lastguru

VWMA with kNN Machine Learning: MFI/ADX

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.
リリースノート: fixed a misleading comment
リリースノート: 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)

Tips in TradingView Coins are appreciated
オープンソーススクリプト

TradingViewの精神に則り、このスクリプトの作者は、トレーダーが理解し検証できるようにオープンソースで公開しています。作者に敬意を表します!無料で使用することができますが、このコードを投稿で再利用するには、ハウスルールに準拠する必要があります。 お気に入りに登録してチャート上でご利用頂けます。

免責事項

これらの情報および投稿は、TradingViewが提供または保証する金融、投資、取引、またはその他の種類のアドバイスや推奨を意図したものではなく、またそのようなものでもありません。詳しくは利用規約をご覧ください。

チャートでこのスクリプトを利用したいですか?