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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

from tqdm import tqdm

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

from watchlist import Watchlist # Is this failing? If so, copy it locally. See above.

print(f"\nPandas TA v{ta.version}\nTo install the Latest Version:\n$ pip install -U git+https://github.com/twopirllc/pandas-ta\n")
%pylab inline
Pandas TA v0.2.74b0
To install the Latest Version:
$ pip install -U git+https://github.com/twopirllc/pandas-ta

Populating the interactive namespace from numpy and matplotlib

What is a Pandas TA Strategy?

A Strategy is a simple way to name and group your favorite TA indicators. Technically, a Strategy is a simple Data Class to contain list of indicators and their parameters. Note: Strategy is experimental and subject to change. Pandas TA comes with two basic Strategies: AllStrategy and CommonStrategy.

Strategy Requirements:

  • name: Some short memorable string. Note: Case-insensitive "All" is reserved.
  • ta: A list of dicts containing keyword arguments to identify the indicator and the indicator's arguments

Optional Requirements:

  • description: A more detailed description of what the Strategy tries to capture. Default: None
  • created: At datetime string of when it was created. Default: Automatically generated.

Things to note:

  • A Strategy will fail when consumed by Pandas TA if there is no {"kind": "indicator name"} attribute.

Builtin Examples

All

Default Values

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 = Monday May 3, 2021, NYSE: 12:00:44, Local: 16:00:44 PDT, Day 123/365 (34.00%)
ta = None

Common

Default Values

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 = Monday May 3, 2021, NYSE: 12:00:44, Local: 16:00:44 PDT, Day 123/365 (34.00%)
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

Strategies require a name and an array of dicts containing the "kind" of indicator ("sma") and other potential parameters for ta.

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='Monday May 3, 2021, NYSE: 12:00:44, Local: 16:00:44 PDT, Day 123/365 (34.00%)')

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='Monday May 3, 2021, NYSE: 12:00:44, Local: 16:00:44 PDT, Day 123/365 (34.00%)')

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='Monday May 3, 2021, NYSE: 12:00:44, Local: 16:00:44 PDT, Day 123/365 (34.00%)')
In [ ]:

Strategy Management and Execution with Watchlist

Initialize AlphaVantage Data Source

In [7]:
AV = AlphaVantage(
    api_key="YOUR API KEY", premium=False,
    output_size='full', clean=True,
    export_path=".", export=True
)
AV
Out [7]:
AlphaVantage(
  end_point:str = https://www.alphavantage.co/query,
  api_key:str = YOUR API KEY,
  export:bool = True,
  export_path:str = .,
  output_size:str = full,
  output:str = csv,
  datatype:str = json,
  clean:bool = True,
  proxy:dict = {}
)

Create Watchlist and set it's 'ds' to AlphaVantage

In [8]:
data_source = "av" # Default
# data_source = "yahoo"
watch = Watchlist(["SPY", "IWM"], ds_name=data_source, timed=False)

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

In [9]:
watch
Out [9]:
Watch(name='Watch: SPY, IWM', ds_name='av', tickers[2]='SPY, IWM', tf='D', strategy[5]='Common Price and Volume SMAs')

Help about Watchlist

In [10]:
help(Watchlist)
Help on class Watchlist in module watchlist:

