pandas-ta/examples/example.ipynb

Technical Analysis with Pandas (pandas_ta)

• Below contains examples of simple charts that can be made from pandas_ta indicators
• Examples below are nothing more than for educational purposes only

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()

pandas.ta - Technical Analysis Indicators
Total Indicators: 82
Abbreviations:
    accbands, ad, adosc, adx, amat, ao, aobv, apo, aroon, atr, bbands, bop, cci, cmf, cmo, coppock, cross, decreasing, dema, donchian, dpo, efi, ema, eom, fwma, hl2, hlc3, hma, ichimoku, increasing, kc, kst, kurtosis, linreg, log_return, long_run, macd, mad, massi, median, mfi, midpoint, midprice, mom, natr, nvi, obv, ohlc4, percent_return, ppo, pvi, pvol, pvt, pwma, qstick, quantile, rma, roc, rsi, short_run, skew, sma, stdev, stoch, swma, t3, tema, trend_return, trima, trix, true_range, tsi, uo, variance, vortex, vp, vwap, vwma, willr, wma, zlma, zscore

In [3]:

# Individual Indicator help
help(ta.sma)

Help on function sma in module pandas_ta.overlap.sma:

sma(close, length=None, offset=None, **kwargs)
    Simple Moving Average (SMA)
    
    The Simple Moving Average is the classic moving average that is the equally
    weighted average over n periods.
    
    Sources:
        https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/
    
    Calculation:
        Default Inputs:
            length=10
        SMA = SUM(close, length) / length
    
    Args:
        close (pd.Series): Series of 'close's
        length (int): It's period.  Default: 10
        offset (int): How many periods to offset the result.  Default: 0
    
    Kwargs:
        adjust (bool): Default: True
        presma (bool, optional): If True, uses SMA for initial value.
        fillna (value, optional): pd.DataFrame.fillna(value)
        fill_method (value, optional): Type of fill method
    
    Returns:
        pd.Series: New feature generated.

In [ ]:

Load Daily SPY from AlphaVantage and clean it up a little

In [4]:

ticker = 'DIA'
AV = AlphaVantage(premium=False, clean=True, output_size='full')
df = AV.data(symbol=ticker, function='DA') # Daily Adjusted
df.name = ticker
df.set_index(['date'], inplace=True)
df.drop(['dividend', 'split_coefficient'], axis=1, inplace=True) if 'dividend' in df.columns and 'split_coefficient' in df.columns else None

opendf = df['open']
closedf = df['close']
volumedf = df['volume']

In [5]:

last_ = df.shape[0]
# last_ = 100 # Uncomment for remaining subset
print(f"{df.name}{df.shape}")
df.head()

Out [5]:

DIA(5368, 6)

	open	high	low	close	adj_close	volume
date
1998-01-20	77.81	78.84	77.41	78.81	49.5083	1744600.0
1998-01-21	78.09	78.38	77.28	77.84	48.8989	1839600.0
1998-01-22	77.19	77.86	76.94	77.19	48.4906	1662600.0
1998-01-23	77.50	77.75	76.31	77.00	48.3712	1693700.0
1998-01-26	77.38	77.67	76.94	77.31	48.5660	1172800.0

Create some constants for some indicators

In [6]:

#help(df.ta.constants) # for more info
df.ta.constants(True, -4, 4)
df.tail()

Out [6]:

	open	high	low	close	adj_close	volume	-4	-3	-2	-1	0	1	2	3	4
date
2019-05-14	254.40	257.2300	254.24	255.81	255.1720	4308796.0	-4	-3	-2	-1	0	1	2	3	4
2019-05-15	254.17	257.6300	253.74	256.92	256.2792	4151403.0	-4	-3	-2	-1	0	1	2	3	4
2019-05-16	258.00	260.0800	257.85	259.16	258.5136	3889707.0	-4	-3	-2	-1	0	1	2	3	4
2019-05-17	256.36	259.3926	256.21	257.44	257.4400	3652916.0	-4	-3	-2	-1	0	1	2	3	4
2019-05-20	256.21	257.4100	255.49	256.67	256.6700	2580122.0	-4	-3	-2	-1	0	1	2	3	4

