"""
Build test_data.pkl from 5min adjusted CSV files.
Only loads the test time range to save memory/disk.
"""
import os
import pickle
import pandas as pd
from tqdm import tqdm

# ── Config ────────────────────────────────────────────────────────────────────
DATA_DIR   = "/home/hanyueju/MinModel/data/one_stock_one_csv_adjusted"
OUTPUT_DIR = "./data/processed_datasets"
OUTPUT_FILE = os.path.join(OUTPUT_DIR, "test_data.pkl")

# Match config.py test_time_range.
# Start a bit earlier to cover the lookback_window (240 bars) before backtest begins.
TEST_START = "2024-12-20"
TEST_END   = "2026-04-07"

MIN_BARS = 300  # drop stocks with fewer than this many bars in the test window
# ─────────────────────────────────────────────────────────────────────────────

def build(data_dir, test_start, test_end):
    files = sorted(f for f in os.listdir(data_dir) if f.endswith(".csv"))
    test_data = {}

    for fname in tqdm(files, desc="Loading"):
        stock_code = fname.replace(".csv", "")
        path = os.path.join(data_dir, fname)

        try:
            df = pd.read_csv(
                path,
                usecols=["datetime", "open", "high", "low", "close", "volume", "turnover"],
                parse_dates=["datetime"],
            )
        except Exception as e:
            print(f"  skip {fname}: {e}")
            continue

        df = df.rename(columns={"volume": "vol", "turnover": "amt"})
        df = df.set_index("datetime").sort_index()
        df = df[["open", "high", "low", "close", "vol", "amt"]]
        df = df.dropna()

        df = df[(df.index >= test_start) & (df.index <= test_end)]

        if len(df) < MIN_BARS:
            continue

        test_data[stock_code] = df

    return test_data


if __name__ == "__main__":
    os.makedirs(OUTPUT_DIR, exist_ok=True)

    print(f"Building test_data.pkl")
    print(f"  Source : {DATA_DIR}")
    print(f"  Range  : {TEST_START} ~ {TEST_END}")
    print(f"  Output : {OUTPUT_FILE}")
    print()

    test_data = build(DATA_DIR, TEST_START, TEST_END)

    print(f"\nStocks loaded: {len(test_data)}")
    sample_key = next(iter(test_data))
    print(f"Sample ({sample_key}): {len(test_data[sample_key])} bars")
    print(test_data[sample_key].head(3))

    with open(OUTPUT_FILE, "wb") as f:
        pickle.dump(test_data, f)

    size_mb = os.path.getsize(OUTPUT_FILE) / 1024 / 1024
    print(f"\nSaved → {OUTPUT_FILE}  ({size_mb:.1f} MB)")