Pandas TA (pandas_ta) Strategies for Custom Technical Analysis

Topics

What is a Pandas TA Strategy?
- Builtin Strategies: AllStrategy and CommonStrategy
- Creating Strategies
Watchlist Class
- Strategy Management and Execution
- NOTE: The watchlist module is independent of Pandas TA. To easily use it, copy it from your local pandas_ta installation directory into your project directory.
Indicator Composition/Chaining for more Complex Strategies
- Comprehensive Example: MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns

In [1]:

%matplotlib inline
import datetime as dt

import pandas as pd
import pandas_ta as ta
from alphaVantageAPI.alphavantage import AlphaVantage  # pip install alphaVantage-api

from watchlist import Watchlist # Is this failing? If so, copy it locally. See above.

print(f"\nPandas TA v{ta.version}\nTo install the Latest Version:\n$ pip install -U git+https://github.com/twopirllc/pandas-ta\n")
%pylab inline

Pandas TA v0.2.42b0
To install the Latest Version:
$ pip install -U git+https://github.com/twopirllc/pandas-ta

Populating the interactive namespace from numpy and matplotlib

What is a Pandas TA Strategy?

A Strategy is a simple way to name and group your favorite TA indicators. Technically, a Strategy is a simple Data Class to contain list of indicators and their parameters. Note: Strategy is experimental and subject to change. Pandas TA comes with two basic Strategies: AllStrategy and CommonStrategy.

Strategy Requirements:

name: Some short memorable string. Note: Case-insensitive "All" is reserved.
ta: A list of dicts containing keyword arguments to identify the indicator and the indicator's arguments

Optional Requirements:

description: A more detailed description of what the Strategy tries to capture. Default: None
created: At datetime string of when it was created. Default: Automatically generated.

Things to note:

A Strategy will fail when consumed by Pandas TA if there is no {"kind": "indicator name"} attribute.

Builtin Examples

All

In [2]:

AllStrategy = ta.AllStrategy
print("name =", AllStrategy.name)
print("description =", AllStrategy.description)
print("created =", AllStrategy.created)
print("ta =", AllStrategy.ta)

name = All
description = All the indicators with their default settings. Pandas TA default.
created = 02/18/2021, 12:39:47
ta = None

Common

In [3]:

CommonStrategy = ta.CommonStrategy
print("name =", CommonStrategy.name)
print("description =", CommonStrategy.description)
print("created =", CommonStrategy.created)
print("ta =", CommonStrategy.ta)

name = Common Price and Volume SMAs
description = Common Price SMAs: 10, 20, 50, 200 and Volume SMA: 20.
created = 02/18/2021, 12:39:47
ta = [{'kind': 'sma', 'length': 10}, {'kind': 'sma', 'length': 20}, {'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}, {'kind': 'sma', 'close': 'volume', 'length': 20, 'prefix': 'VOL'}]

In [ ]:

Creating Strategies

Simple Strategy A

In [4]:

custom_a = ta.Strategy(name="A", ta=[{"kind": "sma", "length": 50}, {"kind": "sma", "length": 200}])
custom_a

Out [4]:

Strategy(name='A', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}], description='TA Description', created='02/18/2021, 12:39:47')

Simple Strategy B

In [5]:

custom_b = ta.Strategy(name="B", ta=[{"kind": "ema", "length": 8}, {"kind": "ema", "length": 21}, {"kind": "log_return", "cumulative": True}, {"kind": "rsi"}, {"kind": "supertrend"}])
custom_b

Out [5]:

Strategy(name='B', ta=[{'kind': 'ema', 'length': 8}, {'kind': 'ema', 'length': 21}, {'kind': 'log_return', 'cumulative': True}, {'kind': 'rsi'}, {'kind': 'supertrend'}], description='TA Description', created='02/18/2021, 12:39:47')

Bad Strategy. (Misspelled Indicator)

In [6]:

# Misspelled indicator, will fail later when ran with Pandas TA
custom_run_failure = ta.Strategy(name="Runtime Failure", ta=[{"kind": "percet_return"}])
custom_run_failure

Out [6]:

