Add in Guides documentation to the Hugo documentation (#1845)
* Add in Guides documentation to the terminal * Don't need eg twice * Update _index.md * Delete website/content/terminal/getting_started/images directory * Update _index.md * Delete website/content/terminal/introduction_to_stocks/images directory * Adjust css so that tables and lists also have white text * Small small change * update white svg Co-authored-by: jose-donato <zmcdonato@gmail.com>
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---
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geekdocCollapseSection: true
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---
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OpenBB is a leading open source investment analysis company. We represent millions of investors who want to leverage
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state-of-the-art data science and machine learning technologies to make sense of raw unrefined data. Our mission
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is to make investment research effective, powerful and accessible to everyone.
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---
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<center><b><span style="color:#00AAFF">If you are new to the terminal, please visit our </span>
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<a href="getting_started" style="color: white">Getting Started page</a>
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<span style="color:#00AAFF">that explains how the terminal works. To get an understanding
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of each menu (e.g. <a href="introduction_to_stocks" style="color: white">stocks</a>
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or <a href="introduction_to_economy" style="color: white">economy</a>)
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visit the related Introduction page.</span></b></center>
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---
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### Why Python?
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Python is one of the most used programming languages due to its simplified syntax and shallow learning curve.
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On top of this, it is highly used in data science and academia world (particularly on finance, economics
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or business related degrees). This is very important, as it is the first time in history that users - regardless
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of their background - can so easily add features to an investment research platform.
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The OpenBB Terminal provides a modern Python-based integrated environment for investment research, that allows an
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average joe retail trader to leverage state-of-the-art Data Science and Machine Learning technologies. As a modern
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Python-based environment, the OpenBB Terminal opens access to numerous Python data libraries in Data Science (Pandas,
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Numpy, Scipy, Jupyter), Machine Learning (Pytorch, Tensorflow, Sklearn, Flair), and Data Acquisition (Beautiful Soup,
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and numerous third-party APIs).
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### Why Open Source?
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An open source product allows for higher quality, greater reproducibility, lower costs, more transparency,
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and faster go-to-market due to the strong community created. In addition, users can adapt the platform to their
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needs or even build proprietary features on top of our infrastructure - which ultimately can result in a marketplace.
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The OpenBB Terminal empowers individuals, regardless of whether they want to invest on the stock market in Japan,
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invest real estate in Portugal or invest in the cryptomarket, to contribute and share their passion, dreams and desires
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about the OpenBB Terminal. This brings together contributors that are not only software engineers but also financial
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advisors, data scientists, machine learning researchers, analysts, day traders, mathematicians, academics, physicists
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and portfolio managers.
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The terminal provides a modern, and fully customizable, python-based integrated environment for investment research. Leveraging state-of-the-art data science and machine learning, the individual trader can now harness technologies used by institutions and professionals around the world to make better financial decisions.
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The terminal opens access to numerous python data libraries in data science (Pandas, Numpy, Scipy, Jupyter), machine learning (Pytorch, Tensorflow, Sklearn, Flair), and data acquisition (Beautiful Soup, and numerous third-party APIs).
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You can access a video of the terminal capabilities [here](https://www.youtube.com/watch?v=fqGPK8OVHLk).
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<iframe width="560" height="315" src="https://www.youtube.com/embed/fqGPK8OVHLk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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**Command Groups:**
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{{< toc-tree >}}
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website/content/terminal/commands/_index.md
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---
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geekdocCollapseSection: true
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---
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**Command Groups:**
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{{< toc-tree >}}
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