gg4

ai-engine/scripts/extract_upcoming_features.py

@@ -0,0 +1,198 @@
+"""
+Extract Upcoming Features — leak-free feature rows for UPCOMING (NS) matches,
+produced by the EXACT same pipeline that built training_data_v27.csv.
+=============================================================================
+Why this exists: the picker (generate_daily_picks.py) needs the 133 leak-free
+features for tomorrow's matches, computed IDENTICALLY to training (any drift =
+train/serve skew = broken model). So we reuse V27Loader + V27Extractor verbatim:
+
+  1. load_all() builds ELO / team history / league / squad caches from FT
+     matches ONLY (untouched — guarantees identical feature computation).
+  2. We then APPEND upcoming NS matches as targets and inject their odds from
+     live_matches.odds (all markets, same mapping as the trainer's _load_odds).
+  3. extract_all() replays FT chronologically (ELO fully built), then computes
+     features for the NS targets at the end. ELO update + labels are guarded
+     for null scores (NS has no result yet); the 133 model features never use
+     the current score, so they come out identical to training.
+  4. Write ONLY the upcoming rows -> data/upcoming_features.csv
+
+Then: generate_daily_picks.py --features data/upcoming_features.csv --log
+
+Run nightly (heavy: full ELO replay, like training). Read-only on the DB.
+"""
+from __future__ import annotations
+import csv
+import json
+import os
+import sys
+import time
+
+if sys.stdout and hasattr(sys.stdout, "reconfigure"):
+    try:
+        sys.stdout.reconfigure(encoding="utf-8")
+    except Exception:
+        pass
+
+AI_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, AI_DIR)
+
+from scripts.extract_training_data_v27 import (  # noqa: E402
+    V27Loader, V27Extractor, ALL_COLS, get_conn,
+)
+
+OUTPUT = os.path.join(AI_DIR, "data", "upcoming_features.csv")
+DAYS_AHEAD = 4
+
+
+def map_live_odds(odds_json) -> dict:
+    """Map live_matches.odds JSON → odds_cache keys, IDENTICAL to the trainer's
+    _load_odds category/selection logic (so odds features match training)."""
+    out: dict = {}
+    if isinstance(odds_json, str):
+        try:
+            odds_json = json.loads(odds_json)
+        except Exception:
+            return out
+    if not isinstance(odds_json, dict):
+        return out
+    for cat, sels in odds_json.items():
+        if not isinstance(sels, dict):
+            continue
+        c = str(cat).lower().strip()
+        for sel, val in sels.items():
+            try:
+                v = float(val)
+            except (TypeError, ValueError):
+                continue
+            if v <= 0:
+                continue
+            sn = str(sel)
+            s = sn.lower().strip()
+            if c == "maç sonucu":
+                if sn == "1": out["ms_h"] = v
+                elif sn in ("0", "X"): out["ms_d"] = v
+                elif sn == "2": out["ms_a"] = v
+            elif c == "1. yarı sonucu":
+                if sn == "1": out["ht_ms_h"] = v
+                elif sn in ("0", "X"): out["ht_ms_d"] = v
+                elif sn == "2": out["ht_ms_a"] = v
+            elif c == "karşılıklı gol":
+                if "var" in s: out["btts_y"] = v
+                elif "yok" in s: out["btts_n"] = v
+            elif c == "0,5 alt/üst":
+                if "alt" in s: out["ou05_u"] = v
+                elif "üst" in s: out["ou05_o"] = v
+            elif c == "1,5 alt/üst":
+                if "alt" in s: out["ou15_u"] = v
+                elif "üst" in s: out["ou15_o"] = v
+            elif c == "2,5 alt/üst":
+                if "alt" in s: out["ou25_u"] = v
+                elif "üst" in s: out["ou25_o"] = v
+            elif c == "3,5 alt/üst":
+                if "alt" in s: out["ou35_u"] = v
+                elif "üst" in s: out["ou35_o"] = v
+            elif c == "1. yarı 0,5 alt/üst":
+                if "alt" in s: out["ht_ou05_u"] = v
+                elif "üst" in s: out["ht_ou05_o"] = v
+            elif c == "1. yarı 1,5 alt/üst":
+                if "alt" in s: out["ht_ou15_u"] = v
+                elif "üst" in s: out["ht_ou15_o"] = v
+    return out
+
