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momentum-system/trading_execution.py
T
Claude e3cb2b8e5f Add local-LLM (Ollama) news/catalyst scoring 
Leverage a local Ollama model to interpret news catalysts for the small-cap
momentum strategy — distinguishing bullish catalysts (FDA approval, M&A) from
dilutive offerings the technicals alone can't see. Advisory-first, then automate.

Phase 1 (advisory; no change to trade behavior):
- research/ingestion/news_enrichment.py: stdlib urllib call to Ollama with
  structured JSON output, classifying each headline into a catalyst type,
  sentiment, conviction and dilution flag. Graceful degradation (None on any
  failure) so trading is never blocked.
- news_catalyst_cache table + populate the previously-dead news_events.sentiment.
- Runs on the existing interval Scheduler (off the hot path), gated by
  OllamaConfig; deduped by the raw_news_items payload hash.
- Surface the advisory on the dashboard approval queue (projection + api + UI).

Phase 2 (automation; ships gated OFF):
- Optional catalyst_score blended into calculate_setup_quality (None reproduces
  the exact legacy weights); pure pass-through via evaluate_setup.
- Dilution veto in TradingExecutionService (slow + fast paths) emitting a
  catalyst_dilution_veto risk event, behind NEWS_DILUTION_VETO_ENABLED.

Config (OllamaConfig.from_env), .env.example docs, and tests for parsing,
persistence/dedupe, the projection, the score blend, the veto, and an
end-to-end enrich->advisory flow (Ollama mocked). Default model moves to
qwen2.5:7b-instruct (best small-model JSON, fits a 12GB GPU).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M5HTMkaadGDnAPtXe2hWwg
2026-06-19 17:29:24 +00:00

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"""Trading execution service (Milestone 4/5 glue).
Connects ready signals to broker execution with risk controls and an
approval workflow:
signal_ready
-> risk checks (sizing, max concurrent, daily-loss circuit breaker)
-> order_approval_requested
-> [manual approval in the dashboard] or [auto-approve]
-> order_approved -> broker submit -> order_submitted (+ filled)
Exit orders close existing broker positions on demand.
"""
from __future__ import annotations
import functools
import json
import logging
import os
import threading
from dataclasses import dataclass
from datetime import datetime, timedelta
from runtime.flatten import cancel_protective_and_close
from alpaca_paper.execution import (
AlpacaPaperExecutor,
ExecutionRequest,
)
from storage.event_schema import (
EventMode,
OrderApprovalRequestedEvent,
OrderApprovedEvent,
OrderRejectedEvent,
RiskRuleTriggeredEvent,
)
from storage.event_store import EventStore
from storage.projections import (
query_account_positions_snapshot,
query_approval_queue,
query_ready_signals_snapshot,
)
from strategy.risk.position_sizing import (
PositionSizingConfig,
calculate_position_size,
)
from trading_mode import TradingModeSettings
logger = logging.getLogger(__name__)
def _locked(method):
"""Serialize a mutating service method behind self._lock (a re-entrant lock).
The fast trigger thread (submit_breakout_now) and the main loop (tick,
approvals) both mutate execution state (_armed, _requested_symbols) and emit
events; without this they would race on the shared psycopg2 connection and
the in-memory sets. RLock so a public method may call another (tick ->
expire_stale_entries) without deadlocking.
"""
@functools.wraps(method)
def wrapper(self, *args, **kwargs):
with self._lock:
return method(self, *args, **kwargs)
return wrapper
@dataclass
class ExecutionSettings:
enabled: bool = True
auto_approve: bool = False
max_orders_per_tick: int = 1
max_concurrent_positions: int = 3
risk_per_trade_pct: float = 0.01
default_equity: float = 100_000.0
max_daily_loss_pct: float = 0.03
# on a daily-loss breach, also flatten open positions + cancel unfilled entries
flatten_on_breach: bool = True
# --- entry mechanism (Ross-Cameron-style; all tunable) -----------------
# reward target as a multiple of risk (entry + reward_multiple * (entry-stop))
reward_multiple: float = 2.0
# how the entry order is placed: "limit" rests at the entry trigger so an
# unfilled order is a real, cancellable state; "market" fills immediately
entry_order_type: str = "limit"
