CoT Early Warning Index

A cross‑market instability signal detecting extremes, crowding, squeeze/exhaustion pressure, and broad risk accumulation in futures positioning.

Abstract

The Early Warning Index aggregates multiple instability dimensions across global futures markets: extreme positioning, crowded trades, and squeeze/exhaustion risks. These elements are normalised and combined to identify elevated stress conditions, signalling the potential for volatility clusters, forced unwinds, and cross‑asset regime shifts.

1. Inputs

df_cot_signals — enriched unified CoT dataset.
Spec_zscore, Hedger_zscore — primary z‑score measures.
crowded_long / crowded_short — crowding flags.
squeeze_risk / exhaustion_risk — tactical reversal pressure.
Market_and_Exchange_Names — breadth denominator.

2. Extreme Positioning Flags

Extreme conditions are identified using ±2σ thresholds:

spec_extreme = |Spec_zscore| ≥ 2

hedger_extreme = |Hedger_zscore| ≥ 2

These identify statistically significant deviations indicative of stress, imbalance, or unsustainable positioning.

3. Daily Aggregation

Counts and shares are computed across all markets:

spec_extreme_count
hedger_extreme_count
any_extreme_count
crowded_long_count / crowded_short_count
squeeze_count / exhaustion_count

All counts are normalised into breadth shares by dividing by the number of active markets.

4. Normalisation

A robust z‑score is applied to each breadth measure to create stable, comparable indicators:

z = (X − median(X)) / (1.4826 × MAD)

Robust statistics protect the signal from fat‑tailed shocks and structural drift in market composition.

5. Composite Early Warning Index

The composite index averages the core instability dimensions:

Early_Warning_Index = mean(z_extremes, z_crowding, z_squeeze, z_exhaustion)

Higher values reflect broad, multi‑factor pressure consistent with rising reversal or volatility risk.

6. Regime Labels

x ≥ 1.0 → High Alert

0.5 ≤ x < 1.0 → Elevated

−0.5 < x < 0.5 → Normal

x ≤ −0.5 → Subdued

High Alert phases often precede VAR spikes, deleveraging cycles, or trend exhaustion.

7. Implementation (Python)

# Prep and validation
spec_z = pd.to_numeric(df_ew["Spec_zscore"], errors="coerce")
hedger_z = pd.to_numeric(df_ew["Hedger_zscore"], errors="coerce")

# Extreme flags
df_ew["spec_extreme"] = spec_z.abs() >= 2.0
df_ew["hedger_extreme"] = hedger_z.abs() >= 2.0
df_ew["any_extreme"] = df_ew["spec_extreme"] | df_ew["hedger_extreme"]

# Daily aggregation
df_cot_early_warning_daily = df_ew.groupby("Report_Date").agg(...)

# Robust z
for name in [...]:
    df_cot_early_warning_daily[f"{name}_z"] = _robust_z(df_cot_early_warning_daily[name])

# Composite index
df_cot_early_warning_daily["Early_Warning_Index"] = \
 df_cot_early_warning_daily[components].mean(axis=1)

8. Interpretation

High Alert — broad instability, positioning extremes, high squeeze/exhaustion probability.
Elevated — increasing risk of rotation, volatility or unwind.
Normal — typical market functioning.
Subdued — markets unusually aligned and calm.

9. Limitations

CoT reporting lag may delay early-warning detection.
Extremes can persist in trending environments.
Thin markets can overweight certain signals.

10. Practical Use Cases

Portfolio VAR adjustments and hedging overlays.
Identifying cross‑asset stress build‑up.
Timing reversals in speculative positioning.
Macro regime transition monitoring.