Energy spread

experiments/betting/extractor.py

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+#!/usr/bin/env python3
+"""
+Download (snapshot) NFL Divisional Round weekend spreads from multiple books
+using The Odds API v4, and append as JSONL.
+
+Output: one JSON object per line (JSONL). Easy to grep / load later.
+
+Setup:
+  pip install requests
+  export ODDS_API_KEY="YOUR_KEY"
+
+Run:
+  python nfl_weekend_spreads_jsonl.py
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from dataclasses import asdict, dataclass
+from datetime import datetime, timedelta, timezone
+from typing import Any
+from zoneinfo import ZoneInfo
+
+import requests
+
+BASE_URL = "https://api.the-odds-api.com/v4"
+TZ_LOCAL = ZoneInfo("America/Mexico_City")  # your timezone
+
+
+@dataclass
+class SpreadRow:
+    snapshot_utc: str
+    snapshot_local: str
+
+    sport_key: str
+    event_id: str
+    commence_time: str  # ISO
+
+    home_team: str
+    away_team: str
+
+    bookmaker_key: str
+    bookmaker_title: str
+    bookmaker_last_update: str | None
+
+    outcome_name: str
+    spread_point: float | None
+    american_price: int | None
+
+
+def iso_utc_now() -> str:
+    return datetime.now(timezone.utc).replace(microsecond=0).isoformat()
+
+
+def iso_local_now() -> str:
+    return datetime.now(TZ_LOCAL).replace(microsecond=0).isoformat()
+
+
+def weekend_window_local(anchor_local: datetime) -> tuple[datetime, datetime]:
+    """
+    Returns (start, end) local datetimes covering THIS weekend:
+      Saturday 00:00 local -> Monday 00:00 local
+    If it's already weekend, it still returns the current weekend window.
+    """
+    # Monday=0 ... Sunday=6
+    dow = anchor_local.weekday()
+    # Find upcoming Saturday (or today if Saturday)
+    days_until_sat = (5 - dow) % 7
+    sat = anchor_local.replace(hour=0, minute=0, second=0, microsecond=0) + timedelta(
+        days=days_until_sat
+    )
+    mon = sat + timedelta(days=2)
+    return sat, mon
+
+
+def get_odds(api_key: str, sport_key: str) -> list[dict[str, Any]]:
+    """
+    v4 odds endpoint (featured markets like spreads/totals/h2h).
+    """
+    params = {
+        "apiKey": api_key,
+        "regions": "us",
+        "markets": "spreads",
+        "oddsFormat": "american",
+        "dateFormat": "iso",
+    }
+    url = f"{BASE_URL}/sports/{sport_key}/odds"
+    r = requests.get(url, params=params, timeout=30)
+    r.raise_for_status()
+    return r.json()
+
+
+def parse_iso_as_utc(dt_str: str) -> datetime:
+    # The Odds API returns ISO timestamps; treat "Z" as UTC.
+    # Python fromisoformat doesn't like "Z" directly.
+    if dt_str.endswith("Z"):
+        dt_str = dt_str[:-1] + "+00:00"
+    return datetime.fromisoformat(dt_str).astimezone(timezone.utc)
+
+
+def in_window(
+    commence_time_iso: str, start_local: datetime, end_local: datetime
+) -> bool:
+    commence_utc = parse_iso_as_utc(commence_time_iso)
+    commence_local = commence_utc.astimezone(TZ_LOCAL)
+    return start_local <= commence_local < end_local
+
+
+def flatten_spreads(
+    events: list[dict[str, Any]], snapshot_utc: str, snapshot_local: str
+) -> list[SpreadRow]:
+    rows: list[SpreadRow] = []
+
+    for ev in events:
+        sport_key = ev.get("sport_key", "")
+        event_id = ev.get("id", "")
+        commence_time = ev.get("commence_time", "")
+        home_team = ev.get("home_team", "")
+        away_team = ev.get("away_team", "")
+
+        for bk in ev.get("bookmakers", []) or []:
+            bk_key = bk.get("key", "")
+            bk_title = bk.get("title", "")
+            bk_last_update = bk.get("last_update")
+
+            for mkt in bk.get("markets", []) or []:
+                if mkt.get("key") != "spreads":
+                    continue
+
+                for out in mkt.get("outcomes", []) or []:
+                    rows.append(
+                        SpreadRow(
+                            snapshot_utc=snapshot_utc,
+                            snapshot_local=snapshot_local,
+                            sport_key=sport_key,
+                            event_id=event_id,
+                            commence_time=commence_time,
+                            home_team=home_team,
+                            away_team=away_team,
+                            bookmaker_key=bk_key,
+                            bookmaker_title=bk_title,
+                            bookmaker_last_update=bk_last_update,
+                            outcome_name=out.get("name"),
+                            spread_point=out.get("point"),
+                            american_price=out.get("price"),
+                        )
+                    )
+    return rows
+
+
+def append_jsonl(path: str, rows: list[SpreadRow]) -> None:
+    with open(path, "a", encoding="utf-8") as f:
+        for row in rows:
+            f.write(json.dumps(asdict(row), ensure_ascii=False) + "\n")
+
+
+def main() -> None:
+    api_key = os.getenv("ODDS_API_KEY")
+    if not api_key:
+        raise SystemExit("Missing ODDS_API_KEY env var.")
+
+    sport_key = os.getenv("SPORT_KEY", "americanfootball_nfl")
+
+    now_local = datetime.now(TZ_LOCAL)
+    start_local, end_local = weekend_window_local(now_local)
+
+    snapshot_utc = iso_utc_now()
+    snapshot_local = iso_local_now()
+
+    print(f"[{snapshot_local}] Weekend window (local): {start_local} -> {end_local}")
+    print(f"[{snapshot_local}] Fetching spreads for: {sport_key}")
+
+    events = get_odds(api_key=api_key, sport_key=sport_key)
+
+    weekend_events = [
+        ev
+        for ev in events
+        if ev.get("commence_time")
+        and in_window(ev["commence_time"], start_local, end_local)
+    ]
+    print(
+        f"[{snapshot_local}] Events returned: {len(events)} | "
+        f"weekend-matching: {len(weekend_events)}"
+    )
+
+    rows = flatten_spreads(
+        weekend_events, snapshot_utc=snapshot_utc, snapshot_local=snapshot_local
+    )
+    out_path = os.getenv("OUT_PATH", f"spreads_weekend_{sport_key}.jsonl")
+
+    append_jsonl(out_path, rows)
+    print(f"[{snapshot_local}] Wrote {len(rows)} rows -> {out_path}")
+
+
+if __name__ == "__main__":
+    main()

experiments/energia/first_run.txt

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+[METRICS] Top rows:
+  unique_id        MAE       RMSE   n  sign_acc  recall_pos  recall_neg  mcc_sign
+__overall__ 134.314707 230.578105 305  0.593443    0.335938    0.779661  0.128672
+  06HUI-230 110.478317 183.457210  61  0.655738    0.300000    0.829268  0.148186
+  03GDU-230 156.631519 252.001792  61  0.606557    0.100000    0.853659 -0.064446
+  04HLI-230  91.313545 178.034161  61  0.590164    0.710526    0.391304  0.105130
+  05GPL-230 112.453093 182.419840  61  0.590164    0.304348    0.763158  0.074376
+  01CEI-230 200.697060 322.019723  61  0.524590    0.037037    0.911765 -0.102733
+