Category Archives: Quant Books

Quant Books

The Best Quant Papers of 2018 from Savvy Investor

Quantlabs is pleased to be partnering with Savvy Investor, the world’s leading knowledge network for institutional investors. You may wish to consider joining their platform – it’s entirely free.

Fama and French win “Best Quant Paper 2018”

Savvy Investor curates the best pensions and investment white papers from around the world. Having uploaded more than 25,000 papers since launch, they have a unique platform from which to host these Awards. The Savvy Investor Awards are judged on the basis of the quality and readability of the paper and its appeal to their institutional investor audience.

Call us sentimental, but we’re delighted to be awarding the Savvy Investor trophy for the best quant paper of 2018 to Eugene Fama and Kenneth French. Unlike some earlier papers authored by this duo, the winning paper is in no way ground-breaking. However, it reminds us all of the nature of equity market volatility, and the implications for long-term investment returns. As the name suggests, it is a “volatility lesson” for professional investors, coming from two of the most respected names in the business.

See the winning papers below, or visit Savvy Investor for the full list of winners and short-listed papers across all 15 categories. 

Best Quant Paper 2018

Volatility Lessons (Financial Analysts Journal – CFA Institute)
In this paper, Fama and French examine the return distribution of equities versus cash over a variety of time periods, and show that the probability of negative equity returns over three and five-year periods is substantial. Interestingly, for longer-term horizons (say 10 or 20 years) there is a marked increase in right skewness and kurtosis. In other words, compared to a normal distribution of returns, the left tail almost disappears and the likelihood of negative equity returns versus cash diminishes substantially. Another key conclusion from the data relates to drawing inferences about future risk premia from observed returns over 3-, 5- or 10-year periods. The duo argue that, due to the high volatility, the evidence from such a “short” time period will be too “noisy” to be reliable.

Robust Asset Allocation for Robo-Advisors (Amundi Asset Management)
Quant researchers from Amundi Asset Management examine the challenges faced by robo-advisors attempting to automate the portfolio allocation and rebalancing process. This is a detailed, complex and formula-rich paper which will appeal primarily to quant managers and analysts involved in portfolio optimization, specifically using a mean-variance approach.

The Correlation See-Saw (Axioma)
The correlation of returns between different asset classes is critical to overall portfolio risk. However, these relationships are not necessarily stable over time. Axioma analyzes the way that shifts in cross-asset correlations impact overall portfolio risk, examining a case study of the first five months of 2018 when there was an unusual pattern of correlation reversals. How should this impact an investor’s approach to risk analytics?

Combining Investment Signals in Long/Short Strategies (Goldman Sachs Asset Management)
This paper examines the best way to combine quantitative investment signals in the context of managing a long-short portfolio. Is it better to create one combined signal, or is it preferable to consider the portfolio exposures indicated by each signal and combine the different exposures? The authors carry out their own empirical study and compare the results with other academic evidence.

If We Don’t Believe Markets are “Efficient”, What Do We Believe? (Winton)
Despite the well-known faults that are inherent in the efficient market hypothesis, it still underpins several prominent investment strategies. The authors of this paper examine an ecological theory that could be more applicable to financial markets.

The Current State of Quantitative Equity Investing (CFA Institute Research Foundation)
In this 74-page paper, CFA Institute Research Foundation reviews the concepts of risk and return, anomalies and the onset of factor investing, as well as the influence of big data on the quantitative equity field.

Pulling the Goalie: Hockey and Investment Implications (Cliff Asness/Aaron Brown)
Harkening back to the 1980 ‘Miracle on Ice,’ the authors build a model to determine the precise time that a hockey coach should choose to pull the goalie when behind. They then apply these lessons to a portfolio management environment.

About Savvy Investor

Savvy Investor is the world’s leading resource hub for the institutional investors. Since launch in March 2015, more than 33,000 members from across the globe have registered for the site, with 200-250 new members joining every week.

Savvy Investor allows you to search and immediately find the top white papers on any investment topic, ranked by popularity.

