Regularization — Part 4

Initialization & Transfer Learning

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Deep Learning at FAU

These are the lecture notes for FAU’s YouTube Lecture “Deep Learning”. This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know!


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In convex problems, initialisation does not play a role.

Welcome back to deep learning! So today, we want to look at a couple of initialization techniques that will come in really handy throughout your work with deep learning networks. So, you may wonder why does initialization matter if you have a convex function, actually, it doesn’t matter at all because you follow the negative gradient direction and you will always find the global minimum. So, no problem for convex optimization.

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Initialization can make a big difference in non-convex problems.

However, many of the problems that we are dealing with are non-convex. A non-convex function may have different local minima. If I start at this point you can see that I achieve one local minimum by the optimization. But if I were to start at this point, you can see that I would end up with a very different local minimum. So for non-convex problems, initialization is actually a big deal. Neural networks with non-linearities are in general non-convex.

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Simple ideas for initialization.

So, what can be done? Well, of course, you have to work with some initialization. For the biases, you can start quite easily and initialize them to 0. This is very typical. Keep in mind that if you’re working with a ReLU, you may want to start with a small positive constant, This is better because of the dying ReLU issue. For the weights, you need to be random to break the symmetry. We already had this problem. In dropout, we saw that we need additional regularization in order to break the symmetry. Also, it would be especially bad to initialize them with zeros because then the gradient is zero. So, this is something that you don’t want to do. Similar to the learning rate, their variance influences the stability of the learning process. Small uniform gaussian values work.

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How can the variances be calibrated?

Now, you may wonder how can we calibrate those variances. Let’s suppose we have a single linear neuron with weights W and input X. Remember that the capital letters here mark them as random variables. Then, you can see that the output is W times X. So, this is this linear combination of the respective inputs plus some bias. Now, we are interested in the variance of Y hat. If we assume that W and X are independent, then the variance of every product can be actually computed as the expected value of X to the power of 2 times the variance of W plus the expected value of W to the power of 2 times the variance of X and then you add the variances of the two random variables. Now if we require W and X to have 0 mean, then this would simplify the whole issue. The means would be 0, so the expected values cancel out and our variance would simply be the multiplication of the two variances. Now, we assume that X subscript n and W subscript n are independent and identically distributed. In this special case, we can then see that essentially the N here scaled our variances. So, it’s actually dependent on the number of inputs that you have towards your layer. This is a scaling of the variance with your W subscript n. So, you see that the weights are very important. Effectively, the more weights you have, the more it scales the variance.

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Regularization — Part 4
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Autocomplete textbox search from database in codeigniter 4 using jQuery Typeahead js. In this tutorial, you will learn how to implement an autocomplete search or textbox search with database using jquery typehead js example.

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Autocomplete Textbox Search using jQuery typeahead Js From Database in Codeigniter

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Dylan  Iqbal

Dylan Iqbal


Big Data Visualization: What, Why, Tips and Tools

Wondering what is big data visualization and how you can apply it for your business? Here's a guide to help you get started.

Because we live in a data-driven society, it’s likely that you’re constantly bombarded with complex sets of data that you need to transmit to your coworkers in an easy-to-grasp way.

The challenge is that almost no one wants to look at large lists of numbers and data, and important information can be easily lost within the midst of chaotic spreadsheets. But there is a solution, and that is big data visualization.

Today, we’ll be covering what big data visualization is and why it’s important, different big data visualization techniques you can use, tips and tricks for creating easily intelligible large data sets and the best big data visualization tools you can use.

By the end of this article, you’ll feel like a real data scientist and be competent in creating pie charts, bar charts, heat maps, histograms, interactive charts and more for big data visualization.

So let’s get into it, shall we?

Table of Contents

What is Big Data Visualization?

Why is Data Visualization Important in Big Data?

What Are the Types of Big Data Visualization?

5 Big Data Visualization Tips for Beginners

4 Tools for Big Data Visualization


What is Big Data Visualization?

Big data visualization is the representation of large sets of data through visual aids, whether that be through pie charts, heat maps, bar charts or any other kind of chart types or visual representation.

