/market-regime

name: market-regime
description: Use when characterizing the current market environment. Covers trend and volatility regimes, risk-on versus risk-off signals, macro context, and matching strategy to regime rather than fighting it. Trigger on "what kind of market are we in", "is it risk on or risk off right now", "my strategy stopped working this year". Do not use for advance-decline and participation internals — see the market-breadth skill.
metadata:
  category: finance
  version: 1.0.0
  tags: [regime, macro, volatility, risk-on, environment]

Market Regime

Purpose

Characterize the environment before selecting a strategy. A strategy that works in a trending, low-volatility regime frequently loses money in a choppy, high-volatility one — and the most common cause of a strategy "stopping working" is a regime change it was never designed to survive.

When to Use

• Setting overall exposure and strategy selection.
• Explaining why a strategy that worked has stopped.
• Assessing whether conditions favor trend-following or mean-reversion.
• Periodic review of the environment.

Capabilities

• Trend regime: trending versus range-bound.
• Volatility regime: expanding, contracting, and the level.
• Risk appetite: credit spreads, defensive versus cyclical leadership.
• Macro context: rates, the dollar, inflation expectations.
• Regime-appropriate strategy selection.

Inputs

• Index price and volatility history.
• Cross-asset data: credit spreads, rates, currencies, commodities.
• Sector and factor performance.

Outputs

• A regime characterization.
• Which strategies the regime favors and which it punishes.
• An exposure recommendation.

Workflow

1. **Establish the trend regime** — Is the index above a rising long-term average, or chopping around a flat one? Trend-following requires a trend; in a range it bleeds. 2. **Establish the volatility regime** — The level and, more importantly, the direction. Expanding volatility from a low base is the most dangerous configuration for leveraged and short-volatility positions. 3. **Read the risk appetite** — Credit spreads widening while equities hold is a warning that the bond market disagrees. Defensive sectors leading in an advance is the same message. 4. **Note the macro backdrop** — Rates, the dollar, and inflation expectations set the constraint within which everything else operates. 5. **Match the strategy to the regime** — Do not run a mean-reversion strategy in a strong trend, and do not run a breakout strategy in a range. This is the entire point of the exercise. 6. **Re-assess on a schedule, not on impulse** — Weekly. Regimes change slowly; reacting to daily noise is the failure mode this is meant to prevent.

Best Practices

• The most common cause of a strategy failing is a regime change, not a broken strategy. Diagnose the environment before you rewrite the code.
• Volatility clusters. A high-volatility day is followed by more high-volatility days far more often than chance. Expanding volatility means reduce size, immediately.
• Credit markets lead equity markets more often than the reverse. Widening high-yield spreads with equities at a high is a disagreement worth respecting.
• No regime lasts. The strategy that has worked for eighteen months is the one most at risk when conditions change, and it will have attracted the most capital by then.
• Do not predict the regime change. Observe it and adapt. Positioning for a change that has not happened is expensive.
• Reduce exposure when the regime is ambiguous. Ambiguity is itself information.

Examples

**A regime read across dimensions:**

def regime(market: MarketData) -> RegimeReport:
    spx, vix = market.spx, market.vix

    trend = (
        "trending_up"   if spx.close.iloc[-1] > spx.ma200.iloc[-1] and spx.ma200.diff(20).iloc[-1] > 0 else
        "trending_down" if spx.close.iloc[-1] < spx.ma200.iloc[-1] and spx.ma200.diff(20).iloc[-1] < 0 else
        "range"
    )

    vix_now, vix_avg = vix.close.iloc[-1], vix.close.rolling(63).mean().iloc[-1]
    vol = (
        "expanding" if vix_now > vix_avg * 1.25 else
        "contracting" if vix_now < vix_avg * 0.80 else
        "stable"
    )
    vol_level = "low" if vix_now < 15 else "elevated" if vix_now < 25 else "high"

    # Cross-asset risk appetite: does the credit market agree with the equity market?
    credit_stress = market.hy_spread.iloc[-1] > market.hy_spread.rolling(126).quantile(0.75).iloc[-1]
    defensive_leading = (
        market.sector_returns_63d[["utilities", "staples", "healthcare"]].mean()
        > market.sector_returns_63d[["tech", "discretionary", "financials"]].mean()
    )

    return RegimeReport(
        trend=trend,
        volatility=f"{vol_level}_{vol}",
        risk_appetite="off" if (credit_stress or defensive_leading) else "on",

        favors=STRATEGY_FIT[(trend, vol)],
        punishes=STRATEGY_ANTI_FIT[(trend, vol)],
    )


STRATEGY_FIT = {
    ("trending_up", "contracting"):  ["trend-following", "momentum", "breakouts"],
    ("trending_up", "expanding"):    ["reduce size", "tighten stops"],
    ("range", "contracting"):        ["mean-reversion", "premium selling"],
    ("range", "expanding"):          ["cash", "wait"],
    ("trending_down", "expanding"):  ["cash", "defensive", "hedges"],
}

**A read that explains a strategy's failure:**

Regime, as of this week:

Trend         : range. SPX has oscillated in a 6% band for 11 weeks. The 200-day
                is flat.
Volatility    : elevated and expanding. VIX 24, up from a 63-day average of 17.
Risk appetite : OFF. High-yield spreads at the 82nd percentile of the last six
                months. Utilities and staples leading over 63 days.
Macro         : 10-year yield +80bp over the quarter. Dollar strengthening.

Favors  : mean-reversion at the range extremes, reduced size, cash.
Punishes: breakout strategies (every breakout has failed — that IS the range),
          trend-following (there