1/ A lot of quant traders (including myself at many times) have a knee jerk instinct to believe that if a strategy is technically challenging it must mean there’s more alpha underneath.
1/ A lot of quant traders (including myself at many times) have a knee jerk instinct to believe that if a strategy is technically challenging it must mean there’s more alpha underneath.
I went to school for math. I got trained in quant trading, and eventually shifted to quant trading in crypto. Over time, I've relied less on math and more on intuition, e.g. for analyzing Elon's tweets.
Yesterday I looked at ... cartoons? for 4 hours.
A thread about adapting.
For those interested in developing #HFT strategies in #DeFi, this presentation by @jrdothoughts can give you some approach angles and interesting ideas
Last week, we presented a great session at the @BlockchainZA DeFi Conference 2021.
This session was focused on High-Frequency Trading in DeFi, covering key building blocks of DeFi HFT Strategies as well as some crazy ideas.
Here's the recording👇
youtu.be/MsWIS436do4
A misconception about DeFi is that HFT doesn't exist, because blocks only occur every few seconds. The truth is that HFT-like price discovery *does* occur. In the mempool (and private networks like Flashbots). If you're only looking on-chain, you're missing a lot of the ecosystem
⚠️ It’s 📰💧 time of the month again 🚨
Don’t want to read 100s of pages of CFMM literature?
You’re in luck! We review the known theory of CFMMs (plus some new goodies!) for an upcoming *textbook* chapter on crypto DeFi w/ two new authors:
(Paper: stanford.edu/~guillean/paper…)
Optimal starting amount for maximizing arbitrage profit on arbitrary-length paths of constant product market makers. (note v3, fixed a typo in numerator of eq 3)
Read my latest article: A Guide for Choosing Optimal @Uniswap V3 LP Positions, Part 1
In this post, I derive a (known) relationship relating the probability that the price of an asset will fall within a predefined range.
medium.com/@lambert-guillaum…
Easier to explain with an high level architecture diagram. The raw feeds are stored and there is a scheduled script to convert them to the binary flat files
The main problem with trading options as retail is the vig you pay on each trade.
You start the game a goal behind.
Can your edge be expressed in a cheaper more liquid instrument?
quick rant about "constructing a neutral exposure" while I wait for something to load. a qualitative description about step by step optimization for us peons who take seconds to calculate (not microseconds on a custom ASIC like some here)
1/ Have a quant strategy. What's that?
"In the 90s and early 2000s, we focused on longer-term price momentum and a relatively slow trading frequency. The current approach is very nimble. The systems are designed to evolve, making fine-tuned adjustments throughout the day, in real-time."
hedgeweek.com/2021/07/16/303…
I wrote about the rise of quants in the credit world. With the jump in electronic trading during the pandemic, quants say their methods are set to sweep the bond world as they did equities decades ago @marketsbloomberg.com/news/articles/…
Trade six types of strategies
If you have momentum trades, add in a system for mean-reversion. If you have trend-following trades, add some carry strategies. 5 from one strategy, could be -4 from the other and it'll auto-reduce scale
h/t discussion w/ @SimpelAlpha
Thread👇
So you like trade tips, eh?
Get some features that are correlated with future returns, combine and stick em in a constrained optimization with a txn cost model and risk model, then trade the delta to your exposures using algorithmic execution to reduce impact.
You're welcome.
"Empyrial is a Python-based open-source quantitative investment library dedicated to portfolio management [combining] risk analysis, quantitative analysis, fundamental analysis, factor analysis, and prediction making." github.com/ssantoshp/Empyria…
"Statistical arbitrage identifies and exploits temporal price differences between similar assets. We propose a unifying conceptual framework and develop a novel deep learning solution, which finds commonality and time-series patterns from large panels." arxiv.org/pdf/2106.04028.pdf
Finally took the plunge and published NSE F&O Bhavcopy summarizer on Github.
A lot of moving objects there and impossible to explain everything so I just commented the code
github.com/beinghorizontal/B…
This is the code flow and sample diagram it will generate