stocks_toolkit_india
Toolkit for NSE/BSE stocks comprises of various functionalities like historical technical data and candle pattern detection.
To Install the package:
pip install stocks-toolkit-india
Note- This library needs TA-lib to be installed. To know more about how to install it https://github.com/mrjbq7/ta-lib
Note- Functionality for Graph and better visualisation of data will be added.
UPDATE - NSE no more allows to get data more than 365 days. This library is designed to provide in depth analysis of recent trend pattern of a stock.
For getting technical Data of a stock, the data is in pandas DataFrame Form.
from stocks_toolkit import stocks_toolkit
st = stocks_toolkit()
stock_df = st.technical_data("INFY","01-01-2021","01-7-2021")
print(stock_df)
| Symbol | Series | Date | Prev Close | Open Price | High Price | Low Price | Last Price | Close Price | Average Price | Total Traded Quantity | Turnover | No. of Trades | Deliverable Qty | % Dly Qt to Traded Qty | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | RELIANCE | EQ | 01-Jan-2021 | 1985.3 | 1988 | 1997 | 1982 | 1988 | 1987.5 | 1989.5 | 4.622e+06 | 9.19548e+09 | 139680 | 1.01331e+06 | 21.92 |
| 1 | RELIANCE | EQ | 04-Jan-2021 | 1987.5 | 1995.1 | 1998.9 | 1968 | 1990.25 | 1990.85 | 1982.61 | 1.1313e+07 | 2.24292e+10 | 316871 | 3.8696e+06 | 34.2 |
| 2 | RELIANCE | EQ | 05-Jan-2021 | 1990.85 | 1969 | 1983.6 | 1956 | 1965 | 1966.1 | 1968.87 | 1.11328e+07 | 2.1919e+10 | 300233 | 5.20805e+06 | 46.78 |
| 3 | RELIANCE | EQ | 06-Jan-2021 | 1966.1 | 1965.9 | 1966 | 1905.15 | 1915.5 | 1914.25 | 1928.17 | 2.14143e+07 | 4.12904e+10 | 659284 | 9.0381e+06 | 42.21 |
| 4 | RELIANCE | EQ | 07-Jan-2021 | 1914.25 | 1920.5 | 1945 | 1905.15 | 1912.8 | 1911.15 | 1919.86 | 1.49184e+07 | 2.86413e+10 | 454695 | 6.45266e+06 | 43.25 |
| 5 | RELIANCE | EQ | 08-Jan-2021 | 1911.15 | 1918 | 1938.4 | 1912.1 | 1934 | 1933.7 | 1923.06 | 1.27098e+07 | 2.44418e+10 | 270966 | 5.30355e+06 | 41.73 |
For getting the candle analysis, candle patter found in the stock:
candles = st.candle_analysis("INFY","01-01-2021","01-7-2021")
print(candles)
| open | close | high | low | date | candles_found | |
|---|---|---|---|---|---|---|
| 0 | 1988 | 1987.5 | 1997 | 1982 | 01-Jan-2021 | [] |
| 1 | 1995.1 | 1990.85 | 1998.9 | 1968 | 04-Jan-2021 | [] |
| 2 | 1969 | 1966.1 | 1983.6 | 1956 | 05-Jan-2021 | [] |
| 3 | 1965.9 | 1914.25 | 1966 | 1905.15 | 06-Jan-2021 | [] |
| 4 | 1920.5 | 1911.15 | 1945 | 1905.15 | 07-Jan-2021 | [] |
| 5 | 1918 | 1933.7 | 1938.4 | 1912.1 | 08-Jan-2021 | [] |
| .. ... ... ... ... ... ... | ||||||
| 118 | 1572.00 | 1574.20 | 1578.00 | 1543.00 | 25-Jun-2021 | [CDLDOJI:Bullish, CDLDOJISTAR:Bearish, CDLLONG... |
| 119 | 1572.90 | 1571.80 | 1580.15 | 1560.60 | 28-Jun-2021 | [CDLDOJI:Bullish, CDLHIGHWAVE:Bearish, CDLLONG... |
| 120 | 1561.00 | 1563.05 | 1573.65 | 1559.20 | 29-Jun-2021 | [CDLDOJI:Bullish, CDLGRAVESTONEDOJI:Bullish, C... |
| 121 | 1572.05 | 1580.80 | 1591.00 | 1572.05 | 30-Jun-2021 | [CDLSHOOTINGSTAR:Bearish] |
The 'candles_found' column contains array of candle name along with their characterstics i.e Bullish/Bearish
CDLGRAVESTONEDOJI:Bullish => Grave Stone Doji candle pattern indicating Bullish behavoiour.
Note - A single array can have candles indicating both Bearish and Bullish behavious. Read more about Japanese Candle stick patterns - https://www.nomuradirect.com/pdf/21_Candlesticks.pdf
Both NSE symbol and BSE symbol can be provided as symbol in both of the above functions. If the data is available with NSE, both of the function will return data.
To convert NSE symbol to bse symbol use:
print(st.nse_to_bse("INFY")))
500209
and to convert bse symbol to nse symbol use:
print(st.bse_to_nse("500325")))
RELIANCE
Metadata
Release files for stocks-toolkit-india 0.1.49
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| stocks-toolkit-india-0.1.49.tar.gz | 28.4 kB | Without provenance | Details for stocks-toolkit-india-0.1.49.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| stocks_toolkit_india-0.1.49-py3-none-any.whl | Python 3 | none | any | Details for stocks_toolkit_india-0.1.49-py3-none-any.whl |
Total release size: 56.7 kB
Release files / stocks-toolkit-india-0.1.49.tar.gz
| Download URL | stocks-toolkit-india-0.1.49.tar.gz |
|---|---|
| Size | 28.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d2b36b92fe808503e191401df49641476d5a5ac918e35ec042d67851bcb84735
|
|
BLAKE2b-256 checksum How to use checksums |
5e0ea34fb6793a5ed9c73fb348a1542773f636a8358bec7f5984c6ff6b925042
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.4
|
Release files / stocks_toolkit_india-0.1.49-py3-none-any.whl
| Download URL | stocks_toolkit_india-0.1.49-py3-none-any.whl |
|---|---|
| Size | 28.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
5076630a05d4dca729f482f1b50bbd12bc8e5259770eaa27dd4f37486c7d22e1
|
|
BLAKE2b-256 checksum How to use checksums |
81b51508057386b4756e805ba88c217e204f0d488d1524da170e608201f9a29d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.4
|
Security for stocks-toolkit-india 0.1.49
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.