Pular para o conteúdo principal
Página inicialPythonMachine Learning for Finance in Python

Machine Learning for Finance in Python

Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.

Comece O Curso Gratuitamente
4 Horas15 Videos59 Exercicios
27.208 AprendizesTrophyDeclaração de Realização

Crie sua conta gratuita

GoogleLinkedInFacebook

ou

Ao continuar, você aceita nossos Termos de Uso, nossa Política de Privacidade e que seus dados são armazenados nos EUA.
GroupTreinar 2 ou mais pessoas?Experimente o DataCamp For Business

Amado por alunos de milhares de empresas


Descrição do Curso

How to Predict Stock Prices with Machine Learning

Machine learning has a huge number of applications within the finance industry and is commonly used to predict stock values and maintain a strong stock portfolio. This course will teach you how to use Python to calculate technical indicators from historical stock data and create features and targets.

Build Your Knowledge of ML Models

Strong stock predictions start with good data preparation. You’ll learn how to prepare your financial data for ML algorithms and fit it into various models, including linear models, xgboost models, and neural network models.

The second chapter moves on to using Python decision trees to predict future values for your stock, and forest-based machine learning methods to enhance your predictions.

The second half of this course will cover how to scale your data for use in KNN and neural networks before using those tools to predict the future value of your stock. You’ll learn how to plot losses, measure performance, and visualize your prediction results.

Use the Sharpe Ratio to Build Your Ideal Portfolio

Machine learning can also help you find the optimal stock portfolio. You’ll learn how to use modern portfolio theory (MPT) and the Sharpe ratio as part of your process to predict the best portfolios. Once you’ve completed this course, you’ll also understand how to evaluate the performance of your machine learning-predicted portfolio.

You’ll use a variety of real-world data sets from NASDAQ and apply robust theories and techniques to them so that you can create your own predictions and optimize for your risk appetite and budget. "
Para Empresas

GroupTreinar 2 ou mais pessoas?

Obtenha acesso à biblioteca completa do DataCamp, com relatórios, atribuições, projetos e muito mais centralizados
Experimente O DataCamp for BusinessPara uma solução sob medida , agende uma demonstração.
  1. 1

    Preparing data and a linear model

    Grátis

    In this chapter, we will learn how machine learning can be used in finance. We will also explore some stock data, and prepare it for machine learning algorithms. Finally, we will fit our first machine learning model -- a linear model, in order to predict future price changes of stocks.

    Reproduzir Capítulo Agora
    Machine learning for finance
    50 xp
    Explore the data with some EDA
    100 xp
    Correlations
    100 xp
    Data transforms, features, and targets
    50 xp
    Create moving average and RSI features
    100 xp
    Create features and targets
    100 xp
    Check the correlations
    100 xp
    Linear modeling
    50 xp
    Create train and test features
    100 xp
    Fit a linear model
    100 xp
    Evaluate our results
    100 xp
Para Empresas

GroupTreinar 2 ou mais pessoas?

Obtenha acesso à biblioteca completa do DataCamp, com relatórios, atribuições, projetos e muito mais centralizados

Conjuntos De Dados

NASDAQ: AAPLNASDAQ: AMDQQQ ETFSPYLNGSMLV

Colaboradores

Collaborator's avatar
Chester Ismay
Collaborator's avatar
David Campos
Collaborator's avatar
Shon Inouye
Nathan George HeadshotNathan George

Assistant Professor of Data Science at Regis University

Veja Mais

O que os outros alunos têm a dizer?

Junte-se a mais de 13 milhões de alunos e comece Machine Learning for Finance in Python hoje mesmo!

Crie sua conta gratuita

GoogleLinkedInFacebook

ou

Ao continuar, você aceita nossos Termos de Uso, nossa Política de Privacidade e que seus dados são armazenados nos EUA.