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.

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Algo Trading News Headlines 8/29/2018

Brazilian crypto trading platform hacked, over 264000 user data leaked

(www.coingeek.com)

Portal do Bitcoin reported that the hack was initially brought to light through a YouTube video posted by Investimentos Digitais. A total of 14,500 accounts containing a total of 5,813 BTC, currently worth about $40 million, were reportedly affected by the security breach.

Photo by  Pedro Menezes  on  Unsplash

Photo by Pedro Menezes on Unsplash

Forecasting Market Movements Using Tensorflow

(alpaca.markets)

In this post we’ll be looking at a simple model using Tensorflow to create a framework for testing and development, along with some preliminary results and suggested improvements.

$6 Billion Daily Crypto Volume is Being Faked, How Can it be Combated?

(www.ccn.com)

Earlier this week, the Blockchain Transparency Institute (BTI) published a report claiming that the global crypto exchange market is faking $6 billion of its daily volume. The researchers at BTI evaluated the user activity and traffic of the market’s biggest crypto exchanges, comparing their projected trading volume to other metrics.

Empower Sebi to crack down on erring CAs, says panel

(http://www.asianage.com/)

New Delhi: The fair market committee report submitted to securities market regulator Sebi has many more unpleasant surprises for various market-connected entities. For instance, it opens a pandora’s box on whether Sebi has jurisdiction over errant chartered accountants or not.

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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.

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Algo Trading News Headlines 8/17/2018

Startup Raises $23 Million To Make Crypto Trades Faster And Stealthier

(www.forbes.com)

Silicon Valley-based cryptocurrency trading platform Sfox has raised $23 million in new investment, led by venture firms Tribe Capital and Social Capital. The 20-person startup aims to help investors make large trades by routing their orders to multiple places, enabling faster execution and better prices. Khosla Ventures, startup accelerator Y Combinator, and crypto investors Blockchain Capital and Digital Currency Group also participated in the Series A funding round. Forbes estimates Sfox has reached $15 million in revenue over the past 12 months.

Photo by  John-Paul Henry  on  Unsplash

Blockchain Firm Exscudo To Add Trading Robots Support On Its Crypto Exchange

(blocktribune.com)

Exscudo is currently testing a trading terminal API that will allow Exscudo exchange users to utilize a trading robot that automatically makes trading decisions based on market data analysis and pre-programmed set of parameters.

Forecasting Market Movements Using Tensorflow — Intro into Machine Learning for Finance

(blog.alpaca.markets)

In this post we’ll be looking at a simple model using Tensorflow to create a framework for testing and development, along with some preliminary results and suggested improvements.

The World Economic Forum warns that AI may destabilize the financial system

(www.technologyreview.com)

Compiled through interviews with dozens of leading financial experts and industry leaders, the report concludes that artificial intelligence will disrupt the industry by allowing early adopters to outmaneuver competitors. It also suggests that the technology will create more convenient products for consumers, such as sophisticated tools for managing personal finances and investments.

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9 Most Commonly Asked Questions About MarketStore And Answers To Them

Photo by  William Stitt  on  Unsplash

Each of these articles seeks to explain the technology we build along with our Alpaca algo trading brokerage. These articles led active discussions on Reddit and Medium, and it became clear to us that there is a lot of interest and a pretty large need in the community for a timeseries database dedicated for financial market data. The database world and software engineering in general have changed so much over the last decade as we’ve seen an explosion in open source programming and databases. We are seeing some people now actively using open source to and contributing some code in the GitHub repository.

In social media and offline, we’ve been answering questions and responding to comments, but today we wanted to take the opportunity to put all the queries and responses together in one post and share it with the entire community so everyone can get a look at the responses on a single post.

Q: Does MarketStore store data in memory?

