$TSLA Skyrocketed Ahead of Stock Split
$TSLA Skyrocketed Ahead of Stock Split
Shown in the spikes in the acceleration of change of volatility spread.
Quantamental
Quantament is the merger of Quantitative research and trading with Fundamental research and trading. Many data scientists, algo traders, and market participants are finding that the merger of the quantitative techniques and fundamental data are allowing them to build much better algorithms for trading.
Posts
ESG for the Short Seller with Alt Data
Short-selling opportunities exist using ESG Alternative Data
CloudQuant Proves the Value in Environmental Social and Governance Alternative Data
CloudQuant LLC has proven the value in the G&S Quotient Environmental, Social, and Governance (ESG) alternative dataset. The detailed data science study shows a long-short portfolio with a five-day or twenty-day position holding period produces an investment strategy with single year Sharpe Ratios as high as 2.046 and Alphas as high as 5.24%. The study shows that the dataset is highly distinct from other standard and so-called “smart-beta” factors.
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
Quants discuss evaluating and adapting their models – Including CloudQuant
In a world where robots, voice recognition and artificial intelligence are growing in importance, Peltz International’s Meet the Quantitative Manager …
In January, Morgan Slade participated in a Panel Discussion for Quantitative Fund Managers and how they are adapting their model. This link to the article published by Peltz International after the conference requires registration and a user ID on their web site.
Futures Radio Show interviews Morgan Slade December 12, 2017
CloudQuant’s CEO was interviewed by Anthony Crudele of Futures Radios show to discuss topic including Artificial Intelligence, Machine Learning, and Deep Learning applied to algorithmic trading. Alternative datasets are a major topic of discussion. People are saying that data is being created faster than ever before. That really isn’t true. What is really happening is that data is being captured and stored at a faster rate than ever before. Vendors are now making AltData available for traders to change the way that they interact with the markets. This applies to futures and stocks with the popularity of Deep Learning in algorithmic trading strategy development.
Technical Analysis Library (TA-LIB) for Python Backtesting
Anyone who has ever worked on developing a trading strategy from scratch knows the huge amount of difficulty that is required to get your logic right. … TA-LIB Turbo-Charges Your Research Loop: TA-Lib is widely used by quantitative researchers and software engineers developing automated trading systems and charts. This freely available tool allows you to gather information on over 200 stock market indicators.
Industry News: Machine Learning and Artificial Intelligence News November 20, 2017
AI & ML news covering: Overvalued Markets, crypto-currencies, Quantamental funds, Social Sentiment Deceptions, Hiring of Buy-Side Quantitative Trading, …
Quantitative Trading and Data Science in the News August 28, 2017
Quantitative Trading and Data Science in the News August 28 2017: CloudQuant opens door to crowd algo traders, RBC AI pilot, RBC pilots AI-based financial insight tools, AI focussed Chip, Momentum trading guide…
Battle of The Quants – Discusses Crowd Researching in NY
Crowdsourcing in fund management and trading is the move to utilize anyone with an internet connection to participate in the research with the goal of finding new and better ways of trading. During the discussion the differing approaches being taken with the business models, and the technology, and the challenges each are facing.
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.
Vote for CloudQuant in the Benzinga Fintech Award 2020
