The Analytical Foundation Behind My Approach to Search
How Paul Truscott's qualifications and training in financial technical analysis transfer directly into diagnosing search engine behaviour and AI visibility, producing original measurement frameworks for semantic SEO and generative engine optimisation.
Credentials and Qualifications
I hold the STA Diploma in Technical Analysis (Parts 1 and 2, with Distinction) from the Society of Technical Analysts in the UK. I hold the IFTA Certified Financial Technician (CFTe) designation, recognised internationally as the standard professional qualification in technical analysis. I hold an Advanced Diploma of Financial Market Analysis from the Australian Technical Analysts Association. I carry Full Membership (MSTA) of the Society of Technical Analysts, member number 11311.
In 2011, I won the Bronwen Wood Memorial Prize. The Bronwen Wood is the STA's premier annual award, given for the highest-scoring paper in the Part 2 examination. The prize requires a merit-based score of 90% or higher. Fewer than twenty people received it between 2005 and 2025.
Training and Mentorship
I trained under Trevor Neil, one of the most recognised names in global technical analysis education and a former Bloomberg TV presenter. I studied advanced Market Profile methodology under Jim Dalton, the author whose work defined how institutional traders read auction-based price structure. Both mentorships reinforced the same principle: the data tells you what is happening if you know how to read it. Opinion is a liability. Measurement is the edge.
What Technical Analysis Actually Teaches
Technical analysis is the study of how systems behave under uncertainty. Financial markets are driven by momentum, sentiment, mean reversion, and the interaction between participants who hold conflicting views of value. The analyst's job is not to predict the future. It is to read the current state of the system accurately, identify when a structural condition has shifted, and act on evidence before the majority recognises the change.
That description applies to search engines and AI retrieval systems with almost no modification. Google's ranking systems evaluate signals that fluctuate, overcorrect, and revert. Entity visibility in AI-generated answers follows momentum cycles. Citation frequency overshoots corroboration strength and then corrects. A brand's trust profile in the knowledge graph behaves like a price chart: it trends, it consolidates, it breaks out, and it breaks down.
The analytical training I carry is not decorative background. It is the reason I see patterns in AI search behaviour that practitioners without a statistical foundation do not see.
The Disciplines That Transfer
Technical analysts live inside noisy data. Separating a genuine trend from random fluctuation is the foundational skill. In search, the same discipline applies: distinguishing a real ranking shift from normal volatility, reading whether a change in AI citation frequency reflects a structural cause or statistical noise.
Every momentum indicator I studied measures the same thing: how far a value has moved from its baseline and whether that movement is accelerating or decelerating. Entity visibility in AI answers follows the same mechanics. A brand mentioned in 40% of AI responses to a category query is either supported by deep corroboration or overextended on thin evidence. The distinction determines whether the visibility holds or reverts.
In financial markets, support and resistance describe price levels where buying or selling pressure concentrates. In AI visibility, the same concept applies to trust thresholds. Once an entity's corroboration reaches a certain depth, its baseline visibility becomes difficult to erode. Conversely, without a corroboration breakthrough, there is a ceiling the entity cannot pass regardless of content volume.
A drawdown measures the decline from a peak to a trough before recovery. Traders use it to quantify risk. In AI visibility, the same measurement applies after a brand suffers a reputational event, a hallucinated fact, or a rebrand that disrupts entity reconciliation. Quantifying the severity and duration of the decline is the first step toward engineering the recovery.
Coined Measurement Frameworks
The analytical foundation described above is not abstract. It has produced four original measurement frameworks that I apply to AI visibility diagnosis.
Adapted from the Relative Strength Index. Citation RSI measures the speed and magnitude of change in an entity's mention frequency across AI-generated answers, identifying when visibility is overextended relative to underlying corroboration or undervalued relative to actual standing.
Adapted from the support and resistance framework. Entity Support and Resistance describes the corroboration baseline an entity will not fall below once established, and the citation ceiling it cannot break through without a stronger independent corroboration event.
Adapted from Bollinger Bands. Visibility Bollinger Bands plot the statistical range of normal citation fluctuation around a moving average, separating genuine shifts in an entity's AI standing from ordinary noise.
Adapted from drawdown analysis. Visibility Drawdown quantifies the severity and duration of a decline in entity visibility, whether after a rebrand, a hallucinated fact, or negative press coverage, before recovery begins.
Why This Matters for Your Brand
Most practitioners in semantic SEO and generative engine optimisation do not carry a background in statistical analysis. They optimise content. They build links. They write schema. What they do not do is measure the behaviour of the systems they are trying to influence with any statistical rigour.
I measure. That is the difference.
Read how the methodology works or read the full career timeline.