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
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 matplotlib.pyplot as plt
# import mplfinance as mpf

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

from watchlist import Watchlist
%pylab inline

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 = 07/25/2020, 11:24:19
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 = 07/25/2020, 11:24:19
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=None, created='07/25/2020, 11:24:19', last_run=None, run_time=None)

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=None, created='07/25/2020, 11:24:19', last_run=None, run_time=None)

Bad Strategy. (Misspelled Indicator)

In [6]:

# Misspelled indicator, will fail later when ran with Pandas
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=None, created='07/25/2020, 11:24:19', last_run=None, run_time=None)

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]:

watch = Watchlist(["SPY", "IWM"], ds=AV)

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

In [9]:

watch

Out [9]:

Watch(name='Watchlist: SPY, IWM', tickers[2]='SPY, IWM', tf='D', strategy[0]='All')

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: object = None, **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.AllStrategy.
 |  
 |  Requirements:
 |  - Pandas TA (pip install pandas_ta)
 |  - AlphaVantage (pip install alphaVantage-api) for the Default Data Source.
 |      To use another Data Source, update the load() method after AV.
 |  
 |  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: object = None, **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 = ['dividend', 'split_coefficient'], file_path: str = '.', **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
 |      Pandas TA Strategy Class. Default: pandas_ta.AllStrategy
 |  
 |  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 "All"

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, timed=False)

[!] Loading All: SPY, IWM

[+] Downloading['D']: SPY
[+] Strategy: All
[i] Indicators with the following arguments: {'append': True}
[i] Excluded[10]: above, above_value, below, below_value, cross, cross_value, long_run, short_run, trend_return, vp
[i] Total indicators: 101
[i] Columns added: 152

[+] Downloading['D']: IWM
[+] Strategy: All
[i] Indicators with the following arguments: {'append': True}
[i] Excluded[10]: above, above_value, below, below_value, cross, cross_value, long_run, short_run, trend_return, vp
[i] Total indicators: 101
[i] Columns added: 152

In [12]:

watch.data

Out [12]:

{'SPY':                 open      high       low     close      volume  ABER_ZG_5_15  \
 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    136.332267   
 ...              ...       ...       ...       ...         ...           ...   
 2020-07-20  321.4300  325.1300  320.6200  324.3200  56150230.0    320.673333   
 2020-07-21  326.4500  326.9300  323.9400  325.0100  57245315.0    322.353333   
 2020-07-22  324.6200  327.2000  324.5000  326.8600  57792915.0    323.313333   
 2020-07-23  326.4700  327.2300  321.4800  322.9600  75737989.0    324.014000   
 2020-07-24  320.9500  321.9900  319.2460  320.8800  73766597.0    323.886400   
 
             ABER_SG_5_15  ABER_XG_5_15  ABER_ATR_5_15    ACCBL_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  ...   
 ...                  ...           ...            ...         ...  ...   
 2020-07-20    326.296123    315.050544       5.622790  300.906776  ...   
 2020-07-21    327.800604    316.906063       5.447270  301.895656  ...   
 2020-07-22    328.577452    318.049214       5.264119  302.558926  ...   
 2020-07-23    329.310511    318.717489       5.296511  303.787477  ...   
 2020-07-24    329.077410    318.695390       5.191010  305.041909  ...   
 
                VAR_30   VTXP_14   VTXM_14        VWAP     VWMA_10         WCP  \
 date                                                                            
 1999-11-01        NaN       NaN       NaN  136.041667         NaN  135.921875   
 1999-11-02        NaN       NaN       NaN  135.693303         NaN  135.257775   
 1999-11-03        NaN       NaN       NaN  135.682462         NaN  135.625000   
 1999-11-04        NaN       NaN       NaN  135.950504         NaN  136.546825   
 1999-11-05        NaN       NaN       NaN  136.393305         NaN  137.910125   
 ...               ...       ...       ...         ...         ...         ...   
 2020-07-20  45.091757  1.223611  0.623952  153.261128  318.474591  323.597500   
 2020-07-21  46.469833  1.211493  0.661493  153.278065  319.600245  325.222500   
 2020-07-22  50.919143  1.153997  0.689436  153.295250  320.677611  326.355000   
 2020-07-23  52.999190  1.085706  0.763560  153.317466  321.522738  323.657500   
 2020-07-24  48.773943  1.027629  0.904621  153.338694  321.846761  320.749000   
 
