Tag Archives: popular

Python becoming the world’s most popular coding language

It seems even the influential Economist magazine claims that Python is the most popular programming language. Here is the headline:

Python is becoming the world’s most popular coding language

Here are some highlights:

In the past 12 months Americans have searched for Python on Google more often than for Kim Kardashian, a reality-TV star. The number of queries has trebled since 2010, while those for other major programming languages have been flat or declining. 

are its simplicity and flexibility…

This versatility means that the Central Intelligence Agency has used it for hacking, Google for crawling webpages,….

Some of the most popular packages harness “machine learning”, by crunching large quantities of data to pick out patterns that would otherwise be imperceptible.

rendering all other competitors obsolete…

Here is that article:

https://www.economist.com/graphic-detail/2018/07/26/python-is-becoming-the-worlds-most-popular-coding-language

Do I really need to continue stress this point about taking the above offer. Either that comes down to the last 48 hours to decide.

Thanks

Bryan

 

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

 

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Thoughts on Debugging from algos from a popular forex trading platform

I am also currently deep in the weeds trying to debug a transformed algorithm routine from a very popular forex trading platform. I want this to run in Python which is for my 100% vision of using one programming language for my entire infrastructure. Another one is planned next week. Both are similar to the Head and Shoulders but more specific for Flag/Pennants and Wedges. Both indicators will be very useful to help filter out bad potential trades but more will be revealed as it gets closer to completion.

More notes on my thoughts of debugging these indicators. I find this the best way to understand trading algorithms that you may use for systematic or automation. If you want to learn, get a decent debugger but I am quite surprised I have recently started using this Microsoft Visual Code for debugging. This can be the most challenging part of the whole process in automating your trades.

More from this link:

Visual Studio Code advantages

Both IDEs (Integrated Development Environments) are free but PyCharm has bugs with no simple help. Just go with the Microsoft Visual Code Studio just for the Intellisense or debugging capabilities. I still prefer my Sublime and running the Python interpreter for quicker development.

Both IDEs will run on all operating systems including Linux, Windows, and Mac OS

This was sent out last Friday:

I am doing yet another demo of my new automated head and shoulders indicator. I competed this a few days ago which really pinpoint market entry and exits for cryptocurrency trading opportunities. After my last Python script I highlighted for download, this one works with Bitcoin crypto market data, I have leveraged that power for this new indicator. Do realize that this can be used for any asset class and timeframe. I will demonstrate this on Wed May 23 at 7PM Eastern Standard Time.

 

Automated Head and Shoulders Indicator Details to follow

Note that I will be live streaming on my Youtube channels at youtube.com/quantlabs

I also plan to keep this video playback for my members only for 24 hours after the live stream is complete. As usual, I don’t need the world to know about this.

More details on this automated indicator here.

More corrections on this indicators which was revealed from the video:

As the more experienced folks in my private Telegram group, someone hinted to develop the inverted automated head and shoulders for market reversals. This is even for a longing strategy like the one I plan to implement. Do note my further corrections in the link above. 

I am also currently deep in the weeds trying to debug a transformed algorithm routine from a very popular forex trading platform. I want this to run in Python which is for my 100% vision of using one programming language for my entire infrastructure. Another one is planned next week. Both are similar to the Head and Shoulders but more specific for Flag/Pennants and Wedges. Both indicators will be very useful to help filter out bad potential trades but more will be revealed as it gets closer to completion.

Did I also mention about the detailed Java based components I need to use to work with Dukascopy API and JForex for both forex and CFD instruments? That is another big step for weeks coming own the line.

There you go, quite bit on the go.

Bryan

P.S. Don’t forget about this Tuesday if you are in Downtown Toronto. More details here if interested:

https://www.meetup.com/quant-finance/events/250588027/

or

https://www.meetup.com/TOForexTraders/events/250588184/

We are coming to the last set of people we can accommodate.

 

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!

Popular indicators from TA-Lib Python wrapper

Different analytical indicators

Popular indicators were introduced from 2 recent videos. These were from the TA-Lib package Python wrapper.  I further tested to see which indicators could be found useful. Although, I would give you a different answer from my recent analysis video where I demonstrated popular indicators Williams R% and Stochastic, you would think everything looks clean. This is just another way of saying they do not lag when they change direction at fast timeframes like Tick or 1 Minute. I really emphasized the importance of this in the videos.

Different results vs Dukascopy JForex

I went ahead to start analyzing the same indicator with exact timeframes using TA-Lib within Python. As compared from within Dukascopy JForex, It seems I got different results so I tested a complete pile of them. This included:

1. Stochastics was just to jumpy to work with any form of trend.

2. Beta was more consistent which did not move around as much.

3. Williams R was again to volatile (in short periods) moving between in upper and lower ranges

4. Linear Regression and Chaikin A/D Oscillator was the same effect as Williams R

The only ones that seem to work were the popular indicators which include:

1. Normalized ATR

2. SMA

3. Bollinger Bands

4. Rate of Change Percentage

As a result, I will stick these 4 above to find out the magical combination. There are others to pick from within TA-LIB but I find you want to keep the combination simple. This also is important to really understand how properly interpret the unpredictable indicators I list above.

