Backtesting

Backtesting is the process of testing a trading strategy on historical market data to see how it would have performed under those trading conditions. Quantitative Developers and Analysts will use a market simulator (like CloudQuant) to evaluate the trading strategy. Key statistics that show performance are shown on the CloudQuant scorecard. Statistics include Sharpe Ratio, Calmar Ratio, Kelly Edge Percentages, Profit/Loss, Drawdown.

Backtesting Quantitative Algorithms on CloudQuant

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Algorithmic Trading with other peoples money

Skills to Become a Quantitative Trader

How do you get from being a Data Scientist, Software Engineer, or Markets Enthusiast to being a Quantitative Algo Developer? Algorithmic Trading requires both technical, and functional skills.

Overview of Core Technical Skills

Programming

Programming is the ability to express your trading ideas so that a computer can repeat the process. You need this skill to be able to code your algo. Structured backtesting is another use of your programming skills. CloudQuant uses Python, a high-level language that is easy for anyone to learn who has ever worked with any programming or macro language, like MS Excel VBA.

Simulation (BackTesting)

To test your algo you will need to test it against historical data. This is called “Backtesting.” Backtesting is more than checking to see if you made a profit or loss. It includes understanding how and why you made a profit and loss and systematically improving that algo.

Statistics

CloudQuant’s backtesting provides several reports that are full of statistics. Understanding what each of these statistics means is essential to improving your algo. Having a base understanding of statistics is also important.

Management of Risk

Order processing and trading involves risk. CloudQuant meets our regulatory required risk and our own functional requirements for pre-trade and post-trade risk management within our production trading system and our backtesting simulation tools. Understanding how risk works will help you improve your algorithm skills.

Learn more

Read the Full Post and See the training available at Experfy.com
Sample python code from the CloudQuant trading strategy backtesting and trade simulation platform

Python Algorithm Trading – The 4 Basic Elements

Creating a python algorithm for trading means that one must cover four basic building elements. Market data, order processing, tracking/analysis, and backtesting. These four elements are all required to build a successful trading strategy.
Trend Analysis in a Candlestick Market Data Chart

Algorithms for Trading

The hardest part of starting any project, including building a quantitative trading strategy, is figuring out where to start. To that end, this post covers a basic overview of a few algorithms for trading. We hope to help you get your creative energy to level up.
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
Sample python code from the CloudQuant trading strategy backtesting and trade simulation platform

Code-Dependent: Pros and Cons of the Algorithm Age

Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment.

Algorithms are instructions for solving a problem or completing a task. Recipes are algorithms, as are math equations. Computer code is algorithmic. The internet runs on algorithms and all online searching is accomplished through them. Email knows where to go thanks to algorithms. Smartphone apps are nothing but algorithms. Computer and video games are algorithmic storytelling. Online dating and book-recommendation and travel websites would not function without algorithms. GPS mapping systems get people from point A to point B via algorithms. Artificial intelligence (AI) is naught but algorithms. The material people see on social media is brought to them by algorithms. In fact, everything people see and do on the web is a product of algorithms. Every time someone sorts a column in a spreadsheet, algorithms are at play, and most financial transactions today are accomplished by algorithms. Algorithms help gadgets respond to voice commands, recognize faces, sort photos and build and drive cars. Hacking, cyberattacks and cryptographic code-breaking exploit algorithms. Self-learning and self-programming algorithms are now emerging, so it is possible that in the future algorithms will write many if not most algorithms.

Algorithms are often elegant and incredibly useful tools used to accomplish tasks. They are mostly invisible aids, augmenting human lives in increasingly incredible ways. However, sometimes the application of algorithms created with good intentions leads to unintended consequences. Recent news items tie to these concerns:

Read the full article on Pew Research Center
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

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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.