class Watchlist(builtins.object)
 |  Watchlist(tickers: list, tf: str = None, name: str = None, strategy: pandas_ta.core.Strategy = None, ds_name: str = 'av', **kwargs)
 |  
 |  # Watchlist Class (** This is subject to change! **)
 |  A simple Class to load/download financial market data and automatically
 |  apply Technical Analysis indicators with a Pandas TA Strategy.
 |  
 |  Default Strategy: pandas_ta.CommonStrategy
 |  
 |  ## Package Support:
 |  ### Data Source (Default: AlphaVantage)
 |  - AlphaVantage (pip install alphaVantage-api).
 |  - Python Binance (pip install python-binance). # Future Support
 |  - Yahoo Finance (pip install yfinance). # Almost Supported
 |  
 |  # Technical Analysis:
 |  - Pandas TA (pip install pandas_ta)
 |  
 |  ## Required Arguments:
 |  - tickers: A list of strings containing tickers. Example: ["SPY", "AAPL"]
 |  
 |  Methods defined here:
 |  
 |  __init__(self, tickers: list, tf: str = None, name: str = None, strategy: pandas_ta.core.Strategy = None, ds_name: str = 'av', **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |  
 |  __repr__(self) -> str
 |      Return repr(self).
 |  
 |  indicators(self, *args, **kwargs) -> <built-in function any>
 |      Returns the list of indicators that are available with Pandas Ta.
 |  
 |  load(self, ticker: str = None, tf: str = None, index: str = 'date', drop: list = [], plot: bool = False, **kwargs) -> pandas.core.frame.DataFrame
 |      Loads or Downloads (if a local csv does not exist) the data from the
 |      Data Source. When successful, it returns a Data Frame for the requested
 |      ticker. If no tickers are given, it loads all the tickers.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  data
 |      When not None, it contains a dictionary of DataFrames keyed by ticker. data = {"SPY": pd.DataFrame, ...}
 |  
 |  name
 |      The name of the Watchlist. Default: "Watchlist: {Watchlist.tickers}".
 |  
 |  strategy
 |      Sets a valid Strategy. Default: pandas_ta.CommonStrategy
 |  
 |  tf
 |      Alias for timeframe. Default: 'D'
 |  
 |  tickers
 |      tickers
 |      
 |      If a string, it it converted to a list. Example: "AAPL" -> ["AAPL"]
 |          * Does not accept, comma seperated strings.
 |      If a list, checks if it is a list of strings.
 |  
 |  verbose
 |      Toggle the verbose property. Default: False

Default Strategy is "Common"

In [11]:
# No arguments loads all the tickers and applies the Strategy to each ticker.
# The result can be accessed with Watchlist's 'data' property which returns a 
# dictionary keyed by ticker and DataFrames as values 
watch.load(verbose=True)
[!] Loading All: SPY, IWM
[+] Downloading[av]: SPY[D]
[+] Strategy: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': False, 'append': True}
[i] Multiprocessing 5 indicators with 7 chunks and 8/8 cpus.
[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Monday May 3, 2021, NYSE: 12:00:47, Local: 16:00:47 PDT, Day 123/365 (34.00%)
[+] Downloading[av]: IWM[D]
[+] Strategy: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': False, 'append': True}
[i] Multiprocessing 5 indicators with 7 chunks and 8/8 cpus.
[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Monday May 3, 2021, NYSE: 12:01:05, Local: 16:01:05 PDT, Day 123/365 (34.00%)
In [12]:
", ".join([f"{t}: {d.shape}" for t,d in watch.data.items()])
Out [12]:
'SPY: (5410, 10), IWM: (5266, 10)'
In [13]:
watch.data["SPY"]
Out [13]:
open high low close volume SMA_10 SMA_20 SMA_50 SMA_200 VOL_SMA_20
date
1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN NaN NaN NaN NaN
1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN NaN NaN NaN NaN
1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN NaN NaN NaN NaN
1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN NaN NaN NaN NaN
1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ...
2021-04-27 417.9300 418.1400 416.3000 417.5200 51303056.0 415.217 410.3710 397.7478 363.27265 71792983.05
2021-04-28 417.8100 419.0100 416.9000 417.4000 51238854.0 415.812 411.5045 398.2498 363.78545 70541813.30
2021-04-29 420.3200 420.7200 416.4400 420.0600 78544329.0 416.231 412.6910 398.8032 364.29115 68832319.05
2021-04-30 417.6300 418.5400 416.3400 417.3000 85527030.0 416.235 413.5255 399.3348 364.76840 68124526.75
2021-05-03 419.4300 419.8400 417.6650 418.2000 67702070.0 416.534 414.1175 399.8982 365.25545 66925392.05

5410 rows × 10 columns

In [ ]:
In [14]:
watch.load("SPY", plot=True, mas=True)
Out [14]:
[i] Loaded SPY[D]: SPY_D.csv
open high low close volume SMA_10 SMA_20 SMA_50 SMA_200 VOL_SMA_20
date
1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN NaN NaN NaN NaN
1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN NaN NaN NaN NaN
1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN NaN NaN NaN NaN
1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN NaN NaN NaN NaN
1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ...
2021-04-27 417.9300 418.1400 416.3000 417.5200 51303056.0 415.217 410.3710 397.7478 363.27265 71792983.05
2021-04-28 417.8100 419.0100 416.9000 417.4000 51238854.0 415.812 411.5045 398.2498 363.78545 70541813.30
2021-04-29 420.3200 420.7200 416.4400 420.0600 78544329.0 416.231 412.6910 398.8032 364.29115 68832319.05
2021-04-30 417.6300 418.5400 416.3400 417.3000 85527030.0 416.235 413.5255 399.3348 364.76840 68124526.75
2021-05-03 419.4300 419.8400 417.6650 418.2000 67702070.0 416.534 414.1175 399.8982 365.25545 66925392.05

