Studies

A Study is a Python DataClass and is used to group such, name, description, cores, ta, et al. Studies simplify both custom and bulk (multi)processing analysis.

Builtin Studies#

Pandas TA has two builtin studies: "All" and "Common".

"ALL"#

import pandas_ta as ta

print(ta.AllStudy)

"Common"#

import pandas_ta as ta

print(ta.CommonStudy)

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The DataClass#

Study DataClass Class to name and group indicators for processing.

Parameters:

Name	Type	Description	Default
`name`	`str`	Name.	required
`ta`	`list of dicts`	i.e [{"kind": "ema", "length", 50}]	`list()`
`cores`	`int`	The number cores to use for multiprocessing. Default: `cpu_count()`	`cpu_count()`
`description`	`str`	Description of what the Study. Default: `""`	`''`
`created`	`str`	DateTime String at creation. Default: Automatically generated.	`get_time(to_string=True)`

Returns:

Type	Description
`DataClass`	The Study to be processed by `df.ta.study()`

All or Common Study

Run

# All
df.ta.study(ta.AllStudy, **kwargs)

# Common
df.ta.study(ta.CommonStudy, **kwargs)

Custom Study

Create

DemoStudy = ta.Study(
    name="Demo Study",
    description="Example Study Group",
    cores=0,  # Usually faster than multiprocessing
    ta = [
        {"kind": "sma", "length": 200},
        {"kind": "sma", "close": "volume", "length": 50},
        {"kind": "bbands", "length": 20},
        {"kind": "rsi"},
        {"kind": "macd", "fast": 8, "slow": 21},
        {"kind": "sma", "close": "volume", "length": 20, "prefix": "VOLUME"}
    ]

Run

df.ta.study(DemoStudy, **kwargs)

Note

See also the Pandas TA "Study" Examples
Case-insensitive "All" is reserved.

Multiprocessing

Not recommended for:

Small sets of indicators
Indicator chains