A: No. MarketStore is designed to run in a reasonable size of host without huge hardware investment. If you have lots of cash, software technology is irrelevant, but what software engineering can bring is that you can do a lot better job with cheaper hardware. MarketStore’s primary use case is to be able to store and distribute years of data at second level granularity for more than tens of thousands of series (US equities and crypto coins across exchanges can easily become this size). The data size can be a few terabytes, and it is not still very common to have this big size RAM in a commodity hardware. MarketStore instead stores everything in disk, but the on-disk format is nearly identical to the layout in the memory, and thanks to SSD evolution, MarketStore can load the data at the speed competitive to in-memory storage.

Q: How does it make sense to compare with PostgreSQL and includes DataFrame loading?

A: Even if you can store the data, offloading it from application processes, it is not useful if you cannot use it. MarketStore is mainly used in the context of AI machine learning and backtesting, and the application typically loads it into some tabular structure such as Pandas DataFrame. That is why MarketStore’s network protocol is byte sequence in MessagePack so the inefficient JSON deserialization can be avoided. The client can load the delivered byte data into memory as C array, which is what is used behind DataFrame.

Q: How is it better compared to InfluxDB?

A: We have not compared the performance with InfluxDB, but InfluxDB and other general-purpose timeseries databases use-case is as system metrics or activity log analysis. Those require more flexible data structure and don’t necessarily need specific functions such as timezone-aware aggregate. The flexibility comes with necessary overhead as tradeoffs as always, and MarketStore should be much faster and cost effective if the use case is the financial market data.

Q: Why are you comparing with PostgreSQL when Timescale should be faster?

A: You can send us the benchmark results if you have them, but in our internal experiments, Timescale is even slower than PostgreSQL compared to MarketStore. The loading time at the database server level for Timescale is 2–3x slower than PostgreSQL, since Timescale makes use of table partitioning (aka table constraints exclusion) that needs to open lots of files from disk. It will give advantage to filter a small slice of the data out of large amount of data, but it will not work better if you scan most of it. MarketStore stores the data in an optimal way on disk and reads sequentially direct to memory compared to those relational databases, so it is way faster.

Q: MarketStore can be used only for historical data but not for real-time data right?

A: There is a new feature coming soon to MarketStore that will allow streaming and realtime push on every new data write. MarketStore was originally designed to help our algo trading platform that builds trading algorithms using deep learning, and run them in the real market, and had JSON websocket streaming. The feature has been for the time being so that Marketstore can find a way to fit in larger use cases. But thankfully it is now back in as a plugin. We have been testing this with thousands of updates every few seconds and so far it is working perfectly.

Q: Why do I need this for machine learning? I can load the data from disk without a problem

A: If your training process doesn’t use much data (e.g. just daily bars from one stock), then yes probably you don’t need MarketStore for performance reasons. What we needed to do on Alpaca trading platform requires a server that is large enough to store an amount of intraday data across the entire market (can be up to terabyte range), and load the necessary series data back and forth. If you are familiar with typical machine learning training process, you can tell how the training iteration can load random data from the pool. That said, MarketStore is not just for performance, but also for the convenience to prove the uniformed way to access historical and real-time timeseries data the same way without worrying about how to manage local files etc. And the built-in data ingestor can load the data without even writing any code.

Q: Where is the installer?

A: Sorry, at the moment, we are not providing the one-click installer! But instead, we package the server process into a docker container image, so if you have docker, you can just start it in a second.

Q: Why is it open sourced?

A: Because there is a problem to be solved! MarketStore was implemented proprietary for our internal use and has been used in our production, but we have also seen the common problems affecting many people in the space. Our mission at Alpaca is to help individual investors with technology, and improve the algo trading environment, regardless of whether we give that information away to users or offer it in a premium package. This kind of product has only been accessible by financial institutions with large capital resources. But now we are making it available to anyone who is eager to try out! That’s awesome, isn’t it!?

Q: I found a bug

A: Please report it in the GitHub issue!

