[](https://python.stockindicators.dev/)
[](https://badge.fury.io/py/stock-indicators)
[](https://pypistats.org/packages/stock-indicators)
# Stock Indicators for Python
**Stock Indicators for Python** is a PyPI library package that produces financial market technical indicators. Send in historical price quotes and get back desired indicators such as moving averages, Relative Strength Index, Stochastic Oscillator, Parabolic SAR, etc. Nothing more.
It can be used in any market analysis software using standard OHLCV price quotes for equities, commodities, forex, cryptocurrencies, and others. We had trading algorithms, machine learning, and charting systems in mind when originally creating this community library. [Stock Indicators for .NET](https://dotnet.stockindicators.dev/) is also available.
Visit our project site for more information:
- [Overview](https://python.stockindicators.dev/)
- [Indicators and overlays](https://python.stockindicators.dev/indicators/)
- [Guide and Pro tips](https://python.stockindicators.dev/guide/)
- [Release notes](https://github.com/facioquo/stock-indicators-python/releases)
- [Discussions](https://github.com/DaveSkender/Stock.Indicators/discussions)
- [Contributing](https://github.com/facioquo/stock-indicators-python/blob/main/docs/contributing.md#readme)
## Getting started
### Windows
1. Install .NET SDK (6.0 or newer):
- Download from [Microsoft .NET Downloads](https://dotnet.microsoft.com/download)
- Or using winget: `winget install Microsoft.DotNet.SDK.6`
- Verify: `dotnet --info`
2. Install the package:
```bash
pip install stock-indicators
```
### macOS
1. Install .NET SDK (6.0 or newer):
```bash
brew install dotnet-sdk
dotnet --info # Verify installation
```
2. Install the package:
```bash
pip install stock-indicators
```
## Example usage
```python
from stock_indicators import indicators
# fetch your data
quotes = get_history("MSFT")
# calculate 20-period SMA
results = indicators.get_sma(quotes, 20)
```
> **Note:** This is a simple example. For a step-by-step guide, see the [QuickStart Guide](https://github.com/facioquo/stock-indicators-python-quickstart#readme) or our [documentation](https://python.stockindicators.dev/) site.
Raw data
{
"_id": null,
"home_page": null,
"name": "stock-indicators",
"maintainer": "Dong-Geon Lee",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "Stock Indicators, Technical Indicators, Market, Technical Analysis, Algorithmic, Trading, Trade, Momentum, Finance, Algorithm, Algo, AlgoTrading, Financial, Strategy, Chart, Charting, Oscillator, Overlay, Equity, Bitcoin, Crypto, Cryptocurrency, Forex, Quantitative, Historical, Quotes, Accumulation, Distribution, ADL, Aroon, True Range, ATR, Bollinger Bands, Commodity, CCI, Chandelier Exit, Chaikin Money Flow, CMF, MFI, Kaufman Adaptive, Moving Average, Convergence, Divergence, MACD, SMA, EMA, DEMA, TEMA, TRIX, Smoothed, Elder-ray, Bull, Bear, Power, Force, Hurst Exponent, Relative, Strength, Index, RSI, Connors RSI, Stochastic, Directional, Movement, ADX, Donchian, Keltner Channel, Heikin-Ashi, Hilbert Transform Instantaneous Trendline, Hull, HMA, Ichimoku Cloud, On-balance Volume, OBV, Price, PMO, PRS, Parabolic SAR, PSAR, Pivot Points, Rate of Change, ROC, Linear Regression, R-Squared, Renko, Schaff Cycle, Slope, Standard Deviation, Beta, Convexity, Correlation, SuperTrend, Ulcer, Ultimate, Vortex, Williams %R, Alligator, Gator, Fractal, Chaos, Choppiness, Endpoint, WMA, ZigZag",
"author": "Dave Skender",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/95/da/f329ed69bbddd25de968847c663e7242470eab8f0049dbb4b76f3b64dcd1/stock_indicators-1.3.3.tar.gz",
"platform": null,
"description": "[](https://python.stockindicators.dev/)\n\n[](https://badge.fury.io/py/stock-indicators)\n[](https://pypistats.org/packages/stock-indicators)\n\n# Stock Indicators for Python\n\n**Stock Indicators for Python** is a PyPI library package that produces financial market technical indicators. Send in historical price quotes and get back desired indicators such as moving averages, Relative Strength Index, Stochastic Oscillator, Parabolic SAR, etc. Nothing more.\n\nIt can be used in any market analysis software using standard OHLCV price quotes for equities, commodities, forex, cryptocurrencies, and others. We had trading algorithms, machine learning, and charting systems in mind when originally creating this community library. [Stock Indicators for .NET](https://dotnet.stockindicators.dev/) is also available.