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I need Alternative Data to improve my portfolio’s performance

I am a Researcher who wants to develop my own trading strategy and get paid by CloudQuant for licensing and running it.

I am a Data Vendor that needs to Showcase my Dataset

I am interested in news about Alternative Data, AI and ML

about CloudQuant - Showcasing Alternative Data

I am Curious About CloudQuant

Alternative Data for Portfolio Managers and Quantitative Analysts

The world of the professional Portfolio Manager (PM) is rapidly changing. Modern PMs and Quantitative Analysts are challenged with continually adapting their fund’s performance. Alternative Datasets allow the discovery and harvesting of alpha.

Prior to harvesting alpha the PM must discover the alpha.

Prior to discovering alpha in a data set the PM must analyze the data.

Prior to anlayzing the data the PM’s team must wrangle the data into a usable format with correct symbology.

This process looks like:

1️⃣ Onboard the data
2️⃣ Wrangle the data
3️⃣ Map the symbology
4️⃣ Formulate and code a working trading strategy
5️⃣ Backtest
6️⃣ Review results

This process can take months. Only after this effort does the analyst decide if it is profitable to license the dataset.

CloudQuant’s Data Set Library allows you to browse the datasets and jump straight to step 6️⃣.

Begin by browsing our dataset library.

Browse The Alternative Data Set Library

The CloudQuant Data Showcase Service for Data Vendors

We demonstrate the value in alternative data.

We are

quantitative investors, proprietary traders, machine learning experts,
fundamental investors, CPAs, CFAs, MBAs, PhDs, MFMs, and CIOs
data scientists, and AI research consultants.

We are a global crowd research network.

We use

visualization, data science, advanced data APIs, and artificial intelligence.

We use

alternative data, fundamental data, proprietary data, crowdsourcing,
high-resolution backtesting, and high-frequency trading engines.

We use

python notebooks, Machine Learning, and Natural Language Processing
to produce and leverage proprietary alpha signals.
Learn more about Data Showcasing Browse the Dataset Library

The CloudQuant Free Backtesting & Research Tools

The Trading Strategy Incubator

Your trading strategies are your proprietary way to trade. You have extraordinary ideas that can be developed into a profitable trading strategy.

CloudQuant® provides you the platform to bring your ideas, your approaches to trading to life. You develop your trading strategy, choose the inputs, choose the parameters, choose the stocks, and run the backtests. Once you are happy with your algo we will fund (provide the capital) and run the strategy using our production platform, with dedicated professional traders to handle the execution, compliance oversight, and technology.

When there is profit, you will get paid a share as a licensing fee for your algorithm. When there is a loss, we lose, not you.

In short, we have the platform (technology, team, data, oversight, risk, and trading capital), you have the ideas. We put those together and partner with you to generate trading profits together.

Learn more about our Trading Strategy Incubator

Click To Register

Start building your strategy today

AdobeStock_53734123

Registration is Free. Algo Development is Free. Backtesting is Free.

You get paid a portion of all trading profits using your algorithm using our capital.

We license your algo.

You maintain full ownership and rights.

At no point do you have to put up your own capital.

Start building your trading strategy now for free.

CloudQuant Is

⭐ a data showcase for alternative data vendors,

⭐ a high-performance quantitative research platform,

⭐ an educator,

⭐ a community for traders, quants, and data scientists,

⭐ an investor in profitable strategies

⭐ a technology provider,

\We are The Trading Strategy Incubator. Our mission is to provide independent, curious researchers with tools to develop and prove trading strategies.

