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  • What technical skills needed for algorithmic trading, HFT, etc?

    - by alchemical
    I'm interested in getting into developing trading systems, black box, HFT, etc. My primary experience is with C# and .Net (7 years). I've also done some sockets programming. I have some experience in finance working on analysis applications (2 years). My goal is to move into developing automated trading systems for a hedge fund, bank, etc. Is there any way to learn the skills needed for this without somehow getting the job first? I've looked at the open source tradelink, IB interactive brokerage, etc. I'm playing around with this framework, and may hook it up and do some paper trading. However, I'm not sure if this has much relationship with how a well-funded entity would be conducting a high-level automated trading operation. I.e. would the tools and frameworks they prefer be a totally different skill-set? Also wondering if I need to learn C++ and/or Java for these types of apps.

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  • java+netbeans+mysql+ubuntu 64 bits LTS VS C#+MS SQL for fast develop trading system

    - by crunchor
    I am going to build a trading system and use with the broker "Interactive Broker" API. The API supports C++ Socket, Java Socket, DDE, Active X APIs in Windows. The API supports Java Socket API, Posix C++ Socket API in Unix kind like Ubuntu. My trading system has some real time long calculations to do and a lot of maths for backtest. I am using a retail trading program Amibroker which is written in C++ and I run it in windows xp 32 bits, it will take me days to do one serious backtest with my G620 Sandy Bridge CPU and 3GB of ram. So for my trading system, I need to have 1. speed, 2. stability, 3. fast development I have done some research, C++ is fastest but I am not good at it and it takes much longer time to develop. Other than C++, Java in linux has the best speed. I also did some research for database and look like mysql has the best speed. Mysql and PostgreSQL both are very popular open source sql db, which one should I choose? I see MySQL has Workbench now which looks like similar to MS SQL management studio so look like a good start. Netbeans should be the most popular Java IDE now and seem like its GUI design can be as easy as Virtual Studio now. I am not sure if made by Netbeans would affect the speed and if its GUI design is really that good and easy to use. Ubuntu 64 bits LTS has good long term support, good community support, and stable. I will buy a new computer if I can create a good trading system for live trading and backtest. Very likely I will buy a I7 or I5 depends on if I7 can really have better speed for my case. Actually I mainly deal with C# in my jobs and I just knew java but not good at java. What would you guys recommend? Any better solution? This will be a big project and very likely life long project for me so I seriously do research including asking you guys before I start and focus on what I should, thanks!

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  • Algorithmic trading software safety guards

    - by Adal
    I'm working on an automatic trading system. What sorts of safe-guards should I have in place? The main idea I have is to have multiple pieces checking each other. I will have a second independent little process which will also connect to the same trading account and monitor simple things, like ensuring the total net position does not go over a certain limit, or that there are no more than N orders in 10 minutes for example, or more than M positions open simultaneously. You can also check that the actual open positions correspond to what the strategy process thinks it actually holds. As a bonus I could run this checker process on a different machine/network provider. Besides the checks in the main strategy, this will ensure that whatever weird bug occurs, nothing really bad can happen. Any other things I should monitor and be aware of?

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  • C++ : Avoid lot of boolean variable for multiple verification conditions in trading app

    - by Naveen
    Hi i am a junior dev in trading app... we have a order refresh verification unit. It has to verify order confirmation from exchange. We send a bunch of different request in bulk ( NEW, MODIFY, CANCEL ) to exchange... Verification has to happen for max N times with each T intervals for all orders. if verification successful for all the order before N retry then fine.. otherwise we need to indicate as verification unsuccessfull. i hv done a basic coding done in very urgent like below for( N times ) { for_each ( sent_request_order ) // SENT { 1) get all the refreshed order from DB or shared mem i.e REFRESHED 2) find current sent order in REFRESHED if( not_found ) not refreshed from exchange, continue to next order if( found ) case NEW : //check for new status, mark verification done case MODIFY : //check for modified status.. //if not mark pending, go to next order, //revisit the same after T time case CANCEL : //check for cancelled status.. //if not mark pending, go to next order, //revisit the same after T time } if( all_verified ) exit from verification. wait ( T sec ) } order_verification_pending, order_verification_done, order_visited, order_not_visited, all_verified, all_not_verified ... lot of boolean flags used for indication.. is there any better approach for doing this.... splitting responsibilities across the classes......???? i know this is not a general question.... but still flags are making me tidious to handle...

