Alessio Farhadi posted “A.I. Trading – A Question of Ethics” on LinkedIn. His main point is that machine learning and algos do not have ethics. … Fairness to the industry requires that one should review the steps that have been taken by innovators, regulators, broker-dealers, and exchanges to mitigate any potential dangers of using computers and algorithms to trade.
Tag: Backtesting
The CloudQuantAI github repository holds the share_ordering_demo tutorial/code that demonstrates ways to buy and sell stocks in the CloudQuant backtesting engine using Market, Limit, and Midpoint Peg Order types.
There is no single “right way” to do any of these. You will have to think carefully about
The podcast on Massive 30,000 Trades Daily, High-Frequency Quant Trading with Morgan Slade including an interesting breakout trading strategy.
The Quantitative Strategy Backtest ScoreCard is saving time for crowd researchers who are able to visualize the results of multi-day backtests quickly, even as the backtest is running.
Categories
Improving A Trading Strategy
TD Sequential is a technical indicator for stock trading developed by Thomas R. DeMark in the 1990s. It uses bar plot of stocks to generate trading signals. … Several elements could be modified in this strategy. Whether to include the countdown stage, the choice of the number of bars in the setup stage and countdown stage, the parameters that help to decide when to exit and the size of the trade will affect strategy performance. In addition, we could use information other than price to decide whether the signal should be traded.
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Skills to Become a Quantitative Trader
Your Proprietary Trading Algorithm is always your property on CloudQuant. Any trading strategy that you develop is yours. Not ours. You do not transfer ownership of the algo to CloudQuant. You do not transfer any copyrights to CloudQuant. This is fundamental to the operations and success of CloudQuant.
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.
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.
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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.
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.