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averaged_wiener_process_autocorrelation_autocovariance

Gives analytic formulas to calculate autocovariance matrix and autocorrelation matrix for averaged Wiener process with equal-distance time points. Is supplemented with Python numpy code to verify those formulas with a Monte Carlo simulation.

hyperplane_and_unit_ball

Monte Carlo simulation to get L1-norm adjusted square of diagonal hyperplane intersection with n - dimentional unit ball

nodeweightedbudget

This package (non-exactly) maximizes the total prize with a budget constraint for an undirected node-weighted rooted graph loosely inspired by Bateni 2018 algorithm from DOI:10.1137/15M102695X

non_cyclic_pandiagonal_latin_squares_order_13

This is an implementation of Dabbaghian & Wu's algorithm of constructing non-cyclic pandiagonal Latin squares for the order 13 from Dabbaghian V, Wu T. Constructing non-cyclic pandiagonal Latin squares of prime orders. Journal of Discrete Algorithms. 2015;30:70–7. https://doi.org/10.1016/j.jda.2014.12.001

normal_ball_positive_convolution

random_speculation

The code to confirm by simulation that a random speculation results in a positive expected return on historical prices of a growing asset

rank_matrix_vba

This VBA code transforms a matrix into the row echelon form and calculates its rank

semi_cyclic_pandiagonal_latin_squares_order_13

A new computational fast algorithm of constructing semi-cyclic pandiagonal Latin squares for an arbitrary order.

sha256_collision_check

This code was written to ensure there are no collisions among the SHA256 hashes gotten from numeric strings in the range of 0000 000 000 to 9999 999 999

volatility_adjustment_to_near_normal

This code aims to show that a proper SPX adjustment by an expected volatility may give a near-normal distribution of the log returns