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 v0.3.32b0
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 7, 2022, NYSE: 5:46:16, Local: 9:46:16 PST, Day 38/365 (10.00%)'
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 7, 2022, NYSE: 5:46:16, Local: 9:46:16 PST, Day 38/365 (10.00%)'
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 7, 2022, NYSE: 5:46:16, Local: 9:46:16 PST, Day 38/365 (10.00%)')

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 7, 2022, NYSE: 5:46:16, Local: 9:46:16 PST, Day 38/365 (10.00%)')

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 7, 2022, NYSE: 5:46:16, Local: 9:46:16 PST, Day 38/365 (10.00%)')

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
[+] Downloading[yahoo]: SPY[D]
[+] yf | SPY(7310, 7): 4522.0008 ms (4.5220 s)
[+] Saving: /Users/kj/av_data/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<00:00, 122.64it/s]

[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Monday February 7, 2022, NYSE: 5:46:21, Local: 9:46:21 PST, Day 38/365 (10.00%)
[i] Analysis Time: 55.7978 ms (0.0558 s)
[+] Downloading[yahoo]: IWM[D]

[+] yf | IWM(5460, 7): 3960.7030 ms (3.9607 s)
[+] Saving: /Users/kj/av_data/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<00:00, 1266.47it/s]

[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Monday February 7, 2022, NYSE: 5:46:25, Local: 9:46:25 PST, Day 38/365 (10.00%)
[i] Analysis Time: 5.0981 ms (0.0051 s)

In [12]:

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

Out [12]:

'SPY: (7310, 12), IWM: (5460, 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	25.645573	25.645573	25.517983	25.627346	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.645579	25.809624	25.645579	25.809624	480500	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.791393	25.882529	25.736712	25.864302	201300	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-03	25.900767	26.155947	25.882540	26.137720	529400	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-04	26.228843	26.301752	25.937209	26.247070	531500	0.0	0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
2022-02-01	450.679993	453.630005	446.940002	452.950012	123155400	0.0	0	442.022003	454.610001	461.063572	440.467170	126778345.0
2022-02-02	455.500000	458.119995	453.049988	457.350006	117361000	0.0	0	442.582004	453.600002	460.865603	440.713518	129087460.0
2022-02-03	450.950012	452.970001	445.709991	446.600006	118024400	0.0	0	442.567004	452.511002	460.478942	440.886810	129761735.0
2022-02-04	446.350006	452.779999	443.829987	448.700012	118335600	0.0	0	443.639005	451.549002	460.121924	441.089413	131335570.0
2022-02-07	449.510010	450.859985	446.769989	447.652405	39513832	0.0	0	444.420245	450.627122	459.719042	441.264651	129058021.6

7310 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: 4.2129 ms (0.0042 s)

	Open	High	Low	Close	Volume	Dividends	Stock Splits	SMA_10	SMA_20	SMA_50	SMA_200	VOL_SMA_20
Date
1993-01-29	25.645573	25.645573	25.517983	25.627346	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.645579	25.809624	25.645579	25.809624	480500	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.791393	25.882529	25.736712	25.864302	201300	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-03	25.900767	26.155947	25.882540	26.137720	529400	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-04	26.228843	26.301752	25.937209	26.247070	531500	0.0	0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
2022-02-01	450.679993	453.630005	446.940002	452.950012	123155400	0.0	0	442.022003	454.610001	461.063572	440.467170	126778345.0
2022-02-02	455.500000	458.119995	453.049988	457.350006	117361000	0.0	0	442.582004	453.600002	460.865603	440.713518	129087460.0
2022-02-03	450.950012	452.970001	445.709991	446.600006	118024400	0.0	0	442.567004	452.511002	460.478942	440.886810	129761735.0
2022-02-04	446.350006	452.779999	443.829987	448.700012	118335600	0.0	0	443.639005	451.549002	460.121924	441.089413	131335570.0
2022-02-07	449.510010	450.859985	446.769989	447.652405	39513832	0.0	0	444.420245	450.627122	459.719042	441.264651	129058021.6

7310 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 7, 2022, NYSE: 5:46:16, Local: 9:46:16 PST, Day 38/365 (10.00%)')

In [16]:

watch.load("IWM")

Out [16]:

[i] Loaded IWM[D]: IWM_D.csv
[i] Analysis Time: 2.1510 ms (0.0022 s)

