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VietNam Data Stock Market Price
| Date | Stars |
|---|---|
| 2026-07-24 | 472 |
| 2026-07-25 | 472 |
| 2026-07-28 | 472 |
| 2026-07-30 | 472 |
| 2026-08-06 | 472 |
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growth rate 0.00%/day
# vnquant package
<img src="./vnquant/imgs/stock_1.png" style="display: block;margin-left: auto;margin-right: auto;width:50%;" />
## 1. Introduction
This project offers comprehensive financial information and advanced visualization tools for the Vietnam stock market to researchers. Specifically, it provides extensive data, including historical prices, finacial, business, and cashflow reports for individual or multiple symbols over the same period. This enables investors to conduct in-depth quantitative analyses and forecasting. Additionally, the available stock prices can be utilized to create visualizations with advanced metrics such as Bollinger Bands and Relative Strength Index (RSI), aiding in identifying optimal buying and selling points.
## 2. Setting:
### 2.1. Setup on local machine
This project is in developing process, So it is only distributed on github channel. To install requiring you open the command line and type the below commands:
```
git clone https://github.com/phamdinhkhanh/vnquant
cd vnquant
python setup.py install
```
you must install git command line in your computer to run above command.
### 2.2. Google colab
To use package in google colab, you have to mount point to google drive folder first and setup the same as in local machine. Reference to [google colab - vnquant example](https://drive.google.com/file/d/10KNNA4sZTk2dKW6wvv3tLMivgQDgd-5-/view?usp=sharing) for detail.
## 3. Visualization: (0.0.2)
from version 0.0.2 vnquant enable to you visualize stock price from any symbols code at source cafe or vnd or pandas data frame which have OHLC type. OHLC type meaning that your data frame columns is enough ['open', 'high', 'low', 'close'] list.
Below is general syntax of visualization function supported on vnquant package.
```{python}
import vnquant.plot as pl
pl.vnquant_candle_stick(data,
title=None,
xlab='Date', ylab='Price',
start_date=None, end_date=None,
colors=['blue', 'red'],
width=800, height=600,
show_advanced=[],
data_source='cafe',
**kargs)
```
**Arguments**
* `data`: is pandas data frame of OHLC type or OHLCV type, or string symbol of any VietNam stock index.
in case symbol, data is automatically cloned from open source.
* `title`: General title of candle stick chart. If data is a symbol, title is going to be created based on symbol and cloned datetime interval.
* `xlab`: x label. Default Date.
* `ylab`: y label. Default Price.
* `start_date`: start date. Default None. Must to be declared when data is symbol.
* `end_date`: end date. Default None. Must to be declared when data is symbol.
* `colors`: list colors defines increasing and decreasing color stick candle in order.
* `width`: with of plot frame. Default 800px
* `height`: height of plot frame. Default 600px
* `show_advanced`: list of advanced stock index to show up. Each element belongs to ['volume', 'macd', 'rsi'].
* `data_source`: invalid when use symbol intead of data frame. Source to clone data, 'VND' or 'CAFE'.
### 3.1. Visualization from source VND or CAFE
In this way, you can visualize stock price clone from VND or CAFE source by pass symbol, start_date, end_date into module as below:
```{python}
from vnquant import plot as plt
plt.vnquant_candle_stick(
data='VND',
title='VND symbol from 2019-09-01 to 2019-11-01',
xlab='Date', ylab='Price',
start_date='2019-09-01',
end_date='2019-11-01',
data_source='CAFE',
show_advanced=['volume', 'macd', 'rsi'],
width=1600,
height=800
)
```
<img src="./vnquant/imgs/stock_2.png" style="display: block;margin-left: auto;margin-right: auto;width:50%;" />
You can suppress volume by set up show_vol=False. Result as below:
<img src="./vnquant/imgs/stock_3.png" style="display: block;margin-left: auto;margin-right: auto;width:50%;" />
### 3.2. Visualization from dataExcerpt of 27,943 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:53ded585dbde21cb, topic:trading, readme:financial, readme:quantitative