Binary Options: Scam or Opportunity?

It’s always a good idea to include help within your functions! You never know who might benefit from it. With PowerShell adding help to your script, function and module is a really easy thing to do.

It boosted the development of artificial intelligence and allowed all sorts of new applications from Go-playing machines to self-driving cars. Otherwise you had to change the script every time when you want to test it with a different broker or account. Probably via lot size, but I found no detailed documentation.

73 thoughts on “Binary Options: Scam or Opportunity?”

The new Bod have taken 62,, shares almost 10% of the company. In total the share owned by Management = 74,, or almost 12% of the company.

IB is an NFA compliant broker. You can not close trades on NFA accounts. You must set the NFA flag for opening a reverse position instead. And you must enable trading costs, otherwise including the commission has no effect.

Those account issues are not related to machine learning, and are better asked on the Zorro forum. Or even better, read the Zorro manual where all this is explained. The code is below: Let me know what you think: Hi, deep learning researcher and programmer here. I have some comments: I have also heard that ReLUs make a network so fast that you can brute force train it in some cases, with no pretraining.

But I have not yet experimented with that. The network structure must still be defined in Python, but Zorro can use the network for training and prediction. Would you do YouTube Tutorials to your work, this series of articles. And where can I subscribe this kinda of algorithmic trading tutorials. Thanks for your contribution. I would do YouTube tutorials if someone payed me very well for them. Until then, you can subscribe this blog with the link on the right above.

Why not feed economic data from a calendar like forexfactory. I suggested that several times before. This data is what makes me a profitable manual trader rookie though , if there is any intelligence in these neuronal networks it should improve performance greatly. Some human institutional traders claim its possible to trade profitably without a chart from this data alone. Detecting static support and resistance areas horizontal lines should be superior to any simple candle patterns.

It can be mathematically modeled, as the Support and Resistance indicator from Point Zero Trading proves. Unfortunately i dont have a clue how Arturo the programmer did it. We also made the experience that well chosen external data, not derived from the price curve, can improve the prediction.

I read the book German here, too and am working through your blog articles right now. I already learnt a lot and still am learning more and more about the really important stuff other than: Your mindset must be perfect and you need to have well-defined goals.

I never was a fan of such things and finally I found someone that is on the same opinion and actually teaches people how to correctly do it. So, thank you very much and thanks in advance for all upcoming articles that I will read and you will post.

As a thank you I was thinking about sending you a corrected version of your book there are some typos and wrong articles here and there…. Would you be interested in that? Thank you for this interesting post. I ran it on my pc and obtained similar results as yours.

Then I wanted to see if it could perform as well when commission and rollover and slippage were included during test.

I used the same figures as the ones used in the workshops and included in the AssetFix. The modifications I did in your DeepLearn. Simulated account AssetsFix Bar period 1 hour avg 86 min Simulation period No, your results look absolutely ok. The predictive power of 4 candles is very weak. This is just an experiment for finding out if price action has any predictive power at all.

Although it apparently has, I have not yet seen a really profitable system with this method. Is it still the training process in R despite the seed? It is indeed so. Deepnet apparently uses also an internal function, not only the R random function, for randomizing some initial value. Is it not clear for me after reading inside the package.

Does that make sense? Was wondering about this point you made in Step 5: With frequently retrained models especially if using relatively short blocks of training data it is easy to train a model offline and get impressive results with data you will not have available for training in real time. Then reality kicks in. Therefore truncating your offline training set by N bars where N is the number of bars ahead you are trying to predict may well be advisable….

I was able to run the code till neural. Thank you so much. Dear jcl, in the text you mentioned that you could predict the current leg of zig-zag indicator, could you please elaborate on how to do that? I would never claim that I could predict the current leg of zigzag indicator.

But we have indeed coded a few systems that attempted that. For this, simply use not the current price movement, but the current zigzag slope as a training target. Which parameters you use for the features is completely up to you. Hi jcl, Nice work. I was wondering if you ever tried using something like a net long-short ratio of the asset I. But similar data of other markets, such as order book content, COT report or the like, have been used as features to a machine learning system.

