Pandas TA (pandas_ta) Studies for Custom Technical Analysis

Topics

What is a Pandas TA Study?
- Builtin Studies: AllStudy and CommonStudy
- Creating Studies
Watchlist Class
- Study 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 Studies
- 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 v0.3.48b0
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 Study?

A Study is a simple way to name and group TA indicators. Technically, a Study is a simple Data Class to contain list of indicators and their parameters. Note: Study is experimental and subject to change. Pandas TA comes with two basic Studies: AllStudy and CommonStudy.

Study 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 Study tries to capture. Default: None
created: At datetime string of when it was created. Default: Automatically generated.

Things to note:

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

Builtin Examples

All

Default Values

In [2]:

AllStudy = ta.AllStudy
print(f"{AllStudy.name = }")
print(f"{AllStudy.description = }")
print(f"{AllStudy.created = }")
print(f"{AllStudy.ta = }")
print(f"{AllStudy.cores = }")

AllStudy.name = 'All'
AllStudy.description = 'All the indicators with their default settings. Pandas TA default.'
AllStudy.created = 'Monday February 28, 2022, NYSE: 7:02:32, Local: 11:02:32 PST, Day 59/365 (16.00%)'
AllStudy.ta = None
AllStudy.cores = 8

Common

Default Values

In [3]:

CommonStudy = ta.CommonStudy
print(f"{CommonStudy.name = }")
print(f"{CommonStudy.description = }")
print(f"{CommonStudy.created = }")
print(f"{CommonStudy.ta = }")
print(f"{CommonStudy.cores = }")

CommonStudy.name = 'Common Price and Volume SMAs'
CommonStudy.description = 'Common Price SMAs: 10, 20, 50, 200 and Volume SMA: 20.'
CommonStudy.created = 'Monday February 28, 2022, NYSE: 7:02:32, Local: 11:02:32 PST, Day 59/365 (16.00%)'
CommonStudy.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'}]
CommonStudy.cores = 0

In [ ]:

Creating Studies

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

Simple Study A

In [4]:

custom_a = ta.Study(name="A", cores=0, ta=[{"kind": "sma", "length": 50}, {"kind": "sma", "length": 200}])
custom_a

Out [4]:

Study(name='A', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}], cores=0, description='', created='Monday February 28, 2022, NYSE: 7:02:32, Local: 11:02:32 PST, Day 59/365 (16.00%)')

Simple Study B

In [5]:

custom_b = ta.Study(name="B", cores=0, ta=[{"kind": "ema", "length": 8}, {"kind": "ema", "length": 21}, {"kind": "log_return", "cumulative": True}, {"kind": "rsi"}, {"kind": "supertrend"}])
custom_b

Out [5]:

Study(name='B', ta=[{'kind': 'ema', 'length': 8}, {'kind': 'ema', 'length': 21}, {'kind': 'log_return', 'cumulative': True}, {'kind': 'rsi'}, {'kind': 'supertrend'}], cores=0, description='', created='Monday February 28, 2022, NYSE: 7:02:32, Local: 11:02:32 PST, Day 59/365 (16.00%)')

Bad Study. (Misspelled Indicator)

In [6]:

# Misspelled indicator, will fail later when ran with Pandas TA
custom_run_failure = ta.Study(name="Runtime Failure", cores=0, ta=[{"kind": "peret_return"}])
custom_run_failure

Out [6]:

Study(name='Runtime Failure', ta=[{'kind': 'peret_return'}], cores=0, description='', created='Monday February 28, 2022, NYSE: 7:02:32, Local: 11:02:32 PST, Day 59/365 (16.00%)')

In [ ]:

Study 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=True)

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

In [9]:

watch

Out [9]:

Watch(name='Watch: SPY, IWM', ds_name='yahoo', tickers[2]='SPY, IWM', tf='D', study[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, study: pandas_ta.utils._study.Study = 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 Study.
 |  
 |  Default Study: pandas_ta.CommonStudy
 |  
 |  ## 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, study: pandas_ta.utils._study.Study = 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
 |  
 |  study
 |      Sets a valid Study. Default: pandas_ta.CommonStudy
 |  
 |  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 Study is "Common"

In [11]:

# No arguments loads all the tickers and applies the Study to each ticker.
# The result can be accessed with Watchlist's 'data' property which returns a 
# dictionary keyed by ticker and DataFrames as values 
watch.load(verbose=True)

[!] Loading All: SPY, IWM
[i] Loaded SPY[D]: SPY_D.csv
[+] Study: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': True, 'append': True}
[i] No mulitproccessing (cores = 0).

[i] Progress: 100%|█| 5/5 [00:00<

[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Monday February 28, 2022, NYSE: 7:02:33, Local: 11:02:33 PST, Day 59/365 (16.00%)
[i] Analysis Time: 57.8152 ms (0.0578 s) for 5 columns (avg 11.5647 ms / col).
[i] Loaded IWM[D]: IWM_D.csv
[+] Study: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': True, 'append': True}
[i] No mulitproccessing (cores = 0).

[i] Progress: 100%|█| 5/5 [00:00<

[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Monday February 28, 2022, NYSE: 7:02:33, Local: 11:02:33 PST, Day 59/365 (16.00%)
[i] Analysis Time: 5.0522 ms (0.0051 s) for 5 columns (avg 1.0112 ms / col).

In [12]:

", ".join([f"{t}: {d.shape}" for t,d in watch.data.items()])

Out [12]:

'SPY: (7324, 12), IWM: (5474, 12)'

In [13]:

watch.data["SPY"]

Out [13]:

	Open	High	Low	Close	Volume	Dividends	Stock Splits	SMA_10	SMA_20	SMA_50	SMA_200	VOL_SMA_20
Date
1993-01-29	25.645567	25.645567	25.517978	25.627340	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.645570	25.809614	25.645570	25.809614	480500	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.791378	25.882514	25.736697	25.864286	201300	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-03	25.900760	26.155940	25.882533	26.137712	529400	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-04	26.228853	26.301761	25.937218	26.247080	531500	0.0	0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
2022-02-22	431.890015	435.500000	425.859985	429.570007	124391800	0.0	0	443.084003	443.732504	456.582517	442.721047	1.252365e+08
2022-02-23	432.660004	433.260010	421.350006	421.950012	132578000	0.0	0	440.185004	443.106505	455.639678	442.761832	1.234655e+08
2022-02-24	411.019989	428.760010	410.640015	428.299988	213942900	0.0	0	437.261002	442.852504	454.906946	442.852832	1.248431e+08
2022-02-25	429.609985	437.839996	427.859985	437.750000	121715600	0.0	0	436.104001	443.178004	454.427191	443.034645	1.234349e+08
2022-02-28	432.029999	438.200012	431.859985	433.720001	81093943	0.0	0	435.430002	442.766504	453.722542	443.172199	1.192668e+08

7324 rows × 12 columns

In [ ]:

In [14]:

watch.load("SPY", plot=True, mas=True)

Out [14]:

[i] Loaded SPY[D]: SPY_D.csv
[i] Analysis Time: 4.1666 ms (0.0042 s) for 5 columns (avg 0.8351 ms / col).

