Market Regime Detection

Market Regime Detection

Regime labels summarize observable trend and volatility states. They are model outputs with uncertainty, not natural market categories; measure whether they add held-out value to the downstream strategy.

Regime Types

Detection Inputs (all in the free indicator whitelist)

EMA slope candidate — compute over a predeclared horizon and normalize by price or ATR:

ema50_slope_pct = (ema50[-1] - ema50[-5]) / ema50[-5] * 100

Derive positive, negative, and near-zero bands from training data or rolling historical percentiles for the instrument/timeframe; freeze them before evaluation.

BB Width = (bb_upper - bb_lower) / bb_middle * 100 — rolling-100-period percentile:

• > 80th pctl → expansion, supports a trend read
• < 20th pctl → squeeze candidate; no direction or imminent break is implied
• mid-band → ranging

ATR Ratio = atr_14 / rolling_median(atr_14, 20): Use rolling percentiles or training-set thresholds; ATR ratio measures volatility, not whether price is trending or choppy.

BB Inside KC — if bb_lower > kc_lower AND bb_upper < kc_upper, the Bollinger band is inside the Keltner channel → compression. Pair with low BB Width to confirm squeeze.

Donchian Position — (close - donchian_lower) / (donchian_upper - donchian_lower):

• near 0 or 1 with EMA slope agreeing → confirmed trend continuation
• mid-band (0.4–0.6) with flat slope → ranging

Composite Classification

The table is a seed specification only. Do not count correlated inputs as independent agreement or map them directly to confidence/size. Calibrate a classifier, report posterior/calibration if available, otherwise report features plus classified, uncertain, or no trade.

Regime Change Signals

Strategy Selection Matrix

Workflow

1. Pull indicators (get_indicators returns one indicator per call — fetch each separately):

   get_indicators(indicator_code="ema_50",      symbol=<symbol>, exchange=<exchange>, interval=<interval>, count=120)
   get_indicators(indicator_code="bbands",      symbol=<symbol>, exchange=<exchange>, interval=<interval>, count=120)
   get_indicators(indicator_code="atr_14",      symbol=<symbol>, exchange=<exchange>, interval=<interval>, count=120)
   get_indicators(indicator_code="keltner_20",  symbol=<symbol>, exchange=<exchange>, interval=<interval>, count=120)
   get_indicators(indicator_code="donchian_20", symbol=<symbol>, exchange=<exchange>, interval=<interval>, count=120)
   

2. Compute the four inputs in one execute block:

   execute python3 -c "
   import numpy as np
   ema50 = [<ema50 array>]
   bb_lower, bb_upper, bb_middle = [<...>], [<...>], [<...>]
   kc_lower, kc_upper = [<...>], [<...>]
   atr = [<atr_14 array>]
   don_lo, don_hi = [<...>], [<...>]
   close = [<...>]
   
   slope_pct = (ema50[-1] - ema50[-5]) / ema50[-5] * 100
   bb_width  = [(u - l) / m * 100 for u, l, m in zip(bb_upper, bb_lower, bb_middle)]
   bb_width_pctl = sum(1 for w in bb_width if w <= bb_width[-1]) / len(bb_width) * 100
   atr_ratio = atr[-1] / np.median(atr[-20:])
   bb_in_kc  = bb_lower[-1] > kc_lower[-1] and bb_upper[-1] < kc_upper[-1]
   don_pos   = (close[-1] - don_lo[-1]) / (don_hi[-1] - don_lo[-1])
   
   print(f'slope_pct={slope_pct:.3f} bb_width_pctl={bb_width_pctl:.1f} atr_ratio={atr_ratio:.2f} bb_in_kc={bb_in_kc} don_pos={don_pos:.2f}')
   "
   

3. Classify using thresholds frozen from training data. Report label, uncertainty, numeric inputs, data window, and which downstream strategies have validated conditional results.

4. Visualize (optional, only if user-facing report needs it):

   draw_chart_analysis(action="create", drawing={
       "type": "highlight",
       "points": [
           {"time": <regime_start>, "price": <candle_high>},
           {"time": <current_time>, "price": <candle_low>}
       ],
       "options": {"text": "TRENDING bullish (EMA50 slope +0.84%)"}
   })
   

Evidence and Validation

• Treat the setup as a testable hypothesis, not a prediction. Define thresholds, entry, invalidation, and exit before evaluating outcomes.
• Calibrate on the same instrument, venue, session, and timeframe. Use closed candles and a held-out or walk-forward sample; record every variant tried.
• Include spread, fees, slippage, borrow or funding, partial fills, and latency. Reject the setup when net expectancy is not positive or depends on one narrow parameter.
• Return observed inputs, missing data, cost assumptions, entry, invalidation, exit, and a valid, watch, or no-trade status.
• Research basis: Ang & Bekaert supports regime-dependent volatility and correlation, while backtest-overfitting research cautions against selecting thresholds after repeated trials.

Key Rules

• Use regime detection only when it improves the downstream strategy versus a no-regime baseline.
• Treat transitional/uncertain states according to predeclared policy; never map them to arbitrary size.
• The inputs are correlated; feature count does not prove accuracy or reduce false calls.
• Model higher/lower-timeframe conflicts explicitly; the higher timeframe does not automatically override.
• Compute EMA slope via execute; do NOT call ADX / DMI / Supertrend (API-billed, not in the free whitelist)

Related Skills

• multi-timeframe-analysis — regime on HTF sets context; MTF refines entries within the regime
• bollinger-bands — BB Width is a key detection input; BB strategies adapt to detected regime
• trailing-stop — trail method per regime (TRENDING: Donchian/chandelier; RANGING: structural; SQUEEZE: don't enter)