Why haven't there been personal finance apps or softwares that use regression modeling or A.I.?
What would they be trying to predict?  The value YNAB and Mint provide is objective truth about what you've spent. They can force you to think about the tradeoffs inherent in budgeting by showing that you've overspent one category, and making you decide where to find the money to cover it. They can call your attention to a credit card swipe that's larger than you intended, to a subscription you didn't intend to keep, etc. by just generally getting you to read and think about your transaction history and the sums of transactions per category and overall. Prediction doesn't really enter into it. One way to understand Mint's business model is as a service that collects training data for machine learning models that do try to predict things, such as how stock prices will move or whether users will click on certain ads. How would they make money from it?   They sell you the software for $100 (US example; could as easily be 100 Euros or 10,000 Japanese Yen).  You use it to make recommendations on your blog.  Your blog becomes rich from advertising.  They sold $100 worth of software.  If they spent $1 million in labor developing it, they're way behind.   Another problem is that the software would stop working and need adjusted periodically.  This is easy to do on a server but annoying on a PC.  And who pays for the adjustments?   Put both those things together, and it's a lot easier to do on a server.  Another advantage is that a server can get a better data feed as well.  Pay a premium for the detailed information rather than relying on public sources.  And people are used to renting server access where they expect to buy software once.   Another issue is that they are unlikely to beat the market this way.  Yes, AIs have done so.  But that's the latest AI, constantly adjusted.  This is going to be a previous generation AI.  It's more likely to match the market.  And we already have a way to match the market:  an index fund.   If someone had a brilliant AI, the best use would probably be to sell it to a fund manager.  The fund manager could then use the AI to find opportunities for its existing investors.  Note that a $10 billion fund with a 10% return that gives a .1% commission would be paying $1 million.  And that has no marketing or packaging overhead.  Think $10 billion is a lot?  Fidelity has $2 trillion. "Consumer facing finance is heavily regulated. You are liable for the recommendations you make; if they are based on a black box you risk problems when sued. It is difficult to explain in a court of law why a neural network came to a particular conclusion. It is much easier to provide advice (models) in the ""educated counterparty"" market. Not only do institutional investors in general expect to pay for a quality advice (consumers in general expect to get online advice for free) but the legal implications are different."
"Aside from the fundamental points brought up in other answers (which is important), there are a number of choices when it comes to budgeting software. Software like this can help you organize fixed reoccurring costs (rent and phone) as well as variable costs (food and events). Mint is probably the most popular, but just search ""budgeting software"" to get an idea of what's out there. Some can also help you visualize your spending with reports and dashboards. Some help out with paying bills on a schedule (this may help avoiding late payments). Some even link up with your bank/credit card accounts." I  found that  an application already exists which does virtually everything I want to do with a reasonable interface.  Its called My Personal Index.  It has allowed me  to look at my asset allocation all in one place. I'll have to enter: The features which solve my problems above include:  Note - This is related to an earlier post I made regarding dollar cost averaging and determining rate of returns.   (I finally got off my duff and did something about it) well there are many papers on power spot price prediction, for example. It depends on what level of methodology you would like to use. Linear regression is one of the basic steps, then you can continue with more advanced options. I'm a phd student studying modelling the energy price (electricity, gas, oil) as stochastic process. Regarding to your questions: 1. mildly speaking, it's really hard, due to its random nature! (http://www.dataversity.net/is-there-such-a-thing-as-predictive-analytics/)  2. well, i would ask what kind of measure of success you mean? what level of predicted interval one could find successful enough? 3. would you like me to send you some of the math-based papers on? 4. as i know, the method is to fully capture all main characteristics of the price. If it's daily power price, then these are mean-reversion effect, high volatility, spike, seasonality (weekly, monthly, yearly).  Would you tell me what kind of method you're using? Maybe we can discuss some shared ideas? Anna "Note that mutual funds' quarterly/annual reports usually have this number. I generally just let my home-accounting software project my future net worth; its numbers agree well enough with those I've gotten from more ""professional"" sources such as monte-carlo modelling. (They'd agree better if I fed in all the details of my paycheck, but I don't feel like doing the work to keep that up to date.) I'm using Quicken, but I assume MS Money and other competitors have the same capability if you buy the appropriate version." I mean, it doesn't really apply to a lease/buy decision in a home.  The variability due to the variables I discussed makes it not worth the analysis when you're talking about return differences of maybe 1-2%.  Even if your house costs $1 million, that's only a difference of $10,000... MAYBE if there's no difference in the other variables, but there will be.  My point is that it's not worth doing.  DCF models are used for valuation of large projects, not simple home buying problems.  There are way better ways to evaluate such a simple problem. The software he is talking about readjusts your portfolio weights back to your original allocation on a semi-constant basis. Basically it's designed to replace the stereotypical, lazy financial advisor.  Your second point is quite valid though... One such example for anyone reading who has a young child is a coverdell ESA which allows you to put pretax income into an investment fund and withdraw tax free to spend on anything education related including the primary and secondary level (ie books, Supplies, field trips, tutoring, etc.). Basically money youd probably spend anyway, but this way you skip out on some taxes. nice work! really enjoyed looking through your website. do you see any possible application of Machine Learning (specifically tensorflow) to this? I was thinking about building a trading bot that uses data from various APIs as a strategy just as an experiment but I'm wondering what your insights are. "If your financial needs aren't complex, and mostly limited to portfolio management, consider looking into the newish thing called robo-advisers (proper term is ""Automated investing services"").  