Rather, before taking the judge position Faruqui was one of a group of prosecutors in the U. There, Faruqui prosecuted cases that involved terrorism, child pornography, and weapons proliferation. But the ways Faruqui has weighed on cases that have come before him can give lawyers clues as to what legal frameworks will pass muster.

Crypto lawyers have drawn on his prior decisions in the context of the Tornado Cash sanctions, for example. Faruqui spoke with Protocol about the power of his position, and what people in crypto should understand about the law. There was another prosecutor, Christopher Brown — you know, the other Chris Brown — and he had taken an interest in this when we were both working on financial crime in the Washington, D.

Our U. attorney at the time, Jessie Liu, had this idea of using financial investigations in a way that was not limited to just white collar crime, or even narcotics cases, but also for cyber investigations, to national security investigations, and in civil cases. A lot of what we were investigating was related to following the money and so she wanted us to be this multidisciplinary unit. But I have to say, we started with the goal of wanting to make T-shirts, and we never did that while I was there.

Your decisions have also gotten a lot of attention. We're public servants! And in order for the public to have faith and trust us, they need to understand what it is that we're doing and what we're saying. Humor is one way, not using a lot of legalese is another way. But I think there are many judges who are trying to make the judiciary more accessible, and so people can see the work that we're doing and understand what we're doing and then make their own opinions about if it's right or wrong.

But at least, if it's understandable, then there's still some trust in the framework even if you don't agree with how our decisions are stated. We are ambassadors for the judiciary to the people in our courtroom — it's a very frightening proposition being in court if you've been federally charged, and people have perceptions of what they think can happen there in terms of fairness or unfairness. But then it goes far beyond that.

I do a lot of work with the Administrative Office of the Courts, our central body doing civic education and outreach to high schools, because I want college and high school students and law students to have an experience where they get a chance to talk to a judge. So my goal is certainly not just getting to one segment of the population, but it's making decisions accessible to whoever's interested in reading them. What has it felt like for you switching from that prosecutor role to magistrate judge?

Lawyers are trying to take different frameworks from one topic and apply them to another, and then convince you that that is or is not appropriate. Being a judge is very different because you're evaluating what the parties present to you as the applicable legal frameworks, and deciding how new, groundbreaking technology fits into legal frameworks that were written 10 or 15 years ago.

But that's not really a place where judges get involved in saying how it ought to be regulated. There was, famously, a judge in Florida that said cryptocurrency was not money because you couldn't put it underneath your bed, and that's what money is: something that is tangible.

So different people are going to have different decisions. And that's not just true for crypto, but also other areas of the law. Your best-known crypto decisions strongly assert that crypto is traceable. One way people try to make it less traceable is with mixers, and Tornado Cash was sanctioned by OFAC not too long ago.

Do you think the legal reasoning was sound enough for similar sanctions to be applied to other mixers, or decentralized exchanges? I don't know. I think there's been some discussion that people may litigate some of these things, so I can't comment, because those frequently do come to our courthouse.

And I think there are certainly people opining on that, yes and no. So much of what judges do is that we rely on the parties that are before us to tell us what's right and what's wrong.

And then, you know, obviously, they'll have different views, and we make a decision based on what people say in front of us. Are you aware that some legal analysis of the Tornado Cash sanctions references your recent decision in a cryptocurrency sanctions case? That's what good lawyers will always do. Even legislators might look at that as they try to think about where the gaps are. As a prosecutor I had a case where we sued three Chinese banks to give us their bank records, and it had never been done before.

Afterwards, Congress passed a new law, using the decisions from judges in this court and the D. circuit court, the court above us.

So I'm sure people look at prior decisions and try to apply them in the ways that they want to. Are there any misconceptions about how the law applies to crypto, or how your decisions should be interpreted, that you wish you could get across?

