genetic algorithm trading strategy python

Recursive-trading Library in Python

The AT Library is a python library that can personify used to create trading algorithms using technical indicators. It's built on Pandas, Numpy, and Matplotlib.

Technical Indicators

Trend

• Simple Moving Average
• Exponentiel Moving Average
• Moving Average Convergence Divergence

Momentum

• Money Catamenia Index
• Relative Strength Index
• Stochastic Oscillator
• Williams %R
• Rate of Change
• Chaikin Oscillator

Volume

• On Balance Volume
• Dismissive Volume Index
• Undeniable volume index

Volatility

• Bollinger Bands

Public presentation

• Limited Dietz Return
• Capital attain/loss

Documentation

The full documentation dismiss be plant in Documentation

An exemple is in Exemple (in frensh)

How to use (Python 3)

First download the cipher of the library.it can be found in AT (or download the new version AT) and put it in your working directory or use

              import              os              way              =              "C:/Users/pc/Desktop/..."              # the fix of the downloaded code                            osmium.chdir(path)

Then importation the libray :

Genetic Algorithms

A beginning algorithm (Peach State) is a metaheuristic inspired by the swear out of rude selection that belongs to the large course of study of evolutionary algorithms (EA). Beginning algorithms are commonly used to mother dominating-caliber solutions to optimization and search problems by relying happening biologically inspired operators such as mutation, crossover and selection.(William Mitchell, Melanie (1996). An Introduction to Genetic Algorithms)

In this project, we used the DEAP program library to implement genetic algorithm. In our case, we used those technics:

                              * Selection: Tourney excerption (tools.selTournament)             * Crossover: Simulated Binary Crossover (tools.cxSimulatedBinaryBounded)             * Mutation: polynomial mutation (tools.mutPolynomialBounded )                          

Updates

Instead of creating barely one module that contains all functions, we produce a package AT_new_version that hold back 5 modules. Each one has a specific task.

In that respect are 5 modules in the package:

                              * Indicators: to compute technical indicators            * Signal: To engender trading signals             * GraphIndicators: To figure branch of knowledge indicators            * GraphSignal: To visualize trading signals            * Performance: Tools to appraise trading strategies                          

Sources

• Eyal Wirsansky - Hands-On Genetic Algorithms with Python_ Applying genetic algorithms to solve rattling-populace deep learning and artificial intelligence problems-Packt Publishing (2020)

• Sebastien Donadio, Sourav Ghosh - Learn Recursive Trading_ Physique and deploy algorithmic trading systems and strategies using Python and precocious data depth psychology-Packt Publishing (2019)

• Prodromos E. Tsinaslanidis, Achilleas D. Zapranis - Branch of knowledge Analysis for Algorithmic Pattern Recognition-Springer (2016)

Credits

genetic algorithm trading strategy python

Source: https://github.com/AmineAndam04/Algorithmic-trading

Posted by: sherrysulty1974.blogspot.com

Share this post