Stock Statistics/Indicators Calculation Helper

VERSION: 0.5.1

Introduction

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

Supported statistics/indicators are:

• change (in percent)
• delta
• permutation (zero-based)
• log return
• max in range
• min in range
• middle = (close + high + low) / 3
• compare: le, ge, lt, gt, eq, ne
• count: both backward(c) and forward(fc)
• cross: including upward cross and downward cross
• SMA: Simple Moving Average
• EMA: Exponential Moving Average
• MSTD: Moving Standard Deviation
• MVAR: Moving Variance
• RSV: Raw Stochastic Value
• RSI: Relative Strength Index
• KDJ: Stochastic Oscillator
• Bolling: Bollinger Band
• MACD: Moving Average Convergence Divergence
• CR: Energy Index (Intermediate Willingness Index)
• WR: Williams Overbought/Oversold index
• CCI: Commodity Channel Index
• TR: True Range
• ATR: Average True Range
• DMA: Different of Moving Average (10, 50)
• DMI: Directional Moving Index, including
  • +DI: Positive Directional Indicator
  • -DI: Negative Directional Indicator
  • ADX: Average Directional Movement Index
  • ADXR: Smoothed Moving Average of ADX
• TRIX: Triple Exponential Moving Average
• TEMA: Another Triple Exponential Moving Average
• VR: Volume Variation Index
• MFI: Money Flow Index
• VWMA: Volume Weighted Moving Average
• CHOP: Choppiness Index
• KAMA: Kaufman's Adaptive Moving Average
• PPO: Percentage Price Oscillator
• StochRSI: Stochastic RSI
• WT: LazyBear's Wave Trend
• Supertrend: with the Upper Band and Lower Band

Installation

pip install stockstats

Compatibility

The build checks the compatibility for the last two major releases of python3 and the last release of python2.

License

BSD-3-Clause License

Tutorial

Initialization

StockDataFrame works as a wrapper for the pandas.DataFrame. You need to Initialize the StockDataFrame with wrap or StockDataFrame.retype.

import pandas as pd
from stockstats import wrap

data = pd.read_csv('stock.csv')
df = wrap(data)

Formalize your data. This package takes for granted that your data is sorted by timestamp and contains certain columns. Please align your column name.

• date: timestamp of the record, optional.
• 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

Note these column names are case-insensitive. They are converted to lower case when you wrap the data frame.

By default, the date column is used as the index. Users can also specify the index column name in the wrap or retype function.

Example: DataFrame loaded from CSV.

          Date      Amount  Close   High    Low   Volume
0     20040817  90923240.0  11.20  12.21  11.03  7877900
1     20040818  52955668.0  10.29  10.90  10.29  5043200
2     20040819  32614676.0  10.53  10.65  10.30  3116800
...        ...         ...    ...    ...    ...      ...
2810  20160815  56416636.0  39.58  39.79  38.38  1436706
2811  20160816  68030472.0  39.66  40.86  39.00  1703600
2812  20160817  62536480.0  40.45  40.59  39.12  1567600

After conversion to StockDataFrame

              amount  close   high    low   volume
date
20040817  90923240.0  11.20  12.21  11.03  7877900
20040818  52955668.0  10.29  10.90  10.29  5043200
20040819  32614676.0  10.53  10.65  10.30  3116800
...              ...    ...    ...    ...      ...
20160815  56416636.0  39.58  39.79  38.38  1436706
20160816  68030472.0  39.66  40.86  39.00  1703600
20160817  62536480.0  40.45  40.59  39.12  1567600 

Use unwrap to convert it back to a pandas.DataFrame. Note that unwrap won't reset the columns and the index.

Access the Data

StockDataFrame is a subclass of pandas.DataFrame. All the functions of pandas.DataFrame should work the same as before.

Retrieve the data with symbol

We allow the user to access the statistics directly with some specified column name, such as kdjk, macd, rsi.

The values of these columns are calculated the first time you access them from the data frame. Please delete those columns first if you want the lib to re-evaluate them.

Retrieve the Series

Use macd = stock['macd'] or rsi = stock.get('rsi') to retrieve the Series.