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Guided tour walkthrough Elite trial to build primitive algo trading system

Guided tour walkthrough Elite trial to build primitive algo trading system

 

Here it is for the 2 month trial to my Quant Elite membership. Get complete details here if interested.

Here is the link to try out

Introduction to Quant Elite Membership

 

 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Is this Python code of linear regression really machine learning

Is this Python code of linear regression really machine learning? Seriously, why do less knowledgable people just rely on the result of some popular machine learning framework like TensorFlow. Don’t you think it is wise to understand the underlying math? I have used this stuff with MATLAB well before the terms big data and machine learning  became popular. I am no expert here but I would like to have some experts add their opinion on it.

Looking for input

Comment away in my video where I am wrong. I like to learn what you think. All I ask is be respectful about it

Robert Pardo book for forward walking

https://onlinelibrary.wiley.com/doi/book/10.1002/9781119196969

https://en.wikipedia.org/wiki/Linear_function

TensorFlow and Nutonian machine learning for algo trading tips

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Webinar: Python algo trading system with Bitcoin an crypto currency overview

Webinar: Python algo trading system with Bitcoin an crypto currency overview

Let’s chit chat about this course I have created.

Here is where you can find the outline.

This can be found here with a detailed video below
https://quantlabs.net/blog/2018/03/new-course-building-python-algo-trading-system-with-bitcoin-an-crypto-currency-focus/

Zoom.US info details below. I am thinking of streaming this on my Youtube channel at Youtube.com/quantlabs at this time instead of the Facebook group.

 

You are invited to a Zoom webinar.
When: Mar 26, 2018 7:00 PM Eastern Time (US and Canada)
Topic: Python algo trading system with Bitcoin an crypto currency overview

Please click the link below to join the webinar:
https://zoom.us/j/574479815

Or iPhone one-tap :
US: +16699006833,,574479815# or +16465588656,,574479815#
Or Telephone:
Dial(for higher quality, dial a number based on your current location):
US: +1 669 900 6833 or +1 646 558 8656
Webinar ID: 574 479 815
International numbers available: https://zoom.us/zoomconference?m=zrBXTaEby6bD81Q2hevDPAIpZlwB6X8G

 

Thanks

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

New course!! Building Python algo trading system with Bitcoin an crypto currency focus

New course!! Building Python algo trading system with Bitcoin an crypto currency focus

More details will follow on how to get access with dates of the live bootcamp editions. Note that the total number of videos in between 3-4 hrs.

Remember this is the first draft of this:


COURSE NAME – Cryptocurrency and Algo Trading Infrastructure with Python: A Practical tutorial for Python developers

Sub-title – Discover the secrets behind trading infrastructure components, high speed in-memory NOSQL database options, cryptocurrency exchanges and simple technical indicators, and more. And use the same techniques in your Python code.

AUTHOR NAME – Bryan Downing

DURATION –

OVERVIEW – As Bitcoin ruled the financial news cycle in recent times, many have had a peak interest in cyrpto currency along with systematic trading. There are many advanced trading research techniques including machine learning, AI, or quant. This course was created for the ‘newbie’ who has a basic understand in popular programming languages but easy to learn such as Python. A focus of technical analysis was chosen since it is popular among retail traders. It is also seems to offer more predictable results as opposed to harder to learn concepts such as quant or machine learning. In essence, a simpler with more effective techniques were purposely chosen to get someone with basic programming knowledge (eg. Python) to get ramped up faster! It is very hopeful any student will become more confident in their capabilities to complete this without the unnecessary complexities that usually hold back their success.

TARGET AUDIENCE – Python programmers (including data scientists etc.) who want to understand algo techniques and apply them into a primitive automated trading system. Pro or Retail traders who have an interest in learning the basics of Python and systematic trading.