	Open	High	Low	Close	Volume	Dividends	Stock Splits	SMA_10	SMA_20	SMA_50	SMA_200	VOL_SMA_20
Date
1993-01-29	25.645567	25.645567	25.517978	25.627340	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.645570	25.809614	25.645570	25.809614	480500	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.791378	25.882514	25.736697	25.864286	201300	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-03	25.900760	26.155940	25.882533	26.137712	529400	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-04	26.228853	26.301761	25.937218	26.247080	531500	0.0	0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
2022-02-22	431.890015	435.500000	425.859985	429.570007	124391800	0.0	0	443.084003	443.732504	456.582517	442.721047	1.252365e+08
2022-02-23	432.660004	433.260010	421.350006	421.950012	132578000	0.0	0	440.185004	443.106505	455.639678	442.761832	1.234655e+08
2022-02-24	411.019989	428.760010	410.640015	428.299988	213942900	0.0	0	437.261002	442.852504	454.906946	442.852832	1.248431e+08
2022-02-25	429.609985	437.839996	427.859985	437.750000	121715600	0.0	0	436.104001	443.178004	454.427191	443.034645	1.234349e+08
2022-02-28	432.029999	438.200012	431.859985	433.720001	81093943	0.0	0	435.430002	442.766504	453.722542	443.172199	1.192668e+08

7324 rows × 12 columns

In [ ]:

Easy to swap Studies and run them

Running Simple Study A

In [15]:

# Load custom_a into Watchlist and verify
watch.study = custom_a
watch.study

Out [15]:

Study(name='A', ta=[{'kind': 'sma', 'length': 50}, {'kind': 'sma', 'length': 200}], cores=0, description='', created='Monday February 28, 2022, NYSE: 7:02:32, Local: 11:02:32 PST, Day 59/365 (16.00%)')

In [16]:

watch.load("IWM")

Out [16]:

[i] Loaded IWM[D]: IWM_D.csv
[i] Analysis Time: 1.7527 ms (0.0018 s) for 2 columns (avg 0.8802 ms / col).

	Open	High	Low	Close	Volume	Dividends	Stock Splits	SMA_50	SMA_200
Date
2000-05-26	34.332581	34.473965	34.167634	34.473965	74800	0.0	0.0	NaN	NaN
2000-05-30	34.968825	35.746433	34.968825	35.746433	57600	0.0	0.0	NaN	NaN
2000-05-31	35.864218	36.335495	35.864218	35.875999	36000	0.0	0.0	NaN	NaN
2000-06-01	36.612378	36.688961	36.612378	36.688961	7000	0.0	0.0	NaN	NaN
2000-06-02	38.350228	38.597649	38.350228	38.597649	29400	0.0	0.0	NaN	NaN
...	...	...	...	...	...	...	...	...	...
2022-02-22	198.479996	200.449997	195.350006	196.660004	31847100	0.0	0.0	209.63768	220.087778
2022-02-23	198.240005	198.899994	192.550003	192.979996	31491500	0.0	0.0	209.11240	219.962649
2022-02-24	188.320007	198.479996	187.919998	198.039993	52221100	0.0	0.0	208.75240	219.866293
2022-02-25	198.690002	202.619995	197.000000	202.500000	33631400	0.0	0.0	208.51880	219.827520
2022-02-28	200.470001	204.600006	200.479904	202.509995	20468009	0.0	0.0	208.21740	219.769443

5474 rows × 9 columns

Running Simple Study B

In [17]:

# Load custom_b into Watchlist and verify
watch.study = custom_b
watch.study

Out [17]:

Study(name='B', ta=[{'kind': 'ema', 'length': 8}, {'kind': 'ema', 'length': 21}, {'kind': 'log_return', 'cumulative': True}, {'kind': 'rsi'}, {'kind': 'supertrend'}], cores=0, description='', created='Monday February 28, 2022, NYSE: 7:02:32, Local: 11:02:32 PST, Day 59/365 (16.00%)')

In [18]:

watch.load("IWM")

Out [18]:

[i] Loaded IWM[D]: IWM_D.csv
[i] Analysis Time: 247.7720 ms (0.2478 s) for 8 columns (avg 30.9721 ms / col).