5410 rows × 10 columns

In [ ]:

Easy to swap Strategies and run them

Running Simple Strategy A

In [15]:
# Load custom_a into Watchlist and verify
watch.strategy = custom_a
# watch.debug = True
watch.strategy
Out [15]:
Strategy(name='A', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}], description='TA Description', created='Monday May 3, 2021, NYSE: 12:00:44, Local: 16:00:44 PDT, Day 123/365 (34.00%)')
In [16]:
watch.load("IWM")
Out [16]:
[i] Loaded IWM[D]: IWM_D.csv
open high low close volume SMA_50 SMA_200
date
2000-05-26 91.06 91.440 90.63 91.44 37400.0 NaN NaN
2000-05-30 92.75 94.810 92.75 94.81 28800.0 NaN NaN
2000-05-31 95.13 96.380 95.13 95.75 18000.0 NaN NaN
2000-06-01 97.11 97.310 97.11 97.31 3500.0 NaN NaN
2000-06-02 101.70 102.400 101.70 102.40 14700.0 NaN NaN
... ... ... ... ... ... ... ...
2021-04-27 228.71 229.390 227.51 228.45 19648108.0 223.0372 185.87295
2021-04-28 228.08 229.490 227.10 228.84 16222739.0 223.0974 186.31955
2021-04-29 230.85 230.950 225.79 227.99 25500342.0 223.1760 186.75035
2021-04-30 225.72 227.800 224.14 224.89 27005991.0 223.2620 187.13965
2021-05-03 227.21 227.407 224.92 225.99 18549520.0 223.2780 187.53880

5266 rows × 7 columns

Running Simple Strategy B

In [17]:
# Load custom_b into Watchlist and verify
watch.strategy = custom_b
watch.strategy
Out [17]:
Strategy(name='B', ta=[{'kind': 'ema', 'length': 8}, {'kind': 'ema', 'length': 21}, {'kind': 'log_return', 'cumulative': True}, {'kind': 'rsi'}, {'kind': 'supertrend'}], description='TA Description', created='Monday May 3, 2021, NYSE: 12:00:44, Local: 16:00:44 PDT, Day 123/365 (34.00%)')
In [18]:
watch.load("SPY")
Out [18]:
[i] Loaded SPY[D]: SPY_D.csv
open high low close volume EMA_8 EMA_21 CUMLOGRET_1 RSI_14 SUPERT_7_3.0 SUPERTd_7_3.0 SUPERTl_7_3.0 SUPERTs_7_3.0
date
1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN NaN 0.000000 NaN 0.000000 1 NaN NaN
1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN NaN -0.007172 NaN NaN 1 NaN NaN
1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN NaN -0.000461 NaN NaN 1 NaN NaN
1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN NaN 0.007120 NaN NaN 1 NaN NaN
1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN NaN 0.016915 NaN NaN 1 NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2021-04-27 417.9300 418.1400 416.3000 417.5200 51303056.0 415.319173 409.798636 1.124900 65.982183 405.421805 1 405.421805 NaN
2021-04-28 417.8100 419.0100 416.9000 417.4000 51238854.0 415.781579 410.489669 1.124612 65.732538 406.547390 1 406.547390 NaN
2021-04-29 420.3200 420.7200 416.4400 420.0600 78544329.0 416.732339 411.359699 1.130965 68.571173 407.070077 1 407.070077 NaN
2021-04-30 417.6300 418.5400 416.3400 417.3000 85527030.0 416.858486 411.899727 1.124373 62.761728 407.070077 1 407.070077 NaN
2021-05-03 419.4300 419.8400 417.6650 418.2000 67702070.0 417.156600 412.472479 1.126527 63.837618 407.543637 1 407.543637 NaN

