VENUS MANUAL OF INPUT

VENUS MANUAL OF INPUT

20 septiembre 2020, 15:28
Marta Gonzalez
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VENUS MANUAL OF INPUT

See Video Manual Input here:


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Magic Number: 
Magic Mumber, differentiate the different eas use one per pair and robot. it is interesting that the numbers are separated by at least 10 units.

  



Input basic:  General system values.

Autodistance: Automatic distance between levels depending on the main supports and resistances.

Distance: Fixed distance between levels.

Max lot general: Maximum system lot in most algorithms.

Basket: Money value for the overall output of the algorithms.

Partial Closure: Activates / deactivates a partial recovery of losses.

Basket only one: Value for individual profit output in certain algorithms.

Power of signal: Value for the power filter, it is recommended to use values ​​between 15 and 25.

Sensitivity Signal: Signal sensitivity recommended value between 1 and 5.  

Impulse Power: Factor that filters the force of the impulse. Recommended  Value 2.

Impulse Time: Factor that filters the duration of the impulse.  Recommended  Value 10.

Impulse Filter: Impulse filter sensitivity. Recommended value 2.

Factor Volatility: Volatility filter factor. Recommended value 14.

S R power:  Support and resistance detection filter factor. Recommended value between 40 and 100.

Signal input:  Regulates the power and sensitivity of the signal that marks the market entry. The higher the value, the more secure the input, but the fewer inputs  the system makes, values ​​between 50 and 1500 are recommended. This is the most importan input for controled the signal of entry.



Algorithm PR Specific inputs for the neural net of algorithm PR.

BreakEven: Activated/desactivated  Break Event of  the neural net of algorithm PR .
BreakEven At: initial Value of Break Event  of  the neural net of algorithm PR . 
BreakEven To: Vaule of moving of Break Event  of  the neural net of algorithm PR . 


Algorithm 1 Specific inputs for the neural net of algorithm 1.

Used_algorithm_1: Activated/desactivated  the neural net of algorithm 1.

Trailing al1:  Activated/desactivated   Trailing Stop of  the neural net of algorithm 1 . 

TrailingStop al1: Value of Trailing Stop, Distance from price to SL.



Algorithm  2Specific inputs for the neural net of algorithm 2. 

Used algorithm 2: Activated/desactivated  the neural net of algorithm 2.

Lot init al2: Initial lot of system in the algoritm 1, recommended 0.01 lots.

Trailing al2:  Activated/desactivated   Trailing Stop of  the neural net of algorithm 2 . 

TrailingStop al2: Value of Trailing Stop, Distance from price to SL.



Algorithm  3: Specific inputs for the neural net of algorithm 3. 

Used algorithm 3: Activated/desactivated  the neural net of algoritm 3.

Nun al3: Number of steps that the neural net of algorithm 3 has.

Trailing al3 Activated/desactivated Trailing Stop of  the neural net of algorithm 3 . 

TrailingStop al3: Value of Trailing Stop, Distance from price to SL.



Algorithm  4: Specific inputs for the neural net of algorithm 4. 

Used algorithm 4: Activated/desactivated  the neural net of algoritm 4.

Nun al4: Number of steps that the neural net of algorithm 4 has.

Trailing al4 Activated/desactivated Trailing Stop of  the neural net of algorithm 4 .

TrailingStop al4:  Value of Trailing Stop, Distance from price to SL.



Algorithm  5: Specific inputs for the neural net of algorithm 5. 

Used algorithm 5: Activated/desactivated  the neural net of algoritm 5.

Lot init al5:  Initial lot of system in the algoritm 5, recommended the same value to the input  Lot_init_al1 .



Algorithm  6: Specific inputs for the neural net of algorithm 6. 

Used algorithm 6: Activated/desactivated  the neural net of algoritm 6.

Lot init al6:  Initial lot of system in the algoritm 6, recommended the same value to the input Lot_init_al2 .



 Algorithm  7: Specific inputs for the neural net of algorithm 7. 

Used algorithm 7: Activated/desactivated  the neural net of algoritm 7.

Nun al7: Number of steps that the neural net of algorithm 7 has.

Factor al7: Factor to controled the increase of lot of  the neural net of algoritm 7. Recommended value 10.



Algorithm   Specific inputs for the neural net of algorithm 8. 

Used algorithm 8: Activated/desactivated  the neural net of algoritm 8. 

Nun l8: Number of steps that the neural net of algorithm 8 has.

Factor al8: Factor to controled the increase of lot of  the neural net of algoritm 8.  Recommended value 10.



Algorithm  9: Specific inputs for the neural net of algorithm 9.

Used algorithm 9: Activated/desactivated the neural net of algoritm 9.

Dist factor al9: Increase factor of the distance between orders algorithm 9 .

Lot al9: Recovery Lot algorithm 9.

