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  • How Should I Generate Trade Statistics For CouchDB/Rails3 Application?

    - by James
    My Problem: I am trying to developing a web application for currency traders. The application allows traders to enter or upload information about their trades and I want to calculate a wide variety of statistics based on what the user entered. Now, normally I would use a relational database for this, but I have two requirements that don't fit well with a relational database so I am attempting to use couchdb. Those two problems are: 1) Primarily, I have a companion desktop application that users will be able to work with and replicate to the site using couchdb's awesome replication feature and 2) I would like to allow users to be able to define their own custom things to track about trades and generate results based off of what they enter. The schema less nature of couch seems perfect here, but it may end up being harder than it sounds. (I already know couch requires you to define views in advance and such so I was just planning on sticking all the custom attributes in an array and then emitting the array in the view and further processing from there.) What I Am Doing: Right now I am just emitting each trade in couch keyed by each user's system and querying with the key of the system to get an array of trades per system. Simple. I am not using a reduce function currently to calculate any stats because I couldn't figure out how to get everything I need without getting a reduce overflow error. Here is an example of rows that are getting emitted from couch: {"total_rows":134,"offset":0,"rows":[ {"id":"5b1dcd47221e160d8721feee4ccc64be", "key":["80e40ba2fa43589d57ec3f1d19db41e6","2010/05/14 04:32:37 +0000"], null, "doc":{ "_id":"5b1dcd47221e160d8721feee4ccc64be", "_rev":"1-bc9fe763e2637694df47d6f5efb58e5b", "couchrest-type":"Trade", "system":"80e40ba2fa43589d57ec3f1d19db41e6", "pair":"EUR/USD", "direction":"Buy", "entry":12600, "exit":12700, "stop_loss":12500, "profit_target":12700, "status":"Closed", "slug":"101332132375", "custom_tracking": [{"name":"signal", "value":"Pin Bar"}] "updated_at":"2010/05/14 04:32:37 +0000", "created_at":"2010/05/14 04:32:37 +0000", "result":100}} ]} In my rails 3 controller I am basically just populating an array of trades such as the one above and then extracting out the relevant data into smaller arrays that I can compute my statistics on. Here is my show action for the page that I want to display the stats and all the trades: def show @trades = Trade.by_system(:startkey => [@system.id], :endkey => [@system.id, Time.now ]) @trades.each do |trade| if trade.result > 0 @winning_trades << trade.result elsif trade.result < 0 @losing_trades << trade.result else @breakeven_trades << trade.result end if trade.direction == "Buy" @long_trades << trade.result else @short_trades << trade.result end if trade["custom_tracking"] != nil @custom_tracking << {"result" => trade.result, "variables" => trade["custom_tracking"]} end end end I am omitting some other stuff that is going on, but that is the gist of what I am doing. Then I am calculating stuff in the view layer to produce some results: <% winning_long_trades = @long_trades.reject {|trade| trade <= 0 } %> <% winning_short_trades = @short_trades.reject {|trade| trade <= 0 } %> <ul> <li>Total Trades: <%= @trades.count %></li> <li>Winners: <%= @winning_trades.size %></li> <li>Biggest Winner (Pips): <%= @winning_trades.max %></li> <li>Average Win(Pips): <%= @winning_trades.sum/@winning_trades.size %></li> <li>Losers: <%= @losing_trades.size %></li> <li>Biggest Loser (Pips): <%= @losing_trades.min %></li> <li>Average Loss(Pips): <%= @losing_trades.sum/@losing_trades.size %></li> <li>Breakeven Trades: <%= @breakeven_trades.size %></li> <li>Long Trades: <%= @long_trades.size %></li> <li>Winning Long Trades: <%= winning_long_trades.size %></li> <li>Short Trades: <%= @short_trades.size %></li> <li>Winning Short Trades: <%= winning_short_trades.size %></li> <li>Total Pips: <%= @winning_trades.sum + @losing_trades.sum %></li> <li>Win Rate (%): <%= @winning_trades.size/@trades.count.to_f * 100 %></li> </ul> This produces the following results, which aside from a few things is exactly what I want: Total Trades: 134 Winners: 70 Biggest Winner (Pips): 1488 Average Win(Pips): 440 Losers: 58 Biggest Loser (Pips): -516 Average Loss(Pips): -225 Breakeven Trades: 6 Long Trades: 125 Winning Long Trades: 67 Short Trades: 9 Winning Short Trades: 3 Total Pips: 17819 Win Rate (%): 52.23880597014925 What I Am Wondering- Finally The Actual Questions: I am starting to get really skeptical of how well this method will work when a user has 5,000 trades instead of just 134 like in this example. I anticipate most users will only have somewhere under 200 per year, but some users may have a couple thousand trades per year. Probably no more than 5,000 per year. It seems to work ok now, but the page load times are already getting a tad high for my tastes. (About 800ms to generate the page according to rails logs with about a 250ms of that spent in the view layer.) I will end up caching this page I am sure, but I still need the regenerate the page each time a trade is updated and I can't afford to have this be too slow. Sooo..... Is doing something similar here possible with a straight couchdb reduce function? I am assuming handing this off to couch would possibly help with larger data sets. I couldn't figure out how, but I suppose that doesn't mean it isn't possible. If possible, any hints will be helpful. Could I use a list function if a reduce was not available due to reduce constraints? Are couchdb list functions suitable for this type of calculations? Anyone have any idea of whether or not list functions perform well? Any hints what one would look like for the type of calculations I am trying to achieve? I thought about other options such as running the calculations at the time each trade was saved or nightly if I had to and saving the results to a statistics doc that I could then query so that all the processing was done ahead of time. I would like this to be the last resort because then I can't really filter out trades by time periods dynamically like I would really like to. (I want to have a slider that a user can slide to only show trades from that time period using the startkey and endkey in couchdb if I can.) If I should continue running the calculations inside the rails app at the time of the page view, what can I do to improve my current implementation. I am new to rails, couch and programming in general. I am sure that I could be doing something better here. Do I need to create an array for each stat or is there a better way to do that. I guess I just would really like some advice on how to tackle this problem. I want to keep the page generation time minimal since I anticipate these being some of the highest trafficked pages. My gut is that I will need to offload the statistics calculation to either couch or run the stats in advance of when they are called, but I am not sure. Lastly: Like I mentioned above, one of the primary reasons for using couch is to allow users to define their own things to track per trade. Getting the data into couch is no problem, but how would I be able to take the custom_tracking array and find how many winning trades for each named tracking attribute. If anyone can give me any hints to the possibility of doing this that would be great. Thanks a bunch. Would really appreciate any help. Willing to fork out some $$$ if someone wants to take on the problem for me. (Don't know if that is allowed on stack overflow or not.)