Strategy(name='Runtime Failure', ta=[{'kind': 'percet_return'}], description='TA Description', created='02/18/2021, 12:39:47')

In [ ]:

Strategy Management and Execution with Watchlist

Initialize AlphaVantage Data Source

In [7]:

AV = AlphaVantage(
    api_key="YOUR API KEY", premium=False,
    output_size='full', clean=True,
    export_path=".", export=True
)
AV

Out [7]:

AlphaVantage(
  end_point:str = https://www.alphavantage.co/query,
  api_key:str = YOUR API KEY,
  export:bool = True,
  export_path:str = .,
  output_size:str = full,
  output:str = csv,
  datatype:str = json,
  clean:bool = True,
  proxy:dict = {}
)

Create Watchlist and set it's 'ds' to AlphaVantage

In [8]:

data_source = "av" # Default
# data_source = "yahoo"
watch = Watchlist(["SPY", "IWM"], ds_name=data_source, timed=False)

Info about the Watchlist. Note, the default Strategy is "All"

In [9]:

watch

Out [9]:

Watch(name='Watch: SPY, IWM', ds_name='av', tickers[2]='SPY, IWM', tf='D', strategy[5]='Common Price and Volume SMAs')

Help about Watchlist

In [10]:

help(Watchlist)

Help on class Watchlist in module watchlist:

class Watchlist(builtins.object)
 |  Watchlist(tickers: list, tf: str = None, name: str = None, strategy: pandas_ta.core.Strategy = None, ds_name: str = 'av', **kwargs)
 |  
 |  # Watchlist Class (** This is subject to change! **)
 |  A simple Class to load/download financial market data and automatically
 |  apply Technical Analysis indicators with a Pandas TA Strategy.
 |  
 |  Default Strategy: pandas_ta.CommonStrategy
 |  
 |  ## Package Support:
 |  ### Data Source (Default: AlphaVantage)
 |  - AlphaVantage (pip install alphaVantage-api).
 |  - Python Binance (pip install python-binance). # Future Support
 |  - Yahoo Finance (pip install yfinance). # Almost Supported
 |  
 |  # Technical Analysis:
 |  - Pandas TA (pip install pandas_ta)
 |  
 |  ## Required Arguments:
 |  - tickers: A list of strings containing tickers. Example: ["SPY", "AAPL"]
 |  
 |  Methods defined here:
 |  
 |  __init__(self, tickers: list, tf: str = None, name: str = None, strategy: pandas_ta.core.Strategy = None, ds_name: str = 'av', **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |  
 |  __repr__(self) -> str
 |      Return repr(self).
 |  
 |  indicators(self, *args, **kwargs) -> <built-in function any>
 |      Returns the list of indicators that are available with Pandas Ta.
 |  
 |  load(self, ticker: str = None, tf: str = None, index: str = 'date', drop: list = [], plot: bool = False, **kwargs) -> pandas.core.frame.DataFrame
 |      Loads or Downloads (if a local csv does not exist) the data from the
 |      Data Source. When successful, it returns a Data Frame for the requested
 |      ticker. If no tickers are given, it loads all the tickers.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  data
 |      When not None, it contains a dictionary of DataFrames keyed by ticker. data = {"SPY": pd.DataFrame, ...}
 |  
 |  name
 |      The name of the Watchlist. Default: "Watchlist: {Watchlist.tickers}".
 |  
 |  strategy
 |      Sets a valid Strategy. Default: pandas_ta.CommonStrategy
 |  
 |  tf
 |      Alias for timeframe. Default: 'D'
 |  
 |  tickers
 |      tickers
 |      
 |      If a string, it it converted to a list. Example: "AAPL" -> ["AAPL"]
 |          * Does not accept, comma seperated strings.
 |      If a list, checks if it is a list of strings.
 |  
 |  verbose
 |      Toggle the verbose property. Default: False

Default Strategy is "Common"

In [11]:

# No arguments loads all the tickers and applies the Strategy to each ticker.
# The result can be accessed with Watchlist's 'data' property which returns a 
# dictionary keyed by ticker and DataFrames as values 
watch.load(verbose=True)

