Cleanup: remove dead code, stale docs, and all non-main branches

- Delete back-compat shims data_provider.py / oanda_provider.py;
  test_all.py now imports providers.* directly (ForexDataProvider name
  retired; the two shim-only tests removed, 268 -> 266 tests)
- Delete backtest/research/calendar_store.py (no importer, no entry
  point); config.py audit comment reworded
- Delete executed plan doc (2026-07-21 centralized-config) and the two
  unbuildable guide PDFs; HTML guides remain the source of truth
- OANDA_MIGRATION_ROADMAP.md status header corrected: phases 1-2
  shipped 2026-07-28, feed is live on Oanda; phases 3-6 still unbuilt
- Branches deleted locally + on both remotes: oanda_demo (merged),
  web-dashboard, eurusd-modular, reversal-exit-alert (owner-approved
  permanent deletion); July stash dropped

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019zR58p8ACW1oJ5QXVUZ5es

backtest/research/calendar_store.py

@@ -1,94 +0,0 @@
-"""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"])             # 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",),
-    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