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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Innovation in Trading

The next wave of broker innovation will be Crowdsourced algos

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Are Institutional Brokers Innovative?

Markets Media is reporting that “47% of buy-side firms, mostly those with modest levels of assets under management, did not think their brokers were doing anything innovative.” Andy Kershner, the CEO of CloudQuant’s parent company was interviewed.  He believes:

The next wave of broker innovation likely will be geared toward democratizing quantitative trading, according to Kershner Trading Group Founder and CEO Andy Kershner. That would vastly expand the universe of high-level quant traders globally, which Kershner roughly estimated stands at perhaps 5,000 today.

Andy Kershner, Kershner Trading

“It will be very similar to what you saw when you had access to the market open up in the mid-90s for day traders,” Kershner said. “You had lots of innovation, lots of people coming in with lots of ideas, and lots of software that really changed and knocked out all the market makers. Then high-frequency came along and killed all the specialists and also knocked out some of the day traders. I think now we’ll see a reversion.”

“You’ve got ‘big data’ out there everywhere that everybody talks about, but the moat is access to the data, access to capital, and access to some knowledge of what to do with it,” Kershner continued. “I think in the next three years if you do not have an auto- or quantitative-trading system that’s more than just APIs — you’ll need back-testing, forward-testing, live, the whole package — if you don’t have that for people to sign up for, your brokerage will be left behind.”

Read the full story on Markets Media

Battle of the Quants June 2017

Battle of The Quants – Discusses Crowd Researching in NY

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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.
Python based Trading Strategies by Machine Learning

Trading Strategy development—Powered by Machine Learning

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Morgan Slade, the CEO of CloudQuant spent some time with discussing the world of crowd research, technology, and data science with ChatWithTraders.com Topics covered:
  • How large funds and institutions put on $100-million positions; how they work orders into the market, structure the trade and handle market impact etc.
  • Morgan explains why he feels as though the common approach to strategy development is counter intuitive, and shares an alternative 3-step formula.
  • A simple description of how machine learning and data science is being used by traders, and an example of how ML has been used to improve existing strategies.
This interview is the follow on interview that Chat With Traders had Andy Kershner. Towards the end of that episode, Andy briefly mentioned a cloud-based algo development platform and fund, CloudQuant, which is a subsidiary of Kershner Trading Group.

About Morgan Slade

Learn more about Morgan, his 20 years of experience as a trader, portfolio manager, researcher, technologist, executive and entrepreneur, and the team at CloudQuant.
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Let The Market Take You Out Of Your Trade

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LearnToTradeTheMarkets.com published a very interesting article advocating Why You Should Almost Never Manually Close Trades. This post goes into detail examining that most traders “self-sabotage.” In other words, traders are their own worst enemy. They get emotional when trading. This is one of the main reasons that CloudQuant uses algorithms. The use of stop loss orders, and programmatic reasons to enter or exit the market are essential to our trading strategies. “If you manually close a trade when it’s against you, you are voluntarily taking a loss. Read that last sentence again, maybe even a third time. Trading is about maximizing your winners so that they offset your losing trades, that’s how you make money. You’re going to have losing trades, but you don’t need to voluntarily take them, most of the time.” An example of using programmatic stop loss and programmatic profit taking are included in several of the CloudQuant public trading strategy scripts written in python. Read the full posting on LearnToTradeTheMarkets.com    
Sharpe Ratio distribution

Four Problems with the Sharpe Ratio

If you are an algorithmic trader, developer, or data scientists they you have already heard of the Sharpe Ratio. Many of you use this measurement as your score card for how well your algo performs.

The Sharpe Ratio, named after William Forsyth Sharpe,  measures the excess return per unit of deviation in an investment asset or a trading strategy. There are the four potential problems in using the Sharpe Ratio to measure trading performance. The first two problems are relevant if trading results in different intervals are correlated, while the latter two problems are relevant even if trading results are uncorrelated.

Problem 1. Failure to distinguish between intermittent and consecutive losses

Problem 2. Dependency on time interval

Problem 3. Failure to distinguish between upside and downside fluctuations

Problem 4. Failure to distinguish between retracements in unrealized profits versus retracements from “Trade Entry Date” equity.

Read the four problems with more detail and graphs on Oxford Capital Strategies
News for Machine Learning and Algo Trading

Recommend Investment Blogs

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Wesley R. Gray (@alphaarchitect), the CEO and CIO of Alpha Architect, a quantitative asset manager published a list of “high-quality research produced by financial professionals in the blogosphere” on the Wall Street Journal The list includes: Read the full article on the Wall Street Journal to see why Mr. Gray likes these blogs.
Andy Kershner, veteran trader discusses CloudQuant

Risk tolerance, daily habits and trade critiques with Andy Kershner

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Trading industry veteran Andy Kershner, the CEO of CloudQuant’s parent company Kersher Trading spoke with Chat With Traders about risk tolerance, daily habits and trade critiques. Andy has been an active stock trader since the early 90’s, and in 2001 he founded Kershner Trading Group—a proprietary trading and technology firm in Austin (Texas) and, through a partnership with SMB Capital, Kershner Trading have a second office in Midtown (Manhattan) too.

Topics of discussion:

  • Andy talks about his ability to take pain on trades moving against him, and whether or not day traders with a greater risk tolerance make more money.
  • Beyond good strategies; the daily habits which have contributed to Andy’s trading success. Plus, Andy critiques his trades from the session prior to recording.
  • Andy shares several types of strategies which have been working for him lately, how he “ladders” into favorable positions and a few tips for doing the same.
  • As someone who’s hired many new traders, Andy describes some of the typical errors made by traders just starting out and how they could be better prepared.
  The follow up interview with Morgan Slade is now available too at https://info.cloudquant.com/2017/06/crowd-research/
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Social Sentiment in Trading Algorithms

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Bloomberg recently wrote that “It’s no secret that hedge fund managers are always looking for new sources of data that will help them in their never-ending quest to beat the market.” (1) One of the most interesting new sources of data is social sentiment. We have found that the incorporation of social sentiment data is definitely improving the quality of algorithms as shown in our backtesting on CloudQuant. Over the next couple of weeks an intern from the University of Chicago who is mastering in Financial Mathematics is working on incorporating social signals into a DeMark Indicators script that is available for all registered users to see in the CloudQuant base working scripts. I look forward to seeing how this improves. And I look forward to seeing her quantitative reasons for why social sentiment and other changes to the TD Sequential script improves. (1) Finding Novel Ways to Trade on Sentiment Data | Tech At Bloomberg
Quantitative Strategy, Trading, and Algo Development Industry News

Discretionary Managers Seek Alpha in Alternative Data

Alternative data providers see huge potential in providing their data to discretionary asset managers who are losing assets to quantitative and systematic funds.

As active managers trail the performance of passive index funds and exchange-traded funds (ETFs), discretionary fund managers are scrambling to consume big data analytics into their decision making process.
While early movers in the big data analytics industry have mainly been quant hedge funds and systematic fund managers, the next wave is going to be discretionary fund managers, according to panelists at an event sponsored by Wall Street Horizon, EstimizeOTAS Technologies and FlexTrade Systems.
Read the full story on Traders Magazine Online
Morgan Slade, Python Data Scientist and Trader

June 22nd 2017 Battle of Quants

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CloudQuant’s CEO Morgan Slade will be speaking on Crowdfunding Algo Developers and Data Scientists. The primary question on his panel will be “How does the mechanism work and which business model is showing early signs of success?” Please join us in New York for this important discussion. NY Battle of Quants Speakers Event Date: June 22, 2017 Didn’t make it? See our discussion review.