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  • Java, Massive message processing with queue manager (trading)

    - by Ronny
    Hello, I would like to design a simple application (without j2ee and jms) that can process massive amount of messages (like in trading systems) I have created a service that can receive messages and place them in a queue to so that the system won't stuck when overloaded. Then I created a service (QueueService) that wraps the queue and has a pop method that pops out a message from the queue and if there is no messages returns null, this method is marked as "synchronized" for the next step. I have created a class that knows how process the message (MessageHandler) and another class that can "listen" for messages in a new thread (MessageListener). The thread has a "while(true)" and all the time tries to pop a message. If a message was returned, the thread calls the MessageHandler class and when it's done, he will ask for another message. Now, I have configured the application to open 10 MessageListener to allow multi message processing. I have now 10 threads that all time are in a loop. Is that a good design?? Can anyone reference me to some books or sites how to handle such scenario?? Thanks, Ronny

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  • What grid distributed computing frameworks are currently favoured for trading systems

    - by Rich
    There seems to a quite a few grid computing frameworks out there, but which ones are actually being used to any great extent by the investment banks for purposes of low latency distributing calculation? I'd be interested to hear answers covering both windows,Linux and cross platform. Also, what RPC mechanisms seem to be favoured most? I've heard that for reason of low latency and speed, the calculations themselves are quite often written in C++/C as calculations running on VMs are several orders of magnitude slower than native code. Does this seem to be a common scenario in practice? e.g distributed .NET grid framework running calculations written in native c++/c?

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  • Programming a trading strategy

    - by Rob
    Excuse me if I'm not descriptive enough, as I do not have much of a background when it comes to these things: How would I go about coding a primitive trading strategy and link it to some sort of artificial trading environment? Where do I start, and what are some other essential questions I should be asking? I am interested more in doing this because it interests me than making returns. Ideally it utilizes random/historical market data and doesn't actually execute any real trades. My background: I'm almost done my undergrad degree in computer science, and have had intro finance and economic courses. Familiar mostly with C and Java.

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  • Advice for a getting a job in algorithmic trading - writing faster code

    - by Alex
    I am currently an intermediate Java developer working in the financial industry. I am considering trying to get into an algorithmic trading developer position. I am looking for any advice/resources that may help me obtain such a job. My naive initial thoughts are to concentrate on learning how to write faster, more memory efficient code whilst maintaining readability. Can anyone point me in the right direction of some useful resources for what I am aiming to achieve?

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  • Microeconomical simulation: coordination/planning between self-interested trading agents

    - by Milton Manfried
    In a typical perfect-information strategy game like Chess, an agent can calculate its best move by searching the state tree for the best possible move, while assuming that the opponent will also make the best possible move (i.e. Mini-max). I would like to use this approach in a "game" modeling economic activity, where the possible "moves" would be to buy or sell for a given price, and the goal, rather than a specific class of states (e.g. Checkmate), would be to maximize some function F of the agent's state (e.g. F(money, widget) = 10*money + widget). How to handle buy/sell actions that require coordination between both parties, at the very least agreement upon a price? The cheap way out would be to set the price beforehand, maybe based upon the current supply -- but the idea of this simulation is to examine how prices emerge when freely determined by "perfectly rational" agents. A great example of what I do not want is the trading algorithm in SugarScape -- paraphrasing from Growing Artificial Societies p101-102: when a pair of agents interact to trade, they each compute their internal valuations of the goods, then a bargaining process is conducted and a price is agreed to. If this price makes both agents better off, they complete the transaction The protocol itself is beautiful, but what it cannot capture (as far as I can tell) is the ability for an agent to pay more than it might otherwise for a good, because it knows that it can sell it for even more at a later date -- what appears to be called "strategic thinking" in this pape at Google Books Multi-Agent-Based Simulation III: 4th International Workshop, MABS 2003... to get realistic behavior like that, it seems one would either (1) have to build an outrageously-complex internal valuation system which could at best only cover situations that were planned for at compile-time, or otherwise (2) have some mechanism to search the state tree... which would require some way of planning future trades. Note: The chess analogy only works as far as the state-space search goes; the simulation isn't intended to be "zero sum", so a literal mini-max search wouldn't be appropriate -- and ideally, it should work with more than two agents.

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  • Why does derivative trading position always require C++ knowledge?

    - by Jeffrey
    I’ve never worked in trading environment before and I was curious to see that few of the trading houses seem to use C# but most of them do heavily rely on C++. Why is it? Is it because C++ is better performance wise? Is it because of legacy code base? Is it because cross platform issue? What about dynamic languages (ruby, python)? Are they too slow for this kind of work in terms of performance? Updated: If realibility and performance are important would "Erlang" be the "next big thing" in trading platform?

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  • Option Trading: Getting the most out of the event session options

