Hedger Pressure

Cross‑market commercial hedger pressure indicator, capturing supply expansion stress and physical shortage signals using hedger z‑scores and hedger net open interest.

Abstract

This signal measures commercial hedger sentiment across the futures markets by aggregating deep‑short and deep‑long hedger positioning. Using hedger z‑scores, it identifies when producers or commercial entities are heavily short (indicating supply expansion or bearish forward pricing) or heavily long (indicating physical shortage or inventory stress). These breadth measures are normalised with robust z‑scores and mapped into intuitive supply/shortage pressure regimes.

1. Inputs

df_cot_signals — unified enriched CoT dataset.
Hedger_zscore — z‑score of hedger net positioning.
Hedger_Net_%OI — optional mean hedger net % of open interest.
Market_and_Exchange_Names — used for market counts.

2. Per‑Market Hedger Pressure Flags

Hedger pressure is defined using hedger z‑scores:

z ≤ −1.0 → hedger_deep_short (supply expansion / bearish pricing)

z ≥ +1.0 → hedger_deep_long (physical shortage / supply stress)

A generic hedger_pressure flag is created if missing, defaulting to deep‑short classification.

3. Daily Aggregation

For each CoT reporting date, the model aggregates hedger pressure metrics:

hedger_deep_short_count — number of markets where hedgers are heavily short.
hedger_deep_long_count — number of markets where hedgers are heavily long.
avg_hedger_net_oi — mean hedger net share of open interest.
avg_hedger_zscore — average hedger z‑score across all markets.

Shares are derived to make the signal comparable across time:

deep\_short\_share = hedger\_deep\_short\_count / n\_markets

deep\_long\_share = hedger\_deep\_long\_count / n\_markets

4. Normalisation (Robust Z‑Scores)

To minimise sensitivity to outliers and structural shifts, robust z‑scores are applied to hedger pressure shares:

z\_{rob}(X) = \dfrac{X - median(X)}{1.4826 \cdot MAD(X)}

This method strengthens signal stability when market composition or liquidity changes over time.

5. Regime Labels

Each robust z‑score is mapped into intuitive hedger regimes:

z ≥ 1.0 → High Supply Expansion Pressure

0.5 ≤ z < 1.0 → Moderate Supply Expansion

z < 0.5 → Normal

The same thresholds are used for shortage pressure, replacing the labels with High Shortage / Stress and Moderate Shortage / Stress.

6. Implementation (Python)

df_hp = df_cot_signals.copy()
df_hp["Report_Date"] = pd.to_datetime(df_hp["Report_Date"], errors="coerce")

# Per‑market hedger flags
HEDGER_DEEP_SHORT_Z = -1.0
HEDGER_DEEP_LONG_Z  =  1.0

hz = pd.to_numeric(df_hp["Hedger_zscore"], errors="coerce")

df_hp["hedger_deep_short"] = hz <= HEDGER_DEEP_SHORT_Z
df_hp["hedger_deep_long"]  = hz >= HEDGER_DEEP_LONG_Z

if "hedger_pressure" not in df_hp.columns:
    df_hp["hedger_pressure"] = df_hp["hedger_deep_short"]

# Daily aggregation
df_hedger_pressure_daily = (
    df_hp.groupby("Report_Date").agg(
        n_markets=("Market_and_Exchange_Names", "nunique"),
        hedger_deep_short_count=("hedger_deep_short", "sum"),
        hedger_deep_long_count=("hedger_deep_long", "sum"),
        avg_hedger_net_oi=("Hedger_Net_%OI", "mean"),
        avg_hedger_zscore=("Hedger_zscore", "mean"),
    ).reset_index()
)

df_hedger_pressure_daily["n_markets"] = df_hedger_pressure_daily["n_markets"].replace(0, np.nan)

df_hedger_pressure_daily["hedger_deep_short_share"] = (
    df_hedger_pressure_daily["hedger_deep_short_count"] / df_hedger_pressure_daily["n_markets"]
)

df_hedger_pressure_daily["hedger_deep_long_share"] = (
    df_hedger_pressure_daily["hedger_deep_long_count"] / df_hedger_pressure_daily["n_markets"]
)

# Robust z‑score helper
def _robust_z(series: pd.Series) -> pd.Series:
    x = pd.to_numeric(series, errors="coerce").

7. Interpretation

Supply Expansion Pressure — hedgers heavily short, signalling expectations of increased production or weaker forward prices.
Shortage / Stress Pressure — hedgers heavily long, indicating tightening physical supply or inventory stress.
Normal — commercial positioning is within typical historical bounds.

8. Limitations

Hedger behaviour varies across asset classes, making cross-sector comparison imperfect.
Static thresholds may require periodic recalibration.
Missing or sparse Hedger_Net_%OI values reduce signal precision.

9. Practical Use Cases

Identifying early warnings of physical market tightness or oversupply.
Enhancing allocation models with a fundamental commercial-positioning overlay.
Cross-checking speculative crowding signals for confirmation or divergence.