Sector Positioning Signal

Composite sector‑level signal measuring speculative crowding, hedger pressure, and unwind risk across major futures sectors (Energy, Metals, Ags, FX, Rates). Derived from enriched CoT positioning.

Abstract

This signal aggregates market‑level Commitments of Traders (CoT) positioning into macro sectors. It identifies whether a sector is experiencing broad speculative crowding, systemic hedger pressure, or heightened unwind risk. Using market‑level flags (crowded_long, crowded_short, hedger_pressure) and 4‑week flow dynamics, the model classifies sector‑wide stress and positioning imbalance.

1. Inputs

df_cot_signals — unified enriched CoT dataset with market‑level signals.
Market classifications: Commodity_Class, Classification, Sub‑Classification, Commodity_SubClass.
Market flags: crowded_long, crowded_short, hedger_pressure.
Flow_4w: 4‑week speculative flow (directionally used to assess unwind risk).

2. Sector Classification Logic

Markets are mapped into macro sectors using metadata first and name‑based heuristics second. The sectors include:

Energy
Metals (Precious & Base)
Ags (Grains, Softs, Livestock)
FX
Rates
Other

Classification uses string‑matching on both metadata and exchange‑provided market names when metadata is missing or inconsistent.

3. Preparation & Flag Construction

The base dataset is cleaned, sectors assigned, and missing flags defaulted to False. Unwind risk is defined as:

UnwindRisk = (crowded_long ∧ Flow_4w < 0) ∨ (crowded_short ∧ Flow_4w > 0)

This captures conditions where positioning is both extreme and moving against traders, signalling elevated squeeze or unwind probability.

4. Sector Aggregation

For each sector and report date, the following aggregates are computed:

n_markets: number of unique markets.
spec_crowded_long_share: share of markets flagged crowded long.
spec_crowded_short_share: share of markets flagged crowded short.
hedger_pressure_share: share of markets with hedger pressure.
unwind_risk_share: share of markets with unwind risk.
avg_spec_zscore: mean speculative z‑score across markets.
avg_hedger_zscore: mean hedger z‑score across markets.

5. Broad Macro Flags

Sector‑wide stress conditions are triggered using static thresholds:

broad_spec_crowding: spec_crowded_long_share ≥ 0.40
broad_hedger_pressure: hedger_pressure_share ≥ 0.40
broad_unwind_risk: unwind_risk_share ≥ 0.30

These thresholds identify when positioning stress is not isolated but systemic within a sector.

6. Latest Snapshot

The most recent reporting date is extracted, and sector aggregates for that date form the latest positioning table.

7. Implementation (Python)

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

# Classification and flags
df_sector["Sector"] = df_sector.apply(_classify_sector, axis=1)
flow = pd.to_numeric(df_sector.get("Flow_4w", np.nan), errors="coerce")
df_sector["unwind_risk"] = (
    (df_sector["crowded_long"] & (flow < 0)) |
    (df_sector["crowded_short"] & (flow > 0))
)

# Aggregation
group_cols = ["Report_Date", "Sector"]
df_sector_positioning = (
    df_sector.groupby(group_cols).agg(
        n_markets=("Market_and_Exchange_Names", "nunique"),
        spec_crowded_long_share=("crowded_long", "mean"),
        spec_crowded_short_share=("crowded_short", "mean"),
        hedger_pressure_share=("hedger_pressure", "mean"),
        unwind_risk_share=("unwind_risk", "mean"),
        avg_spec_zscore=("Spec_zscore", "mean"),
        avg_hedger_zscore=("Hedger_zscore", "mean")
    ).reset_index()
)

# Threshold flags
BLS, BH, BU = 0.40, 0.40, 0.30
df_sector_positioning["broad_spec_crowding"] = df_sector_positioning["spec_crowded_long_share"] >= BLS
df_sector_positioning["broad_hedger_pressure"] = df_sector_positioning["hedger_pressure_share"] >= BH
df_sector_positioning["broad_unwind_risk"] = df_sector_positioning["unwind_risk_share"] >= BU

# Latest snapshot
latest_date = df_sector_positioning["Report_Date"].max()
df_sector_positioning_latest = df_sector_positioning[

8. Interpretation

Broad Spec Crowding: reveals directional consensus that can reverse sharply when flows shift.
Hedger Pressure: highlights defensive commercial positioning, often preceding volatility.
Unwind Risk: identifies when stretched positioning meets adverse flow, increasing squeeze probability.

9. Limitations

Sector mapping depends on exchange naming conventions, which may contain inconsistencies.
Static thresholds may require periodic recalibration to reflect structural regime changes.
Flow metrics can be noisy for low-liquidity contracts, reducing signal reliability.

10. Practical Use Cases

Detect sectors at risk of rapid position unwinds or squeezes.
Monitor systemic crowding across futures markets to inform macro-level risk management.
Combine with trend, volatility, or macro composites for tactical allocation timing.