Ether Classic Purchase…

Yesterday we went long Ether Classic (ETC) at 18.29. This moves us back into a long position across all cryptocurrencies except bitcoin.

ETC is our second best performing trading market for the bot since starting the live trade diary, showing a gain of +472.4%. Today I also thought it would be worth looking at the impact our trading methodology has on relative % holdings in the trade pool.

When we started the real-time experiment we decided to pro-rate the trading capital across all coins based roughly on their coin market capitalisation. The purpose of this was to reflect how we would approach trading a larger fund – while our small test account would have zero impact on some of the smaller coins, a larger fund would be harder to trade if they were equally split.

The decision was also taken not to rebalance this split once trading had commenced. Coins that were more profitable to trade for the AI Bot would, over time, have larger trade sizes associated with them – this was to reward the better performing model behaviours.

In the 10 weeks since the start of our experiment this has already started to see a significant change in the % applied to each coin as can be seen below:

CoinStarting %Current %
Ether Classic0.85%2.07%

bitcoin has shown a decline in relative holdings at the expense of some of the rapidly rising coins such as ETC and XRP. ETH has also made inroads into the dominance of bitcoin within the portfolio. I’m not sure we have enough information to draw anything from this trend at this stage but I think it may be interesting to monitor over time.

Total performance now sits at +135.01% since the start of the experiment and the models appear to have sidestepped some of the extreme volatility seen in some coins during this time.

We have also made a change to the learning capabilities of the models – I will detail these changes in a separate post.

— Wintermute —

4 thoughts on “Ether Classic Purchase…

  1. It seems to be going well. I am appreciative of the presence of weighting the portfolio with the anticipation of trading a larger amount of capital.

    I am curious on how the models choose the coins to include in the portfolio. Blockchain tech is gaining traction and there are currently a number of very good applications coming out in ICOs. I would use Encrypgen as an example; the human genome on the blockchain ensuring privacy and encouraging a wide database open for research. Is there a specific criteria for choosing coins? I am sure liquidity would be one of the factors. As the universe of alt-coins expands at the pace it is currently is there a way to take that factor into account within the model?

    Thank You for the update and I look forward to more!

    1. Hi,
      the initial selection was a combination of traded coin capitalisation (as opposed to total coins in issue where many of them may be tied up) and personal bias 🙂 There were a few others such as Litecoin that were also candidates for inclusion and, with a larger fund, would probably be included more for liquidity reasons than anything else.
      We try and keep a distinction between coins that are good for trading and coins that have a good underlying use case which may be more suitable for long term investment but have, at the moment, limited trading appeal.

  2. hi , why not show entry stoploss take profit amount .. for each trade (after it’s finished ofcourse) .. so i can see whats happening? like one can do on fxbook .. more details. Now i just see profit or loss which does not tell me much.. also many coins have had a simple trend.. how will ai perform in crazy sideways trend?.. what timeframe does it trade in?

    1. Hi, thanks for the question. The models are designed to handle institutional volumes, not individual investor volumes. To accommodate this requirement we can’t enter trades on a simple stop basis, the slippage would be too high.
      Instead the models operate on a continuous analysis basis, pulling in data from the markets. When a change of position is triggered then the trade is passed through to the hedging module which attempts to execute the trades at the best levels across the available exchanges. This will minimise slippage when entering and exiting trades.
      Although the test account for our real-time diary is very small, we have designed the model to accommodate much higher levels of trading.

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