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.
%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 = 01/23/2021, 12:36:24
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 = 01/23/2021, 12:36:24
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='01/23/2021, 12:36:24')

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='01/23/2021, 12:36:24')

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='01/23/2021, 12:36:24')

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"], timed=False)

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 = [], 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
[i] Loaded['D']: SPY_D.csv
[+] 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
[i] Loaded['D']: IWM_D.csv
[+] 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   
 ...              ...       ...       ...       ...          ...      ...   
 2020-09-28  333.2200  334.9600  332.1500  334.1900   64584614.0  331.181   
 2020-09-29  333.9700  334.7700  331.6209  332.3700   51531594.0  330.401   
 2020-09-30  333.0900  338.2900  332.8800  334.8900  104081136.0  330.008   
 2020-10-01  337.6900  338.7400  335.0100  337.0400   88698745.0  330.128   
 2020-10-02  331.7000  337.0126  331.1900  333.8400   89431112.0  330.447   
 
               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  
 ...              ...       ...        ...          ...  
 2020-09-28  336.9395  334.8642  310.30500  86281647.00  
 2020-09-29  336.0925  335.0252  310.38120  85553267.55  
 2020-09-30  335.2070  335.2228  310.46905  88007358.10  
 2020-10-01  334.1740  335.4264  310.55675  88965293.60  
 2020-10-02  333.5965  335.6440  310.62810  86036292.75  
 
 [5265 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   
 ...            ...     ...      ...     ...         ...      ...       ...   
 2020-09-28  148.37  150.44  146.404  150.02  17600904.0  149.672  151.4385   
 2020-09-29  149.85  150.28  148.000  149.34  18703037.0  149.269  151.1340   
 2020-09-30  149.90  151.97  148.490  149.79  29073746.0  148.766  150.7630   
 2020-10-01  150.81  152.20  149.490  152.18  25871962.0  148.615  150.4490   
 2020-10-02  149.41  153.58  148.990  152.85  29451992.0  148.571  150.4025   
 
               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  
 ...              ...        ...          ...  
 2020-09-28  152.3430  144.94380  24197653.10  
 2020-09-29  152.4106  144.87070  24280229.40  
 2020-09-30  152.4458  144.80300  24951209.50  
 2020-10-01  152.5272  144.74450  25406635.15  
 2020-10-02  152.6190  144.68525  25273355.50  
 
 [5121 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
...	...	...	...	...	...	...	...	...	...	...
2020-09-28	333.2200	334.9600	332.1500	334.1900	64584614.0	331.181	336.9395	334.8642	310.30500	86281647.00
2020-09-29	333.9700	334.7700	331.6209	332.3700	51531594.0	330.401	336.0925	335.0252	310.38120	85553267.55
2020-09-30	333.0900	338.2900	332.8800	334.8900	104081136.0	330.008	335.2070	335.2228	310.46905	88007358.10
2020-10-01	337.6900	338.7400	335.0100	337.0400	88698745.0	330.128	334.1740	335.4264	310.55675	88965293.60
2020-10-02	331.7000	337.0126	331.1900	333.8400	89431112.0	330.447	333.5965	335.6440	310.62810	86036292.75

5265 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='TA Description', created='01/23/2021, 12:36:24')

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	91.44	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
...	...	...	...	...	...	...	...
2020-09-28	148.37	150.44	146.404	150.02	17600904.0	152.3430	144.94380
2020-09-29	149.85	150.28	148.000	149.34	18703037.0	152.4106	144.87070
2020-09-30	149.90	151.97	148.490	149.79	29073746.0	152.4458	144.80300
2020-10-01	150.81	152.20	149.490	152.18	25871962.0	152.5272	144.74450
2020-10-02	149.41	153.58	148.990	152.85	29451992.0	152.6190	144.68525

5121 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='TA Description', created='01/23/2021, 12:36:24')

