dennisthiessen
21ed83c56c
Closes the feedback loop on R:R scanner signals: - Nightly outcome_evaluator job replays unresolved setups against daily OHLCV bars: target_hit / stop_hit / ambiguous (same-bar, counted as loss) / expired after OUTCOME_EVALUATION_MAX_BARS (default 30) - Migration 004: evaluated_at + outcome_date on trade_setups - GET /trades/performance: hit rate, expectancy (avg R), total R with breakdowns by direction, recommended action, and confidence bucket - New Performance page (stat cards, breakdown tables, Evaluate Now, methodology disclosure) wired into sidebar and mobile nav - 17 new unit tests for evaluation logic and stats aggregation Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
35 lines
809 B
Python
35 lines
809 B
Python
"""add outcome evaluation fields to trade_setups
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Revision ID: 004
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Revises: 003
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Create Date: 2026-06-10 00:00:00.000000
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"""
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from typing import Sequence, Union
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from alembic import op
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import sqlalchemy as sa
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# revision identifiers, used by Alembic.
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revision: str = "004"
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down_revision: Union[str, None] = "003"
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branch_labels: Union[str, Sequence[str], None] = None
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depends_on: Union[str, Sequence[str], None] = None
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def upgrade() -> None:
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op.add_column(
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"trade_setups",
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sa.Column("evaluated_at", sa.DateTime(timezone=True), nullable=True),
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)
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op.add_column(
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"trade_setups",
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sa.Column("outcome_date", sa.Date(), nullable=True),
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)
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def downgrade() -> None:
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op.drop_column("trade_setups", "outcome_date")
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op.drop_column("trade_setups", "evaluated_at")
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