Rate of Change Strategy

Rate of Change Strategy :


This strategy is based on the Rate of Change indicator. It compares the current price with that of a user-defined period of time ago. This makes it easy to spot trends and even speculative bubbles. The strategy is long term and very risky, which is why we've added a Stop Loss. There's also a money management method that allows you to reinvest part of your profits or reduce the size of your orders in the event of substantial losses.


As explained above, the ROC is used to situate the current price compared to that of a certain period of time ago. The formula for calculating ROC in relation to the previous year is as follows :

ROC (365) = (close/close (365) - 1) * 100

With this formula we can find out how many percent the change in the current price is compared with 365 days ago, and thus assess the trend.


  • ROC Length : Length of the ROC to be calculated. The current price is compared with that of the selected length ago.
  • ROC Bubble Signal : ROC value indicating that we are in a bubble. This value varies enormously depending on the financial product. For example, in the equity market, a bubble exists when ROC = 40, whereas in cryptocurrencies, a bubble exists when ROC = 150.
  • Stop Loss (in %) : Stop Loss value in percentage. This is the maximum trade value percentage that can be lost in a single trade.
  • Fixed Ratio : This is the amount of gain or loss at which the order quantity is changed. The default is 400, which means that for each $400 gain or loss, the order size is increased or decreased by an amount chosen by the user.
  • Increasing Order Amount : This is the amount to be added to or subtracted from orders when the fixed ratio is reached. The default is $200, which means that for every $400 gain, $200 is reinvested in the strategy. On the other hand, for every $400 loss, the order size is reduced by $200.
  • Initial capital : $1000
  • Fees : Interactive Broker fees apply to this strategy. They are set at 0.18% of the trade value.
  • Slippage : 3 ticks or $0.03 per trade. Corresponds to the latency time between the moment the signal is received and the moment the order is executed by the broker.
  • Important : A bot has been used to test the different parameters and determine which ones maximize return while limiting drawdown. This strategy is the most optimal on BTCUSD in 1D timeframe with the following parameters :
ROC Length = 365
ROC Bubble Signal = 180
Stop Loss (in %) = 6


We are in a LONG position if ROC (365) > 0 for at least two days. This allows us to limit noise and irrelevant signals to ensure that the ROC remains positive.


We are in a SHORT position if ROC (365) < 0 for at least two days. We also open a SHORT position when the speculative bubble is about to burst. If ROC (365) > 180, we're in a bubble. If the bubble has been in existence for at least a week and the ROC falls back below this threshold, we can expect the asset to return to reasonable prices, and thus a downward trend. So we're opening a SHORT position to take advantage of this upcoming decline.


The strategy is self-regulating. We don't exit a LONG trade until a SHORT signal has arrived, and vice versa. So, to exit a winning position, you have to wait for the entry signal of the opposite position.


This strategy is very risky, and we can easily end up on the wrong side of the trade. That's why we're going to manage our risk with a Stop Loss, limiting our losses as a percentage of the trade's value. By default, this percentage is set at 6%. Each trade will therefore take a maximum loss of 6%.
If the SL has been triggered, it probably means we were on the wrong side. This is why we change the direction of the trade when a SL is triggered. For example, if we were SHORT and lost 6% of the trade value, the strategy will close this losing trade and open a long position without taking into account the ROC value. This allows us to be in position all the time and not miss the best opportunities.


The fixed ratio method was used to manage our gains and losses. For each gain of an amount equal to the value of the fixed ratio, we increase the order size by a value defined by the user in the "Increasing order amount" parameter. Similarly, each time we lose an amount equal to the value of the fixed ratio, we decrease the order size by the same user-defined value. This strategy increases both performance and drawdown.


Please note that the strategy is backtested from 2017-01-01. As the timeframe is 1D, this strategy is a medium/long-term strategy. That's why only 34 trades were closed. Be careful, as the test sample is small and performance may not necessarily reflect what may happen in the future.

Enjoy the strategy and don't forget to take the trade :)
نص برمجي مفتوح المصدر

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