Quantitative Trading

Quantitative trading utilizes trading strategies based on quantitative analysis to systematically identify trading opportunities and to execute trades as identified. A Quant trader may work for the buy side or sell side of the trading industry. Buy side quants are looking to trade for investment purposes. These trades typically seek to make a profit from either short-term price movements (alpha) or from longer-term investment returns (beta.) Sell side quants provide quantitative trading in their brokerage activities. The sell side algos are typically accumulation type strategies (i.e. volume weighted average pricing – VWAP) that help investors get the best price for their overall order.

Quantitative trading techniques include high-frequency trading, sentiment analysis trading, and statistical arbitrage.

Quantitative trading is not synonymous with High-Frequency Trading (HFT) even though all HFT firms employ some form of algorithmic trading.

CloudQuant utilizes crowd researchers to provide the quantitative analysis that is then used by our quantitive traders.

Posts

Environmental, Social, & Governance (ESG) Short Term Trading

Short-term ESG data is showing some very interesting opportunities. Our recent whitepaper shows that 5-day and 20-day positions are profitable.

Spot checking the white paper shows the following simulated trades from last week.

The simulated trades were MOO on Monday, MOC on Friday.
ADBE 2019-12-14

$ADBE ESG Simulated Trading, 5 Day Hold

DE 2019-12-14

$DE ESG Simulated Trading, 5 Day Hold

 
FOX 2019-12-14

$FOX ESG Simulated Trading, 5 Day Hold

    Please note, this is not trading advice in any way. It is merely showing the potential of Environmental, Social, and Governance (ESG) data.  
bulls and bears

Quantitative Trading & Algos in the news. 23, January 2019

News clips provided algorithmically.

New EU rules electrify fixed income trading volumes: survey

LONDON (Reuters) – Volumes of electronic trading in fixed income have jumped since European Union rules introduced a year ago made it more cumbersome to execute over-the-counter (OTC) business, in a bid to make deals more transparent, a report said. Total average daily volumes of EU government bonds traded electronically in the first three quarters of last year rose by 36 percent to $57 billion, the report by Greenwich Associates based on a surv… 2019-01-17 15:02:56+00:00 Read the full story. Interest Score: 2.0093, Positive Sentiment: 0.1932, Negative Sentiment 0.1159

technical analysis of stocks and commodities

Technical Analysis Of Stocks And Commodities Using Basic Indicators Last weekend I was asked by numerous traders who practice technical analysis of stocks and commodities, what indicator I preferred to use the most. The answer was the Exponential Moving Average or (EMA) for short. I demonstrated yesterday how to use the EMA to measure pullbacks away from the main trend. The method is rather simple but works remarkably well for finding stocks a… 2019-01-07 01:47:16+00:00 Read the full story. Interest Score: 1.9561, Positive Sentiment: 0.1796, Negative Sentiment 0.3992

Difference between Big Data & The Internet of Things

Faisal is based in Canada with a background in Finance/Economics & Computers. He has been actively trading FOREX for the past 11 years. Faisal is also an active Stocks trader with a passion for everything Crypto. His enthusiasm & interest in learning new technologies has turned him into an avid Crypto/Blockchain & Fintech enthusiast. Currently working for a Mobile platform called Tradelike as the Senior Technical Analyst. His interest for writing… 2019-01-14 00:00:00 Read the full story. Interest Score: 1.8519, Positive Sentiment: 0.1543, Negative Sentiment 0.0000

3 ETFs to Play the China-Driven Basic Materials Sector Breakout

Basic materials stocks bore the full brunt of a “risk off” market environment in the fourth quarter of 2018 as investors dumped names caught in the crossfire of a tit-for-tat tariffs trade war between the United States and China and those with exposure to slumping oil prices. Over the period, the Dow Jones U.S. Basic Materials Index (^DJUSBMT) fell nearly 15%, plunging the sector into correction territory. As news of planned January trade talks … 2019-01-23 14:25:45.540000+00:00 Read the full story. Interest Score: 1.7738, Positive Sentiment: 0.0954, Negative Sentiment 0.1717

Hedge fund veteran joins Dynamic Beta Investments as COO

Hedge fund advisory firm Dynamic Beta investments (DBi), a specialist in the liquid alternatives market, has expanded its management team with the appointment of former AQR Global Head of Execution Douglas Cilento as Chief Operating Officer. Cilento (pictured) joins DBi with an extensive 18-year track record. At AQR, he managed the Trading team responsible for execution and strategy implementation across global markets in all liquid asset classe… 2019-01-23 00:00:00 Read the full story. Interest Score: 1.6725, Positive Sentiment: 0.3890, Negative Sentiment 0.0000

Top hedge fund industry trends for 2019

By Donald A Steinbrugge, CFA – CEO, Agecroft Partners – The hedge fund industry is dynamic, and participants are best served by anticipating, rather than reacting to, change. Informed by our contact with more than two thousand institutional investors and hundreds of hedge fund organisations, the following is Agecroft’s 10th annual list of top trends that we anticipate for the year ahead. Hedge fund industry reaches maturity The good news fo… 2019-01-08 00:00:00 Read the full story. Interest Score: 1.6354, Positive Sentiment: 0.2726, Negative Sentiment 0.1265

ALGO UPDATE: IBM’s Algo-Powered AIEQ ETF

IBM, historically know for its computers, is in the artificial intelligence business. And the algorithmic trading business too. As noted in DataTrek’s recent Daily Disruption Feature, there’s the AIEQ ETF, which uses IBM Watson powered artificial intelligence to manage a portfolio of US stocks. DataTrek co-founder Nicolas Colas told Traders Magazine that while there are several US listed ETFs that use some form of algorithmic analysis to manage capital, this one “goes a bit further down the road to being a … 2019-01-23 15:53:08+00:00 Read the full story. Interest Score: 1.4381, Positive Sentiment: 0.1403, Negative Sentiment 0.1754

