## 👋👋 We are looking for volunteers who are interested in taking over the maintenance and development of this library. If you are interested in contributing, please reach out to us or create an issue on GitHub to express your interest.

# casased
casased is a Python library to retrieve historical and intraday data from the Casablanca Stock Exchange (Bourse de Casablanca). It provides functions to download historical time series and intraday prices for listed stocks and indices.

<p align="center">
<img  width="350" height="350" src="media/casased_logo.png">
</p>

casased stands for "Casablanca Stock Exchange data retriever".

- PyPI: https://pypi.org/project/casased/
## Requirements 
In order to use casased you should download the following packages: requests, beautifulsoup4, lxml, json, and datetime.

The outputs of this library are DataFrames or dictionaries, so Pandas should be installed 

## Install

```bash
pip install casased
```

```python
import casased as cas
# use cas.get_history(...) or cas.get_intraday(...)
```
## What's new :
For each stock you can get: Session data, latest transactions, best limit and  data of the last 5 sessions:

_Demo videos removed — see the example notebooks or the `media/` folder for visuals._


You can get all the key indicators available at "Bourse de Casablanca"


You can also get indexes summary for each session. Examples and more functions are available at  [this notebook](/casased_Exemple.ipynb)
![indicateur_02](https://user-images.githubusercontent.com/49843367/159123465-bd556c63-bbbf-4354-aea8-fbd43f900983.png)

## How to use
To use this libary there is a notation to respect: names of stocks.
You can find jupyter notebooks on [GitHub](https://github.com/QuantBender/casased)


## Release notes
- v0.1.1 — 2026-01-06 — Remove demo videos & GIF, update branding with new logo, documentation cleanup.
- v0.1.0 — initial release. 
### Get the notation
```python
import casased as cas
cas.notation()
```

### Getting historical data
```python
import casased as cas
# Single asset
df = cas.get_history('Addoha', start='2020-01-01', end='2020-12-31')
print(df.head())

# Multiple assets
df_multi = cas.loadmany(['Addoha','AFMA'], start='2020-01-01', end='2020-12-31')

# Intraday
intraday = cas.get_intraday('Addoha')
```

```

### Data of one single stock
Syntaxe :`loadata(name, start=None,end=None,decode="utf-8")`

To get data from date 0 (The data is provided from Septembre 2016)
```python
import casased  as bvc
data=bvc.loadata('BCP')
data.tail()
```
```{r, engine='python', count_lines}
             Value	  Min	   Max	    Variation	       Volume
   date                                  
22/09/2021	271.00	 269.60	  271.00	0.00		52908
23/09/2021	272.60	 271.00	  273.00	0.59		37230
24/09/2021	276.00	 271.00	  278.00	1.25		162109
27/09/2021	275.00	 272.05	  276.95       -0.36		51533
28/09/2021	276.05	 272.70	  276.05	0.38		17676
```
You can get data between two periods :
```python
data=bvc.loadata('CIH',start='2018-01-01',end='2019-01-01')
data
```
```{r, engine='python', count_lines}
	       Value	Min	 Max   Variation  	Volume
date					
02/01/2018	278.0	278.00	279.5	-2.80	  	312
03/01/2018	278.0	278.00	279.5	0.00		312
...	...	...	...	...	...
28/12/2018	294.0	294.00	301.0	-2.00		211865
31/12/2018	300.0	300.00	300.0	2.04		12
```
You can get the historical data of MASI and MSI20
```python
data=bvc.loadata('MASI',start='2022-09-01',end='2022-09-5')
data
```
```{r, engine='python', count_lines}
		   Value
labels	
2022-09-01	12127.1717
2022-09-02	12136.2882
2022-09-05	12140.7196
```

```python
data=bvc.loadata('MSI20',start='2022-09-01',end='2022-09-5')
data
```
```{r, engine='python', count_lines}
		   Value
labels	
2022-09-01	980.633689
2022-09-02	981.350658
2022-09-05	982.005686
```
Sometime you may face some encoding\decoding issues, you can change the value of `decode` argument from its default value "utf-8" to another format  (e.g "utf-8-sig" is working )

### Data of many stocks
Syntaxe :`loadmany(*args,start=None,end=None,feature="Value",decode="utf-8")`
```python
data=bvc.loadmany('BCP','CIH')
data.tail()
```
```{r, engine='python', count_lines}
             BCP     CIH
22/09/2021	271.00	301.0
23/09/2021	272.60	305.0
24/09/2021	276.00	313.0
27/09/2021	275.00	310.0
28/09/2021	276.05	305.8
```
You can use start and end arguments :
```python
data=bvc.loadmany('BCP','CIH',start='2018-01-01',end='2019-01-01')
data.tail()
```
```{r, engine='python', count_lines}
	         BCP	CIH
date		
02/01/2018	293.0	278.0
03/01/2018	289.9	278.0
04/01/2018	285.3	280.8
05/01/2018	283.0	280.8
08/01/2018	285.4	280.8
...	...	...
25/12/2018	279.0	294.2
26/12/2018	277.0	296.0
27/12/2018	279.9	300.0
28/12/2018	280.0	294.0
31/12/2018	280.0	300.0
```
In case you want to have data of lots of stocks you can give the function a list of these stocks. Moreover `feature` argument let you choose another variable (Value, Min, Max, Variation, Volume")