Analyzing and understanding large data sets and data analytics is no easy task and it can be especially difficult trying to relay that same information to colleagues who are not data-driven or data scientists.

That’s where big data visualization comes in. By transforming your large data sets into visually appealing infographics or interactive charts, you can easily convey your data points to fellow decision-makers.

When your data is plotted out on graphs in a visual way and metrics are made easily readable, no data gets lost in the mix, no matter how large or small, and it makes decision-making for the future a breeze.

Because you can’t make adequate decisions or advance significantly without analyzing your raw data, it’s important that companies use great data visualization methods to keep everyone in the loop.

Let’s take BMW for example.

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In 2020, BMW was able to track the number of sales for electric cars that they had and then compare it to other car companies’ sales, but not only.

They also were able to track the countries that bought the highest amount of their electric cars.

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This is a prime example of big data visualization in action. When you track your analytics and data, you can see where your wins are and when to celebrate or where your losses are and how you can make adjustments for the future.

Now, imagine for a moment that all this information was just written out plainly on a spreadsheet and had unstructured data all over it.

It would be hard to understand and assess how the company is doing and would take a long time to communicate to employees how their work has affected the sales of the cars.

This is why visualizing big data is so important. With just a glance and within seconds, you can easily see what cars are selling best and in what countries.

No time is wasted going through spreadsheets and trying to make sense of unstructured data — just visual analytics laid out for all to see and understand.


Why is Data Visualization Important in Big Data?

We live in a time where the internet and social media have exploded at an extraordinary rate, and information can be gathered within seconds and at the tips of anyone’s fingers.

With the rise of this technological era, it’s important that data can be visualized and consumed quickly and efficiently — especially since the human brain now has an attention span of about 8 seconds, according to this study by the Technical University of Denmark.

Because companies, businesses and organizations can gather data more quickly than ever, this means that they need to be able to visualize that data in an equally quick and easily consumable way.

The best way to efficiently communicate your ever-coming, new data is through visualizing big data. This will bring your complex data to life and anyone who looks at it will be able to understand and grasp it with just a glance.

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Take the image above as an example. With just a quick look at the statistics that are clearly visualized, you can make a data-based assumption.

Now, imagine if this data was just written out plainly on a spreadsheet. It would take much longer to understand and make an assumption based on the numbers.

By using big data visualization techniques, you’ll be able to get the most value from your data and analytics and make sure that everyone who says your data analysis will be able to interpret, understand and use your data. This, in turn, will help your company excel.

When you use data visualization techniques, it will optimize your use of data, help decision making and planning go smoothly, you’ll be able to identify and mitigate risk, extract loads of useful data and insights and improve your overall strategy and direction of your company.

There are no losses to using a visual representation of data, only wins. But there are lots of different types of data visualization that you can use.

Let’s discuss the different types of big data visualization and assess which one will work best for you.


What Are the Types of Big Data Visualization?

There are lots of different types of data visualization that data analysts like to use and depending on the amount of data. A data analyst may choose to use a pie chart to express their numerical data or a bar chart.

When looking at big data analytics regarding locations, one might choose to use an interactive heat map or maybe a pivot table.

We’re going to look at 8 common types of big data visualization and some data visualization examples for each to help you decipher which one will work best for you.


Type #1: Line Charts

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A line chart, also known as a line graph, is a graphic representation of data that plots a fixed value on one side and a variable on the other.

A line chart is a fantastic way to represent the relationship of data. You can use a line chart to represent changes and fluctuations of things within a certain period of time.


Type #2: Bar Charts

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A bar chart, also known as a bar graph, uses bars to compare different data points or data sets.

Many data scientists will use bar charts to visually represent their data analysis. You can use a bar chart to compare large amounts of data, fluctuations of quantities or different categories.

The taller the bar, the larger the numerical value and vice versa.


Type #3: Pie Charts

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Pie charts, donut charts, circle graphs or whatever you choose to call them, are representations of data that are split into smaller segments and sizes to represent their numerical value.