5410 rows × 13 columns

Running Bad Strategy. (Misspelled indicator)

In [19]:
# Load custom_run_failure into Watchlist and verify
watch.strategy = custom_run_failure
watch.strategy
Out [19]:
Strategy(name='Runtime Failure', ta=[{'kind': 'percet_return'}], description='TA Description', created='Monday May 3, 2021, NYSE: 12:00:44, Local: 16:00:44 PDT, Day 123/365 (34.00%)')
In [20]:
try:
    iwm = watch.load("IWM")
except AttributeError as error:
    print(f"[X] Oops! {error}")
[i] Loaded IWM[D]: IWM_D.csv
[X] Oops! 'AnalysisIndicators' object has no attribute 'percet_return'
In [ ]:

Indicator Composition/Chaining

  • When you need an indicator to depend on the value of a prior indicator
  • Utilitze prefix or suffix to help identify unique columns or avoid column name clashes.

Volume MAs and MA chains

In [21]:
# Set EMA's and SMA's 'close' to 'volume' to create Volume MAs, prefix 'volume' MAs with 'VOLUME' so easy to identify the column
# Take a price EMA and apply LINREG from EMA's output
volmas_price_ma_chain = [
    {"kind":"ema", "close": "volume", "length": 10, "prefix": "VOLUME"},
    {"kind":"sma", "close": "volume", "length": 20, "prefix": "VOLUME"},
    {"kind":"ema", "length": 5},
    {"kind":"linreg", "close": "EMA_5", "length": 8, "prefix": "EMA_5"},
]
vp_ma_chain_ta = ta.Strategy("Volume MAs and Price MA chain", volmas_price_ma_chain)
vp_ma_chain_ta
Out [21]:
Strategy(name='Volume MAs and Price MA chain', ta=[{'kind': 'ema', 'close': 'volume', 'length': 10, 'prefix': 'VOLUME'}, {'kind': 'sma', 'close': 'volume', 'length': 20, 'prefix': 'VOLUME'}, {'kind': 'ema', 'length': 5}, {'kind': 'linreg', 'close': 'EMA_5', 'length': 8, 'prefix': 'EMA_5'}], description='TA Description', created='Monday May 3, 2021, NYSE: 12:00:44, Local: 16:00:44 PDT, Day 123/365 (34.00%)')
In [22]:
# Update the Watchlist
watch.strategy = vp_ma_chain_ta
watch.strategy.name
Out [22]:
'Volume MAs and Price MA chain'
In [23]:
spy = watch.load("SPY")
spy
Out [23]:
[i] Loaded SPY[D]: SPY_D.csv
open high low close volume VOLUME_EMA_10 VOLUME_SMA_20 EMA_5 EMA_5_LR_8
date
1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN NaN NaN NaN
1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN NaN NaN NaN
1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN NaN NaN NaN
1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN NaN NaN NaN
1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN NaN 136.012480 NaN
... ... ... ... ... ... ... ... ... ...
2021-04-27 417.9300 418.1400 416.3000 417.5200 51303056.0 6.806114e+07 71792983.05 416.300295 415.367660
2021-04-28 417.8100 419.0100 416.9000 417.4000 51238854.0 6.500254e+07 70541813.30 416.666863 415.960662
2021-04-29 420.3200 420.7200 416.4400 420.0600 78544329.0 6.746469e+07 68832319.05 417.797909 416.829211
2021-04-30 417.6300 418.5400 416.3400 417.3000 85527030.0 7.074875e+07 68124526.75 417.631939 417.333800
2021-05-03 419.4300 419.8400 417.6650 418.2000 67702070.0 7.019481e+07 66925392.05 417.821293 417.779676

5410 rows × 9 columns

In [ ]:

