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Backtrader, using mongodb as datafeed instead CSV

I'm quite new to backtrader and since I've started I couldn't stop wondering why there's no database support for the datafeed. I've found a page on the official website where's described how to implement a custom datafeed. The implementation should be pretty easy, but on github (or more in general on the web) I couldn't fine a single one implementation of a feed with MongoDb. I understand that CSV are easier to manage and so on, but in some cases could require a lot of RAM for storing data all at once in memory. On the other hand, having a db can be "RAM friendly" but will take longer during the backtesting process even if the DB is a documental one. Does anyone have any experience with both of these two approaches? And if yes, there's some code I can take a look at?
Thanks!

Need advice on writing a document control software with Python and MySQL

I'm looking for open-ended advice on the best approach to re-write a simple document control app I developed, which is really just a custom file log generator that looks for and logs files that have a certain naming format and file location. E.g., we name all our Change Orders with the format "CO#3 brief description.docx". When they're issued, they get moved to an "issued" folder under another folder that has the project name. So, by logging the file and querying it's path, we can tell which project it's associated with and whether it's been issued.
I wrote it with Python 3.3. Works well, but the code's tough to support because I'm building the reports while walking the file structure, which can get pretty messy. I'm thinking it would be better to build a DB of most/all of the files first and then query the DB with SQL to build the reports.
Sorry for the open-ended question, but I'm hoping not to reinvent the wheel. Anyone have any advice as to going down this road? E.g., existing apps I should look at or bundles that might help? I have lots of C/C++ coding experience but am still new to Python and MySQL. Any advice would be greatly appreciated.

Really nice answer by #GCord. I'd add just two bits:
If it's a relatively small database, consider sqlite3 instead of
MySQL (it is nicely supported out of the box, multiplatform, no
dependencies on a running rdbms)
If it's expected to grow, and/or you
just want to play with some new technology, try to write automated
ingestion scripts for a real document management system (e.g., http://www.alfresco.com/). I'd
recommend Apache Solr (based on Apache Lucene) as a full text
indexing service and then you could use Apache Tika to automatically
extract text and metadata from your documents (see
http://wiki.apache.org/solr/ExtractingRequestHandler)

Firstly, if it works well as you suggest, then why fix it?
Secondly, before doing any changes to your code I would ask myself the following questions:
What are the improvements/new requirements I want to implement that I can't easily do with the current structure?
Do I have a test suite of the current solution, so that I can regression-test any refactoring? When re-implementing something it is easy to overlook some specific behaviors which are not very well documented but that you/users rely on.
Do those improvements warrant an SQL database? For instance:
Do you need to often run reports out of an SQL database without walking the directory structure?
Is there a problem with walking the directories?
Do you have network or performance issues?
Are you facing an increase in usage?
When implementing an SQL solution, you will need a new task to update the SQL data. If I understand correctly, the reports are currently generated on-the-fly, and therefore are always up-to-date. That won't be the case with SQL reports, so you need to make sure they are up-to-date too. How frequently will you update the SQL database:
a) In real-time? That will necessitate a background service. That could be a operational hassle.
b) On-demand? Then what would be the difference with the current solution?
c) At scheduled times? Then your data may be not up-to-date between the updates.
I don't have any packages or technical approaches to recommend to you, I just thought I'd give you those general software management advices.
In any case, I also have extensive C++ and Python and SQL experience, and I would just stick to Python on this one.
On the SQL side, why stick to traditional SQL engines? Why not MongoDB for instance, which would be well suited to storing structured data such as file information.