	Open	High	Low	Close	Volume	Dividends	Stock Splits	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
2000-05-26	34.332581	34.473965	34.167634	34.473965	74800	0.0	0.0	NaN	NaN	0.000000	NaN	0.000000	1	NaN	NaN
2000-05-30	34.968825	35.746433	34.968825	35.746433	57600	0.0	0.0	NaN	NaN	0.036246	NaN	NaN	1	NaN	NaN
2000-05-31	35.864218	36.335495	35.864218	35.875999	36000	0.0	0.0	NaN	NaN	0.039864	NaN	NaN	1	NaN	NaN
2000-06-01	36.612378	36.688961	36.612378	36.688961	7000	0.0	0.0	NaN	NaN	0.062272	NaN	NaN	1	NaN	NaN
2000-06-02	38.350228	38.597649	38.350228	38.597649	29400	0.0	0.0	NaN	NaN	0.112987	NaN	NaN	1	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-02-22	198.479996	200.449997	195.350006	196.660004	31847100	0.0	0.0	200.961183	202.721204	1.741272	40.121546	213.017055	-1	NaN	213.017055
2022-02-23	198.240005	198.899994	192.550003	192.979996	31491500	0.0	0.0	199.187586	201.835640	1.722382	36.468006	211.403898	-1	NaN	211.403898
2022-02-24	188.320007	198.479996	187.919998	198.039993	52221100	0.0	0.0	198.932565	201.490581	1.748265	44.016839	211.164767	-1	NaN	211.164767
2022-02-25	198.690002	202.619995	197.000000	202.500000	33631400	0.0	0.0	199.725328	201.582346	1.770535	49.691015	211.164767	-1	NaN	211.164767
2022-02-28	200.470001	204.600006	200.479904	202.509995	20468009	0.0	0.0	200.344143	201.666678	1.770585	49.703318	211.164767	-1	NaN	211.164767

5474 rows × 15 columns

Running Bad Study. (Misspelled indicator)

In [19]:

# Load custom_run_failure into Watchlist and verify
watch.study = custom_run_failure
watch.study

Out [19]:

Study(name='Runtime Failure', ta=[{'kind': 'peret_return'}], cores=0, description='', created='Monday February 28, 2022, NYSE: 7:02:32, Local: 11:02:32 PST, Day 59/365 (16.00%)')

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 'peret_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.
Set cores=0 for better performance when few indicators

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.Study("Volume MAs and Price MA chain", cores=0, ta=volmas_price_ma_chain)
vp_ma_chain_ta

Out [21]:

Study(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'}], cores=0, description='', created='Monday February 28, 2022, NYSE: 7:02:32, Local: 11:02:32 PST, Day 59/365 (16.00%)')

In [22]:

# Update the Watchlist
watch.study = vp_ma_chain_ta
watch.study.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
[i] Analysis Time: 2.6769 ms (0.0027 s) for 4 columns (avg 0.6714 ms / col).

	Open	High	Low	Close	Volume	Dividends	Stock Splits	VOLUME_EMA_10	VOLUME_SMA_20	EMA_5	EMA_5_LR_8
Date
1993-01-29	25.645567	25.645567	25.517978	25.627340	1003200	0.0	0	NaN	NaN	NaN	NaN
1993-02-01	25.645570	25.809614	25.645570	25.809614	480500	0.0	0	NaN	NaN	NaN	NaN
1993-02-02	25.791378	25.882514	25.736697	25.864286	201300	0.0	0	NaN	NaN	NaN	NaN
1993-02-03	25.900760	26.155940	25.882533	26.137712	529400	0.0	0	NaN	NaN	NaN	NaN
1993-02-04	26.228853	26.301761	25.937218	26.247080	531500	0.0	0	NaN	NaN	25.937207	NaN
...	...	...	...	...	...	...	...	...	...	...	...
2022-02-22	431.890015	435.500000	425.859985	429.570007	124391800	0.0	0	1.159478e+08	1.252365e+08	436.476400	438.103977
2022-02-23	432.660004	433.260010	421.350006	421.950012	132578000	0.0	0	1.189715e+08	1.234655e+08	431.634270	434.610989
2022-02-24	411.019989	428.760010	410.640015	428.299988	213942900	0.0	0	1.362390e+08	1.248431e+08	430.522843	431.390383
2022-02-25	429.609985	437.839996	427.859985	437.750000	121715600	0.0	0	1.335984e+08	1.234349e+08	432.931895	429.881644
2022-02-28	432.029999	438.200012	431.859985	433.720001	81093943	0.0	0	1.240521e+08	1.192668e+08	433.194597	429.579707