[!] Loading All: SPY, IWM
[+] Downloading[av]: SPY[D]
[+] Strategy: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': False, 'append': True}
[i] Multiprocessing: 8 of 8 cores.
[i] Total indicators: 5
[i] Columns added: 5
[+] Downloading[av]: IWM[D]
[+] Strategy: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': False, 'append': True}
[i] Multiprocessing: 8 of 8 cores.
[i] Total indicators: 5
[i] Columns added: 5

In [12]:

watch.data

Out [12]:

{'SPY':                 open      high       low     close      volume   SMA_10  \
 date                                                                      
 1999-11-01  136.5000  137.0000  135.5625  135.5625   4006500.0      NaN   
 1999-11-02  135.9687  137.2500  134.5937  134.5937   6516900.0      NaN   
 1999-11-03  136.0000  136.3750  135.1250  135.5000   7222300.0      NaN   
 1999-11-04  136.7500  137.3593  135.7656  136.5312   7907500.0      NaN   
 1999-11-05  138.6250  139.1093  136.7812  137.8750   7431500.0      NaN   
 ...              ...       ...       ...       ...         ...      ...   
 2021-02-10  392.1200  392.2800  387.5000  390.0800  58362935.0  383.207   
 2021-02-11  391.2400  391.6900  388.1000  390.7100  42913288.0  384.515   
 2021-02-12  389.8500  392.9000  389.7700  392.6400  50593270.0  386.772   
 2021-02-16  393.9600  394.1700  391.5300  392.3000  50972366.0  388.379   
 2021-02-17  390.4200  392.6600  389.3300  392.3900  51746878.0  389.463   
 
               SMA_20    SMA_50    SMA_200   VOL_SMA_20  
 date                                                    
 1999-11-01       NaN       NaN        NaN          NaN  
 1999-11-02       NaN       NaN        NaN          NaN  
 1999-11-03       NaN       NaN        NaN          NaN  
 1999-11-04       NaN       NaN        NaN          NaN  
 1999-11-05       NaN       NaN        NaN          NaN  
 ...              ...       ...        ...          ...  
 2021-02-10  381.9135  374.9464  338.55695  64542395.00  
 2021-02-11  382.4595  375.5194  339.08185  64422878.45  
 2021-02-12  383.1685  376.0518  339.57900  64453086.30  
 2021-02-16  383.9985  376.5620  340.08810  61643706.50  
 2021-02-17  384.6855  377.0760  340.63610  61669385.40  
 
 [5358 rows x 10 columns],
 'IWM':               open     high      low   close      volume   SMA_10    SMA_20  \
 date                                                                          
 2000-05-26   91.06   91.440   90.630   91.44     37400.0      NaN       NaN   
 2000-05-30   92.75   94.810   92.750   94.81     28800.0      NaN       NaN   
 2000-05-31   95.13   96.380   95.130   95.75     18000.0      NaN       NaN   
 2000-06-01   97.11   97.310   97.110   97.31      3500.0      NaN       NaN   
 2000-06-02  101.70  102.400  101.700  102.40     14700.0      NaN       NaN   
 ...            ...      ...      ...     ...         ...      ...       ...   
 2021-02-10  229.92  230.315  224.945  226.88  27682789.0  217.559  215.1440   
 2021-02-11  228.14  229.000  223.420  226.62  25847586.0  219.349  215.9875   
 2021-02-12  225.93  227.740  224.640  227.26  17440191.0  221.519  216.6535   
 2021-02-16  229.47  229.630  224.790  225.83  22999508.0  223.041  217.4075   
 2021-02-17  223.61  224.740  220.960  224.06  24950507.0  224.086  217.9380   
 
               SMA_50    SMA_200   VOL_SMA_20  
 date                                          
 2000-05-26       NaN        NaN          NaN  
 2000-05-30       NaN        NaN          NaN  
 2000-05-31       NaN        NaN          NaN  
 2000-06-01       NaN        NaN          NaN  
 2000-06-02       NaN        NaN          NaN  
 ...              ...        ...          ...  
 2021-02-10  202.9438  163.56290  27734044.40  
 2021-02-11  203.8558  164.05045  27887312.30  
 2021-02-12  204.7470  164.50945  27063635.45  
 2021-02-16  205.6058  164.98705  26161437.20  
 2021-02-17  206.4086  165.48165  26421747.15  
 
