If you want to learn algorithmic trading, check out this free course that I created here on RU-vid: ru-vid.com/group/PLtqRgJ_TIq8Y6YG8G-ETIFW_36mvxMLad
Amazing, thank you. How long did it take you to get this good at coding? I have been coding for about 6-12 months and still struggle producing and algorithm such as this one on my own.
I have plenty of years of programming experience, but nonetheless I still can’t just write down an algorithm such as this one from scratch. I also have to constantly look up things in the documentation, forum, and other sites. To be able to write a trading algorithm on QuantConnect (or any other site) requires you to not only know the supported programming language (Python) but also the supported API. Learning this basically is like learning a new programming language. So don’t worry if you feel like you don’t understand too much at first. Just start with very basic stuff and work yourself up from there. If you haven’t already, I recommend going through QuantConnect’s boot camp since that gives you a good introduction to its API.
Thanks for the comment. As far as I know, QuantConnect currently does not yet offer Indian stock data. If you have your own data, you might be able to make that work through Lean. They have made plans to add international markets such as India in the future. I can't tell you when this will be released but they will likely post an announcement when they do.
Thanks for the comment. You need the $8 research pack to get access to everything. For a live trading node, you need to pay an additional $20 a month (or less with a yearly subscription). So in total it costs $28 per month for a live trading node + the research pack (or less with a yearly subscription).
@@TradeOptionsWithMe alright, thanks for the clarification. I think that's kinda expensive if you're not immediately trading with upwards of $10k, but it is what it is
Runtime Error: AttributeError : 'FundamentalFactorAlphaModel' object has no attribute 'lastMonth' at Update in AlphaModel.py:line 19 :: if algorithm.Time.month == self.lastMonth: AttributeError : 'FundamentalFactorAlphaModel' object has no attribute 'lastMonth'
Create your free QuantConnect account here: www.quantconnect.com/?ref=towm You can clone the code from this algorithm here: www.quantconnect.com/forum/discussion/10209/fundamental-investing-example-algorithm/p1?ref=towm
Hi, did they get rid of the module selection and algorithm builder functions? I cannot find them anywhere on QuantConnect, neither in the GUI nor any references in the supporting documents or the community..? How to access them?
Hey! I just found your channel and subscribed, love what you're doing! Particularly, I like how clear and detailed your explanations are as well as the depth of knowledge you have surrounding the topic! Since I run a tech education channel as well, I love to see fellow Content Creators sharing, educating, and inspiring a large global audience. I wish you the best of luck on your RU-vid Journey and can't wait to watch your next upload. Cheers, happy holidays, and keep up the great work :)
The code in this video, have you tested it by giving it small capital to work with and allowed it to work over the course of 1-3 weeks? If not, what would your predictions be based solely on the information in this video.
Thanks for your comment. I have not yet done that, but that’s something that you definitely should do before live trading an algorithm since paper trading is immune to backtesting mistakes/errors such as overfitting, look ahead bias etc. It’s hard to say how good this would perform, especially over such a short time-frame. But I certainly believe that it would be profitable over a long enough time frame.
The problem is that paper trading requires your algorithm to be up and running at all times since it has to be able to react to new data on a constant basis. That’s why most firms charge for that. Theoretically, it’s possible to develop and host algorithms on your own machine. This, however, requires you to get the data from somewhere. Furthermore, you would have to leave it running while it paper trades.
Just discover this channel, awesome work guy and thanks for all your videos, that's exactly what I'm looking for 👍 the only thing is I have to adapt these knowledges to Futures , let's see what Will happen 🧐
thank you for the video. only starting out but I noticed that the algo is doing a lot of unexpected trading (avg 8 trades per day) selling/buying small quantities (mostly 1 share) . what could be the issue?
The tutorial is amazing, but just wondering that I cannot find the Insight Portfolio model when I create the project. Do I need to pay for it or is it not available anymore?
Thanks for the comment. They recently changed the UI for the algorithm creation, they might’ve forgotten to add that portfolio model. But I don’t believe you need to pay to use it. You just have to copy the import statement and set the portfolio construction model. That should do the trick.
A question about the Universe Selection... From where do these stocks and symbols come from? Is it from all of NASDAQ and NYSE? There are some Universe Modules that can find securities from Technology Indexes, etc, so I was wondering what grouping of securities does this algorithm search its stocks to construct the Insight Weighted Portfolio Model? Edit: Because I would like to execute a similar, but modified framework on a certain industry, or index. A video on how to do this would be fantastic! Also would I be able to insert individual stocks to invest in along with the selected Universe?
Thanks for the question und suggestion. QuantConnect has data for pretty much all US stocks (close to 20000 symbols). For a full list and more details on this, I recommend checking out the documentation page here: www.quantconnect.com/docs/data-library/equity?ref=tow There are plenty of other universe selection models including a manual selection model. This manual universe selection model allows you to manually add whatever securities you want to. If you want to learn some more details about universe selection, you could check out the boot camp example algorithms on QC that give you an introduction to universe selection and QC in general. I hope this helps.
congratulations on the channel and video, I wanted your contact because I am wanting to set up a grid robot that will act according to RSI would have and have the function of dragging upwards?
Thanks for the comment. I recommend checking out my free algorithmic trading course playlist. The 6th video is coming this Friday and in it you will learn how to use indicators like RSI in QuantConnect.
@@TradeOptionsWithMe The only message I get is that the backtest is complete. Looking at the purchase page i see you get a second research node when you buy the researcher subscription so Im guessing that has something to do with it. Great videos bro - this is the best yt channel to actually learn about algo trading imo. Keep up the good work!
@@bluejazzman Thanks for the positive feedback. As I said, they might have added a backtest limit recently, but it should say so if that's the reason. You could try contacting QuantConnect support. But buying a research node likely isn't the solution. If you would have to buy something, you would have to upgrade your backtesting node from free to a premium one.
@bluejazzman @TradeOptionsWithMe I have the same problem. I clicked on the Logs tab and found this message near the bottom: "You have exceeded maximum number of orders (10000), for unlimited orders upgrade your account."
A researcher seat + live trading node is $28 a month, but you’re right, there might be some additional costs if that’s all you want. I believe I’m currently paying $48 a month.
I have been paper trading this algorithm since the release of this video. So far it has accomplished a return of about 4%. But note that such a short time frame isn’t very significant, so these results don’t say too much. If you want to test it in a more detailed manner, you can just use the link in the description box to backtest it yourself.
It appears that there is mistake in the code in the line "size_scores = self.Scores(added, [(lambda x: x.Fundamentals.MarketCap, False, 1)])"; i believe it ought to be size_scores = self.Scores(added, [(lambda x: x.Fundamentals.MarketCap, True, 1)]) as larger the marketcap the better the stock is deemed to be. Am I correct?
Better is very subjective. This strategy tries to exploit the so-called size effect which says that smaller cap stocks tend to outperform larger cap stocks. See the Quantpedia entry that I linked in the description box for more details.
@@TradeOptionsWithMe Thank you for the reply and the answer does make sense. One more question: when you construct the score, you multiply a rank (lower numerical value is better) with quality wt, size wt, and value wt. Now that seems to be incorrect to me. You should divide the rank with the weight to reflect your intent of giving more siginficant to quality and value ranks and lesser weight to size ranks. Agree?
