dennisthiessenandClaude Fable 5
ba2df8b9fd
The manual paper book only contains trades taken by hand, inside a 20
minute window, on days someone was available. The backtest that validated
this strategy auto-takes the top-ranked qualified setups up to capacity
every session. The forward record was therefore measuring strategy plus
discretion plus availability -- and degrading silently on busy days.
The shadow book closes that gap: it mirrors the backtest's selection rule
(top strategy_rank qualified, up to capacity, 1% fixed-fractional risk)
and shares the manual book's exit policy, so the only difference between
the two books is which setups get taken. Selection ordering reuses the
strategy_rank the scanner already stores rather than recomputing it, so
the two cannot drift apart. It runs as a near-close pipeline step right
after the scan, marking entries at the same prices a human would see.
Gate-reset re-entry state is now scoped per book -- the books diverge as
soon as their entries differ, and each must see only its own stops.
Performance view rewritten around the comparison:
- three series (shadow, manual, SPY) from a new endpoint
- SPY changes from a per-trade cost-basis counterfactual to plain
buy-and-hold %, since one line has to serve two books
- headline stats are R-multiples, not currency: the books size
differently, so only R compares across them
- configurable start date, because the strategy has been revised
repeatedly and pre-cutover trades ran under rules that no longer
exist
Migration 024 also repairs the numeric weekday crons written by 023,
rewriting only rows still holding the broken form so hand-corrected
settings survive. Its literals are inlined because bound parameters
render as NULL under 'alembic upgrade --sql'.
The shadow book is opt-in and writes nothing until enabled. Verify its
first selections match a backtest of that day's cross-section before
trusting any point on the curve.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
131 lines
4.9 KiB
Python
131 lines
4.9 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 JobTriggerRequest(BaseModel):
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"""Optional parameters for a one-time manual job run."""
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target_model: Literal["production_gtl", "structural_sr"] | None = None
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cadence: Literal["weekly", "daily"] | None = None
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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_momentum_percentile: float | None = Field(default=None, ge=0, le=100)
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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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require_high_conviction: bool | None = None
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exclude_conflicts: bool | None = None
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exclude_neutral: bool | None = None
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class ScheduleConfigUpdate(BaseModel):
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"""Cron schedule for the pipelines + fundamentals. Crons are 5-field
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(min hour dom month dow); timezone is an IANA name (e.g. America/New_York)."""
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schedule_timezone: str | None = Field(default=None, max_length=64)
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schedule_daily_pipeline_cron: str | None = Field(default=None, max_length=120)
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schedule_near_close_pipeline_cron: str | None = Field(default=None, max_length=120)
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schedule_after_close_pipeline_cron: str | None = Field(default=None, max_length=120)
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schedule_intraday_pipeline_cron: str | None = Field(default=None, max_length=120)
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schedule_fundamentals_cron: str | None = Field(default=None, max_length=120)
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class PerformanceConfigUpdate(BaseModel):
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"""Window for the Performance comparison.
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``start_date`` is an ISO date, or empty string to show all history. The
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strategy has been revised repeatedly; pinning a start keeps the shadow-vs-
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manual comparison inside one configuration instead of averaging across
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rules that no longer exist.
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"""
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start_date: str | None = Field(default=None, max_length=10)
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class ShadowBookConfigUpdate(BaseModel):
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"""Auto-traded shadow book: the validated strategy with no human input."""
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enabled: bool | None = None
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capacity: int | None = Field(default=None, ge=1, le=100)
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risk_pct: float | None = Field(default=None, gt=0, le=10)
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start_equity: float | None = Field(default=None, ge=1000)
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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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class AlertConfigUpdate(BaseModel):
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"""Telegram alert config. bot_token is write-only; omit/empty to keep the
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stored token."""
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enabled: bool | None = None
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bot_token: str | None = Field(default=None, max_length=200)
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telegram_chat_id: str | None = Field(default=None, max_length=64)
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qualified_enabled: bool | None = None
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sr_proximity_enabled: bool | None = None
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score_drop_enabled: bool | None = None
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digest_enabled: bool | None = None
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regime_quadrant_enabled: bool | None = None
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trade_closed_enabled: bool | None = None
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