FX Desk Pro (에 의해 user954)
| : 게인 | +4058.49% |
| 드로다운 | 80.49% |
| 핍스: | 185182.8 |
| 거래 | 895 |
| 원 : |
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| 손실: |
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| : 종류 | 데모 |
| 레버리지: | 1:30 |
| 거래 : | 자동화 |
FX Desk Pro 토론
Jan 27, 2016 at 08:19
456 개의 뷰
3 Replies
Jun 29, 2016 at 09:07
(편집됨 Jun 29, 2016 at 09:22)
FX Desk Pro is a scalable, high GHPR program tailored for Institutions and HNW individuals - It is an aggressive with advanced risk management that aims to maximize cash flow by taking advantage of available leverage.
With an average expectancy of over 100 pips per trade, FX Desk Pro is extremely tradable for followers unlike most positive systems on myFXbook that are scalping systems (1 to 15 pips expectancy per trade) with high slippage relative to the reference account.
The system trades major currency pairs and spot metals based on quantitative models that follow momentum on different timeframes.
With an average expectancy of over 100 pips per trade, FX Desk Pro is extremely tradable for followers unlike most positive systems on myFXbook that are scalping systems (1 to 15 pips expectancy per trade) with high slippage relative to the reference account.
The system trades major currency pairs and spot metals based on quantitative models that follow momentum on different timeframes.
Jun 29, 2016 at 16:42
(편집됨 Jun 29, 2016 at 16:46)
Yes it is an aggressive model. The 80% drawdown is high, that is at its default settings. It is intended for small institutions and high net worth individuals who are looking for aggressive returns and maximizing cash flow. This allows a fund manager to allocate much less margin to FX Desk Pro
A follower always has the option of reducing leverage. for less aggressive settings.
The model Is support vector based and does train itself as time progresses so theoretically the maximum drawdown should reduce. Another expected behavior is that after a drawdown, expectancy of a above average run up is high. The data so far bears this out.
A follower always has the option of reducing leverage. for less aggressive settings.
The model Is support vector based and does train itself as time progresses so theoretically the maximum drawdown should reduce. Another expected behavior is that after a drawdown, expectancy of a above average run up is high. The data so far bears this out.
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