When you use a pie chart, it becomes easy to see and compare how the different segments relate and differ from each other.

When using a pie chart, try not to overload it with too many different values. When you split the pie chart into more than 7 segments, it can become difficult to understand the data.


Type #4: Heat Maps

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A heat map is a visual representation of data that is laid out on a map or table and uses different nuances and intensities of colors to represent its data.

Using a heat map can be especially helpful when you need to analyze data that seems to be never-ending. When you have an extremely wide value range, using a heat map makes it much more simple to quickly visualize and analyze large amounts of complex data at a glance.


Type #5: Histograms

histogram - weights of newborns

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A histogram is a graphical and visual representation of complex data sets and the frequency of said numerical data displayed through bars.

Histograms are very similar to bar graphs but vary in the fact that they mostly focus on the repeated frequency of numerical data.

Type #6: Scatter Plots

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A scatter plot, scatter chart or scatter graph, is a diagram that uses dots to represent and emphasize the different values of two or more numeric variables on an X and Y-axis.

Scatter plots are extremely useful to use when you have multiple large data sets and you want to know how they relate to each other and compare the importance of each value.


Type #7: Treemaps

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Treemaps are the visual representation of hierarchical data by using color-coded rectangles.

Users can use treemaps as a method to compare multiple sets of data and reflect the weight of each value in a project.


Type #8: Funnel Charts

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Funnel charts are typically used in sales and represent the different stages that your users or customers go through during the sales process and demonstrate decreasing values as they move through your funnel.

By using a funnel chart, you can accurately see where you are losing or gaining your customers during the sales process.


5 Big Data Visualization Tips for Beginners

Now that we’ve covered what big data visualization is, its importance and 9 different types of data visualization, you may feel like you’re a professional in data science.

Now that you’re familiar with the basics of data visualization, it’s time that we equip you with some of our best data visualization techniques.

Here are our top 5 best data visualization techniques for you to use when creating a visual representation of your data.


Tip #1: Use a Powerful Data Visualization Tool

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You can’t create powerful graphs without a powerful data visualization tool.

Sure, you could use something like Google charts, but to create unique, engaging charts, you’ll want to use a data visualization tool like Visme that's packed with amazing functionality.

Visme is a powerful data visualization tool with many integration functionalities. As you can see in the image above, you can create everything from funnel charts and tables to interactive data maps and graphs in this editor.

When you need to visualize big data, Visme is the way to go. When you create a graph in our big data visualization tool, your data can be updated in real-time with our integration tools.

You can import all your data from Google Sheets, Microsoft Excel, Google Analytics and other data sources, then see it come to life automatically on your project while you sit back and relax.

Visme also has many open-source elements and graphics for you to use to keep your infographic intriguing. To have the perfect interactive data visualization, you can use word clouds, tables, treemaps, animated characters and graphic design elements and more to implement into your design.

They’re also a powerhouse filled with lots of useful and educational tutorials on how to create the perfect chart for your raw data. Visme also has lots of tutorials for all things graphic design.

So why not use a tool that has everything you need for creating visuals for your data analysis and tons of tutorials to go with it? You can start your free account with Visme today and start living out your data analyst dreams.

It’s important to use a strong data visualization software for your data analysis and presentations. Stick around and soon we’ll get into our list of best tools for big data visualization.


Tip #2: Pick the Correct Form of Big Data Visualization

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When it comes to visualizing your data, you need to make sure that you choose the correct chart type.

Because there are so many different ways to display your data, you need to weigh out the cons and pros of each and find out which one will work best for your infographic or presentation.

Take for example pie charts and bar graphs.

When you analyze data that is very different, you might want to use a pie chart. But if you want to represent data entries that are close together, you could use a bar chart for that.

If you’re trying to create data visualization for sales, you could use a funnel chart, pyramid chart or cone chart for that.

Each different visualization method has its time and place, and you need to analyze your data and think about what method will work best for your respective data.

Refer above to the “Big Data Visualization Types” section above to see which one will suit you best.


