Hi everyone! You can find the code for this tutorial here - github.com/dataquestio/project-walkthroughs/tree/master/sp_500 .

@feiziihu

Жыл бұрын

Thanks Vik!

@AudaiLouri

Жыл бұрын

Thanks Vic, However your F1 score is at 0.5. How does that factor in?

@aarondelarosa3146

10 ай бұрын

Thanks, but it's incomplete.

@saip6126

7 ай бұрын

Hey Viki. You should have used the pd.dropna(inplace=True).

@majorkuntz

2 ай бұрын

Great video. Will you or can you provide additional information on other useful classifiers and also how to merge other data sources like news and sentiment into this code?

Clear and to the point. I hate super long videos full of things that don't provide much value. This one was great. I like that he walked through general data science/machine learning steps. In particular the data cleansing which many skip over, but it is actually an important step. Also, a pet peeve of mine is audio quality. This video you can hear the presenter clearly and he doesn't sound like his is working from a tin can.

I’m new to coding but have always been an avid market watcher and looking for opportunities. Best video I’ve seen since I started scouring the depths of KZread for this content last week. Thank you sir!

thank yiu so much fir the video. I have taken varius courses in different places, and your video and teaching style are certainly the best !

I cannot thank you enough! It's very straight to point and I've learned more in this video than in n online courses and articles.

Great video. Thank you for the insights. Going to be tuning into more of your work.

This was an amazing walkthrough. I have learned so much!

Very thorough and loved it sir. Thanks for the video lesson.

Searched & watched a LOT of videos. This is the best. Well done man.

@jsonbourne5085

2 ай бұрын

have you tried them? do they work on real data?

Excellent. This tutorial corrects an error that pretty much every other video from others that I have seen has made. Don't seek MSE precision in your target as your goal. That's not what practitioners are looking for. Do what this educator has done instead. This model gets it right as used in the real world. Solid base to work with. Well done!

@user-gj3kz7cm3x

12 күн бұрын

No, this is not even close to how practitioners have approached the problem in the last 30 years…

Vik, I echo the compliments on the excellent video. I was able to use my own bespoke weekly market timing signals aligned with weekly S&P closes to finally get a grounded statistical "opinion" on the predictability of forward returns - as only my second Python exercise! Thanks!

My man is doing noble work. Kudos!

Watched up to 2:26 and I already know this is going to be excellent. Clear and concise explanation from the start and you know this is going to be more than your ordinary YT tutorial

@alang.2054

10 ай бұрын

It's not excellent, you can't beat the market as regular person. You basically compete with Harvard graduates with math, computer science, etc. Degrees. Again, one KZread video won't make you beat the market

@killerstar718

9 ай бұрын

@@alang.2054 someone had to break this kids dreams of being rich off a youtube vid

@okoo7385

5 ай бұрын

​@alang.2054 Where'd you get that she said she would beat the market from her comment? I read an observation just stating that, this video is higher quality than most YT videos that claim to teach you something specific yet just give you fluff..

Great video. Really clear and at a pace that allowed me to follow it easily and learn some new and simple techniques in how to manipulate data.

This is awesome, instead of showing what you need to learn or try it shows how to actually build a model. This is very usefull. Thank you!

@idkidkidk3488

11 ай бұрын

Could we get a similar video bus featuring a deep learning model instead?

@alang.2054

10 ай бұрын

What are you talking about? Do you really think this guy would show you real ways to make money? On market you compete with professionals in multi billion hedge funds with degrees, you can't beat them with KZread video

Excellent video, thank you for sharing this. Hopefully I can see more ML related videos going forward.

This is very nice way to get started using data science with the markets. This gives a nice framework to get started. And attempt to expand the predictors (on RSI based or Change in Open Interest , some correlation with the major stocks composing that index) . Thank you for sharing.

Thats a really good video and it seems you really know what you are talking about. Thanks!

Explaining is on top. Thank you!

Very useful man, thanks for show us the way!

Congratulations for your explanation and it was very clear. I would like to suggest you to prepare a vide including news about the stock into this model. Thanks

I'm hoping you can do a follow up video to this. Would be great to see how you would incorporate macro data into your model, such as news or interest rates.

