Stock Statistics/Indicators Calculation Helper

Introduction

Supply a wrapper StockDataFrame for pandas.DataFrame with inline stock statistics/indicators support.

Supported statistics/indicators are:

Moving Averages: SMA, EMA, SMMA, TEMA, LRMA, KAMA, VWMA, DMA

Momentum: RSI, StochRSI, MACD, PPO, KDJ, ROC, CMO, KST, Coppock, AO, BOP, CTI, Inertia, PSL

Trend: Supertrend, Aroon, Ichimoku, CR, DMI (+DI/-DI/ADX/ADXR), TRIX, WT

Volatility: Bollinger Bands, ATR, TR, CCI, WR, CHOP, KER, Z-Score, MAD, PGO

Volume: VR, MFI, PVO, VWMA

Oscillators: QQE, RVGI, ERI, FTR

Utilities: delta, shift, log return, cross/cross-up/cross-down, comparisons (le/ge/lt/gt/eq/ne), count, max/min in range, permutation

Installation

pip install stockstats

Compatibility

Requires Python 3.9+. CI tests against Python 3.10, 3.11, 3.12, and 3.13.

License

BSD-3-Clause License

Quick Start

Load and wrap data

StockDataFrame works as a wrapper for pandas.DataFrame. Initialize it with wrap or StockDataFrame.retype.

import pandas as pd
from stockstats import wrap

# from CSV
df = wrap(pd.read_csv('stock.csv'))

# from yfinance (disable multi-level index for compatibility)
import yfinance as yf
df = wrap(yf.download('AAPL', multi_level_index=False))

Your data should contain these columns (case-insensitive):

• close: the close price of the period
• high: the highest price of the interval
• low: the lowest price of the interval
• volume: the volume of stocks traded during the interval
• date: timestamp of the record, optional (used as index by default)

You can specify the index column name in wrap or retype. Use unwrap to convert back to a plain pandas.DataFrame.

Access indicators

Indicators are calculated on first access. Delete a column to force re-evaluation.

# indicators with default windows
rsi = df['rsi']           # 14-period RSI (default)
rsi6 = df['rsi_6']        # 6-period RSI

# moving averages on any column
sma = df['close_20_sma']  # 20-period SMA of close
ema = df['high_10_ema']   # 10-period EMA of high

Multi-line indicators

Some indicators generate multiple columns at once.

# MACD generates three columns at once
df.get('macd')
print(df[['macd', 'macds', 'macdh']].tail())

# Bollinger Bands
df.get('boll')
print(df[['boll', 'boll_ub', 'boll_lb']].tail())

Signal detection

# cross-over detection
golden_cross = df['close_10_sma_xu_close_50_sma']  # 10 SMA crosses above 50 SMA

# comparison operators
overbought = df['rsi_ge_70']  # True when RSI >= 70

Initialize all indicators with shortcuts

Some indicators, such as KDJ, BOLL, MFI, have shortcuts. Use df.init_all() to initialize all these indicators.

This operation generates lots of columns. Please use it with caution.

Tutorial

Column naming patterns

Use pattern <column>_<window>_<indicator> for full control:

• high_5_sma - 5 periods simple moving average of the high price
• close_10_ema - 10 periods exponential moving average of the close
• high_-1_d - 1 period delta of the high price (minus means looking backward)

Use pattern <indicator>_<window> when only the window varies:

• rsi_6 - 6 periods RSI
• cci_10 - 10 periods CCI
• atr_13 - 13 periods ATR

Some indicators have default windows. Check their documentation for details.

Configurable parameters

Some statistics have configurable parameters. They are class-level fields. Changes are global and won't affect existing results. Remove existing columns so that they will be re-evaluated the next time you access them.