    - by extended_events
    You can control different aspects of how an event session behaves by setting the event session options as part of the CREATE EVENT SESSION DDL. The default settings for the event session options are designed to handle most of the common event collection situations so I generally recommend that you just use the defaults. Like everything in the real world though, there are going to be a handful of “special cases” that require something different. This post focuses on identifying the special cases and the correct use of the options to accommodate those cases. There is a reason it’s called Default The default session options specify a total event buffer size of 4 MB with a 30 second latency. Translating this into human terms; this means that our default behavior is that the system will start processing events from the event buffer when we reach about 1.3 MB of events or after 30 seconds, which ever comes first. Aside: What’s up with the 1.3 MB, I thought you said the buffer was 4 MB?The Extended Events engine takes the total buffer size specified by MAX_MEMORY (4MB by default) and divides it into 3 equally sized buffers. This is done so that a session can be publishing events to one buffer while other buffers are being processed. There are always at least three buffers; how to get more than three is covered later. Using this configuration, the Extended Events engine can “keep up” with most event sessions on standard workloads. Why is this? The fact is that most events are small, really small; on the order of a couple hundred bytes. Even when you start considering events that carry dynamically sized data (eg. binary, text, etc.) or adding actions that collect additional data, the total size of the event is still likely to be pretty small. This means that each buffer can likely hold thousands of events before it has to be processed. When the event buffers are finally processed there is an economy of scale achieved since most targets support bulk processing of the events so they are processed at the buffer level rather than the individual event level. When all this is working together it’s more likely that a full buffer will be processed and put back into the ready queue before the remaining buffers (remember, there are at least three) are full. I know what you’re going to say: “My server is exceptional! My workload is so massive it defies categorization!” OK, maybe you weren’t going to say that exactly, but you were probably thinking it. The point is that there are situations that won’t be covered by the Default, but that’s a good place to start and this post assumes you’ve started there so that you have something to look at in order to determine if you do have a special case that needs different settings. So let’s get to the special cases… What event just fired?! How about now?! Now?! If you believe the commercial adage from Heinz Ketchup (Heinz Slow Good Ketchup ad on You Tube), some things are worth the wait. This is not a belief held by most DBAs, particularly DBAs who are looking for an answer to a troubleshooting question fast. If you’re one of these anxious DBAs, or maybe just a Program Manager doing a demo, then 30 seconds might be longer than you’re comfortable waiting. If you find yourself in this situation then consider changing the MAX_DISPATCH_LATENCY option for your event session. This option will force the event buffers to be processed based on your time schedule. This option only makes sense for the asynchronous targets since those are the ones where we allow events to build up in the event buffer – if you’re using one of the synchronous targets this option isn’t relevant. Avoid forgotten events by increasing your memory Have you ever had one of those days where you keep forgetting things? That can happen in Extended Events too; we call it dropped events. In order to optimizes for server performance and help ensure that the Extended Events doesn’t block the server if to drop events that can’t be published to a buffer because the buffer is full. You can determine if events are being dropped from a session by querying the dm_xe_sessions DMV and looking at the dropped_event_count field. Aside: Should you care if you’re dropping events?Maybe not – think about why you’re collecting data in the first place and whether you’re really going to miss a few dropped events. For example, if you’re collecting query duration stats over thousands of executions of a query it won’t make a huge difference to miss a couple executions. Use your best judgment. If you find that your session is dropping events it means that the event buffer is not large enough to handle the volume of events that are being published. There are two ways to address this problem. First, you could collect fewer events – examine you session to see if you are over collecting. Do you need all the actions you’ve specified? Could you apply a predicate to be more specific about when you fire the event? Assuming the session is defined correctly, the next option is to change the MAX_MEMORY option to a larger number. Picking the right event buffer size might take some trial and error, but a good place to start is with the number of dropped events compared to the number you’ve collected. Aside: There are three different behaviors for dropping events that you specify using the EVENT_RETENTION_MODE option. The default is to allow single event loss and you should stick with this setting since it is the best choice for keeping the impact on server performance low.You’ll be tempted to use the setting to not lose any events (NO_EVENT_LOSS) – resist this urge since it can result in blocking