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 v0.2.64b0
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 = Saturday April 10, 2021, NYSE: 5:39:01, Local: 9:39:01 PDT, Day 100/365 (27.0%)
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 = Saturday April 10, 2021, NYSE: 5:39:01, Local: 9:39:01 PDT, Day 100/365 (27.0%)
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='Saturday April 10, 2021, NYSE: 5:39:01, Local: 9:39:01 PDT, Day 100/365 (27.0%)')

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='Saturday April 10, 2021, NYSE: 5:39:01, Local: 9:39:01 PDT, Day 100/365 (27.0%)')

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='Saturday April 10, 2021, NYSE: 5:39:01, Local: 9:39:01 PDT, Day 100/365 (27.0%)')

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 5 indicators with 7 chunks and 8/8 cpus.
[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Saturday April 10, 2021, NYSE: 5:39:04, Local: 9:39:04 PDT, Day 100/365 (27.0%)
[+] Downloading[av]: IWM[D]
[+] Strategy: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': False, 'append': True}
[i] Multiprocessing 5 indicators with 7 chunks and 8/8 cpus.
[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Saturday April 10, 2021, NYSE: 5:39:21, Local: 9:39:21 PDT, Day 100/365 (27.0%)

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   
 ...              ...       ...       ...       ...         ...      ...   
 2021-04-05  403.4600  406.9400  403.3800  406.3600  91684764.0  394.910   
 2021-04-06  405.7600  407.2400  405.4000  406.1200  62020953.0  396.263   
 2021-04-07  405.9400  406.9600  405.4500  406.5900  55836280.0  397.972   
 2021-04-08  407.9300  408.5800  406.9300  408.5200  57863114.0  400.072   
 2021-04-09  408.3900  411.6700  408.2600  411.4900  61104559.0  402.251   
 
              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  
 ...             ...       ...        ...          ...  
 2021-04-05  393.285  388.3778  355.07135  97644589.35  
 2021-04-06  394.505  388.8426  355.54305  94588174.75  
 2021-04-07  395.476  389.2866  356.03280  91698310.95  
 2021-04-08  396.423  389.7812  356.52230  89096496.15  
 2021-04-09  397.321  390.5228  357.01950  87839472.10  
 
 [5394 rows x 10 columns],
 'IWM':               open     high     low   close      volume   SMA_10    SMA_20  \
 date                                                                         
 2000-05-26   91.06   91.440   90.63   91.44     37400.0      NaN       NaN   
 2000-05-30   92.75   94.810   92.75   94.81     28800.0      NaN       NaN   
 2000-05-31   95.13   96.380   95.13   95.75     18000.0      NaN       NaN   
 2000-06-01   97.11   97.310   97.11   97.31      3500.0      NaN       NaN   
 2000-06-02  101.70  102.400  101.70  102.40     14700.0      NaN       NaN   
 ...            ...      ...     ...     ...         ...      ...       ...   
 2021-04-05  226.40  226.535  223.57  224.97  27826550.0  219.366  223.9090   
 2021-04-06  225.00  226.690  223.84  224.31  24907760.0  219.274  224.1875   
 2021-04-07  224.23  224.370  219.94  220.69  26233700.0  219.637  224.0550   
 2021-04-08  221.84  222.820  219.39  222.56  23989440.0  220.689  223.8220   
 2021-04-09  222.49  223.090  221.24  222.59  23267373.0  221.282  223.3405   
 
               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  
 ...              ...        ...          ...  
 2021-04-05  221.2644  179.31385  34417859.25  
 2021-04-06  221.4506  179.72680  33632261.05  
 2021-04-07  221.5686  180.12530  33331582.25  
 2021-04-08  221.7538  180.52610  32690454.25  
 2021-04-09  222.0178  180.92405  32590989.45  
 
