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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.

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Wondering about the trend in Apple's earnings?

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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
Jun 8
Steal this 8-step framework to take an algorithmic trading strategy from idea to execution.
1/ The idea

Look for market anomalies that repeat.

Your strategy should be based on some economic rationale. In other words, “using machine learning” is not a trading strategy.
2/ Research

Find out if the anomaly you think you found actually exists in history.

Get data and start exploring.
Read 11 tweets
PyQuant News 🐍 Profile picture
May 30
An 8-step, dead-simple algorithmic trading framework billion hedge fund managers follow: Image
1/ The idea

Look for market anomalies that repeat.

Your strategy should be based on some economic rationale. In other words, “using machine learning” is not a trading strategy.
2/ Research

Find out if the anomaly you think you found actually exists in history.

Get data and start exploring.
Read 10 tweets
PyQuant News 🐍 Profile picture
May 22
There is $664,000,000,000 invested in factor strategies.

Factors can help you manage risk and amplify returns.

I spent 3 years figuring it out.

Now you can do it in 10 minutes.

Here’s how in Python.
By reading this thread, you’ll be able to:

• Download historic factor data
• Compute the sensitivities to the factors
• Figure out the risk contribution of the factors

But first…
A quick primer on factor investing:

• Used to target specific return drivers
• Helps manage risk outside diversification
• Important for active managers that get paid for performance

You can use the famous Fama-French 3-factor model for free.

Here’s how.
Read 13 tweets
PyQuant News 🐍 Profile picture
May 8
I read the 161-page Financial Machine Learning paper.

It took me 3 days.

Here are the key topics (in 30 seconds): Image
Understanding Asset Prices

Prices as predictions, reflecting future payoffs and investor valuations.
Large Information Sets

The vast scope of information influencing market prices, highlights the need for complex models.
Read 11 tweets
PyQuant News 🐍 Profile picture
May 4
There is $664,000,000,000 invested in factor strategies.

Factors can help you manage risk and amplify returns.

I spent 3 years figuring it out.

Now you can do it in 10 minutes.

Here’s how in Python.
By reading this thread, you’ll be able to:

• Download historic factor data
• Compute the sensitivities to the factors
• Figure out the risk contribution of the factors

But first…
A quick primer on factor investing:

• Used to target specific return drivers
• Helps manage risk outside diversification
• Important for active managers that get paid for performance

You can use the famous Fama-French 3-factor model for free.

Here’s how.
Read 13 tweets
PyQuant News 🐍 Profile picture
May 3
7 of the best books on algorithmic trading with Python you can buy: Image
Trading Evolved by Andreas Clenow

Professional backtesting environment using Python, and provides strategies for trading both futures and equities.

• Professional backtesting with Python
• Source code and strategy explanation
• Focus on futures and equities trading Image
Algorithmic Trading with Interactive Brokers by Matthew Scarpino

Introduces readers to algorithmic trading through Interactive Brokers' Trader Workstation (TWS) programming interface.

• Guide to using IB TWS API
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• No prior experience needed Image
Read 11 tweets

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