How to Trade IBM Following Better-Than-Expected Earnings

The daily and weekly charts for International Business Machines Corp. (IBM) were setup for a positive reaction to earnings. Tuesday’s close of $122.52 put the stock above my quarterly pivot at $122.46 with the weekly chart positive since Jan. 11. Charts favored a positive reaction to earnings. The price gap higher to the open of $131.37 was a gain of 7.2%, indicating upside potential to the 200-day and 200-week simple moving averages at $137.54 a… 2019-01-23 11:46:34-05:00 Read the full story. Interest Score: 1.2430, Positive Sentiment: 0.3255, Negative Sentiment 0.1184

Unusual Buying in Stocks Is Picking Up

Almost four weeks ago, it seemed the world was ending. You couldn’t open a website or walk past a headline without seeing the words “bear market” everywhere. People were goading me, saying things like, “Are you still bullish?” and “I am guessing you’re just as bullish as ever.” My usual response was that, while it was hard to be bullish when everyone is decidedly not, I had to go with my data. And the data said to be bullish – this will pass. If… 2019-01-22 13:59:00.866000+00:00 Read the full story. Interest Score: 1.0971, Positive Sentiment: 0.2372, Negative Sentiment 0.1927

9 Black Swan Events that changed the Financial World

All of us traders wish we had a magic ball in which we could see the future moves of the financial markets. Alas, that is not the case and we have to stick to our golden rules of due diligence, diversification, hedging, rebalancing and continuous monitoring. More often than not most of the financial market moves are dictated by the greed & fear of the investors. The inherent risks of investing in the financial markets can never be brought down to… 2019-01-18 00:00:00 Read the full story. Interest Score: 1.0272, Positive Sentiment: 0.1127, Negative Sentiment 0.5633

Ford Motor Stock ‘Too Cheap to Ignore’ Heading Into Earnings

Ford Motor Company (F) reports earnings after the closing bell on Wednesday, Jan. 23, and according to Macrotrends, the stock has a P/E ratio of just 6.22 with a dividend yield of 6.99%. This makes the stock “too cheap to ignore.” At the end of 2018, Ford stock closed at $7.65. This level was input into my proprietary analytics, and new monthly, quarterly, semiannual and annual levels were started for the beginning of 2019. On the first day of t… 2019-01-23 14:33:10.659000+00:00 Read the full story. Interest Score: 0.9693, Positive Sentiment: 0.1385, Negative Sentiment 0.2769

Pod Chats: Tom Basso on Self-Awareness

Tom Basso is the former president and founder of Trendstat Capital Management. The retired hedge fund manager, also known affectionately by his nickname “Mr Serenity”, boasts over 40 years of trading experience and was elected to the board of the National Futures Association in 1998. As well as publishing his book Panic-Proof Investing, he now offers trading advice to traders old and new through his website. In episode 700 of Trend Following R… 2019-01-09 11:40:26+00:00 Read the full story. Interest Score: 0.6474, Positive Sentiment: 0.1880, Negative Sentiment 0.2402    
This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. We used natural language processing (NLP) to determine an interest score and to calculate the sentiment of the linked article using the Loughran and McDonald Sentiment Word Lists. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors. This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.
Algo Trading powered by Alternative Data Sets

Conversations: Effects of Alternative Data Sets on Trading Algorithms

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What effect can alternative data sets have on trading algorithms?

We asked a few of our teammates and systematic traders what the effect of alternative data sets is on trading algos. We thought we could spread some insight as to why our alternative data is so valuable the also developers. We all start using the smallest and most basic data sets, such as the generic S&P500 stock prices. This is all just fine until you want to take your development to the next level. You can delve deeper into predicting the rise and fall of each stock using these specified alternative data sets. CloudQuant offers a wide and expanding variety of these data sets for free, giving our users easy access to alternative data in order to help them improve their trading strategies.
The teammates featured are:
  • Tayloe Draughon- Sr. Product Manager
  • James Chang- Quantitative Portfolio Manager
  • Morgan Slade- CEO
  • Steve Pettinato- Portfolio Manager
  • Paul Tunney- Client Success Manager
AdobeStock_108824457

Quantitative Trading & Algos in the news. 11, July 2018

News clips provided algorithmically.

technical analysis of stocks and commodities

Technical Analysis Of Stocks And Commodities Using Basic Indicators Last weekend I was asked by numerous traders who practice technical analysis of stocks and commodities, what indicator I preferred to use the most. The answer was the Exponential Moving Average or (EMA) for short. I demonstrated yesterday how to use the EMA to measure pullbacks away from the main trend. The method is rather simple but works remarkably well for finding stocks a… 2018-07-09 01:47:16+00:00 Read the full story. CloudQuant Thoughts: The Exponential Moving Average (EMA) is easily calculated using the Technical Analysis Library (TA-LIB) in our python based backtest simulation. TA-LIB is a very common tool used for those interested in technical anallysis like the above story indicates. We have easy source coude you can copy. Free registration is required at app.cloudquant.com.  Once you logon you can clone this simple script to backtest your trading strategy ideas using EMA. For those of you who only want to see the TA-LIB Code for calculating the Exponential Moving Average then here is a simple code snipping. Please note, this is for example purposes only to demonstrate TA-LIB.
# TA-LIB to calculate the Exponential Moving Average for long or short in app.CloudQuant.com
myBars = md.bar.daily(start=-21) # grab 21 bars, EMA uses 20, we want yesterdays and the day before so grab 21
close = myBars.close # pull out just the close prices
EMA = talib.MA(close,timeperiod=20,matype=1) # TALIB Moving Average matype=1 = Exponential Moving Average
EMAlist = numpy.ndarray.tolist(EMA) # TALIB returns a numpy array, lets turn that back into a basic list
print self.symbol,EMAlist
if EMAlist[-1] > EMAlist[-2]: # if yesterday's EMA is above the previous day go long
    order.algo_buy(self.symbol,"market","init",order_quantity=100)
    print "long entry"
if EMAlist[-1] < EMAlist[-2]: # if yesterday's EMA is below the previous day go short
    order.algo_sell(self.symbol,"market","init",order_quantity=100)
    print "short entry"
 

