Ultimate List of Automated Trading Strategies You Should Know - Part 1

This is the part 1 of a series “Ultimate List of Automated Trading Strategies

Photo by  Artem Bali  on  Unsplash

Photo by Artem Bali on Unsplash

So many types of automated trading use-cases

Since the public release of Alpaca’s commission-free trading API, many developers and tech-savvy people have joined our community slack to discuss various aspects of automated trading. We are excited to see many have already started running algorithms in production, while others are testing their algorithms with our paper trading feature, which allows users to play with our API in a real-time simulation environment.

When we started thinking about a trading API service earlier this year, we were looking at only a small segment of algo trading. However, the more users we talked with, the more we realized there are many use cases for automated trading, particularly when considering different time horizons, tools, and objectives.

Today, as a celebration of our public launch and as a welcome message to our new users, we would like to highlight various automated trading strategies to provide you with ideas and opportunities you can explore for your own needs.

Please note that some concepts overlap with others, and not every item necessarily talks about a specific strategy per se, and some of the strategies may not be applicable to the current Alpaca offering.

(1) Time-Series Momentum/Mean Reversion

Background

(Time-series) momentum and mean reversion are two of the most well known and well-researched concepts in trading. Billions of dollars are put to work by CTAs employing these concepts to produce alpha and create diversified return streams.

What It Is

The fundamental idea of time-series forecasting is to predict future values based on previously observed values. Time-series momentum, also known as trend-following, seeks to generate excess returns through an expectation that the future price return of an asset will be in the same direction as that asset’s return over some lookback period.

Trend-following strategies might define and look for specific price actions, such as range breakouts, volatility jumps, and volume profile skews, or attempt to define a trend based on a moving average that smooths past price movements. One of the simple, well-known strategies is the “simple moving average crossover”, which buys a stock if its short-period moving average value surpasses its long-period moving average value, and sells if the inverse event happens.

Mean-reversion is the expectation that the future price return of an asset will be in the opposite direction of that asset’s return over some lookback period. One of the most popular indicators is the Relative Strength Index, or RSI, which measures the speed and change of price movements using a scale of 0 to 100. For the purposes of trying to assess the likelihood of mean-reversion, a higher RSI value is said to indicate an overbought asset while a lower RSI value is said to indicate an oversold asset.

For Implementation

Trend-following and mean-reversion strategies are easy to understand since they look at a single asset’s time-series and try to make a prediction about that asset’s future return, but there are many ways to interpret the past behavior. You will need access to historical price data and may benefit from an indicator calculator library such as TA-lib. Virtually every trading framework library, including pyalgotrade, backtrader, and pylivetrader, can support these types of strategies.

Here is the Quantopian tutorial with backtest result for moving average crossover: Quantopian Tutorials

(2) Cross-Sectional Momentum/Mean Reversion

Background

In the U.S. stock market, there are more than 6,000 names listed on the exchanges and actively traded every day. One of the hardest problems in stock trading (and also true for global cryptocurrency trading) is how to pick the stocks.

What It Is

Cross-sectional momentum compares the momentum metrics across different stocks to try to predict the future returns of one or more of them. Even if two stocks such as Facebook and Google are indicating a momentum breakout, this may be driven by the market, but you try to beat the market by taking stronger momentum between those signals. Same for mean reversion. The point is that we consider the market movement that drives each individual stock and consider the relative strength of signals across stocks in an effort to produce a strategy that will outperform the market. This tends to be more computationally heavy, since you need to calculate the metrics with potentially tens to hundreds of time-series.

For Implementation

Again, for this type of strategy libraries like TA-Lib may make it easier to calculate the indicators. Also, you may need simultaneous access to multiple symbols’ price data. IEX’s API can provide up daily bar data for up to 100 stocks per query.

A medium post about cross-sectional study: Basics of Backtest and Cross-sectional Momentum

(3) Dollar Cost Averaging

Background

This is one of the simplest automated trading strategies and it is widely used by many investors.

