Tag Archives: Real time

New minute real time chart with BNB BTC moving big

It seems this pair of the Binance Coin against Bitcoin has been moving a lot lately. I even showed the new real time minute chart. That was flat to. At the moment, this is a close to realtime but really minute.

As for forex, there were the usual suspect but the Swiss Franc was the base quote for New Zealand, Australian, and Singapore. What is going on here outside of the timing ?

I am unsure about the CFD but gold was up there with Hong Kong 33 (HK 33)

More analytics still to come

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Close real time charting with Qt 5 Python and ChartDirector

This is minute timeframe which is pretty close to real time. I will be teaching and improving this script in my upcoming LIVE Python intake. Sign up here below on a super discount!!

Yes in Python with simplicity!

To me this a game changer don’t you think?? Do remember that I have found a way to run Qt 5 Designer with Python in the last posting on this blog.

Python Algo Trading Infrastructure with Crypto Currency

 

 

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New combined parameters to use for optimal real time crypto currency pairs

Lots of parameters I am using to combine proper trading decisions for optimal profit return. I am showcasing the combination of this for highest returns when position are entered. This is important to watch to understand you cannot depend on one or two indicators. You may wonder why folks are losing money left and right. There are so many false positives out there for bad positions to go bad. Do understand these parameters can be used universally for all major asset classes. This includes crypto currency with Binance Exchange for my needs. Also, I am using Python to create these but I am starting to hit limitations with it.

NOTES

Remember this is to kept simple. This includes technical analysis since it seems to work ok versus other analytics techniques I tried.

 

Chart Director trading 38 min video added to Python 3 quant elite course

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Real time P&L algo automated forex trading lesson

Big whale profit algo automated forex trading lesson

Who knew? You make a 43 minute video and you teach yourself certain lessons. It seems the velocity of negative  real time P&L (from the broker) can be a potential guarantee to see if you have a unprofitable trade against you. Check this video to  see a potentially big whale position version others that go negative. It seems shorts have a higher profitability that goes against me. Also, ensure that your broker gives the REAL TIME P&L after you put your position on (entry) to track this. This video also shows on how debugging and logging the evolutions of these positions can help in tracking. I don’t think many brokers offer real time P&L nor proper trading APIs.

One  thing after conversation with someone who has experience with Dukascopy JForex is how the API has lots of detailed metrics with indicators. Not only, it is agreed that the OOP nature the API works to your advantage as compared to Interactive Brokers or LMAX.

get all the cheap Dukascopy courses here https://quantlabs.store

 

Minimize loss with linear regression for algo forex trading

#1 goal in forex trading is not to lose money but profit is 2nd

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What defines strength of forex trading in real time

What defines strength of forex trading in real time

This was a question i asked online to my groups

What does the phrase a particular CURRNECY strengthen? What sort of indicator or math are at work here? Give me the details.

Here are 2 highly valuable responses I got: (BIG THANKS TO THEM FOR ANSWERING)

Hi Bryan,

You asked about individual currency strengths in the FX market and how they are calculated. There are different types (relative and absolute) and many different ways to calculate them, but I’ll give you a very simple example of absolute currency strength.

I’m sure you’re aware that rate-of-change (ROC) is essentially just the percentage move. Let’s say we are trying to calculate the absolute strength of USD:

  • First we decide on a basket of currency pairs that include USD. For example; EURUSD, GBPUSD and USDCHF. In reality you would consider more than 3 pairs, but I’ll keep it simple for the sake of illustration.
  • Then measure the ROC over some time period, e.g. the past week. That is, take each of our 3 currency pairs and calculate the percentage move over the last week. This could yield 1.1%, -0.4% and 0.9%. Note however that USD is the quote currency in EURUSD and GBPUSD, but is the base currency in USDCHF. This means that we have to invert the ROC values for EURUSD and GBPUSD (since an increase EURUSD or GBPUSD implies a devaluation in USD relative to EUR or GBP, respectively). So the final values are -1.1%, 0.4% and 0.9%.
  • Then take the average: (-1.1+0.4+0.9) / 3 = 0.066%.
  • There we have it, the absolute strength of USD. It can be interpreted as the average move for the past week compared to the other currencies in the basket (EUR, GBP and CHF). So in this example, USD slightly outperformed.

As I said above, this is just a very simple example. In reality you’ll use many more pairs in the basket, you’d calculate the absolute strength for each currency (not just USD) so that you can compare them, you could repeat the analysis for a variety of time periods and you may want to plot the strengths as time series in order to analyse the relationships between the individual currencies. You can also use bounded oscillators such as RSI in order to compute relative strength, and some people advocate the use of the geometric average as opposed to the arithmetic average, or even weighting each currency according to the daily traded volume (a 2% increase in EURGBP is more significant than a 2% increase in EURMXN, for example).

There’s a huge amount of information regarding this topic online, but here’s how it might look once everything has been computed:


As you can see, it offers great insight into how the individual currencies have been/are behaving, something that is not immediately obvious when examining the data of single currency pairs in isolation. In this image, you can see that EUR has been the best performing currency during the past year, and that JPY has been the worst. Hence if you’re a momentum trader you should be looking to go long EURJPY, and if you’re a mean-reversion trader you should be looking to short EURJPY. 