Tip #3: Make Sure Your Data is Easily Comprehensible

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The whole point of big data visualization is to make it easy to understand at a glance.

It won’t be easily intelligible if you just start piling in large amounts of unstructured data and simply hope for the best. Or imagine you have tens of tiny little numbers on a bar graph that no one can see or read.

You need to make sure that anyone on your team, whether a data scientist or not, can understand what you’re trying to convey at a glance.

You can do this by using clear and bold text, contrasting font colors and background colors, not adding too many values to one chart and using compelling images to highlight your point, just like in the example above.

By adding too much text or too many values to a single graph, you risk confusing your audience even more. So keep it as simple and concise as possible.


Tip #4: Always Use Legends to Further Explain Your Data

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Using legends is absolutely vital for making your data easy to understand, so whether you’re creating a pie chart or bar graph, make sure you’re using a legend.

A legend is an area of your design that further explains each segment of your chart.

Many times people will assign a color to a segment in their chart, just like in the example above, and on the side add a little graphic element that explains what each color represents.

The legend is responsible for keeping the audience engaged and understanding everything you’re trying to convey.


Tip #5: Use Multiple Charts for Big Data

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If you have a large amount of data that needs to be conveyed to your team, try using multiple graphs to do so.

Incorporating tons of data into a single chart will only make it hard for the human brain to stay on track and focus to try and understand what you want to share.

The best rule of thumb to follow here is KISS — keep it simple, stupid.

So instead of simply adding all your data to one pie chart and making it have 30 pie slices, why not create multiple graphs and break it down into bite-sized pieces? Pun intended.

By creating multiple matching charts, you can keep your data easily intelligible, cohesive and right on brand.

Just like in the example above, you can clearly understand all the data that’s being displayed because it is written out on two different donut charts.

You want to make sure your information is understandable by anyone at a glance, and you can do so by breaking down your data.


4 Tools for Big Data Visualization

Now that you know essentially all there is to know about big data visualization, it’s time you choose a tool that will help you create those visuals.

We’ll be covering 4 data visualization software you can use to get the job done.

Let’s jump right into it.


Tool #1: Visme

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If you want to create compelling and professional data visualization, then you need a tool like Visme.

Visme makes it easy for both designers and non-designers alike to visualize their data in interactive and engaging ways.

For example, you can create incredible animated charts, add your own audio files to them that you can record right within the editor, add tons of professionally design data widgets and import all your data from third-party websites such as Microsoft Excel, Google sheets etc.

The best part? You can save endless amounts of time and effort by using one of our hundreds of customizable templates for displaying your data.

Simply scroll through tons of professionally design templates for charts and data choose one that suits your style.

Everything can be customized on each and every template and you can even add your own brand colors, logo and font to keep everything right on-brand with your other designs.

Not only can you create loads of beautiful charts, graphs and infographics with Visme, but you can also create anything else design-related. You can create presentations, infographics, multi-page reports and proposals, branded social graphics and more.

If you’re looking for a powerful data visualization tool with high functionality for many other types of designs, Visme is the one for you. Plus, you can create a free account and use for as long as you like — no trial period or hidden costs!


Tip #2: Tableau

Tableau is an interactive data visualization software with a focus on business intelligence. Their goal is to help people make data that can be easily understood by anyone.

Tableau is a tool that is used in the business intelligence industry and it can help you simplify raw data into a simple format. With drag and drop functionalities, you can create data visualization fairly quickly and then share it with others.

In Tableau you can create lots of different data visualizations, from a correlation matrix to a simple bar graph.

Another plus for the software is that you can infuse the Tableau dashboard with artificial intelligence and machine learning from Aible.

You can start a free trial with Tableau, but it is a bit pricey after your trial is up. At $70/month billed annually, you’ll have to make sure you absolutely love the product before buying it.


Tool #3: Microsoft’s Power BI

Power BI by Microsoft is a business analytics service that helps you create interactive data visualizations.

Whether your data is on an Excel spreadsheet an on-premises hybrid data warehouses, Power BI will help you bring that data together to create reports and graphs to share with your team.

