A tree-based trend filter for a moving-average MT5 EA

A moving-average crossover EA is the canonical "starter strategy" — cheap to backtest, easy to understand, and famously mediocre on its own because it generates many false signals in ranging markets. This article adds a LightGBM trend filter that the EA consults before acting on a crossover signal: only take the trade if the filter predicts the trend will persist.

The base EA (without filter)

Standard: buy when fast MA crosses above slow MA, sell when it crosses below. We'll keep this exact signal generation — we just wrap it with a yes/no decision from the ONNX model.

Training the trend filter

For each historical MA crossover signal, label whether it would have been profitable (1) or not (0). Use a fixed-horizon exit rule, e.g., 50 bars after entry. Features:

Slope of slow MA over last 20 bars (proxy for prevailing direction).
Distance from price to fast MA, normalized by ATR.
Realized volatility over last 50 bars.
Time since last crossover (sometimes crossovers cluster — usually bad).
Higher-timeframe trend direction (H4/D1 MA slope).

train_filter.py

import lightgbm as lgb from onnxmltools import convert_lightgbm from onnxmltools.convert.common.data_types import FloatTensorType # X: features at crossover. y: 1 if 50-bar return was profitable. params = {"objective": "binary", "learning_rate": 0.05, "num_leaves": 31, "max_depth": 6} booster = lgb.train(params, lgb.Dataset(X, label=y), num_boost_round=200) n_features = X.shape[1] onx = convert_lightgbm( booster, initial_types=[("input", FloatTensorType([None, n_features]))], target_opset=17, ) with open("trend_filter.onnx", "wb") as f: f.write(onx.SerializeToString())

Using the filter in MQL5

ma_cross_with_filter.mq5

#resource "models\trend_filter.onnx" as uchar FilterModel[] input double InpFilterThreshold = 0.55; long hFilter; double FilterProbability() { matrixf in(1, 12); vectorf out(2); // fill 12 features as training did, then: OnnxRun(hFilter, ONNX_NO_CONVERSION, in, out); return out[1]; // probability of class 1 (profitable) } void OnTick() { // ... existing crossover detection ... if(BullishCrossover && FilterProbability() > InpFilterThreshold) PlaceBuy(); if(BearishCrossover && FilterProbability() > InpFilterThreshold) PlaceSell(); }

What to expect from the filter

Trade count drops 40–60%.
Win rate improves by 5–15 percentage points.
Maximum drawdown improves materially — often the biggest practical win.
Total profit may go up or down depending on whether the filter is removing more losers than winners.