60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
import data_manager
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import strategy
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import pandas as pd
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# 선택할 티커 (data_cache에 있어야 함)
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TICKER = '000660.KS' # 예: SK하이닉스
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START = '2022-01-01'
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END = '2023-12-31'
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print('Loading', TICKER)
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df = data_manager.get_stock_data(TICKER, START, END)
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print('raw rows:', len(df))
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df = df.dropna()
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print('clean rows:', len(df))
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# Precompute LAST_PEAK if not present
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if 'LAST_PEAK' not in df.columns:
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df['LAST_PEAK'] = strategy.compute_last_peak_series(df)
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# Vectorized signals
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vec = strategy.compute_buy_signals_vectorized(df)
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vec_buy_dates = vec[vec['BUY_SIGNAL'] == True].index.tolist()
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print('Vectorized buy count:', len(vec_buy_dates))
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# Iterative check_buy_signal
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iter_buys = []
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for i in range(1, len(df)):
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today = df.iloc[i]
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yesterday = df.iloc[i-1]
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history = df.iloc[:i+1]
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ok, sl, stype = strategy.check_buy_signal(today, yesterday, history)
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if ok:
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iter_buys.append((history.index[i], sl, stype))
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print('Iterative buy count:', len(iter_buys))
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# Compare dates
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iter_dates = [d[0] for d in iter_buys]
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only_iter = sorted(set(iter_dates) - set(vec_buy_dates))
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only_vec = sorted(set(vec_buy_dates) - set(iter_dates))
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print('Only iterative but not vectorized:', len(only_iter))
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for d in only_iter[:10]:
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print(' -', d)
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print('Only vectorized but not iterative:', len(only_vec))
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for d in only_vec[:10]:
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print(' -', d)
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# Compare stop loss differences for common dates
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common = sorted(set(iter_dates).intersection(set(vec_buy_dates)))
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print('Common buy dates:', len(common))
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for d in common[:10]:
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iter_sl = next(sl for (dt, sl, st) in iter_buys if dt == d)
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vec_sl = vec.loc[d, 'STOP_LOSS_PRICE']
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print(d, 'iter_sl=', iter_sl, 'vec_sl=', vec_sl)
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print('\nDone')
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