التكامل المشترك في أزواج العملات الأجنبية.
التكامل المشترك في أزواج العملات الأجنبية هو أداة قيمة. بالنسبة لي، التكامل المشترك هو الأساس لاستراتيجية تجارية ميكانيكية محايدة السوق ممتازة التي تسمح لي للاستفادة في أي بيئة اقتصادية. سواء كان السوق في اتجاه صعودي، اتجاه هبوطي أو ببساطة تتحرك جانبية، وتداول أزواج الفوركس يسمح لي لحصاد المكاسب على مدار السنة.
يتم تصنيف إستراتيجية تداول أزواج الفوركس التي تستخدم التكامل المشترك كشكل من أشكال التداول المتقارب على أساس المراجحة الإحصائية والرجوع إلى المتوسط. هذا النوع من الاستراتيجية كان أول شعبية من قبل فريق التداول الكمي في مورغان ستانلي في 1980s باستخدام أزواج الأسهم، على الرغم من أنني والتجار الآخرين وجدوا أنه يعمل أيضا بشكل جيد جدا لتداول أزواج الفوركس، أيضا.
تداول أزواج الفوركس على أساس التكامل المشترك.
تداول أزواج الفوركس على أساس التكامل المشترك هو في الأساس استراتيجية عودة إلى متوسط. وباختصار، عندما يكون زوجين أو أكثر من أزواج الفوركس مدمجة، فإن ذلك يعني أن انتشار السعر بين أزواج الفوركس المنفصلة يميل إلى العودة إلى قيمته المتوسطة باستمرار مع مرور الوقت.
من المهم أن نفهم أن التكامل المشترك ليس ارتباطا. الترابط هو علاقة قصيرة الأجل فيما يتعلق بالتحركات المشتركة للأسعار. ويعني الترابط أن الأسعار الفردية تتحرك معا. على الرغم من الاعتماد على بعض الارتباط من قبل التجار، في حد ذاته انها أداة غير جديرة بالثقة.
ومن ناحية أخرى، فإن التكامل المشترك هو علاقة أطول أجلا مع التحركات المشتركة للأسعار، حيث تتحرك الأسعار معا في حدود أو فروق معينة، كما لو كانت مربوطة معا. لقد وجدت التكامل المشترك ليكون أداة مفيدة جدا في تداول أزواج الفوركس.
خلال تداول أزواج العملات الأجنبية، عندما ينتشر انتشار إلى قيمة عتبة يحسبها بلدي خوارزميات التداول الميكانيكية، وأنا "قصيرة" الفرق بين أسعار أزواج. وبعبارة أخرى، أنا أراهن على انتشار سوف يعود مرة أخرى نحو الصفر بسبب التكامل المشترك بينهما.
استراتيجيات التداول أزواج العملات الأساسية بسيطة جدا، وخصوصا عند استخدام أنظمة التداول الميكانيكية: اخترت اثنين من أزواج العملات المختلفة التي تميل إلى التحرك بالمثل. إنني أشتري زوج العملات الضعيفة وبيع الزوج المتداول. عندما يتقارب الفارق بين الزوجين، أغلق موقفي لتحقيق الربح.
تداول أزواج الفوركس على أساس التكامل المشترك هو استراتيجية محايدة السوق إلى حد ما. وكمثال على ذلك، إذا انخفض زوج العملات، فإن التداول من المحتمل أن يؤدي إلى خسارة على الجانب الطويل وكسب تعويض على الجانب القصير. لذلك، ما لم تفقد جميع العملات والأدوات الأساسية فجأة قيمة، يجب أن يكون صافي التجارة بالقرب من الصفر في أسوأ السيناريوهات.
وعلى نفس المنوال، فإن تداول الأزواج في العديد من الأسواق هو استراتيجية تداول ذاتي التمويل، حيث أن العائدات من المبيعات القصيرة يمكن أن تستخدم أحيانا لفتح الصفقة الطويلة. حتى من دون هذه الفائدة، لا يزال تداول أزواج العملات الأجنبية التي تعمل بالوقود المشترك يعمل بشكل جيد جدا.
فهم التكامل المشترك لتداول أزواج الفوركس.
التكامل المشترك مفيد بالنسبة لي في أزواج الفوركس التداول لأنه يتيح لي برنامج بلدي نظام التداول الميكانيكية على أساس كل من الانحرافات على المدى القصير من أسعار التوازن وكذلك توقعات السعر على المدى الطويل، والتي أعني التصحيحات والعودة إلى التوازن.
ولفهم كيفية عمل أزواج الفوركس التي يحركها التكامل المشترك، من المهم أولا تحديد التكامل المشترك، ثم وصف كيفية عمله في أنظمة التداول الميكانيكية.
كما قلت أعلاه، يشير التكامل المشترك إلى علاقة التوازن بين مجموعات من السلاسل الزمنية، مثل أسعار أزواج الفوركس المنفصلة التي هي في حد ذاتها ليست في حالة توازن. أما في المصطلحات الرياضية، فإن التكامل المشترك هو تقنية لقياس العلاقة بين المتغيرات غير الثابتة في سلسلة زمنية.
وإذا كان لكل سلسلتين زمنيتين أو أكثر قيمة جذر مساوية للقيمة 1، إلا أن تركيبة الخطية هي ثابتة، ثم يقال إنها مركبة كوينيغراتد.
وكمثال بسيط، يجب النظر في أسعار مؤشر سوق الأسهم والعقود الآجلة ذات الصلة: على الرغم من أن أسعار كل من هذين الصكين قد يهيمون على وجوههم بشكل عشوائي على فترات قصيرة من الزمن، في نهاية المطاف سيعودون إلى التوازن، وانحرافاتهم ستكون ثابت.
هنا مثال آخر، ذكر من حيث الكلاسيكية "المشي العشوائي" سبيل المثال: دعونا نقول هناك اثنين من سكران الفردية المشي هومويارد بعد ليلة من الكاروس. دعونا نفترض أيضا أن هذين السكرين لا يعرفون بعضهم البعض، لذلك ليس هناك علاقة يمكن التنبؤ بها بين مساراتهم الفردية. لذلك، ليس هناك تكامل بين تحركاتهم.
في المقابل، والنظر في فكرة أن الفرد في حالة سكر يتجول هوموارد في حين يرافقه كلبه على المقود. في هذه الحالة، هناك صلة محددة بين مسارات هذين المخلوقات الفقيرة.
على الرغم من أن كل من اثنين لا يزال على مسار فردي على مدى فترة قصيرة من الزمن، وعلى الرغم من أن أي واحد من الزوج قد يؤدي عشوائيا أو تأخر الآخر في أي نقطة معينة في الوقت المناسب، لا يزال، وسوف تكون دائما على مقربة من بعضها البعض. المسافة بينهما يمكن التنبؤ بها إلى حد ما، وبالتالي يقال أن الزوج أن يكون كوينيغراتد.
وبعد العودة الآن إلى المصطلحات التقنية، إذا كان هناك نوعان من السلاسل الزمنية غير الثابتة، مثل مجموعة افتراضية من أزواج العملات أب و زي، التي تصبح ثابتة عند حساب الفرق بينهما، وتسمى هذه الأزواج سلسلة متكاملة من الدرجة الأولى - أيضا استدعاء I (1) سلسلة.
على الرغم من أن أيا من هذه السلسلة يبقى في قيمة ثابتة، إذا كان هناك تركيبة خطية من أب و زي التي هي ثابتة (وصفها I (0))، ثم أب و زي هي كوينيغراتد.
المثال البسيط أعلاه يتكون من سلسلتين زمنيتين فقط من أزواج الفوركس الافتراضية. ومع ذلك، فإن مفهوم التكامل المشترك ينطبق أيضا على سلسلة زمنية متعددة، وذلك باستخدام أوامر التكامل أعلى ... فكر من حيث سكر يتجول يرافقه العديد من الكلاب، كل على المقود مختلفة طول.
في اقتصاديات العالم الحقيقي، فإنه من السهل العثور على أمثلة تظهر التكامل بين الأزواج: الدخل والإنفاق، أو قسوة القوانين الجنائية وحجم السجناء. في تداول أزواج العملات الأجنبية، ينصب تركيزي على الاستفادة من العلاقة الكمية التي يمكن التنبؤ بها بين أزواج العملات المتراكمة.
على سبيل المثال، لنفترض أنني أشاهد هذين الزوجين المفترسين للعملة الافتراضية، أب و زي، والعلاقة المشتركة بينهما هي أب & # 8211؛ زي = Z، حيث يساوي Z سلسلة ثابتة بمتوسط صفر، وهذا هو I (0).
ويبدو أن هذا يشير إلى استراتيجية تداول بسيطة: عندما أب - زي & غ؛ V، و V هو بلدي عتبة سعر الزناد، ثم نظام تداول أزواج الفوركس سوف تبيع أب وشراء زي، لأن التوقعات ستكون ل أب لتقليل في الأسعار و زي لزيادة. أو، عندما أب - زي & لوت؛ - V، وأتوقع لشراء أب وبيع زي.
تجنب الانحدار الهامشي في تداول أزواج الفوركس.
ومع ذلك، فإنه ليس بسيطا كما يقترح المثال أعلاه. في الممارسة العملية، يحتاج نظام التداول الميكانيكي لتداول أزواج الفوركس إلى حساب التكامل المشترك بدلا من الاعتماد على قيمة R-سكارد بين أب و زي.
وذلك لأن تحليل الانحدار العادي يقصر عند التعامل مع المتغيرات غير ثابتة. ويسبب ذلك ما يسمى الانحدار الهامشي، مما يوحي العلاقات بين المتغيرات حتى عندما لا يكون هناك أي.
لنفترض، على سبيل المثال، أنني أترتب على مسلسل زمني واحد منفصل "المشي العشوائي" ضد بعضها البعض. عندما اختبر لمعرفة ما إذا كان هناك علاقة خطية، في كثير من الأحيان سوف تجد قيم عالية ل R - التربيع وكذلك القيم P المنخفضة. ومع ذلك، لا توجد علاقة بين هذين المشيين العشوائيين.
الصيغ واختبار التكامل المشترك في تداول أزواج الفوركس.
وأبسط اختبار للتكامل المشترك هو اختبار إنغل-غرانجر الذي يعمل على النحو التالي:
التحقق من أن أب t و زي t هما على حد سواء I (1) حساب العلاقة التكامل المشترك [زي t = أب t + إت] باستخدام طريقة المربعات الصغرى تحقق من أن بقايا التكامل المشترك وثابتة باستخدام اختبار وحدة الجذر مثل المعزز ديكي فولر (أدف) الاختبار.
معادلة غرانجر مفصلة:
I (0) يصف علاقة التكامل المشترك.
ويصف زي T-1 - βAB t-1 مدى الاختلال بعيدا عن المدى الطويل، في حين أن αi هي السرعة والاتجاه الذي تصحح فيه السلاسل الزمنية لزوج العملات نفسها من الاختلال.
عند استخدام طريقة إنغل-غرانجر في تداول أزواج الفوركس، يتم استخدام قيم بيتا للانحدار لحساب أحجام التداول للأزواج.
عند استخدام طريقة إنغل-غرانجر في تداول أزواج الفوركس، يتم استخدام قيم بيتا للانحدار لحساب أحجام التداول للأزواج.
تصحيح الخطأ للتكامل المشترك في أزواج العملات الأجنبية:
عند استخدام التكامل المشترك لتداول أزواج الفوركس، من المفيد أيضا حساب كيفية ضبط المتغيرات المركزة والعودة إلى التوازن على المدى الطويل. لذلك، على سبيل المثال، وهنا هما عينة الوقت أزواج الفوركس سلسلة أظهرت أوتورجريسيفيلي:
تداول أزواج الفوركس على أساس التكامل المشترك.
عندما أستخدم نظام التداول الآلي الخاص بي لتداول أزواج الفوركس، فإن الإعداد والتنفيذ بسيطان إلى حد ما. أولا، أجد أزواج العملات التي تبدو وكأنها قد تكون مشتركة، مثل ور / أوسد و غبب / أوسد.
ثم، أحسب فروق السعر المقدرة بين الزوجين. بعد ذلك، تحقق من وجود استبانة باستخدام اختبار جذر الوحدة أو طريقة شائعة أخرى.
أتأكد من أن خلاصة البيانات الواردة تعمل بشكل مناسب، وأسمح لخوارزميات التداول الميكانيكية بإنشاء إشارات التداول. على افتراض لقد قمت بتشغيل الاختبارات الخلفية كافية لتأكيد المعلمات، وأنا أخيرا على استعداد لاستخدام التكامل المشترك في بلدي أزواج الفوركس التداول.
