Quant Beckman
@quantbeckman
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Quantitative Researcher & Dev. | Financial Data Scientist | Machine Learning Engineer | Mathematical Research | Algorithmic Trading Systems
Fresh takes on Quant Research
Joined July 2019
BNDS actively manages bonds & preferreds seeking stable returns. 📊
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This is the workhorse hedge for equity books. You keep your stock selection while stripping out broad market moves. It sucks...
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A heavy Saturday breakfast, nothing worth reading. Pointless topics like quantum LLMs. Luckily, I'll read this.
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What’s the biggest benefit of AI-driven workload placement?
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In the Tail-risk / CVaR hedging method (Rockafellar–Uryasev formulation) instead of minimizing variance, choose hedge positions to minimize expected losses in the worst (1−α) fraction of outcomes. This targets crash scenarios directly and is useful when returns are
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Correlation is the first truth markets abandon under stress.
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When are we going to tell the truth in the academic world? Sortino: 3.73? 4.42? 😮💨
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Man... who did this?! Now I feel bad for all discretionary traders 🫣
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Big contracts, small contracts... why not both? The Mini-to-Micro Contract Converter lets traders magically swap E-minis for Micro E-minis (and back again) within the same product family, all under one unified risk limit. There’s no math required, just seamless size swapping.
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Here a recipe for neutralizing your portfolio’s exposure to broad market moves.
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-High concurrent I/O and async libraries available → asyncio -Blocking I/O or non-async libraries → ThreadPool -Heavy CPU-bound computation in Python → multiprocessing
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Quadratic case ⇒ explicit linear feedback (Riccati structure)
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"Train up a child in the way he should go, and when he is old he will not depart from it." A Christmas movie that my children and I enjoy watching has one scene in particular that always strikes me: It’s the scene where the parents are looking in on the boy in his room and
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