FXCM Algo Summit 2018

Last month, 5 quant trading experts joined FXCM for an educational event in London. Approximately 200 attendees attended lectures and workshops on topics ranging from machine learning to alpha generation to cryptocurrencies. The speakers conducted both interactive workshops and presentations. You can access the recording of each presentation below. Keynote ROB CARVER Robert Carver is an independent systematic futures trader, writer and researc… 2018-07-05 11:56:39+00:00 Read the full story.  

3 Charts Suggest Gold Miners Could Lead the Way

In the commodities market, there tends to be a negative correlation between the U.S. dollar and gold and other related metals. However, despite the recent weakness in the spot price of gold futures, gold miners and the companies related to the exploration, extraction and processing of precious metals tend to be countering the trend and look poised for a move higher. In the paragraphs below, we’ll examine the charts of the broad commodities market… 2018-07-11 06:11:00-06:00 Read the full story.

Citigroup Reports Earnings Below a ‘Death Cross’

Citigroup Inc. (C), the fourth largest of the four “too big to fail” money center banks, has been trading sideways to down since setting its multi-year intraday high of $80.70 on Jan. 29. This has the stock down 8.3% year to date and in correction territory at 15.5% below this high. The stock is 6% above its 2018 low of $64.38 set on June 26. Citigroup reports quarterly earnings at around 8:00 a.m. during pre-market hours on Friday, July 13. Anal… 2018-07-11 03:47:00-06:00 Read the full story.

Brandywine Asset Management Posts Record Returns In June

Brandywine Asset Management commentary for the month ended June 30, 2018. Get The Timeless Reading eBook in PDF Get the entire 10-part series on Timeless Reading in PDF. Save it to your desktop, read it on your tablet, or email to your colleagues. Q2 hedge fund letters, conference, scoops etc Strong performance from Brandywine Asset Management’s short-term momentum and long-term directional arbitrage strategies combined with Brandywine’s Adapt… 2018-07-10 16:17:59-04:00 Read the full story.

Video: 2 Stock Market Pros Reveal How to Juice Your Portfolio for 2018

The second half of 2018 is off to a bullish start for stocks, buoyed by a goldilocks June jobs report. But don’t get too complacent. TheStreet’s Scott Gamm is hosted our monthly video webinar Trading Strategies to help you prep your portfolio for the second half of 2018. Scott was joined by two top market experts, including: Kristina Hooper, chief global market strategist, Invesco Alicia Levine, head of global investment strategy, BNY Mellon Investment Management Want exclusive investing insight from Jim Cramer? Get 24/7 access to Jim’s charitable trust portfolio with a free trial to Action Alert… 2018-07-11 07:37:00-04:00 Read the full story.

JPMorgan Reports Earnings in Rebound Mode

JPMorgan Chase & Co. (JPM) has been one of the weak bank stocks, recently setting a 2018 low of $102.20 on July 6, but since then, the shares have popped by 5% in anticipation of positive earnings to be released before the opening bell on Friday, July 13. JPMorgan is the largest of the four “too big to fail” money center banks and is the only major bank in the Dow Jones Industrial Average. The stock closed Monday, July 9, at $107.28, up just 0.3%… 2018-07-10 04:00:00-06:00 Read the full story.    
This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors. This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.
bulls and bears

Quantitative Trading & Algos in the news. 22, June 2018

News clips provided algorithmically.

Marks Says Quant Investing Is a `Fringe Activity’ at This Point

Oaktree Capital Group LLC Co-Chairman Howard Marks said quantitative investing isn’t a replacement for the judgment of the best stock pickers. “Some firms are doing it well,” Marks said in an interview with Bloomberg Television’s Erik Schatzker on Wednesday. “But it’s only a fringe activity.” Earlier this week, Marks issued a 17-page memo that analyzed the effects of seve… 2018-06-20 00:00:00 Read the full story. CloudQuant Thoughts: This article’s emphasis on ‘Fringe Activity’ caught our attention. Our first reaction was “Huh? That can’t be right.” After watching the video and reading the full memo we understood what he was really saying. He did call Quant Investing a ‘Fringe Activity’ in the recorded interview but didn’t use that language in the 17-page memo.  The memo concludes “Computers, artificial intelligence and big data will help investors know more and make better quantitative decisions. But until computers have creativity, taste, discernment and judgment, I think there’ll be a role for investors with alpha.” (1) We agree. The role of the analyst is still important. We are closer to the quantitative word and therefore we see things differently than Mr. Marks.  We know that quantitative investing isn’t on the fringe. The role of data science is expanding. Natural Language Processing (NLP), Artificial Intelligence (AI), and Machine Learning (ML) are dramatically growing “things that can be counted.”(2) This includes using these automated techniques to read CEO generated content to score the likelihood that the CEO could become the next Steve Jobs, or that the company is likely to become the next Amazon. One of the reasons we know that Quantitative techniques work is that our own NLP algo flagged this post for us to read out of the thousands of articles that were published in the world of Systematic, Quantitative, and Algorithmic trading!  

Technical Traders Are Starting to Bet Against Agriculture

With the passing of the summer solstice, many investors have started to turn their attention to agriculture-related commodities. A significant breakdown on the chart of the most widely followed agriculture exchange-trade fund (ETF) suggests that active traders will now likely hold a bias to the downside and view the move as a leading indicator that could drag niche agriculture commodities lower. (If you need a quick refresher, check out: A Primer… 2018-06-22 09:44:00-06:00 Read the full story. CloudQuant Thoughts: This article has great graphics to argue its point.  