The difference is that robo-advisers use software to manage portfolios on a large scale, generating big economy of scale and therefore offering a much cheaper services than personal advisor would - and unless your financial needs are extremely complex, the state of the art of scaled up portfolio management is at the point that a human advisor really doesn't give you any value-add (and - as other answers noted - human advisor can easily bring in downsides such as conflict of interest and lack of fiduciary responsibility).  disclaimer: I indirectly derive my living from a company which derives a very small part of their income from a robo-adviser, therefore there's a possible small conflict of interest in my answer" I recently made the switch to keeping track of my finance (Because I found an app that does almost everything for me). Before, my situation was fairly simple: I was unable to come up with a clear picture of how much I was spending vs saving (altho I had a rough idea). Now I here is what it changes: What I can do now: Is it useful ? Since I don't actually need to save more than I do (I am already saving 60-75% of my income), 1) isn't important. Since I don't have any visibility on my personal situation within a few years, 2) and 3) are not important. Conclusion: Since I don't actually spend any time building theses informations I am happy to use this app. It's kind of fun. If I did'nt had that tool... It would be a waste of time for me. Depends on your situation ? Nb: the app is Moneytree. Works only in Japan. "A couple options that I know of: Interactive Brokers offers a ""paper trading"" mode to its account holders that allows you to start with a pretend stack of money and place simulated trades to test trading ideas. They also provide an API that allows you to interface with their platform programmatically for retrieving quotes, placing orders, and the such. As you noted, however, it's not free; you must hold a funded brokerage account in order to qualify for access to their platform. In order to maintain an account, there are minimums for required equity and monthly activity (measured in dollars that you spend on commissions), so you won't get access to their platform without having a decent amount of skin in the game. IB's native API is Java-based; IbPy is an unofficial wrapper that makes the interface available in Python. I've not used IB at all myself, but I've heard good things about their API and its accessibility via IbPy. Edit: IB now supports Python natively via their published API, so using IbPy is no longer needed, unless you wish to use Python 2.x. The officially supported API is based on Python 3. TD Ameritrade also offers an API that is usable by its brokerage clients. They do not offer any such ""paper trading"" mode, so you would need to ""execute"" transactions based on quotes at the corresponding trade times and then keep track of your simulated account history yourself. The API supports quote retrieval, price history, and trade execution, among other functions. TDA might be more attractive than IB if you're looking for a low-cost link into market data, as I believe their minimum-equity levels are lower. To get access, you'll need to sign up for an API developer account, which I believe requires an NDA. I don't believe there is an official Python implementation of the API, but if you're a capable Python writer, you shouldn't have trouble hooking up to the published interfaces. Some caveats: as when doing any strategy backtesting, you'll want to be sure to be pessimistic when doing so, so your optimism doesn't make your trades look more successful than they would be in the real world. At a minimum, you'll want to ensure that your simulations transact at the posted bid/ask prices, not necessarily the last trade's price, as well as any commissions and fees associated with the trade. A more robust scheme would also take into account the depth of the order book (also known as level 2 quotes), which can cause additional slippage in the prices at which you buy/sell your security. An even more robust scheme would take into account the potential latency of trade execution, looking at all prices over some time period that covers the maximum expected latency and simulating the trade at the worst-possible price." At my first job (moderate sized quant prop fund) we inexplicably used Macs. Excel on a mac is a nightmare- if you tried to graph more than, say, 2000 data points it would grind to a halt, though this was some time ago. In retrospect maybe they were conditioning us to program rather than use excel... I still don't see the point of this software; rebalancing frequently is a waste of money (through fees).  If you invest in index funds, you don't have to rebalance at all--effectively, the fund is doing it for you, and since they can generally trade more efficiently than individual investors can, that's a win.  The Coverdell ESA is a great example.  There's a maximum contribution amount, just as there are for almost any tax-exempt account.  A decent financial adviser could help you plan how much to contribute to which accounts, at what time, and when you can/should start to withdraw from them. often financial software is dire, with crappy interfaces and poorly integrated to the wider company. I have an ambition one day to create a modern human centred financial software that is focused on the task at hand rather than forcing the user to jump through unnecessary hoops. Also Excel should be banned for many reasons. "It is very hard to find any ""tough math"" application that isn't already packaged nicely in a library or app. Unless you're out to break grounds, you don't need to do any math beyond knowing what function to call and what button to click. After went through and understanding a bunch of the option pricing equations, I forgot 99.9% of them already. Why? Because in a day to day basis, all I needed to know is which function to call in quantlib and trust whoever wrote quantlib know what they're doing." It's important because it shows that the amount you owe does not decrease linearly with each payment, and you gain equity as a correspondingly slower rate at the beginning of the loan and faster at the end. This has to be figured in when considering refinancing, or when you sell the place and pay off the mortgage. It also shows why making extra payments toward principal (if your loan permits doing so) is so advantageous -- unlike a normal payment that lowers the whole curve by a notch, reducing the length of time over which interest is due and thus saving you money in the long run. (Modulo possible lost-opportnity costs, of course.)