GARCH-LSTM Predictive Convergence

Family: hybrid · Regime: trending · Complexity: high · Asset classes: Equities, Forex, Crypto · Timeframes: 1H, 4H, 1D

Thesis

This strategy hypothesizes that momentum breakouts (V7 Candle) are only predictive when volatility is clustered (GARCH CV) and trend consensus (ML SuperTrend) is confirmed. It uses a look-ahead 'Direction' filter as a theoretical benchmark to identify the maximum possible efficiency of momentum entries in a perfect-information environment.

Components

Indicator Sample (Bar Count Logger) (regime) — Used to ensure the GARCH model (CV) has reached its initialization 'Length' (e.g., 200 bars) before allowing signals.
Unsupported named const comparison ternary negative history (direction) — Serves as a 'Perfect Predictor' filter to test the hypothesis that entry signals only work when the immediate subsequent price movement is favorable.
V7 Corpus-Local-AlertCondition-v1 (entry) — The core execution trigger; identifies local bullish/bearish candle momentum.
ATR SL Finder (exit) — Provides the technical exit levels (Stop Loss) based on recent realized volatility.
Conditional Volatility (CV) (risk) — Calculates position sizing by scaling lot size inversely to the GARCH(1,1) annualized risk estimate.
Momentum Oscillator (confirmation) — Confirms that the rate of change is accelerating in the direction of the ML-filtered trend.
ML SuperTrend (Ultimate) - Auto-Optimized AI with LSTM (volatility_filter) — Provides a non-linear volatility/trend filter to ensure entries occur only within high-probability regimes.

Known failure conditions

Consecutive losses exceeding 5 trades despite 'Perfect Predictor' (Direction) signals, indicating the ATR SL is too tight for the regime.
GARCH CV values remaining stagnant (0 volatility) in a trending market, suggesting parameter miscalibration.

Explore the full interactive blueprint, parameter ranges and evidence on WOBR StrategyVerse, or generate this strategy as an MT4/MT5 Expert Advisor with QuantMogul AI Engine.