Use Artificial Intelligence (AI) to Predict the Stock Market with Python

38 thoughts on “Use Artificial Intelligence (AI) to Predict the Stock Market with Python”

1. This works only for one stock. Is there any way to design a neural network model that can be used to predict stock price of more than one stock?

2. typo? train_data= data.iloc[:int(.99 * len(data)), :]
   
   test_data= data.iloc[int(.99 * len(data)):,:] <———test data 1%
   i also get a xgboost error. probably has something to do with the shape of the train and test data not matching

3. great tutorials 👍👍👍👍👍

4. should it be more reasonable to use the price the day before to predict the price next day in train and testing?

5. Very interesting! But, how could it be used in algotrading?

6. Hey, nice, can you do a tutorial for optimal execution on python using almgren kriss?

7. thanks bro

8. where to get dataset for this

9. Can see you are a programmer but not a data scientist

10. I followed you step by step, but my historical chart is a downtrend for some reason.

11. Are you sure that it is not learning from the high and low prices of the current day that has not yet closed? How to make sure?

12. Hope you had tutorial for deployment of this and for real time price to predit.

13. Where or how did you produce the input file for historical data? Where can it be obtained?

14. How do I, predict more, like not only the test set for example 3 years forward

15. i keep getting an error at the "model.score" it says model is not defined

16. This type of algo is usefull just to predict the next time event. You have 70% accuracy but you are predicting just the next day price based in the last day price multiple times

17. Use Your prediction and check the return %, the results is very very poor. The prediction is basically noise with baseline of open price. You can just plot open price vs close price, the are similar to your prediction.

18. How do I get it to predict more

19. etni jhak marane ke bad prediction acuuracy only 70 %….11:30..!!!

20. Overfitting bruh

21. How the flow of a forwardtest such prediction is:
    Step 1: Download the csv till current date
    Step 2: Delete the last 20% of the timestamps of the csv so if it's like data of 2000 till 2023 then cut till 2019 and save this as training_SP500.csv
    Step 3: Do the opposite of step 2 wich u cut the first 80% off and save it as future_Sp500.csv
    Step 4: train only on the data of training_SP500.csv
    Step 5: u test the the prediction for the missing 20% of the current data
    Step 6: on same chart plot the original data of the current s&p500 data

    And Voila a chart wich u can clearly see is those prediction where in the right direction!!

    Nice vid, good education

22. actually thats almost useless prediction coz we know volume when only when close is known

23. Stopped this video when he included 99% of the data in the train_data. Makes no sense

24. Pregunta que le hago a todos, para que te sirve esto xD. Para nada, no te da ninguna info

25. #Import the libraries
    
    import pandas as pd
    
    import xgboost as xgb
    
    import matplotlib.pyplot as plt

    #load the data
    
    data= pd.read_csv('all_stocks_5yr.csv')
    
    data

    #show th data
    
    data['close'].plot()

    #split the data in to training and testing data sets
    
    train_data= data.iloc[:int(.99*len(data)),:]
    
    test_data= data.iloc[:int(.99*len(data)):,:]

    #Define the features and target variable
    
    features=['open','volume']
    
    target='close'

    #create and train the model
    
    model= xgb.XGBRegressor()
    
    model.fit(train_data[features],train_data[target])

    #makeand show the predictions on the test data
    
    predictions= model.predict(test_data[features])
    
    print('Model predictions:')
    
    print(predictions)

    #Show the actual valurs
    
    print('Actual values:')
    
    print(test_data[target])

    #Show the model's accuracy
    
    accuracy=model.score(test_data[features],test_data[target])
    
    print('Accuracy')
    
    print(accuracy)

    #plot the predictions and the close price
    
    plt.plot(data['close'],label='close price')
    
    plt.plot(test_data[target].index,predictions,label='predictions')
    
    plt.legend()
    
    plt.show()

    😄

26. I'm surprised why this video is getting so many views and likes.
    You made a huge mistake in your logic.
    This is not the accuracy of your model. This is a coefficient of determination that does not play any useful role in building a trading strategy.
    The coefficient of determination shows how strongly you can describe the target variable using your features.
    If you take data from the previous day, and without a model. Your coefficient of determination will be 0.99
    In short, it’s not accurate and it’s useless what you did

27. way of Telling the program is brief . how can we get the csv files for example like yours SPY.csv

28. im new to ML, the final plot only show some orange line near the end, what does it mean and where are the rest?

29. I run the same test on 30 years of data from Yahoo Finance, the score was 96%. Also ran the same on 30 years of data of Nasdaq(NDX) and again the score was 97%. The prediction does not look bad.

30. Nice Video but not useful for real time prediction, volume information will not be available beforehand

31. found on tests with ticker 'SAP.DE' and other stocks
    at 5 min, 1h , 1d interval
    to get max accuracy in those intervals:
    5 min: 99.55 % accuracy
    1 h : 80.12 %
    1 d : 94.46 %
    ————————————-
    using notebook: type into cell: !pip install xgboost
    helped me to import xgboost as xgb (py)
    and
    import yfinance as yf
    data = yf.download(ticker, period='60d', interval='5m')

32. It is totally useless wtf ??

33. Thx and god bless 😊

34. Can I get a sample data, 'SPY.csv'?

35. shame there's no code (just pitching for sign-up), I'll just type it in:)

36. Merci (:

37. What is that csv file and how do i get it

38. 7

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