129 lines
4.2 KiB
Python
129 lines
4.2 KiB
Python
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"""
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Export entry snapshots joined with trade outcomes for later model training.
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Usage:
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python scripts/export_training_dataset.py
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python scripts/export_training_dataset.py data/training_dataset.csv
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"""
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import csv
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import os
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(ROOT))
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from app.db.models import get_conn, init_db
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DEFAULT_OUT = ROOT / "data" / "training_dataset.csv"
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def export_csv(out_path: Path):
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init_db()
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out_path.parent.mkdir(parents=True, exist_ok=True)
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query = """
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WITH post AS (
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SELECT
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trade_id,
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MAX(CASE WHEN elapsed_sec = 60 THEN current_price END) AS price_1m,
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MAX(CASE WHEN elapsed_sec = 60 THEN return_pct END) AS ret_1m,
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MAX(CASE WHEN elapsed_sec = 60 THEN mfe_pct END) AS mfe_1m,
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MAX(CASE WHEN elapsed_sec = 60 THEN mae_pct END) AS mae_1m,
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MAX(CASE WHEN elapsed_sec = 180 THEN current_price END) AS price_3m,
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MAX(CASE WHEN elapsed_sec = 180 THEN return_pct END) AS ret_3m,
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MAX(CASE WHEN elapsed_sec = 180 THEN mfe_pct END) AS mfe_3m,
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MAX(CASE WHEN elapsed_sec = 180 THEN mae_pct END) AS mae_3m,
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MAX(CASE WHEN elapsed_sec = 300 THEN current_price END) AS price_5m,
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MAX(CASE WHEN elapsed_sec = 300 THEN return_pct END) AS ret_5m,
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MAX(CASE WHEN elapsed_sec = 300 THEN mfe_pct END) AS mfe_5m,
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MAX(CASE WHEN elapsed_sec = 300 THEN mae_pct END) AS mae_5m,
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MAX(CASE WHEN elapsed_sec = 600 THEN current_price END) AS price_10m,
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MAX(CASE WHEN elapsed_sec = 600 THEN return_pct END) AS ret_10m,
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MAX(CASE WHEN elapsed_sec = 600 THEN mfe_pct END) AS mfe_10m,
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MAX(CASE WHEN elapsed_sec = 600 THEN mae_pct END) AS mae_10m
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FROM post_entry_snapshots
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GROUP BY trade_id
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)
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SELECT
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s.trade_id,
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s.date,
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s.ticker,
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s.name,
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s.entry_time,
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s.current_price,
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s.entry_price,
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s.target_price,
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s.stop_price,
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s.today_open,
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s.prev_high,
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s.prev_low,
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s.prev_amount,
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s.volume,
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s.change_pct,
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s.market_sentiment,
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s.sentiment_score,
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s.risk_level,
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s.trade_allowed,
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s.hot_sectors,
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s.avoid_sectors,
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s.boosted_tickers,
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s.blacklist_tickers,
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s.ai_boosted,
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s.ai_win_score,
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s.ai_stop_loss_score,
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s.ai_model_version,
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s.position_size_multiplier,
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s.combined_multiplier,
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s.entry_reason,
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post.price_1m,
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post.ret_1m,
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post.mfe_1m,
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post.mae_1m,
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post.price_3m,
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post.ret_3m,
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post.mfe_3m,
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post.mae_3m,
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post.price_5m,
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post.ret_5m,
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post.mfe_5m,
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post.mae_5m,
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post.price_10m,
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post.ret_10m,
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post.mfe_10m,
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post.mae_10m,
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t.exit_time,
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t.exit_price,
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t.quantity,
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t.exit_reason,
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t.pnl,
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CASE WHEN t.pnl > 0 THEN 1 ELSE 0 END AS label_win,
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CASE WHEN t.exit_reason = 'SL' THEN 1 ELSE 0 END AS label_stop_loss
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FROM entry_snapshots s
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LEFT JOIN trades t ON t.id = s.trade_id
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LEFT JOIN post ON post.trade_id = s.trade_id
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ORDER BY s.date, s.entry_time, s.ticker
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"""
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with get_conn() as conn:
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conn.row_factory = None
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cur = conn.execute(query)
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headers = [d[0] for d in cur.description]
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rows = cur.fetchall()
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with out_path.open("w", newline="", encoding="utf-8-sig") as f:
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writer = csv.writer(f)
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writer.writerow(headers)
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writer.writerows(rows)
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print(f"exported {len(rows)} rows -> {out_path}")
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if __name__ == "__main__":
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target = Path(sys.argv[1]) if len(sys.argv) > 1 else DEFAULT_OUT
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if not target.is_absolute():
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target = ROOT / target
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export_csv(target)
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