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finta-1.3
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Common financial technical indicators implemented in Pandas.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | finta-1.3 |
| نام | finta |
| نسخه کتابخانه | 1.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Peerchemist |
| ایمیل نویسنده | peerchemist@protonmail.ch |
| آدرس صفحه اصلی | https://github.com/peerchemist/finta |
| آدرس اینترنتی | https://pypi.org/project/finta/ |
| مجوز | LGPLv3+ |
# FinTA (Financial Technical Analysis)
[](https://www.gnu.org/licenses/lgpl-3.0)
[](https://pypi.python.org/pypi/finta/)
[](https://pepy.tech/project/finta/month)
[](https://www.python.org/download/releases/3.6.0/)
[](https://github.com/ambv/black)
[](https://travis-ci.org/peerchemist/finta)
[](https://img.shields.io/liberapay/patrons/peerchemist.svg?logo=liberapay)
Common financial technical indicators implemented in Pandas.
*This is work in progress, bugs are expected and results of some indicators
may not be accurate.*
## Supported indicators:
Finta supports over 80 trading indicators:
```
* Simple Moving Average 'SMA'
* Simple Moving Median 'SMM'
* Smoothed Simple Moving Average 'SSMA'
* Exponential Moving Average 'EMA'
* Double Exponential Moving Average 'DEMA'
* Triple Exponential Moving Average 'TEMA'
* Triangular Moving Average 'TRIMA'
* Triple Exponential Moving Average Oscillator 'TRIX'
* Volume Adjusted Moving Average 'VAMA'
* Kaufman Efficiency Indicator 'ER'
* Kaufman's Adaptive Moving Average 'KAMA'
* Zero Lag Exponential Moving Average 'ZLEMA'
* Weighted Moving Average 'WMA'
* Hull Moving Average 'HMA'
* Elastic Volume Moving Average 'EVWMA'
* Volume Weighted Average Price 'VWAP'
* Smoothed Moving Average 'SMMA'
* Fractal Adaptive Moving Average 'FRAMA'
* Moving Average Convergence Divergence 'MACD'
* Percentage Price Oscillator 'PPO'
* Volume-Weighted MACD 'VW_MACD'
* Elastic-Volume weighted MACD 'EV_MACD'
* Market Momentum 'MOM'
* Rate-of-Change 'ROC'
* Relative Strenght Index 'RSI'
* Inverse Fisher Transform RSI 'IFT_RSI'
* True Range 'TR'
* Average True Range 'ATR'
* Stop-and-Reverse 'SAR'
* Bollinger Bands 'BBANDS'
* Bollinger Bands Width 'BBWIDTH'
* Momentum Breakout Bands 'MOBO'
* Percent B 'PERCENT_B'
* Keltner Channels 'KC'
* Donchian Channel 'DO'
* Directional Movement Indicator 'DMI'
* Average Directional Index 'ADX'
* Pivot Points 'PIVOT'
* Fibonacci Pivot Points 'PIVOT_FIB'
* Stochastic Oscillator %K 'STOCH'
* Stochastic oscillator %D 'STOCHD'
* Stochastic RSI 'STOCHRSI'
* Williams %R 'WILLIAMS'
* Ultimate Oscillator 'UO'
* Awesome Oscillator 'AO'
* Mass Index 'MI'
* Vortex Indicator 'VORTEX'
* Know Sure Thing 'KST'
* True Strength Index 'TSI'
* Typical Price 'TP'
* Accumulation-Distribution Line 'ADL'
* Chaikin Oscillator 'CHAIKIN'
* Money Flow Index 'MFI'
* On Balance Volume 'OBV'
* Weighter OBV 'WOBV'
* Volume Zone Oscillator 'VZO'
* Price Zone Oscillator 'PZO'
* Elder's Force Index 'EFI'
* Cummulative Force Index 'CFI'
* Bull power and Bear Power 'EBBP'
* Ease of Movement 'EMV'
* Commodity Channel Index 'CCI'
* Coppock Curve 'COPP'
* Buy and Sell Pressure 'BASP'
* Normalized BASP 'BASPN'
* Chande Momentum Oscillator 'CMO'
* Chandelier Exit 'CHANDELIER'
* Qstick 'QSTICK'
* Twiggs Money Index 'TMF'
* Wave Trend Oscillator 'WTO'
* Fisher Transform 'FISH'
* Ichimoku Cloud 'ICHIMOKU'
* Adaptive Price Zone 'APZ'
* Squeeze Momentum Indicator 'SQZMI'
* Volume Price Trend 'VPT'
* Finite Volume Element 'FVE'
* Volume Flow Indicator 'VFI'
* Moving Standard deviation 'MSD'
* Schaff Trend Cycle 'STC'
```
## Dependencies:
- python (3.6+)
- pandas (1.0.0+)
TA class is very well documented and there should be no trouble
exploring it and using with your data. Each class method expects proper `ohlc` DataFrame as input.
## Install:
`pip install finta`
or latest development version:
`pip install git+git://github.com/peerchemist/finta.git`
## Import
`from finta import TA`
Prepare data to use with finta:
finta expects properly formated `ohlc` DataFrame, with column names in `lowercase`:
["open", "high", "low", "close"] and ["volume"] for indicators that expect `ohlcv` input.
### to resample by time period (you can choose different time period)
`ohlc = resample(df, "24h")`
### You can also load a ohlc DataFrame from .csv file
`data_file = ("data/bittrex:btc-usdt.csv")`
`ohlc = pd.read_csv(data_file, index_col="date", parse_dates=True)`
____________________________________________________________________________
## Examples:
### will return Pandas Series object with the Simple moving average for 42 periods
`TA.SMA(ohlc, 42)`
### will return Pandas Series object with "Awesome oscillator" values
`TA.AO(ohlc)`
### expects ["volume"] column as input
`TA.OBV(ohlc)`
### will return Series with Bollinger Bands columns [BB_UPPER, BB_LOWER]
`TA.BBANDS(ohlc)`
### will return Series with calculated BBANDS values but will use KAMA instead of MA for calculation, other types of Moving Averages are allowed as well.
`TA.BBANDS(ohlc, TA.KAMA(ohlc, 20))`
For more examples see examples directory.
------------------------------------------------------------------------
I welcome pull requests with new indicators or fixes for existing ones.
Please submit only indicators that belong in public domain and are
royalty free.
## Contributing
1. Fork it (https://github.com/peerchemist/finta/fork)
2. Study how it's implemented.
3. Create your feature branch (`git checkout -b my-new-feature`).
4. Run [black](https://github.com/ambv/black) code formatter on the finta.py to ensure uniform code style.
5. Commit your changes (`git commit -am 'Add some feature'`).
6. Push to the branch (`git push origin my-new-feature`).
7. Create a new Pull Request.
------------------------------------------------------------------------
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نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | pandas |
نحوه نصب
نصب پکیج whl finta-1.3:
pip install finta-1.3.whl
نصب پکیج tar.gz finta-1.3:
pip install finta-1.3.tar.gz