There are three versions of Power BI that you can use: the desktop app, the mobile app or their website.

You can use Power BI to help you visualize big data with your team by using some of their other popular apps like Microsoft Excel and work together in real-time to create compelling data.

Power BI has some basic templates that you can use to get a jump start on creating your data.

Power BI is quite affordable, coming in at $9.99/month.

If you’re not completely sold on using Power BI, let’s move on to our next tool.


Tool #4: Datawrapper

Datawrapper is an online tool that you can use to create data visualizations that are interactive and responsive, with no code or programming languages like python or javascript required.

With big users like the New York Times and the UN, they do have quite a few things to boast about.

Data wrapper is an open-source and easy-to-use data visualization software where you can create basic charts and graphs, maps and line charts that can be embedded into your website.

As for the price, you can use their free plan and create lots of charts, maps and tables, but they will be watermarked and there are a few other inconveniences that come with the free plan.

The next plan comes in at $599/month, which is definitely on the pricey side.

And that concludes our list of 4 tools for data visualization.


Now Over to You

If you want a data visualization software that will help you convey your data in a fun and engaging way, then you most likely will love using Visme.

Not only is Visme a powerful data visualization tool, but it’s so much more. You can use Visme to create all of your graphic design needs, from sales presentations to pitch decks, social media posts, infographics, videos, eBooks and more.

What are you waiting for? Create your free account today and free your inner data scientist.

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Jeremy Kennedy


4 Mistakes in Strategy Trading

Four Principles of Successful Trading

Why do successful traders keep making money year after year, while newbies lose everything within the first few months? What is it that most beginners get wrong? How do successful traders know what's right?

My colleagues and I are often asked how to succeed in trading. In fact, we have been asked this question so many times, that I have finally decided to write a trading report; a report that will give you straightforward and easy-to-follow advice on how to become a better trader.

Unlike most trading advice articles, this report is written in a clear, plain-English manner. I am going to describe the very essence of the problem in a concise and coherent way. You will read about major mistakes that prevent traders from making money and learn the basic principles that took successful traders years and thousands of dollars to discover. All the facts in this report are based on years of observation and can be easily verified.

Have you ever felt like you have finally learned how to predict market moves after a winning trade? And then felt desperate only a few days later - after a devastating loss?

Now imagine the feelings of a trader who spends years studying price movements, buying expensive indicators, following expert advice, and attending seminars. However, this trader keeps losing money until all their savings are gone. He then raises more funds, loses everything again - all the time wondering why, contrary to all the guru promises, he can't turn trading into a profitable business. Nevertheless trading is just as understandable, predictable and profitable as any other business.

Just imagine that after years invested in trading you still won't be able to understand how markets work. How frustrating would that be?

Or even worse: what if, driven by emotions, you lose control and, as a result, all your savings? Do you have an emergency plan to protect yourself?

How quickly do you think you could recover from heavy losses, if at all?

Not only beginners but also 'experienced' traders tend to ignore or forget about taking steps to protect their capital against these types of catastrophes - until disaster strikes. By then it's too late and the damage is done.

But That Could Never Happen to Me!

After working with over 2000 individual traders and institutional customers in Europe and the USA, we found that 9 out of 10 traders will experience some type of losses that will end up costing them between several thousand to several million dollars.

This doesn't include money spent on manuals, trainings, seminars or months of painstakingly analyzing the market.

Losses incurred in poor trading practices differ in each particular case. However, whatever those losses may be they are always too high for the trader involved. As a rule, people lose all their disposable money. Even worse: sometimes they go even further and get dragged into debt.

Take a look at these statistics:
90% - 95% OF ALL TRADERS LOSE MONEY (Source: Ryan Jones, the author of The Trading Game, Playing by the Numbers to Make Millions)
70 percent of day traders lose money (Source: 1999 study conducted by the North American Securities Administrators Association (NASAA))
95 percent will fail in the first two years (Source: Harvey Houtkin, February issue of Securities Regulation and Law Report)

What Do These Statistics Mean for You?

