Add calendar research infra: FF scraper + verified event store

Scrapes Forex Factory weekly pages (epoch datelines, actual/forecast/
impact/betterWorse) into backtest/cache/calendar.parquet (gitignored;
rebuild with the scraper). Timestamps verified: NFP/CPI/ECB times exact
across DST, and high-impact event bars average 3.5x ATR with the spike
vanishing at +/-1h shifts. calendar_store provides precise_events and
event_window_mask for screens and counterfactuals.

backtest/research/__init__.py

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+"""Research tooling: calendar sourcing/store and edge-screening utilities.
+
+Nothing here is imported by the live bot; these modules support
+backtesting and edge validation only.
+"""

backtest/research/calendar_store.py

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+"""Load and query the historical economic calendar (backtest side).
+
+The store is built by `ff_calendar_scraper.py`. Timestamps are unix
+epoch (UTC) — verified against known release times and price-volatility
+alignment (see scraper docstring).
+
+Key API:
+    cal = load_calendar()                          # full DataFrame, `utc` column
+    ev  = precise_events(cal, ["USD", "EUR"])      # high-impact, minute-accurate
+    mask = event_window_mask(bar_index, ev.utc, before, after)
+"""
+from pathlib import Path
+from typing import Iterable, Optional
+
+import numpy as np
+import pandas as pd
+
+PARQUET = Path(__file__).resolve().parent.parent / "cache" / "calendar.parquet"
+
+# timeLabel values that mean "no intraday time" — these events cannot
+# anchor an M5/M15 window (German Prelim CPI is the notable High one).
+_UNTIMED = {"All Day", "Tentative", ""}
+
+
+def _parse_release_value(raw) -> float:
+    """'0.3%' / '-1.7%' / '228K' / '1.1M' -> float (nan if unparseable)."""
+    import re
+    if raw is None or (isinstance(raw, float) and np.isnan(raw)):
+        return np.nan
+    m = re.search(r"(-?\d+(?:\.\d+)?)\s*([kmbt])?",
+                  str(raw).replace(",", ""), re.IGNORECASE)
+    if not m:
+        return np.nan
+    mult = {"k": 1e3, "m": 1e6, "b": 1e9, "t": 1e12}.get(
+        (m.group(2) or "").lower(), 1.0)
+    return float(m.group(1)) * mult
+
+
+def load_calendar(path: Path = PARQUET) -> pd.DataFrame:
+    df = pd.read_parquet(path)
+    df["utc"] = pd.to_datetime(df.dateline, unit="s", utc=True)
+    for col in ("actual", "forecast", "previous"):
+        df[f"{col}_num"] = df[col].map(_parse_release_value)
+    df["surprise"] = df.actual_num - df.forecast_num
+    return df
+
+
+def precise_events(
+    cal: pd.DataFrame,
+    currencies: Iterable[str] = ("USD", "EUR"),
+    impacts: Iterable[str] = ("high",),
+    require_values: bool = False,
+) -> pd.DataFrame:
+    """Events with a minute-accurate timestamp, filtered by currency/impact."""
+    out = cal[cal.currency.isin(list(currencies))
+              & cal.impactName.isin(list(impacts))
+              & ~cal.timeMasked.astype(bool)
+              & ~cal.timeLabel.fillna("").isin(_UNTIMED)]
+    if require_values:
+        out = out.dropna(subset=["actual_num", "forecast_num"])
+    return out.sort_values("utc").reset_index(drop=True)
+
+
+def event_window_mask(
+    bar_index: pd.DatetimeIndex,
+    event_times: pd.Series,
+    before: pd.Timedelta = pd.Timedelta(minutes=30),
+    after: pd.Timedelta = pd.Timedelta(minutes=30),
+) -> np.ndarray:
+    """Boolean mask: bar OPEN time falls inside [event-before, event+after).
+
+    Use on a backtest's entry times to identify trades entered around
+    releases (avoidance counterfactuals, event-window cost models).
+    """
+    starts = pd.DatetimeIndex(event_times) - before
+    ends = pd.DatetimeIndex(event_times) + after
+    order = np.argsort(starts.values)
+    starts, ends = starts.values[order], ends.values[order]
+    # merge overlapping windows, then interval-stab each bar time
+    merged_s, merged_e = [], []
+    for s, e in zip(starts, ends):
+        if merged_e and s <= merged_e[-1]:
+            merged_e[-1] = max(merged_e[-1], e)
+        else:
+            merged_s.append(s)
+            merged_e.append(e)
+    ms = np.array(merged_s)
+    me = np.array(merged_e)
+    ts = bar_index.values
+    pos = np.searchsorted(ms, ts, side="right") - 1
+    ok = pos >= 0
+    res = np.zeros(len(ts), dtype=bool)
+    res[ok] = ts[ok] < me[pos[ok]]
+    return res

