Predict profitability of trades based on indicator buy / sell signals

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:
    1. Trade is profitable if, profit >0
    2. Buy / sell happen the following day of the signal
    3. 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:
    1. Linear Support Vector Classifier
    2. Decision Tree Classifier
    3. Random Forest Classifier
    4. Gradient Boosting Classifier
    5. XGBoost Classifier
    6. 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

    Owner
    Tomasz Porzycki
    Tomasz Porzycki
    PLUR is a collection of source code datasets suitable for graph-based machine learning.

    PLUR (Programming-Language Understanding and Repair) is a collection of source code datasets suitable for graph-based machine learning. We provide scripts for downloading, processing, and loading the

    Google Research 76 Nov 25, 2022
    50% faster, 50% less RAM Machine Learning. Numba rewritten Sklearn. SVD, NNMF, PCA, LinearReg, RidgeReg, Randomized, Truncated SVD/PCA, CSR Matrices all 50+% faster

    [Due to the time taken @ uni, work + hell breaking loose in my life, since things have calmed down a bit, will continue commiting!!!] [By the way, I'm

    Daniel Han-Chen 1.4k Jan 01, 2023
    Machine Learning approach for quantifying detector distortion fields

    DistortionML Machine Learning approach for quantifying detector distortion fields. This project is a feasibility study for training a surrogate model

    Joel Bernier 1 Nov 05, 2021
    Pydantic based mock data generation

    This library offers powerful mock data generation capabilities for pydantic based models. It can also be used with other libraries that use pydantic as a foundation, for example SQLModel, Beanie and

    Na'aman Hirschfeld 396 Dec 28, 2022
    A complete guide to start and improve in machine learning (ML)

    A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art

    Louis-François Bouchard 3.3k Jan 04, 2023
    This is the code repository for LRM Stochastic watershed model.

    LRM-Squannacook Input data for generating stochastic streamflows are observed and simulated timeseries of streamflow. their format needs to be CSV wit

    1 Feb 14, 2022
    Neighbourhood Retrieval (Nearest Neighbours) with Distance Correlation.

    Neighbourhood Retrieval with Distance Correlation Assign Pseudo class labels to datapoints in the latent space. NNDC is a slim wrapper around FAISS. N

    The Learning Machines 1 Jan 16, 2022
    Stats, linear algebra and einops for xarray

    xarray-einstats Stats, linear algebra and einops for xarray ⚠️ Caution: This project is still in a very early development stage Installation To instal

    ArviZ 30 Dec 28, 2022
    An open-source library of algorithms to analyse time series in GPU and CPU.

    An open-source library of algorithms to analyse time series in GPU and CPU.

    Shapelets 216 Dec 30, 2022
    A Python package for time series classification

    pyts: a Python package for time series classification pyts is a Python package for time series classification. It aims to make time series classificat

    Johann Faouzi 1.4k Jan 01, 2023
    LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms

    LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms Based on the work by Smith et al. (2021) Query

    5 Aug 06, 2022
    [HELP REQUESTED] Generalized Additive Models in Python

    pyGAM Generalized Additive Models in Python. Documentation Official pyGAM Documentation: Read the Docs Building interpretable models with Generalized

    daniel servén 747 Jan 05, 2023
    🤖 ⚡ scikit-learn tips

    🤖 ⚡ scikit-learn tips New tips are posted on LinkedIn, Twitter, and Facebook. 👉 Sign up to receive 2 video tips by email every week! 👈 List of all

    Kevin Markham 1.6k Jan 03, 2023
    ETNA is an easy-to-use time series forecasting framework.

    ETNA is an easy-to-use time series forecasting framework. It includes built in toolkits for time series preprocessing, feature generation, a variety of predictive models with unified interface - from

    Tinkoff.AI 674 Jan 07, 2023
    using Machine Learning Algorithm to classification AppleStore application

    AppleStore-classification-with-Machine-learning-Algo- using Machine Learning Algorithm to classification AppleStore application. the first step : 1: p

    Mohammed Hussien 2 May 02, 2022
    Polyglot Machine Learning example for scraping similar news articles.

    Polyglot Machine Learning example for scraping similar news articles In this example, we will see how we can work with Machine Learning applications w

    MetaCall 15 Mar 28, 2022
    A modular active learning framework for Python

    Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

    modAL 1.9k Dec 31, 2022
    Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

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

    Eduardo Blancas 354 Dec 31, 2022
    MBTR is a python package for multivariate boosted tree regressors trained in parameter space.

    MBTR is a python package for multivariate boosted tree regressors trained in parameter space.

    SUPSI-DACD-ISAAC 61 Dec 19, 2022
    Data from "Datamodels: Predicting Predictions with Training Data"

    Data from "Datamodels: Predicting Predictions with Training Data" Here we provid

    Madry Lab 51 Dec 09, 2022