Goldman Sachs Is Considering Crypto Trades Beyond Futures, Solomon Says

Goldman Sachs Group Inc., one of the biggest U.S. investment banks, is exploring cryptocurrency trades beyond the publicly-traded derivatives that it already handles, according to Chief Operating Officer David Solomon. “We are clearing some futures around Bitcoin, talking about doing some other activities there, but it’s going very cautiously,” said Solomon in an intervie… 2018-06-20 00:00:00 Read the full story.    
Footnotes
(1) Investing Without People page 16, by Howard Marks, Oaktree Capital Management, L.P., https://www.oaktreecapital.com/docs/default-source/memos/investing-without-people.pdf (2) ditto
This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors. This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.
AdobeStock_91734599

Quantitative Trading & Algos in the news. 24, May 2018

News clips provided algorithmically.
 

Alogo Allocation CloudQuant allocates risk capital to crowd-resourced trading algorithm

CloudQuant has allocated risk capital to a crowd resourced trading strategy, according to the company in a press release. CloudQuant is the cloud-based trading strategy incubator. Quantitative analysts, algorithmic developers, data scientists and traders around the world create and test trading strategies leveraging CloudQuant’s superior infrastructure. The strategy’s creator, an Australian based crowd researcher, leveraged CloudQuant’s market s… 2018-05-22 00:00:00 Read the full story.

Quants With 20% of U.S. Stock Funds Puzzle Over Timing of Cycle

Wall Street quants are taking sides on the trajectory of the U.S. business cycle — and money is riding on their investing styles like never before. The high-stakes call — risking some of the stock market’s hottest trades — is reigniting one of the most contested debates in the quantitative community: Can investors time the market? And even if so, should they? It’s all down to conflicting economic signals in factor investing, which slice and … 2018-05-22 00:00:00 Read the full story.

Equity Market Innovation Leads to Venue Proliferation

By Ivy Schmerken, Editorial Director, FlexTrade Several startups are planning to launch either new venues or order types, and even listing standards, to solve problems in US equity trading. At last month’s SIFMA Equity Market Structure conference, executives with the following three startups and one established exchange discussed their particular market innovations and how each one fits into the existing equity market structure. Imperative Exe… 2018-05-24 08:53:24-04:00 Read the full story.

Exclusive Interview: Ninjatrader Platform Expert Chris Dolan

Grace: I wanted to introduce you to the QuantNews audience so that people can learn more about your unique product. Your website mentions that your company has been offering access to advanced charting, trade simulation and real time historical Forex market data. Can you give us more of an overview of your company? Chris: At its core, NinjaTrader is a trading platform that offers a complete front-end technology solution for charting, analytics, … 2018-05-24 06:52:37+00:00 Read the full story.

QB Offers Futures and Fixed Income Algos

Quantitative Brokers is keeping the algorithmic wheels turning as it has partnered with Rebar Systems to provide fixed-income and futures algos. In this partnership, Rebar clients will have access to QB’s multiple algorithms via the former’s Rebar Order Management and execution platform. ROME, formally launched in April 2018, has integrated QB’s fixed-income and futures algorithms – Bolt, Strobe, and Closer – to offer hedge funds and asset manag… 2018-05-22 14:04:59-04:00 Read the full story.  

Discount Retailer Ross Stores Is in Recovery Mode

Discount retailer Ross Stores, Inc. (ROST) reports quarterly earnings after the closing bell on Thursday, May 24. The stock is trading between my semiannual pivot of $78.79 and my annual risky level of $85.13, which was tested at the Jan. 29 high. Ross shares closed Wednesday at $82.61, up 2.9% year to date and down 3.6% from the Jan. 29 high of $85.66. The stock is in recovery mode at 12% above its March 7 low of $73.76. Analysts expect Ross St… 2018-05-24 01:00:00-06:00 Read the full story.

3 Charts That Suggest the Uptrend in Financials Should Continue

The financial sector suffered a dip earlier this year along with most of the market when volatility and uncertainty seemed to dominate headlines. Over the past few years, the financial sector has proven to be one of the most resilient sectors to pullbacks, and recent price action is suggesting that the trend is about to resume its upward trajectory. In this article, we take a look at several key charts that are used by traders to track the perfor… 2018-05-23 08:18:00-06:00 Read the full story.

IG Group appoints long standing senior executives Bridget Messer and Jon Noble as Executive Directors

Demonstrating IG Group’s unfaltering ability to retain and empower top industry talent, IG Group elevates two very senior and longstanding leaders to Executive Director status Evergreen retail FX and CFD giant IG Group has appointed two long standing senior executives to its board, who will report directly to CEO Peter Hetherington. Today, the company’s Chief Commercial Officer Bridget Messer has been elevated to Executive Directorship status, … 2018-05-23 10:02:09+03:00 Read the full story.   stock exchange evolution panel

Tech Firms Vie to Become the “Amazon” of Fixed Income

For independent advisors, getting an accurate sense of the fixed income marketplace can be a challenge, let alone coming up with enough data to make confident trading decisions. But with a rising interest rate environment and large numbers of clients reaching retirement age, expectations are high for advisors’ fixed income strategies. Tech companies with powerful computing capacities and varied strategies are attempting to bridge the gap, bringin… 2018-05-23 06:00:09-04:00 Read the full story.

Has Wall Street Completely Lost Its Mind on General Electric?

