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

---

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.

---

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 of 20-period ROC
• RCMA4=15-period SMA of 30-period ROC

Example:

• df['kst'] retrieves the KST.

Coppock Curve

Coppock Curve is a momentum indicator that signals long-term trend reversals.

Formula:

Coppock Curve = 10-period WMA of (14-period RoC + 11-period RoC) WMA = Weighted Moving Average RoC = Rate-of-Change

Examples:

• df['coppock'] returns the Coppock Curve with default windows
• df['coppock_5,10,15'] returns the Coppock Curve with WMA window 5, fast window 10, slow window 15.

Awesome Oscillator

The AO indicator is a good indicator for measuring the market dynamics, it reflects specific changes in the driving force of the market, which helps to identify the strength of the trend, including the points of its formation and reversal.

Awesome Oscillator Formula

• MEDIAN PRICE = (HIGH+LOW)/2
• AO = SMA(MEDIAN PRICE, 5)-SMA(MEDIAN PRICE, 34)

Examples:

• df['ao'] returns the Awesome Oscillator with default windows (5, 34)
• df['ao_3,10'] returns the Awesome Oscillator with a window of 3 and 10

Balance of Power

Balance of Power (BOP) measures the strength of the bulls vs. bears.

Formula:

BOP = (close - open) / (high - low)

Example:

• df['bop'] returns the Balance of Power

Correlation Trend Indicator

Correlation Trend Indicator is a study that estimates the current direction and strength of a trend.

Implementation is based on the following code:

https://github.com/twopirllc/pandas-ta/blob/main/pandas_ta/momentum/cti.py

Examples:

• df['cti'] returns the CTI of close price with window 12
• df['high_5_cti'] returns the CTI of high price with window 5

Inertia Indicator

In financial markets, the concept of inertia was given by Donald Dorsey in the 1995 issue of Technical Analysis of Stocks and Commodities through the Inertia Indicator. The Inertia Indicator is moment-based and is an extension of Dorsey's Relative Volatility Index (RVI).

Formula:

• inertia = n periods linear regression of RVGI

Examples:

• df['inertia'] retrieves the inertia of 20 periods linear regression of 14 periods RVGI
• df['inertia_10'] retrieves the inertia of 10 periods linear regression of 14 periods RVGI

Psychological Line (PSL)

The Psychological Line indicator is the ratio of the number of rising periods over the total number of periods.

Formula:

• PSL = (Number of Rising Periods) / (Total Number of Periods) * 100

Example:

• df['psl'] retrieves the PSL with default window 12.
• df['psl_10'] retrieves the PSL with window 10.
• df['high_12_psl'] retrieves the PSL of high price with window 12.

---

Trend

Supertrend

Supertrend indicates the current trend. We use the algorithm described here. It includes 3 lines:

• df['supertrend'] is the trend line.
• df['supertrend_ub'] is the upper band of the trend
• df['supertrend_lb'] is the lower band of the trend

It has 2 parameters:

• StockDataFrame.SUPERTREND_MUL is the multiplier of the band, default to 3.
• the default window size is 14. Change it with set_dft_window('supertrend', n)

Aroon Oscillator

The Aroon Oscillator measures the strength of a trend and the likelihood that it will continue.

The default window is 25.

• Aroon Oscillator = Aroon Up - Aroon Down
• Aroon Up = 100 * (n - periods since n-period high) / n
• Aroon Down = 100 * (n - periods since n-period low) / n
• n = window size

Examples:

• df['aroon'] returns Aroon oscillator with a window of 25
• df['aroon_14'] returns Aroon oscillator with a window of 14

Ichimoku Cloud

The Ichimoku Cloud is a collection of technical indicators that show support and resistance levels, as well as momentum and trend direction.

In this implementation, we only calculate the delta between lead A and lead B (which is the width of the cloud).

