What Traders Are Using Now To Select & Time Trades… With 73% Accuracy

If you are an active trader today, and were trading ten years ago, you might have noticed that your tried and true indicators are not delivering near the accuracy they used to.

You are now trading at a risk level that’s higher than you might want.

Time to re-calibrate.

Is it because you haven’t back tested often enough?

Is it because you haven’t found the right combination of indicators that signal you to find a trade faster or get out quicker?

Is it because it’s taking you longer to recover from the last trades that did not give you the results you were looking for?

You might want to STOP looking in the mirror for the right answer, even though every good trader does it.

In this case, it’s counter productive and keeps you in the dark about what’s really going on.

What you use to find and select trades doesn’t meet the bar anymore, even if you are professional trader, have ten or more years of experience or are in charge of managed futures or trade for your clients.

How come?

It’s because of the combined effects of algorithmic trading and high frequency trading.

Yes, you are aware of these volatility drivers. So what about it?

What you might not know is how much these two technical trends disrupt the accuracy of the MACD, Bollinger Bands, RSIs, Moving Averages and well… any combination that you’ve tried.

Just look at your results over the last ten years to see if you notice a trend.

Look into the void

The sad truth is that the human brain doesn’t operate at bullet train speed to out-run the algorithms driving trades today. They’ve come and done their trading just before you’ve got the signal something is worth paying attention to… and the price has changed but you don’t know that.

Chilling isn’t it.

If you want to know how to level the playing field, even if you are an individual trader, or a managed futures trader working on behalf of your clients, you might want to read further.

Or join me for an online workshop to show you how a select group of US equities, options and futures (equities only) traders are discovering a whole new experience selecting and timing their trades with something called Machine Learning. The accuracy rate is quite comforting.

Free Trial here: https://deepstreetedge.clickfunnels.com/get-the-edge.

Curious About Machine Learning And Why It Beats the MACD?

You can read more about why Machine Learning can out pace even algorithmic and High Frequency Trading below.

Artificial intelligence (AI) has been a big player in Hollywood style science fiction for decades. But now it has made its debut on Wall Street.

data-from-star-trek

Known as Predictive Analytics, a supercomputer today is actually able to determine what price and direction a stock or an index is about to move to next. The output provides a true leading indicator.

MACD, RSI, Bollinger Bands and Moving Averages are all lagging indicators, meaning you are hoping that what happened in the past will happen again and you will be alerted by these signals to know the exact time to enter a trade.

The Bad News Is That History Is A Poor Predictor Of Future Action.

The good new is that today, you don’t have to look in the rear view mirror any longer to regain your success as a trader.

To evaluate this statement, it is useful to understand how Machine Learning works.

Artificial intelligence was a nice idea in the beginning: the concept that a computer could learn on it’s own. There really wasn’t anything artificial at work, however, as the programmer supplied all the intelligence to the system by programming the model.

The model was powerful; But not powerful enough to predict the next movement of stocks.

Predicting Stocks And Index Movement Is A Gigantic Big Data Problem.

Programmers couldn’t assemble the right data in the right format, in one location for AI to effectively query the data.

The second problem was the model itself. When you model the question you want answered using the data available, you are limited by the mind of the programmer.

new generation

That all changed a few years back when data scientists changed how they were thinking about the problem. They decided to model the mind, rather than just model the problem in light of the data sources.

Modeling the mind enables AI to be capable of exploring everything about the world.

As long as you provide data to train it so it learns to recognize the patterns you need as it examines all the variables. Then the machine ‘learns’ and can use the patterns it has recognized to make predictions.

However the AI model based on the world (the old problem mindset) becomes obsolete the moment it is finished: the world has moved on already.

The only hope to create intelligent systems is to have the system itself create and maintain its own model: Continuously updating output, in response to sensory input.

With this technological leap, we are not limited by a single human mind to program a predictive model of how stocks and the indices are about to move to a specific price.

Enter today’s AI: Machine Learning. It is NOT a subset of AI. It is truly artificial intelligence at work.

Machine Learning Doesn’t Care About Intelligent Behaviour: It Is Focused On The Accuracy Of Its Patterns So That It’s Output, Predictions, Are Relevant And Reliable

When it comes to predicting the movement of stock prices (leading indicators) traders need accuracy they can trust.

Traditionally, traders have looked backwards, using technical analysis to isolate patterns to ‘predict’ what will happen next.

But to be truly predictive and to get the most out of Machine Learning, you don’t want to analyze the world of stocks in its historical form!

That approach just gives you lagging indicators on steroids and you are back to not knowing any more about the future than history can predict.

You Have To Give The Machine The Right Base And Data To Learn From. What Is The Primary Mover Of Stock Prices? News.

In simple terms, it means you feed it vast streams of news. Then show the machine what happened as a result of the news. This feedback loop increases its confidence that it can predict price movement even more accurately with the next piece of news.

screen-shot-2016-06-27-at-10-17-44-am

Figure 1 – DeepStreet EDGE (a Machine Learning Platform) predicting the direction of the NASDAQ on June 27, 2016, the Monday after the Brexit vote. Trading when the indicator (green line pointing up ahead of the current price) changes from yellow to green or yellow to red tells you the ideal entry point for a futures or options trade.

Then you multiply all that ‘learning’ activity by thousands of news items every day and that’s how the platform builds it’s accuracy.

If you want to see how this Machine Learning platform really works, you can join us for a free trial here. Your trial includes access to the daily trading club so you learn how to interpret the signals by watching the head trader use DeepStreet EDGE for his own account.

You also need to then aggregate those thousands of changes in price movement of every stock so you can make the prediction as to how all that news will move an entire index.

You are now talking about collecting all the inputs that affect price and movement so that Machine Learning can spot thousands of patterns that humans can’t, and turn the patterns into increasingly accurate predictions.

Up until now, this was an immense computation problem as a well as a data source problem. It is very difficult to get all the right input feeds that contribute to stock price movement… and house that data in one massive location so it can be analyzed.

When you get disparate data sources, in the old ‘problem frame’ approach, a data scientist had to organize and label all that data before any analysis could be done.

In Machine Learning, using natural language processing, the computer can move from one data source to another without the data needing to be labeled. It learns and broadens its model off of every bit of information… with the correct algorithms.

While the data and natural language processing side of the problem in analyzing stocks based on news as input, has now been conquered, the other side of the problem is where Machine Learning and leading indicator trading, is in its infancy.

How do you turn a Machine Learning pattern recognition prediction system reading news into a stream of information that makes sense to the human in a way that they can immediately understand what it means, and then know what to do with the result?

In the next post in this series, we will discuss what turned out to be the much bigger problem to solve and what it means for trading today with predictive analytics.

So How Does Machine Learning Level The Playing Field For The Active Trader?

We will leave you with this idea: Algorithmic Trading and High Frequency Trading instructions use lagging indicators to search and time their trades… just like you do.

They just do it faster than you.

Machine Learning Predicts The Future… Acting Long Before The Algorithms And HFT Instructions Ever Get To Follow Their Instructions

Trading with Machine Learning is how you beat them at their own game.

To experience machine learning in action visit http://www.eotpro.com or sign up for  free trial here: https://deepstreetedge.clickfunnels.com/allaccesspass. Your trial includes access to the daily trading club so you learn how to interpret the signals by watching the head trader use DeepStreet EDGE for his own account.

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