Forex Gewinn Matrix Systemlöser

Rechnerische Kosten von Portfolio-Optimierungen mit CVXPY Das Portfolio-Optimierungsproblem war eines der am meisten untersuchten Probleme des Risikomanagements im vergangenen Jahrhundert. Aus der von Markowitz in den 1950er-Jahren veröffentlichten Studie haben sich signifikante Verbesserungen in der Art und Weise ergeben, wie wir Varianzoptimierungen und die zur Lösung des quadratischen Optimierungsproblems verwendeten Algorithmen verwenden. Heute möchte ich mit Ihnen teilen einige Code für die Suche nach der minimalen Varianz-Portfolio in Python sowie einige Vergleiche zwischen den verschiedenen Algorithmen, die wir für die Berechnung solcher Portfolios zur Verfügung haben. Für diese werde ich mit der CVXPY-Bibliothek, die zweifellos die beste Bibliothek für die Lösung dieser Art von Problem in Python ist. Wir gehen ein bisschen durch den Code und die Ergebnisse der verschiedenen Algorithmen verfügbar durch die CVXPY-Implementierung. Minimale Varianzoptimierung Optimierung eines Portfolios ist ganz einfach: Finden Sie einfach die Gewichte, die Sie benötigen, um jedem der Systeme zu geben, so dass einige zugrunde liegende Portfolio-Eigenschaft entweder minimiert oder maximiert wird. Einige dieser Verfahren können nicht leicht analytisch gelöst werden und erfordern daher eine Form der Iteration 8211, wie wenn Sie auf der Suche nach dem besten historischen Sharpe 8211 sind, aber andere Arten von Optimierungen wie das minimale Varianzportfolio können tatsächlich mit der Kovarianzmatrix analytisch gelöst werden. Der obige Code zeigt Ihnen, wie Sie das minimale Varianzportfolio erhalten können, wenn ein pandas DataFrame mit allen Systemrückgängen und einem selectedsolver aus der Liste kommt, die Sie aus dem installsolvers () - Befehl erhalten können (zB CVXOPT). Wie Sie sehen können, der große Vorteil der CVXPY-Bibliothek ist, dass das Problem in einer Weise, die näher an dem, was Sie auf ein Stück Papier schreiben würde, wenn Sie die Mathematik, anstatt in der Matrix-Form, die in der Regel erforderlich ist zu schreiben Durch die direkte Nutzung der Optimierungsbibliotheken. Das CVXPY-Projekt implementiert nicht direkt alle Optimierungsbibliotheken, sondern dient einfach als großartiger Wrapper, um die Verwendung all dieser zugrunde liegenden Bibliotheken mit demselben Eingabeformat und mit einem intuitiveren Satz von Anweisungen zu ermöglichen. Zum Beispiel in diesem Fall ich einfach sagen, dass ich Risiko minimieren wollen und ich möchte die Summe der Gewichte zu 1 und alle von ihnen größer als 0 sein. Da wir sehr leicht zwischen Optimierer wechseln kann es sehr einfach durchzuführen Benchmarks der Optimierung mit den verschiedenen verfügbaren Lösern. Das obige Bild zeigt Ihnen die Zeitdauer, die in Sekunden benötigt wird, um das minimale Varianzproblem zu lösen, indem Sie die 30-jährigen monatlichen Renditen von 500 Handelsstrategien mit den 4 verschiedenen Lösern verwenden, die nach der Installation von CVXPY verfügbar sind. Wie Sie sehen können, ist der SCS-Löser mit Abstand der beste 8211 nicht überraschend, da es8217s codiert in C 8211 mit einer Leistung, die fast 10x, dass der häufiger verwendete CVXOPT-Algorithmus ist. Die Genauigkeit bei SCS ist auch nicht schlechter, wenn die Sharpes und die Gewichte, die durch den SCS-Algorithmus erreicht werden, im wesentlichen gleich 8211 sind, meistens sogar besser 8211 als diejenigen, die durch die anderen Algorithmustypen erreicht werden. Die Unterschiede zwischen CVXOPT, ECOS und anderen nativen Python-Bibliotheken sind grundsätzlich klein, da es nicht möglich ist, den Großteil des Overheads zu entfernen, wenn nicht der Großteil des Codes mit einer nicht interpretierten Sprache wie C ausgeführt wird. Mit dem Wissen, dass der SCS-Algorithmus der beste ist Können wir nun die rechnerischen Kosten für die Durchführung von Portfolio-Optimierungen mit unterschiedlichen Systemnummern bewerten. Wie Sie im Bild unten sehen können, steigen die Rechenkosten als Potenzgesetzfunktion, da die Anzahl der Systeme steigt. Jedoch wie Sie sehen können, ist die Anpassung am Anfang sehr gut, aber die Schätzung beginnt, schlechter zu werden, wie die Zahl von Systemen über die 2500 Systemmarkierung hinausgeht. Dies liegt möglicherweise daran, dass die Arrays groß genug für Datenübertragungen werden und die Berechnung der Kovarianzmatrix beginnt, die Gesamtprogrammausführungskosten deutlich zu erhöhen. Da die Anzahl der Systeme viel größer als die Anzahl der Renditen ist, beginnen wir, einige wichtige Probleme mit der Berechnung der Kovarianzmatrix zu lösen. Im Fall von 4000 Systemen, die 30 Jahre Monatsrendite verwenden, haben wir 360 Rückgabewerte, aber 4000 Systemwerte, beginnen wir, diese Instabilitäten in der Kovarianzmatrix zu sehen. Ein weiteres Problem, das wir zuvor besprochen haben, besteht darin, daß die Musterkovarianzmatrix 8211 diejenige ist, die wir aus den historischen Renditen 8211 ableiten, ist verpflichtet, eine Menge von Schätzfehlern und daher eine geringere Leistungsfähigkeit im Vergleich zu naiv optimierten Portfolios Zum Beispiel gleichermaßen Gewicht Portfolios), vor allem, wenn es eine hohe Anzahl von unkorrelierten Strategien. Es gibt einige Weisen, die wir versuchen können, um dieses zu arbeiten, das uns auch hilft, die Probleme zu verringern, die wir erhalten, wenn die Zahl Strategien der Zahl der Stichprobenrückkehr sehr überlegen ist. Einige davon werden wir in zukünftigen Beiträgen erforschen. Wenn Sie mehr über die Portfolio-Optimierung erfahren möchten und wie Sie auch sehr große Systemportfolios handeln können, sollten Sie sich für Asirikuy bedanken. 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