Tag Archives: Finance

Bitcoin, Bubbles and Tech: Google Top Finance Searches of 2017

Bitcoin, Bubbles and Tech: Google Top Finance Searches of 2017

Here they are to reveal what people looked for

https://www.bloomberg.com/news/articles/2017-12-20/bitcoin-tech-stocks-and-xi-top-2017-google-financial-searches

 

https://trends.google.com/trends/explore?q=crypto%20currency

Extract Google Trends Data with Python

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Selling data to feed hedge fund computers is hottest area of finance

Selling data to feed hedge fund computers is hottest area of finance

Is this just another way of hedge funds who are not profitable. This is sort of like this ‘quants’ to enter into consulting since they most likely cannot trade well on their own

Selling data to feed hedge fund computers is one of the hottest areas of finance right now

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Tutorial on fix to download Yahoo Finance historical data in Python

Tutorial on fix to download Yahoo Finance historical data in Python

 

 

Date manipulation for:

https://stackoverflow.com/questions/5158160/python-get-datetime-for-3-years-ago-today

https://www.cyberciti.biz/faq/howto-get-current-date-time-in-python/

https://stackoverflow.com/questions/10624937/convert-datetime-object-to-a-string-of-date-only-in-python

Fix tutorial from

Fix now for Yahoo Finance with Python Historical Datareader

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Fix now for Yahoo Finance with Python Historical Datareader

Fix now for Yahoo Finance with Python Historical Datareader

Note future changes as well

Visit https://github.com/ranaroussi/fix-yahoo-finance

Note requirements but run in your in console:

pip3 install pandas –upgrade

pip3 install numpy –upgrade

pip3 install requests –upgrade

pip3 install multitasking –upgrade

pip3 install fix_yahoo_finance –upgrade –no-cache-dir

Run the supplied example which appears to work ok:

python3

Python 3.6.1 (default, Apr 4 2017, 09:40:21)

[GCC 4.2.1 Compatible Apple LLVM 8.1.0 (clang-802.0.38)] on darwin

Type “help”, “copyright”, “credits” or “license” for more information.

>>> import fix_yahoo_finance as yf

/usr/local/lib/python3.6/site-packages/fix_yahoo_finance/__init__.py:43: DeprecationWarning:

Auto-overriding of pandas_datareader’s get_data_yahoo() is deprecated and will be removed in future versions.

Use pdr_override() to explicitly override it.

DeprecationWarning)

>>> data = yf.download(“SPY”, start=”2017-01-01″, end=”2017-04-30″)