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Algo Trading News Headlines 6/6/2018

Opinion: AI will change stock-market trading, but it can’t wipe out the human touch

(www.marketwatch.com)

But just as Tesla’s TSLA, +4.83% Elon Musk recently claimed that the use of robots in auto assembly had gone too far and that humans needed to be brought back, the same is likely true for AI on Wall Street and its disappearing trading jobs. It is easy to be enamored by the power and speed of the new prediction machines. However, there are risks that real innovation and competitive advantage will be lost by relying solely on them.

Photo by  Lukas  on  Unsplash

Photo by Lukas on Unsplash

So You Want to Trade Crypto — Market Cap Distribution and Rise of Altcoins (Part 4)

(blog.alpaca.markets)

If someone creates a new coin with a total supply of 100B and manages to get it listed on a small exchange and trades it a few times with their friends for $1 per coin, it technically has a market cap of $100B. But in reality, is has no true value and no trading volume to sustain any kind of selling pressure.

Systematic trading entrepreneur to launch AI fund

(hfm.global)

Entrepreneur and systematic trader Matthew McElligott is launching an artificial intelligence-focused quant equity hedge fund. London-based Ylang Capital will use AI and machine learning trading techniques to trade global equities and is aiming to launch with around $50m, according to sources.

AI Trader: Deep Learning Artificial Intelligence Crypto Trading?

(bitcoinexchangeguide.com)

AI Trader executes trades without emotion, without prejudice, without fatigue and can trade relentlessly for days and months. It’s lightning quick responses to changing market conditions cannot be replicated by human traders. Notwithstanding the significant risks of trading in a highly volatile market, AI Trader was developed to enhance your profit potential.

Intelligent Machines and FX Trading

(www.finextra.com)

A major problem with trading strategies with AI is that they can produce models that are worse than random. The traditional technical analysis is an unprofitable method of trading because strategies based on chart patterns and indicators draw their returns from a distribution with zero mean before any transaction costs.

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Algo Trading News Headlines 6/4/2018

My First Attempt At Systematic Trading Algorithms

(hackernoon.com)

So trend following is different from the strategy you are using in your retirement account, which is probably simply buy and hold. A particularly nice advantage of trend following is that is limits downside risk. So in 2008 a trend following strategy would have given you a sell signal and you would drawdown (lose account value) only 20% instead of 50%. It would also indicate you should “short” the market (bet against it) and you would actually make money. Although, you can do a more simple long-only trend following strategy too.

The quant factories producing the fund managers of tomorrow

(www.ft.com)

“There’s a real war for data scientists right now,” says Alexander Friedman, chief executive at GAM, the Swiss asset manager. “A number of investment firms have developed special relationships with universities to help source talent.”

Quant Guru David Shaw’s Riding Semiconductor Stocks to Top the Market: Advanced Micro Devices (AMD), Nvidia (NVDA)

(www.smarteranalyst.com)

It takes a wise billionaire to know how to stir up Wall Street and change the name of the game to quant, with seven of the ten monster hedge fund empires today all towering as quant funds. Shaw paved the way here, a man whose firm manages around $47 billion in assets and grabbed more than $25 billion for its investors closing out 2016.

Disrupted: The Industries at Risk and the Companies Positioned to Benefit

(www.prnewswire.com)

Barron’s recently reported on a new study from Backend Benchmarking, which tracks the performance of robo-advisors. As of December 31 of last year, Backend has two full years of performance data for certain portfolios within several automated investment platforms. Their calculations show that the portfolio they built using the Charles Schwab Corporation’s Schwab Intelligent Portfolios had the best performance among similar taxable portfolios, with a return of 27.7%.

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Algo Trading News Headlines 5/30/2018

Goldman: Machines Are Taking Over Markets

(www.nasdaq.com)

“Liquidity is the new leverage”: That was the ominous warning fired by Goldman Sachs’ head of Global Credit Strategy, Charles Himmelberg, admonishing traders about the dangers of the ongoing algorithmic transformation in the markets, including the toxic combination of Quant Funds and High-Frequency Trading and how they put the bond and equities markets at high risk of a systemic event.