لقد وجدت مؤشر ميتاتريدر الذي يوفر نقطة انطلاق ممتازة لبناء نظام تداول أزواج الفوركس المشترك. يبدو وكأنه مؤشر بولينجر باند، ولكن في الواقع يظهر مذبذب الفرق السعر بين اثنين من أزواج العملات المختلفة.
عندما يتحرك هذا المذبذب نحو أعلى أو منخفض للغاية، فإنه يشير إلى أن أزواج هي فصل، مما يشير إلى الصفقات.
ومع ذلك، للتأكد من النجاح أعتمد على بلدي نظام البناء الميكانيكية بنيت بشكل جيد لتصفية الإشارات مع اختبار ديكي فولر المعزز قبل تنفيذ الصفقات المناسبة.
وبطبيعة الحال، أي شخص يريد استخدام التكامل المشترك له أو لها أزواج الفوركس التداول، ولكن يفتقر إلى المهارات المطلوبة ألغو البرمجة، ويمكن الاعتماد على مبرمج من ذوي الخبرة لخلق مستشار خبير الفوز.
من خلال سحر التداول الخوارزمي، وأنا برنامج بلدي نظام التداول الميكانيكية لتحديد ينتشر السعر على أساس تحليل البيانات. خوارزميات بلدي خوارزمية لانحرافات الأسعار، ثم تلقائيا يشتري ويبيع أزواج العملات من أجل حصاد أوجه القصور في السوق.
المخاطر التي يجب أن تكون على دراية عند استخدام التكامل المشترك مع تداول أزواج الفوركس.
تداول أزواج العملات الأجنبية ليس خاليا تماما من المخاطر. قبل كل شيء، أنا أضع في اعتبارنا أن أزواج العملات الأجنبية التداول باستخدام التكامل هو استراتيجية انعكاس المتوسط، الذي يقوم على افتراض أن القيم المتوسطة سوف تكون هي نفسها في المستقبل كما كانت في الماضي.
على الرغم من أن اختبار ديكي-فولر المعزز المذكور سابقا مفيد في التحقق من العلاقات المتآزرة لتداول أزواج الفوركس، إلا أنه لا يعني أن فروق الأسعار ستستمر في أن تكون مشتركة في المستقبل.
أنا أعتمد على قواعد قوية لإدارة المخاطر، مما يعني أن نظام التداول الآلي الخاص بي يخرج من الصفقات غير المربحة إذا أو عندما يتم إبطال العائد المحسوب إلى المتوسط.
عندما تتغير القيم المتوسطة، انها تسمى الانجراف. أحاول الكشف عن الانجراف في أقرب وقت ممكن. وبعبارة أخرى، إذا بدأت أسعار أزواج الفوركس التي تم تجميعها سابقا في التحرك في اتجاه بدلا من العودة إلى المتوسط المحسوب سابقا، فقد حان الوقت لخوارزميات نظام التداول الآلي الخاص بي لإعادة حساب القيم.
عندما أستخدم نظام التداول الميكانيكي الخاص بي لتداول أزواج الفوركس، أستخدم صيغة الانحدار الذاتي المذكورة سابقا في هذه المقالة من أجل حساب المتوسط المتحرك للتنبؤ بالانتشار. ثم، أنا الخروج من التجارة في بلدي حدود الخطأ المحسوبة.
التكامل المشترك هو أداة قيمة لأزواج الفوركس بلدي التداول.
استخدام التكامل في أزواج الفوركس التداول هو استراتيجية التداول الميكانيكية محايدة السوق التي تسمح لي التجارة في أي بيئة السوق. انها استراتيجية ذكية تقوم على العودة إلى يعني، ومع ذلك فإنه يساعدني على تجنب المزالق لبعض استراتيجيات العودة إلى المتوسط يعني تداول العملات الأجنبية.
ونظرا لاستخدامه المحتمل في أنظمة التداول الميكانيكية المربحة، فقد اجتذب التكامل المشترك لتداول أزواج العملات الأجنبية الاهتمام من التجار المحترفين وكذلك الباحثين الأكاديميين.
هناك الكثير من المقالات التي نشرت مؤخرا، مثل هذه المادة بلوق تركز على بلوق، أو هذه المناقشة العلمية للموضوع، فضلا عن الكثير من النقاش بين التجار.
التكامل المشترك هو أداة قيمة في بلدي أزواج الفوركس التداول، وأنا أوصي أن ننظر في الأمر لنفسك.
يقول توماسو سيليان.
مادة جيدة جدا. هو ملهم. شكرا لكتابة ذلك!
يقول هاريش ناتشناني.
كما يتم تطبيق الارتباط في الأسهم (الأسهم). ماهو الفرق؟ هل يمكن تطبيق العملية المذكورة أعلاه على الأسهم؟
يقول إدي زهرة.
نعم، يمكن تطبيق نفس العملية على الأسهم وكذلك على المشتقات. ونظرا لوجود مثل هذا الكون الكبير من الأسهم عند مقارنته بأزواج الفوركس، ينبغي أن يكون هناك عدد أكبر من الفرص المحتملة للتداول. مع عدد من الطحن قوة اليوم & # 8217؛ s نظم التداول، العديد من مجموعات من العلاقات يمكن فحصها بسرعة، في الوقت الحقيقي. ويمكن أيضا استخدام التكامل المشترك من قبل التجار الخيارات. فإنه من المتوقع أن تنتج نتائج مثل انتشار كوكا كولا-بيبسي الشعبي الذي تسمح فيه العلاقات السعرية بين بعض الأسهم / الخيارات للمتداولين بالاشتراك في مسرحيات منخفضة المخاطر إلى حد ما مع فرصة جيدة إلى حد ما للفوز.
يقول هاريش ناتشناني.
هل تتداول في غضون يوم أو أكثر من أسابيع باستخدام هذه الاستراتيجية؟ أيضا، ما لغة البرمجة التي تنصح بها. R يستغرق وقتا طويلا لتشغيل الحسابات وإذا كان في غضون التجارة اليوم، الكمون يأتي في اللعب.
لغة البرمجة لا تهم التداول في نهاية اليوم. أي لغة رئيسية مثل بيرل، بايثون، C / C ++ و C # على ما يرام. R يمكن أن يكون سريع للغاية لكنه يبطئ إذا كان & # 8217؛ s اضطر إلى تخصيص حيوي الذاكرة.
أنا التجارة باستخدام الرسوم البيانية اليومية، وأنا البقاء في معظم الصفقات لبضعة أيام لبضعة أسابيع. شون هو مبرمج خبير، وأنا أثق دائما حكمه لاستخدام أفضل لغة البرمجة للحصول على أفضل النتائج لاستراتيجية التداول معين. في الواقع، يمكن لشون خلق برنامج متوازن، الفوز لتحقيق الاستفادة من التكامل المشترك وعوامل أخرى كذلك. إذا كنت & # 8217؛ د مثل الاقتباس، يرجى الاتصال به مباشرة في معلومات @ أونيستيبريموفيد.
يقول كريس زيمر.
وهناك بعض االهتمام بتنفيذ هذا البرنامج ل MT4. إذا كنت تستطيع تقديم بعض التفاصيل على تنفيذ هذه الاستراتيجية في التعليمات البرمجية، يرجى إرسالها إلى زيمر @ أونيستيبريموفد.
أنا أفعل مشروع صغير على استراتيجيات التكامل المشترك في فكس لبلدي ماجستير. أعتقد أنك ركض اختبارات التكامل المشترك على الكثير من أزواج العملات. ما هي تلك التي وجدت أنها ذات دلالة إحصائية مركزة بشكل كبير؟
أنا لا & # 8217؛ ر أعتقد إدي ركض في الواقع الأرقام. والمقصود من هذه المادة أن تكون دليلا شاملا لهذا المفهوم، ولكن ليس تماما لدرجة كونه استراتيجية حسن النية.
1) أوسد / جبي و ور / تشف.
2) ور / بلن و ور / هف.
3) أوسد / تري و أوسد / زار.
4) أود / أوسد و نزد / أوسد.
5) ور / نوك ور / سيك.
وأنا أعلم أن هذه ترتبط ارتباطا وثيقا للغاية، ولكن هذا لا يعني التكامل المشترك.
يقول كاميلو روميرو.
هناك أزواج الفوركس جيدة سوينغاتراتد:
لن يكون الدولار أوسجبي / ورشف زوجا متآلفا لأنه لن يكون هناك استراتيجية محايدة للسوق.
شكرا للمشاركة.
يقول كاميلو روميرو.
هل نفذ أي شخص شفرة باكتست باستخدام إستراتيجية الإرجاع المتوسط؟
هل يجب أن أقيم القيم بين أزواج الفوركس؟
هل أضاف أي شخص تكلفة العمولة إلى باكتست كود وحصلت على نتائج مربحة؟
I & # 8217؛ m متأكد من شخص ما لديه، لكنه & # 8217؛ s ليس شيئا حيث أنت & # 8217؛ سوف تجد لإجابة واضحة على الرسوم البيانية على المدى القصير. قد تجد كوانتيغراتيونس على المدى الطويل، ولكن هذا & # 8217؛ s لا البحوث أنا & # 8217؛ القيام به.
ويتمثل التكامل المشترك الوحيد بين اليورو والفرنك السويسري وبين الدولار الأسترالي والدولار النيوزيلندي حيث أن التجارة والاقتصاد الحميمين الوحيدين بين هذه البلدان والمصارف المركزية يخلقان هذا التكامل المشترك.
ليس اليورو و الجنيه الإسترليني؟
يقول روبرت J أرماغوست.
مرحبا إدي. مقال ممتاز. لقد تم اختبار مرة أخرى 10 سنوات من الرسوم البيانية التفكير & # 8221؛ لا أستطيع أن أكون أول شخص فكر في هذا! & # 8221؛ عندما وجدت هذا الموقع. شكرا جزيلا لكتابة هذا. أنا لا & # 8217؛ ر يشعر تماما حتى وحده بعد الآن. 🙂 فقط أتساءل أي وسيط كنت تستخدم أو هل تستخدم وسطاء متعددة. شكرا على وقتك.
مع خالص التقدير روبرت J. أرماغوست.
الوسيط الرئيسي الذي أستخدمه هو بيبرستون و ستو (عبر توبترادر).
مرحبا شون لقد تم تداول هذه الاستراتيجية يدويا. هل لديك برنامج لأتمتة هذا؟ (حتى أنا لا & # 8217؛ ر لديك للحصول على ما يصل في منتصف الليل بعد الآن) شكرا على وقتك.
ليس من على الرف، ولكن ذلك & # 8217؛ ق شيء يمكننا أن نبني. تبادل لاطلاق النار لي رسالة بالبريد الالكتروني مع قواعد الدخول والخروج للحصول على تقدير. معلومات @ onestepremoved.
روبرت & # 8212؛ شكرا لردود فعل طيبة. شون لديه الأدوات المناسبة لتنفيذ هذا النوع من استراتيجية التداول، وأنا أتفق تماما مع توصيات وسيط له، شكرا مرة أخرى للتعليق! EF.
جيكو كوانت - التداول الكمي.
التداول الكمي، التحكيم الإحصائي، تعلم الآلة والخيارات الثنائية.
آخر الملاحة.
إربيتال أربتريج & # 8211؛ تداول زوج مشترك.
في آخر مشاركة جيكوكانت / 2018/12/17 / إحصائي-التحكيم-اختبار-ل - التكامل المشترك-زيادة-ديكي-أكمل / أثبتت التكامل المشترك، وهو اختبار رياضي لتحديد أزواج ثابتة حيث يجب أن يكون الانتشار حسب التعريف يعني العودة.
في هذا المنصب أعتزم أن أشرح كيفية تداول زوج مشترك، وسوف تستمر في تحليل أسهم رويال داتش شل A مقابل B (ونحن نعلم أنها & # 8217؛ كوينتيغراتد من آخر مشاركة بلدي). تداول زوج كوينغراتد هو مستقيم إلى الأمام، ونحن نعرف متوسط والتباين في انتشار، ونحن نعلم أن تلك القيم هي ثابتة. نقطة الدخول ل أرب ستات هي مجرد البحث عن انحراف كبير بعيدا عن المتوسط.
وتتمثل الاستراتيجية الأساسية فيما يلي:
إذا انتشرت (t) & غ؛ = يعني سبرياد + 2 * الانحراف المعياري ثم انتقل شورت إف سبرياد (t) & لوت؛ = مين سبرياد & # 8211؛ 2 * الانحراف المعياري ثم يذهب طويل.
إذا كان معدل الانتشار (t) & غ؛ = ناداي موفينغ أفيراج + 2 * ناداي رولينغ ستاندارد ديفياتيون ثين غو شورت إف سبرياد (t) & لوت؛ = ناداي موفينغ أفيراج & # 8211؛ 2 * ناداي المتداول الانحراف المعياري ثم يذهب طويلا.
