Help us tweak this new service if you are interested in get coaching or live signals
https://www.surveymonkey.com/r/YRXTRVB
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A CNBC article highlighted a fast moving Quant fund wants to hire the 0.01% of the quants out there which is not top 1%. They also have $100k in prize money
https://www.cnbc.com/amp/2019/07/01/a-wall-street-disrupting-startup-is-searching-for-great-quant-traders.html
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This book was recommended to read for newbies who want to move into these topics
I found this book free book https://jakevdp.github.io/PythonDataScienceHandbook/ It’s a deep dive into numpy, pandas, and all data science, just wanted to share this resource with you.
Also, you didn’t like scikit-learn.
What do you think about plotly instead of chart director? https://github.com/plotly PYTHON:https://github.com/plotly/plotly.py
Javascript( The Toy )https://github.com/plotly/plotly.js
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There were some decent resources recommended to further expand your knowledge in the math behind neural networks. This includes some reference to a Python API as well. I found was useful as well but these will further expand the explanation of the under hood math concepts
These included:
Allow stop loss and take profit
Cryptowatch platform is owned by Kraken
Cryptowatch has a Python API
Trade in fiat currency once you move to the intermediate level
See further helpful reasons to be on Kraken as well here
https://quantlabs.net/blog/2019/05/kraken-advantages-with-cryptowatch-and-python-api/
As I continue to press, without my Python Infrastructure framework course, I highly doubt I would be able to rebuild a new bot for a new exchange like Kraken. It is fast and I hope to show it soon.
Get IMMEDIATE access to the course here
https://quantlabs.net/academy/python-algo-trading-infrastructure-with-crypto-currency/
Thanks Bryan
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NOTE: BOB PARDO WILL NOT BE PRESENTING TOMORROW! Please note I will still be available showcasing if anyone is interested with the same Zoom.us login info!
Tuesday, April 2, 2019 at 10:30 AM – 12 PM
Bob Pardo Returns LIVE
ONline via Zoom.us
Hosted by QuantLabsNet
Bob Pardo will return with some demos of his software Ranger. This will be live only on my Zoom.us link I will provide here in the comments.
Login in details will be posted here
https://www.facebook.com/events/1137846456377230/
Zoom.US Login info
Bryan Downing is inviting you to a scheduled Zoom meeting.
Topic: Bob Pardo
Time: Apr 2, 2019 10:30 AM Eastern Time (US and Canada)
Join Zoom Meeting
https://zoom.us/j/390440539
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Meeting ID: 390 440 539
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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.
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.
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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.
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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.
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
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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
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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:
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
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
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.
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.
Intro to NOSQL for highest performance in your systematic/algo/automated trading
Pretty trading charts with ChartDirector
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:
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.
SECTION DESCRIPTION –Different options for market data including free vs paid
SECTION DESCRIPTION – Simple Python script that does the following
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:
SECTION DESCRIPTION –NOSQL Database of choice
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.
SECTION DESCRIPTION –Over of Python package working examples with no registration needed for popular exchanges
Crypto Currency trading idea with forward looking data for your strategy
SECTION DESCRIPTION –This is a set of ideas where you can use forward looking data to see how institutions gauge Bitcoin. These include:.
SECTION DESCRIPTION –This is an overall demo of features and how to install commercial ChartDirector for Python.
This will vary on a product or Python package basiss. This example is relatively basic.
For successful completion of this course, students will require computer systems with at least the following:
For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
Links and installation are provided in video module as required.
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