The facts above clearly demonstrate that most people underestimate the risks of trading. In most cases, they are simply misled by advertising from brokers and consultants. As a rule, brokers don't care about your long-term success because their goal is to quickly earn back the money invested in attracting a new customer. That's why they want you to start trading as soon as possible. To achieve this goal, brokers provide beginning traders with minimum information that is just sufficient to make trades (and thus to generate commission that brokers live on) and let them fly blind in the market. Such unscrupulous practices have even drawn attention of various governmental agencies supervising and monitoring securities trading. Unfortunately little success has been achieved in curbing these practices.

The sad truth is that most trading consultants sell trading methods that don't work. Of course, these methods are presented not only as working but also as highly profitable. As a rule, a potential customer is shown the few occasions when an indicator (or some other analysis method) happened to predict a good trading opportunity. What happens to be left out of the picture are all the occasions when the method led to disastrous trades.

Furthermore, trading gurus avoid selling their strategies as a set of formally defined objective criteria to enter the market. The main argument is that indicators must be applied differently in different situations. Gurus claim that no algorithm-based system can substitute human intellect. Of course, this kind of reasoning is extremely convenient. Whenever the advertised trading method brings disastrous results they blame the trader not the system. Since everything depends on the trader's subjective determinations, it's impossible to prove that it's the method that doesn't work. You are the only person to be blamed for those losses.

What's most exasperating about this situation is that most of these disasters and unnecessary costs could have been completely avoided or greatly mitigated easily and inexpensively with a little analysis and proactive verification.

Why Are Beginning Traders Particularly Vulnerable

Today's markets are becoming increasingly efficient. To survive in this highly competitive environment, unconventional tools and methods are called for. However, contrary to common sense, beginning traders don't even try to use the latest market analysis tools. Instead, they use methods that worked quite well 30 years ago but are totally useless nowadays.

Institutional players, on the other hand, are equipped with state-of-the-art methods and technologies. Trading futures is a zero sum game. In this game, newbies invariably fall prey to the more advanced players.

$45,000 Spent Just to Discover That a System Doesn't Work

One of my customers purchased a set of indicators from a well-known and respected trading expert. The method consisted in waiting till all the indicators showed a favorable point to enter the market. Of course, such trading opportunities don't come up every day.

You'd think that common sense should have told this customer to paper-trade his method first - to see how well it would work in the real market. Unfortunately, emotions and the expert's convincing arguments proved stronger. He took several trades that emptied his $45,000 trading account.

I tried to persuade the trader to have those indicators coded into a comprehensive and objective system and test it against historical data. My reasoning was simple: what didn't work in the past probably won't work in the future.

Out of pure curiosity I coded those indicators into a system and tested the system on different trading instruments and resolutions. The tests proved that the system didn't work.

If the above trader had spent $900 on a back-testing program and $200 on coding his system, he could have saved $45,000!

How Slow Reaction Once Cost Me $2,000 in 5 Seconds

At some point, I was combining software development with trading FOREX. This active trading gave me a good feel of the tasks and problems that traders face and allowed me to develop software to improve my own results.

I was once trading a system based on the Federal Reserve System interest rate announcement. My strategy correctly indicated the entry direction. Unfortunately, back then I wasn't using automated trading and had to manually adjust the stop loss as soon as the market started moving in the favorable direction. The broker I was using didn't support trailing stops, so manual adjustment was the only way to trade with my method.

As soon as the profit reached the required value I started adjusting the stop loss. Unfortunately it took me too long and a potentially lucrative trade was closed with a loss. The market gets highly volatile following news releases, therefore 5 seconds for manual correction was way too long. If I had managed to adjust the stop within 2 seconds, I would have made $2000.

Automated order execution allows reducing the reaction time. It will take your computer 1 second or less to react and modify an order.

Thus, a one-time investment in automating my strategy worth just 1/10 (or $200) of just one losing trade could have completely changed the outcome. And who knows how many similarly unsuccessful trades will occur in future?

