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 v0.3.63b0
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 = 'Sunday May 1, 2022, NYSE: 14:13:39, Local: 18:13:39 PDT, Day 121/365 (33.00%)'
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 = 'Sunday May 1, 2022, NYSE: 14:13:39, Local: 18:13:39 PDT, Day 121/365 (33.00%)'
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='Sunday May 1, 2022, NYSE: 14:13:39, Local: 18:13:39 PDT, Day 121/365 (33.00%)')

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='Sunday May 1, 2022, NYSE: 14:13:39, Local: 18:13:39 PDT, Day 121/365 (33.00%)')

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='Sunday May 1, 2022, NYSE: 14:13:39, Local: 18:13:39 PDT, Day 121/365 (33.00%)')

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(7367, 7): 3219.4573 ms (3.2195 s)
[+] Saving: /Users/kj/av_data/SPY_D.csv
[+] Study: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': True, 'append': True}
[i] No multiprocessing (cores = 0).

[i] Progress: 100%|███████████████████████████| 5/5 [00:00<00:00, 116.92it/s]

[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Sunday May 1, 2022, NYSE: 14:13:43, Local: 18:13:43 PDT, Day 121/365 (33.00%)
[i] Analysis Time: 57.7287 ms (0.0577 s) for 5 columns (avg 11.5490 ms / col)
[+] Downloading[yahoo]: IWM[D]

[+] yf | IWM(5517, 7): 3059.8179 ms (3.0598 s)
[+] Saving: /Users/kj/av_data/IWM_D.csv
[+] Study: Common Price and Volume SMAs
[i] Indicator arguments: {'timed': True, 'append': True}
[i] No multiprocessing (cores = 0).

[i] Progress: 100%|██████████████████████████| 5/5 [00:00<00:00, 1228.20it/s]

[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Sunday May 1, 2022, NYSE: 14:13:46, Local: 18:13:46 PDT, Day 121/365 (33.00%)
[i] Analysis Time: 5.2669 ms (0.0053 s) for 5 columns (avg 1.0540 ms / col)

In [12]:

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

Out [12]:

'SPY: (7367, 12), IWM: (5517, 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	25.566139	25.566139	25.438944	25.547968	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.566160	25.729696	25.566160	25.729696	480500	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.711524	25.802377	25.657012	25.784206	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	26.147607	26.220289	25.856876	26.165777	531500	0.0	0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
2022-04-25	423.670013	428.690002	418.839996	428.510010	119647700	0.0	0	437.964005	445.552502	438.527184	446.076082	86801825.0
2022-04-26	425.829987	426.040009	416.070007	416.100006	103996300	0.0	0	435.582004	443.562003	438.067266	445.992511	88575150.0
2022-04-27	417.239990	422.920013	415.010010	417.269989	122030000	0.0	0	433.480002	441.348003	437.659459	445.922166	90347575.0
2022-04-28	422.290009	429.640015	417.600006	427.809998	105449100	0.0	0	431.930002	439.803502	437.321290	445.901304	91636685.0
2022-04-29	423.589996	425.869995	411.209991	412.000000	145187900	0.0	0	429.351001	437.821501	436.656953	445.808769	92811085.0

7367 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: 3.1206 ms (0.0031 s) for 5 columns (avg 0.6251 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	25.566139	25.566139	25.438944	25.547968	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.566160	25.729696	25.566160	25.729696	480500	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.711524	25.802377	25.657012	25.784206	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	26.147607	26.220289	25.856876	26.165777	531500	0.0	0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
2022-04-25	423.670013	428.690002	418.839996	428.510010	119647700	0.0	0	437.964005	445.552502	438.527184	446.076082	86801825.0
2022-04-26	425.829987	426.040009	416.070007	416.100006	103996300	0.0	0	435.582004	443.562003	438.067266	445.992511	88575150.0
2022-04-27	417.239990	422.920013	415.010010	417.269989	122030000	0.0	0	433.480002	441.348003	437.659459	445.922166	90347575.0
2022-04-28	422.290009	429.640015	417.600006	427.809998	105449100	0.0	0	431.930002	439.803502	437.321290	445.901304	91636685.0
2022-04-29	423.589996	425.869995	411.209991	412.000000	145187900	0.0	0	429.351001	437.821501	436.656953	445.808769	92811085.0

7367 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='Sunday May 1, 2022, NYSE: 14:13:39, Local: 18:13:39 PDT, Day 121/365 (33.00%)')

In [16]:

watch.load("IWM")

Out [16]:

[i] Loaded IWM[D]: IWM_D.csv
[i] Analysis Time: 1.5740 ms (0.0016 s) for 2 columns (avg 0.7898 ms / col)

