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 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 = 09/27/2020, 09:24:11
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 = 09/27/2020, 09:24:11
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='09/27/2020, 09:24:11')

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='09/27/2020, 09:24:11')

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='09/27/2020, 09:24:11')

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"])

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

In [9]:

watch

Out [9]:

Watch(name='Watch: SPY, IWM', 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: 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.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: 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'], **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, timed=False)

[!] Loading All: SPY, IWM
[i] Loaded['D']: SPY_D.csv
[+] Strategy: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': False, 'append': True}
[i] Multiprocessing: 4 of 4 cores.
[i] Total indicators: 5
[i] Columns added: 5
[i] Loaded['D']: IWM_D.csv
[+] Strategy: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': False, 'append': True}
[i] Multiprocessing: 4 of 4 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   
 ...              ...       ...       ...       ...         ...      ...   
 2020-09-21  325.7000  327.1300  321.7300  326.9700  99450829.0  335.186   
 2020-09-22  328.5700  330.9000  325.8600  330.3000  63612107.0  334.895   
 2020-09-23  330.9000  331.2000  322.1000  322.6400  93112240.0  333.180   
 2020-09-24  321.2200  326.7970  319.8000  323.5000  76681332.0  332.141   
 2020-09-25  322.5800  329.5800  321.6400  328.7300  68610432.0  331.608   
 
               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  
 ...              ...       ...        ...          ...  
 2020-09-21  341.6475  334.0394  309.95180  80051929.15  
 2020-09-22  341.0165  334.3486  310.04320  80803101.40  
 2020-09-23  339.9425  334.4230  310.08205  83535544.35  
 2020-09-24  338.7390  334.4560  310.13015  84830099.10  
 2020-09-25  337.7590  334.6148  310.20615  85358913.60  
 
 [5260 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   
 ...            ...      ...     ...     ...         ...      ...       ...   
 2020-09-21  149.82  150.255  146.33  147.92  40652521.0  151.746  153.8055   
 2020-09-22  148.65  149.300  146.56  149.06  19385790.0  151.666  153.4470   
 2020-09-23  148.41  149.430  143.98  144.07  33403387.0  150.860  152.8305   
 2020-09-24  144.04  146.530  142.09  144.07  31723749.0  150.252  152.2630   
 2020-09-25  143.44  146.850  143.35  146.41  20689266.0  149.978  151.7935   
 
               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  
 ...              ...        ...          ...  
 2020-09-21  152.0930  145.33285  21738963.00  
 2020-09-22  152.2838  145.27345  22022895.70  
 2020-09-23  152.3286  145.17970  22879582.20  
 2020-09-24  152.2694  145.08750  23777133.35  
 2020-09-25  152.2744  145.00650  23936178.00  
 
 [5116 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
...	...	...	...	...	...	...	...	...	...	...
2020-09-21	325.7000	327.1300	321.7300	326.9700	99450829.0	335.186	341.6475	334.0394	309.95180	80051929.15
2020-09-22	328.5700	330.9000	325.8600	330.3000	63612107.0	334.895	341.0165	334.3486	310.04320	80803101.40
2020-09-23	330.9000	331.2000	322.1000	322.6400	93112240.0	333.180	339.9425	334.4230	310.08205	83535544.35
2020-09-24	321.2200	326.7970	319.8000	323.5000	76681332.0	332.141	338.7390	334.4560	310.13015	84830099.10
2020-09-25	322.5800	329.5800	321.6400	328.7300	68610432.0	331.608	337.7590	334.6148	310.20615	85358913.60

5260 rows × 10 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.debug = True
watch.strategy

Out [14]:

Strategy(name='A', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}], description=None, created='09/27/2020, 09:24:11')

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
...	...	...	...	...	...	...	...
2020-09-21	149.82	150.255	146.33	147.92	40652521.0	152.0930	145.33285
2020-09-22	148.65	149.300	146.56	149.06	19385790.0	152.2838	145.27345
2020-09-23	148.41	149.430	143.98	144.07	33403387.0	152.3286	145.17970
2020-09-24	144.04	146.530	142.09	144.07	31723749.0	152.2694	145.08750
2020-09-25	143.44	146.850	143.35	146.41	20689266.0	152.2744	145.00650

5116 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='09/27/2020, 09:24:11')

