People doubt machine learning for various reasons. This insclude the effective financial time series using LTSM. See below the article
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Python Seaborn charting package over Matplotlib for finance time series
Seaborn is very use as a Python package over Matplotlib. Matplotlib is the original popular charting package but the other is much more impressive. If you look at the Gihub.io link, there are better examples on how to manipulate string dates to datetime64 which is needed for Matplotlib. Watch this to help you out to improve your visualization.
Here is the Facebook group these live webinars are in
https://www.facebook.com/groups/quantlabsnet/?ref=bookmarks
Helpful links from video:
https://seaborn.pydata.org/examples/heatmap_annotation.html
https://stackoverflow.com/questions/22795348/plotting-time-series-data-with-seaborn
http://financeandpython.com/SeabornDataVisualization/6/2.html
https://jakevdp.github.io/PythonDataScienceHandbook/03.11-working-with-time-series.html
https://stackoverflow.com/questions/37790429/seaborn-heatmap-using-pandas-dataframe
Update: Which Bitcoin crypto currency bot project? Gekko vs ccxt vs Tribeca vs Blackbird
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Python interface for the creation of graphs and the calculation of statistics for financial time series
Someone from my Telegram made me aware of this project, From the guy behind http://mechanicalforex.com
Project description
The qq-pat library provides you with an easy interface for the creation of graphs and the calculation of statistics for financial time series. The library uses time series stored within pandas dataframes to make its calculations. You can have either one or several columns within your dataframe and qq-pat will always calculate statistics for all of them and return the results as either a list (if you are calculating something like the Sharpe ratio) or an actual pandas dataframe with the same number of columns.
To use qq-pat is very easy. First make sure you create a pandas series or dataframe that contains either the daily returns or the daily price/balance for the assets or strategies you want to evaluate. After this simply initialize the qq-pat analyzer:
https://github.com/QuriQuant/qq-pat
https://github.com/danielfppps
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Financial Time Series Analysis with R in Interactive Brokers
Man, is this broker getting more modern but R is not used as much as I thought
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In order to approach time series analysis and forecasting, we must first answer the question regarding what constitutes time series data. A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are solar activity, ocean tides, stock market behavior, and the spread of disease
https://www.linkedin.com/pulse/time-series-analysis-everything-you-need-know-3-read-shanmugaraj
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Time series analysis in 3 minutes
Not enough here but it will do
https://www.linkedin.com/pulse/time-series-analysis-everything-you-need-know-3-read-shanmugaraj
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In another social media site, I found this popular article about learning time series modelling with R the programming language. From the recent inside trading professionals that I’ve talked to, they still swear by this language. If you’re all new to this, you may want to check this out.
A lot of people don’t know, but I have spent about six months studying the varied uses of R. I particularly scoured the Internet for any articles related around trading. As a result, I have compiled a list of interesting projects that may be useful that use this language.
Check this out to see what I mean
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Join here if you’re one to take Algo Trading to the next level
Or go here if you just want to learn R for automated trading
Thanks Bryan
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Complete Tutorial on Time Series Modeling in R
Ahh..this again .. for new a newbie
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Honestly, this is an interesting way to use Python as it reminds me from when I played with Matlab years ago doing the same thing. You will get the same results using any language. In terms of machine language research, this all boils down to the same thing with same results. But alas, here is another attempt by Google.
Check out these links and brief video tutorial
Over the last 24 hours, I have posted so many technical articles that I do not want you to miss anymore so I suggest you join my Daily Digest so you get everything I blog about.
I will be emailing more highlights over the next few days about these tech postings.
For tomorrow night, I will be doing a general talk of the state of trading tomorrow during our Trading Pow Wow which I have not done in a very long time. I hope to do more of these more frequently. Here is the schedule for Monday night:
1. Please join my meeting, Apr 5, 2016 at 8:00 PM EDT.
https://global.gotomeeting.
2. Use your microphone and speakers (VoIP) – a headset is recommended. Or, call in using your telephone.
Dial +1 (647) 497-9353
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You should consider joining my weekly Meetup events by joining my groups but there are here the links for Monday night:
http://www.meetup.com/quant-
http://www.meetup.com/R-
Thanks for reading
Bryan
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Have you seen this Google machine learning financial time series tutorial?
Sent over by a local Meetup user so thanks to him
https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
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