Statistics/Indicators

Summary

Name	Access Pattern	Default Window	Description
SMA	close_20_sma	-	Simple Moving Average
EMA	close_20_ema	-	Exponential Moving Average
SMMA	close_7_smma	-	Smoothed Moving Average
TEMA	tema	5	Triple Exponential Moving Average
LRMA	close_10_lrma	-	Linear Regression Moving Average
KAMA	close_2_kama	10, 5, 34	Kaufman's Adaptive Moving Average
VWMA	vwma	14	Volume Weighted Moving Average
DMA	dma	10, 50	Difference of Moving Average
RSI	rsi	14	Relative Strength Index
StochRSI	stochrsi	14	Stochastic RSI
MACD	macd	12, 26, 9	Moving Average Convergence Divergence
PPO	ppo	12, 26, 9	Percentage Price Oscillator
KDJ	kdjk	9	Stochastic Oscillator
ROC	close_10_roc	-	Rate of Change
CMO	cmo	14	Chande Momentum Oscillator
KST	kst	-	Know Sure Thing
Coppock	coppock	10, 11, 14	Coppock Curve
AO	ao	5, 34	Awesome Oscillator
BOP	bop	-	Balance of Power
CTI	cti	12	Correlation Trend Indicator
Inertia	inertia	20, 14	Inertia Indicator
PSL	psl	12	Psychological Line
Supertrend	supertrend	14	Supertrend indicator
Aroon	aroon	25	Aroon Oscillator
Ichimoku	ichimoku	9, 26, 52	Ichimoku Cloud
CR	cr	26	Energy Index
DMI	pdi, ndi, adx	14	Directional Movement Index
TRIX	trix	12	Triple Exponential Average
WT	wt1, wt2	10, 21	Wave Trend
Bollinger	boll	20	Bollinger Bands
ATR	atr	14	Average True Range
TR	tr	-	True Range
CCI	cci	14	Commodity Channel Index
WR	wr	14	Williams %R
CHOP	chop	14	Choppiness Index
KER	ker	10	Kaufman's Efficiency Ratio
Z-Score	close_75_z	-	Z-Score
MAD	close_10_mad	-	Mean Absolute Deviation
PGO	pgo	14	Pretty Good Oscillator
VR	vr	26	Volume Variation Index
MFI	mfi	14	Money Flow Index
PVO	pvo	12, 26, 9	Percentage Volume Oscillator
QQE	qqe	14, 5	Quantitative Qualitative Estimation
RVGI	rvgi	14	Relative Vigor Index
ERI	eribull, eribear	13	Elder-Ray Index
FTR	ftr	9	Gaussian Fisher Transform

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Moving Averages

Simple Moving Average

Follow the pattern <columnName>_<window>_sma to retrieve a simple moving average.

Exponential Moving Average

Follow the pattern <columnName>_<window>_ema to retrieve an exponential moving average.

SMMA - Smoothed Moving Average

It requires column and window.

For example, use df['close_7_smma'] to retrieve the 7 periods smoothed moving average of the close price.

TEMA - Triple Exponential Moving Average

TEMA is another implementation for the triple exponential moving average.

TEMA = (3 x EMA) - (3 x EMA of EMA) + (EMA of EMA of EMA)

It takes two parameters, column and window. By default, the column is close, the window is 5.

Use set_dft_window('tema', n) to change the default window.

Examples:

• df['tema'] stands for 5 periods TEMA for the close price.
• df['middle_10_tema'] stands for the 10 periods TEMA for the typical price.

LRMA - Linear Regression Moving Average

Linear regression works by taking various data points in a sample and providing a "best fit" line to match the general trend in the data.

Implementation reference:

https://github.com/twopirllc/pandas-ta/blob/main/pandas_ta/overlap/linreg.py

Examples: * df['close_10_lrma'] linear regression of close price with window size 10

KAMA - Kaufman's Adaptive Moving Average

Kaufman's Adaptive Moving Average is designed to account for market noise or volatility.

It has 2 optional parameters and 2 required parameters: * fast - optional, the parameter for fast EMA smoothing, default to 5 * slow - optional, the parameter for slow EMA smoothing, default to 34 * column - required, the column to calculate * window - required, rolling window size

The default value for window, fast and slow can be configured with set_dft_window('kama', (10, 5, 34))

Examples: * df['close_10,2,30_kama'] retrieves 10 periods KAMA of the close price with fast = 2 and slow = 30 * df['close_2_kama'] retrieves 2 periods KAMA of the close price with default fast and slow

VWMA - Volume Weighted Moving Average

It's the moving average weighted by volume.