Persistant database state strategies

Due to several edits, this question might have become a bit incoherent. I apologize.
I'm currently writing a Python server. It will never see more than 4 active users, but I'm a computer science student, so I'm planning for it anyway.
Currently, I'm about to implement a function to save a backup of the current state of all relevant variables into CSV files. Of those I currently have 10, and they will never be really big, but... well, computer science student and so on.
So, I am currently thinking about two things:
When to run a backup?
What kind of backup?
When to run:
I can either run a backup every time a variable changes, which has the advantage of always having the current state in the backup, or something like once every minute, which has the advantage of not rewriting the file hundreds of times per minute if the server gets busy, but will create a lot of useless rewrites of the same data if I don't implement a detection which variables have changed since the last backup.
Directly related to that is the question what kind of backup I should do.
I can either do a full backup of all variables (Which is pointless if I'm running a backup every time a variable changes, but might be good if I'm running a backup every X minutes), or a full backup of a single variable (Which would be better if I'm backing up each time the variables change, but would involve either multiple backup functions or a smart detection of the variable that is currently backed up), or I can try some sort of delta-backup on the files (Which would probably involve reading the current file and rewriting it with the changes, so it's probably pretty stupid, unless there is a trick for this in Python I don't know about).
I cannot use shelves because I want the data to be portable between different programming languages (java, for example, probably cannot open python shelves), and I cannot use MySQL for different reasons, mainly that the machine that will run the Server has no MySQL support and I don't want to use an external MySQL-Server since I want the server to keep running when the internet connection drops.
I am also aware of the fact that there are several ways to do this with preimplemented functions of python and / or other software (sqlite, for example). I am just a big fan of building this stuff myself, not because I like to reinvent the wheel, but because I like to know how the things I use work. I'm building this server partly just for learning python, and although knowing how to use SQLite is something useful, I also enjoy doing the "dirty work" myself.
In my usage scenario of possibly a few requests per day I am tending towards the "backup on change" idea, but that would quickly fall apart if, for some reason, the server gets really, really busy.
So, my question basically boils down to this: Which backup method would be the most useful in this scenario, and have I possibly missed another backup strategy? How do you decide on which strategy to use in your applications?
Please note that I raise this question mostly out of a general curiosity for backup strategies and the thoughts behind them, and not because of problems in this special case.

Use sqlite. You're asking about building persistent storage using csv files, and about how to update the files as things change. What you're asking for is a lightweight, portable relational (as in, table based) database. Sqlite is perfect for this situation.
Python has had sqlite support in the standard library since version 2.5 with the sqlite3 module. Since a sqlite database is implemented as a single file, it's simple to move them across machines, and Java has a number of different ways to interact with sqlite.
I'm all for doing things for the sake of learning, but if you really want to learn about data persistence, I wouldn't marry yourself to the idea of a "csv database". I would start by looking at the wikipedia page for Persistence. What you're thinking about is basically a "System Image" for your data. The Wikipedia article describes some of the same shortcomings of this approach that you've mentioned:
State changes made to a system after its last image was saved are lost
in the case of a system failure or shutdown. Saving an image for every
single change would be too time-consuming for most systems
Rather than trying to update your state wholesale at every change, I think you'd be better off looking at some other form of persistence. For example, some sort of journal could work well. This makes it simple to just append any change to the end of a log-file, or some similar construct.
However, if you end up with many concurrent users, with processes running on multiple threads, you'll run in to concerns of whether or not your changes are atomic, or if they conflict with one another. While operating systems generally have some ways of dealing with locking files for edits, you're opening up a can of worms trying to learn about how that works and interacts with your system. At this point you're back to needing a database.
So sure, play around with a couple different approaches. But as soon as you're looking to just get it working in a clear and consistent manner, go with sqlite.

If your data is in CSV files, why not use a revision control system on those files? E.g. git would be pretty fast and give excellent history. The repository would be wholly contained in the directory where the files reside, so it's pretty easy to handle. You could also replicate that repository to other machines or directories easily.

Using Excel to work with large amounts of output data: is an Excel-database interaction the right solution for the problem?

I have a situation where various analysis programs output large amounts of data, but I may only need to manipulate or access certain parts of the data in a particular Excel workbook.
The numbers might often change as well as newer analyses are run, and I'd like these changes to be reflected in Excel in as automated a manner as possible. Another important consideration is that I'm using Python to process some of the data too, so putting the data somewhere where it's easy for Python and Excel to access would be very beneficial.
I know only a little about databases, but I'm wondering if using one would be a good solution for what my needs - Excel has database interaction capability as far as I'm aware, as does Python. The devil is in the details of course, so I need some help figuring out what system I'd actually set up.
From what I've currently read (in the last hour), here's what I've come up with so far simple plan:
1) Set up an SQLite managed database. Why SQLite? Well, I don't need a database that can manage large volumes of concurrent accesses, but I do need something that is simple to set up, easy to maintain and good enough for use by 3-4 people at most. I can also use the SQLite Administrator to help design the database files.
2 a) Use ODBC/ADO.NET (I have yet to figure out the difference between the two) to help Excel access the database. This is going to be the trickiest part, I think.
2 b) Python already has the built in sqlite3 module, so no worries with the interface there. I can use it to set up the output data into an SQLite managed database as well!
Putting down some concrete questions:
1) Is a server-less database a good solution for managing my data given my access requirements? If not, I'd appreciate alternative suggestions. Suggested reading? Things worth looking at?
2) Excel-SQLite interaction: I could do with some help flushing out the details there...ODBC or ADO.NET? Pointers to some good tutorials? etc.
3) Last, but not least, and definitely of concern: will it be easy enough to teach a non-programmer how to setup spreadsheets using queries to the database (assuming they're willing to put in some time with familiarization, but not very much)?
I think that about covers it for now, thank you for your time!

