Discussing the article: "MQL5 Wizard Techniques you should know (Part 91): Using Skip Lists and a Hopfield Network in a Custom Trailing Class"
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Check out the new article: MQL5 Wizard Techniques you should know (Part 91): Using Skip Lists and a Hopfield Network in a Custom Trailing Class.
For our next Exploration on notions that are testable with the MQL5 Wizard we examine if Skip Lists and the Hopfield Network can give us a profit-guarding trailing strategy. Trailing Stop Management, as already argued, can be overlooked in most trading systems at the expense of Entry Signals or even Money Management. Trailing stops can make all the difference in certain situations such as trending markets, and thus we test this out with GBP USD.
In the last article, in MQL5-Wizard Techniques, we constructed a robust capital proportioning framework that used the Fenwick Trees as well as a 1-D CNN. Optimizing position size in tandem with volatility is an important aspect to trading but so is guarding profits in live markets. If traders use standard linear trailing stops they could be susceptible to the latency gap. In flash events, which tend to happen more frequently these days and in less predictable periods, price action is seldom contiguous.
In our last article, we coded a custom class that uses a Bloom filter for membership testing. Here, we explore an alternative approach. For this article we are using a probabilistic ‘leap’ over price gaps in order to cater for slippage voids. Also while we used an RNN as the second gate to set the new stop level by tracking sequential memory, some strategies can benefit from a model that is tailored to track holistic market stability instead and that is what we delve into for our neural network.
Therefore, in expanding our toolkit for the MQL5 Wizard series, this article explores a new approach. We will test a skip list for probabilistic gap-jumping as our algorithm, and pair this with a Hopfield network for associative memory in order to filter market noise, as a second check.
Author: Stephen Njuki