	Open	High	Low	Close	Volume	Dividends	Stock Splits	SMA_50	SMA_200
Date
2000-05-26	34.265217	34.406322	34.100593	34.406322	74800	0.0	0.0	NaN	NaN
2000-05-30	34.900198	35.676281	34.900198	35.676281	57600	0.0	0.0	NaN	NaN
2000-05-31	35.793875	36.264228	35.793875	35.805634	36000	0.0	0.0	NaN	NaN
2000-06-01	36.540538	36.616970	36.540538	36.616970	7000	0.0	0.0	NaN	NaN
2000-06-02	38.274976	38.521912	38.274976	38.521912	29400	0.0	0.0	NaN	NaN
...	...	...	...	...	...	...	...	...	...
2022-04-25	190.990005	194.110001	189.210007	193.850006	35556500	0.0	0.0	200.899373	214.747744
2022-04-26	192.320007	192.710007	187.479996	187.740005	40513600	0.0	0.0	200.634474	214.562645
2022-04-27	187.669998	189.779999	186.259995	186.960007	37808000	0.0	0.0	200.367949	214.394826
2022-04-28	189.169998	191.399994	184.710007	190.449997	37405200	0.0	0.0	200.063833	214.261469
2022-04-29	189.589996	191.729996	184.509995	184.949997	41147700	0.0	0.0	199.641135	214.106763

5517 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='Sunday May 1, 2022, NYSE: 14:13:39, Local: 18:13:39 PDT, Day 121/365 (33.00%)')

In [18]:

watch.load("IWM")

Out [18]:

[i] Loaded IWM[D]: IWM_D.csv
[i] Analysis Time: 249.7703 ms (0.2498 s) for 8 columns (avg 31.2219 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	34.265217	34.406322	34.100593	34.406322	74800	0.0	0.0	NaN	NaN	0.000000	NaN	NaN	NaN	NaN	NaN
2000-05-30	34.900198	35.676281	34.900198	35.676281	57600	0.0	0.0	NaN	NaN	0.036246	NaN	NaN	NaN	NaN	NaN
2000-05-31	35.793875	36.264228	35.793875	35.805634	36000	0.0	0.0	NaN	NaN	0.039865	NaN	NaN	NaN	NaN	NaN
2000-06-01	36.540538	36.616970	36.540538	36.616970	7000	0.0	0.0	NaN	NaN	0.062271	NaN	NaN	NaN	NaN	NaN
2000-06-02	38.274976	38.521912	38.274976	38.521912	29400	0.0	0.0	NaN	NaN	0.112987	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-04-25	190.990005	194.110001	189.210007	193.850006	35556500	0.0	0.0	197.294738	199.708898	1.728844	40.519591	205.318726	-1.0	NaN	205.318726
2022-04-26	192.320007	192.710007	187.479996	187.740005	40513600	0.0	0.0	195.171464	198.620817	1.696818	34.286504	204.532482	-1.0	NaN	204.532482
2022-04-27	187.669998	189.779999	186.259995	186.960007	37808000	0.0	0.0	193.346696	197.560743	1.692654	33.576419	201.903553	-1.0	NaN	201.903553
2022-04-28	189.169998	191.399994	184.710007	190.449997	37405200	0.0	0.0	192.702985	196.914312	1.711149	39.603581	201.903553	-1.0	NaN	201.903553
2022-04-29	189.589996	191.729996	184.509995	184.949997	41147700	0.0	0.0	190.980099	195.826647	1.681845	34.318602	201.903553	-1.0	NaN	201.903553

5517 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='Sunday May 1, 2022, NYSE: 14:13:39, Local: 18:13:39 PDT, Day 121/365 (33.00%)')

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='Sunday May 1, 2022, NYSE: 14:13:39, Local: 18:13:39 PDT, Day 121/365 (33.00%)')

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: 2.6022 ms (0.0026 s) for 4 columns (avg 0.6515 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	25.566139	25.566139	25.438944	25.547968	1003200	0.0	0	NaN	NaN	NaN	NaN
1993-02-01	25.566160	25.729696	25.566160	25.729696	480500	0.0	0	NaN	NaN	NaN	NaN
1993-02-02	25.711524	25.802377	25.657012	25.784206	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	26.147607	26.220289	25.856876	26.165777	531500	0.0	0	NaN	NaN	25.856881	NaN
...	...	...	...	...	...	...	...	...	...	...	...
2022-04-25	423.670013	428.690002	418.839996	428.510010	119647700	0.0	0	9.487633e+07	86801825.0	433.629297	436.310946
2022-04-26	425.829987	426.040009	416.070007	416.100006	103996300	0.0	0	9.653451e+07	88575150.0	427.786200	431.983410
2022-04-27	417.239990	422.920013	415.010010	417.269989	122030000	0.0	0	1.011700e+08	90347575.0	424.280797	427.028940
2022-04-28	422.290009	429.640015	417.600006	427.809998	105449100	0.0	0	1.019481e+08	91636685.0	425.457197	423.705404
2022-04-29	423.589996	425.869995	411.209991	412.000000	145187900	0.0	0	1.098098e+08	92811085.0	420.971465	420.144714