 [5214 rows x 10 columns]}

In [13]:

watch.data["SPY"]

Out [13]:

	open	high	low	close	volume	SMA_10	SMA_20	SMA_50	SMA_200	VOL_SMA_20
date
1999-11-01	136.5000	137.0000	135.5625	135.5625	4006500.0	NaN	NaN	NaN	NaN	NaN
1999-11-02	135.9687	137.2500	134.5937	134.5937	6516900.0	NaN	NaN	NaN	NaN	NaN
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	NaN	NaN	NaN	NaN	NaN
1999-11-04	136.7500	137.3593	135.7656	136.5312	7907500.0	NaN	NaN	NaN	NaN	NaN
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...
2021-02-10	392.1200	392.2800	387.5000	390.0800	58362935.0	383.207	381.9135	374.9464	338.55695	64542395.00
2021-02-11	391.2400	391.6900	388.1000	390.7100	42913288.0	384.515	382.4595	375.5194	339.08185	64422878.45
2021-02-12	389.8500	392.9000	389.7700	392.6400	50593270.0	386.772	383.1685	376.0518	339.57900	64453086.30
2021-02-16	393.9600	394.1700	391.5300	392.3000	50972366.0	388.379	383.9985	376.5620	340.08810	61643706.50
2021-02-17	390.4200	392.6600	389.3300	392.3900	51746878.0	389.463	384.6855	377.0760	340.63610	61669385.40

5358 rows × 10 columns

In [ ]:

In [14]:

watch.load("SPY", plot=True, mas=True)

Out [14]:

[i] Loaded SPY[D]: SPY_D.csv

	open	high	low	close	volume	SMA_10	SMA_20	SMA_50	SMA_200	VOL_SMA_20
date
1999-11-01	136.5000	137.0000	135.5625	135.5625	4006500.0	NaN	NaN	NaN	NaN	NaN
1999-11-02	135.9687	137.2500	134.5937	134.5937	6516900.0	NaN	NaN	NaN	NaN	NaN
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	NaN	NaN	NaN	NaN	NaN
1999-11-04	136.7500	137.3593	135.7656	136.5312	7907500.0	NaN	NaN	NaN	NaN	NaN
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...
2021-02-10	392.1200	392.2800	387.5000	390.0800	58362935.0	383.207	381.9135	374.9464	338.55695	64542395.00
2021-02-11	391.2400	391.6900	388.1000	390.7100	42913288.0	384.515	382.4595	375.5194	339.08185	64422878.45
2021-02-12	389.8500	392.9000	389.7700	392.6400	50593270.0	386.772	383.1685	376.0518	339.57900	64453086.30
2021-02-16	393.9600	394.1700	391.5300	392.3000	50972366.0	388.379	383.9985	376.5620	340.08810	61643706.50
2021-02-17	390.4200	392.6600	389.3300	392.3900	51746878.0	389.463	384.6855	377.0760	340.63610	61669385.40

5358 rows × 10 columns

In [ ]:

Easy to swap Strategies and run them

Running Simple Strategy A

In [15]:

# Load custom_a into Watchlist and verify
watch.strategy = custom_a
# watch.debug = True
watch.strategy

Out [15]:

Strategy(name='A', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}], description='TA Description', created='02/18/2021, 12:39:47')

In [16]:

watch.load("IWM")

Out [16]:

[i] Loaded IWM[D]: IWM_D.csv

	open	high	low	close	volume	SMA_50	SMA_200
date
2000-05-26	91.06	91.440	90.630	91.44	37400.0	NaN	NaN
2000-05-30	92.75	94.810	92.750	94.81	28800.0	NaN	NaN
2000-05-31	95.13	96.380	95.130	95.75	18000.0	NaN	NaN
2000-06-01	97.11	97.310	97.110	97.31	3500.0	NaN	NaN
2000-06-02	101.70	102.400	101.700	102.40	14700.0	NaN	NaN
...	...	...	...	...	...	...	...
2021-02-10	229.92	230.315	224.945	226.88	27682789.0	202.9438	163.56290
2021-02-11	228.14	229.000	223.420	226.62	25847586.0	203.8558	164.05045
2021-02-12	225.93	227.740	224.640	227.26	17440191.0	204.7470	164.50945
2021-02-16	229.47	229.630	224.790	225.83	22999508.0	205.6058	164.98705
2021-02-17	223.61	224.740	220.960	224.06	24950507.0	206.4086	165.48165

5214 rows × 7 columns

Running Simple Strategy B

In [17]:

# Load custom_b into Watchlist and verify
watch.strategy = custom_b
watch.strategy

Out [17]:

Strategy(name='B', ta=[{'kind': 'ema', 'length': 8}, {'kind': 'ema', 'length': 21}, {'kind': 'log_return', 'cumulative': True}, {'kind': 'rsi'}, {'kind': 'supertrend'}], description='TA Description', created='02/18/2021, 12:39:47')

In [18]:

watch.load("SPY")

Out [18]:

[i] Loaded SPY[D]: SPY_D.csv

	open	high	low	close	volume	EMA_8	EMA_21	CUMLOGRET_1	RSI_14	SUPERT_7_3.0	SUPERTd_7_3.0	SUPERTl_7_3.0	SUPERTs_7_3.0
date
1999-11-01	136.5000	137.0000	135.5625	135.5625	4006500.0	NaN	NaN	NaN	NaN	0.000000	1	NaN	NaN
1999-11-02	135.9687	137.2500	134.5937	134.5937	6516900.0	NaN	NaN	-0.007172	NaN	NaN	1	NaN	NaN
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	NaN	NaN	-0.000461	NaN	NaN	1	NaN	NaN
1999-11-04	136.7500	137.3593	135.7656	136.5312	7907500.0	NaN	NaN	0.007120	NaN	NaN	1	NaN	NaN
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	NaN	NaN	0.016915	NaN	NaN	1	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2021-02-10	392.1200	392.2800	387.5000	390.0800	58362935.0	386.458726	381.977892	1.056919	64.004906	376.872227	1	376.872227	NaN
2021-02-11	391.2400	391.6900	388.1000	390.7100	42913288.0	387.403454	382.771720	1.058533	64.646206	376.933575	1	376.933575	NaN
2021-02-12	389.8500	392.9000	389.7700	392.6400	50593270.0	388.567131	383.668836	1.063460	66.608891	378.883779	1	378.883779	NaN
2021-02-16	393.9600	394.1700	391.5300	392.3000	50972366.0	389.396657	384.453487	1.062594	65.914662	381.046096	1	381.046096	NaN
2021-02-17	390.4200	392.6600	389.3300	392.3900	51746878.0	390.061845	385.174988	1.062823	66.015633	381.046096	1	381.046096	NaN

5358 rows × 13 columns

Running Bad Strategy. (Misspelled indicator)

In [19]:

# Load custom_run_failure into Watchlist and verify
watch.strategy = custom_run_failure
watch.strategy

Out [19]:

Strategy(name='Runtime Failure', ta=[{'kind': 'percet_return'}], description='TA Description', created='02/18/2021, 12:39:47')

In [20]:

try:
    iwm = watch.load("IWM")
except AttributeError as error:
    print(f"[X] Oops! {error}")

[i] Loaded IWM[D]: IWM_D.csv
[X] Oops! 'AnalysisIndicators' object has no attribute 'percet_return'

In [ ]:

Indicator Composition/Chaining

When you need an indicator to depend on the value of a prior indicator
Utilitze prefix or suffix to help identify unique columns or avoid column name clashes.

