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!

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Index

• Introduction

• Index

• Strategy’s Thesis

• Strategy’s Performance and Results

• Strategy’s Parameters

• Python Code Section

• Sponsor Of Today’s Article

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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.