DUDE THIS IS SO HELPFUL

Incredible video! This helped me a whole lot I really do appreciate it! I Just Liked and Subcribed!

Super helpful - Thank You !!!

What a great framework to ML time-series data for prediction. Thanks for sharing!

Very good explanation, thanks.

This was very well delivered. Thank yo sharing. I will consider the suggestions you made and see how this works. Very exciting with a bit of 😅.....

This was an excellent presentation.

Wow, the concept of predicting the stock market using machine learning and Python is such a fascinating topic! The blend of finance and technology is always an area ripe for innovative approaches. It's impressive how machine learning can analyze vast amounts of data to find patterns that might not be obvious at first glance. Python, with its extensive libraries and community support, is an excellent choice for such complex computations. It's exciting to think about how these tools can provide insights into market trends and possibly even predict future movements. The intersection of machine learning and finance is definitely a space to watch! 📈💡🤖

thank you thank you !! this is great, suscribed :)

Great video, thank you!

Thanks so much, you're a blessing

Thanks for your great video. Im curious to read more about the whole issue of predicting actual prices versus only the direction. Do you have a good source on this?

These are great for practice Keep em coming

@Dataquestio

2 жыл бұрын

Glad you like them, Prathamesh! -Vik

Great tutorial!

Excellen video. I think you have a great teaching ability. I'm surprised you did not start with the usual "THIS IS NOT FINANCIAL ADVICE..." disclaimer 😇

Cool Video! Thank you!!

Thank you very much for this! Truly found this useful for my first ML Project. However, a bit confused by the 'combined' graph - how did you get it? :) (I had to do mine using the train_test_split import.)

I suggest you google the semi strong efficient market hypothesis. Would save a lot of time.

Great tutorial 🙏

Hi Vik. Thank you very much. Is it possible to predict two days in advance instead of just tomorrow?

Great video , I hope to see more tutorials like this in the future.

Excellent video!

Vik thank you for this video! Greetings from Poland. Please explain to me how to connect the model so that operating on a virtual server bought and sold instruments? How do you combine it?

cool went threw the whole process on mini conda.

Actually you forgot to measure the expectancy of a trade in the case it has a precision of 42%. Because what makes a strategy profitable is bit the win rate but rather the expectancy of the trades. Although it is a great video and a good tutorial about programming. Thanks and keep up the good work.

Thank you so much, I’m learning to build and plot models, I’m basically copied your code and tried to understand it, What’s your advice to learn how to do it yourself?

How would you use the volume column? Not sure how to use the volume, can we build some relative volume indicator? Can you give a hint, or maybe a link to a video, where you use volume somehow to improve your model? Volume should influence the model significantly.

Sir your explaining skills are top notch

@dimitriosdesmos4699

2 күн бұрын

your ability to hide though is not....

Thanks for your great video. Im curious to read more about the whole issue of predicting actual prices versus only the direction. Do you have a good source on this? I can see why the latter is more robust, but once you start accounting for transaction costs, the magnitude of the direction is also important. curious to get your thought on this too.

@user-gj3kz7cm3x

11 күн бұрын

No one does what he did because it’s stupid. It’s been common practice for over 40 years to calculate the logged odds of the derivative of the price (logged odds of the returns).

Brilliant video Vik! Towards the end, you mentioned adding news to the model. Could you share how one could integrate that? Thanks!

@Dataquestio

2 жыл бұрын

Hi Jeevan - the easiest way to do it is to scrape daily headlines from say the new york times, and create a "sentiment" model to indicate confidence in the market. The output of that model could then be a predictor column. Of course, you could get a lot more complicated than this :)

Hi, great lesson, I have a question. I'm still new to data science. But why didn't you use the data as a predictor? Im asking because say we want to predict what happens in the next day. How do i pass it to the model when i didn't train with it

Hint: on a recent macbook you can use all its cores by: import joblib N_CORES = joblib.cpu_count(only_physical_cores=True) ... model = RandomForestClassifier(n_estimators='your value', min_samples_split='your other value', random_state=1, n_jobs=N_CORES) The speedup is amazing

@DonFranciscoUSF

6 ай бұрын

you don't need any information about the system to do this, n_jobs = -1 will use all the available cores with no imports or extra lines :)

Thanks, Vic.

