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 histo