What It Is

The idea is to invest a fixed amount of money into an asset periodically. You may doubt it, but some research indicates that this works in the real world, especially long-term. The logic behind it is that price fluctuates many times, and you may buy the stock cheaper overall compared to just investing in the stock at one point in time.

Remember, all of you who contribute to your 401k account are basically doing this. However, you might never think about doing it yourself, simply because there has been no easy way to automate this process.

For Implementation

Now with Alpaca trading API, it’s much simpler and provides much more flexibility.

(4) Market Making

Background

Market makers are important intermediaries who stand ready to buy and sell securities continuously. By doing this, they provide much-needed liquidity and are compensated for their inventory risk primarily by capturing bid-ask spreads.

Market making used to be done primarily by humans, who worked as floor traders in the pits, but now it’s almost entirely performed by machines. As exchanges have become more and more electronic, the strategy market makers employ has naturally required automation.

What It Is

There are a variety of approaches to market making but most typically rely upon successful inventory management through hedging and limiting adverse selection.

Some market makers may have very tight exposure limits and seek to turn over their positions quickly with the goal of being flat at the end of each day. Others may operate on a much longer horizon, carrying a large and diverse portfolio of securities long and short indefinitely. Undoubtedly, for any market maker, speed helps. The speed of calculation allows the market maker to continuously update its pricing and portfolio risk models, while the speed of execution allows the market maker to act on its models in a timely manner in an effort to reduce adverse selection and get better pricing on its hedges.

Competitive market makers need high-resolution data and a low latency infrastructure, although typically the longer their trading horizon is, the less sensitive they are to these things, and a smart but slow model goes a long way.

For Implementation

Also, in order to process vast amounts of data quickly and handle concurrency, languages like python may not be suitable. Go/Rust would be a good choice for balance between ease of concurrency handling and processing speed, as well as functional languages like Erlang/OCaml or good old languages like C++.

Some high-level explanation of market making: How profitable is market making on different exchanges

(5) Day Trading Automation

Background

Lots of day traders develop their trading strategies based on a mechanical set of conditions that are first based on intuition. Since manual day trading involves continuously assessing market conditions and making discretionary trading decisions on the spot, it can often be very physically and emotionally draining. Because the strategies are based on some rules or heuristics which can be codified, it is natural to think they can be automated, which is likely the case.

What It Is

One of the very well-known day trading strategies is the gap-up momentum strategy.

Suppose between the previous market close and next market open there is a positive earnings report. The market opens with a big gap, drawing lots of traders’ attention, and the price keeps going up for a while in the morning (but may not continue for long).

This strategy seeks to capture this follow-through momentum. The challenge here is that not all gap-up stocks keep going up, and among a handful of screened stocks, you need to watch each one’s price action simultaneously.

Some traders may enter on a price breakout from a certain price resistance level, while others may wait to see a chart pattern form to determine the first bottom before going higher. Day trading often relies on analyzing the stock’s price chart and fine-tuning the algorithm to capture the price action can be tricky. That said, once it’s well developed, you are letting your bot trade on your behalf as if you were trading manually, and now you don’t need to monitor the markets and you can also monitor more stocks at the same time without any emotions affecting your trade execution, which is very compelling.

For Implementation

The main thing you need for this is access to market data. You may not even need indicator calculations but instead, you may need a stock screening library such as pipeline-live. The latency typically isn’t so important, so you don’t need to write your system in C++. Python, as well as other lightweight languages, are likely sufficient.

Some reference: Momentum Day Trading Strategies for Beginners: A Step by Step Guide

To Be Continued…

This is part 1 of 3 posts to overview the various types of automated trading strategies. Stay tuned for our next post to cover more.

/

Commission-Free API Stock Brokerage Is Finally Here

Fintech Startup Alpaca Unveils World’s First Commission Free API Stock Broker For Automated Investing And Trading

Just as Technology Transformed Institutional Trading on Wall Street, Now Retail Investing will be Transformed by Algorithms and AI That Automate Trading into Process that Can Easily Execute Thousands of Trades a Day. Company Also Closes Pre-Series A, Bringing Total Funding to $6 Million.