From  Bloomberg

Bloomberg launches market forecasting application powered by artificial intelligence

(www.bloomberg.com)

Bloomberg today announced the launch of a new price forecasting application for investment professionals powered by artificial intelligence (AI). The “Alpaca Forecast AI Prediction Matrix” is an application (app) that provides short-term market price forecasts for major markets such as USD/JPY, EUR/USD, AUD/JPY, CME Nikkei 225 Futures Index and US 10-year treasury bonds, using Bloomberg’s Market Data Feed (B-PIPE).

 

Co-location case: CBI books stock broker & NSE, NIPFP, Sebi officials

(economictimes.indiatimes.com)

In this architecture, data was disseminated in a sequential manner whereby a stock broker who connected first to the server of stock exchange received tick, that is market feed before the stockbroker who connected later.

 

Capital Markets Impact of Regulatory Reform Legislation

(www.lexology.com)

Section 502 of the Act requires the SEC to submit a study on algorithmic trading to committees of the Senate and the House of Representatives, reporting on the risks and benefits of algorithmic trading in the capital markets in the United States.

 

Algo-trading is a threat grocery has to take seriously

(www.thegrocer.co.uk)

Algo-trading has the potential to deliver particularly bad news for the coffee industry, as volatile intra-day trading would likely catch producers, roasters and retailers off guard with sudden price changes. Ultimately, this may lead to increased price volatility for the consumer.

 

Fintech firm Alpaca launches “AlpacaForecast AI Prediction Matrix” for Bloomberg users

(financefeeds.com)

The “AlpacaForecast AI Prediction Matrix” is an application that utilizes Alpaca’s large-scale data processing technology and deep learning technology and shows real-time short-term forecasts for major markets. The company has decided to develop this application in hope that it would bring advanced AI market forecasting capabilities to the global financial community, right to their desks.

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50x Faster Bitcoin Price Data Powered by MarketStore for AI Trading

In our last post “How to Setup Bitcoin Historical Price Data for Algo Trading in Five Minutes”, we introduced how to set up bitcoin price data in five minutes and we got a lot of good feedback and contributions to the open source MarketStore.

Photo by  chuttersnap  on  Unsplash

Photo by chuttersnap on Unsplash

The data speed is really important

Today, I wanted to tell you how fast MarketStore is using the same data so that you can see the performance benefit of using the awesome open source financial timeseries database.

Faster data means more backtesting and more training in machine learning

Faster data means more backtesting and more training in machine learning for our trading algorithm.  We are seeing a number of successful machine learning-based trading algos in the space, but one of the key points we learned is the data speed is really importantIt's important not just for backtesting, but also for training AI-style algorithms since it by nature requires an iterative process.

This is another post to walk you through step by step. But TL;DR, it is really fast.

Setup

structure.001.jpeg

Last time, we showed how to setup the historical daily bitcoin price data with MarketStore.

This time, we store all the minute-level historical prices using the same mechanism called background worker, but with a slightly different configuration.

 

root_directory: /project/data/mktsdb
listen_port: 5993
# timezone: "America/New_York"
log_level: info
queryable: true
stop_grace_period: 0
wal_rotate_interval: 5
enable_add: true
enable_remove: false
enable_last_known: false
triggers:
 - module: ondiskagg.so
   on: "*/1Min/OHLCV"
   config:
     destinations:
       - 5Min
       - 15Min
       - 1H
       - 1D
bgworkers:
 - module: gdaxfeeder.so
   name: GdaxFetcher
   config:
     query_start: "2016-01-01 00:00"
     base_timefame: “1Min”
     symbols:
       - BTC

Almost 2.5 years with more than 1 million bars

The difference from last time is that background worker is configured to fetch 1-minute bar data instead of 1-day bar data, starting from 2016-01-01.  That is almost 2.5 years with more than 1 million bars. You will need to keep the server up and running for a day or so to fill all the data, since GDAX’s historical price API does not allow you to fetch that many data points quickly.

Again, the data fetch worker carefully controls the data fetch speed in case the API returns “Rate Limit” error. So you just need to sleep on it.