              WILLR_14      WMA_10   ZL_EMA_10      Z_30  
 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  
 ...               ...         ...         ...       ...  
 2020-07-20  -3.801032  320.096545  323.215081  1.680556  
 2020-07-21 -10.750280  321.291636  324.115975  1.747819  
 2020-07-22  -2.058111  322.618909  325.718525  1.900614  
 2020-07-23 -25.800604  323.042909  325.442430  1.309102  
 2020-07-24 -38.368580  322.932727  323.987442  0.970050  
 
 [5216 rows x 157 columns],
 'IWM':               open     high     low   close      volume  ABER_ZG_5_15  \
 date                                                                    
 2000-05-26   91.06   91.440   90.63   91.44     37400.0           NaN   
 2000-05-30   92.75   94.810   92.75   94.81     28800.0           NaN   
 2000-05-31   95.13   96.380   95.13   95.75     18000.0           NaN   
 2000-06-01   97.11   97.310   97.11   97.31      3500.0           NaN   
 2000-06-02  101.70  102.400  101.70  102.40     14700.0     96.091333   
 ...            ...      ...     ...     ...         ...           ...   
 2020-07-20  146.12  146.850  145.15  145.96  19581689.0    145.160667   
 2020-07-21  147.47  149.160  147.20  148.03  24467065.0    146.623333   
 2020-07-22  147.09  148.670  147.03  148.11  24424808.0    146.904000   
 2020-07-23  147.98  150.200  146.70  148.26  21704889.0    147.400667   
 2020-07-24  147.29  147.665  145.56  146.08  20015547.0    147.375000   
 
             ABER_SG_5_15  ABER_XG_5_15  ABER_ATR_5_15    ACCBL_20  ...  \
 date                                                               ...   
 2000-05-26           NaN           NaN            NaN         NaN  ...   
 2000-05-30           NaN           NaN            NaN         NaN  ...   
 2000-05-31           NaN           NaN            NaN         NaN  ...   
 2000-06-01           NaN           NaN            NaN         NaN  ...   
 2000-06-02           NaN           NaN            NaN         NaN  ...   
 ...                  ...           ...            ...         ...  ...   
 2020-07-20    149.115307    141.206027       3.954640  133.720919  ...   
 2020-07-21    150.527664    142.719003       3.904331  134.312771  ...   
 2020-07-22    150.657375    143.150625       3.753375  134.568274  ...   
 2020-07-23    151.137150    143.664183       3.736483  135.260831  ...   
 2020-07-24    151.042385    143.707615       3.667385  135.937695  ...   
 
                VAR_30   VTXP_14   VTXM_14       VWAP     VWMA_10        WCP  \
 date                                                                          
 2000-05-26        NaN       NaN       NaN  91.170000         NaN   91.23750   
 2000-05-30        NaN       NaN       NaN  92.454834         NaN   94.29500   
 2000-05-31        NaN       NaN       NaN  93.159976         NaN   95.75250   
 2000-06-01        NaN       NaN       NaN  93.322938         NaN   97.26000   
 2000-06-02        NaN       NaN       NaN  94.592497         NaN  102.22500   
 ...               ...       ...       ...        ...         ...        ...   
 2020-07-20  14.347839  1.085970  0.934117  88.365517  143.089584  145.98000   
 2020-07-21  11.515402  1.030077  0.909147  88.373544  143.813948  148.10500   
 2020-07-22  10.497958  1.013525  0.935595  88.381529  144.438962  147.98000   
 2020-07-23  11.200568  0.993994  0.920849  88.388676  145.342850  148.35500   
 2020-07-24   9.687226  0.945133  0.982616  88.395051  145.807709  146.34625   
 
