For anyone stuck trying to merge the wiki and btc history data frames, if you get a time-zone error (Cannon merge tz-naive with tz-aware), you will need to edit the datetime index of the btc history data frame after converting it to a datetime object to make it time-zone naive. Here was my fix: btc.index = pd.to_datetime(btc.index) [Original Code from Vik] btc.index = btx.index.tz_localize(None) [Fix to make index time-zone naive]
For those stuck in cell 7, be aware that one data entry has no comment (20:44, 10 September 2022 Staxringold) You have to delete that entry with "del revs[x]" being x the position. It depends on when you obtain the data.
Thanks for finding the issue! Replacing the line `comment = rev["comment"]` with `comment = rev.get("comment", "")` should also work. This will just fill in the comment with an empty string if it can't be found.
awesome videos! keep it up! have you tried using transformers for timeseries? also, in your experience, what's your opinion on training using technical indicators (let's say RSI, CCI, stochastic, etc) and VIX, rates (could be us 10 years yield for example)?. an idea I'm exploring is trying to figure out how to use also the rolling correlation with a set of assets (let's say you are fiting for QQQ comparing or correlating with dxy, gold or any other asset to check if the rolling correlation impacts in any way)... take care!
are you putting look ahead bias using trend variable, because you sum the target and in the current bar you dont have the bar closed so in real time wont have the trend predictor
Amazing video. Thank you so much for sharing, I was able to modify it and develop it further to predict the price of Ethereum! Thanks for the inspiration.
Aren't you leaking data by using the Close Value as a predictor? I mean the algorithm just have to look at this to predict next day. Can you reason about it?
if you had other variables such as indices, commodity prices would you need to take differences of the variables in order to make the dataset stationary?
hi! enjoying making this great video, it's the first time I incorporated sentiment analysis at all (in my ML journey). *I'm having a problem with the history attribute/method of yf!* Anyone else?
I can't proceed further in appending (find_sentiment(comment)) to an empty dictionary edit = {}. It keeps loading instead. Can someone help to solve? Thanks!
hello, im trying to run cell 7 and it takes forever to load, i tried the different edits below but it still won't work, is there something else you can advise me to replace it with?
just realized that its because it's trying to pass through +17000 comments, other than cutting the data (revs) into less than 1000 records, is there a way to process this any faster?
What is a multi step forecast? Do you mean a forecast multiple days ahead? If so, then yes, just change the .shift(-1) to the number of days ahead you want to predict.
@@Dataquestio yes forcasting for multiple days ahead. In your video your making a prediction for tommorow. What I wanted was to know if I can make predictions for the next 3 days rather than changing the model with multiple targets
For the line "if len(edits[key]["sentiments"]) > 0:", I got an error saying that "object of type 'float' has no len()" May I kindly ask how to solve this problem?
Hi Dong - the "sentiments" key should be a list, but it looks like it is a single number. This would happen if you assigned the sentiment value directly to the key instead of appending to the list.
Hi Vik! Hope everything is well! On code cell 7 I keep getting KeyError: 'comment' (The find_sentiment function is working) Would really appreciate some help to solve this error :)
It looks like calling `page.revisions()` isn't downloading the comment for you for some reason. Try running `page.revisions(prop='ids|timestamp|flags|comment|user')` instead to see if that helps.
Solved the issue the following way: (code cell 7) edits = {} for rev in revs: date = time.strftime("%Y-%m-%d", rev["timestamp"]) if date not in edits: edits[date] = dict(sentiments=list(), edit_count=0) edits[date]["edit_count"] += 1 for k, v in rev.items(): if k == 'comment': comment = v edits[date]["sentiments"].append(find_sentiment(comment))
That fix looks great. Someone else investigated and found that there is one revision missing the comment key for some reason. This revision is from after I created the original project, so I didn't see it. You could also replace the line `comment = rev["comment"]` with `comment = rev.get("comment", "")`. This will just fill in the comment with an empty string if it can't be found.
😂ha ha ha. Very funny. Kuch bhi. So u r going to predict the price of Bitcoin. 😅. Very funny. It moves according to whim and fancy of Elon Musk and other big players. Your ML will shamefully fail. So don't even give a try. Anyways great conlmexy😂.
Mate, you just arrived on a spaceship from Mars and are a little bit lost... The point of this is to learn how to use ML and to code with jupyterlab. It is in no way focused on actually predicting btc moves.
For anyone stuck trying to merge the wiki and btc history data frames, if you get a time-zone error (Cannon merge tz-naive with tz-aware), you will need to edit the datetime index of the btc history data frame after converting it to a datetime object to make it time-zone naive. Here was my fix:
btc.index = pd.to_datetime(btc.index) [Original Code from Vik]
btc.index = btx.index.tz_localize(None) [Fix to make index time-zone naive]
eres una leyenda, Muchas Gracias tio
is "btx" a typo..you must mean "btc"
For those stuck in cell 7, be aware that one data entry has no comment (20:44, 10 September 2022 Staxringold)
You have to delete that entry with "del revs[x]" being x the position. It depends on when you obtain the data.
Thanks for finding the issue! Replacing the line `comment = rev["comment"]` with `comment = rev.get("comment", "")` should also work. This will just fill in the comment with an empty string if it can't be found.
can you give me the exact code?
