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PyQuant News 🐍 Profile picture
@pyquantnews
Oct 7, 2024 • 10 tweets • 3 min read • Read on X
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A Bloomberg Terminal costs $30,000 per year.

Unaffordable for 99.9% of us.

Reserved for the Wall Street elite to outfox the normal investor.

OpenBB just dropped the $0 cost replacement.

Get 405 free data sources and an AI Copilot.

The 90-second step-by-step guide: OpenBB Terminal Pro
In case you're unfamiliar:

OpenBB is the first AI financial terminal that combines data integration with an AI agent to transform investment research.

• Private (your data is not shared)
• 100s of free data sources
• Custom data backends
• Advanced AI Copilot

How it works:
Set up a custom, local data server with 370 sources of free market data.

No coding required (just double click an icon):
Once the data server is running, add it to the dashboard.

Here's how to do it:
OpenBB's AI copilot can use any data to answer your questions.

Wondering about the crude oil futures curve?

OpenBB Copilot has an answer:
Can't get through the day's news?

OpenBB Copilot can summarize the top news in a single paragraph:
Use pre-built dashboard templates to make your analysis workflow even quicker:
OpenBB Terminal Pro uses the same charts as TradingView.

Get incredible charting capabilities in the same place as your fundamental, news, and economic data:
Select data in a table and generate plots in two clicks.

Then, ask the OpenBB AI Copilot what happened.

Wondering about the trend in Apple's earnings?

Plot it and ask AI:
OpenBB Terminal Pro comes with a simple double-click installation file.

No more virtual environments.

No more dependency hell.

Double click and go: Double click to install

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More from @pyquantnews

PyQuant News 🐍 Profile picture
Jan 9
A Bloomberg Terminal costs $30,000 per year.

Unaffordable for 99.9% of us.

But Bloomberg is not AI-enabled.

OpenBB dropped the $0 replacement with AI agent workspaces.

Free market data and an AI Copilot:

The 90-second step-by-step guide: OpenBB Terminal Pro
In case you're unfamiliar:

OpenBB is the first AI financial terminal that combines data integration with an AI agent to transform investment research.

• Private (your data is not shared)
• 100s of free data sources
• Custom data backends
• Advanced AI Copilot

How it works:
Set up a custom, local data server with 370 sources of free market data.

No coding required (just double click an icon):
Read 12 tweets
PyQuant News 🐍 Profile picture
Dec 31, 2024
Jupyter Notebook is the most powerful tool Python developers have.

But most people don’t know the hidden features.

Need a quick web app?

Or create REST APIs?

Here's the 6 ways to use Jupyter notebook you never knew existed: The notebook to rule them all.
Jupyter Notebook is a web app for creating and sharing computational documents.

When I say powerful, here's what I mean:

• It supports 40 languages
• Produces rich, interactive output
• Leverages big data tools like Spark

So, what else can we do with Jupyter Notebook?
Package Development

nbdev let's you develop and publish Python packages right from Jupyter Notebook.

It generates documentation and publishes on GitHub Pages. You can also write tests and setup CI with GitHub Actions.

github.com/fastai/nbdev
Read 11 tweets
PyQuant News 🐍 Profile picture
Dec 26, 2024
99% of beginners get backtesting wrong:

Build backtest, get poor results.

Tweak backtest, get positive results.

Then they wonder why they lose money.

Here's the dead-simple framework the pros use to get backtesting right: Image
If you replicate this framework, you’ll:

• Setup a backtest with bt
• Run a backtest and analyze results
• Assess how random your results are

Plus, you’ll dramatically reduce the risk of your strategy performing poorly in the market after a great backtest.

But first…
In case you’re new to backtesting.

It's used to:

• Simulate how a strategy might perform live
• Test trading ideas against historic market data
• Optimize for performance parameters like Sharpe

It's easy to setup but hard to get right.

Here’s how to do it with Python.
Read 20 tweets
PyQuant News 🐍 Profile picture
Dec 22, 2024
I'm 43.

If you're still in your 20s or 30s, read this:
1. The compounding effect of doing the same thing over and over for a long period of time is a force of nature. Learn how to compound.

2. Learn how to be comfortable with being uncomfortable. Wealth doesn't accrue to people solving easy problems.
3. As soon as you hit your 30s, you’ll understand. Your 20s actually suck. If you feel like it sucks right now, guess what? It’s supposed to. It gets better.

4. It is very hard to change the world when you are broke. So don't apologize for wanting wealth.
Read 21 tweets
PyQuant News 🐍 Profile picture
Dec 14, 2024
This is Edward Thorp.

The genius mathematician that returned 20%+ over 30 years.

He traded for 19 years, with his worst loss being 1%.

He beat Vegas dealers at blackjack.

His top 7 trading strategies (and how they work): Image
1. Statistical Arbitrage

He used mathematical models to identify price discrepancies between different markets or securities, buying undervalued assets and selling overvalued ones.

2. Quantitative Analysis

Thorp pioneered the use of quantitative methods in financial markets, developing advanced algorithms to analyze market data and identify profitable trades. Image
Read 11 tweets
PyQuant News 🐍 Profile picture
Dec 12, 2024
The best way to lose money with algo trading:

Overfitting to noise.

It’s how traders turn $100,000 accounts into $100.

So I spent 20 hours studying the state-of-the-art technique to learn how to keep my money.

Here’s the step-by-step guide to set it up (for any strategy):
Algo trading is a game of iteration.

The faster you can test parameter combinations, the faster you can discard ones that don’t work.

And nothing is faster than vectorbt.

It’s a state-of-the-art library that runs millions of simulations in a few seconds.

Here’s how it works:
But first, in case you’re unfamiliar with vectorbt:

• Combines backtesting, data science, and machine learning
• Uses a vectorized approach with pandas and NumPy
• Compiles unvectorized code using Numba

All this means it’s blazingly fast.

Here’s how you can use it:
Read 15 tweets

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