It has a parameter for window size. The default window is 14. Change it with set_dft_window('vwma', n).

Examples: * df['vwma'] retrieves the 14 periods VWMA * df['vwma_6'] retrieves the 6 periods VWMA

DMA - Difference of Moving Average

df['dma'] retrieves the difference of 10 periods SMA of the close price and the 50 periods SMA of the close price.

Moving Standard Deviation

Follow the pattern <columnName>_<window>_mstd to retrieve the moving STD.

Moving Variance

Follow the pattern <columnName>_<window>_mvar to retrieve the moving VAR.

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Momentum

RSI - Relative Strength Index

RSI charts the current and historical strength or weakness of a stock. It takes a window parameter.

The default window is 14. Use set_dft_window('rsi', n) to tune it.

Examples:

• df['rsi']: retrieve the RSI of 14 periods
• df['rsi_6']: retrieve the RSI of 6 periods

Stochastic RSI

Stochastic RSI gives traders an idea of whether the current RSI value is overbought or oversold. It takes a window parameter.

The default window is 14. Use set_dft_window('stochrsi', n) to tune it.

Examples:

• df['stochrsi']: retrieve the Stochastic RSI of 14 periods
• df['stochrsi_6']: retrieve the Stochastic RSI of 6 periods

MACD - Moving Average Convergence Divergence

We use the close price to calculate the MACD lines. * df['macd'] is the difference between two exponential moving averages. * df['macds'] is the signal line. * df['macdh'] is the histogram line.

The period of short, long EMA and signal line can be tuned with set_dft_window('macd', (short, long, signal)). The default windows are 12 and 26 and 9.

Note: In July 2017 the code for MACDH was changed to drop an extra 2x multiplier on the final value to align with calculation methods used in tools like cryptowatch, tradingview, etc.

PPO - Percentage Price Oscillator

The Percentage Price Oscillator includes three lines.

• df['ppo'] derives from the difference of 2 exponential moving average.
• df['ppos'] is the signal line.
• df['ppoh'] is the histogram line.

The period of short, long EMA and signal line can be tuned with set_dft_window('ppo', (short, long, signal)). The default windows are 12 and 26 and 9.

KDJ Indicator

The stochastic oscillator is a momentum indicator that uses support and resistance levels.

It includes three lines: * df['kdjk'] - K series * df['kdjd'] - D series * df['kdjj'] - J series

The default window is 9. Use set_dft_window('kdjk', n) to change it. Use df['kdjk_6'] to retrieve the K series of 6 periods.

KDJ also has two configurable parameters named StockDataFrame.KDJ_PARAM. The default value is (2.0/3.0, 1.0/3.0)

ROC - Rate of Change

The Price Rate of Change (ROC) is a momentum-based technical indicator that measures the percentage change in price between the current price and the price a certain number of periods ago.

Formula:

ROC = (PriceP - PricePn) / PricePn * 100

Where: * PriceP: the price of the current period * PricePn: the price of the n periods ago

You need a column name and a period to calculate ROC.

Examples: * df['close_10_roc']: the ROC of the close price in 10 periods * df['high_5_roc']: the ROC of the high price in 5 periods

CMO - Chande Momentum Oscillator

The Chande Momentum Oscillator (CMO) is a technical momentum indicator developed by Tushar Chande.

The formula calculates the difference between the sum of recent gains and the sum of recent losses and then divides the result by the sum of all price movements over the same period.

The default window is 14.

Formula:

CMO = 100 * ((sH - sL) / (sH + sL))

where: * sH=the sum of higher closes over N periods * sL=the sum of lower closes of N periods

Examples: * df['cmo'] returns the CMO with a window of 14 * df['cmo_5'] returns the CMO with a window of 5

KST - Know Sure Thing

The Know Sure Thing (KST) is a momentum oscillator developed by Martin Pring to make rate-of-change readings easier for traders to interpret.

Formula: * KST=(RCMA1x1)+(RCMA2x2)+(RCMA3x3)+(RCMA4x4)

Where: * RCMA1=10-period SMA of 10-period ROC * RCMA2=10-period SMA of 15-period ROC * RCMA3=10-period SMA