https://github.com/santoshlite/quantclean
🧹 Quantclean is a program that reformats financial dataset to US Equity TradeBar (Quantconnect format)
https://github.com/santoshlite/quantclean
algo-trading algorithmic-trading data-cleaning finance financial-data futures lean-engine ohlcv options quandl quant quantconnect quantitative-finance quantitative-trading stock-data stock-market stocks trading-algorithms trading-bot trading-strategies
Last synced: 5 months ago
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🧹 Quantclean is a program that reformats financial dataset to US Equity TradeBar (Quantconnect format)
- Host: GitHub
- URL: https://github.com/santoshlite/quantclean
- Owner: santoshlite
- License: mit
- Created: 2021-05-02T17:21:34.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-05-15T15:40:38.000Z (over 4 years ago)
- Last Synced: 2025-04-09T14:21:40.904Z (6 months ago)
- Topics: algo-trading, algorithmic-trading, data-cleaning, finance, financial-data, futures, lean-engine, ohlcv, options, quandl, quant, quantconnect, quantitative-finance, quantitative-trading, stock-data, stock-market, stocks, trading-algorithms, trading-bot, trading-strategies
- Language: Python
- Homepage:
- Size: 268 KB
- Stars: 18
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: license
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README
# Quantclean 🧹
"Make it cleaner, make it leaner"
Already used by **several people working in the quant and finance industries**, Quantclean is the all-in-one tool that will help you to **reformat your dataset and clean it**.
Quantclean is a program that **reformats** every financial dataset to **US Equity TradeBar** (Quantconnect format)
We all faced the problem of reformating or data to a standard. Manual data cleaning is clearly boring and takes time. Quantclean is here to help you and to make you life easier.
Works great with datas from Quandl, Algoseek, Alpha Vantage, yfinance, and many other more...
## Installation
```
pip install quantclean
```
## Few things you may want to know before getting started 🍉
1) Even if you don't have an open, close, volume, high, low, date column, quantclean will create a blank column for it. No problem!
2) The dataframe generated will look like this if you have a date and time column (or if both are on the same column):
| Date| Open | High | Low | Close | Volume
| ----------- | ---------- | --------- | ---------- | --------- | ---------
| 20131001 09:00 | 6448000 | 6448000 | 6448000 | 6448000 | 90
- Date - String date "YYYYMMDD HH:MM" in the timezone of the data format.
- Open - Deci-cents Open Price for TradeBar.
- High - Deci-cents High Price for TradeBar.
- Low - Deci-cents Low Price for TradeBar.
- Close - Deci-cents Close Price for TradeBar.
- Volume - Number of shares traded in this TradeBar.
3) You can also get something like that if use the ```sweeper_dash``` function instead of ```sweeper```
| Date| Open | High | Low | Close | Volume
| ----------- | ---------- | --------- | ---------- | --------- | ---------
| **2013-10-01 09:00:00** | 6448000 | 6448000 | 6448000 | 6448000 | 90
As you can see, the date format is YYYY-MM-DD and no more YYYYMMDD.
4) If you just have a date column (e.g : something like YYYY-MM-DD), it will look like this:
| Date| Open | High | Low | Close | Volume
| ----------- | ---------- | --------- | ---------- | --------- | ---------
| 20131001 | 6448000 | 6448000 | 6448000 | 6448000 | 90
You can also use the ```sweeper_dash``` function here.
## How to use it? 🚀
First, [here](https://colab.research.google.com/drive/1L6wRRl1l2UnPY50F3qp2cxTcIqC4dtgK?usp=sharing) is a notebook that give you an example of how to use quantclean.
Note : I took this data from Quandl, your dataset doesn't have to look like this one necessarily, quantclean adapts to your dataset as well as possible
```
from quantclean import sweeper
df = pd.read_csv('AS-N100.csv')
df
```
```
_df = sweeper(df)
_df
```
Output:
Now, you may not be happy of this date colum which is presented in the YYYYMMDD format and maybe be prefer YYYY-MM-DD.
In that case do :
```
df_dash = sweeper_dash(df)
df_dash
```
Output:
## Contribution
If you have some suggestions or improvements don't hesitate to create an issue or make a pull request. Any help is welcome!