One misconception is that the judges can't understand this technology — we can. People have these views in two extremes. The lawyer's fundamental job is to take super complex and technical things and boil them down to very easily digestible arguments for a judge, for a jury, or whoever it might be. The financial technology transformation is driving competition, creating consumer choice, and shaping the future of finance. Hear from seven fintech leaders who are reshaping the future of finance, and join the inaugural Financial Technology Association Fintech Summit to learn more.

Financial technology is breaking down barriers to financial services and delivering value to consumers, small businesses, and the economy. Fintech puts American consumers at the center of their finances and helps them manage their money responsibly.

From payment apps to budgeting and investing tools and alternative credit options, fintech makes it easier for consumers to pay for their purchases and build better financial habits. Fintech also arms small businesses with the financial tools for success, including low-cost banking services, digital accounting services, and expanded access to capital. We advocate for modernized financial policies and regulations that allow fintech innovation to drive competition in the economy and expand consumer choice.

Spots are still available for this hybrid event, and you can RSVP here to save your seat. Join us as we discuss how to shape the future of finance. In its broadest sense, Open Banking has created a secure and connected ecosystem that has led to an explosion of new and innovative solutions that benefit the customer, rapidly revolutionizing not just the banking industry but the way all companies do business.

Target benefits are delivered through speed, transparency, and security, and their impact can be seen across a diverse range of use cases. Sharing financial data across providers can enable a customer individual or business to have real-time access to multiple bank accounts across multiple institutions all in one platform, saving time and helping consumers get a more accurate picture of their own finances before taking on debt, providing a more reliable indication than most lending guidelines currently do.

Companies can also create carefully refined marketing profiles and therefore, finely tune their services to the specific need. Open Banking platforms like Klarna Kosma also provide a unique opportunity for businesses to overlay additional tools that add real value for users and deepen their customer relationships.

The increased transparency brought about by Open Banking brings a vast array of additional benefits, such as helping fraud detection companies better monitor customer accounts and identify problems much earlier.

The list of new value-add solutions continues to grow. The speed of business has never been faster than it is today. For small business owners, time is at a premium as they are wearing multiple hats every day. Macroeconomic challenges like inflation and supply chain issues are making successful money and cash flow management even more challenging.

This presents a tremendous opportunity that innovation in fintech can solve by speeding up money movement, increasing access to capital, and making it easier to manage business operations in a central place. Fintech offers innovative products and services where outdated practices and processes offer limited options. For example, fintech is enabling increased access to capital for business owners from diverse and varying backgrounds by leveraging alternative data to evaluate creditworthiness and risk models.

This can positively impact all types of business owners, but especially those underserved by traditional financial service models. When we look across the Intuit QuickBooks platform and the overall fintech ecosystem, we see a variety of innovations fueled by AI and data science that are helping small businesses succeed.

By efficiently embedding and connecting financial services like banking, payments, and lending to help small businesses, we can reinvent how SMBs get paid and enable greater access to the vital funds they need at critical points in their journey. Overall, we see fintech as empowering people who have been left behind by antiquated financial systems, giving them real-time insights, tips, and tools they need to turn their financial dreams into a reality.

Innovations in payments and financial technologies have helped transform daily life for millions of people. People who are unbanked often rely on more expensive alternative financial products AFPs such as payday loans, money orders, and other expensive credit facilities that typically charge higher fees and interest rates, making it more likely that people have to dip into their savings to stay afloat.

A few examples include:. Mobile wallets - The unbanked may not have traditional bank accounts but can have verified mobile wallet accounts for shopping and bill payments. Their mobile wallet identity can be used to open a virtual bank account for secure and convenient online banking.

Minimal to no-fee banking services - Fintech companies typically have much lower acquisition and operating costs than traditional financial institutions. They are then able to pass on these savings in the form of no-fee or no-minimum-balance products to their customers. This enables immigrants and other populations that may be underbanked to move up the credit lifecycle to get additional forms of credit such as auto, home and education loans, etc.

