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[Ralph Sueppel @macro_synergy]

Release: Macrosynergy package 0.1
The standard Python package for working with macro and financial market data in panel form (across countries).
https://t.co/wdfG5z40EE
Designed for JPMaQS, but works with all data of that type and free JPMaQS set on https://t.co/JOuUdSzBzm

[QuantPrep🚀 @quant_prep]

Deep Reinforcement Learning Trading Agent for Bitcoin

- At each step, the Q-Learning agent decides to go either Long, Short or Neutral
- It is then rewarded (PnL) based on the action
- The agent is trained to maximize the total PnL

Get it here👇

https://t.co/NNOTKpWl3t

[Valeriy M., PhD, MBA, CQF @predict_addict]

Econometrics is the original #machinelearning and Greene's Econometric Analysis is the "bible" of econometrics.

#econometrics

[cephalopodshop @macrocephalopod]

Here's a cute story about risk management and how we traded a name that ran up in price from 250 to over 500 in a couple of hours, and then back down to 200 in about twenty minutes. This is the price chart of $TRB over the last 10 days -

[Goshawk Trades @GoshawkTrades]

6 Books That Are Filled With Trading Strategies & Code,

Condensing over 50 years of knowledge on systematic trading:

1."Algorithmic Trading and Quantitative Strategies"

[Curious J @Curious__J]

I’ve said this before but in this bear market, it is apparent to me that there’s really 3 ecosystems to focus on:

1. ETH-L2s
2. Cosmos
3. Solana

Everything else is pure speculation. Obviously eth ecosystem is by far the safest but Solana has proved itself this bear market, and despite the negative sentiment of Cosmos, its a beautiful tech stack that has and will continue to attract giants. I think Cosmos will have a comeback moment/sentiment will shift similar to Solana soon enough.

[Shiro @CryptoShiro_]

Your Weekly Alpha on the Market 👇

Be Early on the Latest Narratives & Catalysts.

🔹 Find ALPHA on:

▪️ Market Outlook
▪️ September Thoughts & Unlocks
▪️ Winning Narratives
▪️ OP Stack Narrative
▪️ Latest News & Catalysts
▪️ Metrics & Data
▪️ Early Projects

& More!

🧵

[Minty @DeFiMinty]

It's funny how people think Crypto will go to zero because it's used for speculation.

The world economy is literally built on speculation.

You think people buy stocks because of its utility? No. People want number to go up just like everything else.

Concepts like revenue, profit margin, etc. is being used as a vehicle of speculation to give a semblance that it's legit, but it's a cat and mouse game just like anything else.

Same with real estate, collectibles, currency value, and everything you can invest in.

If number goes up, people will buy just like every other time because it's in our nature.

Speculation is everywhere. Crypto is just the most efficient vehicle.

[Santiago @svpino]

This was a shocking book.

I just finished it and wasn't expecting what I learned.

Every Machine Learning and Data Science practitioner should learn about causal inference.

It's a different way of thinking. It makes me look at the world with different eyes.

[Giuseppe Cavaliere @CavaliereGiu]

Hi #EconTwitter!

For #economics folks interested in a nice #machinelearning course:🖥️

Andrew Ng (@stanford, @LandingAI) has a very nice set of introductory video lectures and lecture notes, covering the basics of supervised/unsupervised/deep learning.

Don't miss out! 👇

[Piotr Pomorski @PtrPomorski]

Just a reminder that this brand new book on financial machine learning exists

[Cred @CryptoCred]

How To Find Coins for Short-term Trading

1. Price Changes

Look at stuff that's moving the most.

2. Open Interest Changes

Open interest broadly shows you the amount of positioning (and changes in net positioning) in a futures contract.

Outlier OI increases can flag large amounts of speculation, potential front running of news events, participants piling in to trade a breakout/breakdown, positioning building up inside of a narrow range that will eventually resolve violently, and more.

Outlier OI decreases can flag a large number of positions closing, be that voluntarily or via liquidations.

3. Liquidations

Liquidations are generally inefficient - forcibly closing positions can lead to attractive dislocations and the market trading at prices where it would not trade otherwise.

Forced sellers (long liquidations) and forced buyers (short liquidations) are usually sloppy and trade at bad prices, a bit like an old hooker not keeping up with inflation.

4. Funding Rate Anomalies

Outlier funding rates can flag interesting market behaviour.

Immediately raises the question: why hasn't the perp been pushed back in line with spot/vice versa?

Perps aggressive/spot passive, spot aggressive/perps passive, 2023 squeeze shenanigans with max negative funding, and all that good stuff fall into this category.

5. Indicator-Based Alerts

Extreme momentum-oriented readings (whatever version of overbought/oversold), trend-oriented stuff like MAs to highlight anomalies and/or extremities.

There are other categories e.g. 'meta' (what's the popular coin to trade), volume (where is all the trading happening), CVD (who's slugging deltas and where) but this is a solid foundation to get you looking into interesting areas.

[Giuseppe Cavaliere @CavaliereGiu]

Hi #EconTwitter! 📈

Interested in deep-diving into #Econometrics, as seen through the lens of an econ Nobel prize winner?

Check out the material from this intro #econometrics course by Chris Sims (@princeton), covering Time Series #Regression, Asymptotics, Model Checking, Probability, and more.

Very cool stuff to read. Don't miss out! 👇

Link: https://t.co/UrPqUwptOe

[Santiago @svpino]

Scrum is like communism.

It fails everywhere, every time, but they tell you “you aren’t doing it right.”

[Miles Deutscher @milesdeutscher]

Twitter is an alpha gold mine if you know where to look.

I compiled the top 5 alpha tweets I read this week for your convenience.👇

[NameRiffle @NameRiffle]

Taking on the chaotic #ChatGPT plugin store with our own directory, now boasting +349 plugins - the largest on the web! 🏆

Categorized with descriptions and example outputs!

Most popular categories?
1⃣ Utilities/tools
2⃣ News
3⃣ Finance

Free access: https://t.co/VOlK1Nq12x

[tom ッ @tom_antok]

Me debugging my code

[Giuseppe Cavaliere @CavaliereGiu]

Hi #EconTwitter!

Searching for some very nice lecture notes on advanced undergrad #Macroeconomics? 📖

Check out this course by @monacelt (@Unibocconi), covering lots of interesting stuff with very clear language. 👇

Cool stuff!

[Valeriy M., PhD, MBA, CQF @predict_addict]

Data scientists 👨‍🔬 need to learn about forecasting, one can’t just do .fit .predict thinking that #timeseries is the same as iid data.

Having worked with many data scientists and mathematicians who didn’t have previous exposure to time series, econometrics and forecasting one often gets bemused about some data scientists trying to either apply methods that were not designed for time series or reinventing somethings like either creating weird metrics or not even properly validating time series models.

There is certainly no need to reinvent the wheel unless one is already very fluent in time series and is doing research pushing knowledge frontiers further.

One of the best tutorials tailored specifically for data scientists.

#timeseries #machinelearning #datascientists #econometrics #datascience