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parabolic-short-trade-planner

tradermonty/claude-trading-skills

Screen US equities for parabolic exhaustion patterns and generate conditional pre-market short plans with intraday trigger detection.

What is parabolic-short-trade-planner?

A three-phase short-trade planning system for US equities that identifies parabolic exhaustion patterns, generates pre-market conditional plans with explicit borrow/SSR gating, and monitors intraday 5-minute triggers. Use this when building Qullamaggie-style parabolic short watchlists and need to evaluate entry signals against live market data without sending orders.

  • Phase 1: Screen EOD bars for parabolic exhaustion using 5-factor scoring (MA extension, acceleration, volume climax, range expansion, liquidity) with A/B/C/D grades
  • Phase 2: Generate pre-market conditional plans per candidate with three trigger strategies (ORL break, first-red 5-min, VWAP fail) and explicit borrow/SSR/manual-confirmation gating
  • Phase 3: Monitor intraday 5-minute bars via one-shot FSM to detect trigger fires, resolve concrete share counts, and emit entry/stop/size when triggered
  • Earnings-aware screening with configurable blackout windows and recent-catalyst warnings
  • Idempotent intraday monitoring—re-runs during the same minute produce identical state for replay determinism

How to install parabolic-short-trade-planner

npx skills add https://github.com/tradermonty/claude-trading-skills --skill parabolic-short-trade-planner
Prerequisites
  • Python 3.7+
  • FMP_API_KEY environment variable (for Phase 1 EOD bars and company profile)
  • ALPACA_API_KEY and ALPACA_SECRET_KEY (for Phase 2 borrow checks and Phase 3 live bars; optional—falls back to manual broker adapter)
  • Install requirements.txt dependencies
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How to use parabolic-short-trade-planner

  1. 1.Phase 1: Run screen_parabolic.py with --mode safe_largecap (or classic_qm for small-cap) and --as-of YYYY-MM-DD to generate watchlist JSON and Markdown
  2. 2.Phase 2: Run generate_pre_market_plan.py with the Phase 1 JSON, --account-size, and --risk-bps to emit pre-market plans with three entry strategies per candidate
  3. 3.Phase 3: During US regular session, run monitor_intraday_trigger.py every 60 seconds (first 30 min) then every 5 minutes to detect trigger fires and resolve shares
  4. 4.Review plan_status (actionable vs watch_only), blocking_manual_reasons, and advisory_manual_reasons before manual entry at your broker
  5. 5.Confirm borrow/locate, SSR state, and intraday margin rules with your broker before pulling the trigger on any short entry

Use cases

Good for
  • Build a daily parabolic short watchlist from S&P 500 or custom CSV and filter by grade threshold
  • Translate watchlist candidates into pre-market trade plans with explicit borrow inventory and SSR Rule 201 state checks
  • Monitor live 5-minute bars during regular session to detect plan trigger fires and resolve concrete entry/stop/share counts
  • Audit blocking vs advisory manual-confirmation reasons before placing orders at Alpaca
  • Test screening and trigger logic against historical fixtures without live API calls
Who it's for
  • Short-focused swing traders using parabolic exhaustion patterns
  • Traders using Alpaca for borrow inventory and market data
  • Quant researchers building Qullamaggie-style watchlists
  • Risk-aware traders who want explicit gating on borrow/SSR before entry

parabolic-short-trade-planner FAQ

Does this skill send orders?

No. Phase 3 emits a triggered state with concrete entry/stop/share count, but the trader must manually confirm borrow/SSR/margin and fire the order at their broker.

What market data sources does it support?

Phase 1 uses FMP for EOD bars and company profile. Phase 3 uses Alpaca live 5-minute bars (or JSON fixtures for testing). Phase 2 checks Alpaca short inventory or falls back to ManualBrokerAdapter if credentials are missing.

Can I use this for long-side momentum screening?

No. This skill is short-specific and uses parabolic exhaustion patterns. Use vcp-screener or canslim-screener for long-side momentum.

What happens if I don't have Alpaca credentials?

Phase 1 and Phase 2 still work. Phase 2 marks all candidates as watch_only with borrow_inventory_unavailable. Phase 3 requires Alpaca credentials for live 5-minute bars, but you can test with --bars-source fixture.

Is Phase 3 idempotent?

