March 2022
Beginner to intermediate
796 pages
18h 14m
Korean
Content preview from 파이썬을 활용한 금융 분석(2판) : 파이썬의 기초부터 금융공학, 머신러닝, 퀀트 분석, 매매 시스템 구현까지
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599
15
장
매매 전략
그림
15-12
EUR
/
USD
환율과 (두 개의 이진수 특징을 사용한) 분류 기반 매매 전략의 성과
15.6.2
다섯 개의 이진수 특징
전략의 성능을 향상시키기 위해 다음 코드는 이진수 특징값을 두 개가 아닌 다섯 개 사용한다.
특히 서포트 벡터 기반 전략의 성능이 향상되었다(그림
15
-
13
). 반면에 로지스틱 회귀와 가
우스 나이브 베이즈 기반 전략은 성능이 나빠졌다.
In [69]: data = pd.DataFrame(raw[symbol])
In [70]: data['returns'] = np.log(data / data.shift(1))
In [71]: data['direction'] = np.sign(data['returns'])
In [72]: lags = 5
➊
create_lags(data)
data.dropna(inplace=True)
In [73]: create_bins(data)
➋
cols_bin
Out[73]: ['lag_1_bin', 'lag_2_bin', 'lag_3_bin', 'lag_4_bin', 'lag_5_bin']
600
4
부
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