Correlation Hedge

correlation-hedge

Purpose

Two linked jobs:

1. Measure — historical Pearson correlation and OLS regression β between any two futures symbols. Uses log-return series over a lookback window.
2. Size — turn an open position's dollar exposure into a dollar-neutral hedge proposal. Use β and value-per-point math.

Environment routing

Symbol/market data only — no account binding. A sibling skill's account resolution might already pin the session to demo (simulation) or live. If so, stay on that same MCP server.

MCP tools used

Tool names below are bare. The NinjaTrader MCP server provides them. Your client adds its own prefix. See AGENTS.md at the repo root.

• market_history — aligned bar closes for two symbols over the lookback window
• my_portfolio — open positions for hedge-mode input (qty, netPrice). Prune with fields=["positions[].symbol","positions[].netPos","positions[].netPrice","account.netLiq"].
• market_snapshot — valuePerPoint, tickSize, current mark for the hedge candidate. Prune with fields=["snapshots[].symbol","snapshots[].valuePerPoint","snapshots[].tickSize","snapshots[].lastPrice"].
• search_contracts — resolve bare product codes to contract symbols for both legs
• user_profile — discover available account names when the account is unknown

Workflow

1. Classify the ask

2. Gather aligned history

Pick a lookback (default 30–90 sessions for cross-family, 5–30 sessions for within-family):

market_history(symbol=SYM_A, barType="Daily", barSize=1, from=<ISO-8601>, to=<ISO-8601>)
market_history(symbol=SYM_B, barType="Daily", barSize=1, from=<ISO-8601>, to=<ISO-8601>)

For hourly bars use barType="Minute", barSize=60. from requires to. For "last N bars", pass count= instead of a range.

Bar-size choice matters:

• Daily bars: most stable correlation, regime-level signal
• 60-minute bars: captures intra-session drift, more noise
• Avoid sub-hour for cross-product hedging — microstructure noise dominates the signal

3. Compute correlation + β

Save the two symbols' market_history responses to a file (shape {"series": {SYM_A: [...], SYM_B: [...]}}) and pass its path with --file. Never re-type or inline the bar arrays into the command.

python3 scripts/correlation.py --file series.json --window 30

The script aligns bars on shared timestamps, computes log returns, then reports:

• pearson_r — linear correlation of returns (−1 to 1)
• beta_B_on_A — OLS slope of B's returns regressed on A's. Use A as the position symbol, B as the hedge candidate.
• vol_A, vol_B — return stdev of each
• rolling (optional, with --window) — current r vs rolling mean/stdev + regime flag (tightening/loosening/stable)

Compare to priors in references/hedge-patterns.md. If the live r is way outside the typical range for that pair, call it out. The cause may be a regime shift, a data quality issue, or a rare moment.

4. Size the hedge (if asked)

Account resolution. Position data (qty, direction, basis) comes from my_portfolio(account=<name>) — the MCP requires account=. If the account name is unknown, call user_profile() first and pull accounts[].name to discover available accounts.

Pull the position + hedge candidate market snapshot, then:

python3 scripts/hedge_sizing.py --file hedge_input.json

where hedge_input.json holds the position + hedge candidate:

{
  "position": {
    "symbol": "ESU6", "direction": "long", "qty": 4,
    "price": 7170.0, "value_per_point": 50.0
  },
  "hedge_candidate": {
    "symbol": "NQU6", "price": 22640.0, "value_per_point": 20.0,
    "beta": 2.59
  }
}

Output fields:

• hedge.side — Buy or Sell. Opposite of the position side when β > 0 (typical); same side when β < 0 (stock/bond hedge).
• hedge.raw_qty — unrounded contracts (e.g., 3.4 NQ)
• hedge.rounded_qty — whole contracts at --round mode (nearest/up/down)
• hedge.residual_pct — |raw - rounded| / raw × 100
• coverage.coverage_pct — how much of the $ exposure the rounded hedge actually covers
• coverage.residual_exposure_dollars — uncovered $ per 1%

When rounded_qty = 0 or residual_pct > 30%, the script emits a note that suggests a micro (MES/MNQ/MCL/MGC) for finer sizing. Surface that verbatim.

