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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 = Wednesday April 21, 2021, NYSE: 17:14:16, Local: 21:14:16 PDT, Day 111/365 (30.00%)
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 = Wednesday April 21, 2021, NYSE: 17:14:16, Local: 21:14:16 PDT, Day 111/365 (30.00%)
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='Wednesday April 21, 2021, NYSE: 17:14:16, Local: 21:14:16 PDT, Day 111/365 (30.00%)')

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='Wednesday April 21, 2021, NYSE: 17:14:16, Local: 21:14:16 PDT, Day 111/365 (30.00%)')

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='Wednesday April 21, 2021, NYSE: 17:14:16, Local: 21:14:16 PDT, Day 111/365 (30.00%)')
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: Wednesday April 21, 2021, NYSE: 17:14:19, Local: 21:14:19 PDT, Day 111/365 (30.00%)
[+] 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: Wednesday April 21, 2021, NYSE: 17:14:38, Local: 21:14:38 PDT, Day 111/365 (30.00%)
In [12]:
", ".join([f"{t}: {d.shape}" for t,d in watch.data.items()])
Out [12]:
'SPY: (5402, 10), IWM: (5258, 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
... ... ... ... ... ... ... ... ... ... ...
2021-04-15 413.7400 416.1600 413.6900 415.8700 60229842.0 409.151 400.7300 393.4496 359.19885 84100372.00
2021-04-16 417.2500 417.9100 415.7300 417.2600 82037278.0 410.816 402.0190 394.1578 359.74335 82434780.85
2021-04-19 416.2600 416.7400 413.7900 415.2100 78498496.0 411.701 403.3055 394.7382 360.26680 80678481.15
2021-04-20 413.9100 415.0859 410.5900 412.1700 81851828.0 412.306 404.2845 395.2274 360.76650 81082140.25
2021-04-21 411.5100 416.2900 411.3600 416.0700 66792983.0 413.254 405.6130 395.7386 361.26160 79887461.80

5402 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
... ... ... ... ... ... ... ... ... ... ...
2021-04-15 413.7400 416.1600 413.6900 415.8700 60229842.0 409.151 400.7300 393.4496 359.19885 84100372.00
2021-04-16 417.2500 417.9100 415.7300 417.2600 82037278.0 410.816 402.0190 394.1578 359.74335 82434780.85
2021-04-19 416.2600 416.7400 413.7900 415.2100 78498496.0 411.701 403.3055 394.7382 360.26680 80678481.15
2021-04-20 413.9100 415.0859 410.5900 412.1700 81851828.0 412.306 404.2845 395.2274 360.76650 81082140.25
2021-04-21 411.5100 416.2900 411.3600 416.0700 66792983.0 413.254 405.6130 395.7386 361.26160 79887461.80

5402 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='Wednesday April 21, 2021, NYSE: 17:14:16, Local: 21:14:16 PDT, Day 111/365 (30.00%)')
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 91.4400 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
... ... ... ... ... ... ... ...
2021-04-15 225.09 225.1200 222.26 224.29 20587325.0 223.0572 182.59330
2021-04-16 225.40 225.6780 223.01 224.65 23942972.0 223.2652 183.00065
2021-04-19 223.71 224.6571 219.94 221.73 25285933.0 223.3274 183.40020
2021-04-20 220.77 221.6165 215.24 217.19 35570761.0 223.2382 183.77415
2021-04-21 217.02 222.6200 215.50 222.50 31147779.0 223.1420 184.16950

5258 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='Wednesday April 21, 2021, NYSE: 17:14:16, Local: 21:14:16 PDT, Day 111/365 (30.00%)')
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
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2021-04-15 413.7400 416.1600 413.6900 415.8700 60229842.0 410.181984 402.967345 1.120940 73.655303 404.511242 1 404.511242 NaN
2021-04-16 417.2500 417.9100 415.7300 417.2600 82037278.0 411.754876 404.266677 1.124277 74.749576 406.959636 1 406.959636 NaN
2021-04-19 416.2600 416.7400 413.7900 415.2100 78498496.0 412.522682 405.261524 1.119352 70.123431 406.959636 1 406.959636 NaN
2021-04-20 413.9100 415.0859 410.5900 412.1700 81851828.0 412.444308 405.889568 1.112003 63.816107 406.959636 1 406.959636 NaN
2021-04-21 411.5100 416.2900 411.3600 416.0700 66792983.0 413.250017 406.815062 1.121421 67.815585 406.959636 1 406.959636 NaN