backtest/research/ff_calendar_scraper.py

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+"""Forex Factory calendar scraper -> backtest/cache/calendar.parquet.
+
+Fetches weekly calendar pages and parses the event JSON embedded in
+`window.calendarComponentStates` (the `days:` array). Event `dateline`
+is a unix epoch, so timestamps carry no timezone ambiguity — verified
+2026-07-11 against known release times (NFP 12:30/13:30 UTC across DST,
+ECB 12:15/13:15) and against EUR/USD M15 volatility: the bar containing
+a high-impact event averages 3.5x normal range, and shifting event
+times by ±1h destroys that alignment.
+
+Usage:
+    python backtest/research/ff_calendar_scraper.py --start 2024-01-01 --end 2026-07-13
+    python backtest/research/ff_calendar_scraper.py            # refresh: last stored week -> today
+
+Merges into the existing parquet on event (id, dateline), so refreshing
+is idempotent and also picks up revised `actual` values for recent weeks.
+Be polite: ~1.6s between requests (a full year is ~52 requests).
+"""
+import argparse
+import json
+import time
+from datetime import date, timedelta
+from pathlib import Path
+from typing import List, Optional
+
+import pandas as pd
+import requests
+
+CACHE_DIR = Path(__file__).resolve().parent.parent / "cache"
+PARQUET = CACHE_DIR / "calendar.parquet"
+
+UA = ("Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 "
+      "(KHTML, like Gecko) Chrome/126.0 Safari/537.36")
+
+FIELDS = ["id", "dateline", "name", "country", "currency", "impactName",
+          "timeLabel", "timeMasked", "actual", "forecast", "previous",
+          "revision", "actualBetterWorse"]
+
+
+def week_url(d: date) -> str:
+    return (f"https://www.forexfactory.com/calendar?week="
+            f"{d.strftime('%b').lower()}{d.day}.{d.year}")
+
+
+def extract_days(html: str) -> Optional[list]:
+    """Balanced-bracket extraction of the `days:` JSON array."""
+    i = html.find("days:")
+    if i < 0:
+        return None
+    j = html.find("[", i)
+    depth, in_str, esc = 0, False, False
+    for k in range(j, len(html)):
+        ch = html[k]
+        if in_str:
+            if esc:
+                esc = False
+            elif ch == "\\":
+                esc = True
+            elif ch == '"':
+                in_str = False
+        else:
+            if ch == '"':
+                in_str = True
+            elif ch == "[":
+                depth += 1
+            elif ch == "]":
+                depth -= 1
+                if depth == 0:
+                    return json.loads(html[j:k + 1])
+    return None
+
+
+def fetch_week(sess: requests.Session, d: date, retries: int = 4) -> Optional[List[dict]]:
+    for attempt in range(retries):
+        try:
+            r = sess.get(week_url(d), timeout=30)
+            if r.status_code == 200:
+                days = extract_days(r.text)
+                if days is not None:
+                    return [{k: e.get(k) for k in FIELDS}
+                            for day in days for e in day.get("events", [])]
+        except requests.RequestException:
+            pass
+        time.sleep(5 * (attempt + 1))
+    return None
+
+
+def scrape(start: date, end: date) -> pd.DataFrame:
+    sess = requests.Session()
+    sess.headers["User-Agent"] = UA
+    rows: List[dict] = []
+    failed: List[date] = []
+    d = start
+    while d <= end:
+        week_rows = fetch_week(sess, d)
+        if week_rows is None:
+            failed.append(d)
+            print(f"FAILED week {d}")
+        else:
+            rows.extend(week_rows)
+        d += timedelta(days=7)
+        time.sleep(1.6)
+    if failed:
+        print(f"WARNING: {len(failed)} weeks failed: {failed}")
+    return pd.DataFrame(rows)
+
+
+def merge_into_store(new: pd.DataFrame, path: Path = PARQUET) -> pd.DataFrame:
+    if path.exists():
+        old = pd.read_parquet(path)
+        combined = pd.concat([old, new])
+    else:
+        combined = new
+    # keep="last": a refresh overwrites stale rows (actuals fill in
+    # after release, revisions land weeks later).
+    combined = (combined.drop_duplicates(subset=["id", "dateline"], keep="last")
+                .sort_values("dateline").reset_index(drop=True))
+    combined.to_parquet(path, index=False)
+    return combined
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    ap.add_argument("--start", type=date.fromisoformat, default=None)
+    ap.add_argument("--end", type=date.fromisoformat, default=date.today())
+    args = ap.parse_args()
+
+    start = args.start
+    if start is None:
+        if PARQUET.exists():
+            last = pd.read_parquet(PARQUET).dateline.max()
+            start = pd.Timestamp(last, unit="s").date() - timedelta(days=7)
+        else:
+            start = date(2024, 1, 1)
+    print(f"Scraping {start} -> {args.end}")
+    new = scrape(start, args.end)
+    combined = merge_into_store(new)
+    print(f"Store: {len(combined)} events "
+          f"({pd.Timestamp(combined.dateline.min(), unit='s')} -> "
+          f"{pd.Timestamp(combined.dateline.max(), unit='s')}) -> {PARQUET}")
+
+
+if __name__ == "__main__":
+    main()