Happy trading!

 

—–

Hi B, you can use an index for a particular currency to see whether it strengthen or not. E.g. for USD it is DXY. And as all indexes, it is calculated from a basket of assests. In this case it is the basket of currency pairs which is weighted according to its daily traded turnover. You can create your own for every currency out there and to weight it with data from either Bank for International Settlements’ FX Survey 2016 or by a turnover on futures currency market on CME. Then you can play with these values and e.g. add it to 100% scale.

I like to watch the past month particular currency strength and its strength developmnet in this period. U can see the most weakest and the most strength currencies. So you can trade them this way weakest/strength. But as I said it is good to look at the development and as you can see on the pic e.g. USD it is the 3rd most strength currency over the last month, but it is slowly loosing the steam of breaking the highest high point here.

 

 

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Real time trading analysis response with Machine Learning

Real time trading analysis response with Machine Learning

See below for the original email that started this response from one of my newsletter members:

I recall that you were asking about the GP that generate code. There are a few out there and one such example is Adaptrade Builder at http://www.adaptrade.com/  (disclaimer: I own this one and use it myself). Adaptrade Builder generates code in the following scripting languages: EasyLanguage (for TradeStation and MultiCharts), NinjaScript (NinjaTrader 7), MQL4 (MetaTrader 4), and AFL (AmiBroker). If you want to translate the code for another platform, the strategy code generated by Builder is open and commented. In addition, the elements of the strategy logic are listed in the Build Report, although the best and most complete way to understand the strategy logic is to examine the generated strategy code.

 

–> I have never been a fan of these black box technologies

Others are

http://www.stratasearch.com/

http://www.priceactionlab.com/

http://www.tradingsystemlab.com/

Neuroshell Daytrader (neuroshell.com)

Chaoshunter (chaoshunter.com)

Discipulus 5 (rmltech.com)

GeneXProTools (www.gepsoft.com)

 

I think the second point you asked about  is that yes I do use Scikit-learn and the various machine learning packages for data analysis including inference, modelling (prediction), and Bayesian approaches.

 

–> I believe SciKit is the smaller Machine Learning library compare to the more advanced ones like TensorFLow or Karas (I am also new to this ML stuff)

For machine learning you are often confronted with a large n

umber of features and the question as to which set of features are most significant. For this you use Principal Component Analysis for dimension reduction, (sometimes called Eigenanalysis) where you use a statistical procedure using an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called Principal Components. The first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible). This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the

variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal (always at right angles) to the preceding components. The resulting vectors are an uncorrelated orthogonal basis set. PCA is used as an exploratory tool in data analysis and for making predictive models.

–> I keep hearing about these component and factors used

From Scikit-Learn we use the various machine learning algorithms, eg Support Vector Machine, Linear Discriminant analysis, Ada Boost etc…  via a set of randomized tests (test & train), and feed the results into a confusion matrix and  test the model prediction (actual vs. predicted). The constant consideration is that these machine learning tools are designed to deal with stationary data, whereas time series data is non-stationary. So stationarity of data is huge part of your analysis if you want good models.

 

Here is an example of an open source project where you can automate your Machine Learning in Python with TPOT and Genetic Algorithms.

 

See:

https://github.com/rhiever/tpot

http://rhiever.github.io/tpot/using/

https://github.com/rhiever/tpot/blob/master/tutorials/Titanic_Kaggle.ipynb

https://blog.alookanalytics.com/2017/05/25/automate-your-machine-learning/

 –> Good to know all of the above!!!

Real time trading analysis in Redis 4 with Lua Torch machine learning ?

 

I created this video in response

What about bonds? You briefly mentioned treasury bonds, but didn’t include them on your list. I’m particularly interested in first world countries that have an interest rate higher than many third world countries i.e Portugal. Moreover, I code in Java. It’s not as efficient as C++, but it’s quicker to code, because it’s a more conceptual language. The time saved in creating the code with Java is worth more than the efficiency gained with less memory used with optimized C++ code in most cases. Conditions in the market are constantly changing. An algorithm that worked yesterday might not work tomorrow. The slower coding of C++ may not be able to keep up with a changing market. In contrast, Java can allow one to more quickly adapt their strategy. What would be best is to create multiple programs for different types of markets. I like to think of them as different personalities. Perhaps what’s most important though, is an understanding of Bayesian math. That is, new data is constantly affecting probability. The Monty Hall problem is the most well known example of Bayesian math. Indeed, it’s Bayesian math more than anything which helps one understand why Java is better programming language than C++ in most cases.

I made video response to this Youtube comment

https://quantlabs.net/blog/2017/05/real-time-trading-analysis-in-redis-4-with-lua-torch-machine-learning/

A bunch of reminders including tonite:

Your Weekly Trading Plan Webinar Login Details Mon May 21

Bryan Downing is inviting you to a scheduled Zoom meeting.