@@ch-qk8dk the Dataquest's solution is better, it's in my comment
@@atenea3930 okok thanks
Great one Vik. please keep them coming
Vik, thanks for creating these and sharing your knowledge.
Thank you for the video. It helped me alot better than my boot camp course.
awesome videos! keep it up! have you tried using transformers for timeseries? also, in your experience, what's your opinion on training using technical indicators (let's say RSI, CCI, stochastic, etc) and VIX, rates (could be us 10 years yield for example)?. an idea I'm exploring is trying to figure out how to use also the rolling correlation with a set of assets (let's say you are fiting for QQQ comparing or correlating with dxy, gold or any other asset to check if the rolling correlation impacts in any way)... take care!
are you putting look ahead bias using trend variable, because you sum the target and in the current bar you dont have the bar closed so in real time wont have the trend predictor
Amazing video. Thank you so much for sharing, I was able to modify it and develop it further to predict the price of Ethereum! Thanks for the inspiration.
Can u provide me help regarding final year project on bitcoin price prediction in btech
Aren't you leaking data by using the Close Value as a predictor? I mean the algorithm just have to look at this to predict next day. Can you reason about it?
if you had other variables such as indices, commodity prices would you need to take differences of the variables in order to make the dataset stationary?
I also found that rolling by 15 instead of 30 increases the accuracy in 0.5 points
@e7eec37d79c did you manage to get it to work?
you can use timeseries cross validation technique
Awesome video, I learned a lot. Thanks!
line 56:Cannot join tz-naive with tz-aware DatetimeIndex.Any solution?
hi! enjoying making this great video, it's the first time I incorporated sentiment analysis at all (in my ML journey). *I'm having a problem with the history attribute/method of yf!* Anyone else?
how can we show the predection in 54 rather than 0.54
i am trying your tutorial in 2024 and yfinance shows that it has no data regarding bitcoin
Are you using Jupyter notebook??
the mwclient somehow it gives an error
Great sir! Thank so much
How much for this project sir ?
Would/Can you build me one for a certain crypto coin?
Can you send the document of this
I can't proceed further in appending (find_sentiment(comment)) to an empty dictionary edit = {}. It keeps loading instead. Can someone help to solve? Thanks!
Hi Kwok - does the find_sentiment function itself work? It may be that the transformers package isn't installed or configured correctly.
hello, im trying to run cell 7 and it takes forever to load, i tried the different edits below but it still won't work, is there something else you can advise me to replace it with?
just realized that its because it's trying to pass through +17000 comments, other than cutting the data (revs) into less than 1000 records, is there a way to process this any faster?
you are taking yesterday's closing price as tomorrow's closing price that does not make sense...
is it possible to make a multi step forcast with classification models such as this?
What is a multi step forecast? Do you mean a forecast multiple days ahead? If so, then yes, just change the .shift(-1) to the number of days ahead you want to predict.
@@Dataquestio yes forcasting for multiple days ahead. In your video your making a prediction for tommorow. What I wanted was to know if I can make predictions for the next 3 days rather than changing the model with multiple targets
@@Dataquestio I changed the shift-1 to shift-5 what does it show me , prediction for next 5 days ?
I saw the precision score went up to .70 , what does that mean ?
Hey, mine is not predicting next day
Thanks
Awesome!
Where's the plot?
did you manage to get it to work?
Can I try out these codes on pycharm?
Yes, you can use any IDE to complete this project.
For the line "if len(edits[key]["sentiments"]) > 0:", I got an error saying that "object of type 'float' has no len()" May I kindly ask how to solve this problem?
Hi Dong - the "sentiments" key should be a list, but it looks like it is a single number. This would happen if you assigned the sentiment value directly to the key instead of appending to the list.
Hi Vik! Hope everything is well! On code cell 7 I keep getting KeyError: 'comment' (The find_sentiment function is working)
Would really appreciate some help to solve this error :)
It looks like calling `page.revisions()` isn't downloading the comment for you for some reason. Try running `page.revisions(prop='ids|timestamp|flags|comment|user')` instead to see if that helps.
@@Dataquestio Thank you very much! I greatly appreciate your help!
Solved the issue the following way: (code cell 7)
edits = {}
for rev in revs:
date = time.strftime("%Y-%m-%d", rev["timestamp"])
if date not in edits:
edits[date] = dict(sentiments=list(), edit_count=0)
edits[date]["edit_count"] += 1
for k, v in rev.items():
if k == 'comment':
comment = v
edits[date]["sentiments"].append(find_sentiment(comment))
That fix looks great. Someone else investigated and found that there is one revision missing the comment key for some reason. This revision is from after I created the original project, so I didn't see it.
You could also replace the line `comment = rev["comment"]` with `comment = rev.get("comment", "")`. This will just fill in the comment with an empty string if it can't be found.
@@dbarbedillo How long did it take to run that part of the code?
also got stuck on cell 7 :( the fixes from the comments don't help
did you manage to get it work?
plz dont invest in crypto especialy waqar zaka group or u will be destroying ur life
😂ha ha ha. Very funny. Kuch bhi. So u r going to predict the price of Bitcoin. 😅. Very funny. It moves according to whim and fancy of Elon Musk and other big players. Your ML will shamefully fail. So don't even give a try. Anyways great conlmexy😂.
Mate, you just arrived on a spaceship from Mars and are a little bit lost... The point of this is to learn how to use ML and to code with jupyterlab. It is in no way focused on actually predicting btc moves.