# cancel an unfilled entry after this many minutes of resting (the "back
# out" time box; ~1 bar == 1 minute). 0 disables the timeout. Wall-clock
# based, so the invalidation guard can run far more often than this.
entry_timeout_bars: int = 2
# cancel an unfilled entry if price trades back below the entry trigger by
# this fraction. NOT 0.0: at zero tolerance any sub-cent wobble below the
# trigger cancels the entry one tick after it fires, so a breakout that
# oscillates around the level churns and never holds (observed live). Give
# it room — the bracket STOP (opening-range low) is the real protection and
# risk is capped at 1%/trade by sizing. Negative disables price-break cancel.
entry_invalidate_pct: float = 0.015
# live-trigger fast path (submit_breakout_now): how far above the trigger to
# cap the marketable limit so a breakout FILLS on a runner instead of
# resting forever at the trigger, while still bounding slippage.
trigger_slippage_pct: float = 0.004
# don't cancel an entry within this many seconds of arming — a marketable
# fill needs a moment to confirm at the broker; cancelling first strips its
# bracket (the naked-stop failure mode).
entry_grace_seconds: float = 5.0
# --- account-aware sizing (matters most on a small REAL account) -------
# one position's dollar value <= this fraction of equity, so a single trade
# can't exceed buying power (critical at $300: 3 positions must fit, so ~1/3).
max_position_pct: float = 0.33
# liquidity cap: shares <= this fraction of the symbol's day volume so at
# SIZE you don't move the market. 0 = off (irrelevant at small size).
liquidity_max_volume_pct: float = 0.0
# PORTFOLIO gross-notional cap: total $ across ALL open+pending positions must
# stay under this fraction of equity. Without it, max_concurrent (6) x
# max_position_pct (0.16) = ~96% gross in correlated small-cap gappers — one
# regime move hits the whole book together. A later entry is shrunk to fit the
# remaining budget, or blocked if none remains. 0 = off.
max_gross_notional_pct: float = 0.60
# --- backout cooldown (anti-thrash) ------------------------------------
# after an entry backs out (timeout / price-break) the symbol is benched for
# this many seconds before it can re-arm. Without it a breakout that won't
# fill on a marketable limit re-arms every pass and churns (observed live:
# NEOV armed+backed-out 68x in one session, bloating the order list). 0 = off.
backout_cooldown_seconds: float = 180.0
# after this many backouts in a session, bench the symbol for the rest of the
# day (a setup that has failed to fill repeatedly isn't going to start). 0 = no cap.
max_backouts_per_symbol: int = 3
# once a position in a symbol closes at a LOSS, don't re-enter that name this
# session. Stops the "throw good money after bad" re-entry into a name that
# already failed (observed live: APWC stopped out -904, was bought back, lost
# another -1012). Only blocks LOSING exits (see reentry_min_loss_pct) so a
# quick scratch-then-re-enter winner isn't killed (GRAB +513 in the replay).
reentry_block_after_exit: bool = True
# only bench a closed name if it exited DOWN more than this fraction — a real
# stop-out, not a scratch/win. 0 = bench on any close (the blunt original).
reentry_min_loss_pct: float = 0.01
# --- anti-chase / halt protection (the "+100% then 3 halts down" failure) ---
# Block a live entry whose price has already extended more than this fraction
# ABOVE the trigger (opening-range high). A clean break crosses the level
# smoothly (~0% extension and passes); a violent gap-THROUGH — a parabolic
# spike, or a halt reopening past the level — lands far above it, and chasing
# that buys the top tick that then reverses/halts down. 0 = off.
# Default 0.15 calibrated on real fires: historical entries cluster <=9.8%
# extension, with a clean gap to a disaster tail at 26-64% (NEOV x4 @27%,
# ICCM @64%/26%, BIRD @30%). 15% sits in that dead zone — it blocks the
# spike-top chases and passes every normal breakout.
extension_max_pct: float = 0.15
# Day-extension ceiling: block when the entry is more than this fraction ABOVE
# the SESSION OPEN — i.e. the stock is already parabolic on the day. This
# catches what extension_max_pct can't: a name up huge intraday whose ORB-high
# break is itself clean (small trigger-extension) but is a chase of a parabolic
# (e.g. ATPC entered +1.7% above trigger yet +32% above the open, and lost).
# Default 0.30 calibrated on real fires: normal entries are <=14% above the
# open, the parabolic tail is 20-83%. 0 = off. Needs day_open from the trigger.
day_extension_max_pct: float = 0.30
# Skip entries on a name that looks HALTED: in a trading halt you can't exit
# and it gaps through your stop on resume. The caller passes a per-symbol
# halt flag (a bar-gap heuristic over the liquid gappers we trade). This flag
# only gates whether that signal is honoured.
halt_guard_enabled: bool = True
# treat a symbol as halted when, during RTH, its freshest minute bar is older
# than this many minutes WHILE other names are still printing (LULD halts run
# >= 5 min, so 3 catches a real halt without tripping on normal IEX lag).
halt_max_bar_gap_min: float = 3.0
# --- catalyst dilution veto (Phase 2; ships OFF) -----------------------
# block an entry when the LLM catalyst advisory says the move is driven by a
# CONFIRMED dilutive offering (a gap that fades as new shares hit). Requires
# a catalyst_provider AND a conviction at/above the floor below. Default OFF
# so the LLM stays advisory-only until its accuracy is trusted.
catalyst_veto_enabled: bool = False
catalyst_veto_min_conviction: float = 0.6
@classmethod
def from_env(cls, env: dict[str, str] | None = None) -> "ExecutionSettings":
values = dict(os.environ)
if env is not None:
values.update(env)
def flag(key: str, default: str) -> bool:
return values.get(key, default).strip().lower() in {"1", "true", "yes", "on"}
order_type = values.get("TRADING_ENTRY_ORDER_TYPE", "limit").strip().lower()
if order_type not in {"limit", "market"}:
order_type = "limit"
return cls(
enabled=flag("TRADING_EXECUTION_ENABLED", "1"),
auto_approve=flag("TRADING_AUTO_APPROVE", "0"),
max_orders_per_tick=int(values.get("TRADING_MAX_ORDERS_PER_TICK", "1")),
max_concurrent_positions=int(
values.get("TRADING_MAX_CONCURRENT_POSITIONS", "3")
),
risk_per_trade_pct=float(values.get("TRADING_RISK_PER_TRADE_PCT", "0.01")),
default_equity=float(values.get("TRADING_DEFAULT_EQUITY", "100000")),
max_daily_loss_pct=float(values.get("TRADING_MAX_DAILY_LOSS_PCT", "0.03")),
flatten_on_breach=flag("TRADING_FLATTEN_ON_BREACH", "1"),
reward_multiple=float(values.get("TRADING_REWARD_MULTIPLE", "2.0")),
entry_order_type=order_type,
entry_timeout_bars=int(values.get("TRADING_ENTRY_TIMEOUT_BARS", "2")),
entry_invalidate_pct=float(
values.get("TRADING_ENTRY_INVALIDATE_PCT", "0.015")
),
trigger_slippage_pct=float(
values.get("TRADING_TRIGGER_SLIP_PCT", "0.004")
),
entry_grace_seconds=float(values.get("TRADING_ENTRY_GRACE_SECONDS", "5")),
max_position_pct=float(values.get("TRADING_MAX_POSITION_PCT", "0.33")),
liquidity_max_volume_pct=float(
values.get("TRADING_LIQUIDITY_MAX_VOLUME_PCT", "0.0")
),
max_gross_notional_pct=float(
values.get("TRADING_MAX_GROSS_NOTIONAL_PCT", "0.60")
),
backout_cooldown_seconds=float(
values.get("TRADING_BACKOUT_COOLDOWN_SECONDS", "180")
),
max_backouts_per_symbol=int(
values.get("TRADING_MAX_BACKOUTS_PER_SYMBOL", "3")
),
reentry_block_after_exit=flag("TRADING_BLOCK_REENTRY_AFTER_EXIT", "1"),
reentry_min_loss_pct=float(
values.get("TRADING_REENTRY_MIN_LOSS_PCT", "0.01")
),
extension_max_pct=float(
values.get("TRADING_EXTENSION_MAX_PCT", "0.15")
),
day_extension_max_pct=float(
values.get("TRADING_DAY_EXTENSION_MAX_PCT", "0.30")
),
halt_guard_enabled=flag("TRADING_HALT_GUARD_ENABLED", "1"),
halt_max_bar_gap_min=float(
values.get("TRADING_HALT_MAX_BAR_GAP_MIN", "3")
),
catalyst_veto_enabled=flag("NEWS_DILUTION_VETO_ENABLED", "0"),
catalyst_veto_min_conviction=float(
values.get("NEWS_DILUTION_VETO_CONVICTION", "0.6")
),
)
class TradingExecutionService:
"""Drives the signal -> approval -> order pipeline each tick."""
