Trading Strategy

A trading strategy is a plan (often implemented in an algorithm) that defines when a trader will place orders to enter and exit an investment. Trading strategies range from simple sets of rules that an individual follows all the way to highly complicated applied artificial intelligence computer systems.

Many successful trading strategies go beyond orders to buy and sell. They often include risk management, hedging, and money management.


Alternative Data Portfolio Returns on Intraday Hold Trading Strategy

The Value in Machine Learning Alternative Data for Investment Managers

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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 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
Algo Trading powered by Alternative Data Sets

Conversations: Effects of Alternative Data Sets on Trading Algorithms


What effect can alternative data sets have on trading algorithms?

We asked a few of our teammates and systematic traders what the effect of alternative data sets is on trading algos. We thought we could spread some insight as to why our alternative data is so valuable the also developers. We all start using the smallest and most basic data sets, such as the generic S&P500 stock prices. This is all just fine until you want to take your development to the next level. You can delve deeper into predicting the rise and fall of each stock using these specified alternative data sets. CloudQuant offers a wide and expanding variety of these data sets for free, giving our users easy access to alternative data in order to help them improve their trading strategies.
The teammates featured are:
  • Tayloe Draughon- Sr. Product Manager
  • James Chang- Quantitative Portfolio Manager
  • Morgan Slade- CEO
  • Steve Pettinato- Portfolio Manager
  • Paul Tunney- Client Success Manager
Alogo Allocation

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

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.
Facebook drop 19%, July 2018

$FB Decline 19%- What did the Social and technical analysis show? – July 26 2018

$FB’s 19% drop was preceded by TA-LIB and Social Market Analytics indicators to sell. The $120 Billion drop in market cap could have been an opportunity to short sell before the market close the previous night.
$20M Allocation to Crowd Developed Trading Algorithm

CloudQuant Allocates Risk Capital to Crowd-Resourced Trading Algorithm



Chicago, Illinois, USA, May 18, 2018 – CloudQuant, one of 50 Most Promising FinTech Solution Providers of the year, has allocated risk capital to a crowd-resourced trading strategy. The strategy’s creator, an Australian based crowd researcher, leveraged CloudQuant’s market simulation and python based back-testing tools, to prove the algorithm’s performance and profitably within approved risk parameters. As a funded partner, the researcher will receive a share of the trading net profits. “Striking a balance between complexity and simplicity can be a major key towards success,” said the crowd researcher when giving advice to other researchers. The US equity strategy began trading immediately upon approval of the licensing agreement from both the Quantitative Trader and CloudQuant management. “Market enthusiasts are finding new alpha signals in alternative data sets, fundamental data, and market data. Our users are building exciting new trading strategies,” said CEO Morgan Slade. “We are excited about the talent we see emerging within our network of crowd researchers and confident new users will find trading opportunities.” Using research tools originally conceived and developed by proprietary traders in the parent company, Kershner Trading Group, CloudQuant provides data-driven resources to test an algorithm’s profitability. CloudQuant is proving that innovative trading strategies emerge when market enthusiasts are provided institutional grade research tools. The algorithm creator and CloudQuant can then enter into a profit-sharing agreement to trade the algorithm using CloudQuant provided risk capital. About Us CloudQuant is the cloud-based trading strategy incubator. Quantitative analysts, algorithmic developers, data scientists and traders around the world create and test trading strategies leveraging CloudQuant’s superior infrastructure. Approved strategies are licensed from the strategy creator and funded for production by the CloudQuant team, paying the creator a licensing fee from the net profits. 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, established in 2016, is a wholly owned subsidiary of Kershner Trading Group LLC   For Media Inquiries Please Contact: Jessica Titlebaum Darmoni + 1 312 358 3963

CloudQuant presentation for the Students of the University of Chicago Financial Program

Earlier this year Nick Schmandt, one of our Data Scientists, gave an “Introduction to CloudQuant” presentation to the Students of the Financial Program at the University of Chicago. Below is the video of his presentation. His slides are available our Google Drive and his script is available and ready to be cloned and modified by you at  

Video Contents

A general overview of the CloudQuant environment is provided, starting with the basic CallBack functions (such as on_start, on_finish, and on_minute_bar). CloudQuant is a Python-based system, there are several public scripts available which can be easily modified. CQ Lite users have access to minute-by-minute stock data, and Elite users have access to Tick and Quote data as well as sentiment and other market-based factor analysis values. The backtesting engine has some of the best market-predicting logic, allowing its users to accurately predict the prices obtained when stocks are bought or sold at different times. In the video, Nick examines three different ways to purchase and sell stocks in the CloudQuant environment. The accuracy of these predictions is one of the greatest assets for the CloudQuant environment. Forums are available to discuss different technical issues and problems with the CloudQuant website, as well as request additional features.

Factoring Model using Momentum (talib.MOM) and RSI – Relative Strength Index (talib.RSI)

Towards the end of the video, Nick discusses an actual model in development using the TA-LIB library which is included in CloudQuant. A Factored Basket model is a zero beta, market-neutral strategy. This is used to insulate a trading environment from market motion by keeping equal amounts of stock invested in long and short positions by correctly selecting stocks that will outperform/underperform. Shares are rebalanced daily based on a chosen algorithms which predict success. Nick shows three Factors each of which achieve different levels of success, a successful strategy could likely be deduced by combining these algorithms or other factors.

Market Turmoil Generates Opportunity for Proprietary Traders

In these times of market turmoil and volatility, the Kershner Trading Group stands ready to provide traders with a firm built on a strong foundation of significant capital investment, innovation-focused trading technology and decades of experience in the active and proprietary trading space. Kershner Trading is actively seeking experienced US Equities Traders
Trading Strategy Scorecard from CloudQuant

One Minute Trader Podcast with Tayloe Draughon of CloudQuant

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One Minute Trader with Matt Davio recently interviewed Tayloe Draughon to discuss Crowd Sourced Trading Ideas using our trading strategy incubator.