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 v0.3.54b0
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 = 'Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)'
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 = 'Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)'
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='Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)')

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='Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)')

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='Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)')

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<00:00, 130.05it/s]

[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)
[i] Analysis Time: 64.1480 ms (0.0641 s) for 5 columns (avg 12.8315 ms / col)
[+] Downloading[yahoo]: IWM[D]

[+] yf | IWM(5493, 7): 3181.7138 ms (3.1817 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, 1049.73it/s]

[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Friday March 25, 2022, NYSE: 4:36:03, Local: 8:36:03 PDT, Day 84/365 (23.00%)
[i] Analysis Time: 6.5667 ms (0.0066 s) for 5 columns (avg 1.3141 ms / col)

In [12]:

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

Out [12]:

'SPY: (7343, 12), IWM: (5493, 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	25.566158	25.566158	25.438963	25.547987	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.566152	25.729689	25.566152	25.729689	480500	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.711512	25.802366	25.657000	25.784195	201300	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-03	25.820536	26.074926	25.802366	26.056755	529400	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-04	26.147597	26.220280	25.856866	26.165768	531500	0.0	0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
2022-03-21	444.339996	446.459991	440.679993	444.390015	88349800	0.0	0	428.742212	429.172800	440.424402	443.089030	123319985.0
2022-03-22	445.859985	450.579987	445.859985	449.589996	74650400	0.0	0	432.205124	430.240318	440.123272	443.253448	120832915.0
2022-03-23	446.910004	448.489990	443.709991	443.799988	79426100	0.0	0	433.976492	431.398157	439.717905	443.388472	118175320.0
2022-03-24	445.940002	450.500000	444.760010	450.489990	64565700	0.0	0	436.609262	432.573979	439.361801	443.560056	110706460.0
2022-03-25	451.160004	452.980011	448.429993	448.980011	24148266	0.0	0	439.630359	433.203265	438.950176	443.714416	105823648.3

7343 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: 5.2975 ms (0.0053 s) for 5 columns (avg 1.0611 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	25.566158	25.566158	25.438963	25.547987	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.566152	25.729689	25.566152	25.729689	480500	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.711512	25.802366	25.657000	25.784195	201300	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-03	25.820536	26.074926	25.802366	26.056755	529400	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-04	26.147597	26.220280	25.856866	26.165768	531500	0.0	0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
2022-03-21	444.339996	446.459991	440.679993	444.390015	88349800	0.0	0	428.742212	429.172800	440.424402	443.089030	123319985.0
2022-03-22	445.859985	450.579987	445.859985	449.589996	74650400	0.0	0	432.205124	430.240318	440.123272	443.253448	120832915.0
2022-03-23	446.910004	448.489990	443.709991	443.799988	79426100	0.0	0	433.976492	431.398157	439.717905	443.388472	118175320.0
2022-03-24	445.940002	450.500000	444.760010	450.489990	64565700	0.0	0	436.609262	432.573979	439.361801	443.560056	110706460.0
2022-03-25	451.160004	452.980011	448.429993	448.980011	24148266	0.0	0	439.630359	433.203265	438.950176	443.714416	105823648.3

7343 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='Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)')

In [16]:

watch.load("IWM")

Out [16]:

[i] Loaded IWM[D]: IWM_D.csv
[i] Analysis Time: 2.0662 ms (0.0021 s) for 2 columns (avg 1.0370 ms / col)

	Open	High	Low	Close	Volume	Dividends	Stock Splits	SMA_50	SMA_200
Date
2000-05-26	34.265232	34.406338	34.100608	34.406338	74800	0.0	0.0	NaN	NaN
2000-05-30	34.900195	35.676277	34.900195	35.676277	57600	0.0	0.0	NaN	NaN
2000-05-31	35.793886	36.264239	35.793886	35.805645	36000	0.0	0.0	NaN	NaN
2000-06-01	36.540549	36.616982	36.540549	36.616982	7000	0.0	0.0	NaN	NaN
2000-06-02	38.274980	38.521915	38.274980	38.521915	29400	0.0	0.0	NaN	NaN
...	...	...	...	...	...	...	...	...	...
2022-03-21	206.793514	207.771587	203.539915	205.036972	26747500	0.0	0.0	201.746244	217.737591
2022-03-22	205.905258	208.380389	205.396264	207.092926	24699900	0.0	0.0	201.575179	217.631726
2022-03-23	205.745571	206.793514	203.350285	203.499985	19775000	0.0	0.0	201.347227	217.495812
2022-03-24	204.380005	205.899994	202.729996	205.839996	19731700	0.4	0.0	201.120365	217.379275
2022-03-25	206.089996	206.619995	204.449997	205.070007	6913761	0.0	0.0	200.910439	217.267384