 [5250 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
...	...	...	...	...	...	...	...	...	...	...
2021-04-05	403.4600	406.9400	403.3800	406.3600	91684764.0	394.910	393.285	388.3778	355.07135	97644589.35
2021-04-06	405.7600	407.2400	405.4000	406.1200	62020953.0	396.263	394.505	388.8426	355.54305	94588174.75
2021-04-07	405.9400	406.9600	405.4500	406.5900	55836280.0	397.972	395.476	389.2866	356.03280	91698310.95
2021-04-08	407.9300	408.5800	406.9300	408.5200	57863114.0	400.072	396.423	389.7812	356.52230	89096496.15
2021-04-09	408.3900	411.6700	408.2600	411.4900	61104559.0	402.251	397.321	390.5228	357.01950	87839472.10

5394 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
...	...	...	...	...	...	...	...	...	...	...
2021-04-05	403.4600	406.9400	403.3800	406.3600	91684764.0	394.910	393.285	388.3778	355.07135	97644589.35
2021-04-06	405.7600	407.2400	405.4000	406.1200	62020953.0	396.263	394.505	388.8426	355.54305	94588174.75
2021-04-07	405.9400	406.9600	405.4500	406.5900	55836280.0	397.972	395.476	389.2866	356.03280	91698310.95
2021-04-08	407.9300	408.5800	406.9300	408.5200	57863114.0	400.072	396.423	389.7812	356.52230	89096496.15
2021-04-09	408.3900	411.6700	408.2600	411.4900	61104559.0	402.251	397.321	390.5228	357.01950	87839472.10

5394 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='Saturday April 10, 2021, NYSE: 5:39:01, Local: 9:39:01 PDT, Day 100/365 (27.0%)')

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	90.63	91.44	37400.0	NaN	NaN
2000-05-30	92.75	94.810	92.75	94.81	28800.0	NaN	NaN
2000-05-31	95.13	96.380	95.13	95.75	18000.0	NaN	NaN
2000-06-01	97.11	97.310	97.11	97.31	3500.0	NaN	NaN
2000-06-02	101.70	102.400	101.70	102.40	14700.0	NaN	NaN
...	...	...	...	...	...	...	...
2021-04-05	226.40	226.535	223.57	224.97	27826550.0	221.2644	179.31385
2021-04-06	225.00	226.690	223.84	224.31	24907760.0	221.4506	179.72680
2021-04-07	224.23	224.370	219.94	220.69	26233700.0	221.5686	180.12530
2021-04-08	221.84	222.820	219.39	222.56	23989440.0	221.7538	180.52610
2021-04-09	222.49	223.090	221.24	222.59	23267373.0	222.0178	180.92405

5250 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='Saturday April 10, 2021, NYSE: 5:39:01, Local: 9:39:01 PDT, Day 100/365 (27.0%)')

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
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2021-04-05	403.4600	406.9400	403.3800	406.3600	91684764.0	397.751097	393.567248	1.097807	67.670567	390.310373	1	390.310373	NaN
2021-04-06	405.7600	407.2400	405.4000	406.1200	62020953.0	399.610853	394.708407	1.097216	67.264341	392.803177	1	392.803177	NaN
2021-04-07	405.9400	406.9600	405.4500	406.5900	55836280.0	401.161775	395.788552	1.098373	67.673599	393.972009	1	393.972009	NaN
2021-04-08	407.9300	408.5800	406.9300	408.5200	57863114.0	402.796936	396.945956	1.103108	69.367185	396.416722	1	396.416722	NaN
2021-04-09	408.3900	411.6700	408.2600	411.4900	61104559.0	404.728728	398.268142	1.110352	71.814343	398.785047	1	398.785047	NaN

5394 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='Saturday April 10, 2021, NYSE: 5:39:01, Local: 9:39:01 PDT, Day 100/365 (27.0%)')

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='Saturday April 10, 2021, NYSE: 5:39:01, Local: 9:39:01 PDT, Day 100/365 (27.0%)')

