The advantage of using this block in comparison to manually opening each chart is that this block has a single search selector for it, meaning that you change the asset and all the windows will maintain your selected timeframes and apply to the new asset.
CMC Markets technical analysis and charting tools are as good as it gets. In every chart you can open a lower panel for accessing drawing tools, indicators and pretty much everything you need. I am a very big fan of how this lower bar was designed with a little illustration of each indicator and the ability to set your favourites to access them quickly.
CMC Markets Web platform is a monster tool full of amazing capabilities for professional traders that know exactly what they want to do, how to do it and what they need to accomplish it, we couldn't find anything wrong with it. CMC Markets mobile trading platform is one hell of a beautiful mobile app packed with all sorts of features, a simple interface and very useful information at a glance. Regardless of mobile platforms being there just as a complement to the main web trading platform, these guys did it the right way, here are a few points about it:.
Notice the bottom tool bar, from there you can access quickly the most important areas on your mobile trading platform. The home page is fully configurable to your style, the order of the blocks, which ones you want or not.
For example, you can set up quickly all your favourite watchlists in your home along with an open positions view. In there we can search for any asset on the platform and also display several choices on the top of how we want to sort the different assets available. Above you can see the order input window. In my personal opinion this is the best order execution window out there for mobile platforms.
Everything you need is on the right place, easily accesible and identifiable which is a huge concern for me, some platforms don't show clearly what you're doing. There are several other features we could mention such as: CMC Markets TV, news by Reuters, CMC Insights and many more, but you get the idea, the platforms are fully packed of features on both mobile and desktop and they were built the right way.
There's a lot of waste. Then the way to make the data available for this new product to consume, often is ad hoc, clunky, and requires shifting around big datasets, or creating connections to consume the data. There was this gap, and this is where the data mesh came in. The short term ambition is to enable this agility and autonomy for the emerging products, and in the long term is to enable the organization to be able to utilize the data better. There are many areas of the organization that we haven't started working with that will benefit from that.
Again, we chose to add context, we identified these fundamental data sources. We started with one pricing, which is fundamental for the business. That is a key enabler for some of the new products. Then we are now focusing on concrete deliverables, so the bridge I described, and Amundsen, so that we can build trust and confidence in the approach and move it from theory to practice.
This is the backdrop. It is an investment. We are lucky with the context of the transformation, because sometimes some of these things, there is no immediate benefit that is visible. I think in terms of, we made the case for benefits that would not be possible otherwise. That will actually support the transformation, basically. The point is, data mesh has at its core, a collaborative, decentralized way of working, which is what people prefer these days in terms of building products and stuff like that.
Nardon: What was used for metadata management, cataloging to enable self-service? Did you have the concept of data grading, so people knew how and where data could be used? Abedrabbo: When we started with data mesh, we had this initial idea or maybe ambition that was not realistic, that we can look and just map all the data sources in the business and we can identify the right ones.
Then it's easy because there will be five or six. Very quickly, we discovered this is not actually realistic. There are a couple of reasons for that. One is that there are some long term and clearly fundamental data sources like fuzzing, but there are other data sources that are actually useful on the short term, and that might be retained or not, they might move. We needed something incremental.
We need to expose these things and find a starting point. This way, we understood that rather than waiting to hit this idealized view of all the data sources, let's have something, and we looked at a couple of things. We looked at Amundsen from Lyft. We looked at the LinkedIn one. We chose Amundsen for different reasons, but one strong reason is the fact that it's backed by a graph database, and I'm a big fan of graph databases. I've worked with them quite extensively earlier in my career.
Graph databases allow you to represent the connectivity in a native way. Actually, Amundsen then will act as this frontend on top of data stored natively as a graph, and you could also go into the graph and actually query directly the graph to have these ad hoc insights and understand how data relates to each other.
We're using Amundsen and data discovery as a means to surface the data sources we want to onboard into data mesh in an iterative way, and for us also to discover and understand them. What we did is now we are engaging with the different data owners and we are working with them to onboard some of the attributes in terms of grading. It's obvious because they can be ingested directly from the data source, so a database table will have a schema and the database, and stuff like that.
Some of the other attributes are more cross-cutting, like data privacy, or data classification. Because we have a highly regulated business, and because there are people in the business who are accountable for these obligations around the data, like data protection, we are working with them to identify this common language that then we can do across the different data sources. This is where we are now. Amundsen allows you this metadata management. It's immediately useful for a specific use case, which is discovering data that you need to build the product, for example.
Let's say you need a product. You have a product idea. You look into Amundsen and you can find two or three data sources that support your idea. That is fine. There might be more data sources in the business that are not in Amundsen now, so that's fine, the answer might be good enough or not.
If you look at data governance, we need to be able to use metadata management, support data governance, we need a broader coverage of data sources. Because if you have a data governance query, for example, identifying all the datasets with a specific user information, you cannot be happy with a partial answer. You need everything at that stage. This is why we are introducing data discovery to support transformation work, and new products, and onboarding datasets one by one, and iterating through that.
We are expecting this to be a journey rather than a new thing. How we won people over is, again, we found people in the organization who were trying to build new products who couldn't because they didn't know what data they needed and where it was.
We tried to simulate their use case and make it part of the data mesh, basically. Abedrabbo: Currently, from a high level perspective, the data owners need to think about the data sources they need to expose, and expose them. Because we are not starting from a clean slate, we are starting from an existing organization, some of these datasets might be very clear, like pricing.
It's a well identified stream. It has a well-defined format, and stuff like that. Some of them might already be different datasets joined in. We accept that there will be some of that now, so not every dataset needs to be very pure. We will iterate over that, and what is needed and what is not will emerge, and so on. If you do need to cross different things, then again, it's your responsibility as the consumer to identify the data you need, and then build a view that is properly crossing this data together.
