Market Regimes
Give trading agents regime-specific playbooks—when to reduce frequency, avoid leverage, or skip mean reversion—in sideways or low-vol markets.
Overview
Market Regimes is an agent skill for the Operate phase that supplies regime-conditioned trading strategy hints for autonomous or semi-autonomous trading agents.
Install
npx skills add https://github.com/0xhubed/agent-trading-arena --skill market-regimesWhat is this skill?
- Structured regime_strategy entries keyed by conditions such as sideways_flat and low_volatility_mixed
- Actionable guidance: cut trade frequency, favor passive/index-style capital preservation, avoid leverage in tight ranges
- Explicit warnings when mean-reversion fails without deviation to revert from
- Each pattern includes success_rate metadata for agent weighting (sample sizes may be unset in source data)
- Multiple regime_strategy pattern entries with documented success_rate fields (e.g. 0.88, 0.82, 0.75 in source excerpt)
Adoption & trust: 560 installs on skills.sh; 6 GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your trading agent uses the same tactics in every market environment and bleeds fees or leverage risk when volatility and trend structure do not match the strategy.
Who is it for?
Indie builders running agent-trading-arena-style bots who need a curated regime-to-strategy map rather than inventing rules from scratch each session.
Skip if: Investors seeking SEC-grade advice, one-off stock picks, or guaranteed performance claims—the bundled patterns show metadata but not verified live sample sizes in the excerpt.
When should I use this skill?
Classifying market environment or updating agent instructions for sideways, low-volatility, or mean-reversion contexts in a trading arena workflow.
What do I get? / Deliverables
The agent selects descriptions aligned to the detected regime—such as reduced activity in sideways_flat—so position sizing and strategy family match conditions.
- Regime-matched strategy descriptions drawn from the pattern catalog
Recommended Skills
Journey fit
Regime catalogs matter once strategies run live and need tuning; Operate is where builders adjust behavior as conditions change. Iterate covers changing rules and exposure when market state shifts, not the initial hypothesis in Idea or Validate.
How it compares
Reference regime playbooks for agents, not a live market data MCP or brokerage execution integration.
Common Questions / FAQ
Who is market-regimes for?
Developers building or tuning automated trading agents in arena or research setups who want regime labels tied to conservative tactical guidance.
When should I use market-regimes?
During Operate when refreshing strategy prompts after a regime change, and when backtesting agent decisions against labeled sideways or low-volatility periods.
Is market-regimes safe to install?
The skill is JSON knowledge, not execution code, but trading automation carries financial risk; review the Security Audits panel on this Prism page and never wire real capital without your own risk controls.
SKILL.md
READMESKILL.md - Market Regimes
{ "025223f2688e": { "pattern_id": "025223f2688e", "pattern_type": "regime_strategy", "description": "sideways_flat: Reduce trade frequency dramatically; passive/index strategies outperform active trading by preserving capital from transaction costs", "conditions": { "regime": "sideways_flat" }, "success_rate": 0.88, "sample_size": 0, "confidence": 0, "first_seen": "2026-01-13T11:23:59.257020", "last_confirmed": "2026-01-13T11:23:59.257020", "times_seen": 1, "is_active": false }, "16696a9aa3cf": { "pattern_id": "16696a9aa3cf", "pattern_type": "regime_strategy", "description": "low_volatility_mixed: Avoid leveraged positions; small price movements (-0.02% to +0.09%) do not justify leverage costs and risk", "conditions": { "regime": "low_volatility_mixed" }, "success_rate": 0.82, "sample_size": 0, "confidence": 0, "first_seen": "2026-01-13T11:23:59.257020", "last_confirmed": "2026-01-13T11:23:59.257020", "times_seen": 1, "is_active": false }, "271f0a31b6f8": { "pattern_id": "271f0a31b6f8", "pattern_type": "regime_strategy", "description": "sideways_flat: Mean-reversion strategies fail when there is no significant deviation to revert from; stay flat or minimal exposure", "conditions": { "regime": "sideways_flat" }, "success_rate": 0.75, "sample_size": 0, "confidence": 0, "first_seen": "2026-01-13T11:23:59.257020", "last_confirmed": "2026-01-13T11:23:59.257020", "times_seen": 1, "is_active": false }, "e301e9a8b749": { "pattern_id": "e301e9a8b749", "pattern_type": "regime_strategy", "description": "sideways_flat: Reduce trade frequency to <10 trades/day; passive index allocation outperforms active trading; avoid leveraged positions entirely; wait for clear directional breakout before engaging", "conditions": { "regime": "sideways_flat" }, "success_rate": 0.92, "sample_size": 0, "confidence": 0, "first_seen": "2026-01-13T17:50:18.246218", "last_confirmed": "2026-01-13T17:50:18.246218", "times_seen": 1, "is_active": false }, "61605d110d15": { "pattern_id": "61605d110d15", "pattern_type": "regime_strategy", "description": "low_volatility_mixed: When all assets show <0.1% absolute movement, avoid opening new positions; fee drag exceeds potential profit; technical signals (SMA, MACD, multi-timeframe alignment) produce false signals in this regime", "conditions": { "regime": "low_volatility_mixed" }, "success_rate": 0.88, "sample_size": 0, "confidence": 0, "first_seen": "2026-01-13T17:50:18.246218", "last_confirmed": "2026-01-13T17:50:18.246218", "times_seen": 1, "is_active": false }, "5d2a82fce004": { "pattern_id": "5d2a82fce004", "pattern_type": "regime_strategy", "description": "sideways_flat: Mean-reversion and momentum strategies both fail; only capital preservation strategies (hold cash, minimal index allocation) succeed", "conditions": { "regime": "sideways_flat" }, "success_rate": 0.85, "sample_size": 0, "confidence": 0, "first_seen": "2026-01-13T17:50:18.246218", "last_confirmed": "2026-01-13T17:50:18.246218", "times_seen": 1, "is_active": false }, "181329373082": { "pattern_id": "181329373082", "pattern_type": "regime_strategy", "description": "trending_up: Multi-timeframe bullish alignment WITH risk validation and position scaling works in genuine uptrends (+3-5%). Moderate trade frequency (150-200) captures moves without overtrading. Focus on strongest movers (ETH, DOGE) not laggards (SOL).", "conditions": { "regime": "trending_up" }, "success_rate": 0.9, "sample_size": 0, "confidence": 0, "first_seen": "2026-01-14T13:41:05.809644", "last_confirmed": "2026-01-14T13:41:05.809644", "times_seen": 1, "is_active": false }, "c1ebc7a