Tag Archives: options

tensorflow machine learning standard option for algo trading but there is simpler options

tensorflow machine learning standard option for algo trading but there is simpler options

 

Various links below show simple ways to use linear regression with R Squared. It then evolves into using SciKitLean Python Package which is relatively simple but could be effective if you are just after R Square to measure strength of the trend.

towardsdatascience.com/simple-and-multiple-linear-regression-in-python-c928425168f9

blastchart.com/Community/IndicatorGuide/Indicators/LinearRegressionRSquared.aspx

premium.working-money.com/wm/display.asp?art=340

This next link offers a look the complicated TensorFlow Model Library from Google. This is the definite standard defacto in the financial industry. I know this when I saw samples used by high end HFT shops with their workshops for the Newsweek AI conference in NYC last Nov.

Set stock data target with https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877 <– TensorFlow complex but offers many options

I will also say that TensorFlow offer the most choice. See below a tip below from someone in my private Telegram group. This could save you loads of time instead of wasting time by going down rabbit holes.

For crypto use, https://dashee87.github.io/deep%20learning/python/predicting-cryptocurrency-prices-with-deep-learning/

Note that this uses Keras which obviously simpler than TensorFlow. I would probably move towards TF as I get deeper into this.

Here is that useful tip:

My quick-fire tips: in general I’ve had more success constantly-retraining the ML models (every day in your case) as opposed to the usual train/test/validate method. Either that, or use ML models that are specifically designed to capture the time-varying properties of time series (these models can usually be updated in an online fashion). Never used raw prices as inputs—there is so much autocorrelation in the features that the predicted price will just end up being the last close. Instead use returns (I prefer logarithmic returns over simple returns, but it doesn’t make much difference).

Try to use a target that is directly related to the outcome of a potential trade. If that is too complicated, try having separate targets for direction and volatility. I prefer regression algos to classifiers. If possible, require that your model predicts opposite scenarios for both a long and short trade; e.g. it predicts a long trade will make money *AND* a short trade will lose money, and vice-versa. Avoid neural networks when you’re just starting out – there are too many hyperparameters to tune. Ensemble methods are fantastic, and can be very profitable

Here is my conclusion:

If you are like me just trying to figure out general trend through LSR and R-square, I tend to use these for now. As a result, I may use the SciKitLearn example for simplicity but stop there. I really don’t want to go down the various rabbit holes to experiment with more complex ML libraries. I see no reason compared to the recent sub minute study/reseach/playing around experiment at the sub minute level I started back in Dec which became a time waster with no results.

 

For those keeping track, if you don’t want to use Python with TensorFlow there is a simpler C++ option

https://tebesu.github.io/posts/Training-a-TensorFlow-graph-in-C++-API

 

This is the simplest so far but which started this whole level of research

https://stackoverflow.com/questions/893657/how-do-i-calculate-r-squared-using-python-and-numpy

You can see how simple stuff turns complex

 

Williams % and Stochastics most reliable Matlab technical indicator

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complex options backtesting strategy automatically with future courses coming soon

complex options backtesting strategy automatically with future courses coming soon

 

Two queries from newsletter subscribers

Hi Bryan,

Do you have any course module for backtesting any complex option strategies automatedly ?

If you have, please advise.

What I really need is to backtest my complex option trading strategy (more than 4 legs at a time).

 I have backtested manually for the last 5 years, and it proves has a decent return every year. Now, i want to automate the backtest. Since it needs a lot of time to bt option strategy manually.

But since you offer me, futures and option course, do you have backtested the strategy for the last 5 to 10 years and has high performance every year and high sharpe ratio ?

If you have please advise.

Try backtrader.com for backtesting in Python
———–

I would like to join the quant course.

How do I go about doing it?

Video answers here

Get in touch with here in my social media to learn more about these current deals as of Feb 27/2018. This is on for a highly limited time.

http://quantlabs.net/results

 

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Linux cloud options for your remote trading server 

Linux options cloud option for your remote trading server

Xrdp linux Rdp <– Open source RDP server for your Linux/BSD server installation with your JForex installation

1 blu ISP <– https://www.1blu.de affordable German based ISP for your server remote  installation close to Geneva Switzerland for quick connectivity with Dukascopy

Server4u <– Another competitor to Server4u

Ubuntu mate desktop <- one desktop option for Ubuntu Linux with a close look to Windows

Image file of preconfigured software

All thanks again to Apple screwing around in their customer service

Death of Apple starts today 

Hardening FreeBSD is better than Linux anyday

 

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The Highest VIX Options Volume Day in History

 

The Highest VIX Options Volume Day in History
And the short got trapped because there was so much volume

http://www.barrons.com/articles/the-highest-vix-options-volume-day-in-history-1502466104?mod=yahoobarrons&ru=yahoo&yptr=yahoo

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Updated Futures and Options course from UC Davis for 2015

 

Updated Futures and Options course from UC Davis for 2015

This is the course which I base one of my courses with source code. I need to update to this

https://quantlabs.net/analytics/overview-futures-options-strategy/

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Australia options are hard to trade

Australia options are hard to trade

from a newsletter subscriber so thanks to him for sending:

Hello Brian,

 

In AUS options are not that liquid, and dominated by the bank prop traders – it is very much manipulated, towards the expiry date. The banks push the underlying asset pxrice to where most of the retail traders will have their options expire worthless. The best way to trade these here in Australia are to setup butterfly trades about a week out or so –  to exploit the time decay in your favour. You can neutralize the trade very quickly if it does not pan out.

 

BTW the FX landscape continues to evolve:  http://www.futuresmag.com/2017/05/11/blockchain-fx-network-nabs-big-fish

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How to Beat the Market Trading Weekly Options

How to Beat the Market Trading Weekly Options

Always do your own research to confirm these theories. Never ever use this alone as form of a crutch cause you don’t know how. I know most of you won’t believe that

Thanks to Sholom B for sending

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Intro preview to new quant future options courses and Python infrastructure

I just posted this video on how I will be removing the PUBLIC ACCESS from my Quant ELITE membership in the next few days to weeks. This will be the last of my ‘highly valuable’ deals before I go premium in coming weeks fro my new Analytics service.

If you want to take advantage of the last combined courses I have for a low monthly fee before they total $1600 in a few weeks.

Join my FREE newsletter to learn more about these courses will help your automated trading

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