إذا انتشرت (t) & لوت؛ = متوسط انتشار + 2 * الأمراض المنقولة جنسيا وانتشار (t-1) & غ؛ متوسط انتشار + 2 * ستد إذا انتشار (t) & غ؛ = يعني سبرياد & # 8211؛ 2 * الأمراض المنقولة جنسيا وانتشار (t-1) & لوت؛ مين سبرياد & # 8211؛ 2 * ستد ميزة هي أننا التجارة فقط عندما نرى متوسط انعكاس، حيث كما نماذج أخرى تأمل في متوسط انعكاس على انحراف كبير عن المتوسط (هو انتشار تهب؟)
ستنظر هذه المشاركة في المتوسط المتحرك ونموذج الانحراف المعياري المتداول لأسهم شركة رويال داتش شل A مقابل B، وسوف تستخدم نسبة التحوط الموجودة في آخر مشاركة.
شارب راتيو شل A & أمب؛ B ستات أرب شل A.
نسبة شارب السنوية (رف = 0٪):
شل A & أمب؛ B ستات أرب 0.8224211.
شل A 0.166307.
لدى أرباب الحسابات نسبة متفوقة من شارب أكثر من مجرد الاستثمار في شل أ. في النظرة الأولى فإن نسبة شارب 0.8 تبدو مخيبة للآمال، ولكن بما أن الاستراتيجية تنفق معظمها من وقت الخروج من السوق سيكون منخفضا سنويا نسبة محددة. لزيادة نسبة شارب يمكن للمرء أن ننظر إلى تداول ترددات أعلى أو لديك أزواج محفظة بحيث يتم قضاء المزيد من الوقت في السوق.
22 أفكار حول & لدكو؛ إربيتال أربتريج & # 8211؛ تداول زوج كوينيغراتد & رديقو؛
وهذا يعني أيضا أنه عند تحديد الاختلاف الأقصى يمكنني اتخاذ موقف في المشتقات مثل الخيارات؟
-خيار أتم دعوة النداء على الأسهم الأولى.
-buy خيار الاتصال على الثانية.
أو مع باكسبريدكال على الأولى و باكسبريدبوت على الثانية حتى أتمكن من تعيين الحماية وأنا يمكن أن لفة لهم إذا خرجوا السيطرة & # 8230؛
يجب أن تكون المراكز قصيرة المال أتم أو أوتم خفيف في رأيي.
ماذا تفكر؟
هل حاولت استخدام نهج اختبار يوهانسن من أجل إجراء اختبار أكثر صرامة للتكامل المشترك؟ ما رأيك في الجمع بين إنغل-غرانجر مع يوهانسن؟
إن الانتشار في ما سبق لا يتأرجح حوله يعني، من الناحية المثالية، يجب أن يتفاعل الزوج المشترك بين الجانبين بشكل غير متجانس كما هو مبين أعلاه & # 8230؛. لقد كانت عملية الكتابة المثالية مثالية للتكامل المشترك الصحيح الذي أظهرته. ولكن هذا الانتشار ليس انتشارا مثاليا.
أنا 100٪ أتفق معك.
ولكن لأغراض عملية طالما أن متوسط العائد يحدث أسرع من متوسط التغييرات ثم أنت & # 8217؛ ليرة لبنانية بشكل جيد.
أعتقد أن & # 8217؛ شيء أنا & # 8217؛ غاب، كيفية قياس نصف الحياة / سرعة الإرجاع.
يرجى ملاحظة أنه في التجريبي أعلاه نظرة فترة العودة 90 يوما. هذا قصير إلى حد ما. اختيار 200 يوما سوف يؤدي إلى يعني أن أقل استجابة / اتجاه التغييرات. ومن المرجح أن تزيد من حجم نطاقات الانحراف المعياري وتؤدي إلى صفقات أقل في السنة. وهذا يؤدي عادة إلى انخفاض نسبة شارب.
وظيفة مثيرة جدا للاهتمام. أحب أن نرى التنفيذ على سلة من أزواج.
أفعل بعض التغييرات في البرنامج الخاص بك لحساب البولنجر العصابات وأريد أن أعرف لماذا أنت & # 8217؛ وضع الانحراف المعياري إلى اليمين؟ (موفينغستد = رولابلي (سبرياد، لوكباك، سد، ألين = & # 8221؛ رايت & # 8221 ؛، na. pad = ترو))
موافق شكرا لك على الإجابة!
مدونتك تعطيني فرصة لتنفيذ وبناء أسرع استراتيجية بلدي قانون الأحوال الشخصية.
أنا ذاهب لاختبار نماذج مختلفة للمراجحة الإحصائية. أظل جميع الزوار في حلقة!
في البرنامج الخاص بك، وتأثير مارتينغال ليست هنا. كيف يمكنني إضافة هذا التأثير؟
أنا تشغيل بلدي باكتيستس إون مع برامج مختلفة (إكسيل، R إت برورالتيمي (منصة الفرنسية)) ومن أجل القيام ببعض المقارنة، ولست بحاجة لإضافة تأثير مارتينغال.
شكرا على التوضيح. بواسطة نفس الحجة، رولمان يجب أن يكون لها نفس: رولمان (سبرياد، لوكباك، na. pad = ترو، ألين = 'رايت')
مع هذا التعديل الجديد نسبة شارب تنخفض بشكل كبير ..
أشياء عظيمة!! أعتقد أن هناك اثنين من الأخطاء في التعليمات البرمجية الخاصة بك، على الرغم من. الأول هو في حساب المتوسط المتحرك. لقد نسيت تعيين معلمة المحاذاة إلى & # 8220؛ اليمين & # 8221؛ (كما تفعل للإنحراف المعياري). تستخدم الدالة ديفولت & # 8220؛ سينتر & # 8221؛ والبيانات الخاصة بك & # 8211؛ لا يتم محاذاة المتوسط المتحرك والمتوسط المتحرك. يمكنك أن ترى هذا من المؤامرة كذلك. ينتهي متوسط الانتقال قبل 45 يوما من الانتشار. العلة الثانية هي في حساب عوائد التداول. أعتقد أنك يجب أن تأخذ العودة من اليوم التالي ونحن ندخل الموقف بسعر الإغلاق.
شكرا لرمزك الأنيق. لقد لاحظت أن سطر التعليمات البرمجية:
هو تطبيق الدالة شورتبوسيتيونفونك على (-1 * أوفيروبرباند + دونمافغ).
ومع ذلك، فإن الدالة شورتبوسيتيونفونك تأخذ حجتين x و y.
هل هناك أي أخطاء مطبعية في التعليمات البرمجية؟
شكرا لتوضيحكم!
شكرا جيكو للحصول على رمز باكتستينغ. مفيد جدا. زوجين من التعليقات أدناه:
1) وقد علق قارئ آخر بالفعل على هذا أعلاه. موفينغافغ يحتاج إلى تعديل بإضافة المحاذاة = "يمين" من أجل الحصول على أول رقم أفغ تتحرك في اليوم 90:
موفينغافغ = رولمان (سبرياد، لوكباك، ألين = "رايت"، na. pad = ترو)
2) لأننا ندخل الصفقات في نهاية اليوم، والعائد على تاريخ التجارة لا ينبغي الاعتماد. يمكننا ببساطة تحويل كل عنصر في "المواقف" ناقلات أسفل باستخدام وظيفة "التحول" في مكتبة تاريفكس.
أيضا، لا أعتقد أن العائد اليومي هو (أريت - ستوكبير $ هيدجيراتيو * بريت). تخيل إذا كان لديك نسبة التحوط كبيرة، أي إذا كان سعر السهم A في 100 $ ويبلغ سعر B في $ 10، ثم سيكون هدجيراتيو في حي 10. منذ أريت و بريت هي في٪ من حيث، فإن الصيغة لا عمل. يجب أن يكون العائد اليومي أريت - بريت * (النسبة بين نسبة الدولار المحايدة مقابل نسبة التحوط).
#Calculate انتشار ريت اليومي.
دايليريت & لوت؛ - أريت - بريت * هدجيراتيوفيردولارنيوترالراتيو.
ترادينغريت & لوت؛ - دايليريت * شيفت (بوسيتيونس، -1)
أنا أبحث عن استراتيجيات جديدة في تجارة زوج الأسهم التي تحسن نهج التكامل المشترك (على سبيل المثال بدأت أبحث في تداول الزوج مع كوبولاس، الذي لا يزال يبدو & # 8220؛ غير مستقر & # 8221؛ بديل للتكامل المشترك). هل لديك أي ورقة جديدة تشير لي؟ شكرا جزيلا لكم و تهانينا لبلوق كبيرة.
النصف الثاني من الكتاب يذهب من خلال الكثير من التقنيات الأكثر تقدما للتحوط محفظة / العثور على أزواج ثابتة.
أنا مشوشة قليلا في هذه الخطوة.
عندما رسمت لونغبوسيتيونس و شورتبوسيتيونس جنبا إلى جنب مع انتشار، العصابات وخطوط المتوسط المتحرك وجدت ثم هناك إشارات طويلة متتالية وإشارات قصيرة. وفقا لفهمي.
لونغبوستيونس & لوت؛ - إذا كان الانتشار أقل من النطاق السفلي.
لونغكسيت & لوت؛ - إذا انتشر فوق موفافغ في حين طويلة.
شورتبوستيونس & لوت؛ - إذا كان الانتشار فوق النطاق العلوي.
شورتكسيت & لوت؛ - إذا كان انتشار أقل من موفافغ في حين قصيرة.
هو نفس الشيء التعليمات البرمجية الخاصة بك هو القيام به. الرجاء مساعدتي على فهم هذا الجزء.
مرحبا جيكو، قرأت كتب إب تشان التي تتحدث عن هذا الموضوع وأنا مشوشة قليلا حول متوسط الاحتياط. عندما يكون هناك نوعان من أصول آرا، فإننا نفترض أنهما سيعودان إلى متوسطهما، ولكن متوسطهما المتحرك أو متوسطهما الإجمالي في فترة محددة؟ I & # 8217؛ م إعطاء نتائج أفضل باستخدام المعلمات ثابتة من استخدام البولنجر العصابات. وسوف تظهر لك صورة مع شكلي. برنتسكر / 51jofw هل يمكن أن تكتب مقالة أخرى من انعكاس يعني! شكرا للجميع.
مرحبا جيكو. رمز عظيم. هل يمكن أن تشرح أقرب فكرة وراء هذه الدالة كابدكومسوم؟ أنا لا أفهم لحظة عندما كنت سبيسيفينغ اثنين من المتغيرات المدخلات، ولكن في ريدوس () وظيفة معلمة واحدة فقط، & # 8211؛ هل هو بسبب 0؟
هناك خطأ. خوارزمية الخاص ينظر في المستقبل، والمشكلة في وظيفة رولمان. خوارزمية باستخدام المتوسط المتحرك من الأيام المستقبلية لإغلاق الصفقة.
استراتيجية تداول الزوج المشترك
تداول الأزواج هو شكل من أشكال انعكاس المتوسط الذي يتميز بميزة واضحة من التحوط دائما ضد تحركات السوق. وهي عموما استراتيجية ألفا عالية عندما تدعمها بعض الإحصاءات الدقيقة. هذا المفكرة يعمل من خلال المفاهيم التالية.
ويهدف دفتر الملاحظات ليكون مقدمة للمفهوم، وبينما هذا الكمبيوتر المحمول يتميز زوج واحد فقط، وربما كنت تريد خوارزمية الخاص بك للنظر في العديد من أزواج في آن واحد.
تم إنشاء جهاز الكمبيوتر المحمول أصلا لعرض في قسم كس التطبيقية هارفارد ومنذ ذلك الحين استخدمت في ستانفورد، كورنيل، والعديد من الأماكن الأخرى. إذا كنت ترغب في معرفة المزيد عن كيفية استخدام كوانتوبيان كأداة تعليمية في أعلى الجامعات، يرجى الاتصال بي على [إمايل & # 160؛ المحمية]
يتم توفير المواد على هذا الموقع لأغراض إعلامية فقط ولا تشكل عرضا لبيع أو طلب شراء أو توصية أو تأييد لأي أمن أو استراتيجية، كما أنها لا تشكل عرضا لتقديم الخدمات الاستشارية الاستثمارية من قبل كوانتوبيان. وبالإضافة إلى ذلك، لا تقدم المادة أي رأي فيما يتعلق بملاءمة أي ضمان أو استثمار محدد. لا ینبغي اعتبار أي معلومات واردة في ھذه الوثیقة بمثابة اقتراح للانخراط في أي مسار عمل یتعلق بالاستثمار أو الامتناع عنھ حیث لا یقوم أي من کوانتوبيان أو أي من الشرکات التابعة لھ بتقدیم المشورة الاستثماریة أو العمل کمستشار لأي خطة أو کیان خاضع ل وقانون تأمين دخل التقاعد للموظفين لعام 1974، بصيغته المعدلة، أو حساب التقاعد الفردي أو المعاش التقاعدي الفردي، أو تقديم المشورة بصفة الأمانة فيما يتعلق بالمواد المعروضة في هذه الوثيقة. إذا كنت مستقلا فرديا أو مستثمرا آخر، فاتصل بمستشارك المالي أو أي جهة مالية أخرى لا علاقة لها بكوانتوبيان حول ما إذا كانت أي فكرة استثمار أو إستراتيجية أو منتج أو خدمة معينة مذكورة هنا قد تكون مناسبة لظروفك. وتشمل جميع الاستثمارات مخاطر، بما في ذلك خسارة أصل الدين. لا تقدم كوانتوبيان أي ضمانات بشأن دقة أو اكتمال الآراء المعرب عنها في الموقع. وتخضع اآلراء للتغيير، وقد تصبح غير موثوقة ألسباب مختلفة، بما في ذلك التغيرات في ظروف السوق أو الظروف االقتصادية.
