Data Science

Data Science is the application of scientific method, processes, systems, and tools to discover insights and knowledge or insights from all forms of data.  Modern Data Scientists use tools and skills including pythonJupyter Notebooks, linear algebra, statistics, machine learning, and more in their quest to develop those insights.  Some in the business and trading consider Data Science to be synonymous with business analysis or quantitive research.


Trading Technologies and CloudQuant Launch Strategic Partnership to Explore Creation of Alternative Data Offering

, , , ,
Trading Technologies and CloudQuant Launch Strategic Partnership to Explore Creation of Alternative Data Offering
$TSLA Split shown in CloudQuant Analysis

$TSLA Skyrocketed Ahead of Stock Split

$TSLA Skyrocketed Ahead of Stock Split Shown in the spikes in the acceleration of change of volatility spread.
CloudQuant ESG with G&S Quotient 2020 Q1

≈9% Return for Shelter at Home & ESG Investing

Investing in ESG has been crazy in the past few months. A recent market-neutral backtest showed a 8.73% return (Sharpe of 4.98) for the period of January 1, 2020, to April 22, 2020.
Alternative Data Portfolio Returns on Intraday Hold Trading Strategy

The Value in Machine Learning Alternative Data for Investment Managers

, , ,
A long-short portfolio outperforms the equal-weight S&P 500 ETF by 37.9%/year (after transaction costs) using Precision Alpha Alternative Data. Over 91.5% of the total return is pure alpha.

Environmental Social and Governance (ESG) Alternative Data Sets

Performance review of ESG Dataset available from CloudQuant DataSet Catalog…   As Managed Funds decrease in popularity and Passive Funds take over there has been one bright spark for Fund Managers as Investors have realized that they can dramatically influence the behavior of companies through their investments. With each passing month more Millennials come into the trading environment with their well established trends of making purchasing and investing decisions influenced heavily by environmental and social impact. Analysis of ESG friendly ETFs and investment funds have shown them out performing all other asset classes.   We have also seen, over the last year, a dramatic increase in the number of News postings regarding ESG data.   This January 31st post from Barron’s is one of the most recent… “As sustainable investing becomes more popular, independent firms with these principles are more likely to be acquired.” … “…competition has increased among asset managers” …”the number of annual mergers among publicly traded asset managers doubled.” “Sustainable investing has been a bright spot. Last year, flows into these funds in the U.S. more than tripled, marking the fourth year of record flows.”  

CloudQuant Alternative Data Catalog

CloudQuant strives to dramatically increase your odds of finding a suitable, appropriate and valuable Alternative Data Set. If you have been in the Alternative Data environment you know how difficult it is to find Alternative Data Sets with proven performance. Do you trust the results supplied by the data vendor (rarely reproducible) or do you carry out your own analysis (too often a monumental waste of time and resources). CloudQuant researches the most in demand data types, finds sets that have not already had their Alpha consumed. We test the performance of the data and provide a white paper of the results and Python Code to reproduce and confirm the results to your satisfaction (using our leading Python Cloud Backtester CloudQuant Mariner). Head over to our Data Catalog to find out more about our tried and tested Data Sets which include the ESG Data Set from G&S Quotient.  

Environmental, Social, and Governance Data December 30, 2019

In our study of Environmental, Social, and Governance (ESG) data, we looked at holding positions based upon the G&S Quotient short term price predictor score. We found that 5-day holds and 20-day holds using this score were very interesting and produced positive returns. Based on this data set we saw that you could have traded and held some of these stocks and closed your positions at the end of the day last Friday. See the charts for $ADBE, $AAPL, $MSFT, and $LDOS

Environmental, Social, & Governance (ESG) Short Term Trading

Short-term ESG data is showing some very interesting opportunities. Our recent whitepaper shows that 5-day and 20-day positions are profitable.

Spot checking the white paper shows the following simulated trades from last week.

The simulated trades were MOO on Monday, MOC on Friday.
ADBE 2019-12-14

$ADBE ESG Simulated Trading, 5 Day Hold

DE 2019-12-14

$DE ESG Simulated Trading, 5 Day Hold

FOX 2019-12-14

$FOX ESG Simulated Trading, 5 Day Hold

    Please note, this is not trading advice in any way. It is merely showing the potential of Environmental, Social, and Governance (ESG) data.  
Python Plotly Candlestick Chart with annonations

Candlestick Charts in Python with Plotly

Some traders are visually oriented. They need charts. As data scientists, we need to be able to present information in a way that others can understand. Presenting traders a candlestick chart is one of the best ways to transfer useful data. Blog Purpose: ✅ Demonstrate how to create a basic candlestick chart in Python 3 ✅ Demonstrate how to highlight/annotate points on the chart Topics covered in this post: Python, Plotly, OHLC, Candlestick Charts, Jupyter, Pandas, Traders
Morgan Slade, Python Data Scientist and Trader

CloudQuant CEO John Morgan Slade presenting at THE TRADING SHOW – CHICAGO 2019

CloudQuant CEO John Morgan Slade will be taking part in THE TRADING SHOW – CHICAGO 2019 on Wednesday 8th May 2019. At 13:20 (Central) he will be partaking in a Panel to discuss “Deep Neural Networks – how supervised do deep learning machines need to be?” At 14:00 (Central) He will be moderating a discussion on “AI and Machine Learning – how markets will move forward.” See the official website for more information.
backtest chart

CloudQuant Partners with RavenPack to Expand Use of Alternative Data

FOR IMMEDIATE RELEASE: CloudQuant Partners with RavenPack to Expand Use of Alternative Data Chicago, Illinois, USA, November 20, 2018 – CloudQuant LLC today announced the addition of RavenPack analytics within their trading strategy incubator. Crowd researchers can now use RavenPack historical data to discover tradable alpha signals on CloudQuant’s online Python and JupyterLab-based tools. RavenPack is a leading provider of big data analytics for financial services that enables hedge funds, banks and asset managers to query and visualize unstructured data including insights from thousands of news and social media sources. “We are thrilled to include RavenPack analytics in our ecosystem as they have become a vital source of alpha for quantitative investors,” said Morgan Slade, CEO of CloudQuant. “Our community is already finding promising signals that originate from the very popular RavenPack datasets.” Crowd-based research tools are increasing in popularity along with the rapidly growing data science field. RavenPack and Cloudquant are finding that crowd researchers desire access to Wall Street professional-quality tools and datasets, which enable them to thrive in the professional investment field. “We were impressed with how CloudQuant provides anyone with Python-coding skills the opportunity to mine our datasets for alpha signals and earn compensation for their contributions,” said Amando Gonzalez, CEO of RavenPack.  “We strongly support initiatives designed to give data scientists the tools that liberate ideas to improve financial modeling.”   About CloudQuant CloudQuant is the cloud-based trading strategy incubator. Quantitative analysts around the world create and test trading strategies leveraging free institutional grade technology. By providing the capital, technology, and trading acumen to develop and utilize trading strategies, CloudQuant offers a mutually beneficial profit sharing agreement enabling both parties to profit. CloudQuant LLC, who officially launched in 2017, is a wholly owned subsidiary of Kershner Trading Group LLC. Twitter: @CloudQuant About RavenPack RavenPack is the leading provider of big data analytics for the financial services industry.  Financial professionals rely on RavenPack for its speed and accuracy in analyzing large analyzing large amounts of unstructured content.  The company’s products allow clients to enhance returns, reduce risk and increase efficiency by systematically incorporating the effects of public information in their models or workflows. Twitter: @RavenPack   For Media Inquiries Please Contact: Jessica Titlebaum Darmoni + 1 312 358 3963 – END –