Entrepreneurs from every background, in every part of the world, should be empowered to start and scale global businesses. Most businesses still face daunting challenges with very basic matters. These are still very manually intensive processes, and they are barriers to entrepreneurship in the form of paperwork, PDFs, faxes, and forms. Stripe is working to solve these rather mundane and boring challenges, almost always with an application programming interface that simplifies complex processes into a few clicks.

Stripe powers nearly half a million businesses in rural America. The internet economy is just beginning to make a real difference for businesses of all sizes in all kinds of places.

We are excited about this future. The way we make decisions on credit should be fair and inclusive and done in a way that takes into account a greater picture of a person. Lenders can better serve their borrowers with more data and better math.

Zest AI has successfully built a compliant, consistent, and equitable AI-automated underwriting technology that lenders can utilize to help make their credit decisions. While artificial intelligence AI systems have been a tool historically used by sophisticated investors to maximize their returns, newer and more advanced AI systems will be the key innovation to democratize access to financial systems in the future.

D espite privacy, ethics, and bias issues that remain to be resolved with AI systems, the good news is that as large r datasets become progressively easier to interconnect, AI and related natural language processing NLP technology innovations are increasingly able to equalize access. T he even better news is that this democratization is taking multiple forms. AI can be used to provide risk assessments necessary to bank those under-served or denied access.

AI systems can also retrieve troves of data not used in traditional credit reports, including personal cash flow, payment applications usage, on-time utility payments, and other data buried within large datasets, to create fair and more accurate risk assessments essential to obtain credit and other financial services.

By expanding credit availability to historically underserved communities, AI enables them to gain credit and build wealth. Additionally, personalized portfolio management will become available to more people with the implementation and advancement of AI. Sophisticated financial advice and routine oversight, typically reserved for traditional investors, will allow individuals, including marginalized and low-income people, to maximize the value of their financial portfolios.

Moreover, when coupled with NLP technologies, even greater democratization can result as inexperienced investors can interact with AI systems in plain English, while providing an easier interface to financial markets than existing execution tools. Open finance technology enables millions of people to use the apps and services that they rely on to manage their financial lives — from overdraft protection, to money management, investing for retirement, or building credit.

More than 8 in 10 Americans are now using digital finance tools powered by open finance. This is because consumers see something they like or want — a new choice, more options, or lower costs. What is open finance? At its core, it is about putting consumers in control of their own data and allowing them to use it to get a better deal. When people can easily switch to another company and bring their financial history with them, that presents real competition to legacy services and forces everyone to improve, with positive results for consumers.

For example, we see the impact this is having on large players being forced to drop overdraft fees or to compete to deliver products consumers want. We see the benefits of open finance first hand at Plaid, as we support thousands of companies, from the biggest fintechs, to startups, to large and small banks.

All are building products that depend on one thing - consumers' ability to securely share their data to use different services. Open finance has supported more inclusive, competitive financial systems for consumers and small businesses in the U.

and across the globe — and there is room to do much more. As an example, the National Consumer Law Consumer recently put out a new report that looked at consumers providing access to their bank account data so their rent payments could inform their mortgage underwriting and help build credit. This is part of the promise of open finance.

At Plaid, we believe a consumer should have a right to their own data, and agency over that data, no matter where it sits. This will be essential to securing benefits of open finance for consumers for many years to come.

As AWS preps for its annual re:Invent conference, Adam Selipsky talks product strategy, support for hybrid environments, and the value of the cloud in uncertain economic times. Donna Goodison dgoodison is Protocol's senior reporter focusing on enterprise infrastructure technology, from the 'Big 3' cloud computing providers to data centers. She previously covered the public cloud at CRN after 15 years as a business reporter for the Boston Herald.

AWS is gearing up for re:Invent, its annual cloud computing conference where announcements this year are expected to focus on its end-to-end data strategy and delivering new industry-specific services. Both prongs of that are important. But cost-cutting is a reality for many customers given the worldwide economic turmoil, and AWS has seen an increase in customers looking to control their cloud spending.

By the way, they should be doing that all the time. The motivation's just a little bit higher in the current economic situation.