              WILLR_14      WMA_10   ZL_EMA_10      Z_30  
 date                                                     
 2000-05-26        NaN         NaN         NaN       NaN  
 2000-05-30        NaN         NaN         NaN       NaN  
 2000-05-31        NaN         NaN         NaN       NaN  
 2000-06-01        NaN         NaN         NaN       NaN  
 2000-06-02        NaN         NaN         NaN       NaN  
 ...               ...         ...         ...       ...  
 2020-07-20 -17.267552  144.240000  146.914980  0.906142  
 2020-07-21  -9.479866  145.149091  147.299529  1.671168  
 2020-07-22  -8.808725  145.942909  147.801433  1.797089  
 2020-07-23 -14.969136  146.651818  148.188445  1.763914  
 2020-07-24 -31.790123  146.797273  147.826909  1.077936  
 
 [5072 rows x 157 columns]}

In [13]:

watch.data['SPY']

Out [13]:

	open	high	low	close	volume	ABER_ZG_5_15	ABER_SG_5_15	ABER_XG_5_15	ABER_ATR_5_15	ACCBL_20	...	VAR_30	VTXP_14	VTXM_14	VWAP	VWMA_10	WCP	WILLR_14	WMA_10	ZL_EMA_10	Z_30
date
1999-11-01	136.5000	137.0000	135.5625	135.5625	4006500.0	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	136.041667	NaN	135.921875	NaN	NaN	NaN	NaN
1999-11-02	135.9687	137.2500	134.5937	134.5937	6516900.0	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	135.693303	NaN	135.257775	NaN	NaN	NaN	NaN
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	135.682462	NaN	135.625000	NaN	NaN	NaN	NaN
1999-11-04	136.7500	137.3593	135.7656	136.5312	7907500.0	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	135.950504	NaN	136.546825	NaN	NaN	NaN	NaN
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	136.332267	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	136.393305	NaN	137.910125	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-07-20	321.4300	325.1300	320.6200	324.3200	56150230.0	320.673333	326.296123	315.050544	5.622790	300.906776	...	45.091757	1.223611	0.623952	153.261128	318.474591	323.597500	-3.801032	320.096545	323.215081	1.680556
2020-07-21	326.4500	326.9300	323.9400	325.0100	57245315.0	322.353333	327.800604	316.906063	5.447270	301.895656	...	46.469833	1.211493	0.661493	153.278065	319.600245	325.222500	-10.750280	321.291636	324.115975	1.747819
2020-07-22	324.6200	327.2000	324.5000	326.8600	57792915.0	323.313333	328.577452	318.049214	5.264119	302.558926	...	50.919143	1.153997	0.689436	153.295250	320.677611	326.355000	-2.058111	322.618909	325.718525	1.900614
2020-07-23	326.4700	327.2300	321.4800	322.9600	75737989.0	324.014000	329.310511	318.717489	5.296511	303.787477	...	52.999190	1.085706	0.763560	153.317466	321.522738	323.657500	-25.800604	323.042909	325.442430	1.309102
2020-07-24	320.9500	321.9900	319.2460	320.8800	73766597.0	323.886400	329.077410	318.695390	5.191010	305.041909	...	48.773943	1.027629	0.904621	153.338694	321.846761	320.749000	-38.368580	322.932727	323.987442	0.970050

5216 rows × 157 columns

In [ ]:

Easy to swap Strategies and run them

Running Simple Strategy A

In [14]:

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

Out [14]:

Strategy(name='A', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}], description=None, created='07/25/2020, 11:24:19', last_run=None, run_time=None)

In [15]:

watch.load('IWM')

Out [15]:

[i] Loaded['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
...	...	...	...	...	...	...	...
2020-07-20	146.12	146.850	145.15	145.96	19581689.0	139.7062	145.93605
2020-07-21	147.47	149.160	147.20	148.03	24467065.0	140.0196	145.93750
2020-07-22	147.09	148.670	147.03	148.11	24424808.0	140.3478	145.93235
2020-07-23	147.98	150.200	146.70	148.26	21704889.0	140.7736	145.92925
2020-07-24	147.29	147.665	145.56	146.08	20015547.0	141.2408	145.92735

