Research Article #33 - Adaptive Signal-Based Crypto Trading Strategy
Optimizing Trade Allocation Through Multi-Signal Analysis
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I’ve always found interesting the applicability of taking a bunch of signals and using them to dictate your allocation in a portfolio.
Instead of using a simple trend-following or momentum signal, let’s say the typical close > maximum high of a certain lookback period, we will try to aggregate a bunch of signals and scale exposure as the signal gets stronger.
The aim of today’s article is not about:
Validity of the signals
How much individual alpha they add to the portfolio
What’s their effect on portfolio risk adjusted returns
We will be grouping together common technical signals and see how they perform.
Obviously we need to know if that signal is effective in predicting returns and it’s not something random, so we look a bit into that.
Let’s get into today’s article!
Index
Introduction
Index
Strategy’s Thesis
Strategy’s Performance and Results
Strategy’s Parameters
Python Code Section
Sponsor Of Today’s Article
Strategy’s Thesis
The core of the strategy is the aggregation of multiple technical signals to dictate our portfolio allocation.
This is different from a simpler, single-signal strategy like a basic trend-following approach.
A basic approach relies solely on, as an example, if the current close exceeds an highest price of a predefined lookback period.
The key here is to integrate these signals, into a cohesive framework, that scales the investment exposure, based on the strength of the composite of signals. If multiple signals, that are correlated to each other, but are different by nature, show that the market is strong, a higher allocation should provide a better exposure.