Wall Street equity research is supposed to be a value-add to clients. But, one has to wonder what value analysts are providing to their trading desks on struggling General Electric (GE) . In fact, one has to wonder how many clients have been hurt by wrong calls on what has amounted to an industrial stock value trap. Out of the 21 sell-side analysts that cover GE 53% rate the stock a hold, according to Bloomberg data. That’s 53% of a well-paid group of I-bank number crunchers that have been unable to make a clear decision on a stock that has … 2018-05-24 08:32:13-04:00 Read the full story.

Trading-Technology Q&A: John Adam, FactSet (Part 2/2)

Markets Media recently spoke with John Adam, Senior Director, Portfolio Management & Trading Solutions at FactSet. The first segment of the Q&A focused on the big picture; what follows is about the past, present and future of FactSet Trading Solutions. FactSet bought Portware in 2015. How has this acquisition enhanced what FactSet can do for clients and how does the franchise continue to move forward? As good as Portware was before the acquisit… 2018-05-22 13:11:15-04:00 Read the full story.

There’s a Shortage of Expert ETF Traders Who Make the Market Tick

A shortage of specialized traders who oversee ETF transactions on stock exchanges is threatening the boom in exchange-traded funds. Issuers complain that it’s becoming increasingly difficult to hire lead market makers, or LMMs, who work to minimize the difference between the prices that buyers are willing to pay and sellers will accept. What’s the problem? With almost 2,000 funds in the U.S., the resources of these traders are wearing thin. Gol… 2018-05-23 00:00:00 Read the full story.

Virtus InfraCap U.S. Preferred Stock ETF Launches on NYSE: Portfolio Products

Virtus ETF Solutions partnered with Infrastructure Capital Advisors, LLC to introduce the Virtus InfraCap U.S. Preferred Stock ETF, which begins trading today under the ticker “PFFA” on NYSE Arca. The fund seeks current income and capital appreciation in a sector that may be less understood by investors, according to Jay Hatfield, founder and chief executive officer of InfraCap and co-portfolio manager. It will invest in a portfolio of more tha… 2018-05-21 00:00:00 Read the full story.

Goldman Sachs loses senior sales trader to Credit Suisse

Credit Suisse has hired a senior sales trader from Goldman Sachs to bolster the bank’s European client coverage for global portfolio execution. Jack Tierney joins Credit Suisse’s portfolio trading sales team in Europe, as the investment bank continues to build its program risk trading business. He will focus initially on the Benelux region, although his scope will soon extend beyond this. A spokesperson at Credit Suisse declined to comment on t… 2018-05-21 13:38:30+01:00 Read the full story.    
This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors. This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.
$20M Allocation to Crowd Developed Trading Algorithm

CloudQuant Allocates Risk Capital to Crowd-Resourced Trading Algorithm

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Allocation

Chicago, Illinois, USA, May 18, 2018 – CloudQuant, one of 50 Most Promising FinTech Solution Providers of the year, has allocated risk capital to a crowd-resourced trading strategy. The strategy’s creator, an Australian based crowd researcher, leveraged CloudQuant’s market simulation and python based back-testing tools, to prove the algorithm’s performance and profitably within approved risk parameters. As a funded partner, the researcher will receive a share of the trading net profits. “Striking a balance between complexity and simplicity can be a major key towards success,” said the crowd researcher when giving advice to other researchers. The US equity strategy began trading immediately upon approval of the licensing agreement from both the Quantitative Trader and CloudQuant management. “Market enthusiasts are finding new alpha signals in alternative data sets, fundamental data, and market data. Our users are building exciting new trading strategies,” said CEO Morgan Slade. “We are excited about the talent we see emerging within our network of crowd researchers and confident new users will find trading opportunities.” Using research tools originally conceived and developed by proprietary traders in the parent company, Kershner Trading Group, CloudQuant provides data-driven resources to test an algorithm’s profitability. CloudQuant is proving that innovative trading strategies emerge when market enthusiasts are provided institutional grade research tools. The algorithm creator and CloudQuant can then enter into a profit-sharing agreement to trade the algorithm using CloudQuant provided risk capital. About Us CloudQuant is the cloud-based trading strategy incubator. Quantitative analysts, algorithmic developers, data scientists and traders around the world create and test trading strategies leveraging CloudQuant’s superior infrastructure. Approved strategies are licensed from the strategy creator and funded for production by the CloudQuant team, paying the creator a licensing fee from the net profits. By providing the capital, technology, and trading acumen to develop and utilize trading strategies, CloudQuant offers a mutually beneficial profit sharing agreement enabling both parties to profit. CloudQuant LLC, established in 2016, is a wholly owned subsidiary of Kershner Trading Group LLC www.cloudquant.com   For Media Inquiries Please Contact: Jessica Titlebaum Darmoni Jessica@thetitleconnections.com + 1 312 358 3963
Trevor Trinkino Quantitative Trader

Machine Learning FXCM Webinar with Trevor Trinkino of CloudQuant – Part 2/3

On May 15th Trevor Trinkino presented part two of a three-part Machine Learning webinar with FXCM. Part one is here.

Part 2  – Preprocess data for Random Forest. PnL and prediciton improvements…

In part two Trevor goes over how to clean and pre-process data from CloudQuant to use in a Random Forest Classifier. He then looks at the prediction and PnL improvements seen from utilizing this classifier as well as adjusting the hyper-parameters of the model. Files mentioned in the video are available from our Google Drive below.. Ipython Notebook Trades.csv FilteredClfFeatsBF100_50.csv MLClassData.csv    
stock charts

AI & Machine Learning News. 22, April 2018

Cars.com is using Machine Learning to Predict the Sales of Cars

Cars.com has built a machine learning model to help buyers determine when and how to act on a purchase. The technology is called “Hot Car” and has been built on over 20 years of data using over 50 factors. The initial testing resulted in a double-digit increase in sales 2018-04-19 11:10:02+05:30 https://www.analyticsvidhya.com/blog/2018/04/cars-com-is-using-machine-learning-to-predict-the-sales-of-cars/ CloudQuant Thoughts: Double-digit growth is an extremely impressive ROI for AI/ML.  

