https://github.com/lastancientone/simplestockanalysispython
Teaches step-by-step to analysis stock data in python.
https://github.com/lastancientone/simplestockanalysispython
financial-analysis financial-data fundamental-analysis python3 stock-analysis stock-data stock-market stock-prices stock-trading stocks technical-analysis technical-indicators time-series time-series-analysis timeseries
Last synced: 6 months ago
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Teaches step-by-step to analysis stock data in python.
- Host: GitHub
- URL: https://github.com/lastancientone/simplestockanalysispython
- Owner: LastAncientOne
- License: mit
- Created: 2018-03-26T19:46:39.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-02-25T22:48:48.000Z (over 1 year ago)
- Last Synced: 2025-03-29T14:09:09.840Z (6 months ago)
- Topics: financial-analysis, financial-data, fundamental-analysis, python3, stock-analysis, stock-data, stock-market, stock-prices, stock-trading, stocks, technical-analysis, technical-indicators, time-series, time-series-analysis, timeseries
- Language: Jupyter Notebook
- Homepage:
- Size: 6.46 MB
- Stars: 381
- Watchers: 13
- Forks: 121
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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# Simple Stock Analysis in Python
#### This is a tutorial on Simple Stock Analysis in Jupyter and Python. There are two versions of the tutorial available: one in Jupyter and the other in Python. Jupyter also provides Jupyter Notebooks, previously known as iPython notebooks. On the other hand, Python is an interpreted high-level programming language known for its simplicity and ease of understanding, especially for beginners interested in learning about stock analysis and becoming quants. This tutorial is designed for individuals who wish to learn Python coding for stock market analysis. However, if you already have knowledge in stock analysis or coding in Python, this tutorial may not be suitable for you. You can explore more advanced coding options here: https://github.com/LastAncientOne/Stock_Analysis_For_Quant.
### The order goes from #1 through #26.
#### You learn the number 1 first, and then you proceed in order. Once you have finished, you will know how to write code in Python and understand finance and the stock market. :congratulations:
## Prerequistes
Python 3.5+
Jupyter Notebook Python 3
## Dependencies
* fix_yahoo_finance or yfinance
* TensorFlow 1.10.0
* Pandas
* Numpy
* ta-lib
* matlibplot
* sklearn
## How to install library
### conda install -c ranaroussi yfinance
### pip install yfinance --upgrade --no-cache-dir
### Input
Pick a symbol, you want to analyze.
### symbol = '...' <-- ... input a symbol
Pick a 'start' date and 'end' date for certain time frame to analyze.
### start = '...' & end = '...' <-- input a date
______________________________________________________________________________________________________________________________
## Examples
```python
# 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()
```
## Example Stock Charts:
## Example Stock Scripts
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?
### Type: python SimpleStockChartScripts.py
## 🍎 List of questions for simple stock tutorial in python:
______________________________________________________________________________________________________________________________
1. How to get data from yahoo, quandl, or other sites?
2. How to scrape historical data, fundamental data, and news data?
3. How to analyze the stock data?
4. How to make a trendlines?
5. How to use Technical Analysis and Fundamental Analysis?
6. How to add and save to csv file?
7. How to customize table and make beautiful plot?
8. How to create class and function for stock?
9. How to create and run scripts?
10. How to applied statistics and timeseries for stock?
11. How to create buy and sell signals?
12. How to create stock prediction in machine learning and deep learning?
13. How to create simple stock strategy?
14. Example of python libraries for Technical Analysis and fetching historical stock prices.
______________________________________________________________________________________________________________________________
:x: If the code does not load or reload, click here: :point_right: 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://github.com/LastAncientOne/100_Day_Challenge
If you want to learn more advance stock analysis or different language in finance, go to:
https://github.com/LastAncientOne/Stock_Analysis_For_Quant
If you into deep learning or machine learning for finance, go to:
https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock
If you want to learn about Mathematics behind deep learning or machine learning, go to:
https://github.com/LastAncientOne/Mathematics_for_Machine_Learning
## Reading Material
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)
## Authors
### Tin Hang
## Disclaimer
🔻 Do not use this code for investing or trading in the stock market. Stock market is unpredictable. :chart_with_upwards_trend: :chart_with_downwards_trend: However, if you are interest in the stock market, you should read many :books: 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 :blue_book: finance engineering, machine trading, algorithmic trading, and quantitative trading.
## This is not a get-rich-quick scheme; rather, it is intended for research and educational purposes.