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	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
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-09-28	333.2200	334.9600	332.1500	334.1900	64584614.0	330.408828	334.010756	0.902277	49.833349	344.119874	-1	NaN	344.119874
2020-09-29	333.9700	334.7700	331.6209	332.3700	51531594.0	330.844644	333.861596	0.896816	48.051629	344.119874	-1	NaN	344.119874
2020-09-30	333.0900	338.2900	332.8800	334.8900	104081136.0	331.743612	333.955087	0.904369	50.680975	344.119874	-1	NaN	344.119874
2020-10-01	337.6900	338.7400	335.0100	337.0400	88698745.0	332.920587	334.235534	0.910769	52.872627	344.119874	-1	NaN	344.119874
2020-10-02	331.7000	337.0126	331.1900	333.8400	89431112.0	333.124901	334.199576	0.901229	49.357005	344.119874	-1	NaN	344.119874

5265 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='TA Description', created='01/23/2021, 12:36:24')

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='TA Description', created='01/23/2021, 12:36:24')

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
...	...	...	...	...	...	...	...	...	...
2020-09-28	333.2200	334.9600	332.1500	334.1900	64584614.0	7.861837e+07	86281647.00	329.781966	327.921896
2020-09-29	333.9700	334.7700	331.6209	332.3700	51531594.0	7.369350e+07	85553267.55	330.644644	328.610232
2020-09-30	333.0900	338.2900	332.8800	334.8900	104081136.0	7.921853e+07	88007358.10	332.059763	329.854718
2020-10-01	337.6900	338.7400	335.0100	337.0400	88698745.0	8.094220e+07	88965293.60	333.719842	331.449495
2020-10-02	331.7000	337.0126	331.1900	333.8400	89431112.0	8.248564e+07	86036292.75	333.759895	332.881012

5265 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='01/23/2021, 12:36:24')

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	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
...	...	...	...	...	...	...	...	...	...	...	...	...
2020-09-28	333.2200	334.9600	332.1500	334.1900	64584614.0	-2.475445	-1.262367	-1.213078	-5.034888	1.888513	8.811913	733.211957
2020-09-29	333.9700	334.7700	331.6209	332.3700	51531594.0	-2.252083	-0.831203	-1.420879	-5.348731	1.450066	8.248862	937.722469
2020-09-30	333.0900	338.2900	332.8800	334.8900	104081136.0	-1.850393	-0.343611	-1.506782	-5.445367	1.019390	7.484147	1268.357614
2020-10-01	337.6900	338.7400	335.0100	337.0400	88698745.0	-1.343082	0.130960	-1.474042	-5.235913	0.587945	6.411803	1981.090096
2020-10-02	331.7000	337.0126	331.1900	333.8400	89431112.0	-1.185581	0.230769	-1.416350	-4.926333	0.197149	5.320631	5197.569576

5265 rows × 12 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='01/23/2021, 12:36:24')

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")
# Apply constants to the DataFrame for indicators
spy.ta.constants(True, [0, 30, 70])
spy.head()

Out [28]:

[i] Loaded['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
1999-11-01	136.5000	137.0000	135.5625	135.5625	4006500.0	NaN	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	NaN	NaN	-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	NaN	NaN	-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	NaN	NaN	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	NaN	NaN	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='01/23/2021, 12:36:24')

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")
spy.tail()

Out [31]:

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

	open	high	low	close	volume	EMA_10	MACDh_9_19_10	LB	UB	LOGRET_5
date
2020-09-28	333.22	334.9600	332.1500	334.19	64584614.0	331.135274	-0.761769	318.226448	337.517552	0.021841
2020-09-29	333.97	334.7700	331.6209	332.37	51531594.0	331.359770	-0.249828	317.965316	338.606684	0.006247
2020-09-30	333.09	338.2900	332.8800	334.89	104081136.0	332.001630	0.311580	321.342411	340.129589	0.037265
2020-10-01	337.69	338.7400	335.0100	337.04	88698745.0	332.917697	0.832955	327.202692	339.685308	0.041003
2020-10-02	331.70	337.0126	331.1900	333.84	89431112.0	333.085389	0.854917	331.050371	337.881629	0.015425

87 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

87 KiB

Raw Blame History