Price & Volume Charts with Moving Averages

In [7]:

def machart(kind, fast, medium, slow, append=True, last=last_, figsize=(16,8)):
    ma1 = df.ta(kind=kind, length=fast, append=append)
    ma2 = df.ta(kind=kind, length=medium, append=append)
    ma3 = df.ta(kind=kind, length=slow, append=append)
    pricedf = df[['close', ma1.name, ma2.name, ma3.name]]
    title = f"{df.name}: {kind.upper()}s from {df.index[0]} to {df.index[-1]} ({last})"
    pricedf = df[['close', ma1.name, ma2.name, ma3.name]]
    pricedf.tail(last).plot(figsize=figsize, color=['black', 'green', 'orange', 'red'], title=title, grid=True)

def volumechart(kind, length=10, last=last_, figsize=(16, 3)):
    volume = pd.DataFrame({'V+': volumedf[closedf > opendf], 'V-': volumedf[closedf < opendf]})
    title = f"{df.name} Volume: {kind.upper()} from {df.index[0]} to {df.index[-1]} ({last})"
    volume.tail(last).plot(kind='bar', figsize=figsize, width=0.5, color=['green', 'red'], alpha=0.45, stacked=True)
    df.ta(kind=kind, close=volumedf, length=length).tail(last).plot(figsize=figsize, linewidth=1.2, color='black', title=title, grid=True)

In [8]:

machart('ema', 50, 200, 500, last=100)
volumechart('ema', last=100)

Indicator Examples

• Examples of simple and complex indicators. Most indicators return a Series, while a few return DataFrames.
• All indicators can be called one of three ways. Either way, they return the result.

Three ways to use pandas_ta

1. Stand Alone like TA-Lib ta.indicator(kwargs).
2. As a DataFrame Extension like df.ta.indicator(kwargs). Where df is a DataFrame with columns named 'open', 'high', 'low', 'close, 'volume' for simplicity.
3. Similar to #2, but by calling: df.ta(kind='indicator', kwargs).

Cumulative Log Return

In [9]:

clr = df.ta.log_return(cumulative=True, append=True)
# df[['0', f"{clr.name}"]].tail(100).plot(figsize=(16, 3), color=['black'], linewidth=1, title=f"{df.name}: {clr.name} from {df.index[0]} to {df.index[-1]} ({last_})")
df[clr.name].tail(100).plot(figsize=(16, 3), color=['black'], linewidth=1, title=f"{df.name}: {clr.name} from {df.index[0]} to {df.index[-1]} (100)", grid=True)

Out [9]:

<matplotlib.axes._subplots.AxesSubplot at 0x10f7b9c18>

MACD

In [10]:

macddf = df.ta.macd(fast=8, slow=21, signal=9, min_periods=None, append=True)
macddf[[macddf.columns[0], macddf.columns[2]]].tail(100).plot(figsize=(16, 3), color=['black', 'blue'], linewidth=1.3)
macddf[macddf.columns[1]].tail(100).plot.area(figsize=(16, 3), stacked=False, color=['silver'], linewidth=1, title=f"{df.name}: {macddf.name} from {df.index[0]} to {df.index[-1]} (100)", grid=True).axhline(y=0, color="black", lw=1.1)

Out [10]:

<matplotlib.lines.Line2D at 0x10fca2fd0>

ZScore

In [11]:

df.ta.zscore(length=10, append=True)
zcolors = ['maroon', 'red', 'orange', 'silver', 'silver', 'orange', 'red', 'maroon', 'black', 'blue']
zcols = df[['-4', '-3', '-2', '-1', '1', '2', '3', '4', 'Z_10']]
zcols.tail(100).plot(figsize=(16, 3), color=zcolors, linewidth=1, title=f"{df.name}: Z from {df.index[0]} to {df.index[-1]} (100)").axhline(y=0, color="black", lw=1.1)

Out [11]:

<matplotlib.lines.Line2D at 0x10f7e8ac8>

In [ ]:

New Features

Archer Moving Averages Trends

Archer Moving Average Trends (amat) returns the long and short run trends of fast and slow moving averages.

In [12]:

amat = df.ta.amat()
machart('ema', 8, 21, 50, last=100) # Price Chart so we can see the association with AMAT
amat.tail(100).plot(kind='area', figsize=(16, 0.35), color=['green', 'red'], alpha=0.4, stacked=False, title=amat.name)

Out [12]:

<matplotlib.axes._subplots.AxesSubplot at 0x110135470>

In [ ]:

Archer On Balance Volume

Archer On Balance Volume (aobv) returns a DataFrame of OBV, OBV min and max, fast and slow MAs of OBV, and the long and short run trends of the two OBV MAs.

In [13]:

machart('ema', 8, 21, 50, last=100) # Price Chart so we can see the association with AOBV
volumechart('ema', last=100)

In [14]:

aobv = ta.aobv(close=closedf, volume=volumedf, mamode='ema', fast=8, slow=21)
aobv[aobv.columns[:5]].tail(100).plot(figsize=(16, 3), color=['black', 'silver', 'silver', 'green', 'red'], title=aobv.name, grid=True)
print(f"Columns[{len(aobv.columns)}]: {', '.join(list(aobv.columns))}")

Columns[7]: OBV, OBV_min_2, OBV_max_2, OBV_EMA_8, OBV_EMA_21, AOBV_LR_2, AOBV_SR_2

In [15]:

aobv[aobv.columns[-2:]].tail(100).plot(kind='area', figsize=(16, 0.35), color=['green', 'red'], alpha=0.35, stacked=False)

Out [15]:

<matplotlib.axes._subplots.AxesSubplot at 0x10cec3f60>

In [ ]:

Long Run and Short Run

Long Run (long_run) returns 1 if fast and slow averages approach each other from the below or both are increasing. Otherwise returns 0.

