Day trading with cryptocurrencies (“crypto”) is becoming a relatively common avenue to generate supplementary income. Day trading is the buying and selling of assets, such as stocks or crypto, within a single trading day (Reference 1). Determining which crypto to target can be a daunting task since the market as a whole is extremely volatile and there currently exist over 1,300 crypto as of December 26, 2017 (Reference 2). As an investor, it is your responsibility to be well-versed in every crypto within your portfolio. Reading development websites, social media accounts such as twitter, and the technical white pages are all good habits to establish. But, in most cases, simply keeping up with available information and adjusting investment strategies accordingly will not be enough to be a formidable investor. You must track all of your trading data and analyze it regularly to get the most out of your investments.
Here, I will describe a “coin risk index” that I have developed to monitor performance of my crypto investments. It’s important to note that this value will be meaningless if you have a simplistic trading cycle that does not include averaging down (Reference 3). Averaging down can be accomplished manually (I will detail how to do this in a future post) or through functions such as “DOUBLE_UP” in the automated crypto trading program, Gunbot.
So, how can this value be useful to determine risk? For example, a coin risk index of 25% would indicate that for every one sell trade, three buy trades are executed. This would indicate that the crypto regularly reaches unpredictable lows, which is unfavorable when using averaging down functions in programs like Gunbot. Conversely, a coin risk index of 50% would indicate that for every one sell trade, one buy trade is executed. This wouldn’t happen in the real world when averaging down, but a value close to 50% would imply strong predictability regarding price fluctuations, which is favorable.
Developing a “coin risk index”:
Equation: Coin risk index = (# of sell trades) / (# of total trades)
- Download a .csv file with a full trading history from your crypto exchange. In my example, I will be using a “fullorder” file downloaded from Bittrex.
- Open the file in Microsoft Excel.
- Use the “COUNTIF” function to count the total number of trades per pair (Reference 4). In my example, I wanted to count the number of BTC-ETH trades, so I used the equation =COUNTIF(B:B,”BTC-ETH”)
- Use the “COUNTIFS” function to count the number of sells per pair (Reference 5). In my example, I wanted to first count the number of BTC-ETH trades, then count the number of sell trades on this particular pair using the equation =COUNTIFS(B:B,”BTC-ETH”,C:C,”LIMIT_SELL”)
- Divide the value attained in step 4 by the value attained in step 3. In my example, I used the equation =L3/L2
- The resulting value will be a number between 0 and 1, represented as a percent (where 1 is equal to 100%). The lower the value, the more risky the investment is. In my example, BTC-ETH had a risk index of ~41.7%. In my experience, this is a “good” value and indicates relatively low risk.
This is my first post as an author, so any constructive feedback is welcome!
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