5072 rows × 7 columns

Running Simple Strategy B

In [16]:

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

Out [16]:

Strategy(name='B', ta=[{'kind': 'ema', 'length': 8}, {'kind': 'ema', 'length': 21}, {'kind': 'log_return', 'cumulative': True}, {'kind': 'rsi'}, {'kind': 'supertrend'}], description=None, created='07/25/2020, 11:24:19', last_run=None, run_time=None)

In [17]:

watch.load('SPY')

Out [17]:

[i] Loaded['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	0.000000	NaN	1	NaN	NaN
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	NaN	NaN	-0.000461	50.185503	NaN	1	NaN	NaN
1999-11-04	136.7500	137.3593	135.7656	136.5312	7907500.0	NaN	NaN	0.007120	69.153995	NaN	1	NaN	NaN
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	NaN	NaN	0.016915	79.896816	NaN	1	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-07-20	321.4300	325.1300	320.6200	324.3200	56150230.0	319.783320	315.076450	0.872298	63.569724	307.890439	1	307.890439	NaN
2020-07-21	326.4500	326.9300	323.9400	325.0100	57245315.0	320.944805	315.979500	0.874423	64.179426	311.309662	1	311.309662	NaN
2020-07-22	324.6200	327.2000	324.5000	326.8600	57792915.0	322.259293	316.968636	0.880099	65.830627	312.585425	1	312.585425	NaN
2020-07-23	326.4700	327.2300	321.4800	322.9600	75737989.0	322.415005	317.513306	0.868096	59.594031	312.585425	1	312.585425	NaN
2020-07-24	320.9500	321.9900	319.2460	320.8800	73766597.0	322.073893	317.819369	0.861634	56.518677	312.585425	1	312.585425	NaN

5216 rows × 13 columns

Running Bad Strategy. (Misspelled indicator)

In [18]:

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

Out [18]:

Strategy(name='Runtime Failure', ta=[{'kind': 'percet_return'}], description=None, created='07/25/2020, 11:24:19', last_run=None, run_time=None)

In [19]:

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

[i] Loaded['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 [20]:

# 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 [20]:

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=None, created='07/25/2020, 11:24:19', last_run=None, run_time=None)

In [21]:

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

Out [21]:

'Volume MAs and Price MA chain'

In [22]:

spy = watch.load('SPY')
spy

Out [22]:

[i] Loaded['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
...	...	...	...	...	...	...	...	...	...
2020-07-20	321.4300	325.1300	320.6200	324.3200	56150230.0	7.254859e+07	81019145.80	321.398416	320.058000
2020-07-21	326.4500	326.9300	323.9400	325.0100	57245315.0	6.976618e+07	80181050.95	322.602277	321.307000
2020-07-22	324.6200	327.2000	324.5000	326.8600	57792915.0	6.758922e+07	79667351.70	324.021518	322.687127
2020-07-23	326.4700	327.2300	321.4800	322.9600	75737989.0	6.907082e+07	76850881.55	323.667679	323.406458
2020-07-24	320.9500	321.9900	319.2460	320.8800	73766597.0	6.992459e+07	76090907.45	322.738452	323.487321

5216 rows × 9 columns

In [ ]:

MACD BBANDS

In [23]:

# 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 [23]:

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='07/25/2020, 11:24:19', last_run=None, run_time=None)

In [24]:

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

Out [24]:

'MACD BBands'

In [25]:

spy = watch.load('SPY')
spy

Out [25]:

[i] Loaded['D']: SPY_D.csv
[i] Set 'df.ta.mp = True' to enable multiprocessing. This computer has 4 cores. Default: False

