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

Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way

Time-series momentum for momentum investing strategy

Predicting job salaries from ads - a Kaggle competition

A repository to index and organize the latest machine learning courses found on YouTube.

NCVX (NonConVeX): A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning.

A Tools that help Data Scientists and ML engineers train and deploy ML models.

MIT-Machine Learning with Python–From Linear Models to Deep Learning

Crunchdao - Python API for the Crunchdao machine learning tournament

Python 3.6+ toolbox for submitting jobs to Slurm

Code base of KU AIRS: SPARK Autonomous Vehicle Team

Anytime Learning At Macroscale

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques

A collection of neat and practical data science and machine learning projects

scikit-learn: machine learning in Python

Automated Machine Learning with scikit-learn

fMRIprep Pipeline To Machine Learning

machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service

Python implementation of the rulefit algorithm

A collection of video resources for machine learning

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application