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  • AIA Release 3.1 verfügbar

    - by Hans Viehmann
    Nachdem das Foundation Pack 11g inzwischen eine Weile auf dem Markt ist, wurden jetzt auch die darauf aufsetzenden Process Integration Packs (PIPs) freigegeben. In diesem Zuge wurden neben den bestehenden 16 PIPs auch drei neue Integrationen vorgestellt:Oracle Design-to-Release Integration Pack for Agile Product Lifecycle Management for Process and Oracle Process ManufacturingOracle Clinical Trial Payments Integration Pack for Siebel ClinicalOracle Serialization and Tracking Integration Pack for Oracle Pedigree and Serialization Manager and Oracle E-Business SuiteLetztere sind speziell für den Healthcare/Life Sciences Markt gedacht.Zur Freigabe gibt es nicht nur eine entsprechende Pressemeldung (hier), sondern auch einen öffentlichen Launch-Webcast am 23. Februar unter dem Titel "Tackling the Challenges of Application Integration". Leider ist er mehr für amerikanische Zuhörer gedacht und findet um 10:00h PDT statt. Wer aber sein abendliches Fernsehprogramm eintauschen möchte, findet hier die nötigen Details und die Möglichkeit zur Registrierung.

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  • Dice face value recognition

    - by Jakob Gade
    I’m trying to build a simple application that will recognize the values of two 6-sided dice. I’m looking for some general pointers, or maybe even an open source project. The two dice will be black and white, with white and black pips respectively. Their distance to the camera will always be the same, but their position on the playing surface will be random. (not the best example, the surface will be a different color and the shadows will be gone) I have no prior experience with developing this kind of recognition software, but I would assume the trick is to first isolate the faces by searching for the square profile with a dominating white or black color (the rest of the image, i.e. the table/playing surface, will in distinctly different colors), and then isolate the pips for the count. Shadows will be eliminated by top down lighting. I’m hoping the described scenario is so simple (read: common) it may even be used as an “introductory exercise” for developers working on OCR technologies or similar computer vision challenges.

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  • Cannot resume from hibernate (s2disk)

    - by hwjp
    I seem to be able to hibernate OK using s2disk, but when I switch the laptop back on, it seems to hang half-way through the "resuming" menu. The splash screen looks healthy, it seems to be trying to resume from the correct disk, but it hangs about half-way through, with three little pips. One great help would be - where can I find the logs, and how do I get more verbose ones? There is some info in /var/log/pm-suspend.log, but that all seems fine, just lots of hibernate: success messages... How do I switch on more verbose logging in s2disk? And what about the resume process, where are the logs for that, and how can I make them more detailed? Background: the standard Ubuntu hibernate wouldn't work, so I've install uswusp and its associated tools - s2disk etc. That initially made things worse, but a fair amount of fiddling with its config, my swap size and so on seem to have got it at least seemingly successfully suspending...