This interview has been edited and condensed for clarity. Besides the sheer growth of AWS, what do you think has changed the most while you were at Tableau? Were you surprised by anything? The number of customers who are now deeply deployed on AWS, deployed in the cloud, in a way that's fundamental to their business and fundamental to their success surprised me. There was a time years ago where there were not that many enterprise CEOs who were well-versed in the cloud.

It's not just about deploying technology. The conversation that I most end up having with CEOs is about organizational transformation.

It is about how they can put data at the center of their decision-making in a way that most organizations have never actually done in their history. And it's about using the cloud to innovate more quickly and to drive speed into their organizations.

Those are cultural characteristics, not technology characteristics, and those have organizational implications about how they organize and what teams they need to have. It turns out that while the technology is sophisticated, deploying the technology is arguably the lesser challenge compared with how do you mold and shape the organization to best take advantage of all the benefits that the cloud is providing.

How has your experience at Tableau affected AWS and how you think about putting your stamp on AWS? I, personally, have just spent almost five years deeply immersed in the world of data and analytics and business intelligence, and hopefully I learned something during that time about those topics.

I'm able to bring back a real insider's view, if you will, about where that world is heading — data, analytics, databases, machine learning, and how all those things come together, and how you really need to view what's happening with data as an end-to-end story.

It's not about having a point solution for a database or an analytic service, it's really about understanding the flow of data from when it comes into your organization all the way through the other end, where people are collaborating and sharing and making decisions based on that data. AWS has tremendous resources devoted in all these areas.

Can you talk about the intersection of data and machine learning and how you see that playing out in the next couple of years?

What we're seeing is three areas really coming together: You've got databases, analytics capabilities, and machine learning, and it's sort of like a Venn diagram with a partial overlap of those three circles. There are areas of each which are arguably still independent from each other, but there's a very large and a very powerful intersection of the three — to the point where we've actually organized inside of AWS around that and have a single leader for all of those areas to really help bring those together.

There's so much data in the world, and the amount of it continues to explode. We were saying that five years ago, and it's even more true today. The rate of growth is only accelerating.

It's a huge opportunity and a huge problem. A lot of people are drowning in their data and don't know how to use it to make decisions. Other organizations have figured out how to use these very powerful technologies to really gain insights rapidly from their data. What we're really trying to do is to look at that end-to-end journey of data and to build really compelling, powerful capabilities and services at each stop in that data journey and then…knit all that together with strong concepts like governance.

By putting good governance in place about who has access to what data and where you want to be careful within those guardrails that you set up, you can then set people free to be creative and to explore all the data that's available to them. AWS has more than services now. Have you hit the peak for that or can you sustain that growth? We're not done building yet, and I don't know when we ever will be. We continue to both release new services because customers need them and they ask us for them and, at the same time, we've put tremendous effort into adding new capabilities inside of the existing services that we've already built.

We don't just build a service and move on. Inside of each of our services — you can pick any example — we're just adding new capabilities all the time. One of our focuses now is to make sure that we're really helping customers to connect and integrate between our different services. So those kinds of capabilities — both building new services, deepening our feature set within existing services, and integrating across our services — are all really important areas that we'll continue to invest in.

Do customers still want those fundamental building blocks and to piece them together themselves, or do they just want AWS to take care of all that? There's no one-size-fits-all solution to what customers want. It is interesting, and I will say somewhat surprising to me, how much basic capabilities, such as price performance of compute, are still absolutely vital to our customers.

But it's absolutely vital. Part of that is because of the size of datasets and because of the machine learning capabilities which are now being created. They require vast amounts of compute, but nobody will be able to do that compute unless we keep dramatically improving the price performance. We also absolutely have more and more customers who want to interact with AWS at a higher level of abstraction…more at the application layer or broader solutions, and we're putting a lot of energy, a lot of resources, into a number of higher-level solutions.

One of the biggest of those … is Amazon Connect, which is our contact center solution. In minutes or hours or days, you can be up and running with a contact center in the cloud. At the beginning of the pandemic, Barclays … sent all their agents home.

