Use when adding a regime filter to any directional strategy on Superior Trade — anything described as regime gate, trend filter, directional confirmation, ADX gate, EMA-separation filter, trade-or-skip overlay. Provides three reusable building blocks (regime_strong_bear, regime_strong_bull, regime_range) that wrap entry signals with triple-confirmation (EMA separation + ADX + N-bar return). Validated as the difference between fragile and robust trend strategies — fewer trades, cleaner equity curve.
npx skills add https://github.com/Superior-Trade/superior-skills --skill regime-overlay
A reusable filter that wraps any directional strategy and only allows entries when three independent regime confirmations align. Validated as the difference between a fragile and a robust trend strategy.
Searchable under: regime filter, trend gate, trade-or-skip filter, directional confirmation, ADX gate.
After 21 backtests, the single biggest performance lever was not entry logic, stop sizing, or trailing config. It was whether the strategy traded at all during specific market structure.
The proof:
The first version captured a real edge mixed with random noise. The third version captured only the real edge. Smaller absolute return; cleaner equity curve; no losing periods.
A signal is "in regime" when ALL three of these hold simultaneously:
| Indicator | Threshold | What it confirms |
|---|---|---|
| (EMA50 - EMA200) / EMA200 | < -0.06 (for bear) | Structural trend direction — not a fresh cross, real separation |
| ADX(14) | > 25 | Strength of the current trend |
| close.pct_change(30) | < -0.10 (for bear) | Recent momentum is actually moving |
Three independent angles — structure, strength, recency. A market can satisfy any one or two by accident; satisfying all three is genuinely rare and genuinely informative.
For long-side strategies, invert the signs:
(EMA50 - EMA200) / EMA200 > +0.06ADX(14) > 25 (same — ADX is direction-agnostic)close.pct_change(30) > +0.10def regime_strong_bear(df: pd.DataFrame) -> pd.Series:
ema50 = ta.EMA(df, timeperiod=50)
ema200 = ta.EMA(df, timeperiod=200)
adx = ta.ADX(df, timeperiod=14)
ema_sep = (ema50 - ema200) / ema200
ret_30 = df["close"].pct_change(30)
return (ema_sep < -0.06) & (adx > 25) & (ret_30 < -0.10)
def regime_strong_bull(df: pd.DataFrame) -> pd.Series:
ema50 = ta.EMA(df, timeperiod=50)
ema200 = ta.EMA(df, timeperiod=200)
adx = ta.ADX(df, timeperiod=14)
ema_sep = (ema50 - ema200) / ema200
ret_30 = df["close"].pct_change(30)
return (ema_sep > 0.06) & (adx > 25) & (ret_30 > 0.10)
def regime_range(df: pd.DataFrame) -> pd.Series:
adx = ta.ADX(df, timeperiod=14)
return adx < 25
The range-regime check is the opposite end of the spectrum — useful for gating mean-reversion strategies (see bollinger-reverter-4h).
Apply this overlay to any signal-driven entry. The cost is fewer trades; the benefit is the trades you do take fire in the right environment.
| Strategy | Without overlay | With overlay |
|---|---|---|
| Donchian breakdown BTC | 23 trades, 56.5% win, +12.85%, 3.69% DD | 6 trades, 100% win, +6.69%, 0% DD |
| (Same, second-half only) | 8 trades, 25% win, -4.32% | 0 trades, no PnL |
The overlay traded the second column's win rate and drawdown for the first column's absolute return. Most production strategies should prefer the second column.
| Parameter | Conservative | Aggressive | Notes |
|---|---|---|---|
| EMA separation threshold | 0.08 | 0.04 | Tighter = wait for deeper structural moves |
| ADX threshold | 30 | 20 | Higher = stricter trend-strength requirement |
| Return lookback bars | 40 | 20 | Window for "actual momentum" check |
| Return threshold | 0.15 | 0.06 | How much momentum needed |
Start conservative (0.06 / 25 / -0.10). Loosen only if the gate eliminates so many trades that the strategy's edge can't compound.
bollinger-reverter-4h) work IN range regimes — use regime_range (ADX < 25) instead of bear/bull strong gatesMost directional strategies in this repo benefit from this overlay:
donchian-strong-regime — uses bear overlay (validated)The overlay file itself is regime-agnostic — it provides functions for both bear and bull, and a range complement.
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Take superior-trade/regime-overlay from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.