on the server. If you’re worried that you’re losing events you should be increasing your event buffer memory as described in this section. Some events are too big to fail A less common reason for dropping an event is when an event is so large that it can’t fit into the event buffer. Even though most events are going to be small, you might find a condition that occasionally generates a very large event. You can determine if your session is dropping large events by looking at the dm_xe_sessions DMV once again, this time check the largest_event_dropped_size. If this value is larger than the size of your event buffer [remember, the size of your event buffer, by default, is max_memory / 3] then you need a large event buffer. To specify a large event buffer you set the MAX_EVENT_SIZE option to a value large enough to fit the largest event dropped based on data from the DMV. When you set this option the Extended Events engine will create two buffers of this size to accommodate these large events. As an added bonus (no extra charge) the large event buffer will also be used to store normal events in the cases where the normal event buffers are all full and waiting to be processed. (Note: This is just a side-effect, not the intended use. If you’re dropping many normal events then you should increase your normal event buffer size.) Partitioning: moving your events to a sub-division Earlier I alluded to the fact that you can configure your event session to use more than the standard three event buffers – this is called partitioning and is controlled by the MEMORY_PARTITION_MODE option. The result of setting this option is fairly easy to explain, but knowing when to use it is a bit more art than science. First the science… You can configure partitioning in three ways: None, Per NUMA Node & Per CPU. This specifies the location where sets of event buffers are created with fairly obvious implication. There are rules we follow for sub-dividing the total memory (specified by MAX_MEMORY) between all the event buffers that are specific to the mode used: None: 3 buffers (fixed)Node: 3 * number_of_nodesCPU: 2.5 * number_of_cpus Here are some examples of what this means for different Node/CPU counts: Configuration None Node CPU 2 CPUs, 1 Node 3 buffers 3 buffers 5 buffers 6 CPUs, 2 Node 3 buffers 6 buffers 15 buffers 40 CPUs, 5 Nodes 3 buffers 15 buffers 100 buffers   Aside: Buffer size on multi-processor computersAs the number of Nodes or CPUs increases, the size of the event buffer gets smaller because the total memory is sub-divided into more pieces. The defaults will hold up to this for a while since each buffer set is holding events only from the Node or CPU that it is associated with, but at some point the buffers will get too small and you’ll either see events being dropped or you’ll get an error when you create your session because you’re below the minimum buffer size. Increase the MAX_MEMORY setting to an appropriate number for the configuration. The most likely reason to start partitioning is going to be related to performance. If you notice that running an event session is impacting the performance of your server beyond a reasonably expected level [Yes, there is a reasonably expected level of work required to collect events.] then partitioning might be an answer. Before you partition you might want to check a few other things: Is your event retention set to NO_EVENT_LOSS and causing blocking? (I told you not to do this.) Consider changing your event loss mode or increasing memory. Are you over collecting and causing more work than necessary? Consider adding predicates to events or removing unnecessary events and actions from your session. Are you writing the file target to the same slow disk that you use for TempDB and your other high activity databases? <kidding> <not really> It’s always worth considering the end to end picture – if you’re writing events to a file you can be impacted by I/O, network; all the usual stuff. Assuming you’ve ruled out the obvious (and not so obvious) issues, there are performance conditions that will be addressed by partitioning. For example, it’s possible to have a successful event session (eg. no dropped events) but still see a performance impact because you have many CPUs all attempting to write to the same free buffer and having to wait in line to finish their work. This is a case where partitioning would relieve the contention between the different CPUs and likely reduce the performance impact cause by the event session. There is no DMV you can check to find these conditions – sorry – that’s where the art comes in. This is  largely a matter of experimentation. On the bright side you probably won’t need to to worry about this level of detail all that often. The performance impact of Extended Events is significantly lower than what you may be used to with SQL Trace. You will likely only care about the impact if you are trying to set up a long running event session that will be part of your everyday workload – sessions used for short term troubleshooting will likely fall into the “reasonably expected impact” category. Hey buddy – I think you forgot something OK, there are two options I didn’t cover: STARTUP_STATE & TRACK_CAUSALITY. If you want your event sessions to start automatically when the server starts, set the STARTUP_STATE option to ON. (Now there is only one option I didn’t cover.) I’m going to leave causality for another post since it’s not really related to session behavior, it’s more about event analysis. - Mike Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • Option Trading: Getting the most out of the event session options