Six Consecutive Losing Trades Made a Trader Give Up on a Working Trading Method and Miss a Rare $35,000 Trade

The manager of a 50 million dollar investment fund told me about a loss that wouldn't have happened if they had adopted the well-known practice to diversify traded instruments. Richard, one of the fund's analysts, was trading on exotic markets using an automated trading system. The system had been tested before and had proven reliable and profitable. Tested against historical data, it had never shown more than 4 successive losers, which was normal for this particular system.

However, in real-world trading the system generated 6 consecutive losing trades and Richard decided to drop it. He found it psychologically difficult to use the method that seemed to have stopped working - even though he knew that the market was being sluggish and the system's behavior was totally natural under the circumstances. As soon as he stopped using the system, the market entered a growth stage and this trend-following system started working again. As a result an excellent opportunity to earn $35,000 on a single contract was lost! This costly mistake could have been easily avoided, if they had been trading a portfolio based on uncorrelated markets. It would have ensured a steady profit growth irrespective of the conditions on a single market. Other profits would have nullified 6 losing trades on this particular instrument.

When I asked Richard why he didn't use diversification, he said that the reason was quite simple: the company wasn't paying enough attention to the issue and he didn't have the software to make an efficient portfolio.

The unwillingness to factor in quite a predictable situation as well as the desire to save $2,000 on software resulted in losses 20 times exceeding expenses on the necessary research.

Four Things You Must Do at a Minimum to Protect Yourself from Common Mistakes:

While it's impossible to plan for every problem or emergency, a little proactive analysis and a few simple rules will help you avoid or greatly reduce losses.

Unfortunately, I have found that most beginning and even many experienced traders are NOT conducting any type of analysis, which leaves them completely vulnerable to the types of disasters you just read about. This is primarily for four reasons:

#1. They don't understand the importance of verifying trading systems
#2 They use outdated market analysis techniques instead of adopting the latest and most efficient approaches
#3. Even if they do have a good trading method, they can't use it efficiently
#4. They rely on profits from a single strategy/instrument and don't try to diversify their portfolio

While there are over 20 critical tasks that need to be performed to succeed in trading, I'm going to share with you the 6 that are most important for protecting your capital and creating the most favorable conditions for a profitable and consistent strategy.

Step#1: Don't Trust Any Trading Ideas

I never stop wondering what makes people blindly trust the so-called gurus. I have lots of facts proving that most gurus are nothing more than frauds. However, my main point is that you shouldn't trust anybody, not even yourself. You are the only person responsible for your trading failures and successes. If you fail, the only person you should blame is yourself. If you have a trading idea you must test it. All assumptions and untested ideas cost too much. You simply can't afford it!

Therefore, before you start trading with real money, test your idea under conditions as close to real life as possible. The more accurate the simulation is, the more reliable your tests will be. Be critical and objective when making conclusions. Trust statistics, not your feelings or beliefs.

Step #2: Learn From Those Who Really Know How to Make Money While Trading

Study the practices of those traders who take money out of the markets year after year. Unfortunately, finding such people is a major challenge. Most gurus you'll meet will tell you that they are extremely successful and that they teach trading just for the fun of it or out of pure generosity. I'm afraid that in 99% of these cases all their success stories will be lies.

I only trust statistics coming from unbiased sources. For my analysis I rely on ten-year reports on the best Commodity Trading Advisors (CTAs). Obviously, anybody could be making money for 1-3 years due to sheer luck. Data spanning 1-3 years isn't statistically reliable and can't be trusted. This is why in the ten-year reports, I single out those traders who have been making steady profits for more than 3 years.

Most successful CTAs rely exclusively on mechanical methods and automate their trading to be faster than everybody else. Also, they always diversify their trading.

If you analyze the trends in the algorithm-based trading industry, you will see that most of the solutions out there have been created for institutional traders and cost thousands of dollars. The high demand for algorithm-based trading on the part of institutional traders is only natural. They understand that the best trading opportunities can't last long. Mere seconds separate winners from losers.

