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@@ -0,0 +1 @@ +/stockstats-0.5.2.tar.gz diff --git a/python-stockstats.spec b/python-stockstats.spec new file mode 100644 index 0000000..f4f800c --- /dev/null +++ b/python-stockstats.spec @@ -0,0 +1,2086 @@ +%global _empty_manifest_terminate_build 0 +Name: python-stockstats +Version: 0.5.2 +Release: 1 +Summary: DataFrame with inline stock statistics support. +License: BSD +URL: https://github.com/jealous/stockstats +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a6/02/0556f74d82970148179bc0f72a35de409f787391e32c5111c8a3ebf96ea9/stockstats-0.5.2.tar.gz +BuildArch: noarch + +Requires: python3-pandas + +%description +# Stock Statistics/Indicators Calculation Helper + +[](https://github.com/jealous/stockstats/actions/workflows/build-test.yml) +[](https://codecov.io/gh/jealous/stockstats) +[](https://pypi.python.org/pypi/stockstats) + +VERSION: 0.5.2 + +## 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](./LICENSE.txt) + +## Tutorial + +### Initialization + +`StockDataFrame` works as a wrapper for the `pandas.DataFrame`. You need to +Initialize the `StockDataFrame` with `wrap` or `StockDataFrame.retype`. + +``` python +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: + +``` python +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](https://en.wikipedia.org/wiki/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](https://en.wikipedia.org/wiki/Rate_of_return) + +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 + +``` python +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 + +``` python +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](https://en.wikipedia.org/wiki/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](https://www.investopedia.com/terms/s/stochrsi.asp) + +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](https://medium.com/@samuel.mcculloch/lets-take-a-look-at-wavetrend-with-crosses-lazybear-s-indicator-2ece1737f72f) + +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](https://www.investopedia.com/articles/technical/02/092402.asp) + +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](https://www.forextraders.com/forex-education/forex-technical-analysis/triple-exponential-moving-average-the-tema-indicator/) + +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](https://help.eaglesmarkets.com/hc/en-us/articles/900002867026-Summary-of-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](https://www.investopedia.com/terms/w/williamsr.asp) + +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](https://www.investopedia.com/terms/c/commoditychannelindex.asp) + +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](https://en.wikipedia.org/wiki/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](https://economictimes.indiatimes.com/markets/stocks/news/how-to-use-supertrend-indicator-to-find-buying-and-selling-opportunities-in-market/articleshow/54492970.cms) + +Supertrend indicates the current trend. +We use the [algorithm described here](https://medium.com/codex/step-by-step-implementation-of-the-supertrend-indicator-in-python-656aa678c111). +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](https://www.investopedia.com/terms/d/dmi.asp) + +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](https://en.wikipedia.org/wiki/Stochastic_oscillator) + +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](https://support.futunn.com/en/topic167/?lang=en-us) + +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](https://en.wikipedia.org/wiki/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](https://en.wikipedia.org/wiki/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 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. + +#### [MACD - Moving Average Convergence Divergence](https://en.wikipedia.org/wiki/MACD) + +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](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:price_oscillators_ppo) + +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](https://www.investopedia.com/terms/m/mean.asp) + +Follow the pattern `<columnName>_<window>_sma` to retrieve a simple moving average. + +#### [Moving Standard Deviation](https://www.investopedia.com/terms/s/standarddeviation.asp) + +Follow the pattern `<columnName>_<window>_mstd` to retrieve the moving STD. + +#### [Moving Variance](https://www.investopedia.com/terms/v/variance.asp) + +Follow the pattern `<columnName>_<window>_mvar` to retrieve the moving VAR. + +#### [Volume Weighted Moving Average](https://www.investopedia.com/articles/trading/11/trading-with-vwap-mvwap.asp) + +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](https://www.tradingview.com/education/choppinessindex/) + +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](https://www.investopedia.com/terms/m/mfi.asp) + +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](https://school.stockcharts.com/doku.php?id=technical_indicators: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](https://github.com/jealous/stockstats/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> + + +%package -n python3-stockstats +Summary: DataFrame with inline stock statistics support. +Provides: python-stockstats +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-stockstats +# Stock Statistics/Indicators Calculation Helper + +[](https://github.com/jealous/stockstats/actions/workflows/build-test.yml) +[](https://codecov.io/gh/jealous/stockstats) +[](https://pypi.python.org/pypi/stockstats) + +VERSION: 0.5.2 + +## 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](./LICENSE.txt) + +## Tutorial + +### Initialization + +`StockDataFrame` works as a wrapper for the `pandas.DataFrame`. You need to +Initialize the `StockDataFrame` with `wrap` or `StockDataFrame.retype`. + +``` python +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: + +``` python +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](https://en.wikipedia.org/wiki/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](https://en.wikipedia.org/wiki/Rate_of_return) + +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 + +``` python +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 + +``` python +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](https://en.wikipedia.org/wiki/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](https://www.investopedia.com/terms/s/stochrsi.asp) + +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](https://medium.com/@samuel.mcculloch/lets-take-a-look-at-wavetrend-with-crosses-lazybear-s-indicator-2ece1737f72f) + +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](https://www.investopedia.com/articles/technical/02/092402.asp) + +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](https://www.forextraders.com/forex-education/forex-technical-analysis/triple-exponential-moving-average-the-tema-indicator/) + +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](https://help.eaglesmarkets.com/hc/en-us/articles/900002867026-Summary-of-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](https://www.investopedia.com/terms/w/williamsr.asp) + +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](https://www.investopedia.com/terms/c/commoditychannelindex.asp) + +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](https://en.wikipedia.org/wiki/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](https://economictimes.indiatimes.com/markets/stocks/news/how-to-use-supertrend-indicator-to-find-buying-and-selling-opportunities-in-market/articleshow/54492970.cms) + +Supertrend