regime-overlay

Regime Overlay — Triple-Confirmation Gate

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.

Why a separate primitive

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:

• Same Donchian-breakdown short on BTC 4h, no regime gate → +12.85% over 162d (looks great)
• Same strategy, on a different window (the rangy second half) → -4.32% (edge gone)
• Same strategy with triple-confirmed regime gate → +6.69% with 0% drawdown, fires zero trades in the chop window

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.

The three confirmations

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 > +0.06
• ADX(14) > 25 (same — ADX is direction-agnostic)
• close.pct_change(30) > +0.10

Implementation

def 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