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 4, 2022, NYSE: 4:10:37, Local: 8:10:37 PST, Day 63/365 (17.00%)'
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 4, 2022, NYSE: 4:10:37, Local: 8:10:37 PST, Day 63/365 (17.00%)'
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 4, 2022, NYSE: 4:10:37, Local: 8:10:37 PST, Day 63/365 (17.00%)')

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 4, 2022, NYSE: 4:10:37, Local: 8:10:37 PST, Day 63/365 (17.00%)')

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 4, 2022, NYSE: 4:10:37, Local: 8:10:37 PST, Day 63/365 (17.00%)')

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(7328, 7): 3410.8553 ms (3.4109 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, 118.73it/s]

[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Friday March 4, 2022, NYSE: 4:10:40, Local: 8:10:40 PST, Day 63/365 (17.00%)
[i] Analysis Time: 59.7448 ms (0.0597 s) for 5 columns (avg 11.9521 ms / col).
[+] Downloading[yahoo]: IWM[D]

[+] yf | IWM(5478, 7): 3192.7451 ms (3.1927 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, 1062.44it/s]

[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Friday March 4, 2022, NYSE: 4:10:44, Local: 8:10:44 PST, Day 63/365 (17.00%)
[i] Analysis Time: 6.1147 ms (0.0061 s) for 5 columns (avg 1.2238 ms / col).

In [12]:

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

Out [12]:

'SPY: (7328, 12), IWM: (5478, 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	25.645575	25.645575	25.517985	25.627348	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.645572	25.809616	25.645572	25.809616	480500	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.791401	25.882536	25.736719	25.864309	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	26.228843	26.301752	25.937209	26.247070	531500	0.0	0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
2022-02-28	432.029999	438.200012	430.700012	436.630005	145347600	0.0	0	435.721002	442.912004	453.780742	443.186750	122484490.0
2022-03-01	435.040009	437.170013	427.109985	429.980011	137785900	0.0	0	434.817004	441.915504	453.084002	443.274417	121761215.0
2022-03-02	432.369995	439.720001	431.570007	437.890015	117726500	0.0	0	433.996005	441.162505	452.644402	443.406882	121489770.0
2022-03-03	440.470001	441.109985	433.799988	435.709991	104097600	0.0	0	432.907004	440.080504	452.259002	443.546169	120826600.0
2022-03-04	431.750000	432.489990	428.109985	428.225006	35680916	0.0	0	432.023505	439.161754	451.562302	443.653377	116709425.8

7328 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: 3.4316 ms (0.0034 s) for 5 columns (avg 0.6878 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	25.645575	25.645575	25.517985	25.627348	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.645572	25.809616	25.645572	25.809616	480500	0.0	0	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.791401	25.882536	25.736719	25.864309	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	26.228843	26.301752	25.937209	26.247070	531500	0.0	0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...
2022-02-28	432.029999	438.200012	430.700012	436.630005	145347600	0.0	0	435.721002	442.912004	453.780742	443.186750	122484490.0
2022-03-01	435.040009	437.170013	427.109985	429.980011	137785900	0.0	0	434.817004	441.915504	453.084002	443.274417	121761215.0
2022-03-02	432.369995	439.720001	431.570007	437.890015	117726500	0.0	0	433.996005	441.162505	452.644402	443.406882	121489770.0
2022-03-03	440.470001	441.109985	433.799988	435.709991	104097600	0.0	0	432.907004	440.080504	452.259002	443.546169	120826600.0
2022-03-04	431.750000	432.489990	428.109985	428.225006	35680916	0.0	0	432.023505	439.161754	451.562302	443.653377	116709425.8

7328 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 4, 2022, NYSE: 4:10:37, Local: 8:10:37 PST, Day 63/365 (17.00%)')

In [16]:

watch.load("IWM")

Out [16]:

[i] Loaded IWM[D]: IWM_D.csv
[i] Analysis Time: 1.9032 ms (0.0019 s) for 2 columns (avg 0.9548 ms / col).