KEY FEATURES

  • Infrastructure modern component implementation fo an algo trading system

  • Basic coverage of crypto currency Python packages and crypto currency exchanges

  • Technical analysis library package use for strategy idea development

APPROACH –

Videos and sample Python source code scripts are made available at time of presentation. Note that scripts are meant to to be small and simple for easiest consumption. Advanced techniques are stripped out to ensure completion of this course. This can be used as a launching to pad to understand the components of a primitive algo trading system In short, it is devised in a way to get you up and running as quickly as possible.

WHAT WILL YOU LEARN

  • The major components in any trading system
  • Broker and asset class options
  • How crypto currency (e.g. Bitcoin) has becomes a hotbed of profit potential

  • Strategy coding basics to help in analyzing using popular techncial analysis techniques

  • All software mentioned is open and source with the exception of ChartDirector which is used for visualization.

ABOUT THE AUTHOR

SUMMARY OF CONTENT

  1. Why Python for my algos and automated trading? Which tools for development? Why Python over other programming languages?

  2. Intro to NOSQL for highest performance in your systematic/algo/automated trading

  3. Crypto Currency Exchange Overview and Python CCXT Package

  4. Crypto Idea strategy generation using forward looking data as similar to financial institutions who analyze and trade popular alt coins including Bitcoin

  5. Read exchange data with flat files

  6. Pretty trading charts with ChartDirector

  7. Using TA-Lib like package popular technical analysis for strategy development

COURSE ROADMAP

SECTION 1Programming Language Overview

SECTION DURATION – 28 minutes

SECTION DESCRIPTION – Covers pros and cons of each language you may come across on your journey with algo/automated trading. I also ist items you will need to consider when you want do live trading as well using correct concepts of High Frequency Trading (HFT) I cover 5 languages including:

  • R
  • Python
  • Java
  • C#/.NET
  • C and C++
  • MATLAB

SECTION 2Python tools and methodology

SECTION DURATION – 43 minutes

SECTION DESCRIPTION – Covers all elements of Python 2 vs 3 including a high level of ‘HelloWorld’ example. Also covers from a newbie’s POV on non essential Jypter and Machine Learning packages.

  • Python 2 vs 3
  • PIP the package manager
  • Python install options on operating systems
  • Tools like popular editors/IDEs e.g. Sublime
  • Simple Debugging techniques
  • Very high level concepts of popular machine learning frameworks and Juptyr use cases

SECTION 3Options to Capture Market Data

SECTION DURATION – 28 minute

SECTION DESCRIPTION –Different options for market data including free vs paid

  • IEX and IQFeed
  • LMAX and Interactive Brokers broker overview options
  • Dukascopy Forex.CFD broker Duka Python Package
  • Intro to JForex platform to create CSV using Java example

SECTION 4How to create Pandas dataframe to manipulate data

SECTION DURATION – 10 minutes

SECTION DESCRIPTION – Simple Python script that does the following

  • Read a comma separated value file (CSV)
  • Apply statistical calculation and manipulate rows
  • Export data to CSV or Excel format

SECTION 5Demo technical analysis indicator functions

SECTION DURATION – 7 minutes

SECTION DESCRIPTION – As the original TA-LIB can be complicated to build yet along with the buggy TA-Lib Python package, there was another alternative that came up. A much simpler approach with the popular TA-LIB shows a simpler demo of running various indicators. These examples include:

  • Bollinger Band
  • Exponential Moving Average
  • Standard Deviation
  • Relative Strength Indicator

SECTION 6 – Redis NOSQL Overview and Sample

SECTION DURATION – 30 minutes

SECTION DESCRIPTION –NOSQL Database of choice

  • Why this is used
  • Open source and benchmarks
  • Compared against popular open source HFT project
  • Server and client edition
  • Python code demos

SECTION 7Funding Crypto Currency account

SECTION DURATION – 6 minute

SECTION DESCRIPTION –Options to cover to fund via bank credit card or use an anonymous credit card. Note that these option can be removed, invalid, or change without notification.