5493 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='Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)')

In [18]:

watch.load("IWM")

Out [18]:

[i] Loaded IWM[D]: IWM_D.csv
[i] Analysis Time: 291.3743 ms (0.2914 s) for 8 columns (avg 36.4225 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	34.265232	34.406338	34.100608	34.406338	74800	0.0	0.0	NaN	NaN	0.000000	NaN	NaN	NaN	NaN	NaN
2000-05-30	34.900195	35.676277	34.900195	35.676277	57600	0.0	0.0	NaN	NaN	0.036245	NaN	NaN	NaN	NaN	NaN
2000-05-31	35.793886	36.264239	35.793886	35.805645	36000	0.0	0.0	NaN	NaN	0.039865	NaN	NaN	NaN	NaN	NaN
2000-06-01	36.540549	36.616982	36.540549	36.616982	7000	0.0	0.0	NaN	NaN	0.062271	NaN	NaN	NaN	NaN	NaN
2000-06-02	38.274980	38.521915	38.274980	38.521915	29400	0.0	0.0	NaN	NaN	0.112987	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-03-21	206.793514	207.771587	203.539915	205.036972	26747500	0.0	0.0	201.810704	200.489627	1.784950	55.186551	209.222114	-1.0	NaN	209.222114
2022-03-22	205.905258	208.380389	205.396264	207.092926	24699900	0.0	0.0	202.984531	201.089927	1.794927	57.336042	209.222114	-1.0	NaN	209.222114
2022-03-23	205.745571	206.793514	203.350285	203.499985	19775000	0.0	0.0	203.099077	201.309023	1.777425	52.588795	209.222114	-1.0	NaN	209.222114
2022-03-24	204.380005	205.899994	202.729996	205.839996	19731700	0.4	0.0	203.708170	201.720930	1.788858	55.190946	209.222114	-1.0	NaN	209.222114
2022-03-25	206.089996	206.619995	204.449997	205.070007	6913761	0.0	0.0	204.010800	202.025391	1.785111	54.138003	209.222114	-1.0	NaN	209.222114

5493 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='Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)')

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='Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)')

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: 2.7383 ms (0.0027 s) for 4 columns (avg 0.6859 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	25.566158	25.566158	25.438963	25.547987	1003200	0.0	0	NaN	NaN	NaN	NaN
1993-02-01	25.566152	25.729689	25.566152	25.729689	480500	0.0	0	NaN	NaN	NaN	NaN
1993-02-02	25.711512	25.802366	25.657000	25.784195	201300	0.0	0	NaN	NaN	NaN	NaN
1993-02-03	25.820536	26.074926	25.802366	26.056755	529400	0.0	0	NaN	NaN	NaN	NaN
1993-02-04	26.147597	26.220280	25.856866	26.165768	531500	0.0	0	NaN	NaN	25.856879	NaN
...	...	...	...	...	...	...	...	...	...	...	...
2022-03-21	444.339996	446.459991	440.679993	444.390015	88349800	0.0	0	1.095882e+08	123319985.0	438.254397	435.509988
2022-03-22	445.859985	450.579987	445.859985	449.589996	74650400	0.0	0	1.032359e+08	120832915.0	442.032930	440.881722
2022-03-23	446.910004	448.489990	443.709991	443.799988	79426100	0.0	0	9.890684e+07	118175320.0	442.621949	444.560222
2022-03-24	445.940002	450.500000	444.760010	450.489990	64565700	0.0	0	9.266299e+07	110706460.0	445.244630	447.088724
2022-03-25	451.160004	452.980011	448.429993	448.980011	24148266	0.0	0	8.020577e+07	105823648.3	446.489757	448.436489