We don't mandate anything. People are free. This might look like an overhead, but it's not. Because this is actually, you just build the views you need, as opposed to the current situation where you need to find the data and get someone to do it for you. If someone else needs the same thing, then potentially the same work needs to be done slightly differently again. This is where we are with it now. Abedrabbo: Again, just to explain it by an example, think about the fact that we have existing data sources on-premise on physical data centers.
There are different reasons for that. This will be the case for a long time, because of colocation reasons, and latency, and what have you. Then we have these new products on the cloud. If you build a new product, any product that needs to be built will need a massive amount of data that now exists mostly on-premise. There are two ways of doing it.
One is you go and ask for the data and then move the data around, and build custom, which is not viable at all because it will require a lot of people, a lot of effort, and it just doesn't scale very well.
The other way is that we identify the datasets you need. Again, we are assuming that in a cloud context, you have a more flexible environment in terms of technology, so you can try different things and so on. Then for pricing, for example, we are building this bridge to be able to stream the prices in a low-latency and resilient fashion. Again, then as a new product on the cloud, you will be able to consume this on the cloud directly rather than going and doing ad hoc integration from on-cloud to on-premise.
This is roughly the idea that the data need to be available in a way that can be consumed by the different applications, rather than this being a problem every time, basically.
In our case, it means on the cloud. Abedrabbo: I need some clarification, is it for the pricing data specifically? Because it depends on the data source, there are different techniques. For pricing, we have some guarantees from the data sources. Actually, the problem is not duplication, in this case, the problem is more latency and how recent the data is. The risk is to get data that is out of date or to have a gap, and ordering is more of an issue.
Specifically for the datasets we're working on now, it's not an issue, but for other datasets there are different techniques that range from ordering, delivery guarantees, idempotent writes, deduplication filters.
We will use the right technology to support the right use case, when it matches. Nardon: How do you convince data owners to provide to the data mesh? I can see how to convince people that need the data, but people that need to provide mostly see as only more work, and more feed to take care of.
Abedrabbo: This is the idea of good data citizenship. In a data mesh, it's like building a town or a country, what have you, everyone needs to do their bit so that everyone can benefit. The result of this is an ability to develop and implement sophisticated strategies. You will also get access to the client sentiment indicator. To add to your trade arsenal, the external source CMC Trading Central provides in-depth investment and research analysis. This can help you accurately evaluate price data and hone your strategy.
CMC trading software also extends to mobile and tablet apps. In fact, their spread betting and CFD trading apps have, like the desktop platform, also won awards. You can benefit from full order ticket functionality. The app is available for download via the website or app stores. Not to mention mobile-optimised charting, plus over 40 technical indicators, signals and trade tools.
A useful tip is to rotate your phone to horizontal mode, this makes conducting chart customisations far easier. Drawing trend-lines, plus zooming in and out is also cleaner. The default screen when you log in can be customised in settings, or auto-select will show open positions. The iPhone mobile trading app provides live streaming prices, charts, and pending order executions.
It also shows Reuter news feeds as well as displaying the client sentiment indicator. Overall, CMC mobile phone trading allows for a smooth transition from their desktop-based platform, maintaining the same look and feel.
Applications are sleek and easy-to-navigate, while you retain all the features needed for live trading. You can also find a range of CMC trading videos on their official website that will help walk you through getting the most of their iOS and Android apps. CMC user reviews have highlighted deposit and withdrawal methods are fairly industry standard, regardless of whether you are currency, stock, or commodity trading.
You can make payments with any major credit and debit card. They accept 10 different forms of currency for deposits. Alternatively, you can make live wire transfers. However, to stay in line with international banking law, you may be required to hand over personal ID data and banking information.
Without these documents, withdrawal requests will not be processed. On top of that, telephone verification may also be required. This is a measure taken to ensure security. Before you can develop a CMC day trading strategy, you will need to open an account. CMC offers these accounts to both individual corporate clients. Accounts are actually surprisingly similar.
With both account types, you also get account netting, telephone trading, position hedging, plus a price depth ladder. Overall you get nearly all the same functionality and features with all of the CMC trading accounts.
You can also get a CMC trading platform demo account. Legal disclosures also admit the two platforms can display different spreads for identical instruments. Margin and overnight holding costs are average to competitive.
That spread is charged through all hours of the trading day, including local time in the U. In contrast, the Nasdaq spread at 1. CMC does not have a minimum deposit requirement for customers wishing to open a live account, but logic dictates that this amount will be subject to the margin requirements of the smallest trade size that the customer wishes to place. Clients with high account balances are eligible for premium services, such as higher trading leverage, a personal account manager, perks rebates and rewards , priority access to new products, and segregated accounts.
The proprietary Next Generation trading platform for web, tablet, and mobile will please technically oriented clients wanting to upgrade from MetaTrader 4, which is also offered.
However, navigation is not as intuitive as some other platforms, which may be a function of all the features that are packed into this interface. Cryptocurrency CFD trading and spread betting is seamless, requiring no special interface or exchange, but relatively high average spreads could reduce client interest. Charts can be popped out to build and optimize complex desktop layouts.
Clients who want to study long-term trends will find price histories going back 20 years on major instruments. Additionally, the trader can set an initial stop loss at the time of execution for market orders and then amend it to a guaranteed stop loss after execution. Market — The simplest order where a trader signals that their trade request should be executed at the prevailing market rate. The trader also has the option of selecting the expiration time of this order.
The homegrown app outshines MT4 in nearly all aspects but includes fewer features than the web version. More importantly, chart optimization has been emphasized.
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