def __init__(
self,
store: EventStore,
executor: AlpacaPaperExecutor | None = None,
settings: ExecutionSettings | None = None,
trading_mode: TradingModeSettings | None = None,
session_id: str | None = None,
equity: float | None = None,
price_provider=None,
now_fn=None,
catalyst_provider=None,
):
self.store = store
self.settings = settings or ExecutionSettings.from_env()
# optional callable: symbol -> catalyst advisory dict (or None), from the
# LLM enrichment cache. Used ONLY for the (gated) dilution veto. Injected
# so the execution service never reads the DB itself.
self.catalyst_provider = catalyst_provider
self.trading_mode = trading_mode or TradingModeSettings.from_env()
self.session_id = session_id
self.equity = equity or self.settings.default_equity
self.executor = executor or AlpacaPaperExecutor(
store, session_id=session_id
)
self.mode = EventMode.PAPER
# optional callable: symbol -> latest price, used to invalidate unfilled
# entries that break back below the entry trigger before filling
self.price_provider = price_provider
# injectable clock so the entry timeout is wall-clock based (and so the
# invalidation check can run far more often than the timeout window
# without changing the timeout's meaning); tests pass a fake clock
self._now = now_fn or datetime.now
# signals we've already requested approval for this session
self._requested_symbols: set[str] = set()
# armed (submitted, awaiting fill) entries we may need to cancel.
# order_id -> {symbol, entry_price, broker_order_id, armed_at, checks}
self._armed: dict[str, dict] = {}
# backout cooldown (anti-thrash): symbol -> wall-clock time it may re-arm,
# and how many times it has backed out this session. A symbol that won't
# fill is benched instead of re-arming every pass. datetime.max == benched
# for the rest of the session (hit max_backouts_per_symbol).
self._cooldown_until: dict[str, datetime] = {}
self._backout_counts: dict[str, int] = {}
# re-entry block: symbols whose position has CLOSED this session (detected
# by a name leaving the broker's held set) — benched from re-entry so we
# don't buy back a name that just stopped out. _prev_held is the last
# fresh broker snapshot used to spot departures.
self._exited_today: set[str] = set()
self._prev_held: set[str] = set()
# last-seen unrealized return (fraction) per held symbol — used to decide,
# when a name leaves the book, whether it exited at a real loss (bench) or
# a scratch/win (allow re-entry).
self._held_ret: dict[str, float] = {}
# daily-loss circuit breaker: once tripped, no new entries this session
# (a REAL loss is permanent for the session — a bounce can't re-open it)
self._halted = False
# transient data-halt: equity unreadable (network/DNS). Blocks new entries
# WHILE unreadable but RECOVERS on the next good read — a DNS blip must not
# end the trading day the way a real loss does.
self._data_halt = False
# session closed (end-of-day flatten): no new entries for the rest of the day
self._session_closed = False
# consecutive equity-read failures; after the limit we pause (recoverable)
self._equity_read_failures = 0
self._equity_fail_limit = int(
os.environ.get("TRADING_EQUITY_FAIL_LIMIT", "5")
)
# serializes state mutations across the main loop and the trigger thread
self._lock = threading.RLock()
# -- pipeline -----------------------------------------------------------
def _open_position_count(self) -> int:
snapshots = query_account_positions_snapshot(
self.store, broker_name=self.executor.broker_name
)
if not snapshots:
return 0
return len(snapshots[-1].get("positions") or [])
def _held_symbols(self) -> set[str]:
snapshots = query_account_positions_snapshot(
self.store, broker_name=self.executor.broker_name
)
if not snapshots:
return set()
return {
str(p.get("symbol"))
for p in snapshots[-1].get("positions") or []
if p.get("symbol")
}
def _current_equity(self) -> float:
"""Size off the REAL broker equity, not the $100k default.
On a $300 live account the default would size $1,000 positions and get
rejected; this reads actual equity (the get_account read is cached, so
it's cheap) and caches it on the service for fallback.
"""
client = getattr(self.executor, "client", None)
if client is not None and hasattr(client, "get_account"):
try:
eq = float((client.get_account() or {}).get("equity") or 0.0)
if eq > 0:
self.equity = eq
return eq
except Exception: # noqa: BLE001
pass
return self.equity
def _remaining_gross_budget(self, equity: float) -> float:
"""$ of new notional still allowed under the portfolio gross-notional cap
(cap*equity minus the market value of currently-open positions). Inf when
the cap is disabled. Caps how big the NEXT entry can be so the whole book
can't pile into ~100% gross of correlated small-caps."""
cap = float(self.settings.max_gross_notional_pct or 0.0)
if cap <= 0:
return float("inf")
positions = self._broker_positions() or []
open_gross = 0.0
for p in positions:
try:
open_gross += abs(float(p.get("market_value") or 0.0))
except (TypeError, ValueError):
pass
return max(0.0, cap * equity - open_gross)
def _broker_held_symbols(self) -> set[str] | None:
"""Symbols the broker reports as OPEN POSITIONS right now (truth).
Used to guard entry cancellation: our own order_filled events lag the
actual fill (they arrive via the 60s account sync), so the entry-timeout
guard once cancelled an entry it thought was unfilled AFTER it had really
filled — and cancelling a bracket parent cancels its stop/take-profit
legs, leaving the position NAKED. This queries the broker directly so a
filled name is never cancelled. None if positions can't be read (caller
then falls back to the synced snapshot and stays conservative).