    - by extended_events
    You can control different aspects of how an event session behaves by setting the event session options as part of the CREATE EVENT SESSION DDL. The default settings for the event session options are designed to handle most of the common event collection situations so I generally recommend that you just use the defaults. Like everything in the real world though, there are going to be a handful of “special cases” that require something different. This post focuses on identifying the special cases and the correct use of the options to accommodate those cases. There is a reason it’s called Default The default session options specify a total event buffer size of 4 MB with a 30 second latency. Translating this into human terms; this means that our default behavior is that the system will start processing events from the event buffer when we reach about 1.3 MB of events or after 30 seconds, which ever comes first. Aside: What’s up with the 1.3 MB, I thought you said the buffer was 4 MB?The Extended Events engine takes the total buffer size specified by MAX_MEMORY (4MB by default) and divides it into 3 equally sized buffers. This is done so that a session can be publishing events to one buffer while other buffers are being processed. There are always at least three buffers; how to get more than three is covered later. Using this configuration, the Extended Events engine can “keep up” with most event sessions on standard workloads. Why is this? The fact is that most events are small, really small; on the order of a couple hundred bytes. Even when you start considering events that carry dynamically sized data (eg. binary, text, etc.) or adding actions that collect additional data, the total size of the event is still likely to be pretty small. This means that each buffer can likely hold thousands of events before it has to be processed. When the event buffers are finally processed there is an economy of scale achieved since most targets support bulk processing of the events so they are processed at the buffer level rather than the individual event level. When all this is working together it’s more likely that a full buffer will be processed and put back into the ready queue before the remaining buffers (remember, there are at least three) are full. I know what you’re going to say: “My server is exceptional! My workload is so massive it defies categorization!” OK, maybe you weren’t going to say that exactly, but you were probably thinking it. The point is that there are situations that won’t be covered by the Default, but that’s a good place to start and this post assumes you’ve started there so that you have something to look at in order to determine if you do have a special case that needs different settings. So let’s get to the special cases… What event just fired?! How about now?! Now?! If you believe the commercial adage from Heinz Ketchup (Heinz Slow Good Ketchup ad on You Tube), some things are worth the wait. This is not a belief held by most DBAs, particularly DBAs who are looking for an answer to a troubleshooting question fast. If you’re one of these anxious DBAs, or maybe just a Program Manager doing a demo, then 30 seconds might be longer than you’re comfortable waiting. If you find yourself in this situation then consider changing the MAX_DISPATCH_LATENCY option for your event session. This option will force the event buffers to be processed based on your time schedule. This option only makes sense for the asynchronous targets since those are the ones where we allow events to build up in the event buffer – if you’re using one of the synchronous targets this option isn’t relevant. Avoid forgotten events by increasing your memory Have you ever had one of those days where you keep forgetting things? That can happen in Extended Events too; we call it dropped events. In order to optimizes for server performance and help ensure that the Extended Events doesn’t block the server if to drop events that can’t be published to a buffer because the buffer is full. You can determine if events are being dropped from a session by querying the dm_xe_sessions DMV and looking at the dropped_event_count field. Aside: Should you care if you’re dropping events?Maybe not – think about why you’re collecting data in the first place and whether you’re really going to miss a few dropped events. For example, if you’re collecting query duration stats over thousands of executions of a query it won’t make a huge difference to miss a couple executions. Use your best judgment. If you find that your session is dropping events it means that the event buffer is not large enough to handle the volume of events that are being published. There are two ways to address this problem. First, you could collect fewer events – examine you session to see if you are over collecting. Do you need all the actions you’ve specified? Could you apply a predicate to be more specific about when you fire the event? Assuming the session is defined correctly, the next option is to change the MAX_MEMORY option to a larger number. Picking the right event buffer size might take some trial and error, but a good place to start is with the number of dropped events compared to the number you’ve collected. Aside: There are three different behaviors for dropping events that you specify using the EVENT_RETENTION_MODE option. The default is to allow single event loss and you should stick with this setting since it is the best choice for keeping the impact on server performance low.You’ll be tempted to use the setting to not lose any events (NO_EVENT_LOSS) – resist this urge since it can result in blocking on the server. If you’re worried that you’re losing events you should be increasing your event buffer memory as described in this section. Some events are too big to fail A less common reason for dropping an event is when an event is so large that it can’t fit into the event buffer. Even though most events are going to be small, you might find a condition that occasionally generates a very large event. You can determine if your session is dropping large events by looking at the dm_xe_sessions DMV once again, this time check the largest_event_dropped_size. If this value is larger than the size of your event buffer [remember, the size of your event buffer, by default, is max_memory / 3] then you need a large event buffer. To specify a large event buffer you set the MAX_EVENT_SIZE option to a value large enough to fit the largest event dropped based on data from the DMV. When you set this option the Extended Events engine will create two buffers of this size to accommodate these large events. As an added bonus (no extra charge) the large event buffer will also be used to store normal events in the cases where the normal event buffers are all full and waiting to be processed. (Note: This is just a side-effect, not the intended use. If you’re dropping many normal events then you should increase your normal event buffer size.) Partitioning: moving your events to a sub-division Earlier I alluded to the fact that you can configure your event session to use more than the standard three event buffers – this is called partitioning and is controlled by the MEMORY_PARTITION_MODE option. The result of setting this option is fairly easy to explain, but knowing when to use it is a bit more art than science. First the science… You can configure partitioning in three ways: None, Per NUMA Node & Per CPU. This specifies the location where sets of event buffers are created with fairly obvious implication. There are rules we follow for sub-dividing the total memory (specified by MAX_MEMORY) between all the event buffers that are specific to the mode used: None: 3 buffers (fixed)Node: 3 * number_of_nodesCPU: 2.5 * number_of_cpus Here are some examples of what this means for different Node/CPU counts: Configuration None Node CPU 2 CPUs, 1 Node 3 buffers 3 buffers 5 buffers 6 CPUs, 2 Node 3 buffers 6 buffers 15 buffers 40 CPUs, 5 Nodes 3 buffers 15 buffers 100 buffers   Aside: Buffer size on multi-processor computersAs the number of Nodes or CPUs increases, the size of the event buffer gets smaller because the total memory is sub-divided into more pieces. The defaults will hold up to this for a while since each buffer set is holding events only from the Node or CPU that it is associated with, but at some point the buffers will get too small and you’ll either see events being dropped or you’ll get an error when you create your session because you’re below the minimum buffer size. Increase the MAX_MEMORY setting to an appropriate number for the configuration. The most likely reason to start partitioning is going to be related to performance. If you notice that running an event session is impacting the performance of your server beyond a reasonably expected level [Yes, there is a reasonably expected level of work required to collect events.] then partitioning might be an answer. Before you partition you might want to check a few other things: Is your event retention set to NO_EVENT_LOSS and causing blocking? (I told you not to do this.) Consider changing your event loss mode or increasing memory. Are you over collecting and causing more work than necessary? Consider adding predicates to events or removing unnecessary events and actions from your session. Are you writing the file target to the same slow disk that you use for TempDB and your other high activity databases? <kidding> <not really> It’s always worth considering the end to end picture – if you’re writing events to a file you can be impacted by I/O, network; all the usual stuff. Assuming you’ve ruled out the obvious (and not so obvious) issues, there are performance conditions that will be addressed by partitioning. For example, it’s possible to have a successful event session (eg. no dropped events) but still see a performance impact because you have many CPUs all attempting to write to the same free buffer and having to wait in line to finish their work. This is a case where partitioning would relieve the contention between the different CPUs and likely reduce the performance impact cause by the event session. There is no DMV you can check to find these conditions – sorry – that’s where the art comes in. This is  largely a matter of experimentation. On the bright side you probably won’t need to to worry about this level of detail all that often. The performance impact of Extended Events is significantly lower than what you may be used to with SQL Trace. You will likely only care about the impact if you are trying to set up a long running event session that will be part of your everyday workload – sessions used for short term troubleshooting will likely fall into the “reasonably expected impact” category. Hey buddy – I think you forgot something OK, there are two options I didn’t cover: STARTUP_STATE & TRACK_CAUSALITY. If you want your event sessions to start automatically when the server starts, set the STARTUP_STATE option to ON. (Now there is only one option I didn’t cover.) I’m going to leave causality for another post since it’s not really related to session behavior, it’s more about event analysis. - Mike Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • High Frequency Trading