[*********************100%***********************] 1 of 1 downloaded>>>

>>> data

Open High Low Close Adj Close \

Date

2017-01-03 225.039993 225.830002 223.880005 225.240005 223.176010

2017-01-04 225.619995 226.750000 225.610001 226.580002 224.503738

2017-01-05 226.270004 226.580002 225.479996 226.399994 224.325378

2017-01-06 226.529999 227.750000 225.899994 227.210007 225.127960

2017-01-09 226.910004 227.070007 226.419998 226.460007 224.384842

2017-01-10 226.479996 227.449997 226.009995 226.460007 224.384842

2017-01-11 226.360001 227.100006 225.589996 227.100006 225.018967

2017-01-12 226.500000 226.750000 224.960007 226.529999 224.454193

2017-01-13 226.729996 227.399994 226.690002 227.050003 224.969421

2017-01-17 226.309998 226.779999 225.800003 226.250000 224.176743

2017-01-18 226.539993 226.800003 225.899994 226.750000 224.672165

2017-01-19 226.839996 227.000000 225.410004 225.910004 223.839874

2017-01-20 226.699997 227.309998 225.970001 226.740005 224.662262

2017-01-23 226.740005 226.809998 225.270004 226.149994 224.077667

2017-01-24 226.399994 228.080002 226.270004 227.600006 225.514389

2017-01-25 228.699997 229.570007 228.509995 229.570007 227.466339

2017-01-26 229.399994 229.710007 229.009995 229.330002 227.228531

2017-01-27 229.419998 229.589996 228.759995 228.970001 226.871826

2017-01-30 228.169998 228.199997 226.410004 227.550003 225.464844

2017-01-31 226.979996 227.600006 226.320007 227.529999 225.445023

2017-02-01 227.529999 228.589996 226.940002 227.619995 225.534195

2017-02-02 227.619995 228.100006 226.820007 227.770004 225.682831

2017-02-03 228.820007 229.550003 228.460007 229.339996 227.238434

2017-02-06 228.869995 229.330002 228.539993 228.929993 226.832184

2017-02-07 229.380005 229.660004 228.720001 228.940002 226.842117

2017-02-08 228.940002 229.389999 228.309998 229.240005 227.139359

2017-02-09 229.240005 230.949997 229.240005 230.600006 228.486893

2017-02-10 231.000000 231.770004 230.619995 231.509995 229.388550

2017-02-13 232.080002 233.070007 232.050003 232.770004 230.637009

2017-02-14 232.559998 233.710007 232.160004 233.699997 231.558472

… … … … … …

2017-03-17 237.750000 237.970001 237.029999 237.029999 235.879700

2017-03-20 237.029999 237.360001 236.320007 236.770004 235.620972

2017-03-21 237.470001 237.610001 233.580002 233.729996 232.595718

2017-03-22 233.770004 234.610001 233.050003 234.279999 233.143051

2017-03-23 234.279999 235.339996 233.600006 234.029999 232.894257

2017-03-24 234.380005 235.039993 232.960007 233.860001 232.725082

2017-03-27 231.929993 233.919998 231.610001 233.619995 232.486252

2017-03-28 233.270004 235.809998 233.139999 235.320007 234.178009

2017-03-29 234.990005 235.809998 234.729996 235.539993 234.396927

2017-03-30 235.470001 236.520004 235.270004 236.289993 235.143295

2017-03-31 235.899994 236.509995 235.679993 235.740005 234.595978

2017-04-03 235.800003 236.029999 233.910004 235.330002 234.187958

2017-04-04 235.000000 235.580002 234.559998 235.479996 234.337219

2017-04-05 236.259995 237.389999 234.539993 234.779999 233.640625

2017-04-06 234.940002 236.039993 234.429993 235.440002 234.297424

2017-04-07 235.149994 236.000000 234.639999 235.199997 234.058578

2017-04-10 235.360001 236.259995 234.729996 235.339996 234.197906

2017-04-11 234.899994 235.179993 233.339996 235.059998 233.919266

2017-04-12 234.740005 234.960007 233.770004 234.029999 232.894257

2017-04-13 233.639999 234.490005 232.509995 232.509995 231.381638

2017-04-17 233.110001 234.570007 232.880005 234.570007 233.431656

2017-04-18 233.720001 234.490005 233.080002 233.869995 232.735031

2017-04-19 234.520004 234.949997 233.179993 233.440002 232.307129

2017-04-20 234.149994 235.850006 233.779999 235.339996 234.197906

2017-04-21 235.250000 235.309998 234.130005 234.589996 233.451538

2017-04-24 237.179993 237.410004 234.559998 237.169998 236.019028

2017-04-25 237.910004 238.949997 237.809998 238.550003 237.392334

2017-04-26 238.509995 239.529999 238.350006 238.399994 237.243057

2017-04-27 238.770004 238.949997 237.979996 238.600006 237.442093

2017-04-28 238.899994 238.929993 237.929993 238.080002 236.924606

Volume

Date

2017-01-03 91366500

2017-01-04 78744400

2017-01-05 78379000

2017-01-06 71559900

2017-01-09 46939700

2017-01-10 63771900

2017-01-11 74650000

2017-01-12 72113200

2017-01-13 62717900

2017-01-17 61240800

2017-01-18 54793300

2017-01-19 66608800

2017-01-20 129168600

2017-01-23 75061600

2017-01-24 95555300

2017-01-25 84437700

2017-01-26 59970700

2017-01-27 59711100

2017-01-30 79737300

2017-01-31 75880800

2017-02-01 79117700

2017-02-02 69657600

2017-02-03 80563200

2017-02-06 57790100

2017-02-07 57931200

2017-02-08 51566200

2017-02-09 65955200

2017-02-10 66015900

2017-02-13 55182100

2017-02-14 71109000

… …

2017-03-17 89002100

2017-03-20 52537000

2017-03-21 131809300

2017-03-22 97569200

2017-03-23 100410300

2017-03-24 112504900

2017-03-27 87454500

2017-03-28 93483900

2017-03-29 61950400

2017-03-30 56737900

2017-03-31 73733100

2017-04-03 85546500

2017-04-04 56466200

2017-04-05 108800600

2017-04-06 69135800

2017-04-07 74412300

2017-04-10 67615300

2017-04-11 88045300

2017-04-12 81864400

2017-04-13 92880400

2017-04-17 68405400

2017-04-18 83225800

2017-04-19 68699900

2017-04-20 92572200

2017-04-21 110389800

2017-04-24 119209900

2017-04-25 76698300

2017-04-26 84702500

2017-04-27 57410300

2017-04-28 63532800

[81 rows x 6 columns]