"""
client = getattr(self.executor, "client", None)
if client is None or not hasattr(client, "get_positions"):
return None
try:
positions = client.get_positions(fresh=True) or []
return {str(p.get("symbol")) for p in positions if p.get("symbol")}
except Exception as exc: # noqa: BLE001
logger.warning("broker positions unreadable in entry guard: %s", exc)
return None
def _release_and_close(self, client, symbol: str) -> None:
"""Cancel a symbol's resting bracket legs, then market-close it.
Thin wrapper over the shared helper (runtime/flatten.py) — the held-qty
403 fix lives in one place, shared with the live exit manager. Fetches
the symbol's open orders itself (no pre-fetched snapshot at EOD)."""
cancel_protective_and_close(client, symbol)
def _flatten_all(self, reason: str) -> dict:
"""Cancel unfilled entries and market-close every open position.
Used at EOD and when the daily-loss circuit breaker trips: stop the
bleeding by cancelling resting entries and flattening the book.
Best-effort — errors are collected, not raised, so one bad symbol can't
block the rest. Resting bracket legs are cancelled before each close so
the held quantity is freed (else close_position 403s)."""
result: dict = {"cancelled_entries": 0, "closed_positions": [], "errors": []}
# 1) cancel unfilled (armed) entries we are tracking
for order_id in list(self._armed):
armed = self._armed.pop(order_id)
symbol = armed.get("symbol", "")
try:
self.executor.cancel_entry(
order_id, armed.get("broker_order_id"), symbol, reason
)
result["cancelled_entries"] += 1
except Exception as exc: # noqa: BLE001
result["errors"].append(f"cancel {symbol}: {exc}")
self._requested_symbols.discard(symbol)
# 2) market-close open positions
client = getattr(self.executor, "client", None)
for symbol in sorted(self._held_symbols()):
if client is None or not hasattr(client, "close_position"):
result["errors"].append(f"close {symbol}: no broker client to flatten")
continue
try:
self._release_and_close(client, symbol)
result["closed_positions"].append(symbol) # only on a confirmed close
except Exception as exc: # noqa: BLE001
result["errors"].append(f"close {symbol}: {exc}")
return result
@_locked
def _daily_loss_breach(self) -> bool:
"""True while new entries should be blocked.
Two distinct halts: a REAL daily-loss breach (``_halted``) is permanent
for the session so a bounce can't re-open the floodgates; an equity-read
outage (``_data_halt``) blocks WHILE unreadable but recovers on the next
good read — a transient DNS/network blip must not end the trading day.
"""
if self._halted or self._session_closed:
return True
max_loss = abs(float(self.settings.max_daily_loss_pct or 0.0))
if max_loss <= 0:
return False
client = getattr(self.executor, "client", None)
if client is None:
return False
try:
acct = client.get_account()
equity_raw = acct.get("equity")
if equity_raw is None:
raise ValueError("account 'equity' field missing")
equity = float(equity_raw)
baseline = float(acct.get("last_equity") or equity)
# good read -> clear the transient data-halt and resume
if self._data_halt or self._equity_read_failures:
if self._data_halt:
self.store.emit(
RiskRuleTriggeredEvent(
timestamp=datetime.now(), mode=self.mode,
correlation_id=self.session_id,
message="Equity readable again — resuming new entries",
rule_type="equity_recovered", rule_value=0.0,
current_state={"equity": equity},
action_taken="resumed_new_entries",
)
)
self._data_halt = False
self._equity_read_failures = 0
except Exception as exc: # noqa: BLE001
self._equity_read_failures += 1
logger.warning(
"equity read failed (%d consecutive): %s",
self._equity_read_failures, exc,
)
# PAUSE (recoverable): block new entries while equity is unreadable,
# rather than silently disabling the most important safety control —
# but DO NOT permanently halt the session for a transient outage.
if self._equity_read_failures >= self._equity_fail_limit and not self._data_halt:
self._data_halt = True
self.store.emit(
RiskRuleTriggeredEvent(
timestamp=datetime.now(),
mode=self.mode,
correlation_id=self.session_id,
message=(
f"Equity unreadable {self._equity_read_failures}x — "
"pausing new entries until it recovers"
),
rule_type="equity_unreadable",
rule_value=float(self._equity_read_failures),
current_state={"failures": self._equity_read_failures},
action_taken="paused_new_entries",
)
)
return self._data_halt
if baseline <= 0:
return False
pnl_pct = (equity - baseline) / baseline
if pnl_pct <= -max_loss:
self._halted = True
flat = (
self._flatten_all("daily_loss_circuit_breaker")
if self.settings.flatten_on_breach
else {"closed_positions": [], "cancelled_entries": 0}
)
extra = ""
action = "halted_new_entries"
if self.settings.flatten_on_breach:
n_closed = len(flat["closed_positions"])
n_cancel = flat["cancelled_entries"]
flat_errs = flat.get("errors") or []
extra = (
f" — flattened {n_closed} position(s), "
f"cancelled {n_cancel} unfilled entr{'y' if n_cancel == 1 else 'ies'}"
)
if flat_errs:
extra += f"; FLATTEN INCOMPLETE: {flat_errs}"
action = "halted_flatten_incomplete" if flat_errs else "halted_and_flattened"
self.store.emit(
RiskRuleTriggeredEvent(
timestamp=datetime.now(),
mode=self.mode,
correlation_id=self.session_id,
message=(
f"Daily-loss circuit breaker tripped: {pnl_pct:+.2%} "
f"(limit -{max_loss:.0%}) — halting new entries{extra}"
),
rule_type="daily_loss",
rule_value=max_loss,
current_state={
"equity": equity,
"baseline_equity": baseline,
"pnl_pct": round(pnl_pct, 4),
"flatten": flat,
},
action_taken=action,
)
)
return self._halted
@_locked
def _dilution_vetoed(self, symbol: str) -> dict | None:
"""If the gated catalyst veto is on AND the LLM advisory flags a confirmed
dilutive offering at/above the conviction floor, return the advisory (so
the caller can emit a risk event + skip). Otherwise None. Never raises —
a missing/odd advisory simply doesn't veto."""