```python
data=bvc.loadmany(['BCP','BMCI','BOA','CIH'],start="2021-08-30",end='2021-09-04',feature="Volume")
data
```
```{r, engine='python', count_lines}
		BCP	BMCI	BOA	CIH
  Date				
30/08/2021	702	33	172	53
31/08/2021	69575	2515	5853	1005
01/09/2021	28095	2515	3700	1005
02/09/2021	55744	2353	14	50
03/09/2021	26533	2353	8300	500
```
## Intraday data
Syntaxe : `getIntraday(name,decode="utf-8")`
```python
import casased  as load
data=load.getIntraday('MASI')
data
```
```{r, engine='python',count_lines}
 	 Value
09:30	12899.66
09:31	12900.10
09:32	12900.60
09:34	12900.45
09:35	12901.24
...	...
15:12	12975.64
15:14	12976.79
15:17	12976.69
15:18	12978.58
15:30	13019.20
```
## Session data
Syntaxe : `getCours(name)`
```python
cours=bvc.getCours("BOA") 
cours.keys()
```
```{r,engine="python",count_lines}
dict_keys(['Données_Seance', 'Meilleur_limit', 'Dernieres_Tansaction', 'Seance_prec'])
```
```python
import pandas as pd
cours["Données_Seance"]
cours['Meilleur_limit']
pd.DataFrame(cours["Seance_prec"])
pd.DataFrame(cours["Dernieres_Tansaction"])
```
## Key Indicators
Syntaxe : `getKeyIndicators(name,decode='utf-8')`
```python
indicateur=bvc.getKeyIndicators('BOA')
indicateur.keys()
```
```{r,engine="python",count_lines}
dict_keys(['Info_Societe', 'Actionnaires', 'Chiffres_cles', 'Ratio'])
```
## Dividend
Syntaxe: `getDividend(name,decode='utf-8')`
```python
dividends=bvc.getDividend("BOA")
pd.DataFrame(dividends)
```
```{r,engine="python",count_lines}
        Annee	Montant_Dividende Type_Dividende  Date_detachement  Date_paiement
0	2020	  5,00	           Ordinaire	   15/07/2021	    29/07/2021
1	2019	  5,00	           Ordinaire	   10/08/2020	    28/09/2020
2	2018	  5,00		   Ordinaire	   03/07/2019	    15/08/2019
3	2017	  5,00		   Ordinaire	   29/06/2018	    10/07/2018
```
## Indexes summary
Syntaxe : `getIndex()`
```python
index=bvc.getIndex()
index.keys()
```
```{r,engine="python",count_lines}
dict_keys(['Resume indice', 'Indice rentabilite', 'Indices en devises', 'Indice FTSE', 'Indices sectoriels'])
```
## Weights
Syntaxe : `getPond()`
```python
pond=bvc.getPond()
pd.DataFrame(pond)
```
```{r,engine="python",count_lines}
	Code Isin	Instrument    Nombre de titres	Cours	Facteur flottant Facteur plafonnement	Capitalisation flottante Poids
0	MA0000012445	ATTIJARIWAFA BANK	215140839 477,95	0,30	  1,00	                    30847969200,02	 0,1834
1	MA0000011488	ITISSALAT AL-MAGHRIB	879095340 130,10	0,20	  1,00			    22874060746,80	 0,1360
2	MA0000012320	LAFARGEHOLCIM MAR	23431240 1919,00	0,30	  1,00			    13489364868,00	 0,0802
```
## Indexes of the current session
Syntaxe: `getIndexRecap()`
```python
recap=bvc.getIndexRecap()
recap.keys()
```
```{r,engine="python",count_lines}
dict_keys(['Indice', 'Volume Global', 'Plus forte hausse', 'Plus forte baisse'])
```


## Getting Help 
If you are working in Jupyter notebook/lab, you can see the docstring of our  functions by using Shift+Tab. An example is shown below
```python
"""
	Load Data 
	Inputs: 
			Input   | Type                             | Description
			=================================================================================
			 name   |string                            | You must respect the notation. To see the notation see casased.notation
	                 start  |string "YYYY-MM-DD"               | starting date Must respect the notation
	                 end    |string "YYYY-MM-DD"               | Must respect the notation
	Outputs:
	                 Output | Type                             | Description
	                ================================================================================= 
	     	                | pandas.DataFrame (4 columns)     | close high low open vol
"""
```


Question? Contact me on Twitter [@AmineAndam](https://twitter.com/AmineAndam)  or on Linkedin [ANDAM AMINE](https://www.linkedin.com/in/amineandam/).
