first commit

app/schemas/__init__.py

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+

app/schemas/admin.py

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+"""Admin request/response schemas."""
+
+from pydantic import BaseModel, Field
+
+
+class UserManagement(BaseModel):
+    """Schema for user access management."""
+    has_access: bool
+
+
+class PasswordReset(BaseModel):
+    """Schema for resetting a user's password."""
+    new_password: str = Field(..., min_length=6)
+
+
+class CreateUserRequest(BaseModel):
+    """Schema for admin-created user accounts."""
+    username: str = Field(..., min_length=1)
+    password: str = Field(..., min_length=6)
+    role: str = Field(default="user", pattern=r"^(user|admin)$")
+    has_access: bool = False
+
+
+class RegistrationToggle(BaseModel):
+    """Schema for toggling registration on/off."""
+    enabled: bool
+
+
+class SystemSettingUpdate(BaseModel):
+    """Schema for updating a system setting."""
+    value: str = Field(..., min_length=1)
+
+
+class DataCleanupRequest(BaseModel):
+    """Schema for data cleanup — delete records older than N days."""
+    older_than_days: int = Field(..., gt=0)
+
+
+class JobToggle(BaseModel):
+    """Schema for enabling/disabling a scheduled job."""
+    enabled: bool

app/schemas/auth.py

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+"""Auth request/response schemas."""
+
+from pydantic import BaseModel, Field
+
+
+class RegisterRequest(BaseModel):
+    username: str = Field(..., min_length=1)
+    password: str = Field(..., min_length=6)
+
+
+class LoginRequest(BaseModel):
+    username: str
+    password: str
+
+
+class TokenResponse(BaseModel):
+    access_token: str
+    token_type: str = "bearer"

app/schemas/common.py

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+"""Shared API schemas used across all endpoints."""
+
+from typing import Any, Literal
+
+from pydantic import BaseModel
+
+
+class APIEnvelope(BaseModel):
+    """Standard JSON envelope for all API responses."""
+
+    status: Literal["success", "error"]
+    data: Any | None = None
+    error: str | None = None

app/schemas/fundamental.py

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+"""Pydantic schemas for fundamental data endpoints."""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from pydantic import BaseModel
+
+
+class FundamentalResponse(BaseModel):
+    """Envelope-ready fundamental data response."""
+
+    symbol: str
+    pe_ratio: float | None = None
+    revenue_growth: float | None = None
+    earnings_surprise: float | None = None
+    market_cap: float | None = None
+    fetched_at: datetime | None = None

app/schemas/indicator.py

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+"""Pydantic schemas for technical indicator endpoints."""
+
+from __future__ import annotations
+
+from datetime import date
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+
+class IndicatorRequest(BaseModel):
+    """Query parameters for indicator computation."""
+
+    start_date: date | None = None
+    end_date: date | None = None
+    period: int | None = None
+
+
+class IndicatorResult(BaseModel):
+    """Raw indicator values plus normalized score."""
+
+    indicator_type: str
+    values: dict[str, Any]
+    score: float = Field(ge=0, le=100)
+    bars_used: int
+
+
+class IndicatorResponse(BaseModel):
+    """Envelope-ready indicator response."""
+
+    symbol: str
+    indicator: IndicatorResult
+
+
+class EMACrossResult(BaseModel):
+    """EMA cross signal details."""
+
+    short_ema: float
+    long_ema: float
+    short_period: int
+    long_period: int
+    signal: Literal["bullish", "bearish", "neutral"]
+
+
+class EMACrossResponse(BaseModel):
+    """Envelope-ready EMA cross response."""
+
+    symbol: str
+    ema_cross: EMACrossResult

app/schemas/ohlcv.py

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+"""OHLCV request/response schemas."""
+
+from __future__ import annotations
+
+import datetime as _dt
+
+from pydantic import BaseModel, Field
+
+
+class OHLCVCreate(BaseModel):
+    symbol: str = Field(..., description="Ticker symbol (e.g. AAPL)")
+    date: _dt.date = Field(..., description="Trading date (YYYY-MM-DD)")
+    open: float = Field(..., ge=0, description="Opening price")
+    high: float = Field(..., ge=0, description="High price")
+    low: float = Field(..., ge=0, description="Low price")
+    close: float = Field(..., ge=0, description="Closing price")
+    volume: int = Field(..., ge=0, description="Trading volume")
+
+
+class OHLCVResponse(BaseModel):
+    id: int
+    ticker_id: int
+    date: _dt.date
+    open: float
+    high: float
+    low: float
+    close: float
+    volume: int
+    created_at: _dt.datetime
+
+    model_config = {"from_attributes": True}

