Super Bot for Proprietary Trading Desk
In recent days, I’ve been deeply committed to developing a new algorithm tailored to trading EUR/USD.
The goal is always to balance buy and sell orders in a way that creates a sort of hedge, but with the use of mathematics to avoid any imbalance in account equity.
Personally, I don’t trade for proprietary desks, but due to some functionalities of the bot showing excellent resource management, it has proven in initial tests to be capable of handling funded accounts without putting them at risk.
This is because the dynamic approach it employs ensures that the balance always maintains equilibrium between equity and drawdown. Even if there’s an excessive number of open orders, the balance remains stable, avoiding daily or monthly limit violations in funded accounts.
There are numerous functionalities that can be tested in the bot, but the most effective one, which has passed all the backtest requirements, demonstrates that with a funded account already approved for $100,000, it can achieve an average monthly drawdown of 1% and an average financial return of 1.5%.
This means that a $100,000 funded account could yield around $1,500 per month with minimal risk.
As mentioned above, multiple functionalities can be explored. If you’re someone with a greater appetite for risk, you could increase the lot size proportionally and aim for a 3% monthly drawdown, targeting a profitability goal of $4,500 per month.
My initial plan was to offer this bot on the MQL5 marketplace with options for monthly, quarterly, annual, or unlimited rentals.
However, after evaluating the bot, they prohibited me from providing this type of algorithm. Their analysis determined that there isn’t an exact moment when the bot announces the closing of its orders—it waits for the appropriate condition, timing, and net value needed to close a specific set of orders.
With a very short breakeven, it limits losses by closing poorly executed orders while keeping the others open. When a significant market move occurs, it offsets the small losses caused by the breakeven closures and drives the balance into the positive territory.
The capital growth curve is incredibly efficient, as we can observe its development. At times, it may appear static or with little change, but suddenly there’s a sharp upward spike as a result of orders hitting their targets.
If you’re interested, please contact me via Telegram @thecode88 or by email at [email protected].
In recent days, I’ve been deeply committed to developing a new algorithm tailored to trading EUR/USD.
The goal is always to balance buy and sell orders in a way that creates a sort of hedge, but with the use of mathematics to avoid any imbalance in account equity.
Personally, I don’t trade for proprietary desks, but due to some functionalities of the bot showing excellent resource management, it has proven in initial tests to be capable of handling funded accounts without putting them at risk.
This is because the dynamic approach it employs ensures that the balance always maintains equilibrium between equity and drawdown. Even if there’s an excessive number of open orders, the balance remains stable, avoiding daily or monthly limit violations in funded accounts.
There are numerous functionalities that can be tested in the bot, but the most effective one, which has passed all the backtest requirements, demonstrates that with a funded account already approved for $100,000, it can achieve an average monthly drawdown of 1% and an average financial return of 1.5%.
This means that a $100,000 funded account could yield around $1,500 per month with minimal risk.
As mentioned above, multiple functionalities can be explored. If you’re someone with a greater appetite for risk, you could increase the lot size proportionally and aim for a 3% monthly drawdown, targeting a profitability goal of $4,500 per month.
My initial plan was to offer this bot on the MQL5 marketplace with options for monthly, quarterly, annual, or unlimited rentals.
However, after evaluating the bot, they prohibited me from providing this type of algorithm. Their analysis determined that there isn’t an exact moment when the bot announces the closing of its orders—it waits for the appropriate condition, timing, and net value needed to close a specific set of orders.
With a very short breakeven, it limits losses by closing poorly executed orders while keeping the others open. When a significant market move occurs, it offsets the small losses caused by the breakeven closures and drives the balance into the positive territory.
The capital growth curve is incredibly efficient, as we can observe its development. At times, it may appear static or with little change, but suddenly there’s a sharp upward spike as a result of orders hitting their targets.
If you’re interested, please contact me via Telegram @thecode88 or by email at [email protected].
Attached Image(s) (click to enlarge)