	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	34.968799	35.746407	34.968799	35.746407	57600	0.0	0.0	NaN	NaN
2000-05-31	35.864237	36.335514	35.864237	35.876019	36000	0.0	0.0	NaN	NaN
2000-06-01	36.612390	36.688972	36.612390	36.688972	7000	0.0	0.0	NaN	NaN
2000-06-02	38.350220	38.597641	38.350220	38.597641	29400	0.0	0.0	NaN	NaN
...	...	...	...	...	...	...	...	...	...
2022-02-28	200.470001	204.600006	200.460007	203.320007	34893800	0.0	0.0	208.233600	219.773493
2022-03-01	202.660004	203.789993	197.800003	199.490005	40638900	0.0	0.0	207.959800	219.674165
2022-03-02	200.839996	205.300003	200.690002	204.240005	29978600	0.0	0.0	207.741800	219.597099
2022-03-03	205.089996	205.110001	200.289993	201.820007	29828500	0.0	0.0	207.535800	219.516269
2022-03-04	199.699997	200.860001	197.320007	197.380005	10547256	0.0	0.0	207.119801	219.421625

5478 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 4, 2022, NYSE: 4:10:37, Local: 8:10:37 PST, Day 63/365 (17.00%)')

In [18]:

watch.load("IWM")

Out [18]:

[i] Loaded IWM[D]: IWM_D.csv
[i] Analysis Time: 262.2929 ms (0.2623 s) for 8 columns (avg 32.7873 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	34.332574	34.473957	34.167627	34.473957	74800	0.0	0.0	NaN	NaN	0.000000	NaN	0.000000	1	NaN	NaN
2000-05-30	34.968799	35.746407	34.968799	35.746407	57600	0.0	0.0	NaN	NaN	0.036246	NaN	NaN	1	NaN	NaN
2000-05-31	35.864237	36.335514	35.864237	35.876019	36000	0.0	0.0	NaN	NaN	0.039865	NaN	NaN	1	NaN	NaN
2000-06-01	36.612390	36.688972	36.612390	36.688972	7000	0.0	0.0	NaN	NaN	0.062272	NaN	NaN	1	NaN	NaN
2000-06-02	38.350220	38.597641	38.350220	38.597641	29400	0.0	0.0	NaN	NaN	0.112987	NaN	NaN	1	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-02-28	200.470001	204.600006	200.460007	203.320007	34893800	0.0	0.0	200.524146	201.740315	1.774577	50.680772	211.164767	-1	NaN	211.164767
2022-03-01	202.660004	203.789993	197.800003	199.490005	40638900	0.0	0.0	200.294337	201.535742	1.755560	46.117134	211.164767	-1	NaN	211.164767
2022-03-02	200.839996	205.300003	200.690002	204.240005	29978600	0.0	0.0	201.171152	201.781584	1.779092	51.901774	211.164767	-1	NaN	211.164767
2022-03-03	205.089996	205.110001	200.289993	201.820007	29828500	0.0	0.0	201.315342	201.785077	1.767172	49.014699	211.164767	-1	NaN	211.164767
2022-03-04	199.699997	200.860001	197.320007	197.380005	10547256	0.0	0.0	200.440823	201.384616	1.744927	44.161052	211.164767	-1	NaN	211.164767

5478 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 4, 2022, NYSE: 4:10:37, Local: 8:10:37 PST, Day 63/365 (17.00%)')

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 4, 2022, NYSE: 4:10:37, Local: 8:10:37 PST, Day 63/365 (17.00%)')

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: 2.4727 ms (0.0025 s) for 4 columns (avg 0.6194 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	25.645575	25.645575	25.517985	25.627348	1003200	0.0	0	NaN	NaN	NaN	NaN
1993-02-01	25.645572	25.809616	25.645572	25.809616	480500	0.0	0	NaN	NaN	NaN	NaN
1993-02-02	25.791401	25.882536	25.736719	25.864309	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	26.228843	26.301752	25.937209	26.247070	531500	0.0	0	NaN	NaN	25.937211	NaN
...	...	...	...	...	...	...	...	...	...	...	...
2022-02-28	432.029999	438.200012	430.700012	436.630005	145347600	0.0	0	1.357540e+08	122484490.0	434.164598	429.983874
2022-03-01	435.040009	437.170013	427.109985	429.980011	137785900	0.0	0	1.361234e+08	121761215.0	432.769736	430.541195
2022-03-02	432.369995	439.720001	431.570007	437.890015	117726500	0.0	0	1.327785e+08	121489770.0	434.476496	432.167188
2022-03-03	440.470001	441.109985	433.799988	435.709991	104097600	0.0	0	1.275638e+08	120826600.0	434.887661	433.943960
2022-03-04	431.750000	432.489990	428.109985	428.225006	35680916	0.0	0	1.108578e+08	116709425.8	432.666776	434.352225