وهنا خوارزمية بسيطة جدا على أساس النهج المقدم في دفتر الملاحظات.
يتم توفير المواد على هذا الموقع لأغراض إعلامية فقط ولا تشكل عرضا لبيع أو طلب شراء أو توصية أو تأييد لأي أمن أو استراتيجية، كما أنها لا تشكل عرضا لتقديم الخدمات الاستشارية الاستثمارية من قبل كوانتوبيان. وبالإضافة إلى ذلك، لا تقدم المادة أي رأي فيما يتعلق بملاءمة أي ضمان أو استثمار محدد. لا ینبغي اعتبار أي معلومات واردة في ھذه الوثیقة بمثابة اقتراح للانخراط في أي مسار عمل یتعلق بالاستثمار أو الامتناع عنھ حیث لا یقوم أي من کوانتوبيان أو أي من الشرکات التابعة لھ بتقدیم المشورة الاستثماریة أو العمل کمستشار لأي خطة أو کیان خاضع ل وقانون تأمين دخل التقاعد للموظفين لعام 1974، بصيغته المعدلة، أو حساب التقاعد الفردي أو المعاش التقاعدي الفردي، أو تقديم المشورة بصفة الأمانة فيما يتعلق بالمواد المعروضة في هذه الوثيقة. إذا كنت مستقلا فرديا أو مستثمرا آخر، فاتصل بمستشارك المالي أو أي جهة مالية أخرى لا علاقة لها بكوانتوبيان حول ما إذا كانت أي فكرة استثمار أو إستراتيجية أو منتج أو خدمة معينة مذكورة هنا قد تكون مناسبة لظروفك. وتشمل جميع الاستثمارات مخاطر، بما في ذلك خسارة أصل الدين. لا تقدم كوانتوبيان أي ضمانات بشأن دقة أو اكتمال الآراء المعرب عنها في الموقع. وتخضع اآلراء للتغيير، وقد تصبح غير موثوقة ألسباب مختلفة، بما في ذلك التغيرات في ظروف السوق أو الظروف االقتصادية.
وهنا خوارزمية أكثر تطورا كتبه إرني تشان. وتحسب هذه الخوارزمية نسبة التحوط بدلا من مجرد الاحتفاظ بكميات متساوية من كل ضمان.
يتم توفير المواد على هذا الموقع لأغراض إعلامية فقط ولا تشكل عرضا لبيع أو طلب شراء أو توصية أو تأييد لأي أمن أو استراتيجية، كما أنها لا تشكل عرضا لتقديم الخدمات الاستشارية الاستثمارية من قبل كوانتوبيان. وبالإضافة إلى ذلك، لا تقدم المادة أي رأي فيما يتعلق بملاءمة أي ضمان أو استثمار محدد. لا ینبغي اعتبار أي معلومات واردة في ھذه الوثیقة بمثابة اقتراح للانخراط في أي مسار عمل یتعلق بالاستثمار أو الامتناع عنھ حیث لا یقوم أي من کوانتوبيان أو أي من الشرکات التابعة لھ بتقدیم المشورة الاستثماریة أو العمل کمستشار لأي خطة أو کیان خاضع ل وقانون تأمين دخل التقاعد للموظفين لعام 1974، بصيغته المعدلة، أو حساب التقاعد الفردي أو المعاش التقاعدي الفردي، أو تقديم المشورة بصفة الأمانة فيما يتعلق بالمواد المعروضة في هذه الوثيقة. إذا كنت مستقلا فرديا أو مستثمرا آخر، فاتصل بمستشارك المالي أو أي جهة مالية أخرى لا علاقة لها بكوانتوبيان حول ما إذا كانت أي فكرة استثمار أو إستراتيجية أو منتج أو خدمة معينة مذكورة هنا قد تكون مناسبة لظروفك. وتشمل جميع الاستثمارات مخاطر، بما في ذلك خسارة أصل الدين. لا تقدم كوانتوبيان أي ضمانات بشأن دقة أو اكتمال الآراء المعرب عنها في الموقع. وتخضع اآلراء للتغيير، وقد تصبح غير موثوقة ألسباب مختلفة، بما في ذلك التغيرات في ظروف السوق أو الظروف االقتصادية.
أشياء مفيدة جدا.
What makes it lose systematically for nearly 3 months? Does Cointegration fail in that period?
Basically yes, they turned out not to be cointegrated in that time frame, but returned to being conitegrated in the long term.
I think the drawdown you point out is a strong case for why you would actually want many pairs trading at the same time. Pairs can be cointegrated over different time scales, and any given one will not always be in a tradable state (big spread, small spread). By increasing your sample size, you can make it far more likely that at least one pair will be strongly tradable state at a given time, and smooth out the weird bumps you see here.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Thanks for this. Very useful indeed. I noticed you used Augmented-Dickey Fuller test for the cointegration test. Do you have similar implementation using Johansen test? I'm not able to find the johansen test with python.
It appears that whereas there have been some attempts to add the Johansen test to the statsmodels library, currently there is no built-in implementation. Here, for instance, is a 3rd party implementation. I'm not sure when it will get added to the Python libraries, is there a way you can work around not having it?
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
شكر. I did see that link. Pretty complicated to implement and to write it all in the IDE. In fact, Satya B attempted it here quantopian/posts/trading-baskets-co-integrated-with-spy.
The beauty of Johansen test is that it generates eigenvectors, which I think you can use other methods to calculate though I can't recall at the moment, for up to 12 assets and many other things, which can be used to create a basket. I was looking at one of the index arb strategy of Ernie and attempting to replicate it on Q's platform to assess the performance after fees/comm etc. I noticed fees seemed to chew up a lot of the performance. The ABGB & FSLR pair above has an sharpe ratio of 0.75 but ended with sharpe ratio of -0.29. A lot of seeming profitable pairs turned out to be non profitable after bid/ask spread, fees, commission etc. Hence, I am looking at 3 or more stocks pair trading, and index arb. johansen test will make this easier to implement.
I shall keep trying.
The notebook is an excellent statistical introduction to pairs trading, I recommend anyone interested in the topic also look into some of the financial research. Anatomy of Pairs Trading is a good start, and the references are helpful as well. Two more general papers on risk arbitrage strategies are Characteristics of Risk and Return in Risk Arbitrage and Limited Arbitrage in Equity Markets . There are some expensive lessons people have learned about running these kinds of strategies, and it's worth knowing the lessons in advance. Forewarned is forearmed.
Anthony, good to see you here! I have been looking for a good implementation of the Johansen test for a while but couldn't find one. There is a pretty long (but stale) discussion and pull request on github about including it in statsmodels: github/statsmodels/statsmodels/issues/448 and github/josef-pkt/statsmodels/commit/bf79e8ecb12d946f1113213692db6dac5df2b6e9 It's really too bad as definitely in quant finance this is pretty widely used.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
@Aaron. Thank you for the heads up. Appreciate it coming from your. I shall spend some time with those papers.
@Thomas. Thanks for the link. As you said, it is a bit old. Better than naught I suppose.
Here is a python implementation for vector error correction models. You can also use it to find cointegration weights. econ. schreiberlin. de/software/vecmclass. py.
Here is a version of Ernie Chan's algorithm modified to trade multiple pairs. This is a good way to obtain multiple uncorrelated return streams and reduce the beta of the overall strategy.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
@Delany, Are there methods available to screen for pairs using stat tests? Or are those usually too computationally expensive?
We are working on a way to make the notebooks clone-able into one's own research environment. In the meantime those interested in playing around with the notebook from the original post can download it here. After downloading upload it into your research account. If you do not yet have a research account, enter an algorithm into the contest to receive access.
@good trader, The method provided in the notebook will screen a given list of securities for cointegration, the underlying condition necessary for pairs trading. The problem is not as much the computational complexity as it is the loss of statistical power. The more comparisons you do, the less weight you must put on significant p-values. This phenomenon is described here. To be statistically rigorous, you must apply a Bonferroni correction to p-values obtained from a pairwise cointegration script. The reason being that the more p-values you generate, the more likely you are to encounter significant p-values which are spurious and do not reflect actual cointegration behavior in the underlying securities. Since the number of comparisons done when looking for pairwise cointegration in n securities grows at a rate of O(n^2), even looking at 20 securities would render most statistical tests useless. A better approach is to come up with a small set of candidate securities using analysis of the underlying economic links. A small number of statistical tests can then be done to determine which, if any, pairs are cointegrated. Let me know if this is what you meant.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
I disagree somewhat about the problem with too many comparisons. The Bonferroni correction is appropriate when you are looking for truth. For example, if you have a questionnaire with 1,000 items and you give it to people with and without cancer, you'll find on average 50 items that correlate with cancer at the 5% level of statistical significance, even if nothing on the questionnaire is related to cancer. If you consider combinations of two or more items, you can generate as many correlates are you like.
But when designing automated trading strategies, coincidental relations don't hurt you much. They add random noise and trading costs to your results. Since few results are 100% meaningless, most relations have at least some small degree of persistence, it's not critical to filter your strategy down to rigorously validated ones. Profits matter, not truth. Bonferroni and similar metrics push you to the most statistically reliable relations, which are not generally the most economically useful ones.
If by "analysis of the underlying economic links" you mean starting with natural pairs like two similar companies in the same industry, I have not found that useful. Basically people notice the obvious stuff. If you mean thinking about less obvious relations, especially things that are invisible in the usual data people use, then I agree. Ideally you want a validatable economic story for the pair relation, which explains both why it exists and why it is not arbitraged away. Not only does that guard against data mining, but it means you can measure whether the effect continues to work (without that, the only way you know the strategy isn't working is when you lose money).
عمل جيد. I haven't read through your notebook line-by-line, but I can tell that it will be a great addition to the Quantopian example library. And following up with shared algos--good move.
You might have a look at the notebook I posted, quantopian/posts/analysis-of-minute-bar-trading-volumes-of-the-etfs-spy-and-sh. To visualize how a given pair goes in and out of cointegration, you could make a similar plot. Applying the statistical test 390 times per trading day over many years would require some patience, though.
@Aaron Am I correct in reading your argument generally as follows?
- In the real world Bonferroni is too restrictive and the number of profitable pairs you lose via the correction outweighs the statistical certainty you gain.
I think we agree as to the final point you make. I think that many of the economic link analysis folks do are simplistic and ignore the potentially interesting relations that are more likely to contain non-arbitraged alpha.
@Grant Thank you. We're actually planning to expand the example library to a full quant finance curriculum taught with notebooks and companion algorithms. We're going to have a series of summer lectures as we develop more topics, so keep an eye out for those. Your notebook is very cool and I do wonder how stable the cointegration scores are even for strongly cointegrated pairs. Unfortunately, I don't think I'll have time to look into that in the near future what with the production of our other curriculum notebooks. We are looking for guest contributors, however. If you have any notebooks you would like to be featured in our curriculum with full credit to the author(s), send them my way and I'll see if they would fit into our current content.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
In the real world Bonferroni is too restrictive and the number of profitable pairs you lose via the correction outweighs the statistical certainty you gain.
Not precisely. Yes, Bonferroni is too restrictive in the sense that it gives you too few pairs, but Bonferroni also directs you to the wrong pairs.
In the example of a questionnaire with 1,000 items given to cancer patients and non-cancer patients, it's likely that most of the items have no effect on cancer, or at least such weak and complex effects that it's not worth using them for medical advice. So if you want 5% significance, you test each item at the 0.005% level (that is you want 3.9 standard deviations, not just 1.6). You don't mind that, because any real effect strong enough to matter will likely show up with strong significance. If you didn't do Bonferroni, you'd end up with 50 recommendations even when none of the items mattered, and a lot of useless advice.
Incidentally, Bonferroni is a very conservative correction, and there are more sophisticated ones that allow more items.
