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 = 08/21/2020, 11:11:36
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 = 08/21/2020, 11:11:36
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='08/21/2020, 11:11:36', 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='08/21/2020, 11:11:36', 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='08/21/2020, 11:11:36', 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[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.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
 |      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] 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] 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   
 ...              ...       ...       ...       ...         ...      ...   
 2020-08-10  335.0600  335.7700  332.9550  335.5700  44282089.0  329.220   
 2020-08-11  336.8500  337.5400  332.0100  332.8000  69601087.0  330.383   
 2020-08-12  335.4400  338.2800  332.8377  337.4400  53826128.0  331.615   
 2020-08-13  336.6100  338.2514  335.8300  336.8300  41816146.0  332.902   
 2020-08-14  336.4100  337.4200  335.6200  336.8400  47260390.0  333.934   
 
               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  
 ...              ...       ...        ...          ...  
 2020-08-10  325.9365  317.1738  305.38150  60185410.65  
 2020-08-11  326.6305  317.7188  305.54365  59025873.05  
 2020-08-12  327.4100  318.3060  305.72285  57371102.75  
 2020-08-13  328.2120  318.7990  305.89050  56740239.35  
 2020-08-14  328.9680  319.3086  306.05865  55882168.75  
 
 [5231 rows x 10 columns],
 'IWM':               open      high     low   close      volume   SMA_10    SMA_20  \
 date                                                                          
 2000-05-26   91.06   91.4400   90.63   91.44     37400.0      NaN       NaN   
 2000-05-30   92.75   94.8100   92.75   94.81     28800.0      NaN       NaN   
 2000-05-31   95.13   96.3800   95.13   95.75     18000.0      NaN       NaN   
 2000-06-01   97.11   97.3100   97.11   97.31      3500.0      NaN       NaN   
 2000-06-02  101.70  102.4000  101.70  102.40     14700.0      NaN       NaN   
 ...            ...       ...     ...     ...         ...      ...       ...   
 2020-08-14  156.28  157.7585  155.87  157.09  13360915.0  155.071  151.3175   
 2020-08-17  157.50  158.0300  156.74  157.90   9651142.0  155.885  151.9145   
 2020-08-18  157.85  157.8500  155.71  156.39  14647527.0  156.445  152.3325   
 2020-08-19  156.93  158.0400  156.19  156.40  14337271.0  156.706  152.7470   
 2020-08-20  154.80  156.4850  154.54  155.76  15413013.0  156.909  153.1220   
 
               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  
 ...              ...        ...          ...  
 2020-08-14  146.1280  145.93365  20773889.15  
 2020-08-17  146.2824  145.94150  20277361.80  
 2020-08-18  146.3484  145.94620  19786384.90  
 2020-08-19  146.4712  145.93770  19282008.05  
 2020-08-20  146.6628  145.92175  18967414.25  
 
 [5091 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
...	...	...	...	...	...	...	...	...	...	...
2020-08-10	335.0600	335.7700	332.9550	335.5700	44282089.0	329.220	325.9365	317.1738	305.38150	60185410.65
2020-08-11	336.8500	337.5400	332.0100	332.8000	69601087.0	330.383	326.6305	317.7188	305.54365	59025873.05
2020-08-12	335.4400	338.2800	332.8377	337.4400	53826128.0	331.615	327.4100	318.3060	305.72285	57371102.75
2020-08-13	336.6100	338.2514	335.8300	336.8300	41816146.0	332.902	328.2120	318.7990	305.89050	56740239.35
2020-08-14	336.4100	337.4200	335.6200	336.8400	47260390.0	333.934	328.9680	319.3086	306.05865	55882168.75

5231 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='08/21/2020, 11:11:36', 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	91.4400	90.63	91.44	37400.0	NaN	NaN
2000-05-30	92.75	94.8100	92.75	94.81	28800.0	NaN	NaN
2000-05-31	95.13	96.3800	95.13	95.75	18000.0	NaN	NaN
2000-06-01	97.11	97.3100	97.11	97.31	3500.0	NaN	NaN
2000-06-02	101.70	102.4000	101.70	102.40	14700.0	NaN	NaN
...	...	...	...	...	...	...	...
2020-08-14	156.28	157.7585	155.87	157.09	13360915.0	146.1280	145.93365
2020-08-17	157.50	158.0300	156.74	157.90	9651142.0	146.2824	145.94150
2020-08-18	157.85	157.8500	155.71	156.39	14647527.0	146.3484	145.94620
2020-08-19	156.93	158.0400	156.19	156.40	14337271.0	146.4712	145.93770
2020-08-20	154.80	156.4850	154.54	155.76	15413013.0	146.6628	145.92175

5091 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='08/21/2020, 11:11:36', 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
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-08-10	335.0600	335.7700	332.9550	335.5700	44282089.0	331.385297	325.774293	0.906398	70.138852	324.130845	1	324.130845	NaN
2020-08-11	336.8500	337.5400	332.0100	332.8000	69601087.0	331.699676	326.412994	0.898109	64.125274	324.130845	1	324.130845	NaN
2020-08-12	335.4400	338.2800	332.8377	337.4400	53826128.0	332.975303	327.415449	0.911955	68.930669	324.130845	1	324.130845	NaN
2020-08-13	336.6100	338.2514	335.8300	336.8300	41816146.0	333.831903	328.271317	0.910146	67.647776	325.805414	1	325.805414	NaN
2020-08-14	336.4100	337.4200	335.6200	336.8400	47260390.0	334.500369	329.050288	0.910175	67.658402	326.118326	1	326.118326	NaN

5231 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='08/21/2020, 11:11:36', 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='08/21/2020, 11:11:36', 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
...	...	...	...	...	...	...	...	...	...
2020-08-10	335.0600	335.7700	332.9550	335.5700	44282089.0	5.288160e+07	60185410.65	333.190086	331.866824
2020-08-11	336.8500	337.5400	332.0100	332.8000	69601087.0	5.592150e+07	59025873.05	333.060057	332.787803
2020-08-12	335.4400	338.2800	332.8377	337.4400	53826128.0	5.554053e+07	57371102.75	334.520038	333.770083
2020-08-13	336.6100	338.2514	335.8300	336.8300	41816146.0	5.304518e+07	56740239.35	335.290026	334.616881
2020-08-14	336.4100	337.4200	335.6200	336.8400	47260390.0	5.199340e+07	55882168.75	335.806684	335.275420

5231 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='08/21/2020, 11:11:36', 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

	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
...	...	...	...	...	...	...	...	...	...	...	...
2020-08-10	335.0600	335.7700	332.9550	335.5700	44282089.0	5.380544	0.668073	4.712471	3.363096	4.366870	5.370643
2020-08-11	336.8500	337.5400	332.0100	332.8000	69601087.0	5.253724	0.433003	4.820721	3.482362	4.458185	5.434008
2020-08-12	335.4400	338.2800	332.8377	337.4400	53826128.0	5.464634	0.515130	4.949504	3.517851	4.541105	5.564359
2020-08-13	336.6100	338.2514	335.8300	336.8300	41816146.0	5.518942	0.455550	5.063392	3.543211	4.618297	5.693384
2020-08-14	336.4100	337.4200	335.6200	336.8400	47260390.0	5.499394	0.348802	5.150592	3.568073	4.686453	5.804834

5231 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='08/21/2020, 11:11:36', 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, 30, 70])
spy.head(20)

Out [28]:

[i] Loaded['D']: SPY_D.csv
[i] Runtime: 43.0055 ms (0.0430 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 [ ]:

93 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

93 KiB

Raw Blame History