Although you could certainly use a database to do what you're asking, I'm not sure you really want to add that complexity. I don't see much benefit of adding a database to your mix. ...if you were pulling data from a database as well, then it'd make more sense to add some tables for this & use it.
From what I currently understand of your requirements, since you're using python anyway, you could do your preprocessing in python, then just dump out the processed/augmented values into other csv files for Excel to import. For a more automated solution, you could even write the results directly to the spreadsheets from Python using something like xlwt.

Best DataMining Database

I am an occasional Python programer who only have worked so far with MYSQL or SQLITE databases. I am the computer person for everything in a small company and I have been started a new project where I think it is about time to try new databases.
Sales department makes a CSV dump every week and I need to make a small scripting application that allow people form other departments mixing the information, mostly linking the records. I have all this solved, my problem is the speed, I am using just plain text files for all this and unsurprisingly it is very slow.
I thought about using mysql, but then I need installing mysql in every desktop, sqlite is easier, but it is very slow. I do not need a full relational database, just some way of play with big amounts of data in a decent time.
Update: I think I was not being very detailed about my database usage thus explaining my problem badly. I am working reading all the data ~900 Megas or more from a csv into a Python dictionary then working with it. My problem is storing and mostly reading the data quickly.
Many thanks!

Quick Summary
You need enough memory(RAM) to solve your problem efficiently. I think you should upgrade memory?? When reading the excellent High Scalability Blog you will notice that for big sites to solve there problem efficiently they store the complete problem set in memory.
You do need a central database solution. I don't think hand doing this with python dictionary's only will get the job done.
How to solve "your problem" depends on your "query's". What I would try to do first is put your data in elastic-search(see below) and query the database(see how it performs). I think this is the easiest way to tackle your problem. But as you can read below there are a lot of ways to tackle your problem.
We know:
You used python as your program language.
Your database is ~900MB (I think that's pretty large, but absolute manageable).
You have loaded all the data in a python dictionary. Here I am assume the problem lays. Python tries to store the dictionary(also python dictionary's aren't the most memory friendly) in your memory, but you don't have enough memory(How much memory do you have????). When that happens you are going to have a lot of Virtual Memory. When you attempt to read the dictionary you are constantly swapping data from you disc into memory. This swapping causes "Trashing". I am assuming that your computer does not have enough Ram. If true then I would first upgrade your memory with at least 2 Gigabytes extra RAM. When your problem set is able to fit in memory solving the problem is going to be a lot faster. I opened my computer architecture book where it(The memory hierarchy) says that main memory access time is about 40-80ns while disc memory access time is 5 ms. That is a BIG difference.
Missing information
Do you have a central server. You should use/have a server.
What kind of architecture does your server have? Linux/Unix/Windows/Mac OSX? In my opinion your server should have linux/Unix/Mac OSX architecture.
How much memory does your server have?
Could you specify your data set(CSV) a little better.
What kind of data mining are you doing? Do you need full-text-search capabilities? I am not assuming you are doing any complicated (SQL) query's. Performing that task with only python dictionary's will be a complicated problem. Could you formalize the query's that you would like to perform? For example:
"get all users who work for departement x"
"get all sales from user x"
Database needed
I am the computer person for
everything in a small company and I
have been started a new project where
I think it is about time to try new
databases.
You are sure right that you need a database to solve your problem. Doing that yourself only using python dictionary's is difficult. Especially when your problem set can't fit in memory.
MySQL
I thought about using mysql, but then
I need installing mysql in every
desktop, sqlite is easier, but it is
very slow. I do not need a full
relational database, just some way of
play with big amounts of data in a
decent time.
A centralized(Client-server architecture) database is exactly what you need to solve your problem. Let all the users access the database from 1 PC which you manage. You can use MySQL to solve your problem.
Tokyo Tyrant
You could also use Tokyo Tyrant to store all your data. Tokyo Tyrant is pretty fast and it does not have to be stored in RAM. It handles getting data a more efficient(instead of using python dictionary's). However if your problem can completely fit in Memory I think you should have look at Redis(below).
Redis:
You could for example use Redis(quick start in 5 minutes)(Redis is extremely fast) to store all sales in memory. Redis is extremely powerful and can do this kind of queries insanely fast. The only problem with Redis is that it has to fit completely in RAM, but I believe he is working on that(nightly build already supports it). Also like I already said previously solving your problem set completely from memory is how big sites solve there problem in a timely manner.
Document stores
This article tries to evaluate kv-stores with document stores like couchdb/riak/mongodb. These stores are better capable of searching(a little slower then KV stores), but aren't good at full-text-search.
Full-text-search
If you want to do full-text-search queries you could like at:
elasticsearch(videos): When I saw the video demonstration of elasticsearch it looked pretty cool. You could try put(post simple json) your data in elasticsearch and see how fast it is. I am following elastissearch on github and the author is commiting a lot of new code to it.
solr(tutorial): A lot of big companies are using solr(github, digg) to power there search. They got a big boost going from MySQL full-text search to solr.