Next steps and considerations

I need to show how to  properly implement these but it depends when there data stored eg. database vs flat file. It looks the simplest is to use CSV with multiple instrument access is less utilized on the computer hardware front. You can obviously understand hoq critical these decisions are for when using an interpreted scripting language like Python.

There will be lots more to report as I continue along. Do note I find working with Python is much more product than Java within Dukascopy Jforex API.

Package info https://github.com/mrjbq7/ta-lib

Williams % and Stochastics most reliable Matlab technical indicator

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Separating the hype from fiction of popular Bitcoin and crypto currency exchanges

Separating the hype from fiction of popular Bitcoin and crypto currency exchanges

Linked used in this 20 minute video/podcast

https://www.bloomberg.com/news/articles/2017-11-21/tether-theft-isn-t-the-first-controversy-for-cryptocurrency-firm

https://www.bloomberg.com/news/articles/2017-11-30/bitcoin-recovers-from-sudden-selloff-as-large-swings-persist

https://www.moneyweb.co.za/news/markets/bitcoin-falls-after-31m-theft-of-cryptocurrency-tether/

https://www.cryptocompare.com/exchanges/bitfinex/reviews

https://www.bloomberg.com/news/articles/2017-12-05/mystery-shrouds-tether-and-its-links-to-biggest-bitcoin-exchange

Reliable Bitcoin exchanges chosen by CME CBOE for future market

PROOF: IOTA’s Partnerships With Big Tech Not Real

https://news.bitcoin.com/police-posted-at-bithumb-as-users-file-lawsuit-after-server-outage-costs-millions/

https://coinmarketcap.com/exchanges/volume/24-hour/

https://github.com/ccxt/ccxt

 

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Most popular 2016 Quantcon videos from Quantopian

 

Popular 2016 Quantcon videos from Quantopian


Here is a selection of some popular released videos from Quantcon 2016 which is sponsored by Quantopian. Even though they are both a trading platform, trading education and so on, I know they are also hoping to generate alpha as an alternative hedge fund. I have not heard any public recent performance metrics on this alternative fund. Also, one of the videos I list from the founder will offer 10% of trading profit for your IP algo you provide to their trading strategy arsenal. Good luck with that but Quantcon sounds worthy to attend though. Who’s going this year?

Here are those popular videos

Note: I am moving my newsletter contacts over to my new email server hosted on Infusionsoft in coming days to weeks. I just want you to be aware of that so let me know if you are ready to make that switch to continue the conversation. If not, the other way to is to join my Facebook programming group at:

https://www.facebook.com/groups/quantlabsnet/

Overview walkthrough of Quant Courses and Analytics Service

Brand new where you can get details here

Courses and analytic service here

(A new video was recorded)

https://quantlabs.net/blog/2017/03/overview-walkthrough-of-quant-courses-and-analytics-service/

I finally have created some videos on the Courses and new Quant Analytics.

Check them out:

New Analytics Service:

Once big take-away is the low risk intro to the service. I offer $5 2 days trials before joining.

Introducing our new Quant Analytics Service overview

This walkthrough the new dashboard with chat room for new members to exploit our trading systems chart/data generation

Brand new where you can get details here

https://quantlabs.net/analytics/order-analytics/

Note my $5 trial for 48 hours TRIAL of this

BIGGEST NOTE OF ALL: I still need to populate the service starting next week!

https://quantlabs.net/blog/2017/03/introducing-our-new-quant-analytics-service-overview-2/

Thanks 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!

Chat with Traders popular topics of day trading and lucrative forex career

Chat with Traders popular topics of day trading and lucrative forex career

I find it strange how these are more popular videos on Chat With Traders despite the the awesome automation/systematic based episodes.

Join my FREE newsletter to see if these topics are helpful in automated trading

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!

The Most Popular Quant Papers of 2016

Savvy Investor’s Andrew Perrins shares some of the most popular Quant Papers in the year so far.

 (This is my first guest blog posting)

The Research Team at Savvy Investor has curated this list of the most popular quant white papers from 2016 to date. The papers are listed in date order (from most recent to earliest) and cover a wide range of topics including short-selling, factor investing, alpha generation, momentum strategies, diversification, correlation, volatility and illiquid assets.

 

The Long and the Short of It: The Quant Shorting Advantage (QMA, 2016)

Active extension, equity long-short, and equity market neutral products can be attractive for investors at any particular time, given investors’ varied investment objectives and needs. That said, each of the three categories of shorting-enabled products can help address distinct issues facing investors today. QMA’s paper describes how short selling can allow investors to find alpha in often overlooked places, explains the three main categories of shorting-enabled equity products, and highlights the benefits of a systematic quantitative process.

