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.

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Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Machine learning set to shake up equity hedge funds – Financial Times

AI seen becoming powerful enough to forecast market moves better than humans

Financial Times May 25, 2017 by: Lindsay Fortado and Robin Wigglesworth
Machine learning poses a threat to equity hedge funds within the next decade as the technique becomes powerful enough to forecast market moves better than humans, one of the earliest investors in the industry is forecasting. Jeff Tarrant, the founder of Protégé Partners, says that the model of hedge funds charging “2 and 20” — a 2 per cent management fee and 20 per cent performance fee — for investing in large-cap stocks rising and falling “doesn’t work any more” and is ripe for disruption. He pointed to the overhaul of other industries in the past decade at the hands of engineers and scientists. “Jeff Bezos picked off the bookstore business. Apple totally picked off the music business and Netflix totally changed television. Now [machine learning] is going to pick off the hedge funds.”
Read the full story on Financial Times
Python Scripts in CloudQuant's Algorithmic Trading and Quantitative Strategy Backtesting Application

Algo Developers – Entry Level

CloudQuant is THE trading strategy incubator. We’re building a free python data research tool for ordinary people with extraordinary trading ideas. We license and fund the best trading strategies and pay our users a share of the profits. Our group is a FINTECH startup housed under the umbrella of a trading firm with existing infrastructure and financial resources.
Quantitative Strategies and Capital for Trading

Funds Face ‘Alt’ Data Challenge

MarketsMedia by By Rob Daly on 5/18/2017
Although alternative data sets are helping funds with systematic investment strategies, those funds that employ discretionary strategies are finding it harder to separate the new trading signals from the noise.

Much of it comes from the structure of the discretionary funds, which have separated their data science/quant research teams away from their portfolio managers, according to Leigh Drogen, CEO of Estimize and who participated in an alternative data panel hosted by Wall Street Horizon.

“They are left sending reports and Excel spreadsheets to the portfolio managers and asking them to buy in with P&L,” he said. “It’s almost like they were a sell-side shop.”

Even if a managing partner and head quant are convinced that new data sets can capture further alpha, the portfolio managers have to buy into it.

Read the full story on MarketsMedia
 
World Market Access

CloudQuant Announces Users from 72 Countries

May 13, 2017, Chicago, Illinois, For Immediate Release

CloudQuant Announces Users from 72 Countries

CloudQuant’s innovative Trading Strategy Incubator has managed to attract new users from 72 countries. The users represent developers, financial analysts, data scientists, traders, and other trading enthusiast who are interested in developing a trading strategy that may be funded if it proves to be profitable.

These users hope to become the next funded trading strategy. They utilize the cloud based to develop trading strategies. All trading strategies are developed in python, a popular and easy to learn programming language.

About CloudQuant LLC

CloudQuant LLC, established in 2016, is a wholly owned subsidiary of Kersner Trading Group LLC. CloudQuant provides a cloud based trading strategy incubation service for algorithmic developers and traders around the world which includes a partnership agreement and profit sharing arrangements.

Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Short Term Stock Trading Strategies

Both The RSI And Stochastic Can Help You Create Profitable Short Term Stock Trading Strategies

I typically receive dozens of emails from traders who are just starting out asking me for help in creating short-term stock trading strategies. A few weeks ago I demonstrated a strategy using the RSI indicator; I received several emails from readers asking me to explain the difference between the RSI Indicator and the Stochastic Indicator. Without getting into complex mathematical formulas, the RSI indicator measures the momentum or velocity of price movement or in plain English the RSI indicator measures when prices moved too fast too soon. The Stochastic Indicator, on the other hand, is a measurement of the placement of a current price within a recent trading range. The theory is that as prices rise, closes tend to occur nearer to the high end of their recent range. Conversely, when prices drop, closes tend to be near the low end of the range. This is how the Stochastic Oscillator measures price levels. Both indicators are considered momentum oscillators because their primary role in most short-term stock trading strategies is to locate overbought and oversold market conditions. 
Read the full article on MarketGeeks by Roger Scott on May 9, 2017
Kershner Trading, parent of CloudQuant

Kershner Trading Announces the Formation of CloudQuant

,
November 1, 2016

Chicago, Illinois

For Immediate Release

Kershner Trading Group, LLC announces the formation of CloudQuant®, a wholly owned subsidiary.

CloudQuant is:

  • an educator,
  • a facilitator for traders, technologists, and data scientists
  • a technology provider,
In short, CloudQuant is the Trading Strategy Incubator which will allow the trader, software engineer, data scientist or individual with an great trading idea to receive funding for an algorithm that has been tested and proven in backtesting.

CloudQuant began as an internal trading tool for traders with extraordinary trading ideas to be able to explore quantitative trading with the evolution of data science. The technology behind our innovative approach to incubating trading strategies began as an internal system and has been proven through daily use within the Kershner Trading Group. Kershner’s experience as a trading firm focused every day on the work of operating and executing profitable quantitative trading strategies provided the trading and technology expertise to design a platform that enables researchers to go from idea to production implementation in a matter of hours.

CloudQuant’s mission is to provide you with tools to develop and prove your trading strategies. We then license your strategy from you. Once licensed we assign capital from our funds to your strategy. Our professional traders, risk managers, and technologists oversee the running of the trading strategy.

When a client’s trading strategy makes money the client will be paid.  Loses are the responsibility of CloudQuant.