects must be type-inspected at run-time and this carries a performance hit. Libraries for dynamic languages, such as NumPy/SciPy alleviate this issue due to enforcing a type within arrays.
Open Source or Proprietary?
One of the biggest choices available to an algorithmic trading developer is whether to use proprietary (commercial) or open source technologies. There are advantages and disadvantages to both approaches. It is necessary to consider how well a language is supported, the activity of the community surrounding a language, ease of installation and maintenance, quality of the documentation and any licensing/maintenance costs.
The Microsoft stack (including Visual C++, Visual C#) and MathWorks' MatLab are two of the larger proprietary choices for developing custom algorithmic trading software. Both tools have had significant "battle testing" in the financial space, with the former making up the predominant software stack for investment banking trading infrastructure and the latter being heavily used for quantitative trading research within investment funds.
Microsoft and MathWorks both provide extensive high quality documentation for their products. Further, the communities surrounding each tool are very large with active web forums for both. The software allows cohesive integration with multiple languages such as C++, C# and VB, as well as easy linkage to other Microsoft products such as the SQL Server database via LINQ. MatLab also has many plugins/libraries (some free, some commercial) for nearly any quantitative research domain.
There are also drawbacks. With either piece of software the costs are not insignificant for a lone trader (although Microsoft does provide entry-level version of Visual Studio for free). Microsoft tools "play well" with each other, but integrate less well with external code. Visual Studio must also be executed on Microsoft Windows, which is arguably far less performant than an equivalent Linux server which is optimally tuned.
MatLab also lacks a few key plugins such as a good wrapper around the Interactive Brokers API, one of the few brokers amenable to high-performance algorithmic trading. The main issue with proprietary products is the lack of availability of the source code. This means that if ultra performance is truly required, both of these tools will be far less attractive.
Open source tools have been industry grade for sometime. Much of the alternative asset space makes extensive use of open-source Linux, MySQL/PostgreSQL, Python, R, C++ and Java in high-performance production roles. However, they are far from restricted to this domain. Python and R, in particular, contain a wealth of extensive numerical libraries for performing nearly any type of data analysis imaginable, often at execution speeds comparable to compiled languages, with certain caveats.
The main benefit of using interpreted languages is the speed of development time. Python and R require far fewer lines of code (LOC) to achieve similar functionality, principally due to the extensive libraries. Further, they often allow interactive console based development, rapidly reducing the iterative development process.
Given that time as a developer is extremely valuable, and execution speed often less so (unless in the HFT space), it is worth giving extensive consideration to an open source technology stack. Python and R possess significant development communities and are extremely well supported, due to their popularity. Documentation is excellent and bugs (at least for core libraries) remain scarce.
Open source tools often suffer from a lack of a dedicated commercial support contract and run optimally on systems with less-forgiving user interfaces. A typical Linux server (such as Ubuntu) will often be fully command-line oriented. In addition, Python and R can be slow for certain execution tasks. There are mechanisms for integrating with C++ in order to improve execution speeds, but it requires some experience in multi-language programming.
While proprietary software is not immune from dependency/versioning issues it is far less common to have to deal with incorrect library versions in such environments. Open source operating systems such as Linux can be trickier to administer.
I will venture my personal opinion here and state that I build all of my trading tools with open source technologies. In particular I use: Ubuntu, MySQL, Python, C++ and R. The maturity, community size, ability to "dig deep" if problems occur and lower total cost ownership (TCO) far outweigh the simplicity of proprietary GUIs and easier installations. Having said that, Microsoft Visual Studio (especially for C++) is a fantastic Integrated Development Environment (IDE) which I would also highly recommend.
Batteries Included?
The header of this section refers to the "out of the box" capabilities of the language - what libraries does it contain and how good are they? This is where mature languages have an advantage over newer variants. C++, Java and Python all now possess extensive libraries for network programming, HTTP, operating system interaction, GUIs, regular expressions (regex), iteration and basic algorithms.
C++ is famed for its Standard Template Library (STL) which contains a wealth of high performance data structures and algorithms "for free". Python is known for being able to communicate with nearly any other type of system/protocol (especially the web), mostly through its own standard library. R has a wealth of statistical and econometric tools built in, while MatLab is extremely optimised for any numerical linear algebra code (which can be found in portfolio optimisation and derivatives pricing, for instance).
Outside of the standard libraries, C++ makes use of the Boost library, which fills in the "missing parts" of the standard library. In fact, many parts of Boost made it into the TR1 standard and subsequently are available in the C++11 spec, including native support for lambda expressions and concurrency.
Python has the high performance NumPy/SciPy/Pandas data analysis library combination, which has gained widespread acceptance for algorithmic trading research. Further, high-performance plugins exist for access to the main relational databases, such as MySQL++ (MySQL/C++), JDBC (Java/MatLab), MySQLdb (MySQL/Python) and psychopg2 (PostgreSQL/Python). Python can even communicate with R via the RPy plugin!
An often overlooked aspect of a trading system while in the initial research and design stage is the connectivity to a broker API. Most APIs natively support C++ and Java, but some also support C# and Python, either directly or with community-provided wrapper code to the C++ APIs. In particular, Interactive Brokers can be connected to via the IBPy plugin. If high-performance is required, brokerages will support the FIX protocol.
استنتاج.
As is now evident, the choice of programming language(s) for an algorithmic trading system is not straightforward and requires deep thought. The main considerations are performance, ease of development, resiliency and testing, separation of concerns, familiarity, maintenance, source code availability, licensing costs and maturity of libraries.
The benefit of a separated architecture is that it allows languages to be "plugged in" for different aspects of a trading stack, as and when requirements change. A trading system is an evolving tool and it is likely that any language choices will evolve along with it.
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