7324 rows × 11 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.Study("MACD BBands", cores=0, ta=macd_bands_ta, description=f"BBANDS_{macd_bands_ta[1]['length']} applied to MACD")
macd_bands_ta

Out [24]:

Study(name='MACD BBands', ta=[{'kind': 'macd'}, {'kind': 'bbands', 'close': 'MACD_12_26_9', 'length': 20, 'ddof': 0, 'prefix': 'MACD'}], cores=0, description='BBANDS_20 applied to MACD', created='Monday February 28, 2022, NYSE: 7:02:32, Local: 11:02:32 PST, Day 59/365 (16.00%)')

In [25]:

# Update the Watchlist
watch.study = macd_bands_ta
watch.study.name

Out [25]:

'MACD BBands'

In [26]:

spy = watch.load("SPY")
spy

Out [26]:

[i] Loaded SPY[D]: SPY_D.csv
[i] Analysis Time: 4.4833 ms (0.0045 s) for 8 columns (avg 0.5609 ms / col).

	Open	High	Low	Close	Volume	Dividends	Stock Splits	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	MACD_BBP_20_2.0
Date
1993-01-29	25.645567	25.645567	25.517978	25.627340	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.645570	25.809614	25.645570	25.809614	480500	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.791378	25.882514	25.736697	25.864286	201300	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-03	25.900760	26.155940	25.882533	26.137712	529400	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-04	26.228853	26.301761	25.937218	26.247080	531500	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-02-22	431.890015	435.500000	425.859985	429.570007	124391800	0.0	0	-5.233734	-1.083594	-4.150140	-8.736828	-5.040904	-1.344981	-146.637319	0.473913
2022-02-23	432.660004	433.260010	421.350006	421.950012	132578000	0.0	0	-6.404132	-1.803194	-4.600938	-8.676812	-5.018951	-1.361090	-145.761973	0.310657
2022-02-24	411.019989	428.760010	410.640015	428.299988	213942900	0.0	0	-6.741579	-1.712512	-5.029066	-8.484443	-4.964817	-1.445191	-141.782713	0.247592
2022-02-25	429.609985	437.839996	427.859985	437.750000	121715600	0.0	0	-6.175286	-0.916976	-5.258310	-7.980895	-4.839860	-1.698824	-129.798625	0.287423
2022-02-28	432.029999	438.200012	431.859985	433.720001	81093943	0.0	0	-5.982717	-0.579525	-5.403192	-7.467832	-4.719630	-1.971427	-116.458406	0.270198

7324 rows × 15 columns

In [ ]:

Comprehensive Study

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_Study = ta.Study(
    name="Momo, Bands and SMAs and Cumulative Log Returns", # name
    ta=momo_bands_sma_ta, # ta
    description="MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns", # description
    cores=0
)
momo_bands_sma_Study

Out [27]:

Study(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'}], cores=0, description='MACD and RSI Momo with BBANDS and SMAs 50 & 200 and Cumulative Log Returns', created='Monday February 28, 2022, NYSE: 7:02:32, Local: 11:02:32 PST, Day 59/365 (16.00%)')

In [28]:

# Update the Watchlist
watch.study = momo_bands_sma_Study
watch.study.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
[i] Analysis Time: 7.0055 ms (0.0070 s) for 13 columns (avg 0.5394 ms / col).