Thanks for the info with the SSI. Yes, additional market data can have predictive power, especially from the order book. However we plan an own study with ML evaluation of additional data, and that might result in an article on this blog. Thanks jcl, looking forward to it! Hi Jcl, Does random forest algorithm have any advantage over deep net or neural networks for classification problems in financial data?

I make it more clear ; I use number of moving averages and oscillators slope colour change for trading decision buy -sell-hold. Sometimes one oscillator colour change is lagging other is faster etc.. There is no problem at picking tops and bottoms but It is quite challenging to know when to hold. This depends on the system and the features, so there is no general answer.

In the systems we did so far, a random forest or single decision tree was sometimes indeed better than a standard neural network, but a deep network beats anything, especially since you need not care as much about feature preselection. We meanwhile do most ML systems with deep networks. I see thank you. I have seen some new implementations of LSTM which sounds interesting. He is using Bayesian technique gaussian process as dropout http: I downloaded forexfactory news history from Used a algo to convert that into a value of -1 to 1 for EUR.

This value becomes another parameter into the neural training network. Sorry I am not sure what I am missing here, could you please clarify? That was a wrong name in the text. The files in the repository should be correctly named. Thanks for your reply jcl, much appreciated.

I love your work. And I have got lots to learn. In fact it does not seem to make any difference if the neural.

Hi Do you have any experience with generative adversarial networks GANs? Are they suitable for financial time series? We have not yet done a GAN based system. AFAIK GANs are best suited for a different class of problems, not for trading, except maybe in special cases where no immediate success function is available.

Is this, as JCL mentioned, due to an update in the caret package, and if so, does anyone know how to address this, or perhaps advise of a suitable previous version I could install instead? I installed an earlier version I went with 6. Thanks for the info. Algo Can you kindly share your model as i have been struggling to integrate forexfactory data since long.

According to the zorro-manual, the function scale takes 2 parameters: What does this mean? What is the influence here? I'm asking because the function 'scale' somewhere needs to retrieve the median value, the P25 and the P75 value from?! Thanks a lot for this clarification!! Your email address will not be published. This site uses Akismet to reduce spam. Learn how your comment data is processed.

Machine learning strategy development Step 1: The target variable To recap the previous part: The features A price curve is the worst case for any machine learning algorithm. Here are the two simple predictor functions that we use in our experiment in C: Preselecting predictors When you have selected a large number of indicators or other signals as features for your algorithm, you must determine which of them is useful and which not.

There are many methods for reducing the number of features, for instance: Determine the correlations between the signals. Remove those with a strong correlation to other signals, since they do not contribute to the information.

Compare the information content of signals directly, with algorithms like information entropy or decision trees. Determine the information content indirectly by comparing the signals with randomized signals; there are some software libraries for this, such as the R Boruta package. Use genetic optimization for determining the most important signals just by the most profitable results from the prediction process. I would highly recommend to learn how to apply fundamental analysis and how to trade manually instead of spending any energy on binary options because of my own experience.

However, I managed to build a somewhat stable autotrading interface with Winautomation. Would anyone be interested in working together on some of this stuff? If that is the case I am interested. Please guys — I work at the sharp end of the financial industry- these can best be likened to a roulette wheel with a slower time to burn than through your chips. Unless there has been some new market news the price fluctuations cannot be predicted on a five minute interval. The have access to non-public research, 20 years experience, teams of analysts using supercomputers crunching millions of transactions, financial capital billions and brokers that work for them.

Thankfully they only need to be right on very specific transactions. But I have not heard back from the company or from my broker who had promised me that by investing I would make a very good profit.

The problem is that now I lost all my money and I cannot reach them either. I am writing this post because one broker named John, from such called: I transferred that day 10 Euro by Credit Card.

The broker took over my account and started trading. After half an hour, the margin level was under threat and I received a call and broker started to ask for more money. I sent another 5 Euro from my Credit Card!