But if you have 1,000 pairs to test, it's likely that many of them have some degree of cointegral predictability. Even if there is no predictability, including the extra pair only adds a little noise to your strategy, which is not terrible. Also you don't believe that any of them have predictability so strong that anyone would have noticed it and arbitraged it away. So it's reasonable to consider all the pairs with 5% significance or less, and filter them out using economic or other criteria unrelated to the data. Selecting only the strongest statistical relations is not wise.
You can set this up in a Bayesian framework if you like consistency and precision; or you can just use ad hoc rules of thumb.
Just for the il-pair-literated who want to learn. must there be a story behind the pair? Should there be a logical explanation? I played around with pairs and found for example that MorganStanley and Expedia work. but why? Or doesn't one want to know why.
must there be a story behind the pair?
This is actually a semantic question rather than a financial one. If you adopted a pure statistical approach with no consideration of the actual pairs, you would end up with hundreds or thousands of pairs, including some overlapping ones. Then we wouldn't call it a pairs-trading strategy but a long-short equity strategy.
The idea of pairs trading is you can get additional insight by considering specific reasons for the dependence between the stocks; and that insight can result in more accurate positioning, and also avoidance of big losses when the relation breaks.
Obvious relations, like two large-cap stocks in the same industry, tend not to be useful. That's confusing sometimes, because some of the famous early pairs trades involved such pairs, and they're still used for examples in most texts. But too many people are watching those spreads too closely to get the high Sharpe ratios you need for undiversified strategies like pairs trading. Leave those marginal Sharpes to the long-short equity people who have a lot more positions.
Also, when we talk about a reason for the pairs relation, we're talking about both a positive--why is it hard to imagine a world in which the values of these companies diverge from their historical proportions--and a negative--why do these stocks respond to different economic news? So for two near-identical companies the first question is easy, but the second is hard. For two seemingly unrelated companies like MS and EXPE it's the reverse. You might say something like, "In a good economy Morgan Stanley gets a lot of business and people travel a lot," but that's basically true of almost any two companies.
The classic pairs reason was two companies that responded to the same basic economic factors, say oil prices or interest rates or US dollar strength, but at different points in the supply chain, say crude oil prices versus gas station revenues. A single link is not good enough, virtually all companies respond to these factors. But you can find pairs that are matched on narrower factors, say fracking activity in the Northeast US or precipitation in central California, or that match direction on a number of broad factors. Or you can find two companies that are actually in similar businesses today, but that for historical reasons are listed in different sectors. Another common situation is two companies involved at different points of the lifecycle of durable assets; homebuilders and furniture stores with similar geography for example.
Anyway, when you have a reason, you have things to monitor to fine-tune your position; and to alert you if a big dislocation is a great trading opportunity or a sign than the historical relation has broken. If you don't have a reason, you'd better have a lot of diversification, meaning you can't afford the specific analysis work for each pair.
Wouldn't you admit though that if a pair has a story then that story is known and therefore unprofitable by the likes of slow to trade retail traders? And if one could mine the data and discover, through the data, stories that were unexpected that one could at least compete in the pairs trading space? I see your point on maintaining a large pool of pairs if the stories that connect the participants are weak or unexplored, but still, if we underlings wish to participate why wouldn't we use such a technique? Or do you maintain that retail traders can capture and profit from anomalous pair spreads of well known couples?
Wouldn't you admit though that if a pair has a story then that story is known and therefore unprofitable by the likes of slow to trade retail traders?
No, I wouldn't agree with that view. Pairs trading tends to be low capacity, especially in lower-cap stocks, and takes a lot of work. It's not attractive for asset managers because the investment amounts and risk characteristics are erratic. It's mostly pursued by individual full-time professional traders, who might follow a dozen pairs in addition to a few dozen other strategies, and semi-pro traders who are willing to take what the market gives them and stay in cash when none of their strategies are attractive. There are more good pairs than there are competent traders chasing them.
In principle, you could find good pairs using a clever automated filter, or by reading and thinking. My general feeling is the first is harder, and if you're going to do it, you'll want to do it to identify large numbers of pretty good pairs rather than two or three great pairs. In that case, I'd say just switch to long-short equity and forget pairs. The good thing about reading and thinking is most good quants are lazy, and would rather let the computer do the work. So you're competing with non-quants, some of whom are pretty good at reading and thinking, but are at a huge disadvantage to someone with a computer who knows a little math.
I don't want to come across as dogmatic, anyone who does what other people tell them is not likely to find great success in any sort of trading. If you think you can design an algorithm to identify good pairs, there's no harm in trying. It just doesn't strike me as the most promising approach.
. takes a lot of work.
بلى. The easy pairs trade money was made long ago. Lucrative stories in lower-cap stocks though exposes a pair to the aberrations of smaller company volatility no? "Whoops, that solar stock just lost its major contract. Or, wow, that driller just got a windfall state contract." And then the story gets rewritten, or thee or four pages get torn out. One might catch such preludes to story changes if one only watches a dozen or so stories. But here, where we're looking to avoid story watching -- going fully automated, we would get nailed by such narrative breakdowns in just a few pair relationships.
When you say switch to long/short equities you would seem to advocate abandoning the statistical search for obscure (perhaps whimsical) stories in lieu of broader mean reversion -- is this true? But, if one has the tools, why not create dozens and dozens of strange storied pair trades. Sure the stories may not actually exist. But then again, maybe you discover 10 or 20 that are unique. And through a process of eliminating the poorly paired partners, you end up with a manageable set that are capable of dancing with the stars? This site is nothing if not a massive experiment in data mining no?
Again, I'm not trying to law down laws here, but the two straightforward approaches are (a) try to find a few pairs you can understand or (b) forget about pairs and just try to build a large portfolio of longs and shorts without worrying about pairing up stocks or doing unautomated research. In other words (a) niche clever research or (b) massive data mining.
Trying to split the difference by finding dozens of pairs but not doing the tailored research necessary to understand each one seems suboptimal.
try to find a few pairs you can understand.
If I'm reading things correctly, by "understand" you mean that there should be some underlying intuitive story behind the relationship, I suppose so that there is less risk that the relationship will suddenly disappear? Are you talking about a kind of narrative, "The reason we think this is happening, but can't really explain with a model, is. ومثل. or an explanatory quantitative model that provides the story behind the relationship? Say I find a pairs trade based on the idea that when consumers buy lots of eggs, bacon sales drop off, and vice versa. I could make up a story that people can only eat so much for breakfast, and leave it at that. I have a warm, fuzzy feeling, and if I'm a professional trader, hopefully my management will feel warm and fuzzy, too. But is the risk really any different without the story? Unless I actually find a relevant study on breakfast eating, or conduct one myself, then I could just be deluded. And if the underlying cause can't be coded into a set of rules, then it is not really automated quantitative trading, right? As a Quantopian user who doesn't do this sort of thing for a living, I need to get an algo in the Quantopian hedge fund, let it run, and collect a check. No time for doing lots of offline analyses.
There are more good pairs than there are competent traders chasing them.
sounds like the land of milk and honey for us inhabitants of Quantopia. This would say that the Quantopian team should think about churning out candidate pairs for their 35,000+ users to examine like a bunch of ants, trying to come up with stories for a subset of them ("I'll take XYZ & PDQ, do some research, and see if I can find a 'story' to support the relationship.").
I'm just trying to sort out if any of this can be reduced to practice for Joe Schmo Quantopian user, or if it is a hopeless endeavor. Is there a path for Quantopian to get hundreds of lucrative, scalable pairs trading algos for their $10B hedge fund (keep in mind that by my estimation, they need several thousand distinct algos in the fund)? Or is this all a bunch of blah, blah, blah?
I've tried the automated searching of pairs/baskets, using the public knowledge techniques, and though I haven't gone through them all with my tick-level back-tester, the few that I did examine personally were largely worthless; the supposed spread mean-reversion that my grid search turned up was just spurious or due to bid-ask bounce.
However, I do know for a fact that people run decently profitable automated pairs trading portfolios. I take that to mean that it is possible, but the way that I approached it was naive. Perhaps the legwork method is the way to go, coming up with theses about drivers and then looking for portfolios that would express the theses, with the actual hedge ratio construction done "rigorously" using Kalman filters or whatever.
My take is that chatting about pairs trading is wonderful, but there should be a focus on reducing it to practice, with some sort of approachable workflow, so that a Quantopian user can sit down in his pajamas with a cup of coffee on a rainy day and actually come up with a halfway decent algo that would have a shot at getting into the crowd-sourced Q fund. For example, we have:
. try to find a few pairs you can understand.
Perhaps the legwork method is the way to go, coming up with theses about drivers.
O. K. So what's the workflow for your typical Q user? Keep in mind, this needs to be scalable. it won't do Q any good if only users with an advanced degree and 20 years of industry experience can be successful. If the answer is, "Well, there is no workflow. you just need to know" then pairs trading won't be approachable on Q. We have Aaron's "reading and thinking" recommendation above, but read what?
Also, I'd seen somewhere that there are techniques for synthesizing trading pairs, from baskets of securities. Does this work? Or does one effectively end up with the long-short equity portfolio referred to by Aaron Brown above?
The kind of warm-and-fuzzy story you mention is worthless for investing, although as you say it can reassure investors and regulators. What you're looking for is covariates to refine your strategy and, most important, warn you when it's not going to work. The quant trap is that when your relation breaks it simply looks more attractive to your model, and you spiral to doom.
The eggs-and-bacon story is actually the reverse of what you want. That says there is a fixed total consumption, so the total amount consumed of both products is fixed, meaning they are negatively cointegrated. If they were positively correlated, say because investors bid up or down all breakfast foods as a group, you would do anti-pairs trading. You're looking for things that have to be in some kind of long-term balance, but move is opposite directions in the short-term. A warm-and-fuzzy story might be residential construction and furniture sales, in the short run if people are saving for down payments they're not buying furniture, and newly house poor families are making due with old furniture and underfurnishing. But in the long run, houses will get furnished. This would never be a pairs trading story because it's relating entire sectors. To exploit this, you'd build a model tracing the full life cycle, and likely involving other factors like interest rates and family demographics and migration patterns, and trade large numbers of stocks.
To keep this practical, here is a Pairs Trading for Dummies recipe (I mean that respectfully, I'm a big fan for For Dummies books).
Run some kind of statistical screen to identify promising pairs trading targets. Don't look for extreme statistical significance, just some moderate level to screen out the noise like 5% or 1%. It can help to limit one member of each pair to companies or regions you know something about.
Clearly this is for someone who has quant skills, but also general research skills and business judgment.
Run some kind of statistical screen to identify promising pairs trading targets. Don't look for extreme statistical significance, just some moderate level to screen out the noise like 5% or 1%. It can help to limit one member of each pair to companies or regions you know something about.
it sounds like it could be productive for Quantopian to open-source some efficient tools for the screening (and maybe up their game in terms of computing resources). Let's say I'm an expert on company XYZ and maybe I could narrow down my field of candidate securities for comparison to NASDAQ-listed stocks, of which there are about 3,000. So, it is an O(N) computing problem, not O(N^2) as Delaney mentions above for the general screening problem. But, I'd like to compute the statistics on a rolling basis, every trading minute over 2 years. I'd have:
(3000 comparisons/minute)(390 minutes/day)(252 days/year)(2 years) = 589,680,000 comparisons.
Is something like this at all feasible on the Quantopian research platform? If not, how would I scale it back to something that would actually run in a reasonable amount of time (a few days at most) but still provide useful results?
I'm playing around with the algorithm by Ernie Chan that you posted.
Surprisingly, it fails entirely when I swap the pair, see the attached backtest (I've only changed the order).
Also, how to treat the negative hedge (beta from OLS). With the current implementation we go long (short) on both positions when the sign of the hedge is the same as the sign of the z-score, which you don't expect from pair trading. What economic reason can lead to such cointegrations?
Not sure exactly why it's failing when you swap the order. Seems like the math may not be robust to an 'upside-down' pair. The hedge ratio comes from the formal definition of cointegration, which is that for some b and u_t = y_t - b * x_t, u_t is stationary (the mean stays the same). Therefore we try to estimate the b parameter in each trade so that we can correctly produce a stationary drift between the two securities. It can be the case that the two are negatively cointegrated, whether there's a strong economic reason for this I'm not sure. You might try putting in place restrictions to not trade when you have double long or double short positions, or employing a better estimation method for b (more data points for example).
All of the issues you bring up are very sophisticated improvements, and making these improvements to the algorithm could result in something very good. I don't have cut and dried solutions for you, as you are now dancing around the edge of what is known about algorithmic trading. A lot of it comes down to rigorously testing different signal processing methods to see which yield the best out of sample performance. Also, like you said it's important to let the economic reasoning drive the creation of your model.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Thank you for your quick reply.
This is actually a very valuable response, as I was afraid I might have missed something obvious.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Here is a temp website which has similarity of movement information, which is about the same idea as pairs. StockA is the stock you are comparing to, row is how this pair ranks to all pairs, (its row count). It only contains information for the top 5000 or so pairs.