\n\nVisit our project site for more information:\n\n- [Overview](https://python.stockindicators.dev/)\n- [Indicators and overlays](https://python.stockindicators.dev/indicators/)\n- [Guide and Pro tips](https://python.stockindicators.dev/guide/)\n- [Release notes](https://github.com/facioquo/stock-indicators-python/releases)\n- [Discussions](https://github.com/DaveSkender/Stock.Indicators/discussions)\n- [Contributing](https://github.com/facioquo/stock-indicators-python/blob/main/docs/contributing.md#readme)\n\n## Getting started\n\n### Windows\n\n1. Install .NET SDK (6.0 or newer):\n - Download from [Microsoft .NET Downloads](https://dotnet.microsoft.com/download)\n - Or using winget: `winget install Microsoft.DotNet.SDK.6`\n - Verify: `dotnet --info`\n\n2. Install the package:\n\n ```bash\n pip install stock-indicators\n ```\n\n### macOS\n\n1. Install .NET SDK (6.0 or newer):\n\n ```bash\n brew install dotnet-sdk\n dotnet --info # Verify installation\n ```\n\n2. Install the package:\n\n ```bash\n pip install stock-indicators\n ```\n\n## Example usage\n\n```python\nfrom stock_indicators import indicators\n\n# fetch your data\nquotes = get_history(\"MSFT\")\n\n# calculate 20-period SMA\nresults = indicators.get_sma(quotes, 20)\n```\n\n> **Note:** This is a simple example. For a step-by-step guide, see the [QuickStart Guide](https://github.com/facioquo/stock-indicators-python-quickstart#readme) or our [documentation](https://python.stockindicators.dev/) site.\n",
"bugtrack_url": null,
"license": "Apache 2.0",
"summary": "Stock Indicators for Python. Send in historical price quotes and get back desired technical indicators such as Stochastic RSI, Average True Range, Parabolic SAR, etc. Nothing more.",
"version": "1.3.3",
"project_urls": {
"Bug Tracker": "https://github.com/facioquo/stock-indicators-python/issues",
"Documentation": "https://python.stockindicators.dev",
"Homepage": "https://python.stockindicators.dev",
"Release Notes": "https://github.com/facioquo/stock-indicators-python/releases",
"Source Code": "https://github.com/facioquo/stock-indicators-python/tree/main"
},
"split_keywords": [
"stock indicators",
" technical indicators",
" market",
" technical analysis",
" algorithmic",
" trading",
" trade",
" momentum",
" finance",
" algorithm",
" algo",
" algotrading",
" financial",
" strategy",
" chart",
" charting",
" oscillator",
" overlay",
" equity",
" bitcoin",
" crypto",
" cryptocurrency",
" forex",
" quantitative",
" historical",
" quotes",
" accumulation",
" distribution",
" adl",
" aroon",
" true range",
" atr",
" bollinger bands",
" commodity",
" cci",
" chandelier exit",
" chaikin money flow",
" cmf",
" mfi",
" kaufman adaptive",
" moving average",
" convergence",
" divergence",
" macd",
" sma",
" ema",
" dema",
" tema",
" trix",
" smoothed",
" elder-ray",
" bull",
" bear",
" power",
" force",
" hurst exponent",
" relative",
" strength",
" index",
" rsi",
" connors rsi",
" stochastic",
" directional",
" movement",
" adx",
" donchian",
" keltner channel",
" heikin-ashi",
" hilbert transform instantaneous trendline",
" hull",
" hma",
" ichimoku cloud",
" on-balance volume",
" obv",
" price",
" pmo",
" prs",
" parabolic sar",
" psar",
" pivot points",
" rate of change",
" roc",
" linear regression",
" r-squared",
" renko",
" schaff cycle",
" slope",
" standard deviation",
" beta",
" convexity",
" correlation",
" supertrend",
" ulcer",
" ultimate",
" vortex",
" williams %r",
" alligator",
" gator",
" fractal",
" chaos",
" choppiness",
" endpoint",
" wma",
" zigzag"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "18e1b5e0fa0c0051387863b5ec77acc48f161c52c352cb5db02b4392a4fbeefb",
"md5": "f925d76015bf62f43c309f2517eb6a5e",
"sha256": "0119035e049718fa8c8040d88dbcd39e0f3e1bac076ccae1de455e40c24f329f"
},
"downloads": -1,
"filename": "stock_indicators-1.3.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "f925d76015bf62f43c309f2517eb6a5e",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 3311470,
"upload_time": "2025-01-30T01:06:49",
"upload_time_iso_8601": "2025-01-30T01:06:49.684849Z",
"url": "https://files.pythonhosted.org/packages/18/e1/b5e0fa0c0051387863b5ec77acc48f161c52c352cb5db02b4392a4fbeefb/stock_indicators-1.3.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "95daf329ed69bbddd25de968847c663e7242470eab8f0049dbb4b76f3b64dcd1",
"md5": "a81b81d5099344d38195531307432b30",
"sha256": "71be015d5dac47c53baf723854bdd93553c438c1571e8d3bd9ed4b37cd36a179"
},
"downloads": -1,
"filename": "stock_indicators-1.3.3.tar.gz",
"has_sig": false,
"md5_digest": "a81b81d5099344d38195531307432b30",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 4677687,
"upload_time": "2025-01-30T01:06:53",
"upload_time_iso_8601": "2025-01-30T01:06:53.364128Z",
"url": "https://files.pythonhosted.org/packages/95/da/f329ed69bbddd25de968847c663e7242470eab8f0049dbb4b76f3b64dcd1/stock_indicators-1.3.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-01-30 01:06:53",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "facioquo",
"github_project": "stock-indicators-python",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "pythonnet",
"specs": [
[
">=",
"3.0.0"
]
]
},
{
"name": "typing_extensions",
"specs": [
[
">=",
"4.4.0"
]
]
}
],
"lcname": "stock-indicators"
}