    - by Hamza Yerlikaya
    Over the last couple of weeks i have come across lots of articles about high frequency trading. They all talk about how important computers and software is to this but since they are all written from a financial point of view there is no detail about what does software do? Can anyone explain from a programmers point of view what is high frequency trading? and why is computer/software so important in this field?

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  • Database design question (Book Trading System)

    - by Paul
    Hello all! I´m developing a Book Trading System... The user will input your Book to trading... I already have a table tblBook with "all" existing books ... So the user will select one book from that list and fill the book´s CONDITIONS and Edition... So, what is a good Database design to tha case? tblBook = All books tblUserBook = All User Books And making tblUserBook to inheritance tblBook? Thanks

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  • Automating Etrade

    - by iAlexTsang
    Hey everyone, I was wondering how would I start programming an interface to trading stocks in Etrade in python. I am attempting to make an automated trading bot, but there is no api publicly available for automated trading with Etrade. Thanks in advance. ^^

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  • How to use a DHT for a social trading environment

    - by Lirik
    I'm trying to understand if a DHT can be used to solve a problem I'm working on: I have a trading environment where professional option traders can get an increase in their risk limit by requesting that fellow traders lend them some of their risk limit. The lending trader will can either search for traders with certain risk parameters which are part of every trader's profile, i.e. Greeks, or the lending trader can subscribe to requests from certain traders. I want this environment to be scalable and decentralized, but I don't know how traders can search for specific profile parameters when the data is contained in a DHT. Could anybody explain how this can be done?

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  • summer experiment: GWT & python for a trading game- arch question

    - by sadhu_
    Hi, As a summer learning experiment, I'm thinking of coding up a web front end for a trading game i wrote in python, that generates share prices and random snippets of text. I am sort of struggling with how this should work on the back-end though. I'd rather have my GWT client page interact with the python share price generator, than to try and re-code it in java. I suppose i could use an sqlite db, and then use jdbc to pick up the prices, but i was wondering if there is a better way, for me to be able to poll some python script either from my client page, or from the serverside java code ? I found this python wrapper, but i'm not sure how i could use it though: http://code.google.com/apis/visualization/documentation/dev/gviz_api_lib.html Thanks.

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  • iPhone Developer Program registration for UK trading partnership

    - by CMLloyd
    I have been looking into this for a long time and have found no definitive answer. I can't be the only person to have faced this problem and am wondering how you guys proceeded in similar cases. I'm part of a partnership, based in the UK, trading as, lets say, "ABCD iPhone Apps" (legally, a perfectly legitimate way of doing business). I've now developed an iPhone App and I want our company name ("ABCD iPhone Apps") to appear as the seller in the App Store. This way, any future Apps that we develop can all get released under the "ABCD iPhone Apps" aegis too. Given that we aren't an incorporated company (and probably never will be), is it possible for us to enroll in the iPhone Developer Program as a company? Or is there another solution? (Note: I do also have an Individual account but that is for personal projects and is in no way connected to the partnership, and shall remain that way) EDIT: I've just spoken to a guy at ADC UK and he tells me there is no other solution. For a company to be approved on the Developer Program, Apple needs to see a copy of the company's Certificate of Incorporation during the registration process, otherwise no approval.