Topic: PUBLIC QLN TRADING PLAN

Time: May 21, 2017 7:00 PM Montreal

Join from PC, Mac, Linux, iOS or Android: https://zoom.us/j/519828492

Or iPhone one-tap (US Toll): +14086380968,519828492# or +16465588656,519828492#

Or Telephone:

Dial: +1 408 638 0968 (US Toll) or +1 646 558 8656 (US Toll)

Meeting ID: 519 828 492

International numbers available: https://zoom.us/

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!

Real time trading analysis in Redis NOSQL with Lua Torch machine learning ?

Real time trading analysis in Redis 4 with Lua Torch machine learning ?

 

I created this video in response

What about bonds? You briefly mentioned treasury bonds, but didn’t include them on your list. I’m particularly interested in first world countries that have an interest rate higher than many third world countries i.e Portugal. Moreover, I code in Java. It’s not as efficient as C++, but it’s quicker to code, because it’s a more conceptual language. The time saved in creating the code with Java is worth more than the efficiency gained with less memory used with optimized C++ code in most cases. Conditions in the market are constantly changing. An algorithm that worked yesterday might not work tomorrow. The slower coding of C++ may not be able to keep up with a changing market. In contrast, Java can allow one to more quickly adapt their strategy. What would be best is to create multiple programs for different types of markets. I like to think of them as different personalities. Perhaps what’s most important though, is an understanding of Bayesian math. That is, new data is constantly affecting probability. The Monty Hall problem is the most well known example of Bayesian math. Indeed, it’s Bayesian math more than anything which helps one understand why Java is better programming language than C++ in most cases.

I made video response to this Youtube comment

https://quantlabs.net/blog/2017/05/real-time-trading-analysis-in-redis-4-with-lua-torch-machine-learning/

A bunch of reminders including tonite:

Your Weekly Trading Plan Webinar Login Details Mon May 21

Bryan Downing is inviting you to a scheduled Zoom meeting.

Topic: PUBLIC QLN TRADING PLAN

Time: May 21, 2017 7:00 PM Montreal

Join from PC, Mac, Linux, iOS or Android: https://zoom.us/j/519828492

Or iPhone one-tap (US Toll): +14086380968,519828492# or +16465588656,519828492#

Or Telephone:

Dial: +1 408 638 0968 (US Toll) or +1 646 558 8656 (US Toll)

Meeting ID: 519 828 492

International numbers available: https://zoom.us/

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!

Real time trading analysis in Redis 4 with Lua Torch machine learning

Real time trading analysis in Redis 4 with Lua Torch machine learning ?

 

I created this video in response

Comment was:

What about bonds? You briefly mentioned treasury bonds, but didn’t include them on your list. I’m particularly interested in first world countries that have an interest rate higher than many third world countries i.e Portugal. Moreover, I code in Java. It’s not as efficient as C++, but it’s quicker to code, because it’s a more conceptual language. The time saved in creating the code with Java is worth more than the efficiency gained with less memory used with optimized C++ code in most cases. Conditions in the market are constantly changing. An algorithm that worked yesterday might not work tomorrow. The slower coding of C++ may not be able to keep up with a changing market. In contrast, Java can allow one to more quickly adapt their strategy. What would be best is to create multiple programs for different types of markets. I like to think of them as different personalities. Perhaps what’s most important though, is an understanding of Bayesian math. That is, new data is constantly affecting probability. The Monty Hall problem is the most well known example of Bayesian math. Indeed, it’s Bayesian math more than anything which helps one understand why Java is better programming language than C++ in most cases.

 

Comment from this video

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!

Frankfurt Germany DAX Stock exchange real time affordable feed

Frankfurt Germany DAX Stock exchange real time  affordable feed

A member just notified me of this so thanks to them for this:

And I found http://www.boerse-frankfurt.de/aktien/realtime-quotes/DAX (German stock exchange) to be an excellent source of German stocks, which is probably fine for me for the time being. (They do offer more broad and international realtime feeds strating with 100€ per month)

Join my FREE newsletter to learn more juicy tidbits like this for automated trading

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Spark as a Real-Time Web Service PDF

Spark as a Real-Time Web Service PDF

This does 700 updates per second so should I be impressed?

https://www.slideshare.net/mobile/EvanChan2/700-updatable-queries-per-second-spark-as-a-realtime-web-service

For me Redis is super impressive!! http://redis.io/topics/benchmarks

redis-benchmark -t set -r 100000 -n 1000000
====== SET ======
  1000000 requests completed in 13.86 seconds
  50 parallel clients
  3 bytes payload
  keep alive: 1

99.76% `<=` 1 milliseconds
99.98% `<=` 2 milliseconds
100.00% `<=` 3 milliseconds
100.00% `<=` 3 milliseconds
72144.87 requests per second

$ redis-cli dbsize
(integer) 99993

redis-benchmark -n 1000000 -t set,get -P 16 -q
SET: 403063.28 requests per second
GET: 508388.41 requests per second

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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!