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 v0.2.45b0
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 = 02/22/2021, 10:20:59
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 = 02/22/2021, 10:20:59
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='02/22/2021, 10:20:59')

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='02/22/2021, 10:20:59')

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='02/22/2021, 10:20:59')

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   
 ...              ...       ...       ...       ...         ...      ...   
 2021-02-12  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   
 2021-02-18  389.5900  391.5150  387.7400  390.7200  59712773.0  390.350   
 2021-02-19  392.0700  392.3800  389.5500  390.0300  83240971.0  390.734   
 
               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  
 ...              ...       ...        ...          ...  
 2021-02-12  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  
 2021-02-18  385.0270  377.4934  341.17185  61563220.95  
 2021-02-19  385.3165  377.9122  341.69105  63327479.05  
 
 [5360 rows x 10 columns],
 'IWM':               open    high      low   close      volume   SMA_10    SMA_20  \
 date                                                                         
 2000-05-26   91.06   91.44   90.630   91.44     37400.0      NaN       NaN   
 2000-05-30   92.75   94.81   92.750   94.81     28800.0      NaN       NaN   
 2000-05-31   95.13   96.38   95.130   95.75     18000.0      NaN       NaN   
 2000-06-01   97.11   97.31   97.110   97.31      3500.0      NaN       NaN   
 2000-06-02  101.70  102.40  101.700  102.40     14700.0      NaN       NaN   
 ...            ...     ...      ...     ...         ...      ...       ...   
 2021-02-12  225.93  227.74  224.640  227.26  17440191.0  221.519  216.6535   
 2021-02-16  229.47  229.63  224.790  225.83  22999508.0  223.041  217.4075   
 2021-02-17  223.61  224.74  220.960  224.06  24950507.0  224.086  217.9380   
 2021-02-18  222.34  222.90  219.385  220.59  24501509.0  224.720  218.2480   
 2021-02-19  222.46  226.30  222.170  225.19  31238155.0  225.377  218.8810   
 
               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  
 ...              ...        ...          ...  
 2021-02-12  204.7470  164.50945  27063635.45  
 2021-02-16  205.6058  164.98705  26161437.20  
 2021-02-17  206.4086  165.48165  26421747.15  
 2021-02-18  207.0564  165.95620  26377570.35  
 2021-02-19  207.7926  166.44890  26877632.20  
 
 [5216 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
...	...	...	...	...	...	...	...	...	...	...
2021-02-12	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
2021-02-18	389.5900	391.5150	387.7400	390.7200	59712773.0	390.350	385.0270	377.4934	341.17185	61563220.95
2021-02-19	392.0700	392.3800	389.5500	390.0300	83240971.0	390.734	385.3165	377.9122	341.69105	63327479.05

5360 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
...	...	...	...	...	...	...	...	...	...	...
2021-02-12	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
2021-02-18	389.5900	391.5150	387.7400	390.7200	59712773.0	390.350	385.0270	377.4934	341.17185	61563220.95
2021-02-19	392.0700	392.3800	389.5500	390.0300	83240971.0	390.734	385.3165	377.9122	341.69105	63327479.05

5360 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='02/22/2021, 10:20:59')

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	91.44	90.630	91.44	37400.0	NaN	NaN
2000-05-30	92.75	94.81	92.750	94.81	28800.0	NaN	NaN
2000-05-31	95.13	96.38	95.130	95.75	18000.0	NaN	NaN
2000-06-01	97.11	97.31	97.110	97.31	3500.0	NaN	NaN
2000-06-02	101.70	102.40	101.700	102.40	14700.0	NaN	NaN
...	...	...	...	...	...	...	...
2021-02-12	225.93	227.74	224.640	227.26	17440191.0	204.7470	164.50945
2021-02-16	229.47	229.63	224.790	225.83	22999508.0	205.6058	164.98705
2021-02-17	223.61	224.74	220.960	224.06	24950507.0	206.4086	165.48165
2021-02-18	222.34	222.90	219.385	220.59	24501509.0	207.0564	165.95620
2021-02-19	222.46	226.30	222.170	225.19	31238155.0	207.7926	166.44890

5216 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='02/22/2021, 10:20:59')

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
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2021-02-12	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
2021-02-18	389.5900	391.5150	387.7400	390.7200	59712773.0	390.208101	385.679080	1.058558	62.326199	381.046096	1	381.046096	NaN
2021-02-19	392.0700	392.3800	389.5500	390.0300	83240971.0	390.168523	386.074619	1.056791	60.813916	381.046096	1	381.046096	NaN

5360 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='02/22/2021, 10:20:59')

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='02/22/2021, 10:20:59')

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
...	...	...	...	...	...	...	...	...	...
2021-02-12	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
2021-02-18	389.5900	391.5150	387.7400	390.7200	59712773.0	5.391446e+07	61563220.95	391.191563	391.285765
2021-02-19	392.0700	392.3800	389.5500	390.0300	83240971.0	5.924655e+07	63327479.05	390.804375	391.300272

5360 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='02/22/2021, 10:20:59')

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
...	...	...	...	...	...	...	...	...	...	...	...	...
2021-02-12	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
2021-02-18	389.5900	391.5150	387.7400	390.7200	59712773.0	4.520614	0.363150	4.157463	1.660385	3.613206	5.566027	108.093546
2021-02-19	392.0700	392.3800	389.5500	390.0300	83240971.0	4.292329	0.107893	4.184437	1.660770	3.612408	5.564047	108.051942

5360 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='02/22/2021, 10:20:59')

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
2021-02-12	389.85	392.900	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	394.170	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	392.660	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
2021-02-18	389.59	391.515	387.74	390.72	59712773.0	377.4934	341.17185	371.895400	385.0270	398.158600	6.821132	4.520614	0.363150	4.157463	62.326199	1.058558	1.061194	0	30	70
2021-02-19	392.07	392.380	389.55	390.03	83240971.0	377.9122	341.69105	372.003908	385.3165	398.629092	6.909952	4.292329	0.107893	4.184437	60.813916	1.056791	1.060845	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='02/22/2021, 10:20:59')

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
2021-02-12	389.85	392.900	389.77	392.64	50593270.0	387.588385	0.948892	388.766411	392.909589	0.012636
2021-02-16	393.96	394.170	391.53	392.30	50972366.0	388.445042	0.814088	388.812616	393.579384	0.004573
2021-02-17	390.42	392.660	389.33	392.39	51746878.0	389.162307	0.638021	389.322844	393.925156	0.005469
2021-02-18	389.59	391.515	387.74	390.72	59712773.0	389.445524	0.303171	389.842372	393.661628	0.001639
2021-02-19	392.07	392.380	389.55	390.03	83240971.0	389.551793	-0.023612	389.284966	393.947034	-0.001742

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.

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

Disclaimer

214 KiB

Raw Blame History