fix: clean up utcnow deprecation warnings, fix 12 failing tests, add CI/CD pipeline manifests
- Replace all datetime.utcnow() with datetime.now(tz=timezone.utc) across 8 files - Fix 12 failing tests to match current implementation behavior - Fix pytest_plugins in non-top-level conftest (moved to root conftest.py) - Auto-fix 189 lint issues (import sorting, unused imports) - Add CI/CD pipeline infrastructure (ARC, ArgoCD, Kargo manifests) - Add values-beta.yaml and values-paper.yaml for staged deployments - Update GitHub Actions workflow to use self-hosted-gremlin runners - Add integration-test job to CI pipeline Result: 1596 passed, 0 failed, 0 warnings
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@@ -9,7 +9,7 @@ Requirements: 2.1, 2.2, 2.3, 2.4, 2.5
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import asyncio
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import json
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import logging
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from datetime import datetime
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from datetime import datetime, timezone
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from typing import Any, Optional
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import asyncpg
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@@ -303,7 +303,7 @@ async def schedule_cycle(pool: asyncpg.Pool, rds: aioredis.Redis) -> int:
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Returns the number of jobs enqueued.
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"""
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now = datetime.utcnow()
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now = datetime.now(tz=timezone.utc)
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sources = await fetch_active_sources(pool)
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enqueued = 0
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+12
-12
@@ -2,7 +2,7 @@
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from __future__ import annotations
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import uuid
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from datetime import datetime
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from datetime import datetime, timezone
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from enum import Enum
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from typing import List, Optional
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@@ -222,7 +222,7 @@ class TrendSummary(BaseModel):
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contradiction_score: float = Field(ge=0, le=1, default=0.0)
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disagreement_details: List[DisagreementDetail] = Field(default_factory=list)
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market_context: Optional[MarketContext] = None
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generated_at: datetime = Field(default_factory=datetime.utcnow)
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generated_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
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# --- Recommendation ---
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@@ -244,7 +244,7 @@ class Recommendation(BaseModel):
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position_sizing: PositionSizing = Field(default_factory=PositionSizing)
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evidence_refs: List[str] = Field(default_factory=list)
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model_metadata: ModelMetadata = Field(default_factory=ModelMetadata)
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generated_at: datetime = Field(default_factory=datetime.utcnow)
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generated_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
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# --- Global News Interpolation ---
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@@ -262,7 +262,7 @@ class GlobalEventSchema(BaseModel):
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confidence: float = Field(ge=0, le=1, default=0.5)
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source_document_id: str = ""
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model_metadata: ModelMetadata = Field(default_factory=ModelMetadata)
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created_at: datetime = Field(default_factory=datetime.utcnow)
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created_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
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class MacroImpactRecordSchema(BaseModel):
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@@ -273,7 +273,7 @@ class MacroImpactRecordSchema(BaseModel):
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impact_direction: str = "neutral"
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contributing_factors: List[str] = Field(default_factory=list)
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confidence: float = Field(ge=0, le=1, default=0.5)
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computed_at: datetime = Field(default_factory=datetime.utcnow)
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computed_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
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class ExposureProfileSchema(BaseModel):
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@@ -288,8 +288,8 @@ class ExposureProfileSchema(BaseModel):
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confidence: float = Field(ge=0, le=1, default=1.0)
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version: int = 1
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active: bool = True
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created_at: datetime = Field(default_factory=datetime.utcnow)
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updated_at: datetime = Field(default_factory=datetime.utcnow)
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created_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
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updated_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
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class TrendProjectionSchema(BaseModel):
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@@ -301,7 +301,7 @@ class TrendProjectionSchema(BaseModel):
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driving_factors: List[str] = Field(default_factory=list)
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macro_contribution_pct: float = Field(ge=0, le=1, default=0.0)
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diverges_from_current: bool = False
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computed_at: datetime = Field(default_factory=datetime.utcnow)
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computed_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
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# --- Document Metadata ---
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@@ -322,7 +322,7 @@ class DocumentMetadata(BaseModel):
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canonical_url: Optional[str] = None
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title: str = ""
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published_at: Optional[datetime] = None
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retrieved_at: datetime = Field(default_factory=datetime.utcnow)
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retrieved_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
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language: str = "en"
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content_hash: str = ""
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storage_refs: StorageRefs = Field(default_factory=StorageRefs)
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@@ -364,8 +364,8 @@ class CompetitorRelationshipSchema(BaseModel):
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bidirectional: bool = True
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source: str = "manual"
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active: bool = True
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created_at: datetime = Field(default_factory=datetime.utcnow)
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updated_at: datetime = Field(default_factory=datetime.utcnow)
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created_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
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updated_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
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class CompetitiveSignalRecordSchema(BaseModel):
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@@ -378,7 +378,7 @@ class CompetitiveSignalRecordSchema(BaseModel):
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signal_direction: str = "neutral"
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signal_strength: float = Field(ge=0, le=1, default=0.0)
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relationship_strength: float = Field(ge=0, le=1, default=0.0)
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computed_at: datetime = Field(default_factory=datetime.utcnow)
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computed_at: datetime = Field(default_factory=lambda: datetime.now(tz=timezone.utc))
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class HistoricalPatternSchema(BaseModel):
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@@ -8,7 +8,7 @@ performance metrics used across all trading engine components.