Factor lot al9: Factor add to Lot_al9 in the  algorithm 9 .

Entry al9: End of the algorithm 9  and change to algorithm 11.

Distance al9: Distance between orders of algorithm 9.

Recovery support al9: Receive algorithm 10 support to close profitably.




Algorithm  10: Specific inputs for the neural net of algorithm 10. 

Used algorithm 10: Activated/desactivated the neural net of algoritm 10.

Entry al10: Number of orders that activate the algorithm 10.

Dist factor al10:  Increase factor of the distance between orders algorithm 10 .

Factor al10: Increase factor of the distance between orders algorithm 10.

Entry al10: End of the algorithm 10 and change to algorithm 10.

Recovery support al10: Receive algorithm 9 support to close profitably.




Algorithm 11: Specific inputs for the neural net of algorithm 11. 

Used algorithm 11: Activated/desactivated the neural net of  algoritm 11.

Stair step al11: Distance between orders of algorithm 11.

Recovery of an order al11:  Activated/desactivated algoritm 11 recovery of an order by accumulation of positive orders.

Recovery control al11: Activate / Deactivate the recovery control module to close the algorithm  11 with gains.

Compensation closure al11: Activates / deactivates the recovery algorithm  by compensation of the algorithm 11.

Closure by partial recovery al11: Activates / deactivates the recovery closure algorithm with partial algorithm 11.

Factor Closure by partial recovery al11:  Factor for the recovery closure algorithm with partial algorithm 11.

Max order al11: Number of max order open in the  algorithm 11.



Algorithm  12: Specific inputs for the neural net of algorithm 12. 

Used algorithm 12: Activated/desactivated the neural net of algorithm  12.

Stair step al12:  Distance between orders of algorithm 12.

Recovery of an order al12:  Activated/desactivated algoritm 12 recovery of an order by accumulation of positive orders.

Recovery control al12: Activate / Deactivate the recovery control module to close the algorithm  12 with gains.

Compensation closure al12: Activates / deactivates the recovery algorithm  by compensation of the algorithm 12.

Closure by partial recovery al12: Activates / deactivates the recovery closure algorithm with partial algorithm 12.

Factor Closure by partial recovery al12:  Factor for the recovery closure algorithm with partial algorithm 12.

Max order al12: Number of max order open in the  algorithm 12.



Algorithm  13: Specific inputs for the neural net of algorithm 13. 

Used algorithm 13: Activated/desactivated the neural net of algoritm 13 .

Nun al13: Number of steps that the neural net of algorithm 13 has.

Factor al13: Factor to controled the increase of lot of  the neural net of algoritm 13. 




Algorithm  14: Specific inputs for the neural net of algorithm 14.

Used algorithm 14: Activated/desactivated the neural net of 14.

Nun al14: Number of steps that the neural net of algorithm 14 has.

Factor al14: Factor to controled the increase of lot of  the neural net of algoritm 14. 



Algorithm  15  Specific inputs for the neural net of algorithm 15. 

Used algorithm 15: Activated/desactivated the neural net of algoritm 15 .

Distance trend al15: Distance for the entry of trend algorithm of the neural net of the algorithm 15.

Control trend15: Max lot used in the entry of trend algorithm of the neural net of the algorithm 15.



Algorithm  16: Specific inputs for the neural net of algorithm 16. 

Used algorithm 16: Activated/desactivated the neural net of algoritm 16 .

Distance trend al16:   Distance for the entry of trend algorithm of the neural net of the algorithm 16.

Control trend16: Max lot used in the entry of trend algorithm of the neural net of the algorithm 16.



Algorithm  17: Specific inputs for the neural net of algorithm 17. 

Used algorithm 17: Activated/desactivated the neural net of algoritm 17.

Used Phase 2:  Activated/desactivated the phase 2 of  the neural net of algoritm 17.

Factor al 17: Factor used to recupered lost lotje in algoritm17. 

Lot init al13: factor to used for conltroled Lot to init this algorithm.

Stair step al17:  Distance between orders of algorithm 17.

Max order al17: Number of max order open in the  algorithm 17.

Basket control: Profit of lost to recuperation in the algorithm 17.



Algorithm  18: Specific inputs for the neural net of algorithm 18. 

Used phase 2:  Activated/desactivated  the phase 2 of the neural net of the algorithm 18. 

Order Buy For init: Number of buy orders to activate the neural net of the algorithm 18.

Max order al18: Number of max order open in the  algorithm 18.

Factor low Block Buy: Value of the factor that marks the lower Limit of the buy order blocking.

Factor High Block Buy: Value of the factor that marks the upper linite of the buy order blocking.

Order Sell For init: Number of buy orders to activate the neural net of the algorithm 18.

Factor low Block Sell: Value of the factor that marks the lower Limit of the sell order blocking.

Factor High Block Sell: Value of the factor that marks the upper linite of the sell order blocking.


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