It contains three windows:

• window for the conversion line, default to 9
• window for the baseline and the shifts, default to 26
• window for the leading line, default to 52

Formula:

• conversion line = (PH9 + PL9) / 2
• baseline = (PH26 + PL26) / 2
• leading span A = (conversion line + baseline) / 2
• leading span B = (PH52 + PL52) / 2
• result = leading span A - leading span B

Where:

• PH = Period High
• PL = Period Low

Examples:

• df['ichimoku'] returns the ichimoku cloud width with default windows
• df['ichimoku_7,22,44'] returns the ichimoku cloud width with window sizes 7, 22, 44

CR - Energy Index

The Energy Index (Intermediate Willingness Index) uses the relationship between the highest price, the lowest price and yesterday's middle price to reflect the market's willingness to buy and sell.

It contains 4 lines:

• df['cr'] - the CR line
• df['cr-ma1'] - StockDataFrame.CR_MA[0] periods of the CR moving average, the default window is 5
• df['cr-ma2'] - StockDataFrame.CR_MA[1] periods of the CR moving average, the default window is 10
• df['cr-ma3'] - StockDataFrame.CR_MA[2] periods of the CR moving average, the default window is 20

DMI - Directional Movement Index

The directional movement index (DMI) identifies in which direction the price of an asset is moving.

It has several lines:

• df['pdi'] is the positive directional movement line (+DI)
• df['ndi'] is the negative directional movement line (-DI)
• df['dx'] is the directional index (DX)
• df['adx'] is the average directional index (ADX)
• df['adxr'] is an EMA for ADX

It has several parameters.

• default window for +DI is 14, change it with set_dft_window('pdi', n)
• default window for -DI is 14, change it with set_dft_window('ndi', n)
• StockDataFrame.DX_SMMA - window for DX, default to 14
• StockDataFrame.ADX_EMA - window for ADX, default to 6
• StockDataFrame.ADXR_EMA - window for ADXR, default to 6

TRIX - Triple Exponential Average

The triple exponential average is used to identify oversold and overbought markets.

The algorithm is:

TRIX = (TripleEMA - LastTripleEMA) * 100 / LastTripleEMA
TripleEMA = EMA of EMA of EMA
LastTripleEMA = TripleEMA of the last period

It requires column and window. By default, the column is close, the window is 12.

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

Examples:

• df['trix'] stands for 12 periods Trix for the close price.
• df['middle_10_trix'] stands for the 10 periods Trix for the typical price.

WT - Wave Trend

Retrieve the LazyBear's Wave Trend with df['wt1'] and df['wt2'].

Wave trend uses two parameters. You can tune them with set_dft_window('wt', (10, 21)).

---

Volatility

Bollinger Bands

The Bollinger bands includes three lines:

• df['boll'] is the baseline
• df['boll_ub'] is the upper band
• df['boll_lb'] is the lower band

The default window of boll is 20. You can also supply your window with df['boll_10']. It will also generate the boll_ub_10 and boll_lb_10 column.

The default period of the Bollinger Band can be changed with set_dft_window('boll', n). The width of the bands can be tuned with StockDataFrame.BOLL_STD_TIMES. The default value is 2.

TR - True Range of Trading

TR is a measure of the volatility of a High-Low-Close series. It is used for calculating the ATR.

ATR - Average True Range

The Average True Range is an N-period smoothed moving average (SMMA) of the true range value. Default to 14 periods.

Users can modify the default window with set_dft_window('atr', n).

Example:

• df['atr'] retrieves the 14 periods ATR.
• df['atr_5'] retrieves the 5 periods ATR.

CCI - Commodity Channel Index

CCI stands for Commodity Channel Index.

It requires a window parameter. The default window is 14. Use set_dft_window('cci', n) to change it.

Examples:

• df['cci'] retrieves the default 14 periods CCI.
• df['cci_6'] retrieves the 6 periods CCI.

