Forex or day trading algorithms
Do NOT make a post asking for advice, that belongs as a comment in the "Daily advice thread". Keep discussions civil, informative and polite. Off topic comments, attacks or insults will not be tolerated. Strictly no promotional threads. Do not post your app, tool, blog, event, etc. A hedge fund analyst explains his stock research process. Full of excellent links to videos, articles, and books. I have an day trading algorithm that generates a consistent positive return historically, what now? One more caveat, be very careful that you're not using any future information when you back-test. For example, suppose you screen for stocks that hit a week minimum in If your program trades on that information, it is using future information and will fail when you try it with real money. Instead, you would have to make sure that the stock is at a week minimum on the day that you are running the program. I can tell you from experience that it is very easy to make these forex of mistakes, and they can often be subtle and hard to catch. For algorithms working day-trading algorithm you'll also have to account for latency. You may not be able to get in and out of trades at the exact prices that you want, so if your algorithm relies on making money off of penny-sized differences you could have a bad time. I can't stress how important that is. I forex I found in the Holy Grail back in when I had a semi-intraday strategy. It didn't move in and out of the market much, it would buy positions in the volatile NASDAQ, either long or short, usually before noon. Then it would settle its book at the end of the day. The idea was the age old " I almost was going to throw my real money into it but I decided to test it against other exchanges. Not as good but still, very nice. Then I tried the FTSE Classic curve fitting for you. The method "works" if you drop it against a data set it was tweaked to work for. I still am convinced that the method could be modified to try to "detect" what kind of market it is trading in and then modify its strategy based on that. But that's just speculation, somebody needs to do the work. Yeah, curve fitting is a big issue. For machine learning algorithms, what you do is split the data up into training, testing, and validation sets. So while you're tweaking the algorithm on the training set, you test the performance on the testing set, and use the validation set when you are finished tweaking it. This is probably good practice for any type of predictive algorithm, though. What I find is usually a very simple phenomenon obscured by lots of little mathematical rules. The phenomenon is that it's easy to make a strategy that does very well either algorithms a rising market or shorting a falling market. As long as a clearcut trend is in place, the strategy outperforms the market. What sort of machine learning techniques are you using just out of curiosity? I work with bayesian learning techniques as part of my job as a nerd, and I always wondered if they could apply to options trading. I've just started investing, and have always said "one day I'll just apply my research to the stock market" and was wondering how far out in left field that actually is? I think that the best thing to try would be plugging in fundamental data to some of the newer unsupervised learning algorithms like RBM's and deep belief nets. Backprop neural nets might get you somewhere, too. Obviously this would be to find longer-term underlying patterns. A lot trading stuff has already been tried before, forex if anyone has gotten amazing results with them, I haven't seen them. Naive Bayes would actually be a great idea for collecting fundamental data from the SEC. I wrote an SEC filing parser to collect stuff using regular expressions, which worked OK, but the next step for me would have been implementing naive bayes. Honestly, even if you don't figure out how to hack the stock market, a good naive bayes parser could probably make you a lot of money, because obtaining good data sets is a algorithms in and of itself. I've written a stock screener and the filing parser for myself, but that's it. I'd like to try out some deep learning algorithms on my data, but first I'd need to shell out some serious cash for a really reliable data set or I'd have to somehow scrub professor Damodaran's, since I've found numerous errors in itor maybe find a job where I'd have access to better data. Also, this sort of project would basically be a full-time job to implement. Maybe if I algorithms get another programmer on board with me and raise some money for it, I could sit down and spend a year cobbling something together, but otherwise I'd say it would be a pretty impressive achievement for one person. Yeah, that's my problem too. I already spend 8 hours a day hacking c and python, I'm not how much more I realistically have in me, unless I work on company time ; My big idea was to run N-dependence bayes estimation on large historical data sets, but my plan of wget'ing everything from google was too tedious. Also I didn't have any money at the time anyway. When I started learning about ML I got to thinking that it would be a great way to make myself rich in the stock market, but once I started coding stuff I realized that I would have to be able to evaluate my algorithms' investment choices, because the numbers never tell the whole story. So that led to me actually learning about accounting and how to evaluate investments. And what I've learned from that is that a it's a never-ending process, and b if you are good enough to competently evaluate investments on their own merit, you can do perfectly well in the stock market without machine learning. Paper trading ensures that you haven't overfit your data, which is probably the biggest problem in quantitative trading. What provides historical returns in a back tested environment does not always provide real returns in the current one, and paper day can identify this problem. Just build it and let it trade paper for a while. Did the same thing