  • Different credit card option sources
  • Why banks block funding crypto currency via credit card
  • Round about way to fund anonymously via credit

SECTION 9 – Crypto Currency Outlook and CCXT Python Package overview

SECTION DURATION – 20 minute

SECTION DESCRIPTION –Over of Python package working examples with no registration needed for popular exchanges

  • Intro to CoinMarket.com to depend on volume
  • Reliable exchanges at time of recording
  • Advantages of Pyrhon option vs web programming languages
  • Examples of CCXT Python Package

SECTION 10Crypto Currency trading idea with forward looking data tips for your strategy idea

Crypto Currency trading idea with forward looking data for your strategy

SECTION DURATION – 11 minutes

SECTION DESCRIPTION –This is a set of ideas where you can use forward looking data to see how institutions gauge Bitcoin. These include:.

  • NVT Ratio
  • Futures and Options market
  • COT reports
  • Use of CoinMarketCap.com

SECTION 11– Intro to ChartDirector for Python

SECTION DURATION – 19 minutes

SECTION DESCRIPTION –This is an overall demo of features and how to install commercial ChartDirector for Python.

  • How to install on Mac or Linux/Unix like systems
  • Advantages over other open source Python packages like Matplotlib or Seaborn
  • Benefits of ChartDirector
  • Hints of Developing Interactive Self Standalone chart with potential live charting
  • Note that charts are generated via operating platform console terminal

This will vary on a product or Python package basiss. This example is relatively basic.

Minimum Hardware Requirements

For successful completion of this course, students will require computer systems with at least the following:

  • OS: Any
  • Processor: Minimal
  • Memory: 2 GB but may need 4+ GB if you to choose to install VirtualBox with Ubuntu Linux
  • Storage: Minimal

Recommended Hardware Requirements

For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:

  • OS: In order of preference of MacOS, Linux (e.g. Ubuntu), and Windows
  • Processor: Any
  • Memory: 2 GB but may need more 4 GB if you to choose install VirtualBox with Ubuntu Linux
  • Storage: 12 GB

Software Requirements

  • Operating system: See above
  • Browser: Any
  • Python 2.7 but prefer 3.x.
  • Sublime

Links and installation are provided in video module as required.

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

live tick data and risk modeling book questions

I got some details questions for live tick data

More questions from email newsletter subscribers:

Best Book on Risk Models?

I got answer messed up. Sorry. I would recommend if you got the time read:

https://www.amazon.com/Management-Financial-Institutions-Wiley-Finance/dp/1118955943/ref=la_B001IGOKWO_1_6?s=books&ie=UTF8&qid=1518641477&sr=1-6

https://www.amazon.ca/Paul-Wilmott-Quantitative-Finance-Set/dp/0470018704

best but fast place to learn:

https://www.mathworks.com/solutions/financial-services/risk-management.html#risk-modeling

Would appreciate your guidance

I wanted to reach out to you to ask you for some advice & guidance. I am not a programmer, but I am trying to create a solution that will allow me to work with live tick data (Forex mainly) (to create live dashboards, currency strength, momentum etc) and historical data to conduct further analysis. 

I was thinking to feed this data into a database (I saw in your video that you are doing something similar). I wanted to get your advice as to the method of getting live tick data (something that does not cost a fortune would be ideal 🙂 ) ..  

I spoke to a freelancer who said that Jfoex api is only a java library and it would not allow me to work with live tick data.

I’ve also had a look at a few tools to connect to dukascopy to export historical tick data, its just the ‘live tick-data’ part that I am unsure about.

Any advice would be much appreciated.

