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ib-collar

Generate tactical collar strategy reports for protecting PMCC positions through earnings or high-risk events. Requires TWS or IB Gateway running locally.

インストール方法を見る

含まれるファイル(3)

  • SKILL.md4.0 KB
  • scripts/collar.py1.1 KB
  • templates/markdown-template.md3.7 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

IB Tactical Collar

Generate a tactical collar strategy report for protecting PMCC positions through earnings or high-risk events.

IB Connection

TWS or IB Gateway must be running locally with API enabled:

  • Paper trading — port 7497
  • Live trading — port 7496
  • IB_PORT env var — default port when --port is omitted (e.g. IB_PORT=4001 for a Gateway container). Precedence: --port flag > IB_PORT > built-in default. Set it in the shell or a .env file.

Port fallback: If the configured port fails, automatically retry on the other port. If the retry succeeds, save to memory which account type worked (live/paper) and reuse it for all IB skill calls in this and future sessions — until the user explicitly asks for the other account. If both ports fail, ask the user to verify that TWS or IB Gateway is running with API access enabled.

Instructions

Step 1: Gather Data

uv run python scripts/collar.py SYMBOL [--port PORT] [--account ACCOUNT]

The script returns JSON to stdout with all position and scenario data.

Step 2: Format Report

Read templates/markdown-template.md for formatting instructions. Generate a markdown report from the JSON data and save to sandbox/.

Step 3: Report Results

Present key findings to the user: recommended put protection, cost/benefit, and the saved report path.

Arguments

  • SYMBOL - Stock symbol to analyze (must be in portfolio)
  • --port - IB port (default: 7497 for paper trading)
  • --account - Specific account ID (optional, searches all accounts)

JSON Output

The script returns JSON with these key fields:

  • symbol, current_price - Basic info
  • long_strike, long_expiry, long_qty, long_cost - LEAPS position
  • short_positions - List of short calls
  • is_proper_pmcc, short_above_long - PMCC health flags
  • earnings_date, days_to_earnings - Earnings timing
  • put_analysis - List of put scenarios with costs and P&L under gap up/flat/down
  • unprotected_loss_10, unprotected_loss_15, unprotected_gain_10 - LEAPS risk without collar
  • volatility - Historical volatility data

Report Sections

  1. Position Summary: Current PMCC structure (long calls, short calls)
  2. PMCC Health Check: Is structure proper (short > long strike) or broken?
  3. Earnings Risk: Next earnings date and days until event
  4. Put Duration Analysis: Comparison of short vs medium vs long-dated puts
  5. Collar Scenarios: Gap up, flat, gap down outcomes with each put duration
  6. Cost/Benefit Analysis: Insurance cost vs protection value
  7. Implementation Timeline: Step-by-step checklist with dates
  8. Recommendation: Optimal put strike and expiration

Key Concepts

Proper PMCC Structure:

  • Long deep ITM LEAPS call
  • Short OTM calls ABOVE long strike
  • No additional margin required for collar

Broken PMCC Structure:

  • Long call is now OTM (after crash)
  • Short calls BELOW long strike require margin
  • Collar still works but margin implications exist

Tactical Collar:

  • Buy protective puts ONLY before high-risk events (earnings)
  • Sell puts after event passes
  • Balances income generation with crash protection

Put Duration Trade-offs:

  • Short-dated: Cheaper, more gamma, but zero salvage on gap up
  • Medium-dated (2-4 weeks): Best balance of cost, gamma, and salvage
  • Long-dated: Preserves value on gap up, but expensive and less gamma

Example Usage

# Analyze NVDA position (defaults to paper port 7497)
uv run python scripts/collar.py NVDA

# Analyze specific account
uv run python scripts/collar.py AMZN --account U790497

# Use paper trading port instead
uv run python scripts/collar.py NVDA --port 7497

Timezone

All timestamps and time-based calculations must use the America/New_York timezone. All JSON output must include generated_at (NY time string) and data_delay fields.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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