FVG-Logit StatArb Hybrid

Family: hybrid · Regime: trending · Complexity: high · Asset classes: Crypto, Equities · Timeframes: 15m, 1h

Thesis

Institutional imbalances (FVGs) occurring during a confirmed trend represent high-probability continuation signals. By filtering these entries through a logistic regression model (ATOM) and exiting via a statistical arbitrage mean-reversion trigger (AI Pair System), the strategy captures the 'meat' of a trend move while avoiding the decay phase where an asset overextends relative to its benchmark.

Components

• Simple Moving Average (SMA) (regime) — Defines the primary bullish or bearish regime to ensure trades align with the long-term trend.
• HiLo Activator (Pandini Version) (direction) — Provides intermediate trend direction and a trailing baseline for immediate directional bias.
• FVG Crosscheck (Python Definition) (entry) — Identifies specific price imbalances (Fair Value Gaps) as high-probability entry points within the trend.
• AI Pair Trading System v1.0 (exit) — Uses statistical arbitrage logic (Z-score) to identify when the asset has reached an exhaustive momentum state relative to a benchmark pair.
• ATOM Coefficient Indicator (logit) (risk) — Acts as a logistical regression filter for conviction and provides ATR-based risk parameters.
• Local non-scalar UDT typed-na history diagnostic (confirmation) — A technical validation layer to prevent execution on corrupted or 'na' historical data states in complex UDT objects.

Known failure conditions

• The logit model's AUC falls below 0.50 on recent data, rendering the probability filter random.
• The chosen pair for the AI system (e.g., ATOM/ETHBTC) loses cointegration/correlation.
• Price remains in a range smaller than the ATR-based stop for more than 48 hours.

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.