You probably do need a full relational DBMS, if not right now, very soon. If you start now while your problems and data are simple and straightforward then when they become complex and difficult you will have plenty of experience with at least one DBMS to help you. You probably don't need MySQL on all desktops, you might install it on a server for example and feed data out over your network, but you perhaps need to provide more information about your requirements, toolset and equipment to get better suggestions.
And, while the other DBMSes have their strengths and weaknesses too, there's nothing wrong with MySQL for large and complex databases. I don't know enough about SQLite to comment knowledgeably about it.
EDIT: #Eric from your comments to my answer and the other answers I form even more strongly the view that it is time you moved to a database. I'm not surprised that trying to do database operations on a 900MB Python dictionary is slow. I think you have to first convince yourself, then your management, that you have reached the limits of what your current toolset can cope with, and that future developments are threatened unless you rethink matters.
If your network really can't support a server-based database than (a) you really need to make your network robust, reliable and performant enough for such a purpose, but (b) if that is not an option, or not an early option, you should be thinking along the lines of a central database server passing out digests/extracts/reports to other users, rather than simultaneous, full RDBMS working in a client-server configuration.
The problems you are currently experiencing are problems of not having the right tools for the job. They are only going to get worse. I wish I could suggest a magic way in which this is not the case, but I can't and I don't think anyone else will.

Have you done any bench marking to confirm that it is the text files that are slowing you down? If you haven't, there's a good chance that tweaking some other part of the code will speed things up so that it's fast enough.

It sounds like each department has their own feudal database, and this implies a lot of unnecessary redundancy and inefficiency.
Instead of transferring hundreds of megabytes to everyone across your network, why not keep your data in MySQL and have the departments upload their data to the database, where it can be normalized and accessible by everyone?
As your organization grows, having completely different departmental databases that are unaware of each other, and contain potentially redundant or conflicting data, is going to become very painful.

Does the machine this process runs on have sufficient memory and bandwidth to handle this efficiently? Putting MySQL on a slow machine and recoding the tool to use MySQL rather than text files could potentially be far more costly than simply adding memory or upgrading the machine.

Here is a performance benchmark of different database suits ->
Database Speed Comparison
I'm not sure how objective the above comparison is though, seeing as it's hosted on sqlite.org. Sqlite only seems to be a bit slower when dropping tables, otherwise you shouldn't have any problems using it. Both sqlite and mysql seem to have their own strengths and weaknesses, in some tests the one is faster then the other, in other tests, the reverse is true.
If you've been experiencing lower then expected performance, perhaps it is not sqlite that is the causing this, have you done any profiling or otherwise to make sure nothing else is causing your program to misbehave?
EDIT: Updated with a link to a slightly more recent speed comparison.

It has been a couple of months since I posted this question and I wanted to let you all know how I solved this problem. I am using Berkeley DB with the module bsddb instead loading all the data in a Python dictionary. I am not fully happy, but my users are.
My next step is trying to get a shared server with redis, but unless users starts complaining about speed, I doubt I will get it.
Many thanks everybody who helped here, and I hope this question and answers are useful to somebody else.

If you have that problem with a CSV file, maybe you can just pickle the dictionary and generate a pickle "binary" file with pickle.HIGHEST_PROTOCOL option. It can be faster to read and you get a smaller file. You can load the CSV file once and then generate the pickled file, allowing faster load in next accesses.
Anyway, with 900 Mb of information, you're going to deal with some time loading it in memory. Another approach is not loading it on one step on memory, but load only the information when needed, maybe making different files by date, or any other category (company, type, etc..)

Take a look at mongodb.