Jupyter Notebook Python 3
Pick a symbol, you want to analyze.
Pick a 'start' date and 'end' date for certain time frame to analyze.
# Libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt import warnings warnings.filterwarnings("ignore") # fix_yahoo_finance is used to fetch data import fix_yahoo_finance as yf yf.pdr_override() # input symbol = 'AAPL' # Apple Company start = '2018-01-01' end = '2019-01-01' # Read data df = yf.download(symbol,start,end) # View Columns df.head()
In command DOS drive 'C:\ '
Find where you put the code .py in?
How to run python scripts in command prompt(cmd) or Windows PowerShell?
❌ If the code does not load or reload, click here: 👉 https://nbviewer.jupyter.org/
Paste the link in the box.
I tried to make it simple as possible to understand finance data and how to analyze the data by using python language.
If you want to learn different simple function for stock analysis, go to: https://awesomeopensource.com/project/LastAncientOne/100_Day_Challenge
If you want to learn more advance stock analysis or different language in finance, go to: https://awesomeopensource.com/project/LastAncientOne/Stock_Analysis_For_Quant
If you into deep learning or machine learning for finance, go to: https://awesomeopensource.com/project/LastAncientOne/Deep-Learning-Machine-Learning-Stock
If you want to learn about Mathematics behind deep learning or machine learning, go to: https://awesomeopensource.com/project/LastAncientOne/Mathematics_for_Machine_Learning
https://www.investopedia.com/terms/s/stock-analysis.asp (Basic Stock Analysis)
https://www.investopedia.com/articles/investing/093014/stock-quotes-explained.asp (Understand Stock Data)
https://www.investopedia.com/terms/t/trendline.asp (Understand Trendline)
🔻 Do not use this code for investing or trading in the stock market. Stock market is unpredictable. 📈 📉 However, if you are interest in the stock market, you should read many 📚 books that relate to the stock market, investment, or finance. The more books you read, the more you will understand and the more knowledge you gain. On the other hand, if you are into quant or machine learning, read books about 📘 finance engineering, machine trading, algorithmic trading, and quantitative trading.