Trading Algos - 5 Key Metrics and How to Implement Them in Python

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

    Now to implement this one, we'll have to do some manipulation to our account values. Let's use the power of numpy to help us out here (oh and it's also the same in pandas too. We'll be using np.diff to take the returns of our account values and resampling them.

  • optuna

    A hyperparameter optimization framework

    Nothing can beat iteration and rapid optimization. Try running things like grid experiments, batch optimizations, and parameter searches. Take a look at various packages like hyperopt or optuna as packages that might be able to help you here!

  • Scout APM

    Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.

  • NumPy

    The fundamental package for scientific computing with Python.

    Now to implement this one, we'll have to do some manipulation to our account values. Let's use the power of numpy to help us out here (oh and it's also the same in pandas too. We'll be using np.diff to take the returns of our account values and resampling them.

  • hyperopt

    Distributed Asynchronous Hyperparameter Optimization in Python

    Nothing can beat iteration and rapid optimization. Try running things like grid experiments, batch optimizations, and parameter searches. Take a look at various packages like hyperopt or optuna as packages that might be able to help you here!

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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