Volume MAs and MA chains

In [21]:

# Set EMA's and SMA's 'close' to 'volume' to create Volume MAs, prefix 'volume' MAs with 'VOLUME' so easy to identify the column
# Take a price EMA and apply LINREG from EMA's output
volmas_price_ma_chain = [
    {"kind":"ema", "close": "volume", "length": 10, "prefix": "VOLUME"},
    {"kind":"sma", "close": "volume", "length": 20, "prefix": "VOLUME"},
    {"kind":"ema", "length": 5},
    {"kind":"linreg", "close": "EMA_5", "length": 8, "prefix": "EMA_5"},
]
vp_ma_chain_ta = ta.Strategy("Volume MAs and Price MA chain", volmas_price_ma_chain)
vp_ma_chain_ta

Out [21]:

Strategy(name='Volume MAs and Price MA chain', ta=[{'kind': 'ema', 'close': 'volume', 'length': 10, 'prefix': 'VOLUME'}, {'kind': 'sma', 'close': 'volume', 'length': 20, 'prefix': 'VOLUME'}, {'kind': 'ema', 'length': 5}, {'kind': 'linreg', 'close': 'EMA_5', 'length': 8, 'prefix': 'EMA_5'}], description='TA Description', created='02/18/2021, 12:39:47')

In [22]:

# Update the Watchlist
watch.strategy = vp_ma_chain_ta
watch.strategy.name

Out [22]:

'Volume MAs and Price MA chain'

In [23]:

spy = watch.load("SPY")
spy

Out [23]:

[i] Loaded SPY[D]: SPY_D.csv

	open	high	low	close	volume	VOLUME_EMA_10	VOLUME_SMA_20	EMA_5	EMA_5_LR_8
date
1999-11-01	136.5000	137.0000	135.5625	135.5625	4006500.0	NaN	NaN	NaN	NaN
1999-11-02	135.9687	137.2500	134.5937	134.5937	6516900.0	NaN	NaN	NaN	NaN
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	NaN	NaN	NaN	NaN
1999-11-04	136.7500	137.3593	135.7656	136.5312	7907500.0	NaN	NaN	NaN	NaN
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	NaN	NaN	136.012480	NaN
...	...	...	...	...	...	...	...	...	...
2021-02-10	392.1200	392.2800	387.5000	390.0800	58362935.0	5.624199e+07	64542395.00	388.270305	387.091354
2021-02-11	391.2400	391.6900	388.1000	390.7100	42913288.0	5.381859e+07	64422878.45	389.083537	388.372609
2021-02-12	389.8500	392.9000	389.7700	392.6400	50593270.0	5.323217e+07	64453086.30	390.269025	389.537374
2021-02-16	393.9600	394.1700	391.5300	392.3000	50972366.0	5.282130e+07	61643706.50	390.946016	390.380948
2021-02-17	390.4200	392.6600	389.3300	392.3900	51746878.0	5.262595e+07	61669385.40	391.427344	391.019719

5358 rows × 9 columns

In [ ]:

MACD BBANDS

In [24]:

# MACD is the initial indicator that BBANDS depends on.
# Set BBANDS's 'close' to MACD's main signal, in this case 'MACD_12_26_9' and add a prefix (or suffix) so it's easier to identify
macd_bands_ta = [
    {"kind":"macd"},
    {"kind":"bbands", "close": "MACD_12_26_9", "length": 20, "prefix": "MACD"}
]
macd_bands_ta = ta.Strategy("MACD BBands", macd_bands_ta, f"BBANDS_{macd_bands_ta[1]['length']} applied to MACD")
macd_bands_ta

Out [24]:

Strategy(name='MACD BBands', ta=[{'kind': 'macd'}, {'kind': 'bbands', 'close': 'MACD_12_26_9', 'length': 20, 'prefix': 'MACD'}], description='BBANDS_20 applied to MACD', created='02/18/2021, 12:39:47')

In [25]:

# Update the Watchlist
watch.strategy = macd_bands_ta
watch.strategy.name

Out [25]:

'MACD BBands'

In [26]:

spy = watch.load("SPY")
spy

Out [26]:

[i] Loaded SPY[D]: SPY_D.csv

	open	high	low	close	volume	MACD_12_26_9	MACDh_12_26_9	MACDs_12_26_9	MACD_BBL_20_2.0	MACD_BBM_20_2.0	MACD_BBU_20_2.0	MACD_BBB_20_2.0
date
1999-11-01	136.5000	137.0000	135.5625	135.5625	4006500.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1999-11-02	135.9687	137.2500	134.5937	134.5937	6516900.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1999-11-04	136.7500	137.3593	135.7656	136.5312	7907500.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
2021-02-10	392.1200	392.2800	387.5000	390.0800	58362935.0	4.115955	0.768432	3.347522	1.736965	3.487625	5.238285	100.392662
2021-02-11	391.2400	391.6900	388.1000	390.7100	42913288.0	4.287627	0.752083	3.535543	1.736337	3.488320	5.240302	100.448534
2021-02-12	389.8500	392.9000	389.7700	392.6400	50593270.0	4.527226	0.793346	3.733880	1.718321	3.505669	5.293017	101.969021
2021-02-16	393.9600	394.1700	391.5300	392.3000	50972366.0	4.636231	0.721881	3.914350	1.692525	3.545522	5.398519	104.526067
2021-02-17	390.4200	392.6600	389.3300	392.3900	51746878.0	4.675979	0.609303	4.066676	1.671804	3.591144	5.510485	106.892975

5358 rows × 12 columns

In [ ]:

Comprehensive Strategy

MACD and RSI Momentum with BBANDS and SMAs and Cumulative Log Returns

In [27]:

momo_bands_sma_ta = [
    {"kind":"sma", "length": 50},
    {"kind":"sma", "length": 200},
    {"kind":"bbands", "length": 20},
    {"kind":"macd"},
    {"kind":"rsi"},
    {"kind":"log_return", "cumulative": True},
    {"kind":"sma", "close": "CUMLOGRET_1", "length": 5, "suffix": "CUMLOGRET"},
]
momo_bands_sma_strategy = ta.Strategy(
    "Momo, Bands and SMAs and Cumulative Log Returns", # name
    momo_bands_sma_ta, # ta
    "MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns" # description
)
momo_bands_sma_strategy

Out [27]:

Strategy(name='Momo, Bands and SMAs and Cumulative Log Returns', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}, {'kind': 'bbands', 'length': 20}, {'kind': 'macd'}, {'kind': 'rsi'}, {'kind': 'log_return', 'cumulative': True}, {'kind': 'sma', 'close': 'CUMLOGRET_1', 'length': 5, 'suffix': 'CUMLOGRET'}], description='MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns', created='02/18/2021, 12:39:47')

In [28]:

# Update the Watchlist
watch.strategy = momo_bands_sma_strategy
watch.strategy.name

Out [28]:

'Momo, Bands and SMAs and Cumulative Log Returns'

In [29]:

spy = watch.load("SPY")
# Apply constants to the DataFrame for indicators
spy.ta.constants(True, [0, 30, 70])
spy.tail()

Out [29]:

[i] Loaded SPY[D]: SPY_D.csv

	open	high	low	close	volume	SMA_50	SMA_200	BBL_20_2.0	BBM_20_2.0	BBU_20_2.0	BBB_20_2.0	MACD_12_26_9	MACDh_12_26_9	MACDs_12_26_9	RSI_14	CUMLOGRET_1	SMA_5_CUMLOGRET	0	30	70
date
2021-02-10	392.12	392.28	387.50	390.08	58362935.0	374.9464	338.55695	370.722001	381.9135	393.104999	5.860751	4.115955	0.768432	3.347522	64.004906	1.056919	1.054003	0	30	70
2021-02-11	391.24	391.69	388.10	390.71	42913288.0	375.5194	339.08185	370.655462	382.4595	394.263538	6.172699	4.287627	0.752083	3.535543	64.646206	1.058533	1.056330	0	30	70
2021-02-12	389.85	392.90	389.77	392.64	50593270.0	376.0518	339.57900	370.691695	383.1685	395.645305	6.512438	4.527226	0.793346	3.733880	66.608891	1.063460	1.058858	0	30	70
2021-02-16	393.96	394.17	391.53	392.30	50972366.0	376.5620	340.08810	371.405574	383.9985	396.591426	6.558841	4.636231	0.721881	3.914350	65.914662	1.062594	1.059772	0	30	70
2021-02-17	390.42	392.66	389.33	392.39	51746878.0	377.0760	340.63610	371.824830	384.6855	397.546170	6.686329	4.675979	0.609303	4.066676	66.015633	1.062823	1.060866	0	30	70

In [ ]:

Additional Strategy Options

The params keyword takes a tuple as a shorthand to the parameter arguments in order.