Yes. Each run replays the full session bars from open to now_et, so re-running during the same minute produces identical state. prior_state is used only for diff display, never to advance the FSM.

Full instructions (SKILL.md)

Source of truth, from tradermonty/claude-trading-skills.


name: parabolic-short-trade-planner description: Screen US equities for parabolic exhaustion patterns and generate conditional pre-market short plans, then evaluate intraday trigger fires from live 5-min bars. Phase 1 daily 5-factor scorer (MA extension / acceleration / volume climax / range expansion / liquidity), Phase 2 per-candidate plans for ORL break / first-red 5-min / VWAP fail with explicit borrow / SSR / manual-confirmation gating, Phase 3 one-shot intraday FSM that detects trigger fires and resolves concrete share counts. Covers Phase 1 + Phase 2 + Phase 3.

Overview

Generate Qullamaggie-style Parabolic Short watchlists and conditional pre-market plans for US equities. The skill never sends orders. It emits JSON + Markdown that a human reviews against their broker before entry.

Three phases:

  • Phase 1 (screen_parabolic.py): pulls EOD bars + company profile from FMP, applies hard invalidation rules (mode-aware), scores survivors on 5 factors (weights 30/25/20/15/10), and assigns A/B/C/D grades.
  • Phase 2 (generate_pre_market_plan.py): takes the Phase 1 JSON, filters by --tradable-min-grade (default B), checks Alpaca short inventory (or ManualBrokerAdapter), evaluates SEC Rule 201 SSR state from the inherited prior-day close, and renders three trigger plans per candidate.
  • Phase 3 (monitor_intraday_trigger.py): reads the Phase 2 plan, fetches 5-min bars (Alpaca live or fixture), walks each plan's FSM forward by one step, persists per-plan state, and writes an intraday_monitor JSON with state, entry_actual, stop_actual, and shares_actual (when triggered). One-shot — trader runs it every 1–5 min via watch or cron; replay-deterministic so re-runs are byte-identical.

When to Use

Invoke this skill when the user wants to:

  • Build a daily Parabolic Short watchlist from S&P 500 (or a custom CSV).
  • Translate a watchlist into pre-market trade plans with explicit borrow / SSR / state-cap gating.
  • Audit a candidate's blocking vs advisory manual-confirmation reasons before placing an order at Alpaca.

Do NOT invoke for:

  • Long-side momentum screening — use vcp-screener or canslim-screener.
  • 1-minute / sub-minute intraday signals — Phase 3 evaluates 5-min bars only.
  • Live order routing — this skill is detection-only by design; Phase 3 emits a triggered state with concrete entry/stop/share count, but the trader fires the order manually.

Workflow

Phase 1 — daily screener

  1. Confirm FMP_API_KEY is set (env var or --api-key).
  2. Run with the safer-by-default mode:
    python3 skills/parabolic-short-trade-planner/scripts/screen_parabolic.py \
      --mode safe_largecap --as-of 2026-04-30 --output-dir reports/
    
  3. Inspect reports/parabolic_short_<date>.md — the watchlist is grouped by grade (A→D).
  4. Promote interesting names to Phase 2.

For small-cap blow-offs, switch to --mode classic_qm (looser market cap and ADV floors, higher 5-day ROC threshold).

For testing without the API, run --dry-run --fixture <path> against a JSON fixture (one is shipped at scripts/tests/fixtures/dry_run_minimal.json).

Phase 2 — pre-market plan generator

  1. Optional: set ALPACA_API_KEY / ALPACA_SECRET_KEY for live borrow checks. Without them the planner falls back to ManualBrokerAdapter, which marks every candidate as borrow_inventory_unavailable / plan_status: watch_only.
  2. Run:
    python3 skills/parabolic-short-trade-planner/scripts/generate_pre_market_plan.py \
      --candidates-json reports/parabolic_short_2026-04-30.json \
      --account-size 100000 --risk-bps 50 --output-dir reports/
    
  3. Output: reports/parabolic_short_plan_<date>.json. Each plan contains three entry plans (5min ORL break, first red 5-min, VWAP fail) with entry_hint / stop_hint formula strings (no baked-in shares — the trader computes shares at trigger time from the shares_formula).