if not (self.settings.catalyst_veto_enabled and self.catalyst_provider):
return None
try:
adv = self.catalyst_provider(symbol)
except Exception: # noqa: BLE001 — advisory is best-effort, never blocks on its own fault
return None
if not adv or not adv.get("is_dilutive"):
return None
try:
conviction = float(adv.get("conviction") or 0.0)
except (TypeError, ValueError):
return None
if conviction >= self.settings.catalyst_veto_min_conviction:
return adv
return None
def _emit_dilution_veto(self, symbol: str, adv: dict) -> None:
self.store.emit(
RiskRuleTriggeredEvent(
timestamp=datetime.now(),
mode=self.mode,
correlation_id=self.session_id,
message=f"{symbol}: entry blocked — confirmed dilutive catalyst",
rule_type="catalyst_dilution_veto",
rule_value=float(adv.get("conviction") or 0.0),
current_state={
"symbol": symbol,
"catalyst_type": adv.get("catalyst_type"),
"sentiment": adv.get("sentiment"),
"rationale": adv.get("rationale"),
},
action_taken="blocked_dilutive_offering",
)
)
def request_approvals_for_ready_signals(self) -> list[str]:
"""Turn fresh ready signals into approval requests. Returns order ids."""
if not self.settings.enabled:
return []
if self._daily_loss_breach():
return []
if self._open_position_count() >= self.settings.max_concurrent_positions:
self.store.emit(
RiskRuleTriggeredEvent(
timestamp=datetime.now(),
mode=self.mode,
correlation_id=self.session_id,
message="Max concurrent positions reached — no new entries",
rule_type="max_concurrent_positions",
rule_value=float(self.settings.max_concurrent_positions),
current_state={"open_positions": self._open_position_count()},
action_taken="skipped_new_entries",
)
)
return []
held = self._held_symbols()
pending = {row["symbol"] for row in query_approval_queue(self.store)}
signals = query_ready_signals_snapshot(self.store, session_id=self.session_id)
created: list[str] = []
for signal in signals:
if len(created) >= self.settings.max_orders_per_tick:
break
symbol = signal["symbol"]
if (symbol in held or symbol in pending
or symbol in self._requested_symbols
or self._in_cooldown(symbol) # benched after recent backouts
or symbol in self._exited_today): # already round-tripped today
continue
vetoed = self._dilution_vetoed(symbol)
if vetoed is not None:
self._emit_dilution_veto(symbol, vetoed)
self._requested_symbols.add(symbol) # don't re-evaluate every tick
continue
entry = signal.get("entry_price")
stop = signal.get("stop_loss_price")
if not entry or not stop or stop >= entry:
continue
eq = self._current_equity()
# cap this entry to BOTH the per-position limit AND the portfolio's
# remaining gross-notional budget (so the book can't pile into ~100%).
pos_cap = min(eq * self.settings.max_position_pct,
self._remaining_gross_budget(eq))
sizing = calculate_position_size(
float(entry),
float(stop),
equity=eq,
config=PositionSizingConfig(
risk_per_trade_pct=self.settings.risk_per_trade_pct,
default_equity=self.settings.default_equity,
),
max_position_value=pos_cap,
)
if sizing.position_size <= 0:
continue
entry_f = float(entry)
stop_f = float(stop)
risk_per_share = entry_f - stop_f
request = ExecutionRequest(
symbol=symbol,
side="buy",
quantity=sizing.position_size,
entry_price=entry_f,
stop_loss_price=stop_f,
take_profit_price=round(
entry_f + self.settings.reward_multiple * risk_per_share, 2
),
order_type=self.settings.entry_order_type,
)
approval_mode = "auto" if self.settings.auto_approve else "manual"
self.store.emit(
OrderApprovalRequestedEvent(
timestamp=datetime.now(),
mode=self.mode,
correlation_id=self.session_id,
message=(
f"Approval requested ({approval_mode}): buy "
f"{request.quantity} {symbol} @ ~{entry} stop {stop}"
),
order_id=request.order_id,
symbol=symbol,
requested_by="trading_execution",
approval_mode=approval_mode,
execution_mode=self.trading_mode.execution_mode,
execution_request=request.to_payload(),
)
)
self._requested_symbols.add(symbol)
created.append(request.order_id)
logger.info("approval requested for %s (%s)", symbol, request.order_id)
return created
@_locked
def approve_order(
self, order_id: str, approved_by: str = "dashboard", notes: str | None = None
) -> dict:
"""Approve a pending order and execute it."""
entry = self._find_pending(order_id)
if entry is None:
return {"ok": False, "error": f"order {order_id} not pending"}
request = ExecutionRequest.from_payload(entry["execution_request"])
self.store.emit(
OrderApprovedEvent(
timestamp=datetime.now(),
mode=self.mode,
correlation_id=self.session_id,
message=f"Order {order_id} approved by {approved_by}",
order_id=order_id,
symbol=request.symbol,
approved_by=approved_by,
approval_notes=notes,
)
)
result = self.executor.execute(request)
# If the entry didn't fill immediately (a resting limit at the trigger),
# arm it so expire_stale_entries() can back out on timeout or a break
# below the entry. Market orders that fill on submit are never armed.
# Only arm genuinely OPEN resting entries — never a filled, partial, or
# rejected order (a rejected entry would otherwise occupy a position slot
# forever and a partial would be backed out while real shares are held).
if (
result.ok
and result.status in {"new", "accepted", "pending_new", "held", "accepted_for_bidding"}
and request.entry_price
):
self._armed[order_id] = {
"symbol": request.symbol,
"entry_price": float(request.entry_price),
"broker_order_id": result.broker_order_id,
"armed_at": self._now(),
"checks": 0,
}
return {
"ok": result.ok,
"order_id": order_id,
"broker_order_id": result.broker_order_id,
"status": result.status,
"error": result.error,
}
@_locked
def submit_breakout_now(
self,
symbol: str,
trigger: float,
stop: float,
last_price: float | None = None,
reason: str = "orb_live_break",
cum_volume: float = 0.0,
halted: bool = False,
day_open: float | None = None,
) -> dict:
"""Fire a breakout entry immediately on a LIVE price cross.
