VERSION: 0.6.0
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
- ROC: Rate of Change
- 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
- Aroon: Aroon Oscillator
- Z: Z-Score
- AO: Awesome Oscillator
- BOP: Balance of Power
- MAD: Mean Absolute Deviation
- ROC: Rate of Change
- Coppock: Coppock Curve
- Ichimoku: Ichimoku Cloud
- CTI: Correlation Trend Indicator
- LRMA: Linear Regression Moving Average
pip install stockstats
The build checks the compatibility for the last two major releases of python3 and the last release of python2.
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 periodhigh: the highest price of the intervallow: the lowest price of the intervalvolume: 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.
StockDataFrame is a subclass of pandas.DataFrame. All the functions
of pandas.DataFrame should work the same as before.
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.
Use macd = stock['macd'] or rsi = stock.get('rsi') to retrieve the Series.
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.
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.
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.
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.
df['change'] is the change of the close price in percentage.
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 asdf['close_-1_d']df['high_2_d']retrieves the high price delta between current and 2 days later
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 has a configurable window. The default window size is 14 which is
configurable through StockDataFrame.RSI. e.g.
df['rsi']: 14 periods RSIdf['rsi_6']: 6 periods RSI
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 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] 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_maxstands for the max of 2 periods later and 3 periods agoclose_-2~0_minstands for the min of 2 periods ago till now
RSV is essential for calculating KDJ. It takes a window parameter.
Use df['rsv'] or df['rsv_6'] to access it.
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 periodsdf['rsi_6']: retrieve the RSI of 6 periods
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 periodsdf['stochrsi_6']: retrieve the Stochastic RSI of 6 periods
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.
It requires column and window.
For example, use df['close_7_smma'] to retrieve the 7 periods smoothed moving
average of the close price.
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.
Formular:
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 periodsdf['high_5_roc']: the ROC of the high price in 5 periods
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.
Formular:
- Calculate the mean.
- Calculate how far away each data point is from the mean using positive distances. These are called absolute deviations.
- Add those deviations together.
- Divide the sum by the number of data points.
Example:
df['close_10_mad']: the MAD of the close price in 10 periods
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 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.
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.
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 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 is a measure of the volatility of a High-Low-Close series. It is used for calculating the ATR.
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 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 trenddf['supertrend_lb']is the lower band of the trend
It has 2 parameters:
StockDataFrame.SUPERTREND_MULis the multiplier of the band, default to 3.StockDataFrame.SUPERTREND_WINDOWis the window size, default to 14.
df['dma'] retrieves the difference of 10 periods SMA of the close price and
the 50 periods SMA of the close price.
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 +DIStockDataFrame.MDI_SMMA- window for -DIStockDataFrame.DX_SMMA- window for DXStockDataFrame.ADX_EMA- window for ADXStockDataFrame.ADXR_EMA- window for ADXR
The stochastic oscillator is a momentum indicator that uses support and resistance levels.
It includes three lines:
df['kdjk']- K seriesdf['kdjd']- D seriesdf['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)
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 linedf['cr-ma1']-StockDataFrame.CR_MA1periods of the CR moving averagedf['cr-ma2']-StockDataFrame.CR_MA2periods of the CR moving averagedf['cr-ma3']-StockDataFrame.CR_MA3periods of the CR moving average
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.
The Bollinger bands includes three lines
df['boll']is the baselinedf['boll_ub']is the upper banddf['boll_lb']is the lower band
The default window of boll is defined by BOLL_PERIOD. The default value 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
StockDataFrame.BOLL_PERIOD. The width of the bands can be turned with
StockDataFrame.BOLL_STD_TIMES. The default value is 2.
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.
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.
Follow the pattern <columnName>_<window>_sma to retrieve a simple moving average.
Follow the pattern <columnName>_<window>_mstd to retrieve the moving STD.
Follow the pattern <columnName>_<window>_mvar to retrieve the moving VAR.
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 VWMAdf['vwma_6']retrieves the 6 periods VWMA
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 CHOPdf['chop_6']retrieves the 6 periods CHOP
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 MFIdf['mfi_6']retrieves the 6 periods MFI
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 withfast = 2andslow = 30df['close_2_kama']retrieves 2 periods KAMA of the close price
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_kdjdreturns a series that marks the cross of KDJK and KDJDkdjk_xu_kdjdreturns a series that marks where KDJK crosses up KDJDkdjk_xd_kdjdreturns a series that marks where KDJD crosses down KDJD
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 25df['aroon_14']returns Aroon oscillator with a window of 14
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 - μ ) / σ
Where:
z= Z-scorex= the value being evaluatedμ= the meanσ= the standard deviation
Examples:
df['close_75_z']returns the Z-Score of close price with a window of 75
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 (BOP) measures the strength of the bulls vs. bears.
Formular:
BOP = (close - open) / (high - low)
Example:
df['bop']returns the Balance of Power
[Chande Momentum Oscillator] (https://www.investopedia.com/terms/c/chandemomentumoscillator.asp)
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.
Formular:
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 14df['cmo_5']returns the CMO with a window of 5
Coppock Curve is a momentum indicator that signals long-term trend reversals.
Formular:
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 windowsdf['coppock_5,10,15']returns the Coppock Curve with WMA window 5, fast window 10, slow window 15.
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
Formular:
- 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 windowsdf['ichimoku_7,22,44']returns the ichimoku cloud width with window sizes 7, 22, 44
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
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 12df['high_5_cti']returns the CTI of high price with window 5
We use Github Issues to track the issues or bugs.
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.
- Cedric Zhuang [email protected]