OPEN-SOURCE SCRIPT

VWMA with kNN Machine Learning: MFI/ADX

Updated
This is an experimental strategy that uses a Volume-weighted MA (VWMA) crossing together with Machine Learning kNN filter that uses ADX and MFI to predict, whether the signal is useful. k-nearest neighbours (kNN) is one of the simplest Machine Learning classification algorithms: it puts input parameters in a multidimensional space, and then when a new set of parameters are given, it makes a prediction based on plurality vote of its k neighbours.

Money Flow Index (MFI) is an oscillator similar to RSI, but with volume taken into account. Average Directional Index (ADX) is an indicator of trend strength. By putting them together on two-dimensional space and checking, whether nearby values have indicated a strong uptrend or downtrend, we hope to filter out bad signals from the MA crossing strategy.

This is an experiment, so any feedback would be appreciated. It was tested on BTC/USDT pair on 5 minute timeframe. I am planning to expand this strategy in the future to include more moving averages and filters.
Release Notes
fixed a misleading comment
Release Notes
new parameters:
  • Apply kNN filter - if you want to try just the MA crossing without the kNN filter
  • kNN minimum difference - skews the number of votes needed for the decision, so this many more votes are needed to allow taking a position (e.g., if this is 1, the position would not be taken if there are 3 agains 3 votes, but would be taken if there are 4 agains 3 votes)
ADXAverage Directional Index (ADX)DMIknnmachinelearningMFIVolume Weighted Moving Average (VWMA)

Open-source script

In true TradingView spirit, the author of this script has published it open-source, so traders can understand and verify it. Cheers to the author! You may use it for free, but reuse of this code in publication is governed by House rules. You can favorite it to use it on a chart.

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