The disciplined-but-fast counterpart to the watcher->approval cadence:
used by the armed-trigger loop when price crosses a pre-computed
opening-range high. Runs the SAME risk gates as the slow path (daily-loss
breaker, max-concurrent, per-symbol dedup, 1%-risk sizing), but submits a
*marketable* limit (capped a hair above the trigger) so it fills on a
runner instead of resting at the level. Returns a result dict; ``skipped``
explains a no-op (the symbol can simply try again next tick).
"""
if not self.settings.enabled:
return {"ok": False, "skipped": "disabled"}
if self._daily_loss_breach():
return {"ok": False, "skipped": "halted"}
if self._open_position_count() >= self.settings.max_concurrent_positions:
return {"ok": False, "skipped": "max_positions"}
# per-symbol dedup shared with the slow path: never double-enter a name
# that's already held, pending approval, or requested this session.
held = self._held_symbols()
pending = {row["symbol"] for row in query_approval_queue(self.store)}
if symbol in held or symbol in pending or symbol in self._requested_symbols:
return {"ok": False, "skipped": "already_active"}
if self._in_cooldown(symbol): # benched after recent backouts (anti-thrash)
return {"ok": False, "skipped": "cooldown"}
if symbol in self._exited_today: # already round-tripped today — no re-entry
return {"ok": False, "skipped": "reentry_blocked"}
vetoed = self._dilution_vetoed(symbol) # gated catalyst dilution veto
if vetoed is not None:
self._emit_dilution_veto(symbol, vetoed)
return {"ok": False, "skipped": "dilution_veto"}
# Use last_price only when it's a real positive tick. `last_price or
# trigger` would coerce a 0.0/garbage price to the trigger — silently
# skipping the extension guard AND sizing on a phantom price.
if last_price is not None and float(last_price) <= 0:
return {"ok": False, "skipped": "bad_price"}
entry_ref = float(last_price) if last_price is not None else float(trigger)
stop_f = float(stop)
trigger_f = float(trigger)
if stop_f >= entry_ref or trigger_f <= 0:
return {"ok": False, "skipped": "bad_geometry"}
# Anti-chase ceiling: distance of the entry price ABOVE the breakout
# trigger (ORB high). A clean break crosses the level smoothly (~0%) and
# passes; a violent gap-THROUGH — an intraday parabolic, or a halt
# reopening past the level — lands far above it, and chasing that buys
# the top tick that reverses/halts down. (Overnight +100% gappers are
# already excluded upstream by the trigger book's gap_max; this catches
# the intraday chase / gap-through that gap_max can't see.)
ext_max = self.settings.extension_max_pct
if ext_max > 0 and entry_ref > trigger_f * (1.0 + ext_max):
return {"ok": False, "skipped": "over_extended"}
# Day-extension ceiling: the stock is already parabolic on the DAY (far
# above the session open) — chasing that is the "+100% then halts down"
# trap even when the trigger break itself looks clean.
day_max = self.settings.day_extension_max_pct
if (day_max > 0 and day_open is not None and float(day_open) > 0
and entry_ref > float(day_open) * (1.0 + day_max)):
return {"ok": False, "skipped": "over_extended_day"}
# Halt guard: you can't exit during a trading halt and it gaps through the
# stop on resume — don't enter a name the caller flagged as halted.
if halted and self.settings.halt_guard_enabled:
return {"ok": False, "skipped": "halted_symbol"}
eq = self._current_equity()
liq = self.settings.liquidity_max_volume_pct
max_shares = int(liq * cum_volume) if (liq > 0 and cum_volume > 0) else None
# per-position cap AND remaining portfolio gross-notional budget
pos_cap = min(eq * self.settings.max_position_pct,
self._remaining_gross_budget(eq))
if pos_cap <= 0:
return {"ok": False, "skipped": "gross_notional_cap"}
sizing = calculate_position_size(
entry_ref,
stop_f,
equity=eq,
config=PositionSizingConfig(
risk_per_trade_pct=self.settings.risk_per_trade_pct,
default_equity=self.settings.default_equity,
),
max_position_value=pos_cap,
max_shares=max_shares,
)
if sizing.position_size <= 0:
return {"ok": False, "skipped": "zero_size"}
# marketable limit: fill now (price is already at/above the trigger), but
# cap how far above we'll chase so a gap-through doesn't pay any price.
limit_price = round(
max(entry_ref, trigger_f) * (1.0 + self.settings.trigger_slippage_pct), 2
)
risk_per_share = entry_ref - stop_f
request = ExecutionRequest(
symbol=symbol,
side="buy",
quantity=sizing.position_size,
entry_price=limit_price,
stop_loss_price=stop_f,
take_profit_price=round(
entry_ref + self.settings.reward_multiple * risk_per_share, 2
),
order_type="limit",
)
# audit trail mirrors the slow path: requested(auto) -> approved -> submit
self.store.emit(
OrderApprovalRequestedEvent(
timestamp=datetime.now(),
mode=self.mode,
correlation_id=self.session_id,
message=(
f"Live ORB break ({reason}): buy {request.quantity} {symbol} "
f"@ ~{entry_ref:.2f} (trigger {trigger_f:.2f}, stop {stop_f:.2f})"
),
order_id=request.order_id,
symbol=symbol,
requested_by="orb_trigger",
approval_mode="auto",
execution_mode=self.trading_mode.execution_mode,
execution_request=request.to_payload(),
)
)
self.store.emit(
OrderApprovedEvent(
timestamp=datetime.now(),
mode=self.mode,
correlation_id=self.session_id,
message=f"Order {request.order_id} auto-approved (live ORB break)",
order_id=request.order_id,
symbol=symbol,
approved_by="orb_trigger",
approval_notes=reason,
)
)
self._requested_symbols.add(symbol)
result = self.executor.execute(request)
if (
result.ok
and result.status
in {"new", "accepted", "pending_new", "held", "accepted_for_bidding"}
and request.entry_price
):
self._armed[request.order_id] = {
"symbol": symbol,
"entry_price": float(request.entry_price),
"broker_order_id": result.broker_order_id,
"armed_at": self._now(),
"checks": 0,
}
logger.info(
"live ORB break %s qty=%s -> ok=%s status=%s",
symbol, request.quantity, result.ok, result.status,
)
return {
"ok": result.ok,
"order_id": request.order_id,
"broker_order_id": result.broker_order_id,
"status": result.status,
"error": result.error,
"quantity": sizing.position_size,
"entry": limit_price,
"stop": stop_f,
}
@_locked
def reject_order(
self, order_id: str, rejected_by: str = "dashboard", reason: str = "manual"
) -> dict:
entry = self._find_pending(order_id)
if entry is None:
return {"ok": False, "error": f"order {order_id} not pending"}
self.store.emit(
OrderRejectedEvent(
timestamp=datetime.now(),
mode=self.mode,
correlation_id=self.session_id,
message=f"Order {order_id} rejected by {rejected_by}: {reason}",
order_id=order_id,
symbol=entry.get("symbol") or "",
rejected_by=rejected_by,
rejection_reason=reason,
)
)
# allow the symbol to re-signal later
self._requested_symbols.discard(entry.get("symbol") or "")
return {"ok": True, "order_id": order_id}
@_locked
def process_auto_approvals(self) -> list[dict]:
"""Auto-approve any pending requests marked approval_mode=auto."""