Phase 3 — intraday trigger monitor

  1. Confirm ALPACA_API_KEY / ALPACA_SECRET_KEY are set (Phase 3 uses Alpaca market data; data.alpaca.markets works for both paper and live accounts).
  2. During US regular session, run one-shot per cadence — typical is every 60 s during the first 30 min, then every 5 min:
    python3 skills/parabolic-short-trade-planner/scripts/monitor_intraday_trigger.py \
      --plans-json reports/parabolic_short_plan_2026-05-05.json \
      --bars-source alpaca \
      --state-dir state/parabolic_short/ \
      --output-dir reports/
    
    Or wrap in watch -n 60 'python3 ...' / cron.
  3. Output: reports/parabolic_short_intraday_<date>.json lists every monitored plan with state (armed / triggered / invalidated / FSM-specific), bar-derived transition timestamps, and size_recipe_resolved (concrete shares_actual) when triggered.
  4. For testing without the API, use --bars-source fixture --bars-fixture <path> against a JSON fixture (scripts/tests/fixtures/intraday_bars/).

Phase 3 trigger detection is not an order instruction. Before any manual short entry, confirm borrow/locate availability, SEC Rule 201 SSR state, broker short-sale controls, and the broker's current intraday margin or day-trading controls. FINRA replaced the old pattern-day-trader day-count and $25,000 minimum-equity requirements with intraday margin standards effective 2026-06-04, with broker phase-in allowed through 2027-10-20.

Phase 3 is idempotent: each run replays the full session bars from open up to now_et (or --now-et override), so re-running during the same minute produces the same state. prior_state is used only for diff/notification display; it never advances the FSM.

Reviewing a plan before entry

Read three top-level fields per ticker:

  • plan_status: actionable (manual gates can be cleared) or watch_only (hard blockers — borrow unavailable or SSR active).
  • blocking_manual_reasons: must all be resolved before pulling the trigger.
  • advisory_manual_reasons: heads-up only, e.g. manual_locate_required (always set), warning:too_early_to_short, warning:recent_earnings_catalyst (last earnings within --earnings-catalyst-window-days, default 10 trading days — flag the move as event-driven rather than pure technical blow-off).

Earnings-aware screening

Phase 1 fetches the FMP earnings calendar once per run (single call, not per-symbol) and emits two earnings-aware checks:

  • --exclude-earnings-within-days (default 2 calendar days, forward) — hard invalidation when next earnings is within the window. Matches the legacy earnings_blackout_days semantic.
  • --earnings-catalyst-window-days (default 10 trading days, backward) — soft warning recent_earnings_catalyst when last earnings is within the window. Routes to Phase 2 as an advisory manual reason without forcing trade_allowed_without_manual: false.

Per-candidate output exposes last_earnings_date, next_earnings_date, trading_days_since_earnings (TRADING days), earnings_within_days (CALENDAR days, forward), earnings_blackout_days (configured threshold), and earnings_in_blackout_window. The legacy earnings_within_2d is kept for backward compatibility.

Top-level dates: as_of is the planning date (Phase 2 contract — never mutate); run_date mirrors it; market_data_as_of is the latest bar date used for technical metrics (differs from as_of on weekend runs).

Exchange Calendar and Replay

Install requirements.txt before running the planner. Phase 1 --as-of uses strict YYYY-MM-DD, filters bars beyond that ceiling, and counts earnings age with XNYS sessions. Phase 3 uses actual holidays and early closes; the close boundary is exclusive. Historical dates are accepted only with Phase 1 --dry-run fixture data; live universe and profile endpoints are not PIT and therefore fail closed for a non-current --as-of.

Output Format

Phase 1 JSON: parabolic_short_<as_of>.json (schema_version 1.0). Phase 2 JSON: parabolic_short_plan_<as_of>.json (schema_version 1.0). Phase 3 JSON: parabolic_short_intraday_<as_of>.json (schema_version 1.0, phase = intraday_monitor). The contract is pinned by tests/test_schema_contract.py plus tests/test_monitor_intraday_smoke.py for Phase 3.

Resources

  • references/parabolic_short_methodology.md — Qullamaggie's 3-trigger framework and exhaustion signals.
  • references/short_invalidation_rules.md — mode-aware exclusion rules.
  • references/short_risk_management.md — Rule 201, ETB vs HTB, locate.
  • references/intraday_trigger_playbook.md — detail on each trigger type, the FSM transitions Phase 3 implements, and same-bar tie-break semantics.
  • references/broker_capability_matrix.md — what each broker exposes through its API for short inventory.