The World’s First Commission-Free Algo Trading Platform

San Mateo, CA — Oct. 23, 2018 — Alpaca Securities LLC (“Alpaca“), a Silicon Valley based API stock brokerage for developers and bots, today launched the world’s first commission-free trading platform where individuals can easily use algorithms, trading bots and artificial intelligence in their investing and trading activities.

“Alpaca is going to transform stock trading by making available to retail traders all of the technology and science that has transformed institutional trading,” said Yoshi Yokokawa, CEO and co-founder of Alpaca, which is an SEC and FINRA registered broker-dealer.

“In the beginning, individual coders who build algorithmic strategies will be able to connect those algorithms with our commission-free trading API and begin trading on our platform. Over time, we’ll make algorithmic investment strategies available there to non-coder investors who want to manage their investment with customizable strategies such as automated asset allocation and rebalancing strategies, and they’ll never have to manually execute a buy or sell order again.”

Alpaca has been notifying some in the trading and developer community of its impending launch and already more than 4,000 accounts have registered on a waiting list. The large number of accounts attracted to the Alpaca is another indication of the surge in interest in algorithms for trading over the past few years.

Integration With QuantConnect

Alpaca has also been working closely with QuantConnect, an algorithmic trading platform that enables investors to design, build and test quantitative strategies. QuantConnect serves over 65,000 quants from more than 170 countries, and its users have designed more than 1.8 million trading strategies. The QuantConnect integration makes it seamless for users to build algorithms, test them and then trade on Alpaca’s commission-free, automated platform.

“Alpaca is on track to become a much-needed solution for the growing number of algorithmic investing enthusiasts who may require a commission-free brokerage to automate their investing,” said Jared Broad, founder and CEO of QuantConnect. “I anticipate that the quants on our platform, and hundreds of thousands of other coders who are seeking to pioneer algorithmic strategies to deploy in the retail investment environment, will welcome Alpaca to the market. There’s a need in the space for a brokerage dedicated to facilitating automated and quantitative trading, and Alpaca is positioned to meet that need.”

Integration For The Quantopian Community

Quantopian, a platform where freelance quantitative analysts develop, test, and use trading algorithms to buy and sell securities has played a key role, and its community has nearly doubled year-over-year for the last four years. Quantopian’s community of more than 225,000 has run more than 9 million backtests on algorithmic trading strategies. Quantopian also builds and maintains Zipline, and Alpaca has built an integration to allow the Quantopian community to easily run algorithms with Alpaca’s commission free trading API.

“Quantopian provides the world’s leading quantitative finance platform for anyone to come create and test their algorithmic trading strategies,” said John Fawcett, founder and CEO of Quantopian. “Zipline is the standard for backtesting. Alpaca’s new retail quant trading product is a great example of Zipline accelerating innovation in our industry.”

Platform To Enable Automation

Current commission free trading platforms are built still expecting that all trades be executed via manual buy and sell orders. And they are not built to handle the repetitive and frequent trading that is automated by algorithms and artificial intelligence. On Alpaca, traders will be able to open an account, deposit money and then deploy their own algorithms or trading bots so that all stock transactions occur automatically.

The technology Alpaca developed is already in use at institutions in Asia, including Mitsubishi UFJ Financial Group (MUFG), the largest financial institution in Japan.

