pandas-ta/examples/example.ipynb

Example use of pandas_ta

• Simple imports

In [1]:

import pandas as pd
import matplotlib.pyplot as plt

import pandas_ta as ta
from alphaVantageAPI.alphavantage import AlphaVantage  # pip install alphaVantage-api
e = pd.DataFrame()

Help on Indicators (post an issue if the indicator doc needs updating)

In [2]:

e.ta.indicators()
help(ta.accbands)

pandas.ta - Technical Analysis Indicators
Total Indicators: 72
Abbreviations:
    accbands, ad, adosc, adx, ao, apo, aroon, atr, bbands, bop, cci, cmf, cmo, coppock, decreasing, dema, donchian, dpo, efi, ema, eom, fwma, hl2, hlc3, hma, ichimoku, increasing, kc, kst, kurtosis, linreg, log_return, macd, mad, massi, median, mfi, midpoint, midprice, mom, natr, nvi, obv, ohlc4, percent_return, ppo, pvol, pvt, pwma, quantile, rma, roc, rsi, skew, sma, stdev, stoch, t3, tema, trima, trix, true_range, tsi, uo, variance, vortex, vp, vwap, vwma, willr, wma, zscore
Help on function accbands in module pandas_ta.volatility:

accbands(high, low, close, length=None, c=None, drift=None, mamode=None, offset=None, **kwargs)
    Acceleration Bands (ACCBANDS)
    
    Acceleration Bands created by Price Headley plots upper and lower envelope
    bands around a simple moving average.
    
    Sources:
        https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/acceleration-bands-abands/
    
    Calculation:
        Default Inputs:
            length=10, c=4
        EMA = Exponential Moving Average
        SMA = Simple Moving Average
        HL_RATIO = c * (high - low) / (high + low)
        LOW = low * (1 - HL_RATIO)
        HIGH = high * (1 + HL_RATIO)
    
        if 'ema':
            LOWER = EMA(LOW, length)
            MID = EMA(close, length)
            UPPER = EMA(HIGH, length)
        else:
            LOWER = SMA(LOW, length)
            MID = SMA(close, length)
            UPPER = SMA(HIGH, length)
    
    Args:
        high (pd.Series): Series of 'high's
        low (pd.Series): Series of 'low's
        close (pd.Series): Series of 'close's
        length (int): It's period.  Default: 10
        c (int): Multiplier.  Default: 4
        mamode (str): Two options: None or 'ema'.  Default: 'ema'
        drift (int): The difference period.   Default: 1
        offset (int): How many periods to offset the result.  Default: 0
    
    Kwargs:
        fillna (value, optional): pd.DataFrame.fillna(value)
        fill_method (value, optional): Type of fill method
    
    Returns:
        pd.DataFrame: lower, mid, upper columns.

Load Daily EUR.USD from AlphaVantage and clean it up a little

In [4]:

AV = AlphaVantage(premium=False, clean=True, output_size='full')
df = AV.fx(from_currency='USD', to_currency='EUR', function='FXD') # Daily
# df['0'] = 0
df.ta.constants(True, -4, 4)
print(f"Shape: {df.shape}")
df.set_index(['date'], inplace=True)
df.head()

Out [4]:

Shape: (100, 14)

	open	high	low	close	-4	-3	-2	-1	0	1	2	3	4
date
2018-12-05	1.1340	1.1361	1.1310	1.1347	-4	-3	-2	-1	0	1	2	3	4
2018-12-06	1.1348	1.1413	1.1320	1.1377	-4	-3	-2	-1	0	1	2	3	4
2018-12-07	1.1377	1.1424	1.1359	1.1389	-4	-3	-2	-1	0	1	2	3	4
2018-12-09	1.1395	1.1402	1.1380	1.1398	-4	-3	-2	-1	0	1	2	3	4
2018-12-10	1.1398	1.1443	1.1348	1.1350	-4	-3	-2	-1	0	1	2	3	4

Price Chart with Moving Averages

In [5]:

df.ta.ema(length=8, append=True)
df.ta.ema(length=21, append=True)
df.ta.ema(length=50, append=True)
df[['close', 'EMA_8', 'EMA_21', 'EMA_50']].plot(figsize=(16,8), color=['black', 'green', 'orange', 'red'], title="EUR.USD (D)")

Out [5]:

<matplotlib.axes._subplots.AxesSubplot at 0x1108b3908>

Indicator Samples

Cumulative Log Return

In [6]:

df.ta.log_return(cumulative=True, append=True)
df[['0', 'CUMLOGRET_1']].plot(figsize=(16, 3), color=['black'], linewidth=1, title="Cum. Log Return")

Out [6]:

<matplotlib.axes._subplots.AxesSubplot at 0x1129f94e0>

Absolute Price Oscillator (APO)

In [7]:

df.ta.apo(append=True)
apo_12_26 = df[['0', 'APO_12_26']]
apo_12_26.plot(figsize=(16, 3), color=['silver', 'black'], linewidth=1, title='APO_12_26')

Out [7]:

<matplotlib.axes._subplots.AxesSubplot at 0x113067cc0>

MACD

In [8]:

macddf = df.ta.macd(fast=8, slow=21, signal=9, min_periods=None, append=True)
macddf[[macddf.columns[0], macddf.columns[2]]].plot(figsize=(16, 3), color=['black', 'blue'], linewidth=1.3)
macddf[macddf.columns[1]].plot.area(figsize=(16, 3), stacked=False, color=['#dddddd'], linewidth=1, title=macddf.name)
df['0'].plot(figsize=(16, 3), color=['#aaaaaa'], linewidth=1.4)

Out [8]:

<matplotlib.axes._subplots.AxesSubplot at 0x1051cbc50>

ZScore

In [9]:

df.ta.zscore(length=10, append=True)
df.ta.sma(df[df.columns[-1]], length=4, append=True)
zcolors = ['maroon', 'red', 'orange', 'silver', 'black', 'silver', 'orange', 'red', 'maroon', 'black', 'blue']
df[['-4', '-3', '-2', '-1', '0', '1', '2', '3', '4', 'Z_10', 'SMA_4']].plot(figsize=(16, 3), color=zcolors, linewidth=1, title="Z Score")

Out [9]:

<matplotlib.axes._subplots.AxesSubplot at 0x105239c88>

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