7367 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='Sunday May 1, 2022, NYSE: 14:13:39, Local: 18:13:39 PDT, Day 121/365 (33.00%)')

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: 4.9183 ms (0.0049 s) for 8 columns (avg 0.6155 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	25.566139	25.566139	25.438944	25.547968	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.566160	25.729696	25.566160	25.729696	480500	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.711524	25.802377	25.657012	25.784206	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	26.147607	26.220289	25.856876	26.165777	531500	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-04-25	423.670013	428.690002	418.839996	428.510010	119647700	0.0	0	-2.476816	-2.374482	-0.102334	-2.673813	2.605647	7.885107	405.232183	0.018657
2022-04-26	425.829987	426.040009	416.070007	416.100006	103996300	0.0	0	-4.090753	-3.190736	-0.900018	-3.782095	2.201584	8.185264	543.579435	-0.025792
2022-04-27	417.239990	422.920013	415.010010	417.269989	122030000	0.0	0	-5.215284	-3.452213	-1.763071	-4.949416	1.683306	8.316029	788.058859	-0.020042
2022-04-28	422.290009	429.640015	417.600006	427.809998	105449100	0.0	0	-5.196095	-2.746419	-2.449676	-5.858720	1.134857	8.128434	1232.503432	0.047374
2022-04-29	423.589996	425.869995	411.209991	412.000000	145187900	0.0	0	-6.383042	-3.146693	-3.236349	-6.863582	0.534119	7.931821	2770.055766	0.032479

7367 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='Sunday May 1, 2022, NYSE: 14:13:39, Local: 18:13:39 PDT, Day 121/365 (33.00%)')

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: 7.5527 ms (0.0076 s) for 13 columns (avg 0.5813 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
2022-04-25	423.670013	428.690002	418.839996	428.510010	119647700	0.0	0	438.527184	446.076082	426.925628	...	0.042529	-2.476816	-2.374482	-0.102334	39.640020	2.819756	2.838000	0	30	70
2022-04-26	425.829987	426.040009	416.070007	416.100006	103996300	0.0	0	438.067266	445.992511	421.581411	...	-0.124687	-4.090753	-3.190736	-0.900018	32.944671	2.790368	2.824552	0	30	70
2022-04-27	417.239990	422.920013	415.010010	417.269989	122030000	0.0	0	437.659459	445.922166	418.173022	...	-0.019483	-5.215284	-3.452213	-1.763071	34.075197	2.793176	2.811815	0	30	70
2022-04-28	422.290009	429.640015	417.600006	427.809998	105449100	0.0	0	437.321290	445.901304	417.354118	...	0.232877	-5.196095	-2.746419	-2.449676	43.342452	2.818121	2.807079	0	30	70
2022-04-29	423.589996	425.869995	411.209991	412.000000	145187900	0.0	0	436.656953	445.808769	413.025375	...	-0.020676	-6.383042	-3.146693	-3.236349	35.321635	2.780466	2.800377	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='Sunday May 1, 2022, NYSE: 14:13:39, Local: 18:13:39 PDT, Day 121/365 (33.00%)')

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: 5.3013 ms (0.0053 s) for 5 columns (avg 1.0615 ms / col)

	Open	High	Low	Close	Volume	Dividends	Stock Splits	EMA_10	MACDh_9_19_10	LB	UB	LOGRET_5
Date
2022-04-25	423.670013	428.690002	418.839996	428.510010	119647700	0.0	0	437.630600	-2.643389	420.571896	452.372110	-0.021836
2022-04-26	425.829987	426.040009	416.070007	416.100006	103996300	0.0	0	433.715946	-3.590907	410.882598	450.485408	-0.067239
2022-04-27	417.239990	422.920013	415.010010	417.269989	122030000	0.0	0	430.725772	-3.785286	409.127743	441.264262	-0.063689
2022-04-28	422.290009	429.640015	417.600006	427.809998	105449100	0.0	0	430.195631	-2.742518	412.447431	433.844574	-0.023677
2022-04-29	423.589996	425.869995	411.209991	412.000000	145187900	0.0	0	426.887335	-3.187045	407.086343	433.589658	-0.033510

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.

240 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

240 KiB

Raw Blame History