Great stuff!

great channel, will try to get some of my time to get to do something meaningful with the help of dataquest

Great job! I used the majority of your code but for a specific company. My personal aspect is that this "result" is a bit messy. Do you have any tips on how we could make a clear graph towards the end with "predicted values"? I tried graphing with "Tomorrow" with respect to "Close"m but no difference. Part of that reason could because of the wide X-axis. Thanks again, looking forward to your answer! / Alexander

Amazing video!! Have yiou looked at the performances of other ML techniques, e.g, MLPregressor?

Thank you ❤❤

Great video. It seems that the yfinance api is no longer functioning. Could you please do an updated video using a different method to collect the date? Thanks.

Thank you for your videos. But what if I have multiple stocks to predict, and when I parse one stock id in, I want to get the specific prediction for that id only. will it be feasible?

What a deep voice

Great video. The backtest code can be improved. Use vectorized back test instead of doing it in a loop will greatly improve the back test efficiency.

@stevenyoussef7677

10 ай бұрын

Can you elaborate on this? The backtest for me takes about 100 seconds

Excellent video and you are above average by all means. You made things easier for me who is new to Python. At 65 yrs old I tried to work your script and it worked beautifully. So, I tried with TSLA ticker and it gave me no obj to concatenate error and I have no idea how to fix that error.

@bvspa

Жыл бұрын

Hey I'm facing the similar issue. You got any solutions?

@SuperVIN786

Жыл бұрын

@@bvspa Did not work for me yet

@bvspa

Жыл бұрын

@@SuperVIN786 you have to alter the start an step count as per the dataset

@SuperVIN786

Жыл бұрын

@@bvspa Thanks I will try that

@SuperVIN786

Жыл бұрын

@@bvspa Finally ran it after I made start and step number change, watched the video again which helped. Thanks

great video, I hope to see your works into real trading platform. It would be great to see your P&L.

Hello Vik, Thanks for the great tutorial, really informative. Do you know how to add lorentzian classification to the model in your example?

great content

I request you to create a video considering Fundamental Analysis news integration prediction model as its happening behind the scenes to change the values. Its just a request if possible.

You’re the real Mr Money

Super 👏👏💪

thank you

Great video. I am going to try modifying it to predict high and low (as a range %). Perhaps you can give me a few pointers while I explore how to do it.

Excellent Video. Thank you for sharing. Question, how can we compare the 'influence' from another stock in the same industry, ie, two retail stocks, or two energy stocks?

@jitendersinghvirk47

Жыл бұрын

correlation maybe.

The S&P 500 is still up 10% this year. It's not a get-rich-quick scheme, but it's a proven strategy for wealth accumulation over time, Which happens path i'm considering so as to hedge the losses on my $350k portfolio, but are there any drawbacks to buying such quality stocks?

@Curbalnk

7 ай бұрын

Well, one potential downside is that they may not offer the same rapid growth potential as riskier, smaller-cap stocks. So, it depends on your goals and risk tolerance. you may want to work with a financial advisor who can help with right approach.

@TeresaBrickle

7 ай бұрын

this is definitely considerable! think you could suggest any advisors i can get on the phone with? i'm in dire need of proper portfolio allocation

@TeresaBrickle

7 ай бұрын

very much appreciated, your response suggests a person of benevolence.. just inputted her full name on my browser, and came across her site, top-notch qualifications! she seems well-qualified

@upsenloyn

2 ай бұрын

​@@TeresaBricklefuck you bots no ones gonna fall for that

@pinecedar180

2 ай бұрын

Spam comment chain, please remove

Subscribed 🎉

The features used for the random forest cannot be the high, close, low , open values directly without any transformation because what the model is essentially doing is creating a overfit of non linear decisions to certain prices ranges. It is basically memorizing that when the close was above X value and open below Y value predict 1 or 0. You need to normalize the predictors in some way so that the model can use them independently of how high the value the stock is and truly create generalizable rules. Ratios are good since they use percentage instead of using absolute values and allow the model to use information of multiple candles as well.

@kibs_neville

2 ай бұрын

Quite important comment.