Closing $3M Pre-Series A Round

Today the company also announced it has closed a $3 million Pre-Series A. Alpaca has raised more than $6 million in total since the company was founded in 2015. Investors include:

  • Global Brain, one of the largest venture capital firms in Japan

  • Chihiro Asano, a serial entrepreneur, angel investor, also a co-founder and former CTO of Japan’s MoneyForward, which went public in 2017

  • D4V (Design for Ventures), a venture capital firm in partnership with IDEO

  • Eric Di Benedetto, a fintech angel investor with more than 30 IPO and M&A exits

  • Members of Berkeley Angel Network, a group of angel investors comprised of alumni of UC Berkeley

  • Archetype, a seed/early stage venture capital investing in tech-driven companies

  • Joshua S. Levine, FINRA public governer and former CTO/COO of ETRADE Financial

Alpaca Algo Dashboard.jpg

About Alpaca

Alpaca is a Silicon Valley based API stock brokerage for developers and bots. Alpaca is dismantling the old system of stock trading that required investors to constantly monitor stocks and enter buy and sell orders manually by introducing a commission-free trading platform where individuals can easily use algorithms, trading bots and artificial intelligence. The company’s database and AI technology are already used by institutional investors in Asia. Alpaca is currently accepting users at https://alpaca.markets/. Securities are offered through Alpaca Securities LLC.

/

Python Library To Run Quantopian Algorithm In Live

Quantopian — The Online Algo Trading Platform

Quantopian is one of the most popular online algo trading platforms and communities today. It provides the great backtesting environment where you can experiment with your idea, build algorithms and even participate in the contest, as well as share the idea and discuss it with smart people there.

Photo by  Rodion Kutsaev  on  Unsplash

One of the things many people have asked Alpaca during the beta program is how to run the algorithms that they built in Quantopian platform for their own purpose, not just for the contest. While Quantopian has built so much in the platform, they are so great to share the internal framework as open source zipline.

The Newest Open Source Libraries for Quantopian Users

Today, I wanted to share our newest open source libraries for Quantopian users; pylivetrader and pipeline-live.

alpacahq/pylivetrader
Python live trade execution library with zipline interface. - alpacahq/pylivetradergithub.com

alpacahq/pipeline-live
Pipeline Extension for Live Trading. Contribute to alpacahq/pipeline-live development by creating an account on GitHub.github.com

pylivetrader is a zipline API compatible trading framework in python which again focuses on live trading, with much less overhead and dependency problems. It is written from the ground up for live trading use cases, so it removes a lot of heavy lifting that zipline had to do such as price adjustment etc.

This means, you don’t need to build your data bundle to kick off your algorithm in live, but instead you can just start your live trading from the Quantopian algorithm source right away.

At the moment, the supported backend is only Alpaca, but we are happy to connect to IB etc. if someone contributes the code.

Pipeline API — the Core Piece of Quantopian Framework

Pipeline API is the core piece of Quantopian algorithm framework that allows easy stock selection based on the different metrics, much in a pythonic way, and this differentiates the platform from others. I found Pipeline is providing a tremendous value when it comes to trading wide range of universe. Unfortunately, it is not so easy for most people to use this great feature outside of the Quantopian platform.

pipeline-live is a python tool that allows you to do something similar anywhere so that you can do your research somewhere else as well as use it with existing python trading framework such as zipline-live or backtrader, including pylivetrader which I am introducing below. pipeline-live primarily uses IEX public API for pricing and basic fundamental information.

As you know, IEX provides market-wide volume data for daily OHLCV which makes it a perfect choice for pipeline usage. Since pipeline-live focuses on live trading use cases, it does not provide historical view unlike inside Quantopian, but the upside is it is fairly independent and easy to use. It is also very extensible so you can hook up with other paid data sources if you would find useful.

How to Convert Your Quantopian Algorithms to Run in Live Trading

We also put some practices together about how you could convert your Quantopian algorithms to run in live trading. You may want to take a look at these documents if you are interested in.

https://github.com/alpacahq/pipeline-live/blob/master/migration.md
https://github.com/alpacahq/pylivetrader/blob/master/migration.md

I also posted in Quantopian forum with the real example, and you may take a look at it, too.

Long-only non-day trading algorithm for live
This is a modified version of the algorithm presented in…www.quantopian.com

Feel free to give me any feedback/questions/criticism. Happy to help you get started with live trading with these tools too.

