000			02862cam a2200277zu 4500
001			88914793
003			FRCYB88914793
005			20250107234430.0
006			m o d
007			cr un
008			250108s2020 fr | o|||||0|0|||eng d
020			_a9780128156308
035			_aFRCYB88914793
040			_aFR-PaCSA _ben _c _erda
100	1		_aKissell, Robert
245	0	1	_aAlgorithmic Trading Methods _bApplications Using Advanced Statistics, Optimization, and Machine Learning Techniques _c['Kissell, Robert']
264		1	_bElsevier Science _c2020
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aKissell, Robert
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88914793 _qtext/html _a
520			_aAlgorithmic Trading Methods: Applications using Advanced Statistics, Optimization, and Machine Learning Techniques, Second Edition, is a sequel to The Science of Algorithmic Trading and Portfolio Management. This edition includes new chapters on algorithmic trading, advanced trading analytics, regression analysis, optimization, and advanced statistical methods. Increasing its focus on trading strategies and models, this edition includes new insights into the ever-changing financial environment, pre-trade and post-trade analysis, liquidation cost & risk analysis, and compliance and regulatory reporting requirements. Highlighting new investment techniques, this book includes material to assist in the best execution process, model validation, quality and assurance testing, limit order modeling, and smart order routing analysis. Includes advanced modeling techniques using machine learning, predictive analytics, and neural networks. The text provides readers with a suite of transaction cost analysis functions packaged as a TCA library. These programming tools are accessible via numerous software applications and programming languages.           Provides insight into all necessary components of algorithmic trading including: transaction cost analysis, market impact estimation, risk modeling and optimization, and advanced examination of trading algorithms and corresponding data requirements. Increased coverage of essential mathematics, probability and statistics, machine learning, predictive analytics, and neural networks, and applications to trading and finance. Advanced multiperiod trade schedule optimization and portfolio construction techniques. Techniques to decode broker-dealer and third-party vendor models. Methods to incorporate TCA into proprietary alpha models and portfolio optimizers. TCA library for numerous software applications and programming languages including: MATLAB, Excel Add-In, Python, Java, C/C++, .Net, Hadoop, and as standalone .EXE and .COM applications.
999			_c74116 _d74116