dennisthiessen
90618d186f
Track Record: new "Reset" action (POST /admin/track-record/reset) deletes all trade setups so stats start fresh after material scoring/setup changes — live setups regenerate on the next scan. Guarded by a confirm dialog. Recommendation config: remove distance_penalty_factor, which was exposed in the admin UI but consumed nowhere (the touch-probability model superseded it). A knob that silently does nothing is worse than no knob. Remaining defaults are left as-is — they're reasonable, and the honest way to tune them is backtesting against accumulated outcomes, not invented "researched" numbers. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
"""Admin request/response schemas."""
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from typing import Literal
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from pydantic import BaseModel, Field
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class UserManagement(BaseModel):
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"""Schema for user access management."""
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has_access: bool
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class PasswordReset(BaseModel):
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"""Schema for resetting a user's password."""
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new_password: str = Field(..., min_length=6)
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class CreateUserRequest(BaseModel):
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"""Schema for admin-created user accounts."""
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username: str = Field(..., min_length=1)
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password: str = Field(..., min_length=6)
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role: str = Field(default="user", pattern=r"^(user|admin)$")
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has_access: bool = False
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class RegistrationToggle(BaseModel):
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"""Schema for toggling registration on/off."""
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enabled: bool
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class SystemSettingUpdate(BaseModel):
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"""Schema for updating a system setting."""
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value: str = Field(..., min_length=1)
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class DataCleanupRequest(BaseModel):
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"""Schema for data cleanup — delete records older than N days."""
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older_than_days: int = Field(..., gt=0)
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class JobToggle(BaseModel):
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"""Schema for enabling/disabling a scheduled job."""
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enabled: bool
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class RecommendationConfigUpdate(BaseModel):
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high_confidence_threshold: float | None = Field(default=None, ge=0, le=100)
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moderate_confidence_threshold: float | None = Field(default=None, ge=0, le=100)
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confidence_diff_threshold: float | None = Field(default=None, ge=0, le=100)
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signal_alignment_weight: float | None = Field(default=None, ge=0, le=1)
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sr_strength_weight: float | None = Field(default=None, ge=0, le=1)
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momentum_technical_divergence_threshold: float | None = Field(default=None, ge=0, le=100)
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fundamental_technical_divergence_threshold: float | None = Field(default=None, ge=0, le=100)
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class TickerUniverseUpdate(BaseModel):
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universe: Literal["sp500", "nasdaq100", "nasdaq_all"]
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class ActivationConfigUpdate(BaseModel):
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"""Activation gate: what counts as an actionable signal."""
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min_rr: float | None = Field(default=None, ge=0)
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min_confidence: float | None = Field(default=None, ge=0, le=100)
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min_target_probability: float | None = Field(default=None, ge=0, le=100)
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require_high_conviction: bool | None = None
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exclude_conflicts: bool | None = None
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class SentimentConfigUpdate(BaseModel):
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"""Runtime sentiment LLM config. api_key is write-only; omit/empty to keep
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the stored key."""
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provider: Literal["openai", "gemini", "deepseek", "xai", "openai_compatible"] | None = None
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model: str | None = Field(default=None, max_length=100)
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api_key: str | None = Field(default=None, max_length=400)
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base_url: str | None = Field(default=None, max_length=300)
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class SentimentTestRequest(BaseModel):
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ticker: str = Field(default="AAPL", max_length=10)
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