Today's markets respond well to arbitrage strategies and high frequency trading. These methods, however, call for reliable mechanical trading systems and high-quality software to deploy them.

I must point out once again that the primitive systems that most newbies get a hold of ceased to work long ago. In the 1970s, even basic trend-following strategies worked fine because the markets weren't as volatile and fast-paced as they are today. Nowadays, markets require brand-new methods that you won't find in old trading manuals.

Step #3: Run Multiple Tests of Your System under Various Conditions

I am not going to deny the benefits of paper-trading, but I prefer backtesting. Backtesting is the fastest, the most reliable, and most objective way to test a trading method in different situations without letting emotions interfere with your judgment. When testing your trading idea in real-time on a simulated account, it's impossible to ensure that your tests are error-free and extensive enough to be statistically reliable. I've met few people who would be prepared to paper-trade a strategy for at least 3 months before switching to real-world trading. As a result, their conclusions about the strategy's workability and performance are highly subjective and rash.

Backtesting allows trying a method against different historical data and across different financial instruments. It also ensures that the results are unbiased and consistent. Of course, there are a number of backtesting rules but those must be discussed separately.

Invest just a couple of hundred of dollars to have your strategy coded or spend a few hours of your time to do it yourself and you'll find out if the tested idea is worth anything. I can assure you that you'll reject 99% of the systems that you thought were a sure thing!

Step #4: Don't Miss the Benefits of Optimization for Fear of Curve Fitting

Most people regard optimization with apprehension because in most cases it is applied incorrectly and therefore leads to devastating results. Most people optimize their trading systems to find the best parameters. However, optimization must be approached in a completely different fashion.

How do you create a new trading method? You visually scan data for patterns and check how well those patterns work in various situations. Such eyeball tests are nothing less than implicit optimization.

For example, you will use a moving average with the length of 20 and won't use a moving average with the length of 25. Why? Because you can see that the 25 moving average can't predict market moves as precisely. In other words, you have visually optimized your strategy. The danger of such optimization though is that the chosen value of 20 can be completely random and have no rational foundation.

Optimization is vital for solving two tasks. First of all, eyeballing data for the best parameters is too tedious and time-consuming. Moreover, you might simply never manage to find those best parameters. Let the computer do the job and do it much faster than you would. For example, with the help of the genetic optimization I can test hundreds of indicators with different parameters and find out what works and what doesn't. Visual analysis of the same set of indicators would take centuries.

Second, optimization ensures that the discovered optimal parameters aren't random or over-sensitive to changes. Just create a 3D optimization graph with one click and you'll see how robust your strategy is. If even minor changes affect the strategy's performance and there are no logical explanations for each value, you're simply using an over-optimized system that might look nice when tested, but will result in disastrous losses in real-world trading.

Step #5: Trade Several Instruments to Ensure Consistent Profits

According to experts, it is vital to trade several uncorrelated instruments. Diversification allows compensating for unfavorable trading periods for a particular instrument while steadily increasing the overall size of your trading account. Obviously, even the most consistent strategy will run into periods of losses. This is the normal dynamics of trading. At the same time, traders find this phenomenon extremely difficult to deal with. They feel like the system is no longer working because the market has changed. These assumptions can be contrary to reality but more often than not they override logic and common sense and lead to poor decisions.

To eliminate or at least minimize this effect, several strategies must be traded in uncorrelated markets. This way you will ensure a steadier capital growth and abate losses during unfavorable periods. Profits from trading one of the instruments will compensate for the money lost on another. The result will be a modest but steady growth which is the most important thing in trading. Today, creating an efficient portfolio isn't such a difficult task. Portfolio-level backtesting is now available for a reasonable price. Just a few years ago only large companies with enormous budgets could afford portfolio backtesting.

Step #6: Automate Your Trading Method to Avoid Errors and Routine

It is a well-known fact that a good signal is not enough to enter the market. The latest trading methods call for the best possible entry price. This is especially true for high frequency trading. Human reaction isn't quick enough to respond to price changes within milliseconds. At the same time, the price can change several points which will result in a smaller profit or even in a loss.