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  • Optimizing tasks to reduce CPU in a trading application

    - by Joel
    Hello, I have designed a trading application that handles customers stocks investment portfolio. I am using two datastore kinds: Stocks - Contains unique stock name and its daily percent change. UserTransactions - Contains information regarding a specific purchase of a stock made by a user : the value of the purchase along with a reference to Stock for the current purchase. db.Model python modules: class Stocks (db.Model): stockname = db.StringProperty(multiline=True) dailyPercentChange=db.FloatProperty(default=1.0) class UserTransactions (db.Model): buyer = db.UserProperty() value=db.FloatProperty() stockref = db.ReferenceProperty(Stocks) Once an hour I need to update the database: update the daily percent change in Stocks and then update the value of all entities in UserTransactions that refer to that stock. The following python module iterates over all the stocks, update the dailyPercentChange property, and invoke a task to go over all UserTransactions entities which refer to the stock and update their value: Stocks.py # Iterate over all stocks in datastore for stock in Stocks.all(): # update daily percent change in datastore db.run_in_transaction(updateStockTxn, stock.key()) # create a task to update all user transactions entities referring to this stock taskqueue.add(url='/task', params={'stock_key': str(stock.key(), 'value' : self.request.get ('some_val_for_stock') }) def updateStockTxn(stock_key): #fetch the stock again - necessary to avoid concurrency updates stock = db.get(stock_key) stock.dailyPercentChange= data.get('some_val_for_stock') # I get this value from outside ... some more calculations here ... stock.put() Task.py (/task) # Amount of transaction per task amountPerCall=10 stock=db.get(self.request.get("stock_key")) # Get all user transactions which point to current stock user_transaction_query=stock.usertransactions_set cursor=self.request.get("cursor") if cursor: user_transaction_query.with_cursor(cursor) # Spawn another task if more than 10 transactions are in datastore transactions = user_transaction_query.fetch(amountPerCall) if len(transactions)==amountPerCall: taskqueue.add(url='/task', params={'stock_key': str(stock.key(), 'value' : self.request.get ('some_val_for_stock'), 'cursor': user_transaction_query.cursor() }) # Iterate over all transaction pointing to stock and update their value for transaction in transactions: db.run_in_transaction(updateUserTransactionTxn, transaction.key()) def updateUserTransactionTxn(transaction_key): #fetch the transaction again - necessary to avoid concurrency updates transaction = db.get(transaction_key) transaction.value= transaction.value* self.request.get ('some_val_for_stock') db.put(transaction) The problem: Currently the system works great, but the problem is that it is not scaling well… I have around 100 Stocks with 300 User Transactions, and I run the update every hour. In the dashboard, I see that the task.py takes around 65% of the CPU (Stock.py takes around 20%-30%) and I am using almost all of the 6.5 free CPU hours given to me by app engine. I have no problem to enable billing and pay for additional CPU, but the problem is the scaling of the system… Using 6.5 CPU hours for 100 stocks is very poor. I was wondering, given the requirements of the system as mentioned above, if there is a better and more efficient implementation (or just a small change that can help with the current implemntation) than the one presented here. Thanks!! Joel

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  • Which programming language to choose? (for a specific problem/domain, details inside)

    - by Bijan
    I am building a trading portfolio management system that is responsible for production, optimization, and simulation of non-high frequency trading portfolios (dealing with 1min or 3min bars of data, not tick data). I plan on employing Amazon web services to take on the entire load of the application. I have four choices that I am considering as language. a) Java b) C++ c) C# d) Python Here is the scope of the extremes of the project scope. This isn't how it will be, maybe ever, but it's within the scope of the requirements: Weekly simulation of 10,000,000 trading systems. (Each trading system is expected to have its own data mining methods, including feature selection algorithms which are extremely computationally-expensive. Imagine 500-5000 features using wrappers. These are not run often by any means, but it's still a consideration) Real-time production of portfolio w/ 100,000 trading strategies Taking in 1 min or 3 min data from every stock/futures market around the globe (approx 100,000) Portfolio optimization of portfolios with up to 100,000 strategies. (rather intensive algorithm) Speed is a concern, but I believe that Java can handle the load. I just want to make sure that Java CAN handle the above requirements comfortably. I don't want to do the project in C++, but I will if it's required. The reason C# is on there is because I thought it was a good alternative to Java, even though I don't like Windows at all and would prefer Java if all things are the same. Python - I've read somethings on PyPy and pyscho that claim python can be optimized with JIT compiling to run at near C-like speeds.... That's pretty much the only reason it is on this list, besides that fact that Python is a great language and would probably be the most enjoyable language to code in, which is not a factor at all for this project, but a perk. To sum up: - real time production - weekly simulations of a large number of systems - weekly/monthly optimizations of portfolios - large numbers of connections to collect data from There is no dealing with millisecond or even second based trades. The only consideration is if Java can possibly deal with this kind of load when spread out of a necessary amount of EC2 servers. Thank you guys so much for your wisdom.

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  • What online brokers offer APIs?