MACD BBANDS

In [24]:
# MACD is the initial indicator that BBANDS depends on.
# Set BBANDS's 'close' to MACD's main signal, in this case 'MACD_12_26_9' and add a prefix (or suffix) so it's easier to identify
macd_bands_ta = [
    {"kind":"macd"},
    {"kind":"bbands", "close": "MACD_12_26_9", "length": 20, "ddof": 0, "prefix": "MACD"}
]
macd_bands_ta = ta.Strategy("MACD BBands", macd_bands_ta, f"BBANDS_{macd_bands_ta[1]['length']} applied to MACD")
macd_bands_ta
Out [24]:
Strategy(name='MACD BBands', ta=[{'kind': 'macd'}, {'kind': 'bbands', 'close': 'MACD_12_26_9', 'length': 20, 'ddof': 0, 'prefix': 'MACD'}], description='BBANDS_20 applied to MACD', created='Monday May 3, 2021, NYSE: 12:00:44, Local: 16:00:44 PDT, Day 123/365 (34.00%)')
In [25]:
# Update the Watchlist
watch.strategy = macd_bands_ta
watch.strategy.name
Out [25]:
'MACD BBands'
In [26]:
spy = watch.load("SPY")
spy
Out [26]:
[i] Loaded SPY[D]: SPY_D.csv
open high low close volume MACD_12_26_9 MACDh_12_26_9 MACDs_12_26_9 MACD_BBL_20_2.0 MACD_BBM_20_2.0 MACD_BBU_20_2.0 MACD_BBB_20_2.0
date
1999-11-01 136.5000 137.0000 135.5625 135.5625 4006500.0 NaN NaN NaN NaN NaN NaN NaN
1999-11-02 135.9687 137.2500 134.5937 134.5937 6516900.0 NaN NaN NaN NaN NaN NaN NaN
1999-11-03 136.0000 136.3750 135.1250 135.5000 7222300.0 NaN NaN NaN NaN NaN NaN NaN
1999-11-04 136.7500 137.3593 135.7656 136.5312 7907500.0 NaN NaN NaN NaN NaN NaN NaN
1999-11-05 138.6250 139.1093 136.7812 137.8750 7431500.0 NaN NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ...
2021-04-27 417.9300 418.1400 416.3000 417.5200 51303056.0 5.915955 -0.079691 5.995646 8.154250 5.215001 2.275752 -112.722858
2021-04-28 417.8100 419.0100 416.9000 417.4000 51238854.0 5.771638 -0.179207 5.950845 7.976673 5.397597 2.818521 -95.563847
2021-04-29 420.3200 420.7200 416.4400 420.0600 78544329.0 5.804989 -0.116685 5.921674 7.722758 5.573795 3.424833 -77.109493
2021-04-30 417.6300 418.5400 416.3400 417.3000 85527030.0 5.544794 -0.301504 5.846298 7.421640 5.714893 4.008146 -59.729793
2021-05-03 419.4300 419.8400 417.6650 418.2000 67702070.0 5.349544 -0.397403 5.746947 7.193621 5.807538 4.421454 -47.733950

5410 rows × 12 columns

In [ ]:

Comprehensive Strategy

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

In [27]:
momo_bands_sma_ta = [
    {"kind":"sma", "length": 50},
    {"kind":"sma", "length": 200},
    {"kind":"bbands", "length": 20, "ddof": 0},
    {"kind":"macd"},
    {"kind":"rsi"},
    {"kind":"log_return", "cumulative": True},
    {"kind":"sma", "close": "CUMLOGRET_1", "length": 5, "suffix": "CUMLOGRET"},
]
momo_bands_sma_strategy = ta.Strategy(
    "Momo, Bands and SMAs and Cumulative Log Returns", # name
    momo_bands_sma_ta, # ta
    "MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns" # description
)
momo_bands_sma_strategy
Out [27]:
Strategy(name='Momo, Bands and SMAs and Cumulative Log Returns', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}, {'kind': 'bbands', 'length': 20, 'ddof': 0}, {'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='Monday May 3, 2021, NYSE: 12:00:44, Local: 16:00:44 PDT, Day 123/365 (34.00%)')
In [28]:
# Update the Watchlist
watch.strategy = momo_bands_sma_strategy
watch.strategy.name
Out [28]:
'Momo, Bands and SMAs and Cumulative Log Returns'
In [29]:
spy = watch.load("SPY")
# Apply constants to the DataFrame for indicators
spy.ta.constants(True, [0, 30, 70])
spy.tail()
Out [29]:
[i] Loaded SPY[D]: SPY_D.csv
open high low close volume SMA_50 SMA_200 BBL_20_2.0 BBM_20_2.0 BBU_20_2.0 BBB_20_2.0 MACD_12_26_9 MACDh_12_26_9 MACDs_12_26_9 RSI_14 CUMLOGRET_1 SMA_5_CUMLOGRET 0 30 70
date
2021-04-27 417.93 418.14 416.300 417.52 51303056.0 397.7478 363.27265 423.665215 410.3710 397.076785 -6.479120 5.915955 -0.079691 5.995646 65.982183 1.124900 1.121342 0 30 70
2021-04-28 417.81 419.01 416.900 417.40 51238854.0 398.2498 363.78545 423.017539 411.5045 399.991461 -5.595583 5.771638 -0.179207 5.950845 65.732538 1.124612 1.121980 0 30 70
2021-04-29 420.32 420.72 416.440 420.06 78544329.0 398.8032 364.29115 422.463674 412.6910 402.918326 -4.736073 5.804989 -0.116685 5.921674 68.571173 1.130965 1.125724 0 30 70
2021-04-30 417.63 418.54 416.340 417.30 85527030.0 399.3348 364.76840 421.758243 413.5255 405.292757 -3.981734 5.544794 -0.301504 5.846298 62.761728 1.124373 1.125993 0 30 70
2021-05-03 419.43 419.84 417.665 418.20 67702070.0 399.8982 365.25545 421.894229 414.1175 406.340771 -3.755808 5.349544 -0.397403 5.746947 63.837618 1.126527 1.126275 0 30 70
In [ ]:

Additional Strategy Options

The params keyword takes a tuple as a shorthand to the parameter arguments in order.

  • Note: If the indicator arguments change, so will results. Breaking Changes will always be posted on the README.

The col_numbers keyword takes a tuple specifying which column to return if the result is a DataFrame.

In [30]:
params_ta = [
    {"kind":"ema", "params": (10,)},
    # params sets MACD's keyword arguments: fast=9, slow=19, signal=10
    # and returning the 2nd column: histogram
    {"kind":"macd", "params": (9, 19, 10), "col_numbers": (1,)},
    # Selects the Lower and Upper Bands and renames them LB and UB, ignoring the MB
    {"kind":"bbands", "col_numbers": (0,2), "col_names": ("LB", "UB")},
    {"kind":"log_return", "params": (5, False)},
]
params_ta_strategy = ta.Strategy(
    "EMA, MACD History, Outter BBands, Log Returns", # name
    params_ta, # ta
    "EMA, MACD History, BBands(LB, UB), and Log Returns Strategy" # description
)
params_ta_strategy
Out [30]:
Strategy(name='EMA, MACD History, Outter BBands, Log Returns', ta=[{'kind': 'ema', 'params': (10,)}, {'kind': 'macd', 'params': (9, 19, 10), 'col_numbers': (1,)}, {'kind': 'bbands', 'col_numbers': (0, 2), 'col_names': ('LB', 'UB')}, {'kind': 'log_return', 'params': (5, False)}], description='EMA, MACD History, BBands(LB, UB), and Log Returns Strategy', created='Monday May 3, 2021, NYSE: 12:00:44, Local: 16:00:44 PDT, Day 123/365 (34.00%)')
In [31]:
# Update the Watchlist
watch.strategy = params_ta_strategy
watch.strategy.name
Out [31]:
'EMA, MACD History, Outter BBands, Log Returns'
In [32]:
spy = watch.load("SPY")
spy.tail()
Out [32]:
[i] Loaded SPY[D]: SPY_D.csv
open high low close volume EMA_10 MACDh_9_19_10 LB UB LOGRET_5
date
2021-04-27 417.93 418.14 416.300 417.52 51303056.0 414.553148 -0.372667 419.977386 412.106614 0.012897
2021-04-28 417.81 419.01 416.900 417.40 51238854.0 415.070758 -0.446056 420.391986 412.224014 0.003191
2021-04-29 420.32 420.72 416.440 420.06 78544329.0 415.977893 -0.316421 420.143528 415.588472 0.018719
2021-04-30 417.63 418.54 416.340 417.30 85527030.0 416.218276 -0.511511 420.070593 415.885407 0.001343
2021-05-03 419.43 419.84 417.665 418.20 67702070.0 416.578589 -0.584752 420.158604 416.033396 0.001412
In [ ]:

Disclaimer

  • All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, or individual’s trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.

  • Any opinions, news, research, analyses, prices, or other information offered is provided as general market commentary, and does not constitute investment advice. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from use of or reliance on such information.

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