마켓 / MetaTrader 5 / 지표 / SSA Stochastic Limited Edition 4 FREE 게시됨: 18 1월 2017 현재 버전: 2.15 적합한 로봇을 찾지 못하셨나요? 프리랜스에서 나만의 로봇을 주문해 보세요 프리랜스로 이동 트레이딩 로봇 또는 지표 구매 구입 방법 EA를 작동시켜보세요 가상 호스팅으로 구매 전에 지표와 트레이딩 로봇을 테스트해보세요 마켓에서 수익 창출을 원하십니까? 제품의 판매량을 나타내는 방법 미리보기 리뷰 (7) 코멘트 (3) 새 소식 제품을 구매하거나 렌트한 사용자만 코멘트를 남길 수 있습니다 Mohamed Anis 2017.04.25 15:36 #1 hello Mr.Roman, to what time-frame do you recommend ? and what is the %k & %d values do you recommend while using M5 ? thank you. Roman Korotchenko 2017.04.25 16:06 #2 Mohamed Anis: hello Mr.Roman, to what time-frame do you recommend ? and what is the %k & %d values do you recommend while using M5 ? thank you. Hello Mr. Mohamed. I suppose, M15 is more optimal time-frame than M5. M5 is very noisy interval. 1-3 nearest points are more reliable data.In general the next param sets on start:SSA Fast Trend Forecast 2.5:Algorithm: Recurrent forecast,N: Data fragment = 256, Time-dependent lag = N/3, Trend high-freq. limit= 0.25,Forecast high-freq. limit= 0.25,Forecast transform = S[i]/Max(:),Forecast smoothing = Smoothing MA(3).SSACD Forecast (Limited) 2.5:Algorithm: Recurrent forecast,N: Data fragment = 512, Time-dependent lag = N/4, FastTrend high-freq. limit = 0.4SlowTrend high-freq. limit= 0.6Signal SMA period = 4Data preparation = {ln(S[i]-Smin+1)}/Max(:)Forecast preparation = S[i] /Max(:)Forecast smoothing = Smoothing MA(3).SSA Stochastic (Limited) 2.0:Algorithm: Recurrent forecast,N: Data fragment = 256, Time-dependent lag = N/4, %K high-freq. limit = 0.3,%D high-freq. limit = 0.6,Data preparation = S[i] /Max(:),Forecast smoothing = Smoothing MA(3).But that sets are orientirs. Good luck. [삭제] 2023.05.22 12:07 #3 can i get the source code please , thanks 트레이딩 기회를 놓치고 있어요: 무료 트레이딩 앱 복사용 8,000 이상의 시그널 금융 시장 개척을 위한 경제 뉴스 등록 로그인 공백없는 라틴 문자 비밀번호가 이 이메일로 전송될 것입니다 오류 발생됨 Google으로 로그인 웹사이트 정책 및 이용약관에 동의합니다. 계정이 없으시면, 가입하십시오 MQL5.com 웹사이트에 로그인을 하기 위해 쿠키를 허용하십시오. 브라우저에서 필요한 설정을 활성화하시지 않으면, 로그인할 수 없습니다. 사용자명/비밀번호를 잊으셨습니까? Google으로 로그인
Mohamed Anis 2017.04.25 15:36 #1 hello Mr.Roman, to what time-frame do you recommend ? and what is the %k & %d values do you recommend while using M5 ? thank you.
Roman Korotchenko 2017.04.25 16:06 #2 Mohamed Anis: hello Mr.Roman, to what time-frame do you recommend ? and what is the %k & %d values do you recommend while using M5 ? thank you. Hello Mr. Mohamed. I suppose, M15 is more optimal time-frame than M5. M5 is very noisy interval. 1-3 nearest points are more reliable data.In general the next param sets on start:SSA Fast Trend Forecast 2.5:Algorithm: Recurrent forecast,N: Data fragment = 256, Time-dependent lag = N/3, Trend high-freq. limit= 0.25,Forecast high-freq. limit= 0.25,Forecast transform = S[i]/Max(:),Forecast smoothing = Smoothing MA(3).SSACD Forecast (Limited) 2.5:Algorithm: Recurrent forecast,N: Data fragment = 512, Time-dependent lag = N/4, FastTrend high-freq. limit = 0.4SlowTrend high-freq. limit= 0.6Signal SMA period = 4Data preparation = {ln(S[i]-Smin+1)}/Max(:)Forecast preparation = S[i] /Max(:)Forecast smoothing = Smoothing MA(3).SSA Stochastic (Limited) 2.0:Algorithm: Recurrent forecast,N: Data fragment = 256, Time-dependent lag = N/4, %K high-freq. limit = 0.3,%D high-freq. limit = 0.6,Data preparation = S[i] /Max(:),Forecast smoothing = Smoothing MA(3).But that sets are orientirs. Good luck.
hello Mr.Roman, to what time-frame do you recommend ? and what is the %k & %d values do you recommend while using M5 ? thank you.
Hello Mr. Mohamed. I suppose, M15 is more optimal time-frame than M5. M5 is very noisy interval. 1-3 nearest points are more reliable data.
In general the next param sets on start:
Algorithm: Recurrent forecast,
N: Data fragment = 256,
Time-dependent lag = N/3,
Trend high-freq. limit= 0.25,
Forecast high-freq. limit= 0.25,
Forecast transform = S[i]/Max(:),
Forecast smoothing = Smoothing MA(3).
Algorithm: Recurrent forecast,
N: Data fragment = 512,
Time-dependent lag = N/4,
FastTrend high-freq. limit = 0.4
SlowTrend high-freq. limit= 0.6
Signal SMA period = 4
Data preparation = {ln(S[i]-Smin+1)}/Max(:)
Forecast preparation = S[i] /Max(:)
Forecast smoothing = Smoothing MA(3).
Algorithm: Recurrent forecast,
N: Data fragment = 256,
Time-dependent lag = N/4,
%K high-freq. limit = 0.3,
%D high-freq. limit = 0.6,
Data preparation = S[i] /Max(:),
Forecast smoothing = Smoothing MA(3).