And here is the example code migrated from the post above.


/

Algo Trading News Headlines 9/18/2018

5 Reasons Why Cryptocurrency Trading Bots Are So Popular

(www.cryptodisrupt.com)

“Have you been considering using cryptocurrency trading bots or are looking for a way to get involved with crypto trading on exchanges? Many people are thinking the same as you. Here are five reasons why trading bots are so popular.”

Photo by  Fancycrave  on  Unsplash

Photo by Fancycrave on Unsplash

Why Robots Are Bad Financial Advisors

(www.nasdaq.com)

“In an age of rapidly advancing technology, more investors are opting for DIY financial planning and investment management platforms. These trading platforms, retirement calculators, and auto-rebalancers are increasingly sophisticated, but many investors will learn in the next major market meltdown that there is a human element that cannot be replicated by even the most advanced of these tools.”

Can Computers Time The Market?

(www.seekingalpha.com)

“With my background in software, I decided to design, develop, and test out various quantitative models, and leveraged AI to determine the optimal values for each model or “strategy.” Though some excellent quant platforms like Quantopian exist, I opted to develop this simulator from the bottom up.”

10 Years Later, Many Deep Scars From the Financial Crisis Remain

(www.247wallst.com)

“24/7 Wall St. has evaluated those scars. Admittedly, this has more of an American focus than an international focus. Also worth noting is that an indefinite number of additional scars will remain in place for years and are the aftermath of the Great Recession and financial crisis of the past decade. There is also no way to categorize which scar is the most prevalent because that varies from person to person and from group to group.”

/

Algo Trading News Headlines 9/12/2018

Waters Rankings 2018: Best Algorithmic Trading Provider — Wolverine Execution Services

(www.waterstechnology.com)

Early forms of automated order entry provided the catalyst for what would become the foundational trading method of modern markets, but the development of algorithmic trading itself has been fascinating — and at times, controversial — evolution in listed markets. Wolverine Execution Services (WEX) has been at the forefront of broker-supplied algo.

Millennium Shuts Down Pioneering Quant Hedge Fund

(www.bloomberg.com)

The closing of Prediction Company, which Millennium bought in 2013, came as a surprise to employees because the firm was profitable, according to a person familiar with the matter. The hedge fund was started by Doyne Farmer and Norm Packard, who are known for their seminal work in developing chaos theory, and managed about $4 billion at its peak.

Comparing 3 Different Types of Neural Network Architectures in Finance

(alpaca.markets)

When working on a machine learning task, the network architecture and the training method are the two key factors to turning a set of data-points into a functional model. But where should different training methods be applied? How do they work? And which is “best”? In this post, we list up three types of training methods and make comparisons among Supervised, Unsupervised and Reinforcement Learning.

/

Algo Trading News Headlines 9/11/2018

Quant Investing: What are the dangers of the Black Box?

(www.jdsupra.com)

The strong industry consensus is that computers caused that Monday afternoon “flash crash.” Algorithmic trading, where computers automatically pick stocks based on complex pre-programmed instructions, “definitely had an impact” on the market swing that day, according to U.S. Treasury Secretary, Steve Mnuchin.

Photo by  Esther Jiao  on  Unsplash

Photo by Esther Jiao on Unsplash

Rethinking the Order Book: The March Towards Automated Markets

(www.tradersmagazine.com)

Even as the digitization of trading has evolved and blockchain changes the financial landscape, existing market models have been unimaginatively carried over to electronic asset exchanges and now crypto markets. As a result, technology gaps between traders remain economically significant while the current market design perpetuates (and in some cases exacerbates) problematic features of the former system.

Citi hires derivatives duo from rival Goldman Sachs

(www.fnlondon.com)

Citigroup has made three appointments within its electronic trading business, including two hires from rival Goldman Sachs, amid a surge in futures trading this year.

