portfolio AI lead @valsoftcorp / prev tech consultant @nationalbank
Joined January 2015
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Olivier retweeted
I am humbled by the support for my money market interest rate strategy, but to be honest, it was a proof of concept. The real value of the controller is in CDP rate management.
I reviewed the leading CDP infrastructures and assessed their limitations and designed a system that I believe mitigates observed risks with the highest ROI trade-offs.
The result is a fully autonomous stablecoin interest rate system that adapts in real time to optimize borrow rates without inhibiting scaling and while addressing long tail risks. The strategy can be tuned for any collateral type, and the phi-strategy concept I introduce can be developed to remove the need to tune altogether. Of course, I didn't give it all away in my research. We are developing this infrastructure at @Cod3xOrg for future implementation in our native credit layers.
My latest paper is available here (note SSRN review in progress):
Autonomous Money Supply Strategy Utilizing Control Theory @SSRN papers.ssrn.com/sol3/papers.…
Happy to announce my interest rate strategy research paper is now publicly available. I reviewed the leading lenders' strategies for inefficiencies and developed a strategy that mitigates identified risks.
The strategy will be live sooner than you think!
Adaptive Money Market Interest Rate Strategy Utilizing Control Theory @SSRN papers.ssrn.com/sol3/papers.…
Congrats to the beast @crypto_yuvi who just published his PID research paper.
All current and future releases, like @IroncladFinance, will soon feature the smartest interest rates in DeFi.
So excited about our ecosystem. The backend work coupled with what’s being cooked up user-facing is truly novel.
papers.ssrn.com/sol3/papers.…
Olivier retweeted
Happy to announce my interest rate strategy research paper is now publicly available. I reviewed the leading lenders' strategies for inefficiencies and developed a strategy that mitigates identified risks.
The strategy will be live sooner than you think!
Adaptive Money Market Interest Rate Strategy Utilizing Control Theory @SSRN papers.ssrn.com/sol3/papers.…
Turns out Nomad was right and I have to look at books dynamically
actually visualizing markets has been really helpful in my learning recently
Not intuitive enough yet for me to know if these walls will result in rip bozo bears or if we wick down a bit once we blow ATH...Also havent yet seen so many stacked bids as well so will take this as a learning opp
Straight into tonight's diary 💅
Lay off leverage.
Bridge to Mantle.
Hold spot mETH.
Supply on @AureliusFi.
Stack Eigenpoints while we range.
actually visualizing markets has been really helpful in my learning recently
Not intuitive enough yet for me to know if these walls will result in rip bozo bears or if we wick down a bit once we blow ATH...Also havent yet seen so many stacked bids as well so will take this as a learning opp
Straight into tonight's diary 💅
Olivier retweeted
This seems like a good time for a thread on why applying RL to trading is hard. The settings that RL has been successful have the following properties: zero-sum, perfect simulators, (nearly) perfect information, and training on the test set. Trading has none of these properties.
We have built DeepStack, the first AI to beat humans in no-limit poker.
We are now building the next generation AI company for algorithmic trading!
We are growing our research and engineering team.
If you want to work with a world class AI team led by ex-DeepMind researchers and engineers.
If you want to build something that has never been done before - join us.
equilibretechnologies.com
Olivier retweeted
Consider:
UNI fee switch as a potential indicator that CB stands to win case against the SEC.
Paves the way for DeFi resurgence, but also removes the “security” label overhang from tokens like SOL & MATIC.
Easier for CME to list SOL, MATIC,... futures => ETFs to follow
If you read my article on linear correlations and understand their limitations. Using Spearman will help you spot non-linear relationships like shown here
Cross-asset correlations between BTC and major macro assets [Tech Stocks, Gold, TIPs and GSCI Commodity Index] remain low.
We can generate a summary metric by averaging the 4 pairwise, 100 day rolling Spearman rank correlation metrics. This average rank correlation summary is now negative and the lowest since end-2019.
Have you been using linear correlations correctly?
Find out in my latest article or quickly skim this thread.
Just published: The Misleading Allure of Correlations link.medium.com/I6FVkEFMlHb
You wonder if you'd repeat this process over several months, at different points in time -> would you get the same result?