My answer was:

If you just want market data use IQFeed forex option for $30/month. Do understand as part of their terms of service, you cannot redistribute this outside of your own use. If you need it for trading, always use the broker’s data to keep in synch. Also, you will need a live Dukascopy account for the live tick data but again only for your purposes. Hope this helps

More resource links here:

Get the Dukascopy video courses here

Videos

 

https://www.mathworks.com/solutions/financial-services/risk-management.html#risk-modeling

https://www.mathworks.com/solutions/financial-services/risk-management.html#risk-modeling

https://iqfeed.net/symbolguide/index.cfm?symbolguide=guide&displayaction=support&section=guide&web=iqfeed&guide=forex&web=IQFeed&symbolguide=guide&displayaction=support&section=guide&type=TENFORE

http://www.iqfeed.net/index.cfm?displayaction=data&section=fees

https://github.com/giuse88/duka

https://www.dukascopy.com/client/javadoc/com/dukascopy/api/IHistory.html#getLastTick(com.dukascopy.api.instrument.IFinancialInstrument)

Videos

64 bit version Dukascopy Forex tips with Bitcoin crypto addition

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Long-term return forecasts 2018 via industry reports

Long-term return forecasts 2018 via industry reports

This article was posted yesterday

Long-term return forecasts 2018

 

 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Award – Best Quant Paper 2017

 

Savvy Investor curates the best pensions and investment white papers from around the world. Having uploaded more than 20,000 papers since launch, they have a unique platform from which to host these Awards. The Savvy Investor Awards are judged on the basis of the quality and readability of the paper and its appeal to their institutional investor audience.

 

To view the full awards announcement, across 15 categories, visit the Savvy Investor Awards page.

WINNER: AQR Capital Management

Embracing Downside Risk

Equity index option pricing is examined in detail in this paper. The authors conclude that most of the empirical equity risk premium relates to compensation for taking on downside risk; therefore, downside risk is something to be embraced.

HIGHLY COMMENDED

Adding Alpha by Subtracting Beta: A Case Study on how Quant Tools can Improve a Portfolio’s Returns by Axioma

A ‘real world’ portfolio is used to illustrate how fundamental managers can use quantitative tools to identify and lessen potential issues in their portfolio, thereby improving their realized returns.

An Asset Allocation Primer: Connecting Markowitz, Kelly and Risk Parity by PIMCO

Standard asset allocation model mechanics, including the utility based, Kelly, Markowitz, fixed allocation, and risk parity approaches, are described and contrasted in this PIMCO article.

 

Managing equity portfolio volatility by harnessing the volatility risk premium by Eaton Vance

Option-based strategies that attempt to harness the Volatility Risk Premium comprise a new type of solution that investors are currently exploring in order to achieve equity-like returns with less risk.

Start of Something Big: Demystifying the Source of Large Alpha in Small Caps by QMA

Active small-cap managers continue to outperform. QMA posits that capturing alpha in small caps is largely the result of inefficiencies that create pronounced mispricings that diligent managers can exploit on a regular basis.

 

About Savvy Investor

Savvy Investor is the world’s leading resource hub for the institutional investors. Since launch in March 2015, more than 23,000 members from across the globe have registered for the site, with 150-200 new members joining every week.

To find out how you can partner with Savvy Investor this year to enhance your thought leadership credentials in the institutional investor marketplace, please contact our Business Development Manager, Stuart Blake, stuart.blake@savvyinvestor.net.

 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Intro to this free Python Data Science Handbook

Here is a popular data science book found on my Facebook feed. It was quite free with the ability to learn all the necessary packages to get you started in learning data science. Just for your info, Python is now the most popular programming language to learn and implement machine learning with. This means it is an excellent choice to advance your career! This book does cover an old machine learning package called Scikit-learn but it still popular for learning purposes.

 

As usual I made a video on this

 

As promised, I have made some great leeway on this automated forex trading strategy over the last 24 hours. If you want to get a preview on the visualization part, you can check out the first draft video here.

 

As I hope to test this strategy within Dukascopy JForex in coming weeks, I am obviously hoping this works with great success. If so expect my Quant Analyticsand Quant Elite prices to shoot up in coming weeks as I will now have extra credibility to do just that..

 

Join my Quant Analytics membership here

Join my ultra cheap Quant ELITE membership here.

 

Thanks for reading

Bryan

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Intro to free Python Data Science Handbook

Intro to free Python Data Science Handbook

Did I say this is free? It sounds like one of the standard ways to learn machine learning

https://jakevdp.github.io/PythonDataScienceHandbook/

 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!