diff --git a/README.md b/README.md index 36c20e4..c5132fe 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,6 @@ statistics/indicators support. Supported statistics/indicators are: -* change (in percent) * delta * permutation (zero-based) * log return @@ -25,7 +24,6 @@ Supported statistics/indicators are: * 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 @@ -199,10 +197,6 @@ Some statistics have configurable parameters. They are class-level fields. Chang 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 `__d` to retrieve the delta between different periods. @@ -246,7 +240,7 @@ date #### [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. +configurable through `set_dft_window('rsi', n)`. e.g. * `df['rsi']`: 14 periods RSI * `df['rsi_6']`: 6 periods RSI @@ -340,7 +334,7 @@ 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. +The default window is 14. Use `set_dft_window('rsi', n)` to tune it. Examples: @@ -352,7 +346,7 @@ Examples: 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. +The default window is 14. Use `set_dft_window('stochrsi', n)` to tune it. Examples: @@ -364,7 +358,7 @@ Examples: 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`. +`set_dft_window('wt', (10, 21))`. #### SMMA - Smoothed Moving Average @@ -426,7 +420,7 @@ 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. +Use `set_dft_window('trix', n)` to change the default window. Examples: @@ -444,7 +438,7 @@ 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. +Use `set_dft_window('tema', n)` to change the default window. Examples: @@ -455,7 +449,7 @@ Examples: It is the strength index of the trading volume. -It has a default window of 26. Change it with `StockDataFrame.VR`. +It has a default window of 26. Change it with `set_dft_window('vr', n)`. Examples: * `df['vr']` retrieves the 26 periods VR. @@ -467,7 +461,7 @@ 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` +It takes a window parameter. The default window is 14. Use `set_dft_window('wr', n)` to change the default window. Examples: @@ -480,7 +474,7 @@ Examples: CCI stands for Commodity Channel Index. It requires a window parameter. The default window is 14. Use -`StockDataFrame.CCI` to change it. +`set_dft_window('cci', n)` to change it. Examples: @@ -498,7 +492,7 @@ 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`. +Users can modify the default window with `set_dft_window('atr', n)`. Example: @@ -516,7 +510,7 @@ It includes 3 lines: 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. +* the default window size is 14. Change it with `set_dft_window('supertrend', n)` #### DMA - Difference of Moving Average @@ -531,22 +525,22 @@ 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['ndi']` is the negative directional movement line (-DI) * `df['dx']` is the directional index (DX) * `df['adx']` is the average directional index (ADX) * `df['adxr']` is an EMA for ADX It has several parameters. -* `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 +* default window for +DI is 14, change it with `set_dft_window('pdi', n)` +* default window for -DI is 14, change it with `set_dft_window('ndi', n)` +* `StockDataFrame.DX_SMMA` - window for DX, default to 14 +* `StockDataFrame.ADX_EMA` - window for ADX, default to 6 +* `StockDataFrame.ADXR_EMA` - window for ADXR, default to 6 #### [KDJ Indicator](https://en.wikipedia.org/wiki/Stochastic_oscillator) -The stochastic oscillator is a momentum indicator that uses support and +The stochastic oscillator is a momenxtum indicator that uses support and resistance levels. It includes three lines: @@ -554,7 +548,7 @@ It includes three lines: * `df['kdjd']` - D series * `df['kdjj']` - J series -The default window is 9. Use `StockDataFrame.KDJ_WINDOW` to change it. +The default window is 9. Use `set_dft_window('kdjk', n)` to change it. Use `df['kdjk_6']` to retrieve the K series of 6 periods. KDJ also has two configurable parameters named `StockDataFrame.KDJ_PARAM`. @@ -569,9 +563,12 @@ 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 +* `df['cr-ma1']` - `StockDataFrame.CR_MA[0]` periods of the CR moving average, + the default window is 5 +* `df['cr-ma2']` - `StockDataFrame.CR_MA[1]` periods of the CR moving average, + the default window is 10 +* `df['cr-ma3']` - `StockDataFrame.CR_MA[2]` periods of the CR moving average, + the default window is 20 #### [Typical Price](https://en.wikipedia.org/wiki/Typical_price) @@ -588,12 +585,12 @@ The Bollinger bands includes three lines * `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. +The default window of boll is 20. You can also supply your window with `df['boll_10']`. It will also generate the `boll_ub_10` and `boll_lb_10` column. The default period of the Bollinger Band can be changed with -`StockDataFrame.BOLL_PERIOD`. The width of the bands can be turned with +`set_dft_window('boll', n)`. 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) @@ -603,12 +600,9 @@ We use the close price to calculate the MACD lines. * `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 period of short, long EMA and signal line can be tuned with +`set_dft_window('macd', (short, long, signal))`. The default +windows are 12 and 26 and 9. #### [PPO - Percentage Price Oscillator](https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:price_oscillators_ppo) @@ -618,12 +612,9 @@ The Percentage Price Oscillator includes three lines. * `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. +The period of short, long EMA and signal line can be tuned with +`set_dft_window('ppo', (short, long, signal))`. The default +windows are 12 and 26 and 9. #### [Simple Moving Average](https://www.investopedia.com/terms/m/mean.asp) @@ -642,7 +633,7 @@ Follow the pattern `__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`. +`set_dft_window('vwma', n)`. Examples: * `df['vwma']` retrieves the 14 periods VWMA @@ -653,7 +644,7 @@ Examples: 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`. +`set_dft_window('chop', n)`. Examples: * `df['chop']` retrieves the 14 periods CHOP @@ -665,7 +656,7 @@ 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`. +`set_dft_window('mfi', n)`. Examples: * `df['mfi']` retrieves the 14 periods MFI @@ -725,13 +716,14 @@ It has 2 optional parameters and 2 required parameters * 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` +The default value for window, fast and slow can be configured with +`set_dft_window('kama', (10, 5, 34))` Examples: -* `df['close_10_kama_2_30']` retrieves 10 periods KAMA of the close price with - `fast = 2` and `slow = 30` +* `df['close_10,2,30_kama']` retrieves 10 periods KAMA of the close + price with `fast = 2` and `slow = 30` * `df['close_2_kama']` retrieves 2 periods KAMA of the close price + with default fast and slow #### Cross Upwards and Cross Downwards diff --git a/stockstats.py b/stockstats.py index d34c70b..5d718a3 100644 --- a/stockstats.py +++ b/stockstats.py @@ -28,6 +28,7 @@ import itertools import re +from typing import Optional, Callable, Union import numpy as np import pandas as pd @@ -37,6 +38,177 @@ from numpy.lib.stride_tricks import as_strided +class StockStatsError(Exception): + pass + + +_dft_windows = { + # sort alphabetically + 'ao': (5, 34), + 'aroon': 25, + 'atr': 14, + 'boll': 20, + 'cci': 14, + 'change': 1, + 'chop': 14, + 'cmo': 14, + 'coppock': (10, 11, 14), + 