indicates the current trend. +We use the [algorithm described here](https://medium.com/codex/step-by-step-implementation-of-the-supertrend-indicator-in-python-656aa678c111). +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](https://www.investopedia.com/terms/d/dmi.asp) + +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](https://en.wikipedia.org/wiki/Stochastic_oscillator) + +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](https://support.futunn.com/en/topic167/?lang=en-us) + +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](https://en.wikipedia.org/wiki/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](https://en.wikipedia.org/wiki/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 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. + +#### [MACD - Moving Average Convergence Divergence](https://en.wikipedia.org/wiki/MACD) + +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](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:price_oscillators_ppo) + +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](https://www.investopedia.com/terms/m/mean.asp) + +Follow the pattern `<columnName>_<window>_sma` to retrieve a simple moving average. + +#### [Moving Standard Deviation](https://www.investopedia.com/terms/s/standarddeviation.asp) + +Follow the pattern `<columnName>_<window>_mstd` to retrieve the moving STD. + +#### [Moving Variance](https://www.investopedia.com/terms/v/variance.asp) + +Follow the pattern `<columnName>_<window>_mvar` to retrieve the moving VAR. + +#### [Volume Weighted Moving Average](https://www.investopedia.com/articles/trading/11/trading-with-vwap-mvwap.asp) + +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](https://www.tradingview.com/education/choppinessindex/) + +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](https://www.investopedia.com/terms/m/mfi.asp) + +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](https://school.stockcharts.com/doku.php?id=technical_indicators: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](https://github.com/jealous/stockstats/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> + + +%package help +Summary: Development documents and examples for stockstats +Provides: python3-stockstats-doc +%description help +# Stock Statistics/Indicators Calculation Helper + +[](https://github.com/jealous/stockstats/actions/workflows/build-test.yml) +[](https://codecov.io/gh/jealous/stockstats) +[](https://pypi.python.org/pypi/stockstats) + +VERSION: 0.5.2 + +## 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](./LICENSE.txt) + +## Tutorial + +### Initialization + +`StockDataFrame` works as a wrapper for the `pandas.DataFrame`. You need to +Initialize the `StockDataFrame` with `wrap` or `StockDataFrame.retype`. + +``` python +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: + +``` python +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](https://en.wikipedia.org/wiki/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](https://en.wikipedia.org/wiki/Rate_of_return) + +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 + +``` python +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 + +``` python +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](https://en.wikipedia.org/wiki/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](https://www.investopedia.com/terms/s/stochrsi.asp) + +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](https://medium.com/@samuel.mcculloch/lets-take-a-look-at-wavetrend-with-crosses-lazybear-s-indicator-2ece1737f72f) + +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](https://www.investopedia.com/articles/technical/02/092402.asp) + +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](https://www.forextraders.com/forex-education/forex-technical-analysis/triple-exponential-moving-average-the-tema-indicator/) + +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](https://help.eaglesmarkets.com/hc/en-us/articles/900002867026-Summary-of-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](https://www.investopedia.com/terms/w/williamsr.asp) + +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](https://www.investopedia.com/terms/c/commoditychannelindex.asp) + +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](https://en.wikipedia.org/wiki/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](https://economictimes.indiatimes.com/markets/stocks/news/how-to-use-supertrend-indicator-to-find-buying-and-selling-opportunities-in-market/articleshow/54492970.cms) + +Supertrend indicates the current trend. +We use the [algorithm described here](https://medium.com/codex/step-by-step-implementation-of-the-supertrend-indicator-in-python-656aa678c111). +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](https://www.investopedia.com/terms/d/dmi.asp) + +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](https://en.wikipedia.org/wiki/Stochastic_oscillator) + +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](https://support.futunn.com/en/topic167/?lang=en-us) + +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](https://en.wikipedia.org/wiki/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](https://en.wikipedia.org/wiki/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 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. + +#### [MACD - Moving Average Convergence Divergence](https://en.wikipedia.org/wiki/MACD) + +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](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:price_oscillators_ppo) + +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](https://www.investopedia.com/terms/m/mean.asp) + +Follow the pattern `<columnName>_<window>_sma` to retrieve a simple moving average. + +#### [Moving Standard Deviation](https://www.investopedia.com/terms/s/standarddeviation.asp) + +Follow the pattern `<columnName>_<window>_mstd` to retrieve the moving STD. + +#### [Moving Variance](https://www.investopedia.com/terms/v/variance.asp) + +Follow the pattern `<columnName>_<window>_mvar` to retrieve the moving VAR. + +#### [Volume Weighted Moving Average](https://www.investopedia.com/articles/trading/11/trading-with-vwap-mvwap.asp) + +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](https://www.tradingview.com/education/choppinessindex/) + +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](https://www.investopedia.com/terms/m/mfi.asp) + +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](https://school.stockcharts.com/doku.php?id=technical_indicators: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](https://github.com/jealous/stockstats/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> + + +%prep +%autosetup -n stockstats-0.5.2 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-stockstats -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.2-1 +- Package Spec generated @@ -0,0 +1 @@ +9270761266bcfea729b66c077ec4cdbe stockstats-0.5.2.tar.gz |