Additional configuration here is something called “on-disk aggregate” trigger.  What it does is to aggregate 1-minute bar data for lower resolutions (here 5 minutes, 15 minutes, 1 hour, and 1 day).

Check the longer time horizon to verify the entry/exit signals

In a typical trading strategy, you will need to check the longer time horizon to verify the entry/exit signals even if you are working on the minute level. So it is a pretty important feature. You would need pretty complicated LEFT JOIN query to achieve the same time-windowed aggregate in SQL. But with MarketStore, all you need is this small section in the configuration file.

The machine we are using for this test is a typical Ubuntu virtual machine with 8 of Intel(R) Xeon(R) CPU E5-2673 v3 @ 2.40GHz, 32GB RAM and SSD.

The Benchmark

Unfortunately lots of people in this space are using some sort of SQL database

We are going to have a DataFrame object in python which holds all the minute level historical price data of bitcoin since January of 2016 from the server.  We compare MarketStore and PostgreSQL.

PostgreSQL is not really meant to be the data store for this type of data, but unfortunately lots of people in this space are using some sort of SQL database for this purpose since there is no other alternative.  That’s why we built MarketStore.

The table definition of the bitcoin data in PostgreSQL side looks like this.

btc=# \d prices
              Table "public.prices"
 Column |            Type             | Modifiers
--------+-----------------------------+-----------
 t      | timestamp without time zone |
 open   | double precision            |
 high   | double precision            |
 low    | double precision            |
 close  | double precision            |
 volume | double precision            |

The code looks like this.

# For postgres
def get_df_from_pg_one(conn, symbol):
    tbl = f'"{symbol}"'
    cur = conn.cursor()
    # order by timestamp, so the client doesn’t have to do it
    cur.execute(f"SELECT t, open, high, low, close, volume FROM {tbl} ORDER BY t")
    times = []
    opens = []
    highs = []
    lows = []
    closes = []
    volumes = []
    for t, open, high, low, close, volume in cur.fetchall():
        times.append(t)
        opens.append(open)
        highs.append(high)
        lows.append(low)
        closes.append(close)
        volumes.append(volume)

    return pd.DataFrame(dict(
        open=opens,
        high=highs,
        low=lows,
        close=closes,
        volume=volumes,
    ), index=times)

# For MarketStore
def get_df_from_mkts_one(symbol):
    params = pymkts.Params(symbol, '1Min', 'OHLCV')
    return pymkts.Client('http://localhost:6000/rpc'
                         ).query(params).first().df()

You don’t need much client code to get the DataFrame object

The input and output is basically the same, in that one symbol name is given, query the remote server over the network, and get one DataFrame.  One strong benefit of MarketStore is you don’t need much client code to get the DataFrame object since the wire protocol is designed to give an array of numbers efficiently.

The Result

First, PostgreSQL

%time df = example.get_df_from_pg_one(conn, 'prices')
CPU times: user 8.11 s, sys: 414 ms, total: 8.53 s
Wall time: 15.3 s

And MarketStore

%time df = example.get_df_from_mkts_one('BTC') 
CPU times: user 109 ms, sys: 69.5 ms, total: 192 ms Wall time: 291 ms 

Both results of course look the same like below.

In [21]: df.head()
Out[21]:
                       open    high     low   close   volume
2016-01-01 00:00:00  430.35  430.39  430.35  430.39   0.0727
2016-01-01 00:01:00  430.38  430.40  430.38  430.40   0.9478
2016-01-01 00:02:00  430.40  430.40  430.40  430.40   1.6334
2016-01-01 00:03:00  430.39  430.39  430.36  430.36  12.5663
2016-01-01 00:04:00  430.39  430.39  430.39  430.39   1.9530

 

50 times difference

A bitcoin was about $430 back then… Anyway, you can see the difference between 0.3 vs 15 seconds which is about 50 times difference. Remember, you may need to get the same data again and again for different kinds of backtesting and optimization as well as ML training.

