Seasonality-ML Cycle Trend Fusion

Family: trend_following · Regime: trending · Complexity: high · Asset classes: FX, Equities, Crypto · Timeframes: H1, H4

Thesis

Market trends are most persistent during mid-week (Tue-Thu) institutional participation. By aligning machine-learning volatility bands with volume-confirmed momentum (TTF/ADL), we can enter trends at the start of their cycle (STC) and protect capital using zones where high-volume reversal pivots occurred. The edge exists because it filters for time-of-week biases and structural liquidity support.

Components

• Daily Seasonality (regime) — Defines the operational window; mid-week days (Tue-Thu) are prioritized for trend persistence.
• Trend Trigger Factor (TTF) with T3 Smoothing (direction) — Provides the primary trend bias by comparing recent vs. older price ranges, filtered for noise via T3 smoothing.
• Schaff Trend Cycle (STC) (entry) — The tactical entry trigger, providing faster signals than standard MACD by applying stochastic smoothing to the MACD line.
• MACD (exit) — Used for trend exhaustion exits; the signal line crossover provides a conservative exit compared to entry.
• Volume and Pivot Points Correlation with Full Control (risk) — Determines the 'War Zone' for stop loss placement based on pivots occurring on high volume anomalies.
• Accumulation/Distribution Line (confirmation) — Confirms price movement with volume flow to ensure price action is supported by liquidity.
• ML SuperTrend (Ultimate) - Auto-Optimized AI with LSTM (volatility_filter) — Acts as a dynamic volatility floor/ceiling, preventing entries when the ML-optimized ATR threshold is breached.

Known failure conditions

• STC plateaus at 100 or 0 for extended periods while price is ranging.
• TTF fails to cross zero line during high-volatility news events.
• ML SuperTrend 'base_fact' remains static due to LSTM training failure on low-history assets.

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