Discussing the article: "Defining your Edge (Part 5): Using GARCH Variance and Volatility-Scaled LSTM in an Expert Advisor"
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Check out the new article: Defining your Edge (Part 5): Using GARCH Variance and Volatility-Scaled LSTM in an Expert Advisor.
We merge GARCH(1,1) variance projections with ATR plus Bollinger-Bands patterns to form an algorithm that could optionally be used with volatility-scaled LSTM within LSTM Wizard-ready signal class. We cover feature scaling, mode scoring, thresholds, and safety checks. Readers can replicate backtest/forward test results to verify if the recurrent layer gives incremental discrimination over our deterministic baseline.
From our last-piece on developing-Your-Edge where we introduced a new algorithm-network combination as an entry signal, where we decidedly got different test results as far as using just the algorithm vs using both the algorithm and neural network. Therefore, even though we are introducing a new algorithm-network pair in this article, we slightly adjust our focus to having a more all-round algorithm that not only identifies if entry setups exist but also tries to assess if they are worth it.
Most trading approaches concentrate their efforts on choosing whether an entry setup exists or not. Not as much attention is allotted to the narrower problem of when, within a given setup, the actual entry should be taken. This distinction can become very important when price tends to move from a reasonably stable regime into one where volatility expansion is taking foothold. In other words, a breakout can be accurately spotted that still gives a poor trade when the entry is either early, late, or taken in a false expansion.
Our custom signal class for this article uses two engines. First, and primarily, we have GARCH(1,1) that approximates if near-term price volatility is increasing relative to its recent baseline. An optional Volatility-Scaled LSTM, the second engine, could then evaluate a short sequence of normalized price volatility states before contributing to the final entry likelihood. Both of these engines are controlled by seven execution modes that cover: breakouts, squeeze releases, re-entries, impulses, band walks, pullbacks, and range escapes; while utilizing: ATR-, Bollinger-Band- and price-action-based indicators.
The proposition we make is modest. GARCH could spot if a valid setup happens in a favorable setting, whereas the LSTM is engaged to help describe how this regime came about. However, the opposite can also be plausible where ATR, Bollinger Bands and GARCH overlap a lot leaving the network to add only complexity instead of valid information. In this context therefore, rapid proofing implies switching between algorithm only to algorithm plus network versions and trying to validate both with back tests and forward walks.
Author: Stephen Njuki