ML Engine v.2.0.0.0: Commitment of Traders Features and Adaptive Contract Quantity Forecasting in NinjaTrader 8
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Posted inNewsTags: adaptive position sizing, algorithmic trading, auc f1 score trading, automated trading systems, classifier ensemble trading, commercial net positions, commitment of traders, contract quantity forecasting, cot indicator, cumulative delta, day trading signals, dynamic position sizing, ensemble models trading, fastforest regression, fasttree regression, filter trading signals, futures positioning data, futures trading strategy, institutional positioning, lbfgs poisson regression, lightgbm trading model, machine learning regression, machine learning trading, mae trading metric, ml engine, ninjascript api, ninjascript strategy development, ninjatrader 8 strategy, ninjatrader indicator, ninjatrader ml engine, ninjatrader order flow plus, noncommercial net positions, online gradient descent, order flow analysis, poisson regression, position sizing strategy, probability based trading, quant trading models, quantity forecast model, r2 score trading model, reduce false signals, regression ensemble, signal filtering, speculator positioning, trade performance tracking, trade size optimization, trading strategy optimization, tweedie regression, vwap tradingNo Comments
In our previous article we introduced ML Engine — an ensemble machine learning filter that takes the directional signal from your existing indicator and decides whether it is worth taking,…