Home Community Crypto, Trading, Investing, and Technology BTC Beta – Stop Loss Has Tightened Reply To: BTC Beta – Stop Loss Has Tightened

#6864
Justin Justin
Moderator

Hey guys, I’ll chime in here.

@CHOCO, our approach with BTC Beta has been to let it come to the model and solution entirely on its own with no constraints. In other words, we didn’t say you have to have x many trades per day, week, or month. We didn’t put limits on stop losses, etc…

The output is a very aggressive model that, given enough time, performs extremely well (in our testing) across pretty much any market condition. But it does produce losses. And those losses probably exceed most people’s tolerance level.

As such, we posed the problem–can it generate similar results while also being a little more conservative. And the answer is yes. This more conservative approach actually would have done exceptionally well in 2019 (beating the more aggressive model) but slightly worse (albeit still great) in other years. We’ll need to monitor.

*Here’s my token analogy- This would be the same as telling a self-driving car to accelerate and brake a little less harsh. The core model is still intact and it still drives well, but it’s now considering the human factor of comfort.

We prefer to be as hands-off as possible, but there are times that it is appropriate to challenge our models in different ways.

If you want a deep dive into this model–and you haven’t read it yet–I highly encourage you to take a look at this post: https://crypto-ml.com/bitcoin-trading-with-machine-learning-anomaly-detection/

Regarding the Trade History, every single trade listed there is an actual alert delivered by our platform to real customers. It does *not* include any backtesting or hypothetical data.

@MATTHEW55, great question–and yes, the systems do have the ability to adjust these levels on the fly. But it is keeping this aggressive stop loss value. In fact, all of our models seem to be pegged as low as they want to go–we haven’t seen the values change in quite some time.

The manual intervention was to see if it can find a similarly-performing set of parameters while being a little more conservative, which it was able to do. Hopefully that makes sense.

Community Advocate