	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
date
1999-11-01	136.5000	137.0000	135.5625	135.5625	4006500.0	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
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	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
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...
2020-07-20	321.4300	325.1300	320.6200	324.3200	56150230.0	4.422827	0.713695	3.709131	1.351168	3.153296	4.955423
2020-07-21	326.4500	326.9300	323.9400	325.0100	57245315.0	4.651974	0.754274	3.897700	1.341550	3.157320	4.973090
2020-07-22	324.6200	327.2000	324.5000	326.8600	57792915.0	4.926070	0.822696	4.103374	1.279092	3.183563	5.088035
2020-07-23	326.4700	327.2300	321.4800	322.9600	75737989.0	4.773569	0.536156	4.237413	1.215602	3.243110	5.270617
2020-07-24	320.9500	321.9900	319.2460	320.8800	73766597.0	4.433763	0.157080	4.276683	1.211360	3.306770	5.402179

5216 rows × 11 columns

In [ ]:

Comprehensive Strategy

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

In [26]:

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 [26]:

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='07/25/2020, 11:24:19', last_run=None, run_time=None)

In [27]:

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

Out [27]:

'Momo, Bands and SMAs and Cumulative Log Returns'

In [28]:

spy = watch.load('SPY', timed=True)
# Apply constants to the DataFrame for indicators
spy.ta.constants(True, 0, 0, 1) # 0
spy.ta.constants(True, 30, 30, 1) # 30
spy.ta.constants(True, 70, 70, 1) # 70
spy.head(20)

Out [28]:

[i] Loaded['D']: SPY_D.csv
[i] Runtime: 34.4336 ms (0.0344 s)

	open	high	low	close	volume	SMA_50	SMA_200	BBL_20_2.0	BBM_20_2.0	BBU_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
1999-11-01	136.5000	137.0000	135.5625	135.5625	4006500.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	30	70
1999-11-02	135.9687	137.2500	134.5937	134.5937	6516900.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000000	-0.007172	NaN	0	30	70
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	50.185503	-0.000461	NaN	0	30	70
1999-11-04	136.7500	137.3593	135.7656	136.5312	7907500.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	69.153995	0.007120	NaN	0	30	70
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	79.896816	0.016915	NaN	0	30	70
1999-11-08	137.0000	138.3750	136.7500	138.0000	4649200.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	80.574537	0.017821	0.006845	0	30	70
1999-11-09	138.5000	138.6875	136.2812	136.7031	4533700.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	58.528352	0.008379	0.009955	0	30	70
1999-11-10	136.2500	138.3906	136.0781	137.7187	6405600.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	66.303684	0.015780	0.013203	0	30	70
1999-11-11	138.1875	138.5000	137.4687	138.5000	4794100.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	70.833962	0.021438	0.016066	0	30	70
1999-11-12	139.2500	139.9843	137.1250	139.7500	11802900.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	76.319408	0.030422	0.018768	0	30	70
1999-11-15	139.8437	140.2500	139.4062	140.0781	2187500.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	77.514804	0.032767	0.021757	0	30	70
1999-11-16	140.5625	143.0000	140.0937	141.2500	7544800.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	81.170904	0.041099	0.028301	0	30	70
1999-11-17	142.2500	142.9375	141.3125	141.6250	9459000.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	82.169980	0.043750	0.033895	0	30	70
1999-11-18	142.4375	143.0000	141.6250	142.6250	4491000.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	84.527630	0.050786	0.039765	0	30	70
1999-11-19	142.4062	142.9687	142.0000	142.5000	4832100.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	83.049344	0.049909	0.043662	0	30	70
1999-11-22	142.4375	143.0000	141.5000	142.4687	4155400.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	82.659517	0.049690	0.047047	0	30	70
1999-11-23	142.8437	142.8437	140.3750	141.2187	5918000.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	68.775385	0.040877	0.047002	0	30	70
1999-11-24	140.7500	142.4375	140.0000	141.9687	4459700.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	71.832489	0.046174	0.047487	0	30	70
1999-11-26	142.4687	142.8750	141.2500	141.4375	1693900.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	66.840920	0.042425	0.045815	0	30	70
1999-11-29	140.8750	141.9218	140.4375	140.9375	7348600.0	NaN	NaN	134.086598	139.34217	144.597742	NaN	NaN	0.0	62.442534	0.038884	0.043610	0	30	70

In [ ]:

106 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 "All"

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

106 KiB

Raw Blame History