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

Posts

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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Join us at the NY MarketsWiki Education to hear Morgan Slade’s thoughts on the The Algorithmic Trading Tesseract brings cloud computing, alternative data, machine learning, and crowd researchers together forming a revolutionary crowd in the financial industry.
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.
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.
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.
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

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. ...
Kershner Trading, parent of CloudQuant

Kershner Trading Announces the Formation of CloudQuant

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