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.

Posts

CloudQuant ESG with G&S Quotient 2020 Q1

≈9% Return for Shelter at Home & ESG Investing

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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

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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

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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

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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
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

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Allocation

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 www.cloudquant.com   For Media Inquiries Please Contact: Jessica Titlebaum Darmoni Jessica@thetitleconnections.com + 1 312 358 3963

Market Turmoil Generates Opportunity for Proprietary Traders

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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
Backtest Research Life Cycle for Trading Strategies

Backtesting Trading Strategies

If you knew your trading strategy would work 50% of the time, would you commit your scarce savings to trade it? What if it worked 75% of the time? Backtesting gives one the confidence to know that your trading strategy will work.
Harami Sell Signal on Google Dec 20, 2017

Harami Sell Signal with Three Inside Down Demonstrated on $GOOG

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December 19, 2017 Monday Google pressed new highs, but Tuesday closed out the day with a Harami Sell signal. Watch out for today. A negative close today boosts the negative outlook with the emergence of a Three Inside Down pattern. In this event, it will most likely mean that there will be a “little coal in Google stockholders stockings for Christmas”.