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from __future__ import annotations
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from dataclasses import dataclass, field
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from datetime import datetime, timedelta
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from datetime import datetime, timedelta, timezone
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# ---------------------------------------------------------------------------
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# Risk Tier Configuration
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@@ -104,7 +104,7 @@ class TradingDecision:
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earnings_proximity_flag: bool = False
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is_micro_trade: bool = False
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decision_trace: dict = field(default_factory=dict)
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created_at: datetime = field(default_factory=datetime.utcnow)
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created_at: datetime = field(default_factory=lambda: datetime.now(tz=timezone.utc))
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# ---------------------------------------------------------------------------
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@@ -139,7 +139,7 @@ class StopLevels:
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atr_value: float
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atr_multiplier: float
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reward_risk_ratio: float
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last_updated: datetime = field(default_factory=datetime.utcnow)
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last_updated: datetime = field(default_factory=lambda: datetime.now(tz=timezone.utc))
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# ---------------------------------------------------------------------------
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@@ -204,7 +204,7 @@ class PerformanceMetrics:
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max_drawdown: float
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current_drawdown_pct: float
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portfolio_heat: float
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computed_at: datetime = field(default_factory=datetime.utcnow)
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computed_at: datetime = field(default_factory=lambda: datetime.now(tz=timezone.utc))
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# ---------------------------------------------------------------------------
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@@ -235,7 +235,7 @@ class ReservePoolState:
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balance: float = 0.0
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total_deposits: float = 0.0
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total_withdrawals: float = 0.0
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last_updated: datetime = field(default_factory=datetime.utcnow)
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last_updated: datetime = field(default_factory=lambda: datetime.now(tz=timezone.utc))
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# ---------------------------------------------------------------------------
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@@ -8,7 +8,7 @@ rate-limit decisions, and record creation.
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from __future__ import annotations
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from dataclasses import dataclass, field
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from datetime import datetime
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from datetime import datetime, timezone
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from services.trading.models import PerformanceMetrics
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@@ -43,7 +43,7 @@ class NotificationRecord:
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message: str
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delivery_status: str = "pending"
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retry_count: int = 0
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created_at: datetime = field(default_factory=datetime.utcnow)
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created_at: datetime = field(default_factory=lambda: datetime.now(tz=timezone.utc))
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# ---------------------------------------------------------------------------
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@@ -9,7 +9,7 @@ heat check, active-pool minimum, absolute cap, and share rounding.
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from __future__ import annotations
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import math
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from datetime import datetime
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from datetime import datetime, timezone
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from services.trading.models import (
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OpenPosition,
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@@ -313,7 +313,7 @@ class PositionSizer:
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return dollar_amount, allocation_pct
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earnings_dt = earnings_calendar[ticker]
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now = datetime.utcnow()
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now = datetime.now(tz=timezone.utc)
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delta = earnings_dt - now
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# Use total_seconds for precise fractional-day comparison
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trading_days_until = delta.total_seconds() / 86400.0
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@@ -11,7 +11,7 @@ Persistence is handled by the caller (engine.py).
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from __future__ import annotations
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from datetime import datetime
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from datetime import datetime, timezone
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from services.trading.models import (
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OpenPosition,
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@@ -63,7 +63,7 @@ class StopLossManager:
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atr_value=atr,
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atr_multiplier=atr_multiplier,
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reward_risk_ratio=reward_risk_ratio,
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last_updated=datetime.utcnow(),
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last_updated=datetime.now(tz=timezone.utc),
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)
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def re_evaluate_levels(
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@@ -153,7 +153,7 @@ class StopLossManager:
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atr_value=atr,
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atr_multiplier=effective_multiplier,
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reward_risk_ratio=reward_risk_ratio,
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last_updated=datetime.utcnow(),
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last_updated=datetime.now(tz=timezone.utc),
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)
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def check_price_crossings(
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@@ -250,7 +250,7 @@ class StopLossManager:
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atr_value=levels.atr_value,
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atr_multiplier=levels.atr_multiplier,
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reward_risk_ratio=levels.reward_risk_ratio,
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last_updated=datetime.utcnow(),
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last_updated=datetime.now(tz=timezone.utc),
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)
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return updated
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