Machine Learning: Logistic Regression

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Multi-timeframe Strategy based on Logistic Regression algorithm


This strategy uses a classic machine learning algorithm that came from statistics - Logistic Regression ( LR ).

The first and most important thing about logistic regression is that it is not a 'Regression' but a 'Classification' algorithm. The name itself is somewhat misleading. Regression gives a continuous numeric output but most of the time we need the output in classes (i.e. categorical, discrete). For example, we want to classify emails into “spam” or 'not spam', classify treatment into “success” or 'failure', classify statement into “right” or 'wrong', classify election data into 'fraudulent vote' or 'non-fraudulent vote', classify market move into 'long' or 'short' and so on. These are the examples of logistic regression having a binary output (also called dichotomous).

You can also think of logistic regression as a special case of linear regression when the outcome variable is categorical, where we are using log of odds as dependent variable. In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function.

Basically, the theory behind Logistic Regression is very similar to the one from Linear Regression , where we seek to draw a best-fitting line over data points, but in Logistic Regression, we don’t directly fit a straight line to our data like in linear regression . Instead, we fit a S shaped curve, called Sigmoid, to our observations, that best SEPARATES data points. Technically speaking, the main goal of building the model is to find the parameters (weights) using gradient descent.

In this script the LR algorithm is retrained on each new bar trying to classify it into one of the two categories. This is done via the logistic_regression function by updating the weights w in the loop that continues for iterations number of times. In the end the weights are passed through the sigmoid function, yielding a prediction.

Mind that some assets require to modify the script's input parameters. For instance, when used with BTCUSD and USDJPY , the 'Normalization Lookback' parameter should be set down to 4 (2,...,5..), and optionally the 'Use Price Data for Signal Generation?' parameter should be checked. The defaults were tested with EURUSD .

Note: TradingViews's playback feature helps to see this strategy in action.
Warning: Signals ARE repainting.

Style tags: Trend Following, Trend Analysis
Asset class: Equities, Futures , ETFs, Currencies and Commodities
Dataset: FX Minutes/Hours/Days
Release Notes: Minor fix.
Release Notes: Price fix in the custom performance testing module.
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