	Open	High	Low	Close	Volume	Dividends	Stock Splits	SMA_50	SMA_200
Date
2000-05-26	34.332574	34.473957	34.167627	34.473957	74800	0.0	0.0	NaN	NaN
2000-05-30	34.968806	35.746414	34.968806	35.746414	57600	0.0	0.0	NaN	NaN
2000-05-31	35.864248	36.335526	35.864248	35.876030	36000	0.0	0.0	NaN	NaN
2000-06-01	36.612394	36.688976	36.612394	36.688976	7000	0.0	0.0	NaN	NaN
2000-06-02	38.350235	38.597656	38.350235	38.597656	29400	0.0	0.0	NaN	NaN
...	...	...	...	...	...	...	...	...	...
2022-02-01	201.660004	203.570007	197.970001	203.360001	43429400	0.0	0.0	215.571844	221.587410
2022-02-02	203.619995	203.899994	199.289993	201.339996	42777800	0.0	0.0	214.958340	221.516338
2022-02-03	198.839996	201.240005	197.050003	197.529999	39629400	0.0	0.0	214.288376	221.399866
2022-02-04	197.039993	200.100006	194.919998	198.380005	34238900	0.0	0.0	213.643388	221.292209
2022-02-07	198.429993	200.699997	197.990005	199.100006	14568016	0.0	0.0	213.007815	221.167410

5460 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 7, 2022, NYSE: 5:46:16, Local: 9:46:16 PST, Day 38/365 (10.00%)')

In [18]:

watch.load("IWM")

Out [18]:

[i] Loaded IWM[D]: IWM_D.csv
[i] Analysis Time: 256.9024 ms (0.2569 s)

	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	34.332574	34.473957	34.167627	34.473957	74800	0.0	0.0	NaN	NaN	0.000000	NaN	0.000000	1	NaN	NaN
2000-05-30	34.968806	35.746414	34.968806	35.746414	57600	0.0	0.0	NaN	NaN	0.036246	NaN	NaN	1	NaN	NaN
2000-05-31	35.864248	36.335526	35.864248	35.876030	36000	0.0	0.0	NaN	NaN	0.039865	NaN	NaN	1	NaN	NaN
2000-06-01	36.612394	36.688976	36.612394	36.688976	7000	0.0	0.0	NaN	NaN	0.062272	NaN	NaN	1	NaN	NaN
2000-06-02	38.350235	38.597656	38.350235	38.597656	29400	0.0	0.0	NaN	NaN	0.112987	NaN	NaN	1	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-02-01	201.660004	203.570007	197.970001	203.360001	43429400	0.0	0.0	200.301436	206.124694	1.774774	43.961827	213.373369	-1	NaN	213.373369
2022-02-02	203.619995	203.899994	199.289993	201.339996	42777800	0.0	0.0	200.532228	205.689722	1.764791	41.878172	213.373369	-1	NaN	213.373369
2022-02-03	198.839996	201.240005	197.050003	197.529999	39629400	0.0	0.0	199.865066	204.947929	1.745686	38.200475	213.373369	-1	NaN	213.373369
2022-02-04	197.039993	200.100006	194.919998	198.380005	34238900	0.0	0.0	199.535052	204.350845	1.749980	39.477464	213.373369	-1	NaN	213.373369
2022-02-07	198.429993	200.699997	197.990005	199.100006	14568016	0.0	0.0	199.438375	203.873496	1.753603	40.597174	213.373369	-1	NaN	213.373369

5460 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 7, 2022, NYSE: 5:46:16, Local: 9:46:16 PST, Day 38/365 (10.00%)')

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 7, 2022, NYSE: 5:46:16, Local: 9:46:16 PST, Day 38/365 (10.00%)')

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: 3.1504 ms (0.0032 s)

	Open	High	Low	Close	Volume	Dividends	Stock Splits	VOLUME_EMA_10	VOLUME_SMA_20	EMA_5	EMA_5_LR_8
Date
1993-01-29	25.645573	25.645573	25.517983	25.627346	1003200	0.0	0	NaN	NaN	NaN	NaN
1993-02-01	25.645579	25.809624	25.645579	25.809624	480500	0.0	0	NaN	NaN	NaN	NaN
1993-02-02	25.791393	25.882529	25.736712	25.864302	201300	0.0	0	NaN	NaN	NaN	NaN
1993-02-03	25.900767	26.155947	25.882540	26.137720	529400	0.0	0	NaN	NaN	NaN	NaN
1993-02-04	26.228843	26.301752	25.937209	26.247070	531500	0.0	0	NaN	NaN	25.937212	NaN
...	...	...	...	...	...	...	...	...	...	...	...
2022-02-01	450.679993	453.630005	446.940002	452.950012	123155400	0.0	0	1.489213e+08	126778345.0	445.815160	440.058132
2022-02-02	455.500000	458.119995	453.049988	457.350006	117361000	0.0	0	1.431831e+08	129087460.0	449.660109	444.740702
2022-02-03	450.950012	452.970001	445.709991	446.600006	118024400	0.0	0	1.386088e+08	129761735.0	448.640075	448.179915
2022-02-04	446.350006	452.779999	443.829987	448.700012	118335600	0.0	0	1.349227e+08	131335570.0	448.660054	450.446614
2022-02-07	449.510010	450.859985	446.769989	447.652405	39513832	0.0	0	1.175757e+08	129058021.6	448.324171	451.300024