7328 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 4, 2022, NYSE: 4:10:37, Local: 8:10:37 PST, Day 63/365 (17.00%)')

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: 5.5015 ms (0.0055 s) for 8 columns (avg 0.6883 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	25.645575	25.645575	25.517985	25.627348	1003200	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-01	25.645572	25.809616	25.645572	25.809616	480500	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1993-02-02	25.791401	25.882536	25.736719	25.864309	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	26.228843	26.301752	25.937209	26.247070	531500	0.0	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2022-02-28	432.029999	438.200012	430.700012	436.630005	145347600	0.0	0	-5.750580	-0.393816	-5.356764	-7.436681	-4.708023	-1.979365	-115.915231	0.308962
2022-03-01	435.040009	437.170013	427.109985	429.980011	137785900	0.0	0	-5.882783	-0.420815	-5.461968	-7.123321	-4.630572	-2.137823	-107.664858	0.248829
2022-03-02	432.369995	439.720001	431.570007	437.890015	117726500	0.0	0	-5.288323	0.138916	-5.427239	-6.963301	-4.577189	-2.191078	-104.260969	0.350985
2022-03-03	440.470001	441.109985	433.799988	435.709991	104097600	0.0	0	-4.936218	0.392817	-5.329035	-6.949968	-4.569517	-2.189067	-104.188276	0.422977
2022-03-04	431.750000	432.489990	428.109985	428.225006	35680916	0.0	0	-5.201191	0.102275	-5.303466	-6.977203	-4.584723	-2.192242	-104.367512	0.371165

7328 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 4, 2022, NYSE: 4:10:37, Local: 8:10:37 PST, Day 63/365 (17.00%)')

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: 6.8512 ms (0.0069 s) for 13 columns (avg 0.5275 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
2022-02-28	432.029999	438.200012	430.700012	436.630005	145347600	0.0	0	453.780742	443.186750	423.963287	...	0.334237	-5.750580	-0.393816	-5.356764	45.161043	2.835426	2.821986	0	30	70
2022-03-01	435.040009	437.170013	427.109985	429.980011	137785900	0.0	0	453.084002	443.274417	422.454396	...	0.193350	-5.882783	-0.420815	-5.461968	41.057957	2.820079	2.822177	0	30	70
2022-03-02	432.369995	439.720001	431.570007	437.890015	117726500	0.0	0	452.644402	443.406882	422.311628	...	0.413201	-5.288323	0.138916	-5.427239	47.202634	2.838308	2.829593	0	30	70
2022-03-03	440.470001	441.109985	433.799988	435.709991	104097600	0.0	0	452.259002	443.546169	422.638842	...	0.374711	-4.936218	0.392817	-5.329035	45.785943	2.833317	2.833023	0	30	70
2022-03-04	431.750000	432.489990	428.109985	428.225006	35680916	0.0	0	451.562302	443.653377	421.260777	...	0.194521	-5.201191	0.102275	-5.303466	41.212414	2.815989	2.828624	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 4, 2022, NYSE: 4:10:37, Local: 8:10:37 PST, Day 63/365 (17.00%)')

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: 5.4602 ms (0.0055 s) for 5 columns (avg 1.0929 ms / col).

	Open	High	Low	Close	Volume	Dividends	Stock Splits	EMA_10	MACDh_9_19_10	LB	UB	LOGRET_5
Date
2022-02-28	432.029999	438.200012	430.700012	436.630005	145347600	0.0	0	436.035275	-0.201140	419.234187	442.445818	0.005512
2022-03-01	435.040009	437.170013	427.109985	429.980011	137785900	0.0	0	434.934318	-0.253662	419.347488	442.496519	0.000954
2022-03-02	432.369995	439.720001	431.570007	437.890015	117726500	0.0	0	435.471717	0.450856	425.878280	442.341727	0.037081
2022-03-03	440.470001	441.109985	433.799988	435.709991	104097600	0.0	0	435.515040	0.726757	429.759432	441.424577	0.017153
2022-03-04	431.750000	432.489990	428.109985	428.225006	35680916	0.0	0	434.189579	0.294918	425.993151	441.380860	-0.021999

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.

232 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

232 KiB

Raw Blame History