results = []
for entry in query_approval_queue(self.store):
if entry.get("approval_mode") == "auto":
results.append(
self.approve_order(entry["order_id"], approved_by="auto")
)
return results
@_locked
def submit_exit_order(self, symbol: str, reason: str = "manual_exit") -> dict:
"""Close an open broker position for symbol with a market sell."""
held = self._held_symbols()
quantity = None
snapshots = query_account_positions_snapshot(
self.store, broker_name=self.executor.broker_name
)
if snapshots:
for p in snapshots[-1].get("positions") or []:
if str(p.get("symbol")) == symbol:
quantity = int(abs(float(p.get("quantity") or 0)))
if symbol not in held or not quantity:
return {"ok": False, "error": f"no open position for {symbol}"}
# Prefer close_position: closes the FULL position regardless of long/short
# sign and fractional qty. A plain 'sell' of abs(qty) would DOUBLE a short
# position, and int-truncation would leave residual shares un-flat.
client = getattr(self.executor, "client", None)
if client is not None and hasattr(client, "close_position"):
try:
raw = client.close_position(symbol) or {}
return {
"ok": True,
"order_id": str(raw.get("id") or f"close-{symbol}"),
"broker_order_id": raw.get("id"),
"status": raw.get("status") or "closing",
"error": None,
"reason": reason,
}
except Exception as exc: # noqa: BLE001
return {"ok": False, "error": str(exc), "reason": reason}
request = ExecutionRequest(symbol=symbol, side="sell", quantity=quantity)
result = self.executor.execute(request)
return {
"ok": result.ok,
"order_id": request.order_id,
"broker_order_id": result.broker_order_id,
"status": result.status,
"error": result.error,
"reason": reason,
}
@_locked
def close_session(self, reason: str = "eod_flatten") -> dict:
"""End-of-day: block new entries and flatten the book.
A day-trading strategy shouldn't carry overnight gap risk, so at the
configured time the loop calls this to cancel resting entries, market-
close every open position (which also cancels its bracket legs), and
halt new entries for the rest of the session.
"""
self._session_closed = True
result = self._flatten_all(reason)
# belt-and-suspenders: close any broker position the store snapshot missed
client = getattr(self.executor, "client", None)
if client is not None and hasattr(client, "get_positions"):
try:
for p in client.get_positions():
sym = p.get("symbol")
if sym and sym not in result["closed_positions"]:
try:
self._release_and_close(client, sym)
result["closed_positions"].append(sym)
except Exception as exc: # noqa: BLE001
result["errors"].append(f"close {sym}: {exc}")
except Exception as exc: # noqa: BLE001
result["errors"].append(f"positions: {exc}")
self.store.emit(
RiskRuleTriggeredEvent(
timestamp=datetime.now(), mode=self.mode, correlation_id=self.session_id,
message=(f"End-of-day flatten ({reason}): closed "
f"{len(result['closed_positions'])} position(s), "
f"cancelled {result['cancelled_entries']} entr"
f"{'y' if result['cancelled_entries'] == 1 else 'ies'}"
+ (f"; ERRORS {result['errors']}" if result['errors'] else "")),
rule_type="eod_flatten", rule_value=0.0,
current_state=result, action_taken="closed_session",
)
)
return result
def _filled_order_ids(self) -> set[str]:
ids: set[str] = set()
for e in self.store.query_events(event_type="order_filled", limit=None):
payload = json.loads(e.get("payload_json", "{}"))
if payload.get("order_id"):
ids.add(str(payload["order_id"]))
return ids
def _cancelled_order_ids(self) -> set[str]:
ids: set[str] = set()
for e in self.store.query_events(event_type="order_cancelled", limit=None):
payload = json.loads(e.get("payload_json", "{}"))
if payload.get("order_id"):
ids.add(str(payload["order_id"]))
return ids
@_locked
def _in_cooldown(self, symbol: str) -> bool:
"""True if symbol is benched after recent backouts (anti-thrash)."""
until = self._cooldown_until.get(symbol)
if until is None:
return False
if self._now() >= until:
del self._cooldown_until[symbol] # cooldown elapsed
return False
return True
def _register_backout(self, symbol: str) -> str:
"""Record a backout and bench the symbol; returns a short reason suffix."""
n = self._backout_counts.get(symbol, 0) + 1
self._backout_counts[symbol] = n
cap = self.settings.max_backouts_per_symbol
if cap and n >= cap:
self._cooldown_until[symbol] = datetime.max # benched for the session
return f"benched for session ({n} backouts)"
secs = self.settings.backout_cooldown_seconds
if secs > 0:
self._cooldown_until[symbol] = self._now() + timedelta(seconds=secs)
return f"cooldown {secs:.0f}s (backout {n})"
return f"backout {n}"
def _broker_positions(self) -> list[dict] | None:
"""Fresh broker positions (full dicts), or None if unreadable."""