app/schemas/score.py

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+"""Pydantic schemas for scoring endpoints."""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from pydantic import BaseModel, Field
+
+
+class DimensionScoreResponse(BaseModel):
+    """A single dimension score."""
+
+    dimension: str
+    score: float
+    is_stale: bool
+    computed_at: datetime | None = None
+
+
+class ScoreResponse(BaseModel):
+    """Full score response for a ticker: composite + all dimensions."""
+
+    symbol: str
+    composite_score: float | None = None
+    composite_stale: bool = False
+    weights: dict[str, float] = {}
+    dimensions: list[DimensionScoreResponse] = []
+    missing_dimensions: list[str] = []
+    computed_at: datetime | None = None
+
+
+class WeightUpdateRequest(BaseModel):
+    """Request to update dimension weights."""
+
+    weights: dict[str, float] = Field(
+        ...,
+        description="Dimension name → weight mapping. All weights must be positive.",
+    )
+
+
+class RankingEntry(BaseModel):
+    """A single entry in the rankings list."""
+
+    symbol: str
+    composite_score: float
+    dimensions: list[DimensionScoreResponse] = []
+
+
+class RankingResponse(BaseModel):
+    """Rankings response: tickers sorted by composite score descending."""
+
+    rankings: list[RankingEntry] = []
+    weights: dict[str, float] = {}

app/schemas/sentiment.py

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+"""Pydantic schemas for sentiment endpoints."""
+
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class SentimentScoreResult(BaseModel):
+    """A single sentiment score record."""
+
+    id: int
+    classification: Literal["bullish", "bearish", "neutral"]
+    confidence: int = Field(ge=0, le=100)
+    source: str
+    timestamp: datetime
+
+
+class SentimentResponse(BaseModel):
+    """Envelope-ready sentiment response."""
+
+    symbol: str
+    scores: list[SentimentScoreResult]
+    count: int
+    dimension_score: float | None = Field(
+        None, ge=0, le=100, description="Time-decay weighted sentiment dimension score"
+    )
+    lookback_hours: float

app/schemas/sr_level.py

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+"""Pydantic schemas for S/R level endpoints."""
+
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class SRLevelResult(BaseModel):
+    """A single support/resistance level."""
+
+    id: int
+    price_level: float
+    type: Literal["support", "resistance"]
+    strength: int = Field(ge=0, le=100)
+    detection_method: Literal["volume_profile", "pivot_point", "merged"]
+    created_at: datetime
+
+
+class SRLevelResponse(BaseModel):
+    """Envelope-ready S/R levels response."""
+
+    symbol: str
+    levels: list[SRLevelResult]
+    count: int

app/schemas/ticker.py

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+"""Ticker request/response schemas."""
+
+from datetime import datetime
+
+from pydantic import BaseModel, Field
+
+
+class TickerCreate(BaseModel):
+    symbol: str = Field(..., description="NASDAQ ticker symbol (e.g. AAPL)")
+
+
+class TickerResponse(BaseModel):
+    id: int
+    symbol: str
+    created_at: datetime
+
+    model_config = {"from_attributes": True}

app/schemas/trade_setup.py

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+"""Pydantic schemas for trade setup endpoints."""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from pydantic import BaseModel
+
+
+class TradeSetupResponse(BaseModel):
+    """A single trade setup detected by the R:R scanner."""
+
+    id: int
+    symbol: str
+    direction: str
+    entry_price: float
+    stop_loss: float
+    target: float
+    rr_ratio: float
+    composite_score: float
+    detected_at: datetime

app/schemas/watchlist.py

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+"""Pydantic schemas for watchlist endpoints."""
+
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class SRLevelSummary(BaseModel):
+    """Compact SR level for watchlist entry."""
+
+    price_level: float
+    type: Literal["support", "resistance"]
+    strength: int = Field(ge=0, le=100)
+
+
+class DimensionScoreSummary(BaseModel):
+    """Compact dimension score for watchlist entry."""
+
+    dimension: str
+    score: float
+
+
+class WatchlistEntryResponse(BaseModel):
+    """A single watchlist entry with enriched data."""
+
+    symbol: str
+    entry_type: Literal["auto", "manual"]
+    composite_score: float | None = None
+    dimensions: list[DimensionScoreSummary] = []
+    rr_ratio: float | None = None
+    rr_direction: str | None = None
+    sr_levels: list[SRLevelSummary] = []
+    added_at: datetime