import pandas as pd
from pathlib import Path

proj = Path(r"C:\Users\paul_\OneDrive\fx_macro_intraday")

for pair, last_dt in [("EURUSD", "2026-03-13 22:45:00"), ("USDJPY", "2026-03-16 09:30:00")]:
    print(f"=== {pair} — seam check around {last_dt} ===")
    csv = proj / "data" / "raw" / "prices" / pair / f"{pair}_15M_2003_2026.csv"
    df = pd.read_csv(csv)
    df.columns = [c.strip().lower() for c in df.columns]
    df["datetime"] = pd.to_datetime(df["datetime"], format="%Y.%m.%d %H:%M:%S")
    df = df.sort_values("datetime").reset_index(drop=True)
    
    last_dt_ts = pd.Timestamp(last_dt)
    seam_window = df[(df["datetime"] >= last_dt_ts - pd.Timedelta(hours=1)) &
                     (df["datetime"] <= last_dt_ts + pd.Timedelta(days=3))].copy()
    seam_window["range_pips"] = (seam_window["high"] - seam_window["low"]) * (10000 if pair == "EURUSD" else 100)
    print(seam_window[["datetime", "open", "high", "low", "close", "volume", "range_pips"]].head(20).to_string())
    print(f"...gap-detect: last 5 timestamps before {last_dt}, first 5 after:")
    before = df[df["datetime"] <= last_dt_ts].tail(3)
    after  = df[df["datetime"] >  last_dt_ts].head(3)
    print(pd.concat([before, after])[["datetime", "open", "close", "volume"]].to_string())
    print()