The data is pulled from the period of Aug 2017 to Feb 2018 and is an average of each day.
(Change IYR to symbol wanted)
The idea behind the algorithm is not actually for pairs trading, but is for similarity of how a pair moves. I will leave this test site up for a few weeks.
Thanks Delaney. It's a great starting step for pair trading technique.
I am working on the missing piece of this strategy which is how to use Quantopian Research environment to find statistical cointegration stock/ETF pairs from entire universe or from the same sectors. After I construct good pairs, then I can use the Notebook you provided for further analysis and backtest.
Does anyone have any suggestion for me?
I have a question for those trading pairs.
How do you deal with the large processing requirements?
I coded some tests for co-integration and results per combination take roughly 1 second.
I can get this down with parallel processing and by storing data locally but a universe of 2000 stocks will still have 4000000 potential combinations.
Perhaps pointing out the obvious, but .
A pre-screening tool, or pre-screening done for you for a fee .
When I was researching this sort of thing a couple of years ago, the baskets of 3 and 4 of only a few hundred ETFs took months on my MacBook. And they were all mostly garbage, though I never actually went through them all. I probably should.
If I remember correctly, that was 1.6T combinations, or something like that.
The formula is R to the Sterling S, divided by S!
so, for 4000 stocks, it would be.
(4000 x3999)/2! or, about 8 million pairs made from the 4000 typical stocks. for 3 stocks considered together, there would be 4000 x 3999 x 3998 /3!
You can prune the possible tree pretty easily though. I believe most stocks behave as if they really were ETFs (at the market neutral way of looking at it only) and can be represented by a group of other stocks, that move with their same fundamentals. You only have to know what sectors they move with, and then check for pairs against this.
So, for example, with HLF, it moves with consumer, several currencies, emerging markets, and a few others. It is hard to separate out exactly as emerging markets also move with currency, so which is which becomes the question.
For two typical tech stocks that appear to be very similar, it may well be the case that their main difference is which currencies they move with. So, for most of the time, they may appear co-integrated, but then, when there is a difference in currencies that affects one a lot, and not so much the other, they then move apart.
I was working on an algorithm to determine the underlying components, (so to speak) that collectively make each stock behave with the same logic as if it was a multi-sector ETF. (where the underlying stocks are a mystery to be solved) I have most of it done, and I believe I have enough done to prove it does work this way, but I lost my real time quote stream a few months ago, and so stopped working on it.
since my algorithm would need to consider up to 15 underlying components to solve this problem, it would be 4000 x 3999 x3998 . 3985/15! So, I have to trim it. The link I posted a few messages above shows some of the results of this work, where I first determine the possible stocks to consider, for each symbol.
It is my belief that the market is essentially swamped out with pairs trading, and this is why it works so mathematically perfect for each stock to behave as if it is an ETF.
There is certainly a high computational cost to looking at all possible pairs. However, there is a tradeoff to this approach, as you put yourself at a high risk for multiple comparisons bias. Please see earlier in this thread for a fairly complete discussion of this issue. Regardless of which method you use to select pairs, you'll want to do some additional validation using the notebook and then use the algorithms in this thread to try backtesting a strategy.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Indeed, Aaron Brown's advice is gold.
What is "multiple comparisons bias"? I'm lazy and don't feel like sifting through this rather extensive discussion thread.
I find it hard to believe that pairs trading would work as a scalable hedge fund strategy (be able to pour $10's of millions into a single pair). Is there any evidence? In other words, why is Quantopian promoting this?
This is one of the best threads on the site.
It scales; you can trade hundreds of pairs.
Multiple comparisons is a core problem in all of statistics, right up there with overfitting. The general idea is that if you run 100 statistical tests on random data, you should still expect to get 5 below a 5% cutoff and 1 below a 1% cutoff based on random chance. This is true when testing various iterations of a model, or many pairs. Because the number of pairs is O(n^2) you should expect to get a lot of spurious p-values when looking for pairs. A naive strategy of just looping through pairs won't work, you need to be a bit more sophisticated.
And yes you trade many pairs with low exposure to each. That said, I think that long-short equity strategies may be a better first bet to get into the fund at this point, just based on robustness and capacity.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
There is more electricity used in the state of New Jersey doing calculations on the market than there is electricity used in that state for manufacturing. Pairs strategy likely accounts for at least 50% of this usage as even HFT likely often uses some version of deviation from the mean. It is my opinion that the market is so saturated with pairs trading that given the price of any ten tickers that had no big news, one could deduce the price of the rest of the market and be within 0.7% of the actual price, 90% of the time for the top traded 4000 stocks. (and it could probably be done with less than ten tickers. ) So, for a 30 dollar stock, the margin of error would be about a quarter. This is how precisely, compared to each other, I think they move. Until there is news.
It sounds like a corollary to the reciprocal of the law of large numbers; given enough samples you will always find something to fit.
I would reintroduce the concept I proposed in an article in S&C last spring ; the directed acyclic graph or DAG. Using thousands of correlated or cointegrated pairs I built groups from them. Those groups were essentially social graphs of securities. You can search here for DAG, but briefly, you can use the concept of pair trading, that is, fade and favor the divergences, but with a correlated group. And such a group is assembled, dynamically, from a list of pairs that are "friends of friends". It's a pairs strategy, essentially, but with lower risk and less work managing hundreds of separate strategies.
That said, I think that long-short equity strategies may be a better first bet to get into the fund at this point, just based on robustness and capacity.
Have people been coming up with good ones? If so, what proportion are using the new data sets? If not, why not, do you think that is?
I haven't been focusing on them at all, mostly because there's a problem of opportunity cost; if I spend all my time looking for equity long-short algos, not only is there a chance I don't find anything, but if I do, there's still a chance that Quantopian doesn't select it, and since I cannot trade them myself, that time is wasted (unless I pitch it to other funds I suppose). If I look for algos that I personally can trade, and I find some, then I trade them.
I realize there's an unfortunate schism wherein I am using your platform but not contributing to your business model, so if you have any ideas how I can help without wasting my time writing algos that only work high account levels, please let me know. Pairs trading/statistical arbitrage might be one solution, but I've found them very difficult to implement; anything that looks promising in Quantopian fails the backtest when using dividend-adjusted bid-ask tick data, so I might shift my focus back to building my own lower latency infrastructure for a while.
I would reintroduce the concept I proposed in an article in S&C last spring ; the directed acyclic graph or DAG. Using thousands of correlated or cointegrated pairs I built groups from them.
Cool. Yeah, pretty similar. The DAG though was used specifically to find the networked graph. Those trees might embody the same thing, not sure. But I'd guess the idea is approximate.
Why would anyone want to pairs trade when trading a Minimum Spanning Tree or correlated network graph of stocks is so much safer and easier? I've built dozens of pairs strategies and the directionality of the pair always broke the model. And all pairs I ever tested all went directional at some point -- beyond the account's ability to Martingale down.
Have people been coming up with good ones? If so, what proportion are using the new data sets? If not, why not, do you think that is?
I can't release any specific data on this. I can say that there's a lag between when we update product features/try to educate people about algorithm writing techniques (larger universe size, shorting), and when new strategies start appearing. We'd love more large universe strategies right now and I'm trying to figure out ways to make it easier for folks to develop large universe long-short strategies using pipeline.
I haven't been focusing on them at all, mostly because there's a problem of opportunity cost; if I spend all my time looking for equity long-short algos, not only is there a chance I don't find anything, but if I do, there's still a chance that Quantopian doesn't select it, and since I cannot trade them myself, that time is wasted (unless I pitch it to other funds I suppose). If I look for algos that I personally can trade, and I find some, then I trade them.
I realize there's an unfortunate schism wherein I am using your platform but not contributing to your business model, so if you have any ideas how I can help without wasting my time writing algos that only work high account levels, please let me know. Pairs trading/statistical arbitrage might be one solution, but I've found them very difficult to implement; anything that looks promising in Quantopian fails the backtest when using dividend-adjusted bid-ask tick data, so I might shift my focus back to building my own lower latency infrastructure for a while.
Totally reasonable. We don't release our product with the expectation that everybody will use it to develop strategies for the fund, we also want to support your use case of personal trading. We also understand there's a conflict between pushing people to write high capacity market neutral long-short strategies, when those will never work on their own money. What I'm trying to figure out is ways to make the workflow of producing and evaluating factors easier, because once you have a factor-based ranking system, it's pretty easy to slot that into an existing long-short algorithm using pipeline. I'm working on sharing a pipeline algorithm with the community and attaching it to the lectures page in an effort to get more cloning and tweaking going on.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
I share Simon's sentiment. I've continued to participate in the contests, but the idea of spending tens (hundreds?) of hours trying to come up with an uber algo that will compete with the big dogs sounds like a lot of work, with a very uncertain pay-off (it's not even clear that you are still working on the hedge fund. any substantive news?). The pipeline thingy has a bit of a learning curve, so I haven't taken that on yet (the fact that lots of obscure modules need to be imported is a red flag). That said, if there were good working examples that could be tweaked, I might give it a go.
What I'm trying to figure out is ways to make the workflow of producing and evaluating factors easier, because once you have a factor-based ranking system, it's pretty easy to slot that into an existing long-short algorithm using pipeline.
Why don't you get all of the Q eggheads together for 1 week and see if you can come up with a long-short algo that would be Q hedge-fundable, and publish it (and better yet, actually fund it). Not only would this provide an existence proof, but you should also gain some insight into the workflow and the person-hours to accomplish the task.
Here is a pipeline algorithm that I just published as the goto example of a long-short equity strategy. I'm sure it will go through many improvements as the public eye turns to it, but it should at least be a start. It's tricky because we do want to publish algorithms that are 95% of the way done, so that users can take the last 5% and improve the strategies in many different uncorrelated ways. With long-short equity most of the work is in choosing good factors and factor ranking techniques. Unfortunately those are the type of signals that will disappear when shared publicly, but the actual machinery to trade within the algorithm should stay pretty consistent. If you're maybe looking to learn pipeline a bit, I would recommend going through Lectures 17 and 18, then looking at the algorithm.
I can say for certain we are working on the hedge fund. Even if you have strategies that aren't consistently winning the contest, we may be interested in an algorithm that can consistently do ok. Ultimately, my job as the one overseeing the lectures is to keep trying to make it easier so people don't have to spend as much time working on algorithms that may never pay off for them, and so we get more algorithms that do pay off in the long run.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
I start to implement pair trading backtesting in research environment instead of IDE. The main reason is to automatic run multiple pairs performance analysis before I jump into IDE for full backtest. Another reason for this work is to do further analysis for returns from many pairs.
I am wondering where I can find the example of backtesting in research environment to start with. Any comment is very appreciated.
In your research environment there should be a 'Tutorials and Documentation' folder. Inside the folder should be a notebook with the title 'Tutorial (Advanced) - Backtesting with Zipline'. Make a copy of that and let me know if that's enough to get you started.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
May 28 algo falls below benchmark if extended to date and -43% PvR with default slippage and commissions, tanking thru 2018.
Hope it can be rescued b/c it shows good potential.
The example strategies cheat and run on the same timeframe over which we did research and found the securities to be cointegrated. In a real strategy you'd want to find pairs that were cointegrated into the future and not just historically cointegrated. The template should stay largely the same, so it's an issue of swapping in new securities that you have statistical evidence will stay cointegrated.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Could you post a tutorial on calibrating an Ornstein Uhlenbeck process for mean reverting series residuals?
We've added a lecture on this to our queue. No idea when we might currently get to it, but it's on there.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Ages ago I posted, perhaps as anonymole, that a "pair" needn't be made of only two securities. In fact, the whole "we only allow low beta strats" mantra is pretty much an argument that all strategies should be a variation of a pairs strat. That is, over all, a market neutral position is best.
Taking this further however, and applying a more formal model to the pairs strategy (that the security set have a "story" attached to it) I wonder if the two halves of the pair would do better as independent baskets of securities. That if one approached a pairs strategy with the mind to match up two behaviorally opposed baskets of securities that instead of trying to search all pair combinations looking for all the super-great-marvelous attributes a pair should have, that instead, one determine the two sides of the pair coin and fill each side with the most appropriately identified securities -- for each side.
A simplistic model might be described thusly:
Equities which cycle up in the spring/summer and down in the fall/winter would be bundled together and set against equities which cycle oppositely (down in the summer, up in the winter).
No doubt there are more interesting or undiscovered cycles that exist. My point is that rather than identify securities that yin and yang, one discover technical, or macro, or fundamental classifications which zig when the other zags. Then find securities which fit each of those baskets of behavior.
