scholarly journals Developing Bid-Ask Probabilities for High-Frequency Trading

2020 ◽  
Vol 3 (2) ◽  
pp. 7-24
Author(s):  
Lester Ingber
Keyword(s):  
Simulated Annealing ◽  
Path Integral ◽  
High Frequency ◽  
Path Integrals ◽  
High Frequency Trading ◽  
Tick Data ◽  
Adaptive Simulated Annealing ◽  
Algebraic Code

Methods of path integrals are used to develop multi-factor probabilities of bid-ask variables to be used in high-frequency trading (HFT). Adaptive Simulated Annealing (ASA) is used to fit the nonlinear forms, so developed to a day of BitMEX tick data. Maxima algebraic code is used to develop the path integral codes into C codes, and a sampling code is used for the fitting process. After these fits, the resultant C code is very fast and useful for forecasting upcoming ‘ask’, bid, midprice, etc., when narrow and wide windows of incoming data are used. A bonus is the availability of canonical momenta indicators (CMI) useful to forecast direction and strengths of these variables.

scholarly journals Developing Bid-Ask Probabilities for High-Frequency Trading

2019 ◽  
Author(s):  
Lester Ingber
Keyword(s):  
Simulated Annealing ◽  
Path Integral ◽  
High Frequency ◽  
Path Integrals ◽  
High Frequency Trading ◽  
Tick Data ◽  
Adaptive Simulated Annealing ◽  
Algebraic Code

Methods of path integrals are used to develop multi-factor probabilities of bid-ask variables for use in high-frequency trading (HFT). Adaptive Simulated Annealing (ASA) is used to fit the nonlinear forms so developed to a day of BitMEX tick data. Maxima algebraic code is used to develop the path integral codes into C codes, and sampling code is used for the fitting process. After these fits, the resultant C code is very fast and useful for forecasting upcoming ask, bid, midprice, etc., when narrow and wide windows of incoming data are used. A bonus is the availability of canonical momenta indicators (CMI) useful to forecast direction and strengths of these variables.

scholarly journals Developing Bid-Ask Probabilities for High-Frequency Trading

2019 ◽  
Author(s):  
Lester Ingber
Keyword(s):  
Simulated Annealing ◽  
Path Integral ◽  
High Frequency ◽  
Path Integrals ◽  
High Frequency Trading ◽  
Tick Data ◽  
Adaptive Simulated Annealing ◽  
Algebraic Code

Methods of path integrals are used to develop multi-factor probabilities of bid-ask variables for use in high-frequency trading (HFT). Adaptive Simulated Annealing (ASA) is used to fit the nonlinear forms so developed to a day of BitMEX tick data. Maxima algebraic code is used to develop the path integral codes into C codes, and sampling code is used for the fitting process. After these fits, the resultant C code is very fast and useful for forecasting upcoming ask, bid, midprice, etc., when narrow and wide windows of incoming data are used. A bonus is the availability of canonical momenta indicators (CMI) useful to forecast direction and strengths of these variables.

scholarly journals Forecasting COVID-19 with Importance-Sampling and Path-Integrals

2020 ◽  
Author(s):  
Lester Ingber
Keyword(s):  
Simulated Annealing ◽  
Nonlinear Systems ◽  
Financial Markets ◽  
Path Integral ◽  
Stochastic Systems ◽  
Path Integrals ◽  
Strong Support ◽  
Stochastic Nonlinear Systems ◽  
Nonlinear Stochastic Systems ◽  
Adaptive Simulated Annealing

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems. Method: ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Results of detailed calculations have led to new results and insights not previously obtained. Conclusion: These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

scholarly journals Forecasting COVID-19 with Importance-Sampling and Path-Integrals

2021 ◽  
Author(s):  
Lester Ingber
Keyword(s):  
Simulated Annealing ◽  
Nonlinear Systems ◽  
Financial Markets ◽  
Path Integral ◽  
Stochastic Systems ◽  
Path Integrals ◽  
Strong Support ◽  
Stochastic Nonlinear Systems ◽  
Nonlinear Stochastic Systems ◽  
Adaptive Simulated Annealing

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems. Method: ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Results of detailed calculations have led to new results and insights not previously obtained. Conclusion: These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

scholarly journals Forecasting with Importance-Sampling and Path-Integrals

2020 ◽  
Author(s):  
Lester Ingber
Keyword(s):  
Simulated Annealing ◽  
Nonlinear Systems ◽  
Financial Markets ◽  
Path Integral ◽  
Stochastic Systems ◽  
Path Integrals ◽  
Strong Support ◽  
Stochastic Nonlinear Systems ◽  
Nonlinear Stochastic Systems ◽  
Adaptive Simulated Annealing

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are described as being useful to detail such systems. Method: ASA and PATHINT/PATHTREE have been demonstrated as being effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Results: Not only can selected systems in these three disciplines be aptly modeled, but results of detailed calculations have led to new results and insights not previously obtained. Conclusion: While optimization and path-integral algorithms are now quite well-known (at least to many scientists), these applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

scholarly journals Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19

2020 ◽  
Author(s):  
Lester Ingber
Keyword(s):  
Simulated Annealing ◽  
Nonlinear Systems ◽  
Financial Markets ◽  
Path Integral ◽  
Stochastic Systems ◽  
Path Integrals ◽  
Strong Support ◽  
Stochastic Nonlinear Systems ◽  
Nonlinear Stochastic Systems ◽  
Adaptive Simulated Annealing

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems. Method: ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Results of detailed calculations have led to new results and insights not previously obtained. Conclusion: These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

scholarly journals Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19

2020 ◽  
Author(s):  
Lester Ingber
Keyword(s):  
Simulated Annealing ◽  
Nonlinear Systems ◽  
Financial Markets ◽  
Path Integral ◽  
Stochastic Systems ◽  
Path Integrals ◽  
Strong Support ◽  
Stochastic Nonlinear Systems ◽  
Nonlinear Stochastic Systems ◽  
Adaptive Simulated Annealing

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are described as being useful to detail such systems. Method: ASA and PATHINT/PATHTREE have been demonstrated as being effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Not only can selected systems in these three disciplines be aptly modeled, but results of detailed calculations have led to new results and insights not previously obtained. Conclusion: While optimization and path-integral algorithms are now quite well-known (at least to many scientists), these applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

High-Frequency Financial Econometrics

2014 ◽  
Author(s):  
Yacine Aït-Sahalia ◽  
Jean Jacod
Keyword(s):  
High Frequency ◽  
Practical Importance ◽  
Financial Econometrics ◽  
Trading Strategies ◽  
Financial Data ◽  
Jump Processes ◽  
High Frequency Trading ◽  
Microstructure Noise ◽  
Econometric Methods ◽  
Mathematical Foundations

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. The book covers the mathematical foundations of stochastic processes, describes the primary characteristics of high-frequency financial data, and presents the asymptotic concepts that their analysis relies on. It also deals with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As the book demonstrates, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. The book approaches high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.

High-Frequency Trading

2011 ◽  
Author(s):  
Peter Gomber ◽  
Björn Arndt ◽  
Marco Lutat ◽  
Tim Elko Uhle
Keyword(s):  
High Frequency ◽  
High Frequency Trading
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