Intro to Machine Learning with CloudQuant and Jupyter Notebooks
By Digimid Quant
/ September 5, 2017
Trevor Trinkino, a quantitative analysts and trader at Kershner Trading Group recently put together an introduction to Machine Learning utilizing CloudQuant and Jupyter Notebooks. In this video he walks you through a high-level process for implementing machine learning into a trading algorithm, ...
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CloudQuant Launches with Unprecedented Risk Capital Allocation to Crowd Researcher
By Digimid Quant
/ August 22, 2017
CloudQuant, the trading strategy incubator, has launched its crowd research platform by licensing and allocating risk capital to a trading algorithm. The algorithm licensor will receive a direct share of the strategy’s monthly net trading profits.
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Improving A Trading Strategy
By Digimid Quant
/ August 7, 2017
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
By Digimid Quant
/ July 17, 2017
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.
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2017 – The Year of Artificial Intelligence
By Digimid Quant
/ July 11, 2017
World Economic Forum published that Artificial Intelligence (AI) is a rapidly growing discussion point in corporations and governments. This is driven by: 1. Everything is now becoming a connected device. 2. Computing is becoming free. 3. Data is becoming the new oil. 4. Machine learning is becoming the new combustion engine.
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Why You Should Always Question The Status Quo — Tradeciety
By Digimid Quant
/ July 10, 2017
“If you are so worried about algos, why don’t you look at Python? Read up on it. Cloudquant, Quantopian. The tools are there, the information is there, for free.”
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Battle of The Quants – Discusses Crowd Researching in NY
By Digimid Quant
/ July 10, 2017
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.
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The Patient Chart Pattern Trader
By Digimid Quant
/ July 3, 2017
“Chart pattern trading is a style that is more suitable for recreational trading rather than professional. This is one reason it was never considered seriously by the majority of hedge funds. In addition to requiring patience, slow chart pattern formations offer enough time for detection and competition is high at diminishing returns.”
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MarketsWiki Education World of Opportunity July 2017 NY
By Digimid Quant
/ July 3, 2017
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.
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Trading Strategy development—Powered by Machine Learning
By Digimid Quant
/ June 29, 2017
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.
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Let The Market Take You Out Of Your Trade
By Digimid Quant
/ June 26, 2017
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
By Digimid Quant
/ June 26, 2017
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.
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An Index-Fund Evangelist Is Straying From His Gospel
By Digimid Quant
/ June 26, 2017
... Maybe the experts can beat the monkeys after all. That is, if the experts are software engineers writing sophisticated algorithms for computer-generated trading. ...
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Code-Dependent: Pros and Cons of the Algorithm Age
By Digimid Quant
/ June 26, 2017
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.
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Recommend Investment Blogs
By Digimid Quant
/ June 26, 2017
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
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