+
+class UpcomingExtractor(V27Extractor):
+    """Same feature computation as training; only guards null (NS) scores."""
+
+    def _update_elo(self, home_id, away_id, score_home, score_away):
+        if score_home is None or score_away is None:
+            return  # upcoming match — no result, don't move ELO
+        return super()._update_elo(home_id, away_id, score_home, score_away)
+
+    def _extract_one(self, mid, hid, aid, sh, sa, hth, hta, mst, lid, hn, an, ln):
+        if sh is None or sa is None:
+            # Upcoming TARGET. Dummy scores so label/total_goals don't crash;
+            # those columns are labels/LEAKY and are NOT among the 133 model
+            # features, so the served feature vector is identical to training.
+            row = super()._extract_one(mid, hid, aid, 0, 0, 0, 0, mst, lid, hn, an, ln)
+            if row:
+                row["_upcoming"] = 1
+            return row
+        # FT match: needed ONLY to advance ELO (extract_all calls _update_elo
+        # afterwards regardless). Skip the expensive per-match feature
+        # computation — that turns a ~6h full extraction into seconds while
+        # producing the IDENTICAL final ELO the upcoming targets read.
+        return None
+
+
+def main():
+    t0 = time.time()
+    conn = get_conn()
+
+    # ── Cheap check FIRST: are there upcoming matches with odds? ──
+    now_ms = int(time.time() * 1000)
+    hi_ms = now_ms + DAYS_AHEAD * 24 * 3600 * 1000
+    cur = conn.cursor()
+    cur.execute(
+        """
+        SELECT lm.id, lm.home_team_id, lm.away_team_id, lm.mst_utc, lm.league_id,
+               ht.name, at.name, l.name, lm.odds
+        FROM live_matches lm
+        JOIN teams ht ON ht.id = lm.home_team_id
+        JOIN teams at ON at.id = lm.away_team_id
+        JOIN leagues l ON l.id = lm.league_id
+        WHERE lm.sport = 'football'
+          AND lm.odds IS NOT NULL
+          AND lm.mst_utc > %s AND lm.mst_utc <= %s
+        ORDER BY lm.mst_utc ASC
+        """,
+        (now_ms, hi_ms),
+    )
+    upcoming = cur.fetchall()
+    targets = []
+    for mid, hid, aid, mst, lid, hn, an, ln, odds_json in upcoming:
+        oc = map_live_odds(odds_json)
+        if "ms_h" not in oc or "ms_a" not in oc:
+            continue  # need MS odds for the policy
+        targets.append((mid, hid, aid, mst, lid, hn, an, ln, oc))
+    print(f"Upcoming NS matches with MS odds (next {DAYS_AHEAD}d): {len(targets)}", flush=True)
+    if not targets:
+        print("⚠️ Nothing to extract. Deploy the 4-day window + let the odds cron\n"
+              "   populate live_matches, then re-run.")
+        conn.close()
+        return
+
+    print("📦 Loading FT history (ELO/form/league/squad caches; heavy) ...", flush=True)
+    loader = V27Loader(conn)
+    loader.load_all()
+    loader.load_league_matches()
+    print(f"  FT matches: {len(loader.matches)}", flush=True)
+
+    for mid, hid, aid, mst, lid, hn, an, ln, oc in targets:
+        loader.odds_cache[mid] = oc
+        loader.matches.append(
+            (mid, hid, aid, None, None, None, None, mst, lid, hn, an, ln)
+        )
+    # NS targets must be processed AFTER all FT (ELO fully built)
+    loader.matches.sort(key=lambda m: m[7] if m[7] is not None else 0)
+    added = len(targets)
+
+    print("🔄 Extracting features (FT replay + upcoming targets) ...", flush=True)
+    ext = UpcomingExtractor(conn, loader)
+    rows = ext.extract_all()
+    up_rows = [r for r in rows if r.get("_upcoming")]
+
+    os.makedirs(os.path.dirname(OUTPUT), exist_ok=True)
+    with open(OUTPUT, "w", newline="", encoding="utf-8") as f:
+        w = csv.DictWriter(f, fieldnames=ALL_COLS, extrasaction="ignore")
+        w.writeheader()
+        w.writerows(up_rows)
+
+    with_odds = sum(1 for r in up_rows if r.get("odds_ms_h", 0) and r["odds_ms_h"] > 0)
+    print(f"\n✅ Wrote {len(up_rows)} upcoming feature rows ({with_odds} with MS odds) → {OUTPUT}")
+    print(f"   Time: {(time.time()-t0)/60:.1f} min")
+    print("   Next: python scripts/generate_daily_picks.py --features data/upcoming_features.csv --log")
+    conn.close()
+
+
+if __name__ == "__main__":
+    main()