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  • Trouble with a query

    - by Mark Allison
    Hi there, I'm having trouble with a query in SQL Server 2008 on some forex trading data. I have a trades table and an orders table. A trade needs to comprise of 2 or more orders. DDL schema and sample data below. What I want to do is write a query that shows the profit/loss in pips for each trade. A pip is 1/1000th of a currency. So the difference between USD 1.3441 and 1.3442 is 1 pip in forex-speak. A trade usually has one entry order and multiple exit orders. So for example if I buy 3 lots of the currency pair GBP/USD at the exchange rate of 1.6100 and then sell 1 lot at 1.6150, 1 lot at 1.6200 and 1 lot at 1.6250 then the profit is (1.6150 - 1.6100) + (1.6200 - 1.6100) + (1.6250 - 1.6100), or 50 + 100 + 150 = 300 pips profit. The trade could also go the other way (Shorting). For example the currency pair can be sold first before it's bought back later at a cheaper price. I would like a query that returns the following: tradeId, currencyPair, profitInPips It seems like a pretty straightforward query, but it's eluding me right now. Here's my DDL and sample data: CREATE TABLE [dbo].[trades]( [tradeId] [int] IDENTITY(1,1) NOT NULL, [currencyPair] [char](6) NOT NULL, CONSTRAINT [PK_trades] PRIMARY KEY CLUSTERED ( [tradeId] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] GO SET ANSI_PADDING OFF GO SET IDENTITY_INSERT [dbo].[trades] ON INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (1, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (2, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (3, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (4, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (5, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (6, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (7, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (8, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (9, N'GBPUSD') INSERT [dbo].[trades] ([tradeId], [currencyPair]) VALUES (10, N'GBPUSD') SET IDENTITY_INSERT [dbo].[trades] OFF GO CREATE TABLE [dbo].[orders]( [orderId] [int] IDENTITY(1,1) NOT NULL, [tradeId] [int] NOT NULL, [amount] [decimal](18, 1) NOT NULL, [buySell] [char](1) NOT NULL, [rate] [decimal](18, 6) NOT NULL, [orderDateTime] [datetime] NOT NULL, CONSTRAINT [PK_orders] PRIMARY KEY CLUSTERED ( [orderId] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] GO SET ANSI_PADDING OFF GO SET IDENTITY_INSERT [dbo].[orders] ON INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (1, 1, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.606500 AS Decimal(18, 6)), CAST(0x00009CF40083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (2, 1, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.615500 AS Decimal(18, 6)), CAST(0x00009CF400A4CB80 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (3, 2, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.608000 AS Decimal(18, 6)), CAST(0x00009CF500000000 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (4, 2, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.603000 AS Decimal(18, 6)), CAST(0x00009CF50083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (5, 2, CAST(2.0 AS Decimal(18, 1)), N'B', CAST(1.605500 AS Decimal(18, 6)), CAST(0x00009CF50107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (6, 3, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.595500 AS Decimal(18, 6)), CAST(0x00009CF70083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (7, 3, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.590500 AS Decimal(18, 6)), CAST(0x00009CF700C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (8, 3, CAST(2.0 AS Decimal(18, 1)), N'B', CAST(1.594500 AS Decimal(18, 6)), CAST(0x00009CF701499700 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (9, 4, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.611000 AS Decimal(18, 6)), CAST(0x00009CFB0083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (10, 4, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.616000 AS Decimal(18, 6)), CAST(0x00009CFB00A4CB80 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (11, 4, CAST(2.0 AS Decimal(18, 1)), N'S', CAST(1.611500 AS Decimal(18, 6)), CAST(0x00009CFB0107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (12, 5, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.613000 AS Decimal(18, 6)), CAST(0x00009CFC0083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (13, 5, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.618000 AS Decimal(18, 6)), CAST(0x00009CFC0107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (14, 5, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.623000 AS Decimal(18, 6)), CAST(0x00009CFC0083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (15, 5, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.628000 AS Decimal(18, 6)), CAST(0x00009CFD00C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (16, 6, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.632000 AS Decimal(18, 6)), CAST(0x00009D020083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (17, 6, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.637000 AS Decimal(18, 6)), CAST(0x00009D0200A4CB80 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (18, 6, CAST(2.0 AS Decimal(18, 1)), N'S', CAST(1.630000 AS Decimal(18, 6)), CAST(0x00009D0200C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (19, 7, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.634500 AS Decimal(18, 6)), CAST(0x00009D0201499700 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (20, 7, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.639500 AS Decimal(18, 6)), CAST(0x00009D0300000000 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (21, 7, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.644500 AS Decimal(18, 6)), CAST(0x00009D030083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (22, 7, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.637500 AS Decimal(18, 6)), CAST(0x00009D0300C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (23, 8, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.625000 AS Decimal(18, 6)), CAST(0x00009D0400C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (24, 8, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.620000 AS Decimal(18, 6)), CAST(0x00009D050083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (25, 8, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.615000 AS Decimal(18, 6)), CAST(0x00009D0500A4CB80 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (26, 8, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.623000 AS Decimal(18, 6)), CAST(0x00009D050107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (27, 9, CAST(3.0 AS Decimal(18, 1)), N'S', CAST(1.618000 AS Decimal(18, 6)), CAST(0x00009D0600C5C100 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (28, 9, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.613000 AS Decimal(18, 6)), CAST(0x00009D0600D63BC0 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (29, 9, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.608000 AS Decimal(18, 6)), CAST(0x00009D0600E6B680 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (30, 9, CAST(1.0 AS Decimal(18, 1)), N'B', CAST(1.613300 AS Decimal(18, 6)), CAST(0x00009D0601391C40 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (31, 10, CAST(3.0 AS Decimal(18, 1)), N'B', CAST(1.614500 AS Decimal(18, 6)), CAST(0x00009D090083D600 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (32, 10, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.619500 AS Decimal(18, 6)), CAST(0x00009D090107AC00 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (33, 10, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.624500 AS Decimal(18, 6)), CAST(0x00009D0901499700 AS DateTime)) INSERT [dbo].[orders] ([orderId], [tradeId], [amount], [buySell], [rate], [orderDateTime]) VALUES (34, 10, CAST(1.0 AS Decimal(18, 1)), N'S', CAST(1.619000 AS Decimal(18, 6)), CAST(0x00009D0A0083D600 AS DateTime)) SET IDENTITY_INSERT [dbo].[orders] OFF /****** Object: ForeignKey [FK_orders_trades] Script Date: 04/02/2010 15:05:31 ******/ ALTER TABLE [dbo].[orders] WITH CHECK ADD CONSTRAINT [FK_orders_trades] FOREIGN KEY([tradeId]) REFERENCES [dbo].[trades] ([tradeId]) GO ALTER TABLE [dbo].[orders] CHECK CONSTRAINT [FK_orders_trades] GO Thanks in advance for any help!