This is a very interesting idea and definitely something that professional quants do. At the core we just want two assets on either side of a pair, and a portfolio of assets will do just as well as a single equity. There are probably pros and cons of each method, but the idea of using a basket of things rather than a single thing can greatly reduce your position concentration risk and lead to a better algorithm. I'd say it's worth research. You'd still likely want a few different pairs of baskets as each would smooth out the return curve of the other and produce a lower volatility algorithm.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
I have to run an errand, so I only have five minutes, but hopefully I can be clear in that time.
To demonstrate the chops of an AI system, I created an algorithm that can represent the small changes in stocks price, as the sum of a set of ETFs. For example, with MSFT one might have XLK, XLY, FXE, FXI, and some others.
I can show that the typical price movements during a day can be represented in this way. However, when there is specific news, then it is no longer true, if the news is strong.
What I believe this shows is that instead of things "returning to the mean" they are in fact not moving arbitrarily and so, if they return to the mean, it is because one of the underlying components in fact moved. (Of all the underlying components, usually only one or two have news, and the rest are balancing each other out, once the price has adjusted.)
How might one design a trading platform for this as even if you do know it is the sum of other waveforms that are causing one waveform, one still doesn't know what causes them to move until after the fact.
(the reduction in influence is 1/1.6 when looking at the components, so after a couple of feedback loops, the influence is not measurable. Thanks, and sorry for the hurried note,
Have you read Algorithmic Trading written by Ernie Chan? For sure you read it, I have a question: in fact I am not good in programming and working with Matlab, I am really interested in Currency cross rate part of the book and I want to implement the positions in live trading but I don't know how to do that in fact I can't understand what the numbers as positions mean! If somebody can guide me I'm really appreciated.
Not entirely sure I'm understanding your thesis but it seems that you've created an expression that models the returns of a specific stock from it's sector exposures. This is actually a common risk modeling tactic, check out my notebook here. To build a trading strategy off of this I would take your hypothesis about changing news and use that to alter the coefficients of your model. A cool place to start would be to check out the lectures on factor modeling and then maybe look at some news/sentiment data sets to see if you can find any anomalies.
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That is close. It models the returns to within a few cents usually, at any moment in time, depending on the stock and its volatility as a sum of its sectors. (except when it has specific news.) What I envision behind it is a large set of funds using NLP to invest by sector based on news. Because they are so large, then they tend to swamp out the market during normal times.
I can also show that stock prices changes are directly proportional to the sum of the underlying sectors information, for most time periods. For example, the price changes for three months show this and also for three weeks, which is a bit chaos like, as it would seem they wouldnt be so perfectly in tune. Anyway, with this I can sort stocks by their overall market efficiency (the more efficient you are, the more you sync with the relationship stated above).
I also believe that there are huge funds that are interested in doing nothing more than treading water (as one possible explanation) and they move their money around the world, just trying to stay even, and so the result is that at any given time, the sum of everything stays near zero. (when one thing goes up somewhere, something else somewhere else goes down.)
These relationships also break down during periods of very high volatility such as fall 2018.
There are other things I am able to quantify, but again have no idea how to use. When information about a specific stock or sector hits the market, it is my observation that the more objective the information, the faster the market responds, and the more subjective it is, the slower the market responds.
For example, when Ackman says that HLF is a pyramid scheme, then it can sometimes be hours, and sometimes even days before that news is no longer affecting the price of the stock, but when an analyst upgrades or downgrades a stock, that is more objective and the entire price adjustment is over in fifteen minutes. (If you subtract out market movements then an analysts announcement looks like a log curve, with most of the action in the beginning and a bit of a ringing at the last.)
Again, this all happens too fast to be of use, and it is after the fact that I can say, "That was subjective."
I don't think I am able to alter the coefficients as you suggest. I am using a hard coded take on a system of recursive polynomials for my modeling, so there are billions of coefficients.
Hi, I have a quick and possibly dumb question. Why did you use the ratio instead of the difference between S1 and S2 in the Quantopain pairs trading lecture? In the co-integration lecture, you use the difference instead. In other sources, they use the difference as well.
There's an updated notebook, algorithm, and video available on the lecture series page.
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And as a response to pandasaurus' question, which I unfortunately just saw, we have removed the ratio as it was a typo in the lecture.
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Greetings Quantopian Community,
I was at the NYC Event on Pairs Trading, and the current example algorithm is deprecated, such that one cannot deploy it in live trading. With this fix, users can now deploy the algorithm in live trading. The fix is hosted as a pull request on github--thanks.
شكرا جزيلا. Could you please submit your PR to the following repo? It's where we store lectures and examples. Doesn't quite fit in the current form of zipline.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Thanks, Delaney. I submitted the PR to the specified branch.
شكر! Delaney. I am finishing my graduation thesis these days, Your work may help me a lot.
That's great to hear, Dzi. Hope it goes well!
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
I have question in regards to high frequency pairs trading using bid/ask price. One thing that I noticed is during an entry signal if I'm supposed to go long in one and short the other, the Long position that I enter would be using the ask price and this ask price normally is higher than the bid price, so when my exit signals to exit, my bid price that I close my position at will often cause me to loose than make money. What are some of the ways to prevent this from happening or what are some strategies that goes hand in hand with trading high frequently with pairs strategy. Further, how are limit orders used with the bid/ask price.
If you need to make the spread in order for the strategy to be profitable, then you are squarely competing with high-frequency market makers, and it's a whole different ball game. You are unlikely to win. If you have control over the specific order types you send, you could attempt to use mid-point pegs or something, but as soon as you admit any sort of limit orders where execution is not immediate, you now need to be concerned about being exposed unhedged, which is something that you'll need to backtest. (not easy either). What some people do is try and rest or peg an order for the less liquid leg, and attempt to save some of the cost of the wider spread (though again, these days, you'll probably just get adversely selected for no net gain), and then as soon as that fills, you aggressively execute the hedge leg across the narrower spread.
How does one use both bid and ask z score in high frequency trading? For simplicity, I can understand using z score, but when it comes to using both bid and ask price z score, I have trouble picturing how it is used.
Simon's right, mid-frequency strategies generally should be fairly robust to bid-ask spreads. If they're not the edge is probably too small to be consistently profitable. For high frequency trading you do have to consider the bid and ask in many different ways, as your trading will be very sensitive to movements in both. How exactly you use the data would depend on your model.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
You can imagine that the spread is a synthetic asset. For instance, X = 1L -1S so a single unit of X is long one unit of L and short one unit of S. If you need to buy one unit of X immediately, you will buy at the ask of L and sell at the bid of S. If you need to sell one unit of X, you will sell at the bid of L and buy at the ask of S.
You can then easily calculate the bid and ask for X, you have just two "z-scores" to deal with. Then, if you like, you can delay buying until the X_ask_zscore < threshold, and delay selling until the X_bid_zscore > exit_threshold.
أتمنى أن يساعدك هذا.
I had a chance to see this notebook before and I would recommend it to everyone here. Lots of amazing info can be found inside.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Hey Simon. thanks for that last post. I've been thinking through the logic behind that, but I do have some questions. Hope you don't mind explaining or expanding on it a little. 1) If I understood you correctly you mean X being the spread between a pair? in other words one unit of X immediately to be traded immediately, I would think that you will buy at the ask of X rather than L to be immediate wouldn't you? One problem that I would encounter by buying one unit of X at the ask price of L would be that the ask price of L may not be the lowest ask price of X and therefore may cause me to still queue to purchase the unit of X or not even fill. Can you say a little more in regards to this?
2) Further, there is one concept that I'm having a hard time to understand. Let's say that my Z score > entry threshold of +2. I would short L by one unit by selling one unit of L at the bid price of L and go long one unit of Y at the ask price of Y. Assuming hedge ratio is 1 and all. When my Z score < exit threshold of say 0.2. I would then exit my short and long position of the pair. The issue that I would encounter assuming no fees and all is that I would loose money during these trades. I'm having a hard time understanding why that would be if my Z score returned to or close to mean. Is the reason behind this due to the fact that the volatility of the bid/ask price may not be high enough to allow the difference in the entry and exit bid/ask spread price at the start and end of the transaction to pull far enough to earn money?
Please take a look at the last part of the page for this link that shows the true correlations, which are arrived at by saying "from the point of view of a pairs trader, how correlated are these tickers."
If you know how to subtract out the part of the market that floats all boats, to be left only with the information pertaining to neutral, there are extreme correlations. XLK is the ticker used in the example, but there are a thousand I could have used. When you know how to subtract out all but the neutral information, the market becomes completely different in how it appears.
Scroll to the very bottom of the article and look at the two tables with correlation information. These numbers are this way because there is so much interest in pairs trading that it tends to swamp things out. It is even more pronounced in Europe.
1) I think you are getting a bit confused; X is not a real thing, it's a synthetic asset formed by the basket of L and S. X has a price to buy and a price to sell which you calculate from the bids and asks of the components. If you cross the spread, generally, you trade immediately in small enough size. You only have uncertainty about fills if you try to earn the spread. That gets much more difficult.
2) Maybe. If your trades are not making money, I mean, that's a big problem. I can't answer why they are not making money. It could be transaction costs like the bid/ask spreads, you should analyze the volatility of your baskets as a function of the bid/ask spreads you have to pay. If you have to cross four 5-cent spreads to try and capture a spread mean-reversion of 2 cents, well yeah you are going to have problems. A bigger problem I found was that mean reversion happens one of two ways; either the asset reverts to the mean, or the mean converges with the asset (assuming you are constantly recomputing the mean, which seems to be common practice). In both cases your z-score goes back to zero, but only in the first case do you make any money.
@daniel I read your article, the correlations at the end, are those of prices, or returns ?
Thanks for clearing that up for me. The idea of using synthetic assets is relatively new to me. I went and researched it a little and noticed that it is often used to capture streams of cash flow. I'm currently trying to perform residual pairs trading with Chinese Future Contracts. As I research it for the use of Futures, I don’t really find much articles or explanations. Is it applicable to Futures?
At the same time, I'm relatively new at this and trying to go through the lectures and stuff to learn. When you say I should analyze the volatility of my baskets as a function of the bid/ask spreads. Do you know where I can find a lecture that discuss this further? Sorry to ask some fundamental questions. One thing I notice in my data is that the bid/ask spread is really small and by small the it is just a spread of one tick of the futures contract; while the Volume for that tick is also small just around 80 or less contracts for either bid or ask.
The correlations are about prices, but just a subset.
(I have edited this down, as compared to what you probably have in email. Please don't copy anything from the email onto the board.)
James - maybe? You need pairs/baskets with enough variance to profitably trade the mean reversion. There tends to be a spectrum; structurally correlated assets (like ETF vs their component baskets) are perfect to trade, so perfect, that everyone does it and therefore the deviations are probably less than the spread. Then there's really shitty pairs which you find doing brute force analysis of the stock market. These have lots of variance, but they probably don't converge, and/or the relationship is totally spurious. Read closely Aaron Brown's posts on this thread. You want something in the middle.
Danial - I am not sure how useful correlations of prices of any kind are ? They are bound to be super high.
By itself I don't believe there is any one thing that is useful for a neutral strategy.
My approach is to look at the market as being represented by several hundred core waveform, and similar to the idea of Fourier Transform, you can use these fundamental waveform to create the 4000 heaviest played stocks. So, basically everything I believe about the market is based on the idea of correlations, as this is what I used as one of the first steps to find those wave forms. (which are not easy to find.)
Consider if you have Tickers AAA and BBB, and they are two similar stocks.
AAA might have as its composite the waves A, B, C, D, E, F, G, H, I, J, and BBB may have D, E, F, G, H, I, J, K, L.
During the times that there is little to no activity in the components A, B, C, K, L then the two tickers would be nearly perfectly correlated. But if suddenly component A had news (for example), then the perfect correlations would no longer hold, since stock BBB does not have an A component waveform..
If you apply the above to the idea of mean reversion, then you can see what I believe the mean reversion strategy is actually about.
In my opinion the best way to play a neutral strategy would be to devise a portfolio that is about the underlying fundamental wave components..
And in the interest of completeness, I will mention that in the above examples, waves A, B, C, etc are also made of composite waves, (and those composites . ) as the market is self referencing. The several hundred are at the bottom of the self referencing, and are something that exists in theory, that I believe I could "easily" find, but have not spent the time and energy to do so as of this date.
I also believe that if I had data for all the major markets of the world and was able to deduce the underlying component waves for those instruments that are heavily played by the collectively speaking, multi-trillion dollar funds, that the sum of these waves would (except for inflation) most of these times sum to be zero.
Some researchers generate the log price series of two equities with the daily close. Then the spread series is estimated using regression analysis based on log price series data. For equities X and Y, they run linear regression over the log price series and get the coefficient β.
Any reason they use log price series instead?
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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.
In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.
In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Gekko Quant – Quantitative Trading.
Quantitative Trading, Statistical Arbitrage, Machine Learning and Binary Options.