>>>

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Machine-learning in finance are unshackled algo

Machine-learning in finance are unshackled algo

This is a from a member but note copyright info at the end

My response was

I follow this Youtube authors who explains it really easy

Machine-learning in finance

Unshackled algorithms

More firms are experimenting with artificial intelligence

MACHINE-LEARNING is beginning to shake up finance. A subset of artificial intelligence (AI) that excels at finding patterns and making predictions, it used to be the preserve of technology firms. The financial industry has jumped on the bandwagon. To cite just a few examples, “heads of machine-learning” can be found at PwC, a consultancy and auditing firm, at JP Morgan Chase, a large bank, and at Man GLG, a hedge-fund manager. From 2019, anyone seeking to become a “chartered financial analyst”, a sought-after distinction in the industry, will need AI expertise to pass his exams.

Despite the scepticism of many, including, surprisingly, some “quant” hedge funds that specialise in algorithm-based trading, machine-learning is poised to have a big impact. Innovative fintech firms and a few nimble incumbents have started applying the technique to everything from fraud protection to finding new trading strategies—promising to up-end not just the humdrum drudgery of the back-office, but the more glamorous stuff up-front.

Machine-learning is already much used for tasks such as compliance, risk management and fraud prevention. Intelligent Voice, a British firm, sells its machine-learning-driven speech-transcription tool to large banks to monitor traders’ phone calls for signs of wrongdoing, such as insider trading. Other specialists, like Xcelerit or Kinetica, offer banks and investment firms near-real-time tracking of their risk exposures, allowing them to monitor their capital requirements at all times.

Machine-learning excels in spotting unusual patterns of transactions, which can indicate fraud. Firms ranging from startups such as Feedzai (for payments) or Shift Technology (for insurance) to behemoths such as IBM are offering such services. Some are developing the skills in-house. Monzo, a British banking startup, built a model quick enough to stop would-be fraudsters from completing a transaction, bringing the fraud rate on its pre-paid cards down from 0.85% in June 2016 to less than 0.1% by January 2017.

Natural-language processing, where AI-based systems are unleashed on text, is starting to have a big impact in document-heavy parts of finance. In June 2016 JPMorgan Chase deployed software that can sift through 12,000 commercial-loan contracts in seconds, compared with the 360,000 hours it used to take lawyers and loan officers to review the contracts.

Machine-learning is also good at automating financial decisions, whether assessing creditworthiness or eligibility for an insurance policy. Zest Finance has been in the business of automated credit-scoring since its founding in 2009. Earlier this year it rolled out a machine-learning underwriting tool to help lenders make credit decisions, even for people with little conventional credit-scoring information. It sifts through vast amounts of data, such as people’s payment history or how they interact with a lender’s website. Lemonade, a tech-savvy insurance startup, is using machine-learning both to sell insurance policies and to manage claims.

Perhaps the newest frontier for machine-learning is in trading, where it is used both to crunch market data and to select and trade portfolios of securities. The quantitative-investment strategies division at Goldman Sachs uses language processing driven by machine-learning to go through thousands of analysts’ reports on companies. It compiles an aggregate “sentiment score” based on the balance of positive to negative words. This score is then used to help pick stocks. Goldman has also invested in Kensho, a startup that uses machine-learning to predict how events like natural disasters will affect market prices, based on data on similar events.

Quantifiable progress

Quant hedge funds, both new and old, are piling in. Castle Ridge Asset Management, a Toronto-based upstart, has achieved annual average returns of 32% since its founding in 2013. It uses a sophisticated machine-learning system, like those used to model evolutionary biology, to make investment decisions. It is so sensitive, claims the firm’s chief executive, Adrian de Valois-Franklin, that it picked up 24 acquisitions before they were even announced (because of telltale signals suggesting a small amount of insider trading). Man AHL, meanwhile, a well-established $18.8bn quant fund provider, has been conducting research into machine-learning for trading purposes since 2009, and using it as one of the techniques to manage client money since 2014.

