In this post, I discuss how I used GPT embeddings to build a smart search tool for my second brain note-taking system.
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In this Part 2, I discuss the practical implementation of Molecular Notes in Obsidian. I explain how I organise my Second Brain (Tags, Folders, Topics) and present detailed workflows for ingesting different types of content. I also explain how one can extend Obsidian, giving the example of Polymer – a spaced repetition application that I built on top of my Second Brain.
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In this post I present Molecular Notes, a note-taking system I created to help me learn from diverse sources (books, textbooks, articles, courses), distil insights, and synthesise new ideas. Molecular Notes is how I approach my Second Brain – the body of concepts and ideas that are relevant to my understanding of the world, both personally and professionally.
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25 Feb 2022 ·
10 min read
[
finance
]
In this post, we revisit classical DCFs through the lens of convexity. This leads to the counterintuitive finding that increased uncertainty about an asset’s fundamentals can sometimes be a good thing!
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I recently launched a website to open-source some of my book reviews. To accompany this, I’d like to share some thoughts on my philosophy of reading books, and how my current workflow reflects this philosophy.
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10 Jan 2022 ·
7 min read
[
finance
]
In this post, we discuss a cognitive bias called probability matching, explaining how it is rational from a population perspective. We then make an analogy to the Kelly criterion, a betting strategy that finds widespread use in both gambling and finance.
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This post is a fairly comprehensive discussion of how I use Notion (a free personal knowledge management app) to organise various aspects of my life: project management, reading, academics, plans/goals, investing, and more. The post is not designed to be read linearly – pick and choose the bits that are relevant to you.
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17 Jun 2021 ·
7 min read
[
quant
]
At its core, science is about making falsifiable hypotheses about the world (Popper), testing them experimentally, then using the experiment outcomes to refute or refine the hypotheses. The scientific method is an integral part of quantitative finance; it provides a framework we can use to identify and analyse trading signals or anomalies. In this short post, we discuss a general method for hypothesis-testing in finance, using Monte Carlo simulations to compute the probability that an observed signal can be explained by random chance.
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09 Nov 2020 ·
8 min read
[
quant
]
In this short post, we compute and visualise “COVID-19 betas” for stocks in the S&P500 index, to quantitatively and visually understand which companies were most affected (positively and negatively) by COVID-19.
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10 Oct 2020 ·
12 min read
[
finance
]
In Part 1 of this series, we demonstrated that the prices of option butterfly spreads imply a probability distribution of prices for the underlying asset. In this post, we will first examine the limiting case of butterfly spreads. Then, we will tackle the industry-standard approach for constructing PDFs from option prices: interpolating in volatility space to generate a volatility surface, converting this into a continuous set of option prices, then applying the Breeden-Litzenberger formula to find the PDF.
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