In [99]: VaR = scs.scoreatpercentile(equity * hourly['returns'], percs)
➋
In [100]: print_var()
Confidence Level Value-at-Risk
---------------------------------
99.99 389.524
99.90 372.657
99.00 205.662
97.50 186.999
95.00 164.869
90.00 101.835
➊
5
분봉에서
1
시간 간격으로 리샘플링
➋
리샘플링된 데이터의
VaR
값 재계산
16.2.4
모형 객체 보존
일단 백테스팅, 레버리지 분석, 위험 분석 결과에서 알고리즘 트레이딩 전략이 통과되었으면
나중에 사용하기 위해 이 매매 전략 객체를 보존해야 한다. 보존된 객체는 전략 내용을 내포하고
있다.
In [101]: import pickle
In [102]: pickle.dump(model, open('algorithm.pkl', 'wb'))
16.3
온라인 알고리즘
지금까지 테스트한 트레이딩 알고리즘은 오프라인 알고리즘이었다. 이 알고리즘은 문제를 풀
기 위해 필요한 데이터가 완벽하게 주어져 있다고 가정하고 만든 것이다. 여기서 주어진 문제
는 이진화된 특징값과 레이블링된 시장 방향을 사용하여 서포트 벡터 ...
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