In something like 10 days, they got 6, agents up and running on Amazon Connect so they could continue servicing their end customers with customer service. We've built a lot of sophisticated capabilities that are machine learning-based inside of Connect. We can do call transcription, so that supervisors can help with training agents and services that extract meaning and themes out of those calls.

We don't talk about the primitive capabilities that power that, we just talk about the capabilities to transcribe calls and to extract meaning from the calls. It's really important that we provide solutions for customers at all levels of the stack. Given the economic challenges that customers are facing, how is AWS ensuring that enterprises are getting better returns on their cloud investments? Now's the time to lean into the cloud more than ever, precisely because of the uncertainty.

We saw it during the pandemic in early , and we're seeing it again now, which is, the benefits of the cloud only magnify in times of uncertainty. For example, the one thing which many companies do in challenging economic times is to cut capital expense. For most companies, the cloud represents operating expense, not capital expense. You're not buying servers, you're basically paying per unit of time or unit of storage.

That provides tremendous flexibility for many companies who just don't have the CapEx in their budgets to still be able to get important, innovation-driving projects done. Another huge benefit of the cloud is the flexibility that it provides — the elasticity, the ability to dramatically raise or dramatically shrink the amount of resources that are consumed.

You can only imagine if a company was in their own data centers, how hard that would have been to grow that quickly. The ability to dramatically grow or dramatically shrink your IT spend essentially is a unique feature of the cloud.

These kinds of challenging times are exactly when you want to prepare yourself to be the innovators … to reinvigorate and reinvest and drive growth forward again. We've seen so many customers who have prepared themselves, are using AWS, and then when a challenge hits, are actually able to accelerate because they've got competitors who are not as prepared, or there's a new opportunity that they spot.

We see a lot of customers actually leaning into their cloud journeys during these uncertain economic times. The Strategy Tester is multi-threaded, thus allowing to use all available computer resources. Testing and optimization are carried out using special computing agents that are installed as services on the user's computer. Agents work independently and allow parallel processing of optimization passes. An unlimited number of remote agents can be connected to the Strategy Tester. In addition, the Strategy Tester can access the MQL5 Cloud Network.

It brings together thousands of agents around the world, and this computational power is available to any user of the trading platform. In addition to Expert Advisor testing and optimization, you can use the Strategy Tester to test the operation of custom indicators in the visual mode. This feature allows to easily test the operation of demo versions of indicators downloaded from the Market. Optimization means multiple runs of an Expert Advisor using history data with different sets of parameters, aimed at finding their best combination.

During multiple runs, different combinations of the input parameters of an Expert Advisor are tested to find the best ones. Watch the video: How to test Expert Advisors and Indicators before purchase. Watch the video to learn how to test a trading robot before you purchase it from the Market.

Every product on the Market is provided with a free demo version, which can be tested in the Strategy Tester. Please watch the video for further details. After tester launch, instead of multiple settings the user sees a list of standard tasks, by selecting which they can quickly start testing. This will be especially useful for users without previous experience. Some of the major strategy testing and optimization tasks are presented in the start page. In addition, one of the previously performed tasks can be restarted from this page.

If you have run a lot of tasks and they do not fit into the start page, use the search bar. You can find a test by any parameter: program name, symbol, timeframe, modeling mode, etc. After selecting a task, the user proceeds to further testing parameters setup: selection of an Expert Advisor, symbol, testing period, etc.

All irrelevant parameters which are not required for the selected tasks are hidden from the setup page. For example, if mathematical calculations are selected, only two parameters should be specified: selection of a program to be tested and the optimization mode. Testing period, delay and tick generation settings will be hidden.

All available optimization options will be explained below. Click " Test" in the context menu of an Expert Advisor in the Navigator window. After that the Expert Advisor is selected in the Strategy Tester. The Strategy Tester allows backtesting strategies that trade multiple symbols.

