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"Engineering Alpha: Filtering Trading Signals from Market Noise" In increasingly competitive markets, the edge lies not in more data, but in better data and sharper filters. Engineering Alpha: Filtering Trading Signals from Market Noise is written for quantitative researchers, systematic portfolio managers, advanced practitioners, and technically minded traders who want to turn fragile backtests into durable trading strategies. It bridges the gap between academic theory and production-grade implementation, showing how to transform noisy price streams into robust signals that can survive the real world of liquidity constraints, costs, and regime shifts. The book develops a complete research stack, from mathematical foundations and time-series modeling through feature engineering, machine learning, and factor construction. Readers will learn to clean and align market and alternative data, design leak-free targets, and apply filters such as ARIMA, Kalman, and wavelet-based methods. It then formalizes validation via walk-forward testing, purged cross-validation, multiple-testing control, and performance metrics like Sharpe, Information Ratio, and IC-decay. Finally, it connects signals to portfolios-covering risk models, constrained optimization, position sizing, execution algorithms, and monitoring-so that estimated alpha translates into risk-adjusted PnL. The material assumes comfort with Python, basic linear algebra, and probability, but it is self-contained where it matters for practice. Throughout, the emphasis is on reproducible workflows, time-aware evaluation, and eng
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Paperback. Zustand: new. Paperback. "Engineering Alpha: Filtering Trading Signals from Market Noise"In increasingly competitive markets, the edge lies not in more data, but in better data and sharper filters. Engineering Alpha: Filtering Trading Signals from Market Noise is written for quantitative researchers, systematic portfolio managers, advanced practitioners, and technically minded traders who want to turn fragile backtests into durable trading strategies. It bridges the gap between academic theory and production-grade implementation, showing how to transform noisy price streams into robust signals that can survive the real world of liquidity constraints, costs, and regime shifts.The book develops a complete research stack, from mathematical foundations and time-series modeling through feature engineering, machine learning, and factor construction. Readers will learn to clean and align market and alternative data, design leak-free targets, and apply filters such as ARIMA, Kalman, and wavelet-based methods. It then formalizes validation via walk-forward testing, purged cross-validation, multiple-testing control, and performance metrics like Sharpe, Information Ratio, and IC-decay. Finally, it connects signals to portfolios-covering risk models, constrained optimization, position sizing, execution algorithms, and monitoring-so that estimated alpha translates into risk-adjusted PnL.The material assumes comfort with Python, basic linear algebra, and probability, but it is self-contained where it matters for practice. Throughout, the emphasis is on reproducible workflows, time-aware evaluation, and eng This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798896652410
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'Engineering Alpha: Filtering Trading Signals from Market Noise'In increasingly competitive markets, the edge lies not in more data, but in better data and sharper filters. Engineering Alpha: Filtering Trading Signals from Market Noise is written for quantitative researchers, systematic portfolio managers, advanced practitioners, and technically minded traders who want to turn fragile backtests into durable trading strategies. It bridges the gap between academic theory and production-grade implementation, showing how to transform noisy price streams into robust signals that can survive the real world of liquidity constraints, costs, and regime shifts.The book develops a complete research stack, from mathematical foundations and time-series modeling through feature engineering, machine learning, and factor construction. Readers will learn to clean and align market and alternative data, design leak-free targets, and apply filters such as ARIMA, Kalman, and wavelet-based methods. It then formalizes validation via walk-forward testing, purged cross-validation, multiple-testing control, and performance metrics like Sharpe, Information Ratio, and IC-decay. Finally, it connects signals to portfolios-covering risk models, constrained optimization, position sizing, execution algorithms, and monitoring-so that estimated alpha translates into risk-adjusted PnL.The material assumes comfort with Python, basic linear algebra, and probability, but it is self-contained where it matters for practice. Throughout, the emphasis is on reproducible workflows, time-aware evaluation, and eng. Bestandsnummer des Verkäufers 9798896652410
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Taschenbuch. Zustand: Neu. Engineering Alpha | Filtering Trading Signals from Market Noise | Victor Trex | Taschenbuch | Englisch | 2025 | NobleTrex Press | EAN 9798896652410 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 135842018
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