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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 v0.2.89b0
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 = Saturday June 19, 2021, NYSE: 7:59:38, Local: 11:59:38 PDT, Day 170/365 (47.00%)
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 = Saturday June 19, 2021, NYSE: 7:59:38, Local: 11:59:38 PDT, Day 170/365 (47.00%)
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='Saturday June 19, 2021, NYSE: 7:59:38, Local: 11:59:38 PDT, Day 170/365 (47.00%)')

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='Saturday June 19, 2021, NYSE: 7:59:38, Local: 11:59:38 PDT, Day 170/365 (47.00%)')

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='Saturday June 19, 2021, NYSE: 7:59:38, Local: 11:59:38 PDT, Day 170/365 (47.00%)')
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: Saturday June 19, 2021, NYSE: 7:59:42, Local: 11:59:42 PDT, Day 170/365 (47.00%)
[+] 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: Saturday June 19, 2021, NYSE: 8:00:15, Local: 12:00:15 PDT, Day 170/365 (47.00%)
In [12]:
", ".join([f"{t}: {d.shape}" for t,d in watch.data.items()])
Out [12]:
'SPY: (5443, 10), IWM: (5299, 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
... ... ... ... ... ... ... ... ... ... ...
2021-06-14 424.4300 425.3700 423.1000 425.2600 42358478.0 422.067 419.2510 416.2142 377.72930 57830914.60
2021-06-15 425.4200 425.4600 423.5400 424.4800 51508508.0 422.548 419.6990 416.5766 378.11005 57149878.95
2021-06-16 424.6300 424.8700 419.9200 422.1100 79250069.0 422.726 420.2075 416.8964 378.46770 58121870.50
2021-06-17 421.6700 423.0200 419.3200 421.9700 90949659.0 423.046 420.7630 417.2040 378.83100 57346000.85
2021-06-18 417.0900 417.8281 414.7000 414.9200 118676302.0 422.278 420.7450 417.3320 379.14260 59378705.05

5443 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
... ... ... ... ... ... ... ... ... ... ...
2021-06-14 424.4300 425.3700 423.1000 425.2600 42358478.0 422.067 419.2510 416.2142 377.72930 57830914.60
2021-06-15 425.4200 425.4600 423.5400 424.4800 51508508.0 422.548 419.6990 416.5766 378.11005 57149878.95
2021-06-16 424.6300 424.8700 419.9200 422.1100 79250069.0 422.726 420.2075 416.8964 378.46770 58121870.50
2021-06-17 421.6700 423.0200 419.3200 421.9700 90949659.0 423.046 420.7630 417.2040 378.83100 57346000.85
2021-06-18 417.0900 417.8281 414.7000 414.9200 118676302.0 422.278 420.7450 417.3320 379.14260 59378705.05

5443 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='Saturday June 19, 2021, NYSE: 7:59:38, Local: 11:59:38 PDT, Day 170/365 (47.00%)')
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 90.630 91.44 37400.0 NaN NaN
2000-05-30 92.75 94.8100 92.750 94.81 28800.0 NaN NaN
2000-05-31 95.13 96.3800 95.130 95.75 18000.0 NaN NaN
2000-06-01 97.11 97.3100 97.110 97.31 3500.0 NaN NaN
2000-06-02 101.70 102.4000 101.700 102.40 14700.0 NaN NaN
... ... ... ... ... ... ... ...
2021-06-14 232.31 233.3500 230.140 231.02 19182832.0 223.8902 197.88475
2021-06-15 231.12 231.4700 228.475 230.36 17039888.0 223.9980 198.25755
2021-06-16 229.70 230.6800 227.640 229.87 23546462.0 224.1092 198.62130
2021-06-17 229.24 230.2000 224.520 227.29 48292458.0 224.2412 198.98060
2021-06-18 223.70 225.7375 221.130 222.13 54023731.0 224.2326 199.30520

5299 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='Saturday June 19, 2021, NYSE: 7:59:38, Local: 11:59:38 PDT, Day 170/365 (47.00%)')
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
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2021-06-14 424.4300 425.3700 423.1000 425.2600 42358478.0 422.800725 419.952881 1.143268 64.359777 414.067589 1 414.067589 NaN
2021-06-15 425.4200 425.4600 423.5400 424.4800 51508508.0 423.173897 420.364438 1.141432 62.201133 414.647404 1 414.647404 NaN
2021-06-16 424.6300 424.8700 419.9200 422.1100 79250069.0 422.937476 420.523125 1.135833 56.049676 414.647404 1 414.647404 NaN
2021-06-17 421.6700 423.0200 419.3200 421.9700 90949659.0 422.722481 420.654659 1.135501 55.699252 414.647404 1 414.647404 NaN
2021-06-18 417.0900 417.8281 414.7000 414.9200 118676302.0 420.988596 420.133327 1.118653 41.596038 414.647404 1 414.647404 NaN

