import data_manager
import strategy
import pandas as pd

# 선택할 티커 (data_cache에 있어야 함)
TICKER = '000660.KS'  # 예: SK하이닉스
START = '2022-01-01'
END = '2023-12-31'

print('Loading', TICKER)
df = data_manager.get_stock_data(TICKER, START, END)
print('raw rows:', len(df))

df = df.dropna()
print('clean rows:', len(df))

# Precompute LAST_PEAK if not present
if 'LAST_PEAK' not in df.columns:
    df['LAST_PEAK'] = strategy.compute_last_peak_series(df)

# Vectorized signals
vec = strategy.compute_buy_signals_vectorized(df)
vec_buy_dates = vec[vec['BUY_SIGNAL'] == True].index.tolist()
print('Vectorized buy count:', len(vec_buy_dates))

# Iterative check_buy_signal
iter_buys = []
for i in range(1, len(df)):
    today = df.iloc[i]
    yesterday = df.iloc[i-1]
    history = df.iloc[:i+1]
    ok, sl, stype = strategy.check_buy_signal(today, yesterday, history)
    if ok:
        iter_buys.append((history.index[i], sl, stype))

print('Iterative buy count:', len(iter_buys))

# Compare dates
iter_dates = [d[0] for d in iter_buys]
only_iter = sorted(set(iter_dates) - set(vec_buy_dates))
only_vec = sorted(set(vec_buy_dates) - set(iter_dates))

print('Only iterative but not vectorized:', len(only_iter))
for d in only_iter[:10]:
    print('  -', d)

print('Only vectorized but not iterative:', len(only_vec))
for d in only_vec[:10]:
    print('  -', d)

# Compare stop loss differences for common dates
common = sorted(set(iter_dates).intersection(set(vec_buy_dates)))
print('Common buy dates:', len(common))
for d in common[:10]:
    iter_sl = next(sl for (dt, sl, st) in iter_buys if dt == d)
    vec_sl = vec.loc[d, 'STOP_LOSS_PRICE']
    print(d, 'iter_sl=', iter_sl, 'vec_sl=', vec_sl)

print('\nDone')