Such trading robots are conventionally called multicurrency Expert Advisors. The tester automatically downloads the history of required symbols from the trading platform not from the trade server! during the first call of the symbol data. Only the missing price history data are additionally downloaded from the trading server. Before you start optimization of a multi-currency Expert Advisor, enable the symbols required for testing in the Market Watch.

In the context menu, click " Symbols" and enable the required instruments. Before you start optimization, select the financial instrument to test the trading robot operation on, the period and the mode.

Select the main chart for testing and optimization. Symbol selection is required to provide the triggering of OnTick events contained in Expert Advisors. Also, the selected symbol and period affect special functions in the Expert Advisor code that use current chart parameters for example, Symbol and Period.

In other words, the chart to which the Expert Advisor is attached should be selected here. Select testing and optimization period.

You can select one of predefined periods or set a custom time interval. To set a custom period, enter the start and end dates in the appropriate fields to the right. The specific feature of the tester is that it additionally downloads some data preceding the specified period to form no less than bars. This is required for a more accurate testing and optimization. For example, if you test on a one-week timeframe, two additional years are downloaded.

If there is not enough history data for forming additional bars it is especially significant for the monthly and weekly timeframes , for example, when specifying a start of testing close to the start of existing history data, then the start date of testing will be automatically shifted. An appropriate message is added to the Strategy Tester journal.

This option enables the verification of optimization results using a preset forward period in an effort to avoid overfitting in optimization time intervals. During forward optimization , the period set in the Date field is divided into two parts in accordance with the selected forward period a half, one third, one fourth or a custom period when you specify the forward testings tart date. Expert Advisor optimization is performed using the data of the first period.

The results of the best optimization runs on both periods can be compared on tabs Optimization Results and Forward Results. Strategy tester enabled the emulation of network delays during an Expert Advisor operation in order to provide close-to-real conditions for testing.

A certain time delay is inserted between placing a trade request and its execution in the strategy tester. From the moment of request sending till its execution the price can change. This allows users to evaluate how trade processing speed affects the trading results. In the instant execution mode, users can additionally check the EA's response to a requote from the trade server.

If the difference between requested and execution prices exceeds the deviation value specified in the order, the EA receives a requote. Please note that delays work only for trades performed by an EA placing orders , changing stop levels , etc. For example, if an EA uses pending orders, delays are only applied to order placing but not to order execution in real conditions, execution occurs on the server without a network delay.

In this mode, all orders are executed at requested prices without requotes. The mode is used to check how an EA would perform in "ideal" conditions. The Random Delay mode allows testing an Expert Advisor in conditions maximally close to real ones.

The delay value is generated as follows: a number from 0 to 9 is selected randomly - this is the number of seconds for a delay; if a selected number is equal to 9, another number from the same range is selected randomly and added to the first one. You can select one of the predefined delay values or set a custom one.

The platform measures the ping to the trade server and allows you to set that value as a delay in the tester so that you are able to test a robot in the conditions that are as close to the real ones as possible. For more information about tick generation, please read the appropriate section.

Profit calculation in pips can speed up the testing process while there is no need to recalculate profit to deposit currency using conversion rates and thus there is no need to download the appropriate price history. Swap and commission calculations are eliminated in this mode. Please note that margin control is not performed in this mode. You should only use it for quick and rough strategy estimation and then check the obtained results using more accurate modes.

Specify the amount of the initial deposit used for testing and optimization. The deposit currency of the currently connected account is used by default, but you can specify any other currency. Please note that cross rates for converting profit and margin to the specified deposit currency must be available on the account, to ensure proper testing. Only symbols with the "Forex" or "Forex No Leverage" calculation type can be used as cross rates.

Next select the leverage for testing and optimization. The leverage influences the amount of funds reserved on the account as the margin on positions and orders. If you have the source code of the selected Expert Advisor, you can click this button to switch to its editing in MetaEditor.

Use this menu to manage tester settings: save sets of settings for various Expert Advisors in ini files and access them in a couple of clicks later.