7310 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 7, 2022, NYSE: 5:46:16, Local: 9:46:16 PST, Day 38/365 (10.00%)')

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: 5.8750 ms (0.0059 s)

	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	25.645573	25.645573	25.517983	25.627346	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.645579	25.809624	25.645579	25.809624	480500	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.791393	25.882529	25.736712	25.864302	201300	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-03	25.900767	26.155947	25.882540	26.137720	529400	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-04	26.228843	26.301752	25.937209	26.247070	531500	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-02-01	450.679993	453.630005	446.940002	452.950012	123155400	0.0	0	-6.355974	-0.385707	-5.970267	-10.847819	-1.971159	6.905502	-900.654085	0.253014
2022-02-02	455.500000	458.119995	453.049988	457.350006	117361000	0.0	0	-5.089661	0.704485	-5.794146	-10.909064	-2.448053	6.012958	-691.244083	0.343895
2022-02-03	450.950012	452.970001	445.709991	446.600006	118024400	0.0	0	-4.897084	0.717650	-5.614733	-10.902124	-2.880662	5.140800	-556.917940	0.374311
2022-02-04	446.350006	452.779999	443.829987	448.700012	118335600	0.0	0	-4.522875	0.873487	-5.396362	-10.817408	-3.263513	4.290382	-462.930241	0.416642
2022-02-07	449.510010	450.859985	446.769989	447.652405	39513832	0.0	0	-4.261719	0.907714	-5.169433	-10.688294	-3.599817	3.488660	-393.824272	0.453311

7310 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 7, 2022, NYSE: 5:46:16, Local: 9:46:16 PST, Day 38/365 (10.00%)')

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: 7.8356 ms (0.0078 s)

	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
2022-02-01	450.679993	453.630005	446.940002	452.950012	123155400	0.0	0	461.063572	440.467170	426.328965	...	0.470652	-6.355974	-0.385707	-5.970267	49.051706	2.872122	2.847201	0	30	70
2022-02-02	455.500000	458.119995	453.049988	457.350006	117361000	0.0	0	460.865603	440.713518	427.294308	...	0.571277	-5.089661	0.704485	-5.794146	52.739652	2.881789	2.857968	0	30	70
2022-02-03	450.950012	452.970001	445.709991	446.600006	118024400	0.0	0	460.478942	440.886810	426.950170	...	0.384374	-4.897084	0.717650	-5.614733	44.302038	2.858003	2.864968	0	30	70
2022-02-04	446.350006	452.779999	443.829987	448.700012	118335600	0.0	0	460.121924	441.089413	426.953306	...	0.442084	-4.522875	0.873487	-5.396362	46.115639	2.862695	2.867999	0	30	70
2022-02-07	449.510010	450.859985	446.769989	447.652405	39513832	0.0	0	459.719042	441.264651	426.914311	...	0.437276	-4.261719	0.907714	-5.169433	45.322806	2.860357	2.866993	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 7, 2022, NYSE: 5:46:16, Local: 9:46:16 PST, Day 38/365 (10.00%)')

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: 6.0854 ms (0.0061 s)

	Open	High	Low	Close	Volume	Dividends	Stock Splits	EMA_10	MACDh_9_19_10	LB	UB	LOGRET_5
Date
2022-02-01	450.679993	453.630005	446.940002	452.950012	123155400	0.0	0	446.298610	0.683990	424.623547	459.148463	0.041655
2022-02-02	455.500000	458.119995	453.049988	457.350006	117361000	0.0	0	448.307954	1.932347	428.254768	465.105242	0.053834
2022-02-03	450.950012	452.970001	445.709991	446.600006	118024400	0.0	0	447.997418	1.748571	439.214911	460.289105	0.034999
2022-02-04	446.350006	452.779999	443.829987	448.700012	118335600	0.0	0	448.125163	1.778507	443.623448	458.580568	0.015158
2022-02-07	449.510010	450.859985	446.769989	447.652405	39513832	0.0	0	448.039207	1.668326	442.682020	458.618957	-0.005031

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 [ ]:

222 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

222 KiB

Raw Blame History