The Rise of AI Continues – Robots have Mastered the Task of Assembling Furniture

Researchers from Singapore have created 2 robotic arms that can assemble an IKEA chair. It took them 19 minutes and 20 seconds to do it; humans take 15 minutes on average. Science Magazine also has an article on these robots. Their staff tried the same challenge and only beat the robots by, on average, 50 seconds. It took multiple attempts and a lot of training data to make the AI workable. Check out the video below to see this technology in action. 2018-04-21 11:21:31+05:30 https://www.analyticsvidhya.com/blog/2018/04/ai-powered-robots-mastered-task-assembling-furniture/ also 2018-04-18 13:30:44-04:00 http://www.sciencemag.org/news/2018/04/can-robot-build-ikea-chair-faster-you CloudQuant Thoughts: White Glove delivery and assembly of furniture is the golden egg of the furniture business. It costs too much and is too variable (ability of the person assembling the furniture, quality of the delivered goods – faulty/parts missing) . We can easily forsee trained robots delivering and assembling the furniture for you.  

Deep learning in your browser: Use your webcam and Tensorflow.js to detect objects in real time.

Tensorflow.js is a new deep learning library that runs right in your browser. Being a machine learning and Javascript enthusiast, I immediately started working on an object detection library using Tensorflow.js after it was released. Here, I’ll walk through how I got started. You can check out the live demo here. 2018-04-20 19:59:54.715000+00:00 https://towardsdatascience.com/deep-learning-in-your-browser-a-brisk-guide-ca06c2198846?source=collection_home—2——0—————- CloudQuant Thoughts: Sounds great, needs access to your webcam and, unfortunately, didn’t work for me!  

AI Weekly: Why all developers should watch ‘Westworld’

Last year, futurist David Brin predicted a “robot empathy crisis” would come in the years ahead, spurring debate among humans about the right way to treat a robot that looks and acts like a human. It may be easy for some to dismiss ideas like robot personhood or a robot empathy crisis, but it’s a worthwhile discussion, and Westworld is one of the only shows on television that persistently explores how human treatment of machines could change our humanity or reflect our worst demons. After all, slavery doesn’t just affect the enslaved — it also corrupts those who treat them as less than human. 2018-04-20 00:00:00 https://venturebeat.com/2018/04/20/ai-weekly-why-all-developers-should-watch-westworld/ CloudQuant Thoughts: There are already signs that the subservient style of these personal assistants with female voices is affecting our childrens politeness. See another great NPR article (Alexa – are you safe for my kids).  

The 3 most valuable applications of AI in health care

recent report from Accenture analyzed the “near-term value” of AI applications in health care to determine how the potential impact of the technology stacks up against the upfront costs of implementation. Results from the report estimated that AI applications in health care could save up to $150 billion annually for the U.S. health care economy by 2026.
  1. Robot-assisted surgery: Estimated value of $40 billion
  2. Virtual nursing assistants: Estimated value of $20 billion
  3. Administrative workflow assistance: Estimated value of $18 billion
An example of this comes from Nuance. The company provides AI-powered solutions that rely on machine learning to help health-care providers cut documentation time and improve reporting quality. Computer-assisted physician documentation (CAPD) like this provides real-time clinical documentation guidance that helps providers ensure their patients receive an accurate clinical history and consistent recommendations. 2018-04-22 00:00:00 https://venturebeat.com/2018/04/22/the-3-most-valuable-applications-of-ai-in-health-care/ CloudQuant Thoughts: As traders, we have seen health care stocks soar as their profits soar and our Insurance deductions soar. It would be great to see this vicious circle interrupted by AI and ML.  

Using Natural Language Processing To Check Word Frequency In ‘The Adventures of Sherlock Holmes’

Natural Language Processing is one of the most commonly used technique which is implemented in machine learning applications — given the wide range of analysis, extraction, processing and visualising tasks that it can perform. In this article, you will learn how to implement all of these aspects and present your project. The primary goal of this project is to tokenize the textual content, remove the stop words and find the high frequency words. We shall implement this in Python 3.6.4. To start with, we shall look into the libraries that we are going to use:
  • Beautifulsoup: To scrape the data from the HTML of a website and it also helps to process only the text from these HTML codes
  • Regular Expressions: Also known as Regex. It will convert the noise data containing special characters and carry the conversion of uppercase to lowercase characters
  • NLTK (Natural Language Toolkit): For the tokenization of the sentences into a list of words
We are using the eBook “The Adventures of Sherlock Holmes by Sir Arthur Conan Doyle”, which is available here. 2018-04-19 12:45:28+00:00 https://analyticsindiamag.com/using-natural-language-processing-to-check-word-frequency-in-the-adventure-of-sherlock-holmes/ CloudQuant Thoughts: A great starter article. Being able to read and analyze text/news is a huge benefit for trading stocks. Of course you could always just use the sentiment datasets we have available over at app.cloudquant.com which group and categorize earnings, news, twitter and stocktwits tweets for you as a single “sentiment” number.  

9 AI And ML Courses Offered By Tech Giants Which Will Boost Your Career

By now almost every tech company has realised that the world needs more artificial intelligence and machine learning experts. There are only 10,000 people in the world right now with the education, experience and talent needed to develop these AI technologies. This acute lack of skill set is hindering digital transformation at enterprises across the globe. To meet this talent shortage, the tech giants have now become more committed to making ML more accessible to students and developers by offering online courses.
  1. Google – Learn with Google AI
  2. Google – ML Crash Course
  3. Microsoft – Microsoft Professional Program For AI
  4. Amazon – Introduction to ML
  5. Amazon – Deep Learning on AWS
  6. NVIDIA – Deep Learning Course
  7. Baidu – Deeplearning.ai
  8. Intel – Intel Student Ambassador Program for AI
  9. Uber – Uber AI Residency
2018-04-20 07:00:39+00:00 https://analyticsindiamag.com/9-ai-and-ml-courses-offered-by-tech-giants-which-will-boost-your-career/ CloudQuant Thoughts: This is a hot career and there many opportunities out there… Including writing Automated Trading Algorithms at app.cloudquant.com. See more on the demand for data scientists in our new section “below the fold”…  
Below the fold….
 

