"""Per-hour P&L breakdown of a backtest trade book.

Usage:
    python backtest/research/hour_analysis.py BOOK.csv BAR_MINUTES

Reports, per UTC hour (both entry hour and DECISION hour = entry_time
minus one bar, the clock blocked_hours_utc evaluates): trade count,
total R, mean R, win rate, profit factor. Also a per-(hour, direction)
table. Analysis tooling only — never imported by the live code.
"""
import sys

import pandas as pd


def hour_tables(book_csv: str, bar_minutes: int):
    df = pd.read_csv(book_csv)
    et = pd.to_datetime(df["entry_time"], utc=True, format="mixed")
    df["entry_hour"] = et.dt.hour
    df["decision_hour"] = (et - pd.Timedelta(minutes=bar_minutes)).dt.hour

    def table(group_cols):
        g = df.groupby(group_cols)["r_multiple"]
        out = g.agg(count="count", total_r="sum", mean_r="mean",
                    wr=lambda r: (r > 0).mean() * 100)
        out["pf"] = g.apply(
            lambda r: r[r > 0].sum() / max(-r[r <= 0].sum(), 1e-9))
        return out.round(2)

    return (table("decision_hour"), table(["decision_hour", "direction"]),
            table("entry_hour"))


if __name__ == "__main__":
    book, mins = sys.argv[1], int(sys.argv[2])
    by_dec, by_dec_dir, by_ent = hour_tables(book, mins)
    print(f"\n── {book} · by DECISION hour (bar-close clock) ──")
    print(by_dec.to_string())
    print(f"\n── by (decision hour, direction) ──")
    print(by_dec_dir.to_string())