From the same menu, you can quickly select the last used programs, last chart settings and testing periods. Furthermore, you can quickly access any of the previous optimization results , as well as the settings with which the result was achieved. Almost all specification parameters can be overwritten: volumes, trading modes, margin requirements, execution mode and other settings. Set your own trading account parameters when testing strategies, such as trading limits, margin settings and commissions.

This option enables the simulation of different trading conditions offered by brokers. For more details about the available types please read the appropriate section. Optimization criterion is a certain factor, which value defines the quality of a tested set of parameters.

The higher the value of the optimization criterion, the better the testing result with the given set of parameters. It is only used for genetic optimization. The quick optimization based on the genetic algorithm is enabled by selecting optimization criteria in the field located to the right. This field sets the parameter, based on which the most successful Expert Advisor runs are selected.

The larger the value of a selected parameter, the better the result. After setting all the parameters click "Start". This launches the process of testing and optimization. Input parameters allow you to control the behavior of the Expert Advisor, adapting it to different market conditions and a specific financial instrument. For example, you can explore the Expert Advisor performance with different Stop Loss and Take Profit values, different periods of the moving average used for market analysis and decision-making, etc.

To enable the optimization of a parameter, mark the appropriate checkbox. Next set the start and end of the range of values, as well as the step for testing. You can select one or more parameters. The total number of possible combinations is displayed beneath the list of parameters. Parameter sets. You can at any time return to the current settings of your MQL5 program by saving a set of its parameters using a context menu:. You can specify custom trading account settings during strategy testing, such as trading limits, margin settings and commissions.

In this section, you can set the maximum number of open orders and positions, which can simultaneously exist on the account. Additionally, you can configure sessions during which the program is not allowed to trade. The section allows configuration of margin reserving rules and position accounting systems to be used in testing:.

Risk management model : OTC and Exchange, netting or hedging. When this level is reached, all orders are canceled and all trading positions are closed. These levels can be indicated in money and in percentage.

In the former case, they are determined as the account's Equity value. Release fixed profit at the end of day — this option becomes available only if the option "Use daily fixed loss" is selected.

If it is enabled, the accumulated profit will be released and thus included in the free margin at the end of the day. Otherwise this profit amount will remain blocked. This section provides control over commissions charged on all trading operations:. To apply commission settings of the current trading account, enable the option "Use predefined commissions". Enable the option to use current trading account commission settings instead of custom settings specified below.

Specify the name of the symbol for which you are configuring commissions. Several settings can be added for each symbol. Thus, you can set up multi-level commissions that depend on the deal volume or turnover.

Commission can be charged immediately after each trade, or it can be accumulated during the trading day or month and then charged in one operation:.

The selected option determines the entity whose volumes are indicated in the "From" and "To" fields: deal or turnover. For instant commissions, trade direction can be set manually. The following rules shall apply for Close By deals:. The minimum deal volume turnover from which the commission will be charged.

The ranges must not overlap. Otherwise, the commission will be charged for all the ranges, in which the operation falls. The maximum deal volume turnover from which the commission will be charged. The ranges must not overlap..

Commission fee amount. Commission units depend on the commission calculation method selected in the Mode field. Minimum commission amount. Value units depend on the selected calculation mode in the base currency, group currency, points.

If you do not want to limit the minimum commission amount, set the 0 value. Maximum commission amount. The maximum commission value cannot be less than the minimum commission. If you do not want to limit the maximum commission amount, set the 0 value. Thus, if you need to check an Expert Advisor under different conditions, there is no need to create a separate custom symbol and download its history. This can be done by changing standard symbol settings. If the symbol specification is customized, the gear icon and the symbol icon are marked with an asterisk.

This shows that custom parameters are used for the current test. To start optimization, click "Start" on the "Settings" tab. The optimization progress is displayed to the left.

Detailed results of each optimization run are displayed on the "Optimization" tab. The tab contains general testing results, including profit and the number of trades, as well as many statistical values to help assess the performance of the trading robot. See the Testing report section for details. The optimization report can be sorted by any parameter by clicking on the column header.