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	131.968750	1	131.96875	NaN
1999-11-03	136.0000	136.3750	135.1250	135.5000	7222300.0	NaN	NaN	-0.000461	50.185503	131.968750	1	131.96875	NaN
1999-11-04	136.7500	137.3593	135.7656	136.5312	7907500.0	NaN	NaN	0.007120	69.153995	131.968750	1	131.96875	NaN
1999-11-05	138.6250	139.1093	136.7812	137.8750	7431500.0	NaN	NaN	0.016915	79.896816	131.968750	1	131.96875	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-09-21	325.7000	327.1300	321.7300	326.9700	99450829.0	334.624165	337.581041	0.880436	38.586649	344.903390	-1	NaN	344.903390
2020-09-22	328.5700	330.9000	325.8600	330.3000	63612107.0	333.663239	336.919128	0.890569	42.849311	344.903390	-1	NaN	344.903390
2020-09-23	330.9000	331.2000	322.1000	322.6400	93112240.0	331.213630	335.621026	0.867104	36.562589	344.903390	-1	NaN	344.903390
2020-09-24	321.2200	326.7970	319.8000	323.5000	76681332.0	329.499490	334.519114	0.869766	37.668306	344.119874	-1	NaN	344.119874
2020-09-25	322.5800	329.5800	321.6400	328.7300	68610432.0	329.328493	333.992831	0.885804	44.054622	344.119874	-1	NaN	344.119874

5260 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='09/27/2020, 09:24:11')

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='09/27/2020, 09:24:11')

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
...	...	...	...	...	...	...	...	...	...
2020-09-21	325.7000	327.1300	321.7300	326.9700	99450829.0	8.868160e+07	80051929.15	332.563993	335.473066
2020-09-22	328.5700	330.9000	325.8600	330.3000	63612107.0	8.412351e+07	80803101.40	331.809328	333.974346
2020-09-23	330.9000	331.2000	322.1000	322.6400	93112240.0	8.575783e+07	83535544.35	328.752886	331.650953
2020-09-24	321.2200	326.7970	319.8000	323.5000	76681332.0	8.410756e+07	84830099.10	327.001924	329.309445
2020-09-25	322.5800	329.5800	321.6400	328.7300	68610432.0	8.128990e+07	85358913.60	327.577949	327.915225

5260 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='09/27/2020, 09:24:11')

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

	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
...	...	...	...	...	...	...	...	...	...	...	...
2020-09-21	325.7000	327.1300	321.7300	326.9700	99450829.0	-0.892645	-2.268501	1.375856	-1.306935	3.942561	9.192057
2020-09-22	328.5700	330.9000	325.8600	330.3000	63612107.0	-1.311585	-2.149953	0.838368	-2.090184	3.609898	9.309980
2020-09-23	330.9000	331.2000	322.1000	322.6400	93112240.0	-2.235922	-2.459431	0.223510	-2.965507	3.222451	9.410409
2020-09-24	321.2200	326.7970	319.8000	323.5000	76681332.0	-2.866032	-2.471634	-0.394399	-3.834206	2.786167	9.406539
2020-09-25	322.5800	329.5800	321.6400	328.7300	68610432.0	-2.909839	-2.012353	-0.897487	-4.554227	2.334421	9.223069

5260 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='09/27/2020, 09:24:11')

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, 30, 70])
spy.head()

Out [28]:

[i] Loaded['D']: SPY_D.csv
[i] Runtime: 1818.4281 ms (1.8184 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

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 [29]:

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 [29]:

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='09/27/2020, 09:24:11')

In [30]:

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

Out [30]:

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

In [31]:

spy = watch.load("SPY", timed=True)
spy.tail()

Out [31]:

[i] Loaded['D']: SPY_D.csv
[i] Runtime: 334.7583 ms (0.3348 s)

	open	high	low	close	volume	EMA_10	MACDh_9_19_10	LB	UB	LOGRET_5
date
2020-09-21	325.70	327.130	321.73	326.97	99450829.0	335.623943	-2.280086	323.348645	345.631355	-0.034537
2020-09-22	328.57	330.900	325.86	330.30	63612107.0	334.655954	-2.066826	323.032323	341.999677	-0.029444
2020-09-23	330.90	331.200	322.10	322.64	93112240.0	332.471235	-2.419469	319.513906	339.046094	-0.048932
2020-09-24	321.22	326.797	319.80	323.50	76681332.0	330.840101	-2.375870	319.375853	334.248147	-0.037436
2020-09-25	322.58	329.580	321.64	328.73	68610432.0	330.456446	-1.735596	319.831980	333.024020	-0.005824

In [ ]:

86 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

86 KiB

Raw Blame History