7343 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='Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)')

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: 6.3230 ms (0.0063 s) for 8 columns (avg 0.7911 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	25.566158	25.566158	25.438963	25.547987	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.566152	25.729689	25.566152	25.729689	480500	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.711512	25.802366	25.657000	25.784195	201300	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-03	25.820536	26.074926	25.802366	26.056755	529400	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-04	26.147597	26.220280	25.856866	26.165768	531500	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-03-21	444.339996	446.459991	440.679993	444.390015	88349800	0.0	0	-0.963212	3.165579	-4.128790	-8.444926	-5.428754	-2.412583	-111.118362	1.240267
2022-03-22	445.859985	450.579987	445.859985	449.589996	74650400	0.0	0	0.435860	3.651720	-3.215860	-9.101777	-5.146085	-1.190393	-153.735975	1.205559
2022-03-23	446.910004	448.489990	443.709991	443.799988	79426100	0.0	0	1.065150	3.424809	-2.359658	-9.517220	-4.773612	-0.030005	-198.742870	1.115435
2022-03-24	445.940002	450.500000	444.760010	450.489990	64565700	0.0	0	2.079721	3.551503	-1.471782	-9.843831	-4.333591	1.176648	-254.303610	1.081945
2022-03-25	451.160004	452.980011	448.429993	448.980011	24148266	0.0	0	2.730458	3.361792	-0.631334	-10.125351	-3.889260	2.346830	-320.682608	1.030759

7343 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='Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)')

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: 9.0647 ms (0.0091 s) for 13 columns (avg 0.6977 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
2022-03-21	444.339996	446.459991	440.679993	444.390015	88349800	0.0	0	440.424402	443.089030	411.721471	...	0.935990	-0.963212	3.165579	-4.128790	57.318881	2.856144	2.840482	0	30	70
2022-03-22	445.859985	450.579987	445.859985	449.589996	74650400	0.0	0	440.123272	443.253448	410.665112	...	0.994239	0.435860	3.651720	-3.215860	60.266397	2.867778	2.851802	0	30	70
2022-03-23	446.910004	448.489990	443.709991	443.799988	79426100	0.0	0	439.717905	443.388472	411.493926	...	0.811538	1.065150	3.424809	-2.359658	55.657410	2.854815	2.856143	0	30	70
2022-03-24	445.940002	450.500000	444.760010	450.489990	64565700	0.0	0	439.361801	443.560056	411.134941	...	0.917836	2.079721	3.551503	-1.471782	59.510462	2.869777	2.860990	0	30	70
2022-03-25	451.160004	452.980011	448.429993	448.980011	24148266	0.0	0	438.950176	443.714416	410.643102	...	0.849659	2.730458	3.361792	-0.631334	58.279543	2.866420	2.862987	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='Friday March 25, 2022, NYSE: 4:36:00, Local: 8:36:00 PDT, Day 84/365 (23.00%)')

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: 5.4253 ms (0.0054 s) for 5 columns (avg 1.0861 ms / col)

	Open	High	Low	Close	Volume	Dividends	Stock Splits	EMA_10	MACDh_9_19_10	LB	UB	LOGRET_5
Date
2022-03-21	444.339996	446.459991	440.679993	444.390015	88349800	0.0	0	433.646674	3.859133	422.794521	452.299498	0.066718
2022-03-22	445.859985	450.579987	445.859985	449.589996	74650400	0.0	0	436.545460	4.296679	432.162252	452.827702	0.056600
2022-03-23	446.910004	448.489990	443.709991	443.799988	79426100	0.0	0	437.864465	3.814877	438.116668	450.684931	0.021706
2022-03-24	445.940002	450.500000	444.760010	450.489990	64565700	0.0	0	440.160015	3.831127	440.822922	452.293069	0.024234
2022-03-25	451.160004	452.980011	448.429993	448.980011	24148266	0.0	0	441.763651	3.454843	441.875188	453.024812	0.009983

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.

235 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

235 KiB

Raw Blame History