WR - Williams Overbought/Oversold Index

Williams Overbought/Oversold index is a type of momentum indicator that moves between 0 and -100 and measures overbought and oversold levels.

It takes a window parameter. The default window is 14. Use set_dft_window('wr', n) to change the default window.

Examples:

• df['wr'] retrieves the 14 periods WR.
• df['wr_6'] retrieves the 6 periods WR.

CHOP - Choppiness Index

The Choppiness Index determines if the market is choppy.

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

Examples:

• df['chop'] retrieves the 14 periods CHOP
• df['chop_6'] retrieves the 6 periods CHOP

KER - Kaufman's Efficiency Ratio

The Efficiency Ratio (ER) is calculated by dividing the price change over a period by the absolute sum of the price movements that occurred to achieve that change.

The resulting ratio ranges between 0 and 1 with higher values representing a more efficient or trending market.

The default column is close.

The default window is 10.

Formula:

• window_change = ABS(close - close[n])
• last_change = ABS(close-close[1])
• volatility = moving sum of last_change in n
• KER = window_change / volatility

Examples:

• df['ker'] retrieves the 10 periods KER of the close price
• df['high_5_ker'] retrieves 5 periods KER of the high price

Z-Score

Z-score is a statistical measurement that describes a value's relationship to the mean of a group of values.

There is no default column name or window for Z-Score.

The statistical formula for a value's z-score is calculated using the following formula:

z = ( x - mu ) / sigma

Where:

• z = Z-score
• x = the value being evaluated
• mu = the mean
• sigma = the standard deviation

Examples:

• df['close_75_z'] returns the Z-Score of close price with a window of 75

MAD - Mean Absolute Deviation

The mean absolute deviation of a dataset is the average distance between each data point and the mean. It gives us an idea about the variability in a dataset.

Formula:

1. Calculate the mean.
2. Calculate how far away each data point is from the mean using positive distances. These are called absolute deviations.
3. Add those deviations together.
4. Divide the sum by the number of data points.

Example:

• df['close_10_mad']: the MAD of the close price in 10 periods

Pretty Good Oscillator (PGO)

The Pretty Good Oscillator indicator by Mark Johnson measures the distance of the current close from its N-day simple moving average, expressed in terms of an average true range over a similar period.

Formula:

• PGO = (Close - SMA) / (EMA of TR)

Example:

• df['pgo'] retrieves the PGO with default window 14.
• df['pgo_10'] retrieves the PGO with window 10.

---

Volume

VR - Volume Variation Index

It is the strength index of the trading volume.

It has a default window of 26. Change it with set_dft_window('vr', n).

Examples:

• df['vr'] retrieves the 26 periods VR.
• df['vr_6'] retrieves the 6 periods VR.

MFI - Money Flow Index

The Money Flow Index identifies overbought or oversold signals in an asset.

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

Examples:

• df['mfi'] retrieves the 14 periods MFI
• df['mfi_6'] retrieves the 6 periods MFI

Percentage Volume Oscillator (PVO)

The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume. The PVO measures the difference between two volume-based moving averages as a percentage of the larger moving average.

Formula:

• Percentage Volume Oscillator (PVO): ((12-day EMA of Volume - 26-day EMA of Volume)/26-day EMA of Volume) x 100
• Signal Line: 9-day EMA of PVO
• PVO Histogram: PVO - Signal Line

Example:

• df['pvo'] derives from the difference of 2 exponential moving average.
• df['pvos'] is the signal line.
• df['pvoh'] is the histogram line.

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

---

Oscillators

QQE - Quantitative Qualitative Estimation

The Qualitative Quantitative Estimation (QQE) indicator works like a smoother version of the popular Relative Strength Index (RSI) indicator. QQE expands on RSI by adding two volatility based trailing stop lines. These trailing stop lines are composed of a fast and a slow moving Average True Range (ATR). These ATR lines are smoothed making this indicator less susceptible to short term volatility.