5. Multi-candidate hedge comparison

Run hedge_sizing.py N times with different candidates. Rank by coverage_pct near 100% with low residual_pct. Pick the best trade-off between coverage and rounding precision.

This skill ships no basket.py script. Loop over the candidates instead:

for cand in 'NQ 22640 20 2.59' 'MES 7170 5 1.0' 'YM 43500 5 0.9' 'RTY 2400 50 0.7'; do
  # build JSON, pipe to hedge_sizing.py, collect coverage_pct
done

6. Narrate — with regime context

Pair the sizing output with the live correlation, so the user sees the assumption behind it. See references/regime-caveat.md for framing patterns.

Output idioms

Correlation-only:

"ES/NQ over last 16 daily bars: Pearson r 0.99 (very tight), β_NQ_on_ES = 2.59 (NQ moves ~2.6× ES in log returns over this window). Rolling 8-bar window shows r stable at 0.99. This is slightly above the typical 0.85-0.95 range — tight regime."

Hedge proposal:

"You're long 4 ES @ 7170 ($14,340 per 1% ES move).

Short 1 NQ at 22640 covers 82% ($11,739 offset). Residual $2,601 long ES. One-NQ rounds down from raw 1.22; a second NQ would over-hedge. Consider 1 NQ + 2 MNQ for finer coverage.

Math assumes ES/NQ correlation holds (0.99 tight right now, but pairs can decouple in regime shifts). This isn't a guaranteed protection — it's a dollar-neutral offset conditional on the correlation staying similar."

Portfolio cross-check:

"You're long 4 ES AND long 2 NQ. With β=2.59, the 2 NQ longs are effectively +5.2 ES-equivalents of additional exposure — you're 9.2 ES-equivalents long the market, not 6. If that's not intended, consider flattening one or adjusting size."

Disambiguation

• vs contract-intel: contract-intel handles SAME-underlying pairs (ES vs MES — fungible siblings). correlation-hedge handles CROSS-underlying (ES vs NQ, CL vs NG). Different math, different use cases.
• vs market-context's cross_symbol.py: that script does INTRADAY relative strength (who led today). correlation-hedge does HISTORICAL correlation over a lookback window. Distinct semantics — do not mix them.
• vs scale-manager: this skill is informational (what is the hedge?). scale-manager converts a hedge decision into exact place_order payloads. Run correlation-hedge first, then route the chosen hedge through scale-manager for the execution plan.
• vs position-watchdog: watchdog reports current position health per-symbol. correlation-hedge reports cross-symbol exposure interactions. They are complementary.

Explicit non-goals

• No order payloads. Informational sizing only.
• No rule endorsement. One computation is not a rule (see references/regime-caveat.md).
• No basket optimization today. Loop over candidates in the skill workflow instead. A future release may add a multi-position optimizer.
• No options hedging. Correlation- and beta-based sizing is not the right model for options. Options need greeks-based hedging instead. If the user asks about an options hedge, redirect to an external tool.
• No event hedging. Tail-risk hedging against specific events (CPI surprise, FOMC shock) is not correlation-sized. Route to event-watch for event-window alert sizing instead.

Resource layout

• scripts/correlation.py — time-aligned Pearson r + OLS β on log returns of two symbols. Supports --window N for rolling r with regime flagging. Requires ≥3 overlapping bars minimum.
• scripts/hedge_sizing.py — dollar-neutral hedge sizing from position + hedge candidate + β. Reports raw/rounded qty, coverage %, residual $ exposure. Flags rounded=0 and residual>30% with micro-contract suggestion. Supports --round nearest|up|down.
• scripts/fixtures/es_nq_synthetic.json — a 17-bar ES+NQ fixture that produces a near-perfect correlation, for smoke testing.
• references/hedge-patterns.md — typical correlation ranges for 7 product families (equity indexes, energy, metals, bonds, currencies, grains, cross-family). Priors only — always validate with live data. Load it when you compare a live r or β to the typical range.
• references/regime-caveat.md — the stationarity-assumption framing. It explains how to narrate a hedge honestly, when to flag a regime shift, and why tail risk matters. Load it when you narrate a hedge recommendation.