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
...	...	...	...	...	...	...	...	...	...
2021-04-05	403.4600	406.9400	403.3800	406.3600	91684764.0	9.904359e+07	97644589.35	399.857343	397.030535
2021-04-06	405.7600	407.2400	405.4000	406.1200	62020953.0	9.231221e+07	94588174.75	401.944895	399.235555
2021-04-07	405.9400	406.9600	405.4500	406.5900	55836280.0	8.568022e+07	91698310.95	403.493264	401.232830
2021-04-08	407.9300	408.5800	406.9300	408.5200	57863114.0	8.062256e+07	89096496.15	405.168842	403.269982
2021-04-09	408.3900	411.6700	408.2600	411.4900	61104559.0	7.707384e+07	87839472.10	407.275895	405.405385

5394 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='Saturday April 10, 2021, NYSE: 5:39:01, Local: 9:39:01 PDT, Day 100/365 (27.0%)')

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
...	...	...	...	...	...	...	...	...	...	...	...	...
2021-04-05	403.4600	406.9400	403.3800	406.3600	91684764.0	3.496649	1.051942	2.444707	0.173035	1.949555	3.726075	182.248798
2021-04-06	405.7600	407.2400	405.4000	406.1200	62020953.0	4.043917	1.279368	2.764549	0.396500	2.153128	3.909756	163.169877
2021-04-07	405.9400	406.9600	405.4500	406.5900	55836280.0	4.464097	1.359638	3.104459	0.570354	2.365544	4.160734	151.778160
2021-04-08	407.9300	408.5800	406.9300	408.5200	57863114.0	4.896384	1.433541	3.462844	0.658913	2.580554	4.502194	148.932443
2021-04-09	408.3900	411.6700	408.2600	411.4900	61104559.0	5.416195	1.562681	3.853514	0.613266	2.791236	4.969206	156.057773

5394 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='Saturday April 10, 2021, NYSE: 5:39:01, Local: 9:39:01 PDT, Day 100/365 (27.0%)')

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
2021-04-05	403.46	406.94	403.38	406.36	91684764.0	388.3778	355.07135	382.834363	393.285	403.735637	5.314536	3.496649	1.051942	2.444707	67.670567	1.097807	1.078874	0	30	70
2021-04-06	405.76	407.24	405.40	406.12	62020953.0	388.8426	355.54305	384.042695	394.505	404.967305	5.304016	4.043917	1.279368	2.764549	67.264341	1.097216	1.084032	0	30	70
2021-04-07	405.94	406.96	405.45	406.59	55836280.0	389.2866	356.03280	384.334301	395.476	406.617699	5.634577	4.464097	1.359638	3.104459	67.673599	1.098373	1.089953	0	30	70
2021-04-08	407.93	408.58	406.93	408.52	57863114.0	389.7812	356.52230	384.272821	396.423	408.573179	6.129906	4.896384	1.433541	3.462844	69.367185	1.103108	1.096012	0	30	70
2021-04-09	408.39	411.67	408.26	411.49	61104559.0	390.5228	357.01950	383.604942	397.321	411.037058	6.904270	5.416195	1.562681	3.853514	71.814343	1.110352	1.101371	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='Saturday April 10, 2021, NYSE: 5:39:01, Local: 9:39:01 PDT, Day 100/365 (27.0%)')

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
2021-04-05	403.46	406.94	403.38	406.36	91684764.0	396.789148	1.302704	390.173232	407.350768	0.025876
2021-04-06	405.76	407.24	405.40	406.12	62020953.0	398.485666	1.529192	391.193677	410.466323	0.025790
2021-04-07	405.94	406.96	405.45	406.59	55836280.0	399.959181	1.561328	395.008877	411.395123	0.029603
2021-04-08	407.93	408.58	406.93	408.52	57863114.0	401.515694	1.590588	400.329889	410.950111	0.030294
2021-04-09	408.39	411.67	408.26	411.49	61104559.0	403.329204	1.695492	403.766969	411.865031	0.026796

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.

208 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

208 KiB

Raw Blame History