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Notebooks and Code for ML based quant strategies

ML-Quant

Notebooks and Code for ML based quant strategies and research.

Annotations and Sampling

Trend prediction using a Gaussian Mixture Model

Original data distribution, prediction by a mixture distribution implemented using tensorflow probability(TFP) and the prediction by sklearn Gaussian Mixture Model.

TFP mixture model components

Predicted trends by the mixture model

Volume bar sampling

Here we sample and plot 'close' value based on the volume. (Close value at every "xx" volume)

Trend strength and trend period

Here we generate trends (% increase or decrease in SPY) and find the trend strength (value between 0 and 1 depending on how long the trend last) and trend period (how long the trend last).

EMA Optimization

This notebook has a method to find the best EMA lines that represent 'uptrends' and 'downtrends' in S&P 500.

Probabilistic Logistic Regression

This shows how to use probabilistic Logistic Regression to detect outliers in a dataset. Prob LR is useful to guage the confidence of a decision. This is helpful to decide the risk of a bet.

Probabilistic Linear Regression

Unsupervised Buy-Sell detection