How can I assess the impact of trading on my performance?

 

As our product is currently available on 8 different platforms, each with a dedicated fee system, it would be wrong to present figures in line with these 8 platforms. Especially since our users can have multiple exchanges connected to their NapBots account.

The performance statistics of our strategies are based on the prices of CryptoCompare which is based on the average prices indicated on the crypto-currency Exchanges. Most index providers calculate theoretical indices that are not necessarily bound to repeat in the future. But these indices provide very good indications of past returns, once we have incorporated transaction costs.

This is why we decided to set up a simulator to help you better understand past performance with accurate transaction costs.

What transaction costs should we include in our analysis?

First of all, you have the trading fees that have to be paid to the exchange you are using. We have deliberately chosen to only execute market orders, even though they are more expensive than Maker orders.

The current fee levels across all 8 of our platforms are as follows (and provided you do not reach certain 30-day volume levels that would open up the possibility of a discount):

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* 24 bps for 30-day volume above $50,000

** 25 bps for 30-day volume over 10k USD

*** 13.5 bps for an OKB holding above 500.

The reason why we only use market orders

First of all, you need to understand that every hour we execute potentially hundreds of orders for all our users. We have to prioritize them so that we don't come to the market with one big order that comes from several hundred accounts and doesn't get executed at exactly the same time because we would have to go way back in the order book. So we analyze the actual liquidity of the market when we need to send orders. Then we determine an optimal size. And for each user, we divide their target trading amount into N buy/sell orders. We then mark each of these individual orders for all our users and send them randomly at the market price with a timer.

Now let's imagine that we want to place Maker orders to reduce fees and improve slippage. When the market is rising and we have a buy order, you could be waiting a long time to be executed. It is also possible that the buy order will never be executed. Also, since we would have to manage the same queuing process, an unexecuted order means that all subsequent orders would have to wait. This would be very problematic.

We have optimized our performance simulation tool with an option to calculate the slippage cost. The slippage cost is the difference between the actual execution of your order and the reference price that is used to calculate the theoretical performance.

There are two components here: The first is the fact that the prices taken at the same time between the Exchange market and CryptoCompare are not the same. The second is the fact that when your orders are actually executed, the market prices have moved away from your reference price. These criteria are very important to evaluate the difference between the actual execution and the theoretical performance.

Allocation Performance Tool

You now have all the tools to understand the regular differences you will observe between our theoretical performance and the performance of your allocation with our NapBots service. Our new tool will allow you to do several things (you can find it on your Allocation page on your Dashboard):

  • At the bottom of your Allocation page, click on Advanced Settings. 
  • Enter the slippage costs of your choice.
  • Click on View my allocation performance.
  • The tool will calculate a theoretical performance and KPI of your own allocation in use. This will allow you to evaluate the risk factors as well as the average trading gain. Keep in mind that the higher this trading gain, the more likely it is to cover your trading costs (trading fees + slippage fees).

In this way, we hope you will get a better idea of how a particular combination of strategies has performed in the past. And you will see that sometimes the best performing strategies on paper may not be the optimal allocation when you want to maximize your returns.

 
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