A.I. Researchers Are Making More Than $1 Million, Even at a Nonprofit

One of the poorest-kept secrets in Silicon Valley has been the huge salaries and bonuses that experts in artificial intelligence can command. Now, a little-noticed tax filing by a non-profit research lab called OpenAI has made some of those eye-popping figures public.

OpenAI paid its top researcher, Ilya Sutskever, more than $1.9 million in 2016. It paid another leading researcher, Ian Goodfellow, more than $800,000 — even though he was not hired until March of that year. Both were recruited from Google.

Salaries for top A.I. researchers have skyrocketed because there are not many people who understand the technology and thousands of companies want to work with it.

2018-04-19 00:00:00 https://www.nytimes.com/2018/04/19/technology/artificial-intelligence-salaries-openai.html  

Why the transportation sector needs data scientists

The transportation industry is ripe for advancement. With the addition of Internet of Things (IoT) technologies, the industry might not be recognizable in 10 or 20 years. More connectivity means fully optimized operations and manufacturing, decreased downtime and accidents, and — what everyone is waiting for — driverless vehicles and ships. CIO magazine identified the top 10 skills necessary to tackle IoT as machine learning, AutoCAD, Node.js, security infrastructure, security engineering, big data, GPS development, electrical engineering, circuit design, and microcontroller programming. While one data scientist will not have all of those skills, many choose to specialize in some of them, especially machine learning. And since the basis of IoT is using massive amounts of data, there absolutely must be a data person on the IoT team. 2018-04-20 00:00:00 https://venturebeat.com/2018/04/20/why-the-transportation-sector-needs-data-scientists/  

4 Ways to fail a Data scientist job interview

‘Data Scientist’ might well be the sexiest job of the century. But hiring one is anything but that. Actually, it can be excruciatingly painful for companies. It’s an equally big deal for aspirants to bag that perfect offer in core data science, one which is not just a glossed-up, namesake role. While machine learning is tough, training a human who can make machines learn can be tougher. One evolves through various incremental stages of expertise to become a productive data scientist. For companies trying to identify one, it’s like finding a needle in the haystack. After years of hiring data scientists at Gramener, I’ve seen some conspicuously recurring patterns of skill gaps in the market. While there are hundreds of ways to fail an interview, these can be isolated into 4 broad paths.   1. Window dressing the CV with machine learning buzzwords 2. Reducing model-building to just making library calls 3. Lacking the fundamentals essential for data analysis 4. Inability to apply analytics to solve business problems

2018-04-20 13:02:22.165000+00:00 https://towardsdatascience.com/4-ways-to-fail-a-data-scientist-job-interview-d9c4c85c683

 

Job Application Guide for Data Science Students

In the past few years, I have met up with a lot of employers and conducted interviews for training programs. After all the conversations and interviews and seeing the end results, I thought I would share more on how to prepare for your resume and even prepare for the interviews for a data science role. Most of the tips are for people who want to enter into the data science profession with a “green” background. I cannot promise results but I hope it can help those who are passionate about data science. 2018-04-22 03:26:34.217000+00:00 https://towardsdatascience.com/preparing-your-data-science-resume-portfolio-22af6bada8b9?source=collection_home—4——0—————-  

Are High Level APIs Simplifying ML To An Elementary Level?

High-level APIs in machine learning are the best way to accelerate data science workflows. With a slew of tools available in the market, startups and enterprises are deploying machine learning tools to automate repetitive tasks. ML software libraries with APIs such as TensorFlow, Keras and Scikit have simplified the implementation to a great extent by providing more flexibility with user experience as well as the ease of working with ML applications. But data science practitioners have for long argued the effectiveness of APIs that have dumbed down computer science for beginners. While APIs increases the productivity by testing a bunch of models and cranking out the best model, it can also promote the black box problem as practitioner’s lack basic understanding of how algorithms work. In the words of Matthew Mayo, ML researcher, implementing ML models is easier with libraries but does it go too far up in terms of abstraction for beginners to truly understand the theory of the algorithms? 2018-04-20 07:14:59+00:00 https://analyticsindiamag.com/high-level-apis-simplifying-machine-learning/  

How I trained an AI to detect satire in under an hour

I decided to give myself the challenge of seeing if I could teach a machine learning model to detect the difference between Onion articles (and other satire) and real news articles in less than an hour. These were my results: The first step to solving this problem was gathering the training data. This is by far the most difficult part. And there are a number of ways you might accomplish this. One way is to try scraping websites. I’m not really good enough at computers to figure out how to scrape websites, so I decided to gather the data manually (cringe). …

In about 1 second, Classificationbox was trained! Then it took another 2 or 3 seconds to settle and then validate. The results show an accuracy of 83%.

