Predict profitability of trades based on indicator buy / sell signals

Last update: Dec 15, 2021

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Machine Learning TradeProfitability

Overview

Predict profitability of trades based on indicator buy / sell signals

Trade profitability analysis for trades based on various indicators signals:

MACD

Simple Moving Average

Exponential Moving Average

Trading assumptions:

Trade is profitable if, profit >0
Buy / sell happen the following day of the signal
Buy / sell are taken 10% from the open price towards close price

Machine learning assumptions:

Binary classification: 1 - profit, 0 - loss
A separate model for each company / ticker
Model is trained vs optimal precision

Machine learning models used:

Linear Support Vector Classifier
Decision Tree Classifier
Random Forest Classifier
Gradient Boosting Classifier
XGBoost Classifier
Keras classifier

Trade analysis intermediate results:
30-40% of trades based on indicator signals are profitable
In general trades on SMA signals are more often profitable than the ones based on EMA signals

Trade profitability predictions intermediate results (based on test data)/
The precision of the predictions is oscilating around 70%, which is pretty good, considering that the analysts estimate other signals accuracy as 30 to 50% (double top, shoulder & arms, etc). This means, there is ~70% chance that predicted trade will be profitable (Reminder: profitable -> profit > 0)
However, the recall is only around 15%, which means that very the model pick-up very few of the actually profitable trades.

#Detailed analysis tbc

Predict profitability of trades based on indicator buy / sell signals

Related tags

Overview

Predict profitability of trades based on indicator buy / sell signals

Owner

Tomasz Porzycki

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Machine Learning e Data Science com Python

Predict profitability of trades based on indicator buy / sell signals

Related tags

Overview

Predict profitability of trades based on indicator buy / sell signals

Owner

Tomasz Porzycki

This repo implements a Topological SLAM: Deep Visual Odometry with Long Term Place Recognition (Loop Closure Detection)

Provide an input CSV and a target field to predict, generate a model + code to run it.

The code from the Machine Learning Bookcamp book and a free course based on the book

Tools for mathematical optimization region

Customers Segmentation with RFM Scores and K-means

customer churn prediction prevention in telecom industry using machine learning and survival analysis

Distributed deep learning on Hadoop and Spark clusters.

Automated Machine Learning with scikit-learn

MaD GUI is a basis for graphical annotation and computational analysis of time series data.

Combines MLflow with a database (PostgreSQL) and a reverse proxy (NGINX) into a multi-container Docker application

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

MLR - Machine Learning Research

Case studies with Bayesian methods

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Formulae is a Python library that implements Wilkinson's formulas for mixed-effects models.

LibTraffic is a unified, flexible and comprehensive traffic prediction library based on PyTorch

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

a distributed deep learning platform

AutoX是一个高效的自动化机器学习工具，它主要针对于表格类型的数据挖掘竞赛。 它的特点包括: 效果出色、简单易用、通用、自动化、灵活。

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