'cr': 26, + 'cti': 12, + 'dma': (10, 50), + 'eri': 13, + 'eribear': 13, + 'eribull': 13, + 'ichimoku': (9, 26, 52), + 'kama': (10, 5, 34), # window, fast, slow + 'kdjd': 9, + 'kdjj': 9, + 'kdjk': 9, + 'ker': 10, + 'macd': (12, 26, 9), # short, long, signal + 'mfi': 14, + 'ndi': 14, + 'pdi': 14, + 'ppo': (12, 26, 9), # short, long, signal + 'rsi': 14, + 'rsv': 9, + 'stochrsi': 14, + 'supertrend': 14, + 'tema': 5, + 'trix': 12, + 'wr': 14, + 'wt': (10, 21), + 'vr': 26, + 'vwma': 14, +} + + +def set_dft_window(name: str, windows: Union[int, tuple[int, ...]]): + ret = _dft_windows.get(name) + _dft_windows[name] = windows + return ret + + +_dft_column = { + # sort alphabetically + 'cti': 'close', + 'dma': 'close', + 'kama': 'close', + 'ker': 'close', + 'tema': 'close', + 'trix': 'close', +} + + +def dft_windows(name: str) -> Optional[str]: + if name not in _dft_windows: + return None + dft = _dft_windows[name] + if isinstance(dft, int): + return str(dft) + return ','.join(map(str, dft)) + + +def dft_column(name: str) -> Optional[str]: + if name not in _dft_column: + return None + return _dft_column[name] + + +class _Meta: + def __init__(self, + name, + *, + column=None, + windows=None): + self._name = name + self._column = column + self._windows = windows + self._dft_column = dft_column(name) + self._dft_windows = dft_windows(name) + + @staticmethod + def _process_segment(windows): + if '~' in windows: + start, end = windows.split('~') + shifts = range(int(start), int(end) + 1) + else: + shifts = [int(windows)] + return shifts + + @property + def ints(self) -> list[int]: + items = map(self._process_segment, self.windows.split(',')) + return list(itertools.chain(*items)) + + @property + def int(self) -> int: + numbers = self.ints + if len(numbers) != 1: + raise StockStatsError('only accept 1 number') + return numbers[0] + + def _get_int(self, i): + numbers = self.ints + if len(numbers) < i + 1: + # try the defaults + dft_numbers = _dft_windows[self._name] + if len(dft_numbers) > i: + return dft_numbers[i] + raise StockStatsError(f'not enough ints, need {i + 1}') + return self.ints[i] + + @property + def int0(self) -> int: + return self._get_int(0) + + @property + def int1(self) -> int: + return self._get_int(1) + + @property + def int2(self) -> int: + return self._get_int(2) + + @property + def positive_int(self) -> int: + ret = self.int + if ret <= 0: + raise StockStatsError('window must be greater than 0') + return ret + + @property + def windows(self): + if self._windows is None: + return self._dft_windows + return self._windows + + @property + def column(self): + if self._column is None: + return self._dft_column + return self._column + + @property + def name(self): + if self._windows is None and self._column is None: + return self._name + if self.column is None: + return f'{self._name}_{self._windows}' + return f'{self.column}_{self.windows}_{self._name}' + + def name_ex(self, ex): + ret = f'{self._name}{ex}' + if self._windows is None: + return ret + return f'{ret}_{self.windows}' + + +def _call_handler(handler: Callable): + meta = _Meta(handler.__name__[5:]) + return handler(meta) + + def wrap(df, index_column=None): """ wraps a pandas DataFrame to StockDataFrame @@ -55,89 +227,29 @@ def unwrap(sdf): class StockDataFrame(pd.DataFrame): # Start of options. KDJ_PARAM = (2.0 / 3.0, 1.0 / 3.0) - KDJ_WINDOW = 9 - BOLL_PERIOD = 20 BOLL_STD_TIMES = 2 - MACD_EMA_SHORT = 12 - MACD_EMA_LONG = 26 - MACD_EMA_SIGNAL = 9 - - PPO_EMA_SHORT = 12 - PPO_EMA_LONG = 26 - PPO_EMA_SIGNAL = 9 - - PDI_SMMA = 14 - MDI_SMMA = 14 DX_SMMA = 14 ADX_EMA = 6 ADXR_EMA = 6 - CR_MA1 = 5 - CR_MA2 = 10 - CR_MA3 = 20 - - TRIX_EMA_WINDOW = 12 - - TEMA_EMA_WINDOW = 5 - - ATR_SMMA = 14 + CR_MA = (5, 10, 20) SUPERTREND_MUL = 3 - SUPERTREND_WINDOW = 14 - - VWMA = 14 - - CHOP = 14 - - MFI = 14 - - CCI = 14 - - RSI = 14 - - VR = 26 - - WR = 14 - - CMO = 14 - - WAVE_TREND_1 = 10 - WAVE_TREND_2 = 21 - - ER = 10 - - KAMA_SLOW = 34 - KAMA_FAST = 5 - - AO_SLOW = 34 - AO_FAST = 5 - - COPPOCK = (10, 11, 14) - - ICHIMOKU = (9, 26, 52) - - CTI = 12 - - ERI = 13 - - MULTI_SPLIT_INDICATORS = ("kama",) # End of options - @staticmethod - def _change(series, window): - return series.pct_change(periods=-window).fillna(0.0) * 100 - - def _get_change(self): + def _get_change(self, meta: _Meta): """ Get the percentage change column + It's an alias for ROC + :return: result series """ - self['change'] = self._change(self['close'], -1) + self[meta.name] = self.roc(self['close'], meta.int) - def _get_p(self, column, shifts): + def _get_p(self, meta: _Meta): """ get the permutation of specified range example: @@ -147,20 +259,15 @@ def _get_p(self, column, shifts): 2 3 2 (0.x > 0, and assigned to weight 2) 3 5 1 (2.x > 0, and assigned to weight 1) 4 1 3 - - :param column: the column to calculate p from - :param shifts: the range to consider - :return: """ - column_name = '{}_{}_p'.format(column, shifts) # initialize the column if not - self.get(column) - shifts = self.to_ints(shifts)[::-1] - indices = None + col = self.get(meta.column) + shifts = meta.ints[::-1] + indices: Optional[pd.Series] = None count = 0 for shift in shifts: shifted = self.shift(-shift) - index = (shifted[column] > 0) * (2 ** count) + index = (shifted[meta.column] > 0) * (2 ** count) if indices is None: indices = index else: @@ -169,7 +276,7 @@ def _get_p(self, column, shifts): if indices is not None: cp = indices.copy() self.set_nan(cp, shifts) - self[column_name] = cp + self[meta.name] = cp @classmethod def to_ints(cls, shifts): @@ -212,19 +319,18 @@ def _set_nan_of_single_shift(pd_obj, shift): elif shift < 0: pd_obj.iloc[:-shift] = val - def _get_r(self, column, shifts): + def _get_r(self, meta: _Meta): """ Get rate of change of column - :param column: column name of the rate to calculate - :param shifts: periods to shift, accept one shift only - :return: None + Note this function is different to the roc function. + negative values meaning data in the past, + positive values meaning data in the future. """ - shift = self.to_int(shifts) - rate_key = '{}_{}_r'.format(column, shift) - self[rate_key] = self._change(self[column], shift) + shift = -meta.int + self[meta.name] = self.roc(self[meta.column], shift) @staticmethod - def _shift(series, window): + def _shift(series: pd.Series, window: int): """ Shift the series When window is negative, shift the past period to current. @@ -244,159 +350,142 @@ def _shift(series, window): ret.iloc[-window:] = series.iloc[-1] return ret - def _get_s(self, column, shifts): + def _get_s(self, meta: _Meta): """ Get the column shifted by periods - :param column: name of the column to shift - :param shifts: periods to shift, accept one shift only - :return: None + Note this method is different to the shift method of pandas. + negative values meaning data in the past, + positive values meaning data in the future. """ - shift = self.to_int(shifts) - shifted_key = "{}_{}_s".format(column, shift) - self[shifted_key] = self._shift(self[column], shift) + self[meta.name] = self._shift(self[meta.column], meta.int) - def _get_log_ret(self): + def _get_log_ret(self, _: _Meta): close = self['close'] self['log-ret'] = np.log(close / self._shift(close, -1)) - def _get_c(self, column, window): + def _get_c(self, meta: _Meta) -> pd.Series: """ get the