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https://github.com/marketcalls/openchart
OpenChart is a Python library for downloading intraday and EOD (End of Day) historical data from the NSE (National Stock Exchange of India) and NFO (NSE Futures and Options) exchanges
https://github.com/marketcalls/openchart
historical-data nseindia pandas-dataframe pythonlibrary stock-market
Last synced: 8 days ago
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OpenChart is a Python library for downloading intraday and EOD (End of Day) historical data from the NSE (National Stock Exchange of India) and NFO (NSE Futures and Options) exchanges
- Host: GitHub
- URL: https://github.com/marketcalls/openchart
- Owner: marketcalls
- License: other
- Created: 2024-10-20T07:11:58.000Z (2 months ago)
- Default Branch: master
- Last Pushed: 2024-10-20T11:07:39.000Z (2 months ago)
- Last Synced: 2024-12-02T02:14:31.211Z (21 days ago)
- Topics: historical-data, nseindia, pandas-dataframe, pythonlibrary, stock-market
- Language: Python
- Homepage:
- Size: 21.5 KB
- Stars: 11
- Watchers: 3
- Forks: 11
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Download market data from NSEIndia's API
#### \*\*\* IMPORTANT LEGAL DISCLAIMER \*\*\*
---
openchart is **not** affiliated, endorsed, or vetted by NSEIndia. It's
an open-source tool that uses NseIndia's publicly available APIs, and is
intended for research and educational purposes.
# OpenChart
OpenChart is a Python library for downloading intraday and EOD (End of Day) historical data from the NSE (National Stock Exchange of India) and NFO (NSE Futures and Options) exchanges.
## Features
- Download NSE and NFO master data.
- Search for symbols in NSE and NFO exchanges.
- Fetch historical data for equities and derivatives.
- Supports various timeframes: `1m`, `3m`, `5m`, `10m`, `15m`, `30m`, `1h`, `1d`, `1w`, `1M`.
## Installation
You can install the library directly from the GitHub repository:
```bash
pip install openchart
```
or from the GitHub repository:
```bash
pip install git+https://github.com/marketcalls/openchart.git
```
## Usage
### Import the Library
```python
from openchart import NSEData
```
### Initialize the NSEData Class
```python
nse = NSEData()
```
### Download Master Data
Before fetching historical data or searching for symbols, download the master data:
```python
nse.download()
```
### Fetch Historical Data
To fetch historical data for a symbol, use the `historical` method. **Always specify `start` and `end` dates**. You can use `datetime` and `timedelta` to get data from 30 days back.
#### Intraday Data
Fetch intraday data for **TCS** with a 5-minute interval, from 30 days ago until today:
```python
import datetime
end_date = datetime.datetime.now()
start_date = end_date - datetime.timedelta(days=30)
data = nse.historical(
symbol='TCS',
exchange='NSE',
start=start_date,
end=end_date,
interval='5m'
)
print(data)
```
#### EOD Data
Fetch end-of-day data for **Nifty 50**, from 365 days ago until today:
```python
import datetime
end_date = datetime.datetime.now()
start_date = end_date - datetime.timedelta(days=365)
data = nse.historical(
symbol='Nifty 50',
exchange='NSE',
start=start_date,
end=end_date,
interval='1d'
)
print(data)
```
#### NFO Data
Fetch historical data for a futures contract of 15min data, from 30 days ago until today:
```python
import datetime
end_date = datetime.datetime.now()
start_date = end_date - datetime.timedelta(days=30)
data = nse.historical(
symbol='BANKNIFTY24OCTFUT',
exchange='NFO',
start=start_date,
end=end_date,
interval='15m'
)
print(data)
```
### Search for Symbols
#### NSE Exchange
Search for symbols like **Nifty 50**, **TCS**, **RELIANCE** in the NSE exchange.
```python
symbols = nse.search('RELIANCE', exchange='NSE')
print(symbols)
```
#### NFO Exchange
Search for symbols like **NIFTY24OCTFUT**, **BANKNIFTY24OCTFUT**, **NIFTY24N2124800CE**, **NIFTY24N2124800PE** in the NFO exchange.
- **NIFTY24N2124800CE** corresponds to **NIFTY 21 Nov 2024 CE 24800.00**.
- **NIFTY24N2124800PE** corresponds to **NIFTY 21 Nov 2024 PE 24800.00**.
```python
symbols = nse.search('24OCTFUT', exchange='NFO')
print(symbols)
```
Output
```bash
ScripCode Symbol Name Type
0 35006 BANKNIFTY24OCTFUT BANKNIFTY 30 Oct 2024 1
1 35007 NIFTYNXT5024OCTFUT NIFTYNXT50 25 Oct 2024 1
2 35012 FINNIFTY24OCTFUT FINNIFTY 29 Oct 2024 1
3 35239 MIDCPNIFTY24OCTFUT MIDCPNIFTY 28 Oct 2024 1
4 35382 NIFTY24OCTFUT NIFTY 31 Oct 2024 1
.. ... ... ... ...
179 48598 UPL24OCTFUT UPL 31 Oct 2024 3
180 48601 VEDL24OCTFUT VEDL 31 Oct 2024 3
181 48602 VOLTAS24OCTFUT VOLTAS 31 Oct 2024 3
182 48603 WIPRO24OCTFUT WIPRO 31 Oct 2024 3
183 48604 ZYDUSLIFE24OCTFUT ZYDUSLIFE 31 Oct 2024 3
[184 rows x 4 columns]
```
#### Exact Match Search
If you want to perform an exact match search, you can set `exact_match=True`.
```python
symbol_info = nse.search('BANKNIFTY24OCTFUT', exchange='NFO', exact_match=True)
print(symbol_info)
```
### Supported Timeframes
```python
print(nse.timeframes())
# Output: ['1m', '3m', '5m', '10m', '15m', '30m', '1h', '1d', '1w', '1M']
```
## Functions
- `download()`: Downloads NSE and NFO master data.
- `search(symbol, exchange, exact_match=False)`: Searches for symbols in the specified exchange (`'NSE'` or `'NFO'`).
- Returns a pandas DataFrame containing all matching symbols.
- `symbolsearch(symbol, exchange)`: Searches for a symbol and returns the first match.
- Used internally by the `historical` method.
- `historical(symbol, exchange, start, end, interval='1d')`: Fetches historical data for a symbol.
- Uses the `symbolsearch` method to find the symbol, which returns the first match.
- **Always specify `start` and `end` dates when fetching data**.
- `timeframes()`: Returns a list of supported timeframes.
## Notes
- Ensure that you have a stable internet connection.
- The `historical` method uses `symbolsearch`, which returns the first matching symbol. If multiple symbols match your query, consider using an exact symbol name or modifying the `historical` method to accept a symbol code directly.
- **When fetching historical data, always specify `start` and `end` dates. You can use `datetime` and `timedelta` to calculate these dates.**
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request.
## Contact
For any questions or support, please open an issue on the GitHub repository