parabolic-short-trade-planner

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 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).

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.