Conversely, Short Run (short_run) returns 1 if fast and slow averages approach each other from above or both are decreasing. Otherwise returns 0.

In [16]:

machart('hma', 10, 20, 50, last=50, figsize=(16,4))
machart('sma', 10, 20, 50, last=50, figsize=(16,4))

In [17]:

hma10 = df.ta.hma(length=10)  # HMA 10
sma20 = df.ta.sma(length=20)  # SMA 20
lrun = df.ta.long_run(hma10, sma20, append=False)  # Long Run of HMA 10 and SMA 10
srun = df.ta.short_run(hma10, sma20, append=False) # Short Run of HMA 10 and SMA 10
srun.tail(50).plot(kind='bar', figsize=(16,0.25), color=['red'], linewidth=1, alpha=0.45)
lrun.tail(50).plot(kind='bar', figsize=(16,0.25), color=['green'], linewidth=1, alpha=0.45, title=f"{lrun.name}(green) & {srun.name}(red)")

Out [17]:

<matplotlib.axes._subplots.AxesSubplot at 0x1101875f8>

In [ ]:

Cross

Cross (cross) returns 1 if two series cross and 0 if they do not. By default, above=True.

In [18]:

machart('sma', 8, 21, 50, last=50)

In [19]:

maf = df.ta(kind='sma', length=21)
cross_above = ta.cross(df['close'], maf, above=True)
cross_above.tail(50).plot(kind='bar', figsize=(16, 0.5), color=['green'], linewidth=1, alpha=0.55, stacked=False)

cross_below = ta.cross(df['close'], maf, above=False)
cross_below.tail(50).plot(kind='bar', figsize=(16, 0.5), color=['red'], linewidth=1, alpha=0.55, stacked=False, title=f"{df.name}: {cross_above.name} (orange) and {cross_below.name} (blue) from {df.index[0]} to {df.index[-1]}")

Out [19]:

<matplotlib.axes._subplots.AxesSubplot at 0x110856400>

In [20]:

print(f"Most recent {cross_above.name} Dates:\n    {', '.join(list(cross_above[cross_above > 0].tail(6).index[::-1]))}")
print(f"Most recent {cross_below.name} Dates:\n    {', '.join(list(cross_below[cross_below > 0].tail(6).index[::-1]))}")

Most recent close_XA_SMA_21 Dates:
    2019-05-03, 2019-03-29, 2019-03-21, 2019-03-15, 2019-01-08, 2018-11-28
Most recent close_XB_SMA_21 Dates:
    2019-05-07, 2019-05-02, 2019-03-22, 2019-03-20, 2019-03-06, 2018-12-04

In [ ]:

Trend Return

• Trend Return (trend_return) calculates the cumulative log returns from a specified trend.
• There is no limit on how to construct a trend. It can be as simple as: close > SMA(close, 50).

In [21]:

machart('sma', 100, 200, 500)#, last=50)

In [22]:

def ma_strategy(kind, fast, medium, slow, cumulative=True, last=last_):
    """A very basic analysis of the closing price being greater than each moving average"""
    last = last if last is not None else df.shape[0]
    closedf = df['close']
    maf = df.ta(kind=kind, length=fast)
    mam = df.ta(kind=kind, length=medium)
    mas = df.ta(kind=kind, length=slow)
    tdf = pd.DataFrame({
        f"{maf.name} cumlog ret": ta.trend_return(closedf, closedf > maf, cumulative=cumulative),
        f"{mam.name} cumlog ret": ta.trend_return(closedf, closedf > mam, cumulative=cumulative),
        f"{mas.name} cumlog ret": ta.trend_return(closedf, closedf > mas, cumulative=cumulative),
    })
    tdf.set_index(closedf.index, inplace=True)
    window = tdf.tail(last)
    title = f"{df.name}: {kind.upper()} Trend Return from {window.index[0]} to {window.index[-1]}"
    window.plot.area(figsize=(16, 3), color=['red', 'orange', 'yellow'], linewidth=1, alpha=0.35, title=title, stacked=False, grid=True).axhline(y=0, color="black", lw=1.1)

ma_strategy('sma', 100, 200, 500)#, last=50)

In [ ]:

Disclaimer

• All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, or individual’s trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.

• Any opinions, news, research, analyses, prices, or other information offered is provided as general market commentary, and does not constitute investment advice. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from use of or reliance on such information.

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