5402 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='Wednesday April 21, 2021, NYSE: 17:14:16, Local: 21:14:16 PDT, Day 111/365 (30.00%)')
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='Wednesday April 21, 2021, NYSE: 17:14:16, Local: 21:14:16 PDT, Day 111/365 (30.00%)')
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
... ... ... ... ... ... ... ... ... ...
2021-04-15 413.7400 416.1600 413.6900 415.8700 60229842.0 6.719201e+07 84100372.00 412.326473 411.148064
2021-04-16 417.2500 417.9100 415.7300 417.2600 82037278.0 6.989115e+07 82434780.85 413.970982 412.535178
2021-04-19 416.2600 416.7400 413.7900 415.2100 78498496.0 7.145612e+07 80678481.15 414.383988 413.565317
2021-04-20 413.9100 415.0859 410.5900 412.1700 81851828.0 7.334625e+07 81082140.25 413.645992 413.940568
2021-04-21 411.5100 416.2900 411.3600 416.0700 66792983.0 7.215475e+07 79887461.80 414.453995 414.381569

5402 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='Wednesday April 21, 2021, NYSE: 17:14:16, Local: 21:14:16 PDT, Day 111/365 (30.00%)')
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
... ... ... ... ... ... ... ... ... ... ... ... ...
2021-04-15 413.7400 416.1600 413.6900 415.8700 60229842.0 6.478994 1.251027 5.227967 0.107034 3.533810 6.960587 193.942317
2021-04-16 417.2500 417.9100 415.7300 417.2600 82037278.0 6.775655 1.238150 5.537504 0.033429 3.732053 7.430676 198.208557
2021-04-19 416.2600 416.7400 413.7900 415.2100 78498496.0 6.767333 0.983863 5.783470 0.063804 3.947467 7.831129 196.767334
2021-04-20 413.9100 415.0859 410.5900 412.1700 81851828.0 6.441185 0.526172 5.915013 0.184685 4.149350 8.114015 191.098113
2021-04-21 411.5100 416.2900 411.3600 416.0700 66792983.0 6.423364 0.406681 6.016683 0.402246 4.366212 8.330177 181.574596

5402 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='Wednesday April 21, 2021, NYSE: 17:14:16, Local: 21:14:16 PDT, Day 111/365 (30.00%)')
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
2021-04-15 413.74 416.1600 413.69 415.87 60229842.0 393.4496 359.19885 381.895791 400.7300 419.564209 9.399949 6.478994 1.251027 5.227967 73.655303 1.120940 1.113188 0 30 70
2021-04-16 417.25 417.9100 415.73 417.26 82037278.0 394.1578 359.74335 382.340515 402.0190 421.697485 9.789828 6.775655 1.238150 5.537504 74.749576 1.124277 1.115973 0 30 70
2021-04-19 416.26 416.7400 413.79 415.21 78498496.0 394.7382 360.26680 383.714538 403.3055 422.896462 9.715197 6.767333 0.983863 5.783470 70.123431 1.119352 1.117700 0 30 70
2021-04-20 413.91 415.0859 410.59 412.17 81851828.0 395.2274 360.76650 384.993564 404.2845 423.575436 9.543248 6.441185 0.526172 5.915013 63.816107 1.112003 1.117365 0 30 70
2021-04-21 411.51 416.2900 411.36 416.07 66792983.0 395.7386 361.26160 386.960023 405.6130 424.265977 9.197426 6.423364 0.406681 6.016683 67.815585 1.121421 1.119598 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='Wednesday April 21, 2021, NYSE: 17:14:16, Local: 21:14:16 PDT, Day 111/365 (30.00%)')
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
2021-04-15 413.74 416.1600 413.69 415.87 60229842.0 408.805040 1.131842 408.891393 416.432607 0.017832
2021-04-16 417.25 417.9100 415.73 417.26 82037278.0 410.342305 1.100301 408.588484 419.043516 0.013925
2021-04-19 416.26 416.7400 413.79 415.21 78498496.0 411.227341 0.762081 409.841056 419.218944 0.008635
2021-04-20 413.91 415.0859 410.59 412.17 81851828.0 411.398733 0.182182 409.424941 419.359059 -0.001673
2021-04-21 411.51 416.2900 411.36 416.07 66792983.0 412.248055 0.066079 411.499834 419.132166 0.011166
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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