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.

Machine learning for finance

Explore the data with some EDA

Correlations

Data transforms, features, and targets

Create moving average and RSI features

Create features and targets

Check the correlations

Linear modeling

Create train and test features

Fit a linear model

Evaluate our results

Preparing data and a linear model

Learn how to use tree-based machine learning models to predict future values of a stock's price, as well as how to use forest-based machine learning methods for regression and feature selection.

Engineering more features

Feature engineering from volume

Create day-of-week features

Examine correlations of the new features

Decision trees

Fit a decision tree

Try different max depths

Check our results

Random forests

Fit a random forest

Tune random forest hyperparameters

Evaluate performance

Feature importances and gradient boosting

Random forest feature importances

A gradient boosting model

Gradient boosting feature importances

Machine learning tree methods

We will learn how to normalize and scale data for use in KNN and neural network methods.  Then we will learn how to use KNN and neural network regression to predict the future values of a stock's price (or any other regression problem).

Scaling data and KNN Regression

Standardizing data

Optimize n_neighbors

Evaluate KNN performance

Neural Networks

Build and fit a simple neural net

Plot losses

Measure performance

Custom loss functions

Custom loss function

Fit neural net with custom loss function

Visualize the results

Overfitting and ensembling

Combatting overfitting with dropout

Ensembling models

See how the ensemble performed

Neural networks and KNN

In this chapter, you'll learn how to use modern portfolio theory (MPT) and the Sharpe ratio to plot and find optimal stock portfolios.  You'll also use machine learning to predict the best portfolios.  Finally, you'll evaluate performance of the ML-predicted portfolios.

Modern portfolio theory (MPT); efficient frontiers

Join stock DataFrames and calculate returns

Calculate covariances for volatility

Calculate portfolios

Plot efficient frontier

Sharpe ratios; features and targets

Get best Sharpe ratios

Calculate EWMAs

Make features and targets

Plot efficient frontier with best Sharpe ratio

Machine learning for MPT

Make predictions with a random forest

Get predictions and first evaluation

Evaluate returns

Plot returns

Closing remarks and advice

Machine learning with modern portfolio theory

NASDAQ: AAPL

NASDAQ: AMD

QQQ ETF

SMLV

Time series data is all around us; some examples are the weather, human behavioral patterns as consumers and members of society, and financial data. In this course, you'll learn how to calculate technical indicators from historical stock data, and how to create features and targets out of the historical stock data. You'll understand how to prepare our features for linear models, xgboost models, and neural network models. We will then use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. You will also learn how to evaluate the performance of the various models we train in order to optimize them, so our predictions have enough accuracy to make a stock trading strategy profitable.

<h2>How to Predict Stock Prices with Machine Learning </h2>
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. 
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<h2>Build Your Knowledge of ML Models </h2>
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. 
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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. 
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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. 
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<h2>Use the Sharpe Ratio to Build Your Ideal Portfolio</h2>
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. 
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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. "

Supervised Learning with scikit-learn

Learn to model and predict stock data values in Python using linear models, decision trees, random forests, neural networks, and modern portfolio theory. 

Machine Learning for Finance in Python Course | Predict Stock Prices | DataCamp 

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

Machine Learning for Finance in Python

Data Science Professor

Feature importances

Good features

Extracting feature importances

Sorting and plotting

Plotting feature importances

Linear models vs gradient boosting

Boosted models

Fitting a gradient boosting model

Get boosted!

https://s3.amazonaws.com/assets.datacamp.com/production/course_6578/subtitles/course_6578_20d038cc9d1ae15088f92c5981592511.vtt

Machine learning tree methods - Feature importances and gradient boosting

Feature importances and gradient boosting

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