2018-04-20 19:33:49.833000+00:00 https://towardsdatascience.com/how-i-trained-an-ai-to-detect-satire-in-under-an-hour-2b8b300ea805?source=collection_home—4——3—————-  

Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades

I want to show an example of a complete data science pipeline, we’ll walk through how to get started on a data science problem. The first post will concentrate on defining the problem, exploratory data analysis, and setting benchmarks. The second part will focus entirely on implementing Bayesian Linear Regression and interpreting the results, we will implement Bayesian Linear Regression in Python to build a model. After we have trained our model, we will interpret the model parameters and use the model to make predictions. The complete code for this project is available as a Jupyter Notebook on GitHub. 2018-04-20 18:34:29.856000+00:00 https://medium.com/@williamkoehrsen/bayesian-linear-regression-in-python-using-machine-learning-to-predict-student-grades-part-1-7d0ad817fca5  

Defence Against the Data Arts : Python vs R

Like a diplomatic politician I’d say that if your aim is classical statistical analysis then go with R,  if your aim is Machine Learning than go with Python as the support is better and coding easier. But I believe that for a Data Science and ML enthusiast its good to be a jack of both the trades. Harry Potter Python 2018-04-20 17:57:58.522000+00:00 https://towardsdatascience.com/defence-against-the-data-arts-python-v-s-r-5f4529c1d90f?source=collection_home—4——5—————-  

IBM outlines the 5 attributes of useful AI

A few weeks ago, a dejected CTO told me it took his team three weeks to build a machine learning model. I told him a model in just three weeks sounded great, and he agreed. So why the long face? Because 11 months later, the model was still sitting on a shelf. That gap between great AI prototypes and AI in operation is starting to be a common theme as AI and machine learning make contact with the real world. The reason is … Actually, there are a lot of reasons, and we can look at a bunch of them, but underneath all the other reasons is the fact that data doesn’t sit still and never will. Data changes as the world changes. Building an AI or machine learning model means building a way of looking at the world. But as the world and the data change, the models need to adapt. The CTO I met was realizing that building a great model is only the first step. A model on its own is too brittle for the real world. It needs to live as a larger system that’s actually fluid. So how do we make AI systems that are fluid? By building them with five attributes in mind:
  1. Managed – inputs are clean
  2. Resilient – all models fall out of ‘sync’
  3. Performant – fast faster fastest
  4. Measurable – must improve the bottom line
  5. Continuous – continuous learning
2018-04-21 00:00:00 https://venturebeat.com/2018/04/21/ibm-outlines-the-5-attributes-of-useful-ai/  

You’ve got a chip, I’ve got a chip, everybody’s got a chip – Why tech companies are racing each other to make their own custom A.I. chips

Chinese retailer and cloud infrastructure provider Alibaba is the latest company to think up its own design for processors that can run artificial intelligence software. It joins a crowded roster of companies already working on similar custom designs, including Alphabet, Facebook and Apple. The trend could eventually threaten the traditional relationship between big buyers and big suppliers. In particular, chipmaker Nvidia, whose stock has surged as its graphics processing chips have become common for powering AI-based applications, could find its data center business impacted as these roll-your-own-chip projects mature. 2018-04-21 00:00:00 https://www.cnbc.com/2018/04/21/alibaba-joins-google-others-in-making-custom-ai-chips.html  

5 Reasons “Logistic Regression” should be the first thing you learn when becoming a Data Scientist

I started my way in the Data Science world a few years back. I was a Software Engineer back then and I started to learn online first. I remember that as I searched for online resources I saw only names of learning algorithms — Linear Regression, Support Vector Machine, Decision Tree, Random Forest, Neural Networks and so on. It was very hard to understand where I should start. Today I know that the most important thing to learn to become a Data Scientist is the pipeline, i.e, the process of getting and processing data, understanding the data, building the model, evaluating the results (both of the model and the data processing phase) and deployment. So as a TL;DR for this post: Learn Logistic Regression first to become familiar with the pipeline and not being overwhelmed with fancy algorithms.

So here’s my 5 reasons why today I think that we should start with Logistic Regression first to become a Data Scientist. This is only my opinion of course, for other people it might be easier to do things in a different way.

  1. The learning algorithm is just a part of the pipeline
  2. You’ll better understand Machine Learning
  3. “Logistic Regression” is (sometimes) enough
  4. It is an important tool in Statistics
  5. It is a great start to learning Neural Networks
2018-04-21 19:03:32.805000+00:00 https://towardsdatascience.com/5-reasons-logistic-regression-should-be-the-first-thing-you-learn-when-become-a-data-scientist-fcaae46605c4?source=collection_home—4——2—————-  

Python for Finance: Stock Portfolio Analyses

I have been investing in my own stock portfolio since 2002 and developed a financial model for my portfolio a number of years ago. For years, I would download historical prices and load the data into the financial model — while online brokers calculate realized and unrealized returns, as well as income and dividends, I like to have historical data in the model as I conduct my own analyses to evaluate positions. One view / report which I’ve never found from online brokers and services is a “Public Market Equivalent”-like analysis. In short, the Public Market Equivalent (PME) is a set of analyses used in the private equity industry to compare the performance of a private equity fund relative to an industry benchmark. Much more detail here.

Related, the vast majority of equity portfolio managers are unable to select a portfolio of stocks which outperforms the broader market, e.g., S&P 500, over the long-term (~1 in 20 actively managed domestic funds beat index funds). Even when some individual stocks outperform, the underperformance of others often outweighs the better-performing stocks, meaning overall an investor is worse off than simply investing in an index fund.

2018-04-20 13:02:22.165000+00:00 https://towardsdatascience.com/python-for-finance-stock-portfolio-analyses-6da4c3e61054

 

Microsoft Translator Uses AI to Break Language Barriers on Smartphones

Users of the newly-updated Translator app can now download AI-enabled translation packs, a feature that was previously available to a just a couple of smartphones. Overall, both the online and offline flavors of Microsoft’s neural network translation technology yield faster and more fluent translations than the statistical machine translation approach of the past, said Menezes. “[We made] tremendous progress in the past few years because of machine learning and neural networks,” he said. As an added bonus, the new language packs take up less mobile storage. The move to neural machine translation has reduced the size of Translator’s packs by 50 percent. 2018-04-18 00:00:00 http://www.eweek.com/mobile/microsoft-translator-uses-ai-to-break-language-barriers-on-smartphones  
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