آخر الملاحة.
Statistical Arbitrage – Correlation vs Cointegration.
What is statistical arbitrage (stat arb)?
The premise of statistical arbitrage, stat arb for short, is that there is a statistical mispricing between a set of securities which we look to exploit. Typically a strategy requires going long a set of stocks and short another. StatArb evolved from pairs trading where one would go long a stock and short it’s competitor as a hedge, in pairs trading the aim is to select a stock that is going to outperform it’s peers. StatArb is all about mean reversion, in essence you are saying that the spread between any two stocks should be constant (or slowly evolving throughout time), any deviations from the spread present a trading opportunity since in StatArb we believe the spread is mean reverting. Contrary to the name statistical arbitrage isn’t about making risk free money (deterministic arbitrage is risk free).
What type of stocks make good pairs?
The best stocks to use in StatArb are those where there is a fundamental reason for believing that the spread is mean reverting / stationary. Typically this means that the stocks are in the same market sector or even better the same company (some companies have A and B shares with different voting rights or trade on different exchanges)! Some examples of fundamentally similar pairs would be Royal Dutch Shell A vs Royal Dutch Shell B shares, Goldman Sachs vs JP Morgan, Apple vs ARM (their chip supplier), ARM vs ARM ADR, some cross sector groups may also work such as Gold Mining vs Gold Price.
A poor example would be Royal Bank of Scotland vs Tesco since their businesses are completely different / don’t impact each other.
What is the mathematical definition of a good pair?
Upon coming up with a good fundamental stock pairing you next need to have a mathematical test for determining if it’s a good pair. The most common test is to look for cointegration (en. wikipedia/wiki/Cointegration) as this would imply that the pair is a stationary pair (the spread is fixed) and hence statistically it is mean reverting. When testing for cointegration a Pvalue(en. wikipedia/wiki/P-value) hypothesis test is performed, so we can express a level of confidence in the pair being mean reverting.
What is the difference between correlation and cointegration?
When talking about statisitical arbitrage many people often get confused between correlation and cointegration.
Correlation – If two stocks are correlated then if stock A has an upday then stock B will have an upday Cointegration – If two stocks are cointegrated then it is possible to form a stationary pair from some linear combination of stock A and B.
One of the best explanations of cointegration is as follows: “A man leaves a pub to go home with his dog, the man is drunk and goes on a random walk, the dog also goes on a random walk. They approach a busy road and the man puts his dog on a lead, the man and the dog are now cointegrated. They can both go on random walks but the maximum distance they can move away from each other is fixed ie length of the lead”. So in essence the distance/spread between the man and his dog is fixed, also note from the story that the man and dog are still on a random walk, there is nothing to say if their movements are correlated or uncorrelated. With correlated stocks they will move in the same direction most of the time however the magnitude of the moves is unknown, this means that if you’re trading the spread between two stocks then the spread can keep growing and growing showing no signs of mean reversion. This is in contract to cointegration where we say the spread is “fixed” and that if the spread deviates from the “fixing” then it will mean revert.
Lets explore cointegration some more:
Equation for Geometric brownian motion.
where A stands for the price of stock A.
We want to find a cointegrated / stationary pair.
Equation for going long stock A and short n lots of Stock B.
Example of correlated stocks : Notice the spread blowing up.
Example of cointegrated stocks: Notice the spread looks oscillatory.
5 أفكار على & لدكو؛ Statistical Arbitrage – Correlation vs Cointegration ”
I find your blog very interesting. Is it still on ?
The spread plots clearly illustrate the merits of co-integration over correlation for a mean-reverting strategy. Truly looking forward to more posts related to statistical arbitrage.
Thanks, in this post I showed the mathematics that should create a stationary signal. The main assumption was that the growth rate mu was constant (or that the growth of both stocks drifts slow leaving the hedge ratio n constant).
The next post will detail how we test that assumption. Hopefully i’ll have written it by Sunday evening.
Cointegration pair trading strategy
Attached is a pair trading algo that allows the user to toggle on/off different tests for cointegration/mean-reversion of the pair's spread prior to taking any trades. If you choose to turn on one of the tests, the value from the test is recorded as a timeseries viewable from the backtest results page.
The pair being traded in this algo is the oil and gold ETFs (USO and GLD), but you can modify these as you wish.
The 3 different tests are:
-- Effectively, this is a unit-root test for determining whether the spread is cointegrated.
-- As well, a function is included showing how to use the critical-values from the ADF-test instead of p-value.
-- This is the theoretically computed time, based on a historical window of data, that it will take for the spread to mean-revert half of its distance after having diverged from the mean of the spread.
-- Effectively this returns a value between 0 and 1 that tells you whether a time-series is trending or mean-reverting. The closer the value is to 0.5 means the more "random" the time-series has behaved historically. Values below 0.5 imply the time-series is mean-reverting, and above 0.5 imply trending. The closer the value is to 0 implies greater levels of mean-reversion.
-- Trading literature is conflicted as to the usefulness of Hurst exponent, but I included it nonetheless, and have set the default switch to False in the algo.
The backtest results below incorporate two of these tests:
ADF-test p-value, computed over a 63-day (e. g. 3-months) lookback window, with a required minimum p-value of 0.20.
To modify the parameter values of the tests just look in the initialize function, for blocks of code that look like this. Here is how the ADF-test p-value parameters are defined:
Here you see how there is a dictionary defined called 'stat_filter' which you can use to store the parameters of each test. First I create another dictionary inside of 'stat_filter' named 'adf_p_value' and then I load in all of the parameter values relevent to the ADF-test that I want to define when it is acceptable to enter a trade. These exact 5 parameters (e. g. keys of the dictionary) will be defined for all of the tests, as you'll see if you look at the algo code, and notice the adf_critical_value, half_life, hurst_exponent ones are defined following it. The 5 parameters are:
'use': Boolean, True if you want the algo to use this test.
Support for Intraday Frequency.
(Let me know if you run into issues with this, as I haven't done as much testing with it as I have with just daily freq)
You can configure this algo to be run on intraday minutely data as well. مثلا construct a pair spread using 15-min bar closing prices.
First, change the variable 'context. trade_freq' from 'daily' to 'intraday':
context. trade_freq = 'daily' # 'daily' or 'intraday'
Then, look for this code block below in the initialize() function, and specify the 'intraday_freq' value for the frequency of closing prices to use (E. g. 15 minute bars). Then, set 'run_trading_logic' to be how frequently you want the logic to be applied to market data. I chose 60 which means, run this logic every 60-minutes, but if you wish, change it to 1, and the logic will be run every single minute (beware though, as this will result in really long backtest times).
The variable 'check_exit_every_minute' can be set to True if you want the logic to be run every minute if-and-only-if you are currently in a trade. مثلا it checks to see whether you need to exit the trade every minute rather than waiting to the next N periods (e. g. 60 minutes, as specified in the 'run_trading_logic_freq' variable)
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Same algo just start 9 month earlier.
Thanks for the share. I've noticed that there is a coint function in statsmodels. tsa. stattools. Is there a significant difference between the coint function and the ADF test? Any sense in using both?
I've attached a backtest below that attempts to find the pvalue of both tests for each pair, every day. Disclaimer: what I often think is happening in python is actually not.
I haven't tried the coint function in stattools yet, though I imagine it's very similar. I just took a quick glimpse at the code, and it's effectively running a regression of the lagged version of the input timeseries versus the unlagged version which is quite similar to ADF. The difference may lie in how the critical values are computed.
The Engle-Granger test is also sometimes used to test for co-integration, but I haven't looked at that implementation yet.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Great Algo. Its amazing. Very Helpful.
Hello Justin / All.
Could you suggest how I can run this algo on multiple pairs, rather than just one pair?
Try making a pairs trading class that keeps track of all the bookkeeping for each given pair. See David's generalized Kalman filters pair trading algo for a great example of class based pairs trading.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Thanks for sharing info.
I cloned Justin's algo, however when I run a backtest, the performance remains at 0% for the entirety of the backtest window.
I made no changes to the original source code.
Any ideas why this would be occurring?
You probably run algo in daily mode and it only work in minute mode.
Here is my latest backtest of original Justin's Lent algo started just 9 month earlier.
It's worth noting that when I post backtests, code, and research notebooks, the intent is to illustrate a methodology, and provide some code templates to spur the creative thought process of the community and save folks some time by providing cut-and-paste code fragments that can be integrated into their own code. By no means am I posting something that has been fully vetted, and immediately investable in it's exact form, by any stretch of the imagination. I often bias for simpler, rather than overly complex, examples as well, so as to benefit a broader spectrum of readers.
I see you've recognized that the backtest I posted above seems to fail pretty badly over a different timeframe. We see this a lot with strategies we look at, many of which are overfit to just the 2 year period in the contests we run. We try to work with the algo owner and provide advice as to why it may have broken down over the different timeframes. Perhaps you can extend your analysis to provide me some advice as to improving this strategy? Maybe you have some recommendations as to how to incorporate a regime switching model which is very likely to help a strategy such as this given the time frame it seems to fail (the financial/commodity futures crises that occurred in late 2008). Perhaps a stochastic volatility regime switching model might help significantly. If you have experience in this area I'm sure the community would find it a solid addition to incorporate into strategies such as these to make them more robust. I know I would.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Why did you choose the pair USO and GLD? I guess a broader question is can you suggest a process for scanning through a basket of stocks and determining if there are tradable pairs? I'm assuming tests for cointegration would be one method such as ADF as you used. It would be nice if there could be an algo to run through a basket of stocks and auto determine which would make "good" pairs.
I just chose USO/GLD in order to replicate this example that uses those same tickers, from this book: amazon/Quantitative-Trading-Build-Algorithmic-Business/dp/0470284889/
That book is a really good intro to stat arb pair trading (as well as his other books). All the code in the book is in Matlab, so my algo was an attempt to implement it in Python, in our backtester, and incorporate some of the other statistical techniques described throughout the book.
You are correct, that screening a bunch of potential pairs is a reasonable research idea, but you should be cognizant of simply datamining. You first want to determine a sensible economic basis for which the pairs of stocks should be tied (e. g. pairs of stocks in the same sector would be reasonable pairs of stocks to search across). Writing an algo in our backtester to accomplish this would be fairly straightforward: First you can use our Morningstar fundamentals database to grab all stocks in the Energy sector, perhaps even filtering down to stocks of companies of a certain band of marketcap (e. g. only mid-cap energy stocks), then in before_trading_starts(), you loop over each stock pair computing the ADF p-value (or other cointegration stat), keep all the stock pairs that meet your criteria, and then in handle_data() you just run the ones that meet the criteria through an algo similar to the one I shared to enter/exit the trades.
Myself or someone on our team here at Q can try to develop a template for this and share it.
As well you can look at this forum post that shows how to develop a single algo that trades a portfolio of multiple pairs:
It's the algo backtest in the first comment from David Edwards, here:
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
I noticed in the blog section you have a notebook on using a Bayesian optimizer. would you know how i can pull it into Q? its currently on github..thanks!
@Adam, At present it's not possible to use the Bayesian optimizer from the blog post in the Q environment. It was more of a proof of concept implementation idea. As you mentioned, the code I used for the blog post is on github and you can sign up for a trial with SigOpt to get a username/API key to work with it in your own python/zipline environment locally. Offering some of these alternative methods of optimization as a service is an interesting concept which we will have to think about as we develop our Q platform in the future. Thanks for the feedback!
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Thanks Justin! Would be neat to be able to do that type of optimization and / or a particle swarm technique in Q. :)
I believe I found a gap in the trading logic. In the statistic filtering section (lines.
155-176) the algorithm immediately exits if a test fails. That prevents new trades from being opened but does nothing to handle existing trades. Open trades stay open until all of the statistical tests pass again and the algorithm reaches its standard exit logic.
By design we should also have a high likelihood of being in a trade when this happens so the impact could be quite high. The problem in detecting this is that if the relationship re-establishes quickly the performance won't suffer. But if we include a time period in which the relationship doesn't return quickly, as Vladimir did, the results are noticeable.
I added a few lines to close any positions that are open when the statistical tests break down. There are probably better ways of handling the exit logic, but this simple change shows the benefit of having it there. The algorithm doesn't do as well during the original test period but the performance improves over the extended period.
(I also made minor change on lines 20 and 21 to use sid() function to set x and y assets rather than symbol(). The rest of the algorithm is unchanged.)
Could someone kindly explain to me the use of ' hedge Ratio' , Its purpose and construction. I have been working on a Pair trade of my own ( in multicharts )and have been defining the spread as 'stock A / stock B' then using that in a Z-score, with some promising (ish)results and am wondering if a HR can improve my results but I don't understand its impact on the system.
Thanks in advance,
Pair trading using Copula methods instead of cointegration is the new rage. Anyone tried it?
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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.
In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.
In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