count of column in range (shifts) example: change_20_c - - :param column: column name - :param window: range to count, only to previous :return: result series """ - column_name = '{}_{}_c'.format(column, window) - window = self.get_int_positive(window) - self[column_name] = self._rolling( - self[column], window).apply(np.count_nonzero, raw=True) - return self[column_name] + rolled = self._rolling(self[meta.column], meta.int) + counts = rolled.apply(np.count_nonzero, raw=True) + self[meta.name] = counts + return counts - def _get_fc(self, column, shifts): + def _get_fc(self, meta: _Meta) -> pd.Series: """ get the count of column in range of future (shifts) example: change_20_fc - - :param column: column name - :param shifts: range to count, only to future :return: result series """ - column_name = '{}_{}_fc'.format(column, shifts) - shift = self.get_int_positive(shifts) - reversed_series = self[column][::-1] - reversed_counts = self._rolling( - reversed_series, shift).apply(np.count_nonzero, raw=True) + shift = meta.int + reversed_series = self[meta.column][::-1] + rolled = self._rolling(reversed_series, shift) + reversed_counts = rolled.apply(np.count_nonzero, raw=True) counts = reversed_counts[::-1] - self[column_name] = counts + self[meta.name] = counts return counts - def _init_shifted_columns(self, column, shifts): + def _shifted_columns(self, + column: pd.Series, + shifts: list[int]) -> pd.DataFrame: # initialize the column if not - self.get(column) - shifts = self.to_ints(shifts) - shift_column_names = ['{}_{}_s'.format(column, shift) for shift in - shifts] - [self.get(name) for name in shift_column_names] - return shift_column_names - - def _get_max(self, column, shifts): - column_name = '{}_{}_max'.format(column, shifts) - shift_column_names = self._init_shifted_columns(column, shifts) - self[column_name] = np.max(self[shift_column_names], axis=1) - - def _get_min(self, column, shifts): - column_name = '{}_{}_min'.format(column, shifts) - shift_column_names = self._init_shifted_columns(column, shifts) - self[column_name] = np.min(self[shift_column_names], axis=1) - - def _get_rsv(self, window): - """ Calculate the RSV (Raw Stochastic Value) within N periods - - This value is essential for calculating KDJs - Current day is included in N - - :param window: number of periods - :return: None - """ - window = self.get_int_positive(window) - column_name = 'rsv_{}'.format(window) + col = self.get(column) + res = pd.DataFrame() + for i in shifts: + res[int(i)] = self._shift(col, i).values + return res + + def _get_max(self, meta: _Meta): + column = meta.column + shifts = meta.ints + cols = self._shifted_columns(column, shifts) + self[meta.name] = cols.max(axis=1).values + + def _get_min(self, meta: _Meta): + column = meta.column + shifts = meta.ints + cols = self._shifted_columns(column, shifts) + self[meta.name] = cols.min(axis=1).values + + def _rsv(self, window): low_min = self.mov_min(self['low'], window) high_max = self.mov_max(self['high'], window) cv = (self['close'] - low_min) / (high_max - low_min) - self[column_name] = cv.fillna(0.0) * 100 + cv.fillna(0.0, inplace=True) + return cv * 100 - def _get_rsi(self, window=None): - """ Calculate the RSI (Relative Strength Index) within N periods + def _get_rsv(self, meta: _Meta): + """ Calculate the RSV (Raw Stochastic Value) within N periods - calculated based on the formula at: - https://en.wikipedia.org/wiki/Relative_strength_index + This value is essential for calculating KDJs + Current day is included in N - :param window: number of periods - :return: None """ - if window is None: - window = self.RSI - column_name = 'rsi' - else: - column_name = 'rsi_{}'.format(window) - window = self.get_int_positive(window) + self[meta.name] = self._rsv(meta.int) + def _rsi(self, window) -> pd.Series: change = self._delta(self['close'], -1) close_pm = (change + change.abs()) / 2 close_nm = (-change + change.abs()) / 2 p_ema = self.smma(close_pm, window) n_ema = self.smma(close_nm, window) - rs_column_name = 'rs_{}'.format(window) - self[rs_column_name] = rs = p_ema / n_ema - self[column_name] = 100 - 100 / (1.0 + rs) + rs = p_ema / n_ema + return 100 - 100 / (1.0 + rs) + + def _get_rsi(self, meta: _Meta): + """ Calculate the RSI (Relative Strength Index) within N periods + + calculated based on the formula at: + https://en.wikipedia.org/wiki/Relative_strength_index + """ + self[meta.name] = self._rsi(meta.int) - def _get_stochrsi(self, window=None): + def _get_stochrsi(self, meta: _Meta): """ Calculate the Stochastic RSI calculated based on the formula at: https://www.investopedia.com/terms/s/stochrsi.asp - - :param window: number of periods - :return: None """ - if window is None: - window = self.RSI - column_name = 'stochrsi' - else: - column_name = 'stochrsi_{}'.format(window) - window = self.get_int_positive(window) - - rsi = self['rsi_{}'.format(window)] + window = meta.int + rsi = self._rsi(window) rsi_min = self.mov_min(rsi, window) rsi_max = self.mov_max(rsi, window) cv = (rsi - rsi_min) / (rsi_max - rsi_min) - self[column_name] = cv * 100 + self[meta.name] = cv * 100 - def _get_wave_trend(self): - """ Calculate LazyBear's Wavetrend - Check the algorithm described below: - https://medium.com/@samuel.mcculloch/lets-take-a-look-at-wavetrend-with-crosses-lazybear-s-indicator-2ece1737f72f + def _wt1(self, n1: int, n2: int) -> pd.Series: + """ wave trand 1 n1: period of EMA on typical price n2: period of EMA - - :return: None """ - n1 = self.WAVE_TREND_1 - n2 = self.WAVE_TREND_2 - tp = self._tp() esa = self.ema(tp, n1) d = self.ema((tp - esa).abs(), n1) ci = (tp - esa) / (0.015 * d) - tci = self.ema(ci, n2) - self["wt1"] = tci - self["wt2"] = self.sma(tci, 4) + return self.ema(ci, n2) + + def _get_wt1(self, meta: _Meta): + self[meta.name] = self._wt1(meta.int0, meta.int1) + + def _get_wt2(self, meta: _Meta): + wt1 = self._wt1(meta.int0, meta.int1) + self[meta.name] = self.sma(wt1, 4) + + def _get_wt(self, meta: _Meta): + """ Calculate LazyBear's Wavetrend + Check the algorithm described below: + https://medium.com/@samuel.mcculloch/lets-take-a-look-at-wavetrend-with-crosses-lazybear-s-indicator-2ece1737f72f + """ + tci = self._wt1(meta.int0, meta.int1) + self[meta.name_ex('1')] = tci + self[meta.name_ex('2')] = self.sma(tci, 4) @staticmethod def smma(series, window): @@ -406,73 +495,36 @@ def smma(series, window): min_periods=0, adjust=True).mean() - def _get_smma(self, column, windows): - """ get smoothed moving average. + def _get_smma(self, meta: _Meta): + """ get smoothed moving average """ + self[meta.name] = self.smma(self[meta.column], meta.int) - :param column: the column to calculate - :param windows: range - :return: result series - """ - window = self.get_int_positive(windows) - column_name = '{}_{}_smma'.format(column, window) - self[column_name] = self.smma(self[column], window) - - def _get_trix(self, column=None, windows=None): + def _get_trix(self, meta: _Meta): """ Triple Exponential Average https://www.investopedia.com/articles/technical/02/092402.asp - - :param column: the column to calculate - :param windows: range - :return: result series """ - column_name = "" - if column is None and windows is None: - column_name = 'trix' - if column is None: - column = 'close' - if windows is None: - windows = self.TRIX_EMA_WINDOW