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Comparing EATC with Retail Trading
Ability to analyze numerous assets without affecting performance or compromising of trading rules. The ability to manage 1 trading robot is the same as managing 100 or 1000 trading robots. This approach is scalable and can run on many robots for analysis!
Hard for humans to efficiently monitor multiple assets without negatively affecting performance. We are unable to effectively multi-task while maintaining our highest degree of focus. For algorithms this is not a problem
Follows a strict risk management protocol that is adhered to 100% of the time. The rules are based off lines of codes and does not change between trades.
Retail traders who are new have a tendency to overleverage their accounts in hopes of getting rich quick. Once a drawdown occurs this overleveraging can result in large amounts of initial capital being risked. Resulting in increasing odds of losing all money in account, as a result of inconsistent risk management.
EATC Trading Robots can be set to run continuously through all time zones and can enter and exit trades without any human intervention. We do recommend that the user monitors the trades/positions and adjust stoploss to breakeven or exit profitable or losing trades based on market conditions (more on this in tutorial videos)
Humans are required to sleep and have time off, as a result this can lead to missed opportunities in the market. These missed opportunities can result in inconsistencies within a trading strategy. Therefore, this downtime, or inability to monitor the graphs constantly is a flaw of humans versus algorithmic trading.
Latency for MT4 is measured in milliseconds. These speeds for analyzing and reacting to the graph constantly is something that humans are incapable of.
Humans are unable to react as quickly as computers to new information or placing trades.
Does not change trading strategy through good and bad times. Strict execution of coded and tested strategy.
Two human emotions that can lead to inefficient trading are over-confidence and revenge trading. These can result in short term deviation from an investor’s optimal trading strategy that can have long term negative consequences.
EATC uses complex and new data science techniques to allow our data scientist to create our strategies. Each of our strategies are tested against years of historical data, tested for overfitting and to ensure robustness. In addition, the trades placed by the robot are analyzed along with its performance characteristics such as drawdown, sharpe ratio, return to drawdown, and various other parameters.
Most traders lack the ability to carefully test their strategy through a computer/algorithm based approach. As a result they are unable to know the true performance of their strategy and also the associated weaknesses and strengths associated with it. This leads to trading without all the required information.
Here are examples of flyers that will be given out for each trading robot once downloaded and purchased. The data will be specific to the trading robot.
Statistics and values provided for trading robot: stop loss, take profit, average number of bars in trade, number trades made, profit factor (gross profit/gross loss), sharpe ratio, win/loss ratio, return to drawdown ratio, drawdown %, and max consecutive losses.
The above contains the statistics of the trading robot performance. This can be used to get an understanding of what to expect from the trading robot.
The next image will contain all the trades that have been performed by the trading robot in the backtesting history. We have also calculated the values of the trading robots statistics and performance overtime to allow the user to see how the trading robot would have performed through the past few years.