Technical Analysis Library in Python
Project description
# Technical Analysis Library in Python
You can use this library to add features to your finacial time series dataset.
### Volume
* Accumulation/Distribution Index (ADI)
* On-Balance Volume (OBV)
* On-Balance Volume mean (OBV mean)
* Chaikin Money Flow (CMF)
* Force Index (FI)
* Ease of Movement (EoM, EMV)
* Volume-price Trend (VPT)
### Volatility
* Average True Range (ATR)
* Bollinger Bands (BB)
* Keltner Channel (KC)
* Donchian Channel (DC)
### Trend
* Moving Average Convergence Divergence (MACD)
* Average Directional Movement Index (ADX)
* Vortex Indicator (VI)
* Trix (TRIX)
* Mass Index (MI)
* Commodity Channel Index (CCI)
* Detrended Price Oscillator (DPO)
* KST Oscillator (KST)
* Ichimoku Kinkō Hyō (Ichimoku)
### Momentum
* Money Flow Index (MFI)
* Relative Strength Index (RSI)
### Fundamental
* Daily Return (DR)
* Cumulative Return (CR)
# How to use
> pip3 install ta
### Example adding all features
```python
import pandas as pd
from ta import *
# load datas
df = pd.read_csv('your-file.csv', sep=',')
# clean nan values
df = utils.dropna(df)
# add ta features
df = add_all_ta_features(df, "Open", "High", "Low", "Close", "Volume_BTC")
# fill nan values
df = df.fillna(method='backfill')
```
### Example adding one feature
```python
import pandas as pd
from ta.volume import *
# load datas
df = pd.read_csv('your-file.csv', sep=',')
# clean nan values
df = utils.dropna(df)
# add ta feature
df['cmf'] = chaikin_money_flow(df.High, df.Low, df.Close, df.Volume_BTC)
# fill nan values
df['cmf'] = df['cmf'].fillna(method='backfill')
```
If you don't know any feature you can visualize them in "visualize_features.ipynb".
# Deploy to developers
> pip3 install -r requirements.txt
# Based on:
* https://en.wikipedia.org/wiki/Technical_analysis
* https://github.com/FreddieWitherden/ta
* https://github.com/femtotrader/pandas_talib
# Credits:
Developed by Bukosabino at Lecrin Technologies - http://lecrintech.com
We are glad to receive any contribution, idea or feedback.
You can use this library to add features to your finacial time series dataset.
### Volume
* Accumulation/Distribution Index (ADI)
* On-Balance Volume (OBV)
* On-Balance Volume mean (OBV mean)
* Chaikin Money Flow (CMF)
* Force Index (FI)
* Ease of Movement (EoM, EMV)
* Volume-price Trend (VPT)
### Volatility
* Average True Range (ATR)
* Bollinger Bands (BB)
* Keltner Channel (KC)
* Donchian Channel (DC)
### Trend
* Moving Average Convergence Divergence (MACD)
* Average Directional Movement Index (ADX)
* Vortex Indicator (VI)
* Trix (TRIX)
* Mass Index (MI)
* Commodity Channel Index (CCI)
* Detrended Price Oscillator (DPO)
* KST Oscillator (KST)
* Ichimoku Kinkō Hyō (Ichimoku)
### Momentum
* Money Flow Index (MFI)
* Relative Strength Index (RSI)
### Fundamental
* Daily Return (DR)
* Cumulative Return (CR)
# How to use
> pip3 install ta
### Example adding all features
```python
import pandas as pd
from ta import *
# load datas
df = pd.read_csv('your-file.csv', sep=',')
# clean nan values
df = utils.dropna(df)
# add ta features
df = add_all_ta_features(df, "Open", "High", "Low", "Close", "Volume_BTC")
# fill nan values
df = df.fillna(method='backfill')
```
### Example adding one feature
```python
import pandas as pd
from ta.volume import *
# load datas
df = pd.read_csv('your-file.csv', sep=',')
# clean nan values
df = utils.dropna(df)
# add ta feature
df['cmf'] = chaikin_money_flow(df.High, df.Low, df.Close, df.Volume_BTC)
# fill nan values
df['cmf'] = df['cmf'].fillna(method='backfill')
```
If you don't know any feature you can visualize them in "visualize_features.ipynb".
# Deploy to developers
> pip3 install -r requirements.txt
# Based on:
* https://en.wikipedia.org/wiki/Technical_analysis
* https://github.com/FreddieWitherden/ta
* https://github.com/femtotrader/pandas_talib
# Credits:
Developed by Bukosabino at Lecrin Technologies - http://lecrintech.com
We are glad to receive any contribution, idea or feedback.
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