Note: If the indicator arguments change, so will results. Breaking Changes will always be posted on the README.

The col_numbers keyword takes a tuple specifying which column to return if the result is a DataFrame.

In [30]:

params_ta = [
    {"kind":"ema", "params": (10,)},
    # params sets MACD's keyword arguments: fast=9, slow=19, signal=10
    # and returning the 2nd column: histogram
    {"kind":"macd", "params": (9, 19, 10), "col_numbers": (1,)},
    # Selects the Lower and Upper Bands and renames them LB and UB, ignoring the MB
    {"kind":"bbands", "col_numbers": (0,2), "col_names": ("LB", "UB")},
    {"kind":"log_return", "params": (5, False)},
]
params_ta_strategy = ta.Strategy(
    "EMA, MACD History, Outter BBands, Log Returns", # name
    params_ta, # ta
    "EMA, MACD History, BBands(LB, UB), and Log Returns Strategy" # description
)
params_ta_strategy

Out [30]:

Strategy(name='EMA, MACD History, Outter BBands, Log Returns', ta=[{'kind': 'ema', 'params': (10,)}, {'kind': 'macd', 'params': (9, 19, 10), 'col_numbers': (1,)}, {'kind': 'bbands', 'col_numbers': (0, 2), 'col_names': ('LB', 'UB')}, {'kind': 'log_return', 'params': (5, False)}], description='EMA, MACD History, BBands(LB, UB), and Log Returns Strategy', created='02/18/2021, 12:39:47')

In [31]:

# Update the Watchlist
watch.strategy = params_ta_strategy
watch.strategy.name

Out [31]:

'EMA, MACD History, Outter BBands, Log Returns'

In [32]:

spy = watch.load("SPY")
spy.tail()

Out [32]:

[i] Loaded SPY[D]: SPY_D.csv

	open	high	low	close	volume	EMA_10	MACDh_9_19_10	LB	UB	LOGRET_5
date
2021-02-10	392.12	392.28	387.50	390.08	58362935.0	385.522649	1.022084	385.132809	392.763191	0.021324
2021-02-11	391.24	391.69	388.10	390.71	42913288.0	386.465804	0.943560	387.409116	392.294884	0.011636
2021-02-12	389.85	392.90	389.77	392.64	50593270.0	387.588385	0.948892	388.766411	392.909589	0.012636
2021-02-16	393.96	394.17	391.53	392.30	50972366.0	388.445042	0.814088	388.812616	393.579384	0.004573
2021-02-17	390.42	392.66	389.33	392.39	51746878.0	389.162307	0.638021	389.322844	393.925156	0.005469

In [ ]:

214 KiB Raw Blame History Unescape Escape

Pandas TA (pandas_ta) Strategies for Custom Technical Analysis

Topics

What is a Pandas TA Strategy?

Strategy Requirements:

Optional Requirements:

Things to note:

Builtin Examples

All

Common

Creating Strategies

Simple Strategy A

Simple Strategy B

Bad Strategy. (Misspelled Indicator)

Strategy Management and Execution with Watchlist

Initialize AlphaVantage Data Source

Create Watchlist and set it's 'ds' to AlphaVantage

Info about the Watchlist. Note, the default Strategy is "All"

Help about Watchlist

Default Strategy is "Common"

Easy to swap Strategies and run them

Running Simple Strategy A

Running Simple Strategy B

Running Bad Strategy. (Misspelled indicator)

Indicator Composition/Chaining

Volume MAs and MA chains

MACD BBANDS

Comprehensive Strategy

MACD and RSI Momentum with BBANDS and SMAs and Cumulative Log Returns

Additional Strategy Options

214 KiB

Raw Blame History