5443 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='Saturday June 19, 2021, NYSE: 7:59:38, Local: 11:59:38 PDT, Day 170/365 (47.00%)')
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='Saturday June 19, 2021, NYSE: 7:59:38, Local: 11:59:38 PDT, Day 170/365 (47.00%)')
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
... ... ... ... ... ... ... ... ... ...
2021-06-14 424.4300 425.3700 423.1000 425.2600 42358478.0 5.054864e+07 57830914.60 423.686083 422.946481
2021-06-15 425.4200 425.4600 423.5400 424.4800 51508508.0 5.072316e+07 57149878.95 423.950722 423.426186
2021-06-16 424.6300 424.8700 419.9200 422.1100 79250069.0 5.590987e+07 58121870.50 423.337148 423.580830
2021-06-17 421.6700 423.0200 419.3200 421.9700 90949659.0 6.228074e+07 57346000.85 422.881432 423.493411
2021-06-18 417.0900 417.8281 414.7000 414.9200 118676302.0 7.253448e+07 59378705.05 420.227621 422.469933

5443 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='Saturday June 19, 2021, NYSE: 7:59:38, Local: 11:59:38 PDT, Day 170/365 (47.00%)')
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
... ... ... ... ... ... ... ... ... ... ... ... ...
2021-06-14 424.4300 425.3700 423.1000 425.2600 42358478.0 2.786483 0.324042 2.462441 1.018819 1.997419 2.976018 97.986402
2021-06-15 425.4200 425.4600 423.5400 424.4800 51508508.0 2.795023 0.266066 2.528958 1.003087 2.040643 3.078199 101.689172
2021-06-16 424.6300 424.8700 419.9200 422.1100 79250069.0 2.580803 0.041476 2.539327 1.053702 2.091817 3.129932 99.254865
2021-06-17 421.6700 423.0200 419.3200 421.9700 90949659.0 2.372387 -0.133552 2.505939 1.200822 2.152386 3.103950 88.419455
2021-06-18 417.0900 417.8281 414.7000 414.9200 118676302.0 1.619669 -0.709015 2.328685 1.293478 2.173875 3.054273 80.997979

5443 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='Saturday June 19, 2021, NYSE: 7:59:38, Local: 11:59:38 PDT, Day 170/365 (47.00%)')
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
2021-06-14 424.43 425.3700 423.10 425.26 42358478.0 416.2142 377.72930 411.623731 419.2510 426.878269 3.638522 2.786483 0.324042 2.462441 64.359777 1.143268 1.138932 0 30 70
2021-06-15 425.42 425.4600 423.54 424.48 51508508.0 416.5766 378.11005 411.949365 419.6990 427.448635 3.692949 2.795023 0.266066 2.528958 62.201133 1.141432 1.139971 0 30 70
2021-06-16 424.63 424.8700 419.92 422.11 79250069.0 416.8964 378.46770 413.268861 420.2075 427.146139 3.302482 2.580803 0.041476 2.539327 56.049676 1.135833 1.140189 0 30 70
2021-06-17 421.67 423.0200 419.32 421.97 90949659.0 417.2040 378.83100 415.280617 420.7630 426.245383 2.605925 2.372387 -0.133552 2.505939 55.699252 1.135501 1.139413 0 30 70
2021-06-18 417.09 417.8281 414.70 414.92 118676302.0 417.3320 379.14260 415.188859 420.7450 426.301141 2.641097 1.619669 -0.709015 2.328685 41.596038 1.118653 1.134938 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='Saturday June 19, 2021, NYSE: 7:59:38, Local: 11:59:38 PDT, Day 170/365 (47.00%)')
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
2021-06-14 424.43 425.3700 423.10 425.26 42358478.0 422.270911 0.362543 420.791976 426.052024 0.007245
2021-06-15 425.42 425.4600 423.54 424.48 51508508.0 422.672563 0.270356 421.413575 426.310425 0.005196
2021-06-16 424.63 424.8700 419.92 422.11 79250069.0 422.570279 -0.022223 421.832167 426.075833 0.001090
2021-06-17 421.67 423.0200 419.32 421.97 90949659.0 422.461138 -0.230673 420.956510 426.295490 -0.003879
2021-06-18 417.09 417.8281 414.70 414.92 118676302.0 421.090022 -0.927534 414.448692 429.047308 -0.022379
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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