/

Best Starting Kits for Algo Trading with C#

Today, the world is transforming towards automated fashion, including manufacture, cars, marketing and logistics. Personal investment is no exception. At Alpaca, we are pushing this boundary forward so everyone can enjoy the automated investment world.

Photo by  Nikhil Mitra  on  Unsplash

List of .NET/C# Algo Trading Systems

When it comes to algo trading and automated investment, Python is one of the biggest players in the space, but many experts also use .NET/C# for its high performance and robustness. As we did some research on toolset you might look at to start your algo trading, we wanted to share this list for you.

Overall, the ecosystem has grown so much lately, and many open sources and tools are available for you at low cost, without much equipment.

  • QuantConnect

QuantConnect is one of the most popular online backtesting and live trading services, where you can learn and experiment your trading strategy to run with the real time market. The platform has been engineered in C# mainly, with additional language coverage such as python.

  • WealthLab

WealthLab is another C# platform where you can get the real time price and run your algorithm, if you have a Fidelity account.

  • NinjaTrader and MultiCharts

NinjaTrader and MultiCharts are also popular choices for different kind of assets with various broker options.

  • OpenSource Projects

In addition to these, StockSharp is an interesting open source project which is tailor for .NET algo traders and broker integrations.

You should also check out Lean which is an open source library developed by QuantConnect, who also uses this library for their flagship service, supporting multiple assets such as stocks and cryptocurrencies.

List of Data Library

  • Deedle

Deedle is probably one of the most useful libraries when it comes to algo trading. You would run some calculation using Frame and compare data, to get signals.

  • TALibraryInCSharp

TALibraryInCSharp is a great open source library that bridges TA-lib and .NET world, so that you can calculate common indicators such as moving average and RSI. Combining these libraries, you will get the power of trading tools.

  • IEX

Now the question is data to calculate those signals on, but if you are talking about US equities, you can leverage IEX’s free data API and there are libraries like IEXTradingApi that makes your life easy for getting the data instantly. 

  • Others

There are quite a bit of .NET libraries out there for proprietary data sources (e.g. for Quandl) too, so you should check it out.

Announcing Alpaca’s Official .NET Client SDK

Don’t forget about Alpaca! We are committed to providing the best experiences for many algo traders, and today we are happy to announce that our official .NET client SDK for Alpaca Trade API has been released.

Following our Python SDK, .NET SDK takes advantage of its robustness and high performance, as well as wide coverage of platforms. It is an open source project hosted in GitHub and the prebuilt package is up in NuGet. All the classes and methods are documented for IntelliSense so you can get the references right in your IDE.

Here is a snippet of how easily you can place a buy order of a share of Apple.

Alpaca Trade API covers not only retrieving account information and submitting orders, but also allows one to retrieve price and fundamentals information easily. For more details of API, please read our online documents.

Happy algo trading!

/

Python Notebook Research to Replicate ETF Using Free Data

ETF is one of the great investment products in the last decade, and it has allowed so many people to gain the exposure to the wide range of assets easily at low cost. It is easy to buy a share of ETF without knowing what’s in there, but as a tech-savvy guy yourself, you may wonder how it works. By reconstructing the fund yourself, you may even come up with something better.

In this article, we present some basis for you to start your research easily in python to science the ETF world. You can find the complete notebook in GitHub.

Photo by  Kevin Ku  on  Unsplash

Photo by Kevin Ku on Unsplash

What is ETF by the way?

ETF stands for Exchange-Traded Fund. Unlike other types of funds, its shares are traded in exchanges like individual company’s common stocks. The fund is managed by an ETF company and manages portfolio based on the strategy, often diversifying the exposure spread across the market.

One of the most popular ETF is SPY, that tracks S&P 500 index performance. Because of its convenience to manage the risks, not only has it been used by individual investors, but also robo advisors construct their portfolio using ETFs. The convenience doesn’t come for free, of course, and there is an associated cost called expense ratio, that varies an ETF to another.