Use sorting to find the most profitable combination of parameters and run a single test for a detailed report. The following values are displayed for each optimization run:. For convenience, check the "Switch to Optimization Results" option: once the optimization process is complete, the Strategy Tester will automatically switch to the Results tab. The same command is available in the context menu of the Journal tab.

The table with optimization results is colored as follows to enable a more efficient visual analysis:. The cache stores data about previously calculated optimization passes.

The strategy tester stores the data to enable resuming of optimization after a pause and to avoid recalculation of already calculated test passes. Files are named according to the following rule: ExpertName. Cache files allow viewing results of previous optimizations. Open the "Optimization results" tab, select an Expert Advisor and a file with the desired optimization cache:.

The list contains all Expert Advisor optimization cache files available on the disk Optimization date, testing settings symbol, timeframe and interval and input parameters are shown for each file. You can additionally filter optimization results by the trade server. From the result viewing mode, you can also change the optimization criterion , which you selected at the start of optimization. It is displayed in the Results tab and determines the quality of a tested set of input parameters.

The higher the value of the optimization criterion, the better the testing pass is considered to be. To analyze results in third-party programs, for example, Office Excel, optimization report can be saved as a file through the " Export to XML" command of the context menu.

Also, the context menu features commands for exporting and importing cache files. Use these commands to transfer optimization results between different platforms. The Strategy Tester in the trading platform provides a powerful visualization system for presenting optimization results. Open "Optimization graph". The tab contains several types of charts, you can switch between them using the context menu. Zero line plane.

All kinds of graphs, except flat have a zero line or pane if it's a three-dimensional chart. If the balance value is used as the optimization criterion , this line usually means the initial deposit, allowing to visually separate loss-making and profitable passes. In all other cases this line is drawn on the zero value of the optimization criterion. A graph with optimization results opens by default. Each pass of an Expert Advisor with certain input parameters is displayed as a point on the graph.

The number of a pass is shown on the horizontal axis, the value of the parameter that is the optimization criterion is shown on the vertical axis. The graph is colored with a green-to-red gradient, depending on the value of the optimization criterion.

The linear chart 1D shows the variation of the parameter selected as the optimization criterion vertical axis depending on one of the optimization parameters selected for the horizontal axis.

To select a parameter for the horizontal axis, use the "X Axis" command in the context menu. In the two-dimensional graph mode, variations of the selected parameters used for optimization are shown on both axes. Variation of the optimization criterion is shown using the color gradient.

The deeper the color, the higher the value of the optimization criterion. In the three-dimensional visualization mode, changes of the selected parameters used for optimization are shown on the X and Y axes. Variation of the optimization criterion is displayed on the vertical Z axis and using a color gradient.

To select a parameters for the horizontal and vertical axes, use commands "X Axis" and "Y Axis" in the context menu. Switching between solid filling and filling with lines. The camera moves to the right the chart moves to the left. The camera moves to the left the chart moves to the right. Rotate the graph downward around its horizontal axis. Rotate the graph upward around its horizontal axis. Rotate the graph around the vertical axis counterclockwise. Rotate the graph around the vertical axis in a clockwise direction.

Moving the zero plane upward by one. Moving the zero plane to the maximum value of the graph. Moving the zero plane to the minimum value of the graph. Increasing the transparency of the zero plane. Setting the maximum transparency of the zero plane it disappears. Setting the minimum transparency of the zero plane it becomes nontransparent. Forward testing is the repeated run of the best optimization results on a different time period.

This feature allows you to avoid parameters fitting in certain areas of historical data.

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Error Code 12 Subcodes. The point value is calculated as the profit of a position in the same direction, with the volume of 1 lot and the difference between the open and close prices is equal to 1 pip point. RFC Specification Required Expert: Brian Rosen primary , Henning Schulzrinne secondary. Emergency Call Action Failure Reasons. By default, the money turnover is calculated in the deposit currency: the price of each trade is calculated and converted into the deposit currency.

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