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  • Excel data range - to sum series within date range

    - by Mark
    I have a set of data that I would like to manipulate but my problem is not straight forward. In this data I have date ranges that include multiple entries of the same date on some days and not on others. What I need to accomplish is to manage a trading account so that no more than 1% of the account is put at risk on any given day (retrospectively). To do this, when a series of trades falls on the same day, I need to total the risk associated with each of those trades so that I can limit the total risk of the combined trades by limiting the position size I take in each. Here is a sample set of the data I am working with. As you can see, there are 5 trades on Jan 3. Each of these trades comes with a risk value. I need to add the risk values of these 5 trades so that I can compare it to an account value and then determine if I should take more than 1 position in each trade. As you can see there are different numbers of trades that occur on the 4th, 5th 6th and 9th. I need the values returned in each row so that I can further manipulate them in the spreadsheet. I am not new to Excel, but cannot come up with a solution here - your input is much appreciated. Forgive the presentation below - I cannot upload a pic (new user) and the format does not carry across from excel. I have aligned the first several lines manually. Thx. Date ............. Pair ....... L/S ...... Initial Risk .......Win ......Loss ....BE. ....Avg Gain Avg Loss pips/swing 1/3/2012 ....EUR/USD ....S .............15 ................1 ..................................10 ..........................15. .. 1/3/2012 ....USD/CHF .....L ............15 ..........................................1 ..........0 1/3/2012 ....AUD/USD ....S .............15 ................1 .................................16 ...........................18 1/3/2012 ....NZD/USD ....S .............15 ................1 ...................................7 .............................8 1/3/2012 ....AUD/JPY .... S .............10 ................1 .................................25 ............................20 1/4/2012 ....EUR/USD ....L .............20 ................1 .................................19 ...........................19 1/4/2012 ....USD/CHF ....S ............ 15 ................1 .................................17 ...........................20 1/4/2012 EUR/JPY L 20 1 0 1/5/2012 EUR/USD L 15 1 10 20 1/5/2012 GBP/USD L 20 1 15 20 1/5/2012 USD/CHF S 15 1 0 1/5/2012 USD/JPY S 10 1 7 10 1/5/2012 USD/CAD S 15 1 28 36 1/5/2012 AUD/USD L 15 1 20 20 1/6/2012 USD/CAD S 15 1 5 -10 1/6/2012 EUR/JPY L 15 1 7 7 1/9/2012 AUD/USD S 15 1 22 30 1/9/2012 NZD/USD S 15 1 10 15

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