On 30 June, he opened 11 wrong positions with a huge loss and I woke up with all my money lost. I instantly called my broker and this criminal which burned all my money said that he will refund all my positions and I will succed to withdrawal all my money.

I waited for few hours and tryied to call John, and he never asked. Days passed away, I was trying and trying to call him, to write to his email, but without answers. I want to catch this broker which robbed my money, and made hundred of trades on my behalf without my consent and to punish him for every EUR that he lost, to punish him piece with piece just to understand how hard is to make money.

Binary options are great financial product but there is a lot of greedy brokers and firms. They stealing money from innocent people through robots, auto-traders and signal services..

All these systems are usually created by unregulated binary options brokers.. On this site you can find many scam systems: Scammers really were able to convince me by calling me long distance from Cyprus.

This is such a great post in which Binary Options scam is describe in a better way. I am seeking this type of blog from so many days but today i am glad to find this blog.

Love to work with someone to improve this. Let me know if you guys are interested. Things were going really well for me and I believed I had found a quick path to success when I started trading and winning. But, when I needed to liquidize my funds, it was impossible.

Has anyone been successful in getting money out? I have been contacted by a legal team who has informed me that the binary company I invested with will not ever give me my money unless I open a case against them, so I am thinking of doing this. So let me draw this comment out of the spam and answer it:.

If your binary broker refuses to pay out, the first problem is that you normally do not know their real address, not even their country. So the chance to get your money back from a Cyprus mailbox is zero. What I so far never heard is that someone really retrieved money from a fraudulent binary broker.

Hi JCL I was wondering if you or someone could explain me how to modify the objective function so it prefers systems with more trades as you suggested. The objective function is supposed to return a value that is a proxy for performance. The higher, the better. Thanks for the whole write up. Looking forward to getting more information on you manage everything regarding money management, legal issues and other things to get things fancy and manageable.

Binary options is really not for all. It always bears a lot of risks. This kind of information will help the enthusiasts escaping the bad things. Thanks for a fascinating article. Regarding trading costs on short term binary systems…. Is this the same with your system? In binary trading, slippage largely depends on the honesty of the broker.

Since they are usually market makers, it is no problem for them to generate artificial slippage for reducing the win rate. So it may be worth the effort to test the slippage and compare it with different brokers. In serious trading, slippage has a smaller effect on the win rate since asymmetric slippage is illegal under most regulations. MMI can detect trend regimes, but makes no difference between mean reversion and pure randomness. Hi jcl…ahhhh sorry I missed that part in the MMI article where you said just that.

Obviously the mean reversion system is not going to perform well in a trending market or in a random market…however if your MMI eliminates trades during trending periods, would that not at least be partly helpful in filtering out some of the losing trades? If not, do you know of a method to differentiate a mean reverting mode? Yes, there are other methods to detect the market regime, often used is the Hurst exponent.

I have already on my to do list a series of experiments to find out which detection method works best under which circumstances. Glad to find somebody who takes a realistic approach to binary options trading. I believe that profiable strategies can be automated, but they are not available in the public domain. Unfortunately there are hundreds of scam systems see warnings at https: And I see a lot of people fall into these traps, they still believe that somebody will make them money for free.

One more thing to mention is that most binary options platforms has an affiliate program so you cant really find a honest review. Most of the reviews are made to generate revenue and has interest. If you need some assistance in recovering you money lost in binary options there is this company here that will help you get your money back.

Almost died at the end of the article: Do you think it is possible, using data mining, that someone could discover reliable repeating patterns in a data series generated by a cryptographically secure pseudorandom number generator that is programmed to behave like a real market? Not talking about cracking it or finding the seed, just patterns that repeat leading to higher or lower prices over a specified timeframe.

Or is this a hopeless endeavour? If it is a random number generator, then it has per definitionem no reliable repeating patterns. Otherwise it would be a bad programmed random number generator. So I would think they must have some deterministic algorithm that makes the numbers a little less than random.

I wonder if that is by accident or design? There are many ways to simulate a market, the simplest is using real market data. This means the generated index depends on how many users bet on rising and how many on falling.

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