Technical Analysis

The Technical Analysis module is a toolkit for analyzing time-series data, at any resolution. The functions are a collection of formulas that fit into broad categories, and they are mostly derived from the pandas_ta library:

• Momentum
• Overlap (Moving Averages)
• Trend
• Volatility
• Volume
• Other (Fibonacci)

How to Use

Every SDK function also has a secondary _chart command. The table below is a brief description of each SDK function within the ta module; for simplicity, _chart has been omitted. Technical Analysis functions specific to stocks are included as a sub-module, openbb.stocks.ta.

Path	Category	Description
openbb.ta.ad	Volume	Accumulation/Distribution Line
openbb.ta.adosc	Volume	Chaikin Oscillator
openbb.ta.adx	Trend	Average Directional Movement Index
openbb.ta.aroon	Trend	Aroon Indicator
openbb.ta.atr	Volatility	Average True Range
openbb.ta.bbands	Voaltility	Bollinger Bands
openbb.ta.cci	Momentum	Commodity Channel Index
openbb.ta.cg	Momentum	Center of Gravity
openbb.ta.clenow	Momentum	Clenow Volatility Adjusted Momentum
openbb.ta.demark	Momentum	Tom Demark's Sequential Indicator (Unofficial)
openbb.ta.donchian	Volatility	Donchian Channels
openbb.ta.ema	Overlap	Exponential Moving Average
openbb.ta.fib	Other	Fibonacci Retracement
openbb.ta.fisher	Momentum	Fisher Transform
openbb.ta.hma	Overlap	Hull Moving Average
openbb.ta.kc	Volatility	Keltner Channels
openbb.ta.ma	Overlap	Moving Averages (For Charting)
openbb.ta.macd	Momentum	Moving Average Convergence/Divergence
openbb.ta.obv	Volume	On-Balance Volume
openbb.ta.rsi	Momentum	Relative Strength Index
openbb.ta.sma	Overlap	Simple Moving Average
openbb.ta.stoch	Momentum	Stochastic Oscillator
openbb.ta.vwap	Overlap	Volume-Weighted Average Price
openbb.ta.wma	Overlap	Weighted Moving Average
openbb.ta.zlma	Overlap	Zero-Lag Moving Average

The syntax for the data argument can be:

• data = ohlcv_df

  Where functions only require a single column, data = ohlcv_df['Adj Close']

• data = openbb.stocks.load("ticker")

  Target intraday by adding the interval argument to the load syntax.

Best practice is to deploy the first method because the latter will work only with the commands requiring OHLC+V data as inputs. An error message will be returned if this is the case.

openbb.ta.obv(data = openbb.stocks.load('QQQ'))

date	OBV
2019-11-15 00:00:00	1.84279e+07
2019-11-18 00:00:00	3.67938e+07
2019-11-19 00:00:00	5.37171e+07
2019-11-20 00:00:00	1.70881e+07
2022-11-15 00:00:00	-1.09017e+08
2022-11-16 00:00:00	-1.57876e+08
2022-11-17 00:00:00	-2.13339e+08
2022-11-18 00:00:00	-1.59987e+08

The error message:

openbb.ta.rsi(data = openbb.stocks.load('QQQ'))

Please send a series and not a DataFrame.

_chart

To display the chart, instead of raw data, add _chart to the syntax before the (arguments).

openbb.ta.obv_chart(data= openbb.stocks.load('QQQ', start_date = '2022-11-18', interval = 5, prepost = True))

Examples

Import Statements

The examples here assume that this code block is at the top of the Python script of Notebook file:

import pandas as pd
from openbb_terminal.sdk import openbb
# %matplotlib inline (uncomment for Jupyter environments)

MA (Moving Averages)

The different types of moving averages, which also are individual functions (e.g., openbb.ta.ema), are available as an argument (ma_type) to the ma command. There are five accepted arguments, they are listed below in brackets:

• Simple (SMA)
• Exponential (EMA)
• Hull (HMA)
• Weighted (WMA)
• Zero-Lag (ZLMA)

The window argument anticipates a list of integers representing the interval (minutes, days, weeks, months, etc.) to measure against the timestamp of the DataFrame's index. The example below is a daily timeseries of S&P E-Mini Futures:

es = openbb.stocks.load("ES=F")

openbb.ta.ma_chart(
    data = es['Adj Close'],
    symbol = 'E-Mini S&P Futures',
    ma_type = 'SMA',
    window = [21, 150])

Changing, ma_type, to, ZLMA:

ATR (Average True Range)

The atr command requires OHLC data, the data argument can be the load function.

ticker = 'ES=F'
start = '2000-01-01'

df_atr = openbb.ta.atr(data = openbb.stocks.load(f"{ticker}", start_date = f"{start}", monthly = True), window = 6)

df_atr.tail(5)

date	ATRe_6
2022-07-01 00:00:00	454.457
2022-08-01 00:00:00	431.612
2022-09-01 00:00:00	469.08
2022-10-01 00:00:00	455.7
2022-11-01 00:00:00	424.5

Donchian

To use the same data for multiple functions, it is more efficient to first load to a Pandas DataFrame:

ticker = 'ES=F'
start = '2000-01-01'
data_df: pd.DataFrame = openbb.stocks.load(f"{ticker}", start_date = f"{start}", monthly = True)

openbb.ta.donchian_chart(data_df)

The output from a function can be joined to the OHLC data:

ticker = 'ES=F'
start = '2000-01-01'
data_df: pd.DataFrame = openbb.stocks.load(f"{ticker}", start_date = f"{start}", monthly = True)

donchian = openbb.ta.donchian(data_df)

data_df = data_df.join(donchian)

data_df.tail(5)

date	Open	High	Low	Close	Adj Close	Volume	DCL_20_20	DCM_20_20	DCU_20_20
2022-07-01 00:00:00	3782	4144	3723.75	4133.5	4133.5	3.40941e+07	3198	4003.12	4808.25
2022-08-01 00:00:00	4137.5	4327.5	3953	3956.5	3956.5	3.84732e+07	3225	4016.62	4808.25
2022-09-01 00:00:00	3958	4158	3595.25	3601.5	3601.5	4.68698e+07	3595.25	4201.75	4808.25
2022-10-01 00:00:00	3593.25	3924.25	3502	3883	3883	4.80686e+07	3502	4155.12	4808.25
2022-11-01 00:00:00	3884	4050.75	3704.25	3974	3974	2.65215e+07	3502	4155.12	4808.25