Implementation reference: https://github.com/twopirllc/pandas-ta/blob/main/pandas_ta/momentum/qqe.py

Example:

• df['qqe'] retrieves the QQE with RSI window 14, MA window 5.
• df['qqel'] retrieves the QQE long
• df['qqes'] retrieves the QQE short
• df['qqe_10,4'] retrieves the QQE with RSI window 10, MA window 4
• df['qqel_10,4'] retrieves the QQE long with customized windows. Initialized by retrieving df['qqe_10,4']
• df['qqes_10,4'] retrieves the QQE short with customized windows Initialized by retrieving df['qqe_10,4']

The period of short, long EMA and signal line can be tuned with set_dft_window('qqe', (rsi, rsi_ma)). The default windows are 14 and 5.

RVGI - Relative Vigor Index

The Relative Vigor Index (RVI) is a momentum indicator used in technical analysis that measures the strength of a trend by comparing a security's closing price to its trading range while smoothing the results using a simple moving average (SMA).

Formula:

• NUMERATOR= (a+(2xb)+(2xc)+d) / 6
• DENOMINATOR= (e+(2xf)+(2xg)+h) / 6
• RVI= SMA-N of DENOMINATOR / SMA-N of NUMERATOR
• Signal Line = (RVI+(2xi)+(2xj)+k) / 6

where:

• a=Close-Open
• b=Close-Open One Bar Prior to a
• c=Close-Open One Bar Prior to b
• d=Close-Open One Bar Prior to c
• e=High-Low of Bar a
• f=High-Low of Bar b
• g=High-Low of Bar c
• h=High-Low of Bar d
• i=RVI Value One Bar Prior
• j=RVI Value One Bar Prior to i
• k=RVI Value One Bar Prior to j
• N=Minutes/Hours/Days/Weeks/Months

Examples:

• df['rvgi'] retrieves the RVGI line of window 14
• df['rvgis'] retrieves the RVGI signal line of window 14
• df['rvgi_5'] retrieves the RVGI line of window 5
• df['rvgis_5'] retrieves the RVGI signal line of window 5

ERI - Elder-Ray Index

The Elder-Ray Index contains the bull and the bear power. Both are calculated based on the EMA of the close price.

The default window is 13.

Formula:

• Bulls Power = High - EMA
• Bears Power = Low - EMA
• EMA is exponential moving average of close of N periods

Examples:

• df['eribull'] retrieves the 13 periods bull power
• df['eribear'] retrieves the 13 periods bear power
• df['eribull_5'] retrieves the 5 periods bull power
• df['eribear_5'] retrieves the 5 periods bear power

FTR - Gaussian Fisher Transform Price Reversals

The Gaussian Fisher Transform Price Reversals indicator, dubbed FTR for short, is a stat based price reversal detection indicator inspired by and based on the work of the electrical engineer now private trader John F. Ehlers.

Implementation reference:

https://github.com/twopirllc/pandas-ta/blob/084dbe1c4b76082f383fa3029270ea9ac35e4dc7/pandas_ta/momentum/fisher.py#L9

Formula:

• Fisher Transform = 0.5 * ln((1 + X) / (1 - X))
• X is a series whose values are between -1 to 1

Examples:

• df['ftr'] returns the FTR with window 9
• df['ftr_20'] returns the FTR with window 20

---

Utilities

Delta of Periods

Using pattern <column>_<window>_d to retrieve the delta between different periods.

You can also use <column>_delta as a shortcut to <column>_-1_d

Examples:

• df['close_-1_d'] retrieves the close price delta between current and prev. period.
• df['close_delta'] is the same as df['close_-1_d']
• df['high_2_d'] retrieves the high price delta between current and 2 days later

Shift Periods

Shift the column backward or forward. It takes 2 parameters:

• the name of the column to shift
• periods to shift, can be negative

We fill the head and tail with the nearest data.