 

Will Your Factor Deliver? Factor Robustness and Implementation Costs (FAJ, 2016)

Within the indexing world, multifactor investing has become very popular in recent years. Both practitioner and academic researchers have recorded several hundred equity factors. But which of these are likely to profit investors once implemented? This original research was conducted by Noah Beck, Jason Hsu, Vitali Kalesnik and Helge Kostka. It was published in CFA Institute’s Financial Analysts Journal.

 

Academic Lessons on Factor Investing (EDHEC, 2016)

This paper analyses what academic research has to say on equity factors. Our objective is to understand which lessons we can learn from such research in terms of designing and evaluating factor indices. When analysing academic publications on equity factor investing, five important lessons emerge, which provide useful perspective on practical questions about factor indices. This paper looks at the empirical analysis required to identify rewarded factors. It then turns to the economic rationale behind these factors, and looks into the role of diversification for a given factor tilt. Moreover, it discusses the issue of implementation costs and addresses the question of crowding risks. Finally, the paper discusses how popular practical implementations relate to the academic groundings.

 

101 Ways to Generate Alpha (Savvy Research Blog, Sept 2016)

Active equity managers use many different investment strategies and processes to meet the challenge of consistently creating alpha within their portfolios. This Savvy Blog post highlights a list of the top papers, which help equity investors to build successful alpha-generating processes.

 

Trend Following: Equity and Bond Crisis Alpha (Man AHL, 2016)

The authors of this paper study time-series momentum strategies in commodities, currencies, bonds, and equity indices during the period 1960-2015. Their research reveals that there was consistent performance both before and after 1985 – periods that were marked by strong bull and bear markets in bonds. The authors also record a number of important risk factors.

 

Equity/Bond Correlation: History and Future (BlackRock, July 2016)

The correlation between equity and bond markets is of vital importance to asset allocators; for risk control and portfolio construction, for assessing the market outlook, and for building models of how markets work (equity market valuation models, for example). In this 6-page paper, Nuno Luis and David Caplan of BlackRock examine the history of the equity/bond correlation and discuss the likely future path.

 

Understanding and measuring the illiquidity risk premium (Willis Towers Watson)

Illiquidity risk is a potentially appealing means of generating additional yields in a low-return world. The authors of this 10-page document discuss three different dimensions of illiquidity risk premium that investors should demand for a given asset.

 

The Free Lunch: The Value of Decoupling Diversification and Risk (Salient, 2016)

The authors of this very interesting paper discuss why considering diversification and risk independently may help investors build more efficient portfolios. They argue that asset allocators should rethink the impact of low volatility diversifiers in higher risk portfolios. Some low vol asset classes (e.g. hedge funds) may primarily have a “de-risking” impact, but not a “diversifying” impact. The paper demonstrates that, perhaps counter-intuitively, high volatility diversifiers can sometimes be very effective, and allocators should consider these strategies.

 

Multifactor Indexes: The Power of Tilting (FTSE Russell, 2016)

In recent years, institutional investors have become increasingly convinced of the benefits of factor investing, facilitated by the creation of a variety of indices, each focusing on a specific risk factor. The creation of these new indexes has allowed investors to access factor exposure efficiently and at low cost. However, as with any investment strategy, the return from a single-factor index will vary over time, often following different patterns. For instance, the quality risk factor tends to exhibit counter-cyclical performance, whereas the payoff from the value factor normally follows a more cyclical pattern. This paper examines alternative processes for building multifactor indexes, in order to benefit from a diversified exposure to the various source of factor return.

 

Predicting Volatility (Lazard, Jan 2016)

It is widely accepted that financial models always carry the risk of uncertainty. Volatility forecasting, therefore, has huge implications for investors especially employing risk parity, volatility targeting and asset allocation strategies. This paper examines volatility prediction – its characteristics and the effectiveness of different approaches.

 

Savvy Investor www.savvyinvestor.net is the world’s leading platform for the distribution of white papers to global institutional investors. Registration is free, and provides access to over 15,000 white papers, as well as a personalised newsletter, keeping you up to date with investment news and research.

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What does it mean popular programming languages?

What does it mean popular programming languages?

From my perspective, I would only use Python (for ease of use), C++ (for low latency high speed), Java (only for useful legacy software and IB interaction), Matlab (for all the above but integrates with everything).

I will be dumping all things Microsoft including .NET due to unreliable and poor performance thanks to Windows 10

http://www.zdnet.com/article/which-programming-languages-are-most-popular-and-what-does-that-even-mean/

Join my FREE newsletter to learn more about which programming languages is useful for my automated trading

 

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Popular backtested strategies on Quantopian

Popular backtested strategies on Quantopian

I think this is an excellent source to know what works and what does not

https://www.quantopian.com/posts/whats-popular-the-10-most-frequently-cloned-backtests-over-the-last-2-months

Thanks to Sholom Benzevf or sending

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Review of popular deep learning models

Review of popular deep learning models

Can these be used for finance or trading

http://www.kdnuggets.com/2016/06/review-deep-learning-models.html

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