An ETF’s return comes from the returns of underlying assets it holds. ETFs can hold not just individual stocks but also options and swaps, but in the case of market index ETF like SPY, it constructs a simple long position portfolio.

If the constituents are simply long only stocks, is it easy to run some simulation even in python? If it’s possible to build your own ETF-like portfolio, you don’t even need to pay ETF cost? The answer is YES.

Recreating ETF

Various services provide ETF constituent data either through their website or API, with paid and unpaid style. Some provide even historical data. We recommend to find your best services by yourself, but here we automate the process by Selenium to save your time copying and pasting the list of underlying stocks of particular ETF.

get_etf_holdings() will return the list of constituents in pandas DataFrame format, and the columns include weight in the portfolio and an actual number of shares holding as of today. 

Note this does not come with the price data, but you can pull the historical price data from IEX API for free.

get_closes() will take the constituent data from get_etf_holdings() and return the daily closing price history for the last month from IEX API.

Simulate SPY performance

Before doing something unique, let’s just check if our assumption is correct. The task here is to calculate the historical performance of reconstructed portfolio and compare that with the actual ETF.

Remember the constituent list we have is the one as of today. The fund may have rebalanced, but we assume that’s not the case and we build our portfolio a month ago. Putting altogether, we get something like this.

1_OZI7PVciSwZS3MuNxxAudA.png

Even though we took the constituent data as of today, and applied it to simulate the last month, the result isn’t too different. This means this ETF hasn’t changed the holding shares significantly.

So, I don’t need to buy ETF but just buy these stocks?

It’s a natural question whether you can replicate ETF portfolio by buying only underlying stocks.

Yes, you can, only if you have more than $260,000,000,000 ($260BN) which is SPY’s market cap today. But no, you don’t have it, so let’s see how it changes if you do so with $10K. The resulted portfolio we get after some calculation is as below.

The actual total market value of this portfolio is about $2K. The reason why it diverges from the original target is because you don’t buy fractional shares. All fractions are truncated, resulting to much smaller. On the flip side, we found that we can build something similar to SPY with smaller amount of dollar. Running the same historical plotting, we get this.

1_wneCpDX4nv__MddVlztFIw.png

The divergence is much bigger compared to the first one, and the volatility increased, but in terms of the return, it is not too bad. As a study, it is great to see the actual example like this that more diversified portfolio has less volatility, as the modern portfolio theory teaches.

Summary, and now what?

We presented some python research with actual notebook to study how ETF works, and did some simple experiments. You can look at the complete notebook here.

You can try it in your environment! We recommend to clone the notebook and extend the study for your purpose from here. Potential questions you may ask are:

  • what if the cash size is bigger, or smaller?
  • how about other index ETF such as QQQ?
  • how much dollar do you need to have at least one share for each?
  • can you replicate the return more precisely by rebalancing frequently?
  • can you build something similar by using other set of stocks too?

Research is always fun, and you should continue asking these questions. It is a great moment that this kind of research can be done in a day with only your laptop.

We leave it to the readers to what to do from here, but please let us know what you find if you do something in the comment, or to our Twitter @AlpacaHQ! We hope you will leverage the technology to automate your investments.

/

Algo Trading News Headlines 8/23/2018

Barclays hires AI specialist from Goldman intrading revamp

(www.fnlondon.com)

The hire is another sign of Barclays’ intent to ensure its markets business can compete with rivals in the City and on Wall Street, which are increasingly turning to new technologies to improve performance and cut costs.

No Quant Is Safe as Global Stress Hits Risk Parity Where It Hurts

(www.bloomberg.com)

Risk-parity’s travails might test faith in the trading style over traditional allocations. It’s among the worst-performing categories tracked by JPMorgan, falling 1.2 percent this year. That compares to a 2.1 percent gain for long-short equity funds, 1.7 percent for macro funds, and 2.9 percent for balanced mutual accounts, according to data published by the U.S. bank earlier this month.

