Advanced large data optimization python

example problem: We have 100 numbers. Let’s designate (3,54) as buy trade which opens at 3rd number and closes at 54th number. Let’s designate a set ((3,54), (5,8), (10,24), (9,76), (56,98)) as some strategy consisting of 5 buy trades. We are only working with buy \trades So there are many possible strategies - including multiple trades Submit python code that works through all possible \strategies\ to determine the \strategy\ with the highest overall profit. there are 4950 strategies using just single pairs, then exponential including multiple pairs (from the 100 data points) the problem is not actually possible to compute in a traditional iterative manner... There are trillions of \strategies\ when using multiple data points there are many large number models like this in finance. many are not solvable with traditional iterative methods. on the other hand it may be possible to solve with GPU or genetic algorithm generally the tasks i have to work on we have already tried the textbook / chatgpt solution and it requires a more creative solution... the catch with this one is obtaining a result when the traditional iterative approach involves trillions of calculations in the case we determine it is not viable for a particular task to run the full compute time required we will evaluate on: a) sampling compute performance and notes b) python (trading/finance) research (large number optimization) competencies: i) logic, coding & problem solving skills ii) computing (parallel/vector/cpu/gpu etc) iii) mathematics (sampling/genetic/particle swarm algorithm etc) FEATURED OR AI GENERATED APPLICATIONS WILL BE REJECTED