Crypto Regime Filters, Liquidity Sweeps, and Leverage-Based Trade Signals

Summary

This strategy combines a rule-based market regime classifier with macro, open-interest, liquidity, and volume conditions. Its so-called HMM regime is a persistent state machine: a Hurst-style proxy, ADX, RSI, directional indicators, micro-timeframe candle volume flow, and an EMA determine bullish, bearish, or neutral states. A one-bar transition delay and structural reset rules manage state changes. Separately, the script compares USDT dominance and aggregated Bitcoin perpetual open interest with moving averages to label broad flow and leverage conditions.

Trade triggers look for swing-failure patterns that sweep recent highs or lows and close back inside, then require volume anomaly or a small candle body alongside macro and open-interest filters. The excerpt also describes counter-trend risk adjustment and a martingale-style execution engine, but omits much of that engine. Despite its HMM and stationarity labels, the shown regime logic is a deterministic indicator-based approximation, not a fitted probabilistic model. No backtest evidence is included, and results depend on data availability and the extensive assumptions in these proxies.

Key ideas

• The regime state uses a Hurst-style range proxy, ADX, RSI, directional indicators, volume flow, and price relative to an EMA.
• Bullish and bearish trade triggers require a recent swing failure, a volume condition, and corresponding macro and open-interest filters.
• The script treats USDT dominance and aggregated perpetual open interest as contextual flow and leverage measures.
• The regime detector is a rule-based state machine rather than a trained Hidden Markov Model.
• The excerpt omits execution details and gives no performance evidence.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.