    - by Wilco
    So I'm getting really sick of E*TRADE and, being a developer, would love to find an online broker that offers an API. It would be great to be able to write my own trading tools, and maybe even modify existing ones. Based on my research so far, I've only found one option. Interactive Brokers offers a multi-language API (Java/C++/ActiveX/DDE) and has some fairly decent commission rates to boot. I want to make sure there aren't any other options out there I should be considering. Any ideas? Update: Based on answers so far, here's a quick list... Interactive Brokers Java C++ ActiveX DDE for Excel Pinnacle Trading C++ Perl VB.NET Excel MB Trading

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  • Oracle B2B - Synchronous Request Reply

    - by cdwright
    Introduction So first off, let me say I didn't create this demo (although I did modify it some). I got it from a member of the B2B development technical staff. Since it came with only a simple readme file, I thought I would take some time and write a more detailed explanation about how it works. Beginning with Oracle SOA Suite PS5 (11.1.1.6), B2B supports synchronous request reply over http using the b2b/syncreceiver servlet. I’m attaching the demo to this blog which includes a SOA composite archive that needs to be deployed using JDeveloper, a B2B repository with two agreements that need to be deployed using the B2B console, and a test xml file that gets sent to the b2b/syncreceiver servlet using your favorite SOAP test tool (I'm using Firefox Poster here). You can download the zip file containing the demo here. The demo works by sending the sample xml request file (req.xml) to http://<b2bhost>:8001/b2b/syncreceiver using the SOAP test tool.  The syncreceiver servlet keeps the socket connection open between itself and the test tool so that it can synchronously send the reply message back. When B2B receives the inbound request message, it is passed to the SOA composite through the default B2B Fabric binding. A simple reply is created in BPEL and returned to B2B which then sends the message back to the test tool using that same socket connection. I’ll show you the B2B configuration first, then we’ll look at the soa composite. Configuring B2B No additional configuration necessary in order to use the syncreceiver servlet. It is already running when you start SOA. After importing the GC_SyncReqRep.zip repository file into B2B, you’ll have the typical GlobalChips host trading partner and the Acme remote trading partner. Document Management The repository contains two very simple custom XML document definitions called Orders and OrdersResponse. In order to determine the trading partner agreement needed to process the inbound Orders document, you need to know two things about it; what is it and where it came from. So let’s look at how B2B identifies the appropriate document definition for the message. The XSD’s for these two document definitions themselves are not particularly interesting. Whenever you're dealing with custom XML documents, B2B identifies the appropriate document definition for each XML message using an XPath Identification Expression. The expression is entered for each of these document definitions under the document administration tab in the B2B console. The full XPATH expression for the Orders document is  //*[local-name()='shiporder']/*[local-name()='shipto']/*[local-name()='name']/text(). You can see this path in the XSD diagram below and how it uniquely identifies this message. The OrdersReponse document is identified in the same way. The XPath expression for it is //*[local-name()='Response']/*[local-name()='Status']/text(). You can see how it’s path differs uniquely identifying the reply from the request. Trading Partner Profile The trading partner profiles are very simple too. For GlobalChips, a generic identifier is being used to identify the sender of the response document using the host trading partner name. For Acme, a generic identifier is also being used to identify the sender of the inbound request using the remote trading partner name. The document types are added for the remote trading partner as usual. So the remote trading partner Acme is the sender of the Orders document, and it is the receiver of the OrdersResponse document. For the remote trading partner only, there needs to be a dummy channel which gets used in the outbound response agreement. The channel is not actually used. It is just a necessary place holder that needs to be there when creating the agreement. Trading Partner Agreement The agreements are equally simple. There is no validation and translation is not an option for a custom XML document type. For the InboundAgreement (request) the document definition is set to OrdersDef. In the Agreement Parameters section the generic identifiers have been added for the host and remote trading partners. That’s all that is needed for the inbound transaction. For the OutboundAgreement (response), the document definition is set to OrdersResponseDef and the generic identifiers for the two trading partners are added. The remote trading partner dummy delivery channel is also added to the agreement. SOA Composite Import the SOA composite archive into JDeveloper as an EJB JAR file. Open the composite and you should have a project that looks like this. In the composite, open the b2bInboundSyncSvc exposed service and advance through the setup wizard. Select your Application Server Connection and advance to the Operations window. Notice here that the B2B binding is set to Receive. It is not set for Synchronous Request Reply. Continue advancing through the wizard as you normally would and select finish at the end. Now open BPELProcess1 in the composite. The BPEL process is set as a Synchronous Request Reply as you can see below. The while loop is there just to give the process something to do. The actual reply message is prepared in the assignResponseValues assignment followed by an Invoke of the B2B binding. Open the replyResponse Invoke and go to the properties tab. You’ll see that the fromTradingPartnerId, toTradingPartner, documentTypeName, and documentProtocolRevision properties have been set. Testing the Configuration To test the configuration, I used Firefox Poster. Enter the URL for the b2b/syncreceiver servlet and browse for the req.xml file that contains the test request message. In the Headers tab, add the property ‘from’ and give it the value ‘Acme’. This is how B2B will know where the message is coming from and it will use that information along with the document type name to find the right trading partner agreement. Now post the message. You should get back a response with a status of ‘200 OK’. That’s all there is to it.

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