See the example below:

In [15]: df[['close', 'close_-1_s', 'close_2_s']]
Out[15]:
          close  close_-1_s  close_2_s
date
20040817  11.20       11.20      10.53
20040818  10.29       11.20      10.55
20040819  10.53       10.29      10.10
20040820  10.55       10.53      10.25
...         ...         ...        ...
20160812  39.10       38.70      39.66
20160815  39.58       39.10      40.45
20160816  39.66       39.58      40.45
20160817  40.45       39.66      40.45

[2813 rows x 3 columns]

Log Return of the Close

Logarithmic return = ln( close / last close)

From wiki:

For example, if a stock is priced at 3.570 USD per share at the close on one day, and at 3.575 USD per share at the close the next day, then the logarithmic return is: ln(3.575/3.570) = 0.0014, or 0.14%.

Use df['log-ret'] to access this column.

Cross Upwards and Cross Downwards

Use the pattern <A>_xu_<B> to check when A crosses up B.

Use the pattern <A>_xd_<B> to check when A crosses down B.

Use the pattern <A>_x_<B> to check when A crosses B.

Examples:

• kdjk_x_kdjd returns a series that marks the cross of KDJK and KDJD
• kdjk_xu_kdjd returns a series that marks where KDJK crosses up KDJD
• kdjk_xd_kdjd returns a series that marks where KDJD crosses down KDJD

Comparisons

Use patterns <A>_le_<B>, <A>_ge_<B>, <A>_lt_<B>, <A>_gt_<B>, <A>_eq_<B>, <A>_ne_<B> for comparing two columns.

Count of Non-Zero Value

Count non-zero values of a specific range. It requires a column and a window.

Examples:

• Count how many typical prices are larger than close in the past 10 periods

In [22]: tp = df['middle']

In [23]: df['res'] = df['middle'] > df['close']

In [24]: df[['middle', 'close', 'res', 'res_10_c']]
Out[24]:
             middle  close    res  res_10_c
date
20040817  11.480000  11.20   True       1.0
20040818  10.493333  10.29   True       2.0
20040819  10.493333  10.53  False       2.0
20040820  10.486667  10.55  False       2.0
20040823  10.163333  10.10   True       3.0
...             ...    ...    ...       ...
20160811  38.703333  38.70   True       5.0
20160812  38.916667  39.10  False       5.0
20160815  39.250000  39.58  False       4.0
20160816  39.840000  39.66   True       5.0
20160817  40.053333  40.45  False       5.0

[2813 rows x 4 columns]

• Count ups in the past 10 periods

In [26]: df['ups'], df['downs'] = df['change'] > 0, df['change'] < 0

In [27]: df[['ups', 'ups_10_c', 'downs', 'downs_10_c']]
Out[27]:
            ups  ups_10_c  downs  downs_10_c
date
20040817  False       0.0  False         0.0
20040818  False       0.0   True         1.0
20040819   True       1.0  False         1.0
20040820   True       2.0  False         1.0
20040823  False       2.0   True         2.0
...         ...       ...    ...         ...
20160811  False       3.0   True         7.0
20160812   True       3.0  False         7.0
20160815   True       4.0  False         6.0
20160816   True       5.0  False         5.0
20160817   True       5.0  False         5.0

[2813 rows x 4 columns]

Max and Min of the Periods

Retrieve the max/min value of specified periods. They require column and window. Note the window does NOT simply stand for the rolling window.

Examples:

• close_-3,2_max stands for the max of 2 periods later and 3 periods ago
• close_-2~0_min stands for the min of 2 periods ago till now

RSV - Raw Stochastic Value

RSV is essential for calculating KDJ. It takes a window parameter. Use df['rsv'] or df['rsv_6'] to access it.

Typical Price

It's the average of high, low and close. Use df['middle'] to access this value.

When amount is available, middle = amount / volume This should be more accurate because amount represents the total cash flow.

Issues

We use Github Issues to track the issues or bugs.