Also you may want to query not just bitcoins but also other coins, stocks and fiat currencies, since the entire database wouldn’t fit into your main memory usually.

Scalability advantage in MarketStore

MarketStore can serve multiple symbol/timeframe in one query pretty efficiently, whereas with PostgreSQL and other relational databases you will need to query one table at a time, so there is also scalability advantage in MarketStore when you need multiple instruments.

Querying 7.7K symbols for US stocks

To give some sense of this power, here is the result of querying 7.7K symbols for US stocks done as an internal testing.

%time dfs = example.get_dfs_from_pg(symbols) 
CPU times: user 52.9 s, sys: 2.33 s, total: 55.3 s Wall time: 1min 26s 
%time dfs = example.get_dfs_from_mkts(symbols) 
CPU times: user 814 ms, sys: 313 ms, total: 1.13 s Wall time: 6.24 s

Again, the amount of data is the same, and in this case each DataFrame is not as large as the bitcoin case, yet the difference to expand to large number of instruments is significant (more than 10 times).  You can imagine in real life these two (per instrument and multi-instruments) factors multiply the data cost.

Alpaca has been using MarkStore in our production

Alpaca has been using MarkStore in our production for algo trading use cases both in our proprietary customers and our own purposes.  It is actually amazing that this software is available to everyone for free, and we leverage this technology to help our algo trading customers (early access signup is here).

Thanks for reading and enjoy algo trading!


 

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Where Do We Stand in the AI Hype Cycle?

Working for an AI centered algorithmic trading company has allowed me to gain insight on the two most disruptive industries of modern day: AI and finance. This precise positioning in the middle of so many up and coming industries has given me a unique perspective regarding the future of artificial intelligence and crypto trading.

What Is the Hype Cycle?

1_2q01cCYMQC2lKC8aKHfPAQ.png

Great picture explaining the hype cycle from Wikipedia

To begin to understand the opportunities associated with these two technologies, we first must comprehend the hype behind them. Gartner, a prominent IT research firm, spearheaded the hype cycle concept, outlining 5 key phases that a trend goes through. This theory has been proven to work, as there are many examples of trends that have fallen into this established pattern. Not everything is the same, of course, but you can use the patterns this cycle to predict where a particular trend will go. A fascinating part of this trend is that in order to join the mainstream hype, a technology needs to experience both an upward peak and a downward trend of disillusionment, exhibiting an oscillating volatile nature.

The Hype Cycle In Action: The DotCom Bubble a.k.a. Internet

One of the best parts of living in Silicon Valley is that you can hear the real, raw stories about the historical moments that have taken place in technology. I have several friends who experienced the notorious DotCom bubble hype.

The crazy uptrend started in the 90’s where people, especially in the tech space, started to claim there would be the new type of economy within the digital world, one characterized not by tangible products and profit rather an idea of a new way of doing business. Some people compare it with today’s ICO hype, as many of the current ICO projects don’t have real products yet manage to raise considerable amounts of money.

It took roughly 20 years for the Nasdaq index to reach its previous peak in 2017. During that time period, companies like Google, Apple, Amazon and Facebook grew and flourished, and our entire lifestyle was transformed by the Internet. Let me emphasize this again. It took a full 20 years for the original vision of the internet to come to fruition, even with super-smart, hard-working innovators.

How did Crypto Start?

Now let’s turn to crypto. 2017 was a great year for the crypto space, as bitcoin prices not only soared 1,000%, but more importantly, the philosophy behind the bitcoin and blockchain technology traveled into the mainstream hype. Even my mom has now heard about it.

It is easy to mistakenly think that crypto is a quite recent trend, but the Bitcoin paper by Satoshi Nakamoto was actually first published in 2008. It took almost ten years for this trend to enter people’s daily lives and affect the common person. Over the last decade, so many risk-takers have put in enormous efforts to push this once naive technology to such a level, applicable to a wide range of things, from easy-to-use wallet systems to merchant spending infra. If you haven’t checked out the documentary video Banking on Bitcoin by Christopher Cannucciari, which offers a fantastic overview of the origins and path of Bitcoin, I strongly recommend watching it.