So it seems odd that some prominent quant funds are machine-learning sceptics. Martin Lueck of Aspect Capital finds the technique overrated, saying his firm has found only limited useful applications for it. David Siegel, co-founder of Two Sigma, a quant behemoth, and David Harding of Winton Capital, have also argued that the techniques are overhyped.

In other fields, however, machine-learning has game-changing potential. There is no reason to expect finance to be different. According to Jonathan Masci of Quantenstein, a machine-learning fund manager, years of work on rules-based approaches in computer vision—telling a computer how to recognise a nose, say—were swiftly eclipsed in 2012 by machine-learning processes that allowed computers to “learn” what a nose looked like from perusing millions of nasal pin-ups. Similarly, says Mr Masci, a machine-learning algorithm ought to beat conventional trading strategies based on rules set by humans.

The real vulnerability may in any case lie outside trading. Many quant funds depend on human researchers to sift through data and build algorithms. These posts could be replaced by better-performing machines. For all their professed scepticism, Two Sigma and its peers are busy recruiting machine-learning specialists.

Sent from my iPad

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YATC Python For Finance: Algorithmic Trading

YATC Python For Finance: Algorithmic Trading

I call this Yet Another Course

Datacamp came to me on providing content but I am not sure if it is worthwhile

https://www.datacamp.com/community/tutorials/finance-python-trading#gs.QnmYFso

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Intro to Machine Learning for Quantative Finance

Intro to Machine Learning for Quant Finance


This is one from Quantiacs

Thanx to SHolom for semding out

Just so you know I have my own set of resources and attitudes towards machines learning. You can always search my blog for these topics.

http://quantlabs.net/blog/2017/05/intro-to-machine-learning-for-quant-finance/

Note for TONIGHT

May 26 Trading Session

You are invited to a Zoom meeting. 
When: May 29, 2017 8:00 PM Eastern Time (US and Canada)

Register in advance for this meeting:
https://zoom.us/meeting/register/8f3048569318379f7c24e00bf0acd2b8

After registering, you will receive a confirmation email containing information about joining the meeting.

PS. I am also holding my ‘Bargain Harold’ pricing for the Quant Elite Membership.

Holy Intro to the Bargain Harold Sale to be an Elite Quant

–> GO here to sign up <–

Or here for actual details  

P.P.S. (just in case you missed above)

You are invited to a Zoom meeting.
When: May 29, 2017 8:00 PM Eastern Time (US and Canada)

Register in advance for this meeting:
https://zoom.us/meeting/register/8f3048569318379f7c24e00bf0acd2b8

After registering, you will receive a confirmation email containing information about joining the meeting.

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 Machine Learning for Quant Finance

Intro to Machine Learning for Quant Finance

This is one from Quantiacs

Thanx tp SHolom for semding out

Introduction to Machine Learning for Quantitative Finance

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!

Build a historical database with Google or Yahoo Finance

How to Build a historical database with Google or Yahoo Finance

Forget these old school databases. Times have changed since these with legacy data feed sources as well. I just focus on Redis NOSQL which answers a lot of my current data repository problems.

Here is the old story of what was posted:

http://quantlabs.net/blog/2014/04/how-to-build-a-historical-database-with-google-or-yahoo-finance/

Here is my latest Redis searches on the blog of the latest interesting info you should know about:

http://quantlabs.net/blog/?s=redis

Just a reminder that the new Quant Analytics service is now fully underway with daily video trading calls based on my human workflow. The new benefit starting this Monday will be the private event on May 22 at 8PM EDT.

>> JOIN HERE <<


Read the full benefits here

Thanks for reading

Bryan

P.S. Don’t forget as this is important to remember:

We are coming down to the wire over the next weeks where this  newsletter will be retired. To continue with the conversation, you will need to be doing the following:

Get my 2 ebooks to opt in for the automated newsletter found on my new server driven by Infusionsoft.

Go here

All future online events can be found here which points to my Facebook Page events page

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!

Hey you! Help us finance this oil refinery

Hey you! Help us finance this oil refinery

You know when oil prices are staying lower for a LONG time when they cannot find financing for refiners. Now they look for suckers like you who believe in polluting. This is under the Trump era so expect more of these financial arrangements esp when funding globally slows down.

http://www.meridianenergygroupinc.com/lp-investor-opportunity?gclid=CLjdo5TT79MCFZS6wAodG0UNHw

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!