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9872a4cc52
Closes the loop on the asset-routing prompt change. Previously the v5 prompt emitted target_asset (e.g. SOL, TRUMP) but bot_engine still read price_impact_asset and only ever traded BTC/ETH. Now the trade actually fires on whatever perp the AI picked. Schema (alembic 006): posts.target_asset (str) — HL perp ticker, any of the universe posts.category (str) — 6-class enum (direct_named, etc.) posts.expected_move_pct (float) — AI's 1h move estimate Wiring: truth_social.py persists the three fields when creating Post rows. bot_engine.py routing: asset = target_asset || price_impact_asset || BTC Old rows (target_asset=NULL) fall back to legacy BTC/ETH path — no retroactive scoring needed; new rows route correctly from now on. Hyperliquid trader doesn't need changes — `coin` is already a parameter, and analysis.py validated against HL_PERPS before storing target_asset so by the time bot_engine reads the field, it's guaranteed tradeable. Deployment: alembic upgrade head # adds the 3 columns Restart api container Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
"""Add target_asset / category / expected_move_pct to posts (v5 routing)
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Revision ID: 006
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Revises: 005
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Create Date: 2026-05-08 00:00:00.000000
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The v5 AI prompt outputs not just signal direction but also which specific
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perp to trade (could be BTC/ETH/SOL/TRUMP/anything on Hyperliquid). The
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bot reads `target_asset` to route the trade. `price_impact_asset` stays
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bound to BTC/ETH for the existing price_impact_monitor.
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"""
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from typing import Sequence, Union
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import sqlalchemy as sa
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from alembic import op
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revision: str = "006"
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down_revision: Union[str, None] = "005"
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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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with op.batch_alter_table("posts") as batch_op:
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batch_op.add_column(sa.Column("target_asset", sa.String(16), nullable=True))
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batch_op.add_column(sa.Column("category", sa.String(24), nullable=True))
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batch_op.add_column(sa.Column("expected_move_pct", sa.Float(), nullable=True))
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def downgrade() -> None:
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with op.batch_alter_table("posts") as batch_op:
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batch_op.drop_column("expected_move_pct")
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batch_op.drop_column("category")
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batch_op.drop_column("target_asset")
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