We serve our community and partners including:

⭐ Alternative Data Vendors

⭐ Quantitative Researchers

⭐ Independent Trading Algo Developers (crowd researchers)

⭐ Trading Firms and Vendors needing Quantitative Technology

CloudQuant News

Alogo Allocation

Record First Year Growth and a New Allocation to a Trading Strategy

CloudQuant Press, Press
Chicago, Illinois, USA, August 29, 2018 - CloudQuant LLC is pleased to announce a new crowdsourced trading strategy license agreement. This marks the eighth successful partnership with a global algo developer, since their public launch one year ago. Researchers receive 10% of net trading profits under the terms of the license agreement.
0 Comments
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2018-08-29
https://info.cloudquant.com/wp-content/uploads/allocation_8_2018.jpg 720 1280 Tayloe Draughon https://info.cloudquant.com/wp-content/uploads/cloudquant_final_horiz-1030x342.png Tayloe Draughon2018-08-29 17:01:322019-05-22 01:00:29Record First Year Growth and a New Allocation to a Trading Strategy
Trading strategies require thought

Conversations: Recommendations to Someone Starting out at CloudQuant

Blog, CloudQuant Press, Press
The CloudQuant team discusses their helpful thoughts for beginners on CloudQuant. We want to boost everyone starting out on our platform in their algo development and backtesting. Everyone in our company uses the CloudQuant website and coding platform in one way or another. We all use our own application, just like the crowd researchers. When we say that our free backtesting tools are "institutional grade" we really mean it. Every algo we run in our trading and investment strategies is proven in the same backtesting engine as the crowd uses. We rely on the scorecards, the reports, and the simulated trades to ensure that our trading is successful.
1 Comment
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2018-08-13
https://info.cloudquant.com/wp-content/uploads/AdobeStock_106919252_small.jpg 954 1302 Skye Slade https://info.cloudquant.com/wp-content/uploads/cloudquant_final_horiz-1030x342.png Skye Slade2018-08-13 16:57:222018-08-13 18:03:23Conversations: Recommendations to Someone Starting out at CloudQuant
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Learn more about CloudQuant

Industry News

stock exchange evolution panel

AI & Machine Learning News. 03, September 2019

Industry News
ML/AI News covering What Uber drivers hate about being managed by Algorithms, AI vs Fake News, Billionaires Jack Ma and Elon Musk debating good evil and AI, and Northern Trust's use of AI to calculate the ideal rate for loaning Equities.
2019-09-03
https://info.cloudquant.com/wp-content/uploads/as_75951263_small.jpeg 484 726 CQ Auto https://info.cloudquant.com/wp-content/uploads/cloudquant_final_horiz-1030x342.png CQ Auto2019-09-03 15:57:312019-09-09 16:52:16AI & Machine Learning News. 03, September 2019

Alternative Data News. 28, August 2019

Industry News
AltData News covering Over-the-Wall Data Science and How to Avoid Its Pitfalls, how Alternative-data provider Quandl is changing its strategy as industry giants like Bloomberg and S&P push into the $7 billion market and Finance Quants: Entry-Level Pay with a BA, MA, or PhD.
2019-08-28
https://info.cloudquant.com/wp-content/uploads/city_san_fran_AdobeStock_124197731.jpeg 768 1367 CQ Auto https://info.cloudquant.com/wp-content/uploads/cloudquant_final_horiz-1030x342.png CQ Auto2019-08-28 15:17:032019-08-28 15:27:58Alternative Data News. 28, August 2019
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CIO Review 50 Most Promising FINTECH

Topics & Tags for Crowd Researchers

AI AlgoDeveloper Allocation Alternative Data Backtesting Big Data Brokerage Buy Side Candlesticks Career Crowdsourcing Data Science Data Sets Event FIA FINTECH Futures Hedge Funds Intellectual Property JupyterLab and Jupyter Notebooks Machine Learning Paid for Algos Python Python Trading Quantamental Quantitative Finance Quantitative Trading RSI SciKit-Learn Sell Side Social Sentiment STEM Women TA-Lib Technical Analysis TensorFlow Trader Trading Strategy

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The thoughts and opinions on this site do not represent investment recommendations by CloudQuant or our clients. Securities, charts, illustrations and other information contained herein are provided to assist crowd researchers in their efforts to develop algorithmic trading strategies for backtesting on CloudQuant.
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