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Algorithmisches Trading mit Machine Learning und Python: Von Vorhersagemodellen zu automatisierten Strategien
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Publisher: Independently Published
Pages: 434
Weight: 1.15
Height: 1.08 Width: 6.00 Depth: 9.00
Language: German
Maschinelles Lernen hat den algorithmischen Handel grundlegend verändert. Was früher auf starren Regeln und einfachen Indikatoren basierte, wird heute durch datengetriebene Modelle, adaptive Strategien und automatisierte Systeme ersetzt. Dieses Buch zeigt Ihnen den kompletten Weg - von der ersten Vorhersagemodellierung bis zur produktionsreifen Handelsstrategie.
Sie lernen, wie Sie mit Python und modernen Machine-Learning-Techniken Marktdaten analysieren, Prognosemodelle entwickeln und diese in robuste, automatisierte Trading-Systeme überführen. Der Fokus liegt auf der praktischen Umsetzung: von Feature Engineering und Modellvalidierung über Backtesting und Risikomanagement bis hin zur Live-Implementierung.
Was Sie in diesem Buch erwartet- Aufbau und Training von Vorhersagemodellen für Kursbewegungen und Volatilität
- Feature Engineering speziell für Finanzzeitreihen
- Validierung und Vermeidung typischer Fallstricke (Overfitting, Look-Ahead Bias, Data Leakage)
- Entwicklung und Backtesting vollständiger Handelsstrategien
- Integration von Machine Learning in automatisierte Execution-Systeme
- Risikomanagement und Positionsgrö enbestimmung in ML-basierten Strategien
- Praxisnahe Python-Implementierungen mit gängigen Bibliotheken
Das Buch richtet sich an Quant-Trader, Data Scientists und Entwickler, die Machine Learning nicht nur theoretisch verstehen, sondern tatsächlich in handelbare Strategien umsetzen wollen. Vorkenntnisse in Python und grundlegenden statistischen Konzepten werden vorausgesetzt; fortgeschrittene ML-Kenntnisse sind von Vorteil, aber nicht zwingend erforderlich.
Statt abstrakter Theorie erhalten Sie einen klaren, umsetzbaren Fahrplan - von der Idee bis zur automatisierten Strategie.
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