Ranger EA is a robot that was released several months ago, on November 19, 2020, at the MQL5 platform. From the stat, we know that the developer is Ryan Brown from the US. The robot was demo downloaded 1107 times. The last update (3.61) was published on December 3, 2020.
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Is this robot a viable option?
We cannot say for sure. It’s a young expert advisor. Thus, we have to monitor its trading results for half a year or so to be convinced that the system is stable and works smoothly.
How to start trading with Ranger EA
Ranger EA has a few features, settings, and other explanations:
- Ranger EA works automatically for us.
- The system was tested on the 10-year tick data received from a broker.
- An average monthly gain is from 2.5% to 10%.
- The default settings are well-customized.
- The system trades rarely. So, we can expect several deals weekly.
- The main currency pairs to work with are GBP/CAD, AUD/CAD, and NZD/CAD (which wasn’t mentioned).
- We know that the system “uses a corrective trading technique to turn losing trades into winners.” This statement was published without any additional explanation.
- The system works under FIFO rules.
- GBP/CAD works only on the M30 time frame.
- AUD/CAD works on the H1 time frame.
- As we could see, this is almost everything the developer wanted to tell us about the system he sells.
The software is available for $497. We have concerns that it costs this money. Anyway, there are rent options. The one-month rent costs $40 and the three-month rent costs $120. We’re free to download the robot to check its settings.
Ranger EA backtests
There’s a screenshot of the AUD/CAD backtest based on 10-year-data attached. The modeling quality was 99.90%. The absolute gain was +104015.01%. An average monthly gain was +5.53%. The maximum drawdown was – 55.85%. That big drawdown is risky for the real account because it eats margin, decreasing a margin level.
Ranger EA v3.5 (official) has been running a demo USD account on the Trader’s Way broker since January 07, 2020. The system works automatically on MT4 with 1:500 leverage. The account has a verified track record. The deposit was $1000. The absolute gain was +79.79%. An average monthly gain amounted to +4.34%. The maximum drawdown is 10.19%. This account is tracked by 137 traders.
The robot closed 737 deals with 8889 pips. An average win is 27.66 pips when an average loss is -26.05 pips. It traded 8.08 Lots. The wi-rate is 70% for Longs and 71% for Shorts. An average trade length is one day. The Profit Factor is 2.75.
The robot focuses on trading AUD/CAD, GBP/CAD, and NZD/CAD. The most traded and profitable is GBP/CAD with 446 deals and $459 of the profits.
The robot focuses on trading during the European and opening of the American trading sessions.
The most-traded day is Wednesday (193) when the less traded one is Thursday (107 deals).
The robot runs the account with low risk to the account balance.
There are several trades open. It seems for us it’s a Martingale Grid of all these orders waiting for closing with profits.
The robot is profitable in 2021, but there’s still a chance to close February with a loss.
The developer has no photo or cover. The profile has a 5477 rate in total, three signals, two products in the portfolio, and one friend.
There are only two reviews and a single comment. So, people know little about the robot and its performance.
- Backtest reports provided
- Trading results revealed
- Cheap rent options provided
- No strategy explanations provided
- No settings revealed
- No money-management advice given
- The robot uses aggressive Grid with conservative Martingale
- The developer doesn’t mention that there are risky strategies on the board
- High pricing
- No money-back-guarantee provided
- No people feedback published
Ranger EA is a robot that uses an Aggressive Grid plus Martingale to make more profits. As for this combo, the profitability is quite low. The pricing is quite high for this type of robot, taking into account how many symbols we are allowed to trade. The developer didn’t provide a money-back guarantee as well.