UBS trials Netflix-style algorithms for trading suggestions

(www.cnbc.com)

UBS is looking at applying recommendation algorithms to suggest trades to its asset management and hedge fund clients, similar to those used by a host of consumer technology companies.

Market makers, takers and fakers: US exchanges are losing fast

(bravenewcoin.com)

The great ETF debate rages on and there’s a lot of conjecture as to why the SEC should or shouldn’t approve one. But what needs to be addressed foremost is the immaturity and unreliability of many of the exchanges that bitcoin derives its “global spot price” from — that number now over 210 — and the obstacles they may encounter going forward.

Indicators That Could Have Made You 626% ROI During The Recent Bloodbath

(cryptodaily.co.uk)

Those indicators are the 5 EMA and 10 EMA as can be seen on the daily chart for BTC/USD. These indicators have been signaling the direction of every bullish and bearish wave throughout the correction period.

/

Algo Trading News Headlines 8/22/2018

Wall Street Erases the Line Between Its Jocks and Nerds

(www.wsj.com)

There used to be a strict hierarchy: Traders made money and won glory while programmers wrote code and stayed out of sight. Those days are over. Meet the straders. Part risk-taking trader and part computer-whiz “strategist,” they are prowling the halls at Goldman Sachs Group Inc., erasing a once-religious line between the jocks and the nerds.

Photo by  FOTOGRAFIA .GES  on  Unsplash

Israel’s Central Bank Wants Increased Regulation on Algorithmic Trading

(www.calcalistech.com)

Over 90% of the activities on the Tel Aviv Stock Exchange are algorithmically performed, according to a new research published by Israel’s central bank last week. While the number of algorithmic automated high-frequency trading (HFT) activities on the exchange is higher than most stock exchanges, the actual rate of transactions they execute, between 23% and 35%, is significantly lower than global standards, the report said.

Global Algorithmic Trading Market 2018–2025 by Business Players: Virtu Financial, KCG, DRW Trading, Optiver

(threepmnews.com)

Geographically, the global Algorithmic Trading market is designed for the following regional markets: USA, EU, Europe, China, Japan, Southeast Asia, India. It also studies the revenue market status, analysis of main manufacturers. It deciphers the Sales Price and Gross Margin Analysis and Global Sales Price Growth Rate, Marketing Trader or Distributor Analysis. The role of traders and distributors is emphasized in this research. The complete analysis of Algorithmic Trading Market on the global scale provides key details in form of graphs, statistics and tables which will help the market players in making key business decisions.

Wall Street Finds Limits with Current AI Applications

(www.wsj.com)

“A lot of work needs to be done to translate (AI) advancements into benefits for finance,” said Ambika Sukla, executive director of machine learning and AI at Morgan Stanley, at an AI conference Tuesday. “As we work on some of these new models, it’s important to proceed carefully and have a human in the loop.”

Machine Trading: Deploying Computer Algorithms to Conquer the Markets

(www.wiley.com)

Algorithmic trading is booming, and the theories, tools, technologies, and the markets themselves are evolving at a rapid pace. This book gets you up to speed, and walks you through the process of developing your own proprietary trading operation using the latest tools.

Comparing 3 Different Types of Neural Network Architectures in Finance

(blog.alpaca.markets)

When working on a machine learning task, the network architecture and the training method are the two key factors to turning a set of data-points into a functional model. But where should different training methods be applied? How do they work? And which is “best”? In this post, we list up three types of training methods and make comparisons among Supervised, Unsupervised and Reinforcement Learning.

Coinscious Introduces Crypto Prediction Machine Built to Synergize AI and the Blockchain

(www.benzinga.com)

Every day, people spend significant amounts of time reading financial news and checking cryptocurrency prices, gathering information that they hope can lead to better decisions. However, the quantity of information available is overwhelming and calls for effective tools and suitable methodologies to enable us to distill data into actionable insights. By processing large data sets quickly, machine learning algorithms can use news sources such as the Financial Times, The Washington Post, or Twitter to provide key insights.

/