client = getattr(self.executor, "client", None)
if client is None or not hasattr(client, "get_positions"):
return None
try:
return client.get_positions(fresh=True) or []
except Exception as exc: # noqa: BLE001
logger.warning("broker positions unreadable in re-entry guard: %s", exc)
return None
def _note_position_exits(self) -> None:
"""Detect names whose position closed since last pass and bench the ones
that exited at a real LOSS from re-entry this session — re-buying a name
that just stopped out is throwing good money after bad. A name that
scratched or won is left alone (a quick re-entry can be the right call)."""
if not self.settings.reentry_block_after_exit:
return
positions = self._broker_positions()
if positions is None: # broker unreadable — don't trust a stale snapshot
return
held: set[str] = set()
for p in positions:
sym = p.get("symbol")
if not sym:
continue
held.add(sym)
try:
self._held_ret[sym] = float(p.get("unrealized_plpc") or 0.0)
except (TypeError, ValueError):
pass
thr = abs(float(self.settings.reentry_min_loss_pct or 0.0))
for sym in (self._prev_held - held):
last_ret = self._held_ret.pop(sym, 0.0)
if thr > 0 and last_ret >= -thr:
continue # scratched or won — allow re-entry
self._exited_today.add(sym)
self.store.emit(RiskRuleTriggeredEvent(
timestamp=datetime.now(), mode=self.mode,
correlation_id=self.session_id,
message=(f"{sym} closed at {last_ret*100:+.1f}% — benched from "
f"re-entry today"),
rule_type="reentry_block", rule_value=last_ret,
current_state={"symbol": sym, "exit_return": last_ret},
action_taken="reentry_blocked",
))
self._prev_held = held
def expire_stale_entries(self) -> list[dict]:
"""Back out of unfilled entries that timed out or broke their trigger.
This is the disciplined other half of auto-arming: once a setup is
recognized we enter with conviction, but if the fill never comes (the
move didn't follow through) we don't sit there forever — we cancel on a
time box or when price falls back through the entry, freeing risk
budget and the concurrent-position slot for the next setup.
Safe to call as often as you like: the price-break check uses the live
``price_provider`` each call, while the timeout is wall-clock based, so
running this every few seconds reacts fast without shortening the
timeout window.
"""
# track positions that have CLOSED since last pass (so a stopped-out name
# is benched from re-entry). Done BEFORE the early-return so it runs every
# pass, not only when there are armed entries to expire.
self._note_position_exits()
if not self._armed:
return []
filled = self._filled_order_ids()
cancelled = self._cancelled_order_ids()
# broker truth: never cancel an entry whose position is actually open
# (cancelling its bracket parent would strip the protective stop/TP).
# Fall back to the synced snapshot if the broker can't be read.
broker_held = self._broker_held_symbols()
if broker_held is None:
broker_held = self._held_symbols()
invalidate_pct = self.settings.entry_invalidate_pct
timeout_bars = self.settings.entry_timeout_bars
now = self._now()
actions: list[dict] = []
for order_id in list(self._armed):
armed = self._armed[order_id]
symbol = armed["symbol"]
# already resolved at the broker -> stop tracking
if order_id in filled:
self._armed.pop(order_id, None)
continue
if order_id in cancelled:
self._armed.pop(order_id, None)
self._requested_symbols.discard(symbol)
continue
# FILLED at the broker (position open) -> stop tracking, NEVER cancel
# (this is the naked-stop fix: cancelling here killed the stop leg).
if symbol in broker_held:
self._armed.pop(order_id, None)
continue
# grace window: a just-armed entry may still be filling — don't strip
# its bracket before the fill can confirm (belt to the fresh-positions
# check above; together they close the naked-stop race)
if (now - armed["armed_at"]).total_seconds() < self.settings.entry_grace_seconds:
continue
armed["checks"] += 1
reason = None
# (a) price-break invalidation: traded back below the entry trigger.
# Uses the live last-trade price, so this reacts tick-by-tick rather
# than waiting for a bar to close.
if invalidate_pct >= 0 and self.price_provider is not None:
try:
last = self.price_provider(symbol)
except Exception: # noqa: BLE001
last = None
if last is not None:
threshold = armed["entry_price"] * (1.0 - invalidate_pct)
if float(last) < threshold:
reason = (
f"entry invalidated: {float(last):.4f} < trigger "
f"{threshold:.4f}"
)
# (b) time box: unfilled for too long (wall-clock minutes ~ bars)
if reason is None and timeout_bars > 0:
elapsed_min = (now - armed["armed_at"]).total_seconds() / 60.0
if elapsed_min >= timeout_bars:
reason = f"entry timed out: unfilled {elapsed_min:.1f} min"
if reason is not None:
self.executor.cancel_entry(
order_id, armed.get("broker_order_id"), symbol, reason
)
bench = self._register_backout(symbol) # anti-thrash: cool the name
self.store.emit(
RiskRuleTriggeredEvent(
timestamp=datetime.now(),
mode=self.mode,
correlation_id=self.session_id,
message=f"Backed out of {symbol} entry — {reason} [{bench}]",
rule_type="entry_backout",
rule_value=float(timeout_bars),
current_state={
"symbol": symbol,
"order_id": order_id,
"checks": armed["checks"],
"backouts": self._backout_counts.get(symbol, 0),
},
action_taken="cancelled_unfilled_entry",
)
)
self._armed.pop(order_id, None)
self._requested_symbols.discard(symbol)
actions.append({"order_id": order_id, "symbol": symbol,
"reason": reason, "cooldown": bench})
return actions
@_locked
def tick(self) -> dict:
"""One execution pass: expire stale entries, request approvals, auto-execute."""
backed_out = self.expire_stale_entries()
requested = self.request_approvals_for_ready_signals()
auto = self.process_auto_approvals() if self.settings.auto_approve else []
return {
"approvals_requested": requested,
"auto_executed": auto,
"backed_out": backed_out,
}
# -- helpers --------------------------------------------------------------
def _find_pending(self, order_id: str) -> dict | None:
for entry in query_approval_queue(self.store):
if str(entry.get("order_id")) == str(order_id):
return entry
return None
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