π Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
π22β€10π1
Python for Data Analysis Free Resources:
Free Course: https://www.freecodecamp.org/learn/data-analysis-with-python/
Practice: https://www.kaggle.com/learn/python
Free Course: https://www.freecodecamp.org/learn/data-analysis-with-python/
Practice: https://www.kaggle.com/learn/python
π18β€14
Python for Data Analysts - Quick Summary (1).pdf
64.4 KB
π15π5β€1
Python Data Science Handbook
Python Data Science Handbook: full text in Jupyter Notebooks. This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks.
Creator: Jake Vanderplas
StarsβοΈ: 39k
Fork: 17.1K
Repo: https://github.com/jakevdp/PythonDataScienceHandbook
For more, join https://t.iss.one/pythonanalyst
Python Data Science Handbook: full text in Jupyter Notebooks. This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks.
Creator: Jake Vanderplas
StarsβοΈ: 39k
Fork: 17.1K
Repo: https://github.com/jakevdp/PythonDataScienceHandbook
For more, join https://t.iss.one/pythonanalyst
π11β€4π₯°1
Which of the following is/are immutable in Python?
Anonymous Quiz
55%
Tuples
10%
Dictionaries
8%
Sets
26%
All the above
π15β€4
Profound Python Libraries.epub
1.5 MB
Profound Python Libraries
Onder Teker, 2022
Onder Teker, 2022
20 Python Libraries You Aren't Using (But Should).pdf
4.1 MB
20 Python Libraries You
Arenβt Using (But Should)
Caleb Hattingh, 2016
Arenβt Using (But Should)
Caleb Hattingh, 2016
π9π7
Learning Professional Pythonβ
https://www.linkedin.com/posts/sql-analysts_learn-python-activity-7129340888232714240-ZuMM
Like for more πβ€οΈ
https://www.linkedin.com/posts/sql-analysts_learn-python-activity-7129340888232714240-ZuMM
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π5
Python Interview Questions for data analyst interview
Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest.
Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.)
Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously.
Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year.
Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other.
Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average
Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest.
Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.)
Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously.
Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year.
Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other.
Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average
π15β€5
Data Visualization with Python.pdf
7.7 MB
Data Visualization with Python
Dr. Pooja, 2023
Dr. Pooja, 2023
π12β€6π₯°2