	Open	High	Low	Close	Volume	Dividends	Stock Splits	SMA_50	SMA_200	BBL_20_2.0	...	BBP_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
2022-02-22	431.890015	435.500000	425.859985	429.570007	124391800	0.0	0	456.582517	442.721047	427.227554	...	0.070962	-5.233734	-1.083594	-4.150140	36.088546	2.819125	2.840067	0	30	70
2022-02-23	432.660004	433.260010	421.350006	421.950012	132578000	0.0	0	455.639678	442.761832	424.436141	...	-0.066580	-6.404132	-1.803194	-4.600938	32.177252	2.801227	2.828936	0	30	70
2022-02-24	411.019989	428.760010	410.640015	428.299988	213942900	0.0	0	454.906946	442.852832	423.532824	...	0.123376	-6.741579	-1.712512	-5.029066	38.189228	2.816164	2.820568	0	30	70
2022-02-25	429.609985	437.839996	427.859985	437.750000	121715600	0.0	0	454.427191	443.034645	424.441322	...	0.355150	-6.175286	-0.916976	-5.258310	45.878058	2.837988	2.820884	0	30	70
2022-02-28	432.029999	438.200012	431.859985	433.720001	81093943	0.0	0	453.722542	443.172199	423.583827	...	0.264201	-5.982717	-0.579525	-5.403192	43.398750	2.828739	2.820649	0	30	70

5 rows × 23 columns

In [ ]:

Additional Study 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_Study = ta.Study(
    name="EMA, MACD History, Outter BBands, Log Returns", # name
    ta=params_ta, # ta
    description="EMA, MACD History, BBands(LB, UB), and Log Returns Study", # description
    cores=0
)
params_ta_Study

Out [30]:

Study(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)}], cores=0, description='EMA, MACD History, BBands(LB, UB), and Log Returns Study', created='Monday February 28, 2022, NYSE: 7:02:32, Local: 11:02:32 PST, Day 59/365 (16.00%)')

In [31]:

# Update the Watchlist
watch.study = params_ta_Study
watch.study.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
[i] Analysis Time: 4.8132 ms (0.0048 s) for 5 columns (avg 0.9637 ms / col).

	Open	High	Low	Close	Volume	Dividends	Stock Splits	EMA_10	MACDh_9_19_10	LB	UB	LOGRET_5
Date
2022-02-22	431.890015	435.500000	425.859985	429.570007	124391800	0.0	0	440.455752	-1.389834	425.349472	452.074540	-0.021760
2022-02-23	432.660004	433.260010	421.350006	421.950012	132578000	0.0	0	437.091072	-2.234858	417.561858	450.202156	-0.055656
2022-02-24	411.019989	428.760010	410.640015	428.299988	213942900	0.0	0	435.492693	-2.003742	419.815568	440.628438	-0.041839
2022-02-25	429.609985	437.839996	427.859985	437.750000	121715600	0.0	0	435.903112	-0.893305	419.582816	441.137191	0.001577
2022-02-28	432.029999	438.200012	431.859985	433.720001	81093943	0.0	0	435.506183	-0.439231	419.620509	440.895495	-0.001175

In [ ]:

Disclaimer

All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading Study, 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.

In [ ]:

231 KiB Raw Blame History Unescape Escape

Pandas TA (pandas_ta) Studies for Custom Technical Analysis

Topics

What is a Pandas TA Study?

Study Requirements:

Optional Requirements:

Things to note:

Builtin Examples

All

Common

Creating Studies

Simple Study A

Simple Study B

Bad Study. (Misspelled Indicator)

Study 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 Study is "All"

Help about Watchlist

Default Study is "Common"

Easy to swap Studies and run them

Running Simple Study A

Running Simple Study B

Running Bad Study. (Misspelled indicator)

Indicator Composition/Chaining

Volume MAs and MA chains

MACD BBANDS

Comprehensive Study

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

Additional Study Options

Disclaimer

231 KiB

Raw Blame History