day built my platform in python. It's been running for a few months now. I started in July and my returns for that month were positive, August came and I dipped in the negatives, and now it's September and I seemed to have missed that short bull run my program uses lagging indicators that I'm starting to despise. Over all, I'm about even with the market. The only way to make money from what I see in Forex is to cut you losses soon it's apply to any trades but lot more important when you have no clue where the trend goes No, I trading to use as few 3rd party packages as possible. Weird, I know, but it makes the program extremely portable. I'm not a great programmer, but I do have some knowledge of Python that I tried to teach myself and I appreciate you putting this up. It's interesting to see the code and pick out parts, so I get a forex picture of what your strategy is doing. Why does python seem to be a common language for trading programs? Could you not achieve the same result with VBA? I noticed in the link you provided, you have python and VBA. Why not just all VBA? I use python because it's easy to read and write; it feels like I'm writing sentences, not code. This could definitely be done in VBA; I don't know the language well enough to do forex though. The VBA code I do have is used to compile my data algorithms a pretty format. Excel is essentially my GUI. That is awesome and really kinda laid it out for trading. My favorite language is VBA and I'm a lot more comfortable with it than Python Spaces say hi. I was thinking of writing a program using VBA and day the data in a SQL database which would be great for searching out results but excel could also do that while making it look nice. Ontop of this is my very limited stocks knowledge. So, with all the information you've provided, I have a much better idea as to where to start and how I want to go about making it happen. I'm thinking the first step is just to be able to gather all of the data and present it in a meaningful way, which your data detail screenshot kinda covered for me. How often does your data update? Do you run it everyday after the market is closed and get the data that way? Or do you have it constantly monitoring throughout the day? I know I could look into the code for these answers, but you seem very forthcoming and helpful. I may not respond to this yet tonight, bedtime is soon. The data updates daily; I run it every morning and my program downloads the previous day's daily data which is used to calculate signals for the current trading day. The live environment screen scrapes real time quotes from Yahoo and compares it to my signals. I have the program running in Google Drive so throughout the day when I'm curious how it's performing I can log in and check. It's about as real algorithms as free can get you. Thanks for the information man, have you looked into using TeamViewer? It'll allow you to remote into your home PC from a work PC or even your phone. Might give you a little more control than Google Drive. This is wise advice, but some of the wisest is the hardest to follow. We all hate to be pessimistic towards our own ideas. I'm still in the developing stages, but next is testing. It uses a time series concept I developed math degree to forecast prices. I'm looking for connections to help test the theory. So like time series momentum? I recently read a report on how these strategies are used by managed futures funds. Yes, it uses a new type of running average that I'm not sure anyone has investigated before. What I do know is that its probably not currently being used as I don't see the formula pop up anywhere at least in the stuff related to financial markets. I'm working on one myself these days in Python but it's very rudimentary I considered coding in Elliot Wave, moving average flags, and some other technical forex, but there's too many and they often conflict. Forward test it on sim, look for market conditions where it blatantly doesn't work. On sim, increase your targets by one tick or some cent amount if not futures this way you guarantee your fills if it were a live environment don't change stop as it's a market order. I only have access to google finance historic data. Its a day trading strategy so day trades are intra-day. Also, I am looking at high volume stocks so getting into a trade trading be that hard as the assets are constantly shifting hands. If the high of the day is higher then his target, he would have sold for a profit at his target price. If the low of the day was lower than his stop, he would have a loss. No idea how he is determining his entry price though. Maybe buy at open, and accounting for slippage. The market has different types of day. Generally it's either going up, down or sideways choppy. Your algorithm is likely to do day in one of the environments than the others At least mine do. It would be a good idea to try and define when your algorithm doesn't work few algorithms work in day market conditions. Next you need to test out your algorithm in a live market using a simulator. To generate enough statistical data you're going to have to take a minimum of 20 trades, preferably 50 or more to see how the algorithm performs. This should show up where it is weak. Do your best to determine the market condition that caused the most losses I use a simple histogram to visually see what conditions didn't work. When it comes to exiting the trade you should penalize the exit by a certain amount. You want price to trade through your day as this guarantees a fill. When it comes to adjusting your algorithm, change only ONE parameter at a time and redo the 50 trades to gather data. Just curious - what platform are you using for building and testing the system? I'd like to play around with algorithmic trading but am not sure where to start. You can try Ninjatrader or Multicharts as a first step. Both offer some free to use conditions. NT is free till you want to go live. NET is free if you only trade 2 instruments. Its something I constantly battle. I sound the alarm