Entropy Algorithmics is a business-to-business data science consulting company with a primary focus on engineering
turn-key mathematical solutions applied to financial markets. Our proof-based algorithmic expertise helps institutional
investors offer world-class investment performance through Portfolios as a Service (PAAS).
© 2025 Entropy Algorithmics, All Rights Reserved.
MATHEMATICS FIRST, COMPUTING POWER SECOND
Entropy Algorithmics contends that the tacit acceptance of average algorithmic performance can be found in the naive overreliance on computing power as a way to reach beyond its initial promise of smarter mass data crunching capabilities in solving complex stochastic problems. Dependence on such new techniques as artificial intelligence, neural networks or machine learning can certainly be warranted in niche, technology-intensive types of endeavors in search of micro imbalances such as HFT. Still, the broader issue remains that these tools are routinely misportrayed as the in-fashion, catch-all analytical solution in most segments of financial engineering. The reality is that true sophistication in modeling quasi-random data stems more from finding permanent mathematical causation than mining petabytes of data in a brute force-based quest to uncover fleeting statistical correlations.
For that reason, and despite our extensive use of the latest cutting-edge technology, Entropy Algorithmics has made the deliberate choice not to engage in this perpetual arms race for more speed and power as the primary driver of its value creation. Instead, we have allowed theoretical mathematics to take precedence again as we concentrate our research efforts on uncovering new kinds of edges across more vanilla markets and frequencies where the margin for improvement remains tremendous. And as we allow ourselves to bring back the science into data mining, we carefully avoid those new fads such as deep scanning for social media trends or using so-called 'quantamentals' and other ancillary gimmicks dreamt up by an uninspired financial engineering industry gone drunk on technology.
Entropy Algorithmics contends that the tacit acceptance of average algorithmic performance can be found in the naive overreliance on computing power as a way to reach beyond its initial promise of smarter mass data crunching capabilities in solving complex stochastic problems. Dependence on such new techniques as artificial intelligence, neural networks or machine learning can certainly be warranted in niche, technology-intensive types of endeavors in search of micro imbalances such as HFT. Still, the broader issue remains that these tools are routinely misportrayed as the in-fashion, catch-all analytical solution in most segments of financial engineering. The reality is that true sophistication in modeling quasi-random data stems more from finding permanent mathematical causation than mining petabytes of data in a brute force-based quest to uncover fleeting statistical correlations.
For that reason, and despite our extensive use of the latest cutting-edge technology, Entropy Algorithmics has made the deliberate choice not to engage in this perpetual arms race for more speed and power as the primary driver of its value creation. Instead, we have allowed theoretical mathematics to take precedence again as we concentrate our research efforts on uncovering new kinds of edges across more vanilla markets and frequencies where the margin for improvement remains tremendous. And as we allow ourselves to bring back the science into data mining, we carefully avoid those new fads such as deep scanning for social media trends or using so-called 'quantamentals' and other ancillary gimmicks dreamt up by an uninspired financial engineering industry gone drunk on technology.
BREAKING AWAY FROM DOGMATIC ANALYTICS
Entropy Algorithmics enforces a militant scientific approach that directly translates into the unique DNA of our work and its exceptionally straightforward efficiency. We start from the premise that the mainstream intelligence provided to investment professionals has largely become sterile when it comes to adding proof-based operational value to their decision process.
Whereas fundamental research can find in its soft approach a plausible alibi for failing to evolve further than its secular dogmas in establishing causation of financial outcomes, quantitative research on the other hand, has more explaining to do. By asserting its value creation on tired accounting standards, convoluted projection models and endless business "stories", the fundamental analysis industry continues to bring little else than entertainment value to the investor and a blur of disjointed information that can hardly be converted into actual performance by professionals. Likewise, while the technological backdrop of quantitative research has undoubtedly become exponentially more sophisticated, the slow concurrent and paradoxical demise of flagship algorithmic funds and once prominent quantitative strategies is a strong reminder that the financial data engineering industry has mostly failed to harness its new tools until now.
Entropy Algorithmics enforces a militant scientific approach that directly translates into the unique DNA of our work and its exceptionally straightforward efficiency. We start from the premise that the mainstream intelligence provided to investment professionals has largely become sterile when it comes to adding proof-based operational value to their decision process.
Whereas fundamental research can find in its soft approach a plausible alibi for failing to evolve further than its secular dogmas in establishing causation of financial outcomes, quantitative research on the other hand, has more explaining to do. By asserting its value creation on tired accounting standards, convoluted projection models and endless business "stories", the fundamental analysis industry continues to bring little else than entertainment value to the investor and a blur of disjointed information that can hardly be converted into actual performance by professionals. Likewise, while the technological backdrop of quantitative research has undoubtedly become exponentially more sophisticated, the slow concurrent and paradoxical demise of flagship algorithmic funds and once prominent quantitative strategies is a strong reminder that the financial data engineering industry has mostly failed to harness its new tools until now.
PRICE AS A SINGLE POINT OF FOCUS
In this quest for robustness, Entropy Algorithmics refocused on the only unvarnished variable that truly matters: price. Price contains all the information known to the market at any given moment. Price is what professionals trade. Price is what makes their clients money. By combining the exclusive focus on this single string of data with our reversion to theoretical mathematics to understand it, we allowed ourselves to develop a non-parametric, holistic framework that is able to uncover, explain, describe, repeat and exploit without any optimization the true axiomatic causal factors that drive the motion of financial prices. Only then did we proceed with harnessing the power of modern data mining technology to confirm our thesis, which it did.
The outcome is our proprietary True Price Motion Dynamics (TPMD) algorithmic engine, a pioneering solution that underpins our entire data science expertise. Equipped with a proven deterministic underlying framework, we are able to pinpoint the exact drivers that move the needle in extracting the signal out of the noise in your data, yielding unique benefits reaching beyond all ends of what you would expect from a traditional data science company, be it in terms of performance, robustness, actionability, or cost-effectiveness.
In this quest for robustness, Entropy Algorithmics refocused on the only unvarnished variable that truly matters: price. Price contains all the information known to the market at any given moment. Price is what professionals trade. Price is what makes their clients money. By combining the exclusive focus on this single string of data with our reversion to theoretical mathematics to understand it, we allowed ourselves to develop a non-parametric, holistic framework that is able to uncover, explain, describe, repeat and exploit without any optimization the true axiomatic causal factors that drive the motion of financial prices. Only then did we proceed with harnessing the power of modern data mining technology to confirm our thesis, which it did.
The outcome is our proprietary True Price Motion Dynamics (TPMD) algorithmic engine, a pioneering solution that underpins our entire data science expertise. Equipped with a proven deterministic underlying framework, we are able to pinpoint the exact drivers that move the needle in extracting the signal out of the noise in your data, yielding unique benefits reaching beyond all ends of what you would expect from a traditional data science company, be it in terms of performance, robustness, actionability, or cost-effectiveness.