Hey man, how did you get into this kind of work? Im so keen to find some work doing what you did but am finding limited possibilities

Good and clear explanation :) Although there are other factors to be considered like bid offer spread and commissions. Also, when the market goes against you, do you wait before the end of day to close the losing position? Maybe setting a stop loss and including it in the model and back testing can help. Thanks.

@Mike-fm3km

7 ай бұрын

how would commissions help? lol

@alrey72

7 ай бұрын

@@Mike-fm3km In the back testing of the model, it may seem profitable but after considering the commissions/transaction fees, it might be unprofitable instead.

Hi, how do I predict the next , for instance in a new data.

Do a part 2 please!!!

Hi can we use this for Indian stock markets?❤

Hello, thank you very much for the video, I am new to ML, I would like to know how to use the model? How do I see the prediction for the next day? thanks and greetings

Hi! Maybe you can compare your algorithm with the real optimal decision in every season, so you could "asign points" to this algorithm and compare with others!

hello sir , can this be used for day trading , in indian market for options trading of bank nifty and nifty in a 5 minutes candle time frame during market hours and feeding real time data?

Thank you so much for the tutorial and for taking the time to explain each piece of code in such a clear manner. I have two quick questions: 1.) What is the purpose of the .csv file ? 2.) Broadly speaking, what would be the steps to using a different API? Thanks !!

@FlisB

Жыл бұрын

If you can fit the data from the API into a data-frame it would be very easy.

@adamfrench4587

Жыл бұрын

@@FlisB thanks for replying. Would you by any chance know how get (in addition to 1 or 0 when proba >.6) a column with the actual probability?

@FlisB

Жыл бұрын

​@@adamfrench4587 You need to save the result of model.predict_proba to another variable. add probs = preds before changing "preds" with 0.6 condition. And then add "probs" to the array inside pd.concat.

@adamfrench4587

Жыл бұрын

Legend, thank you so much!

What did you use for the risk rate as there is no such thing that exists in finance

is it possible to have a view of the daily basis, but also input training on intraday data to improve the daily view?

Do we have any latest updates to this model? Adding extended logic for improvements?

Alert !!! just won a new suscriber

when you split the data into the training and testing dataset, you are actually performing what is called Simple Random Sampling, this will cause the training data to have the same elements/characteristics of the testing dataset. If you were to calculate the means of each predictor variable in the testing and training dataset it will roughly be the same due to random sampling. The point I am trying to make is that you cannot claim the model has not "seen" the testing data, yet it managed to capture the majority of its properties due to simple random sampling, how about you train the model using the first 70% rows then leave the remaining 30% at the bottom for predictions? In that way the model does not have any idea what's happening with the remaining 30% (though there is an argument one can put forward about this), I think that approach would be the most realistic. I have used the simple random sampling before and I have gotten results which seemed to be accurate, it was not until I used this method I am suggesting to you that I obtained a little bit higher errors.

How do you add additional columns that will display information from yahoo finance such as pe ratio dividens and so on

Hi Vik - thank you for the great video This could be a dumb Qs - in "Improving Our Model" section, why didn't you change Predictors to "NEW_Predictors" when you defined the function/ when you've copy paste? Does this matter? Thank you, AL

@dhananjaysharma3255

9 ай бұрын

"NEW_Predictors" was passed while calling backtest function which calls predict function with "New_Predictors". Hence New_Predictors was used for modelling

Great video but where is the clarification that it will go up or down tomorrow?

How has the model done this year? Does it show a topping formation?

Great, have you tried to improve the model ?

Ill take a notes: the model without hyperparameter tuning. if hyperparamter tuning is done, when backtesting we no longer need to look for the best parameters. In contrast to cross-validation which requires more tuning

Isn’t there leakeage in the ‘trend’ feature, considering it is a function of future values (‘target’)?

Machine learning is artificial learning from a geat many individual experiences. And like the wize man said "Experience is a lantern that we carry on our back and which only ever illuminates the path traveled". The experience of the Stock Market was done with a dominant economic model. Who knows what other models creative minds will come up in the future.

@maalikserebryakov

2 ай бұрын

The stock market always existed.

Awesome