src/tasks/data-fetcher.task.ts

@@ -23,9 +23,8 @@ import {
 import { TaskLockService } from "./task-lock.service";
 import { FeederService } from "../modules/feeder/feeder.service";
 
-// ────────────────────────────────────────────────────────────────
+
 // Types
-// ────────────────────────────────────────────────────────────────
 
 interface LiveScoreTeamPayload {
   id: string;
@@ -93,9 +92,9 @@ interface PendingPredictionRunForSettlement {
 
 type SportType = "football" | "basketball";
 
-// ────────────────────────────────────────────────────────────────
+
 // Service
-// ────────────────────────────────────────────────────────────────
+
 
 @Injectable()
 export class DataFetcherTask {
@@ -108,7 +107,7 @@ export class DataFetcherTask {
     private readonly scraper: FeederScraperService,
     private readonly taskLock: TaskLockService,
     private readonly feeder: FeederService,
-  ) {}
+  ) { }
 
   // ────────────────────────────────────────────────────────────
   // CRON 1: Main sync — every 15 minutes
@@ -200,10 +199,20 @@ export class DataFetcherTask {
 
     this.logger.log("syncLiveMatches START");
 
+    // 4-day forward window: pull today .. +3 days so upcoming matches enter
+    // live_matches early and their odds are refreshed every cycle. That rolling
+    // refresh is what lets the odds-movement / steam monitor (and forward CLV)
+    // see a real opening→closing range. Finished/live matches are already
+    // excluded from odds re-fetch in fetchOddsForMatches(), so closed-match
+    // odds and their ranges are never re-pulled.
+    const SYNC_DAYS_AHEAD = 4;
     const today = getDateStringInTimeZone(new Date(), this.timeZone);
-    const tomorrow = getShiftedDateStringInTimeZone(1, this.timeZone);
     await this.syncMatchList(today);
-    await this.syncMatchList(tomorrow);
+    for (let dayOffset = 1; dayOffset < SYNC_DAYS_AHEAD; dayOffset++) {
+      await this.syncMatchList(
+        getShiftedDateStringInTimeZone(dayOffset, this.timeZone),
+      );
+    }
     await this.updateLiveScores();
     await this.archiveNewlyFinishedMatches(today);
     await this.settlePredictionRuns();
@@ -1167,7 +1176,7 @@ export class DataFetcherTask {
                   updatedAt: new Date(),
                 },
               })
-              .catch(() => {});
+              .catch(() => { });
             this.logger.debug(
               `[${sport}] Marked as POSTPONED: ${match.matchName}`,
             );
@@ -1240,6 +1249,77 @@ export class DataFetcherTask {
   // (Preserved from original — no logic changes)
   // ────────────────────────────────────────────────────────────
 
+  /**
+   * Persist live odds movement into the structured odd_selections / odds_history
+   * tables (the live odds elsewhere are only a JSON blob on live_matches). This
+   * is what makes opening→closing ranges and steam queryable in SQL. Mirrors the
+   * historical feeder's change-detection (feeder-persistence.service.ts) but for
+   * the live HTML odds format. Change-only writes; ALL markets in the payload
+   * are tracked (so anomalies/steam on any bet type are captured).
+   */
+  private async persistOddsHistory(
+    matchId: string,
+    odds: Record<string, Record<string, number>>,
+  ): Promise<void> {
+    try {
+      const cats = await this.prisma.oddCategory.findMany({
+        where: { matchId },
+        include: { selections: true },
+      });
+      for (const [catName, sels] of Object.entries(odds)) {
+        let category = cats.find((c) => c.name === catName);
+        if (!category) {
+          category = await this.prisma.oddCategory.create({
+            data: { matchId, name: catName },
+            include: { selections: true },
+          });
+          cats.push(category);
+        }
+        for (const [selName, value] of Object.entries(sels)) {
+          const sVal = String(value);
+          if (!selName || !sVal || !(value > 0)) continue;
+          const existing = category.selections.find((s) => s.name === selName);
+          if (existing) {
+            if (existing.oddValue !== sVal) {
+              const oldVal = parseFloat(existing.oddValue || "0");
+              const newVal = parseFloat(sVal);
+              if (!isNaN(oldVal) && !isNaN(newVal) && oldVal > 0) {
+                await this.prisma.oddsHistory.create({
+                  data: {
+                    selectionId: existing.dbId,
+                    matchId,
+                    previousValue: oldVal,
+                    newValue: newVal,
+                  },
+                });
+              }
+              await this.prisma.oddSelection.update({
+                where: { dbId: existing.dbId },
+                data: { oddValue: sVal },
+              });
+              existing.oddValue = sVal;
+            }
+          } else {
+            const created = await this.prisma.oddSelection.create({
+              data: {
+                categoryId: category.dbId,
+                name: selName,
+                oddValue: sVal,
+                openingValue: sVal,
+              },
+            });
+            category.selections.push(created);
+          }
+        }
+      }
+    } catch (err: unknown) {
+      const message = err instanceof Error ? err.message : String(err);
+      this.logger.debug(
+        `odds_history persist skipped for ${matchId}: ${message}`,
+      );
+    }
+  }
+
   private async processMatchOdds(match: LiveMatchOddsTarget): Promise<void> {
     const matchSlug = match.matchSlug || "match";
     const sport = String(match.sport || "football").toLowerCase();
@@ -1408,6 +1488,13 @@ export class DataFetcherTask {
       },
     });
 
+    // Fill the structured odds_history table from this pre-match odds refresh,
+    // so opening→closing ranges & steam are captured in the DB (not just the
+    // live_matches JSON blob). Runs only for pre-match matches reached here.
+    if (Object.keys(odds).length > 0) {
+      await this.persistOddsHistory(match.id, odds);
+    }
+
     if (
       Object.keys(odds).length > 0 ||
       refereeName ||