too quick at times. I'm still in a very beta mode, but my algo seems to pick out the days when it is best to purchase at open and sell at close. That's the jist of it without going into the details. Its nothing stellar, but algorithms well, its all for fun anyways, unless of course if it can generate consistent returns. I just need trading from people more experienced in testing and validation as I don't have a background forex that. Proceed to writing it out algorithms an environment that can be traded live through a broker. Look at Ninjatrader or Multicharts for easy retail level access. There are others that cost a lot more. Is the return more than the average daily return, considering commissions? Do I have the time to stick to this? Is there any software out there that allows you to build and test such algorithms? I would like to test if my current trading would have worked in the past as well. I don't mean to be a downer, but it probably isn't a positive historical return after accounting for transaction costs. Maybe not, but it algorithmically picks out the days which are much better to buy at open and sell at close. That, to me, sounds useful to other investors, maybe not though. Sorry, I didn't know the right term. Thank you for correcting me: This is basically the standard out-of-sample testing critique to market anomaly papers. Titman, Asness, and Lo usually just forex a paper, have the EMH crowd say, "overfitting", wait ten years, then revisit their old papers with ten years of out of sample data. My model is very simplistic. Much more so than what a lot of people are suggesting on here. I actually prefer simplicity from experience working in data analysis. Usually the simplest answer does the best job and explains things the easiest. A man after my own heart: I really wasn't trying criticise or undermine in my first comment. I was genuinely curious about your process. Do you mean you ran it against historical data? Or you made some predictions that you think will generate positive outcomes? I'm no super stats guy but you can typically find positive trends after they happen that simply regress to the mean or worse. Since I am working on a moving average concept, my biggest enemy is regression to the mean. However, I combat that by giving the algo no future information and using cross validation. All the historical backtesting is as if it were live. The live stuff is just the test set. I think we're all in a regression to the mean battle. Good luck, just remember the drunkards walk as you invest that first few bucks. I will cross that bridge when necessary. I am just happy to have something that works in a declining market. Just curious, what are your sell triggers and what do you have built in for "sell immediately"? I have a day trading algo that generates a consistent positive return too: I actually respect forex response quite a lot despite the fact that it was aimed towards me. With the right understanding of data analysis, you can overcome this problem. Day why I am good at what I do, because I look beyond what is given to me. That may not be true with this algo, I'm human I can make mistakes, but in general, you bring up a great point about overfitting. You always need to consider generalization. Thanks for pointing out something I should seriously consider. Wow, a positive historical return! I've never seen one of those. I'm sure you'll make a killing in the market. It saddens me that I have to say this, as I thought it was obvious, but previous performance is no guarantee of future performance. You will lose money. I assumed this would be most of the responses. I guess you can't time the market or peoples opinions. The more money day use, the day liquidity you will have. It inevitably will find diminishing returns, find the sweet spot. Things that work on paper don't always work in the real world. You can't know that without literally trading. Use of this site constitutes acceptance of our User Agreement and Privacy Policy. By signing up, you agree to our Terms and that you have read our Privacy Policy and Content Policy. Log in or sign up in seconds. Submit a new text post. Discuss news items relevant to investors 2. Share investment ideas and insights 3. WSJ guide trading how to pick a financial planner Why we recommend Vanguard Compare brokers. Barron's Online-Broker Review Should I invest a lump-sum, or wait? This has been asked and answered many times in the past. Use the search function or check out thisthisthisthisthis or this thread. Useful Online Resources A guide to stock research! You are responsible for your own investment decisions. Please consult with a registered investment advisor before making any investment decision. Investing a windfall - should I put half in investment firm and half with Vanguard? Thinking of moving some or all of it to an index fund instead. All questions about your personal situation should be asked algorithms. It's moronic Monday, your chance to ask any of those lingering questions without fear of harassment. First Tesla Model 3 to be finished ahead of schedule on Friday, company expects to produce 20, per month by December of This is an archived post. You won't be able to vote or comment. What's my next step? I was wondering what sort of job in industry would involve using Naive Bayes. This is just a casual experiment that's meant to help me form an opinion on TA. I use python all the time. When you use it do you use Pyper? Here is a brief snapshot of the program: Algorithms cool, I'm majorly self taught. Here's what the sheets look like: Excel Home Portfolio Details Data Details. If you're right, you won't succeed. If you succeed, you'll save a lot of money. If so, trading did you do it? Data mining isn't inherently bad. See trading response to hedgefundaspirations. Sorry bub, it's impossible to rule out a bias that by trading is impossible to see. Going balls out first try is not a good idea. Look in to it. Posts are automatically forex after 6 months.
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