And Where Is Crypto Today?

If you are not too young, you might remember the prominent event in the bitcoin history about Mt. Gox case. It was 2011 when the firm suffered a security breach and lost almost all of their customer assets. By 2013, I was starting my own startup and had couple of friends in the bitcoin startup community, but I was completely out of the loop regarding the growing mainstream bitcoin hype. I never imagined bitcoin would be something my mom would talk about in 5 years. Note that this 2011–2013 time horizon was a full 5 years after the publishing of the Bitcoin paper, and even bitcoin connoisseurs like me never fathomed the recent crypto craze would occur.

With this being said, I still have no clue what the future that the crypto will be making in the next 10 years. Who could have imagined you would be able to connect with your high school friends through Facebook, and Amazon would start something called cloud business leveraging their online bookstore infrastructure, 20 years back? I’ll be humble and admit I probably don’t know how crypto technology will change the world exactly. People are excited about the opportunity behind this technology as well as how it can change the economy, and some anarchists go as far as to say that our entire sense of governments will be disrupted. The only thing I can say at this point is that this crypto trend just passed the peak of excitement, and will probably see a huge depression over the next few years, as the hype cycle predicts, but will see bigger impact over the next 10–20 years.

When Current AI Boom Started?

It is a very well known fact that the current AI trend is actually the third one in the AI history. The first one started right after the modern computer was born in 60’s-70’s, and the second one arose in the 90’s, when new theories arose. These two AI booms were significant but never reached full fruition, as the computing power was just not enough to accomplish what was aimed by them.

The third era emerged from the memorable 2012 ImageNet competition when the Deep Learning approach by the Toronto team outperformed any other previous techniques by far and approached the human recognition level. Some later research identified the use of GPU realized the theoretic idea with realistic cost. GPU, of course, is only one of many hardware approaches like FPGA, but it did prove that computation power had caught up to theory to some extent.

Since then, the chip maker nVIDIA has jumped into the space, turning itself from a game company to an AI business. Google established the Google Brain project, hiring many top-notch brains from academia, competing with companies like Baidu in self-driving car space, as well as beating human champion of Go by AlphaGo, backed by so many trials and errors with acq-hired startups. Around 2014–2015, we also saw the nativity of many Deep Learning startups that either don’t exist anymore or acquired by big players, around us Alpaca.

And Where Is AI Today?

It’s 2018 and it’s been only less than 6 years from the ImageNet shock. If you compare the bitcoin space, it is around the time Mt. Gox was in trouble and I had no clue what they were talking about. I can now see that AI may have some trouble soon; we are already starting to see technologies in this space fall short of what we expect, such as un performing chatbots self-driving cars, but we will just have to wait and see exactly how the AI trend as a whole plays out.

The best time to invest in AI is right now, based on the lessons learned from crypto. If you compare this trend with the internet boom, it’s either even before the bubble, or in another angle it is only around 2003–2004 where things like Google came out to the mainstream. I sometimes see that people think AI means Deep Learning, but that is not true; it is also not just playing Go or self driving cars. Artificial intelligence possesses a myriad of opportunities and applications, and has the potential to change every aspect of the human life, including finance; we have no idea the potential impact of this monumental technology. There are many leaders who offer specific insights and arguments regarding the future of AI technology, such as Elon Musk or Mark Zuckerberg. They predict it could kill people or there will be singularity. The only thing I can say for sure is that we are underestimating the impact of this trend, and we can only surely determine its effects 20 years from now. Today, however, Alpaca can take pride in the fact that we are the ones that are pushing the boundaries into this undefined space of innovation, paving the way for a new world full of possibility and innovation.

“There is only a “one in billions”chance that we’re not living in a computer simulation. Our lives are almost certainly being conducted within an artificial world powered by AI and highly-powered computers, like in The Matrix” — Elon Musk
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