Retrieve the symbol with 2 arguments

Some statistics need the column name and the window size, such as delta, shift, simple moving average, etc. Use this patter to retrieve them: <columnName>_<windowSize>_<statistics>

Examples:

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

Retrieve the symbol with 1 argument

Some statistics require the window size but not the column name. Use this patter to specify your window: <statistics>_<windowSize>

Examples:

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

Some of them have default windows. Check their document for detail.

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.

Statistics/Indicators

Some statistics have configurable parameters. They are class-level fields. Change of these fields is global. And they won't affect the existing results. Removing existing columns so that they will be re-evaluated the next time you access them.

Change of the Close

df['change'] is the change of the close price in percentage.

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]

RSI - Relative Strength Index

RSI has a configurable window. The default window size is 14 which is configurable through StockDataFrame.RSI. e.g.

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

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.

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.

RSI - Relative Strength Index

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

The default window is 14. Use StockDataFrame.RSI 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 StockDataFrame.RSI to tune it.

Examples:

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

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 StockDataFrame.WAVE_TREND_1 and StockDataFrame.WAVE_TREND_2.

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.

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 StockDataFrame.TRIX_EMA_WINDOW 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.

TEMA - Another Triple Exponential 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 StockDataFrame.TEMA_EMA_WINDOW to change the default window.

Examples:

• df['tema'] stands for 12 periods TEMA for the close price.
• df['middle_10_tema'] stands for the 10 periods TEMA for the typical price.

VR - Volume Variation Index

It is the strength index of the trading volume.

It has a default window of 26. Change it with StockDataFrame.VR.

Examples:

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

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 StockDataFrame.WR to change the default window.

Examples:

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

CCI - Commodity Channel Index

CCI stands for Commodity Channel Index.

It requires a window parameter. The default window is 14. Use StockDataFrame.CCI to change it.

Examples:

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

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 StockDataFrame.ATR_SMMA.

Example:

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

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.
• StockDataFrame.SUPERTREND_WINDOW is the window size, default to 14.

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.

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['mdi'] 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.

• StockDataFrame.PDI_SMMA - window for +DI
• StockDataFrame.MDI_SMMA - window for -DI
• StockDataFrame.DX_SMMA - window for DX
• StockDataFrame.ADX_EMA - window for ADX
• StockDataFrame.ADXR_EMA - window for ADXR

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 StockDataFrame.KDJ_WINDOW 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)

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_MA1 periods of the CR moving average
• df['cr-ma2'] - StockDataFrame.CR_MA2 periods of the CR moving average
• df['cr-ma3'] - StockDataFrame.CR_MA3 periods of the CR moving average

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.

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 period of the Bollinger Band can be changed with StockDataFrame.BOLL_PERIOD. The width of the bands can be turned with StockDataFrame.BOLL_STD_TIMES. The default value is 2.

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 he histogram line.

The period of short and long EMA can be tuned with StockDataFrame.MACD_EMA_SHORT and StockDataFrame.MACD_EMA_LONG. The default value are 12 and 26

The period of the signal line can be tuned with StockDataFrame.MACD_EMA_SIGNAL. The default value is 9.

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 he histogram line.

The period of short and long EMA can be tuned with StockDataFrame.PPO_EMA_SHORT and StockDataFrame.PPO_EMA_LONG. The default value are 12 and 26

The period of the signal line can be tuned with StockDataFrame.PPO_EMA_SIGNAL. The default value is 9.

Simple Moving Average

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

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.

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 StockDataFrame.VWMA.

Examples:

• df['vwma'] retrieves the 14 periods VWMA
• df['vwma_6'] retrieves the 6 periods VWMA

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 StockDataFrame.CHOP.

Examples:

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

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 StockDataFrame.MFI.

Examples:

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

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 fast and slow can be configured with StockDataFrame.KAMA_FAST and StockDataFrame.KAMA_SLOW

Examples:

• df['close_10_kama_2_30'] 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

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

Issues

We use Github Issues to track the issues or bugs.

Others

MACDH Note:

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

Contact author:

• Cedric Zhuang jealous@163.com