Efficient algebraic code-excited linear predictive codebook search

2006 ◽  
Vol 153 (6) ◽  
pp. 761 ◽  
Author(s):  
S.-M. Tsai ◽  
J.-F. Yang
Keyword(s):  
Algebraic Code

scholarly journals Bounds on algebraic code capacities for noisy channels. I

1971 ◽  
Vol 19 (2) ◽  
pp. 124-145 ◽  
Author(s):  
R. Ahlswede ◽  
J. Gemma
Keyword(s):  
Noisy Channels ◽  
Algebraic Code

Private-key algebraic-code encryptions

1989 ◽  
Vol 35 (4) ◽  
pp. 829-833 ◽  
Author(s):  
T.R.N. Rao ◽  
K.-H. Nam
Keyword(s):  
Private Key ◽  
Algebraic Code

scholarly journals Key generation of algebraic-code cryptosystems

1994 ◽  
Vol 27 (2) ◽  
pp. 99-106 ◽  
Author(s):  
Hung-Min Sun ◽  
Tzonelih Hwang
Keyword(s):  
Key Generation ◽  
Algebraic Code

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.

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