parabolic-short-trade-planner

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

Confirm
```
FMP_API_KEY
```
is set (env var or
```
--api-key
```
).

Run with the safer-by-default mode:

bash

python3 skills/parabolic-short-trade-planner/scripts/screen_parabolic.py \
  --mode safe_largecap --as-of 2026-04-30 --output-dir reports/

Inspect
```
reports/parabolic_short_<date>.md
```
— the watchlist is grouped by grade (A→D).
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

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
```
.

Run:

bash

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/

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

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).

During US regular session, run one-shot per cadence — typical is every 60 s during the first 30 min, then every 5 min:

bash

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.

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.

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.

parabolic-short-trade-planner

NPX Install

Tags

SKILL.md Content

Overview

When to Use

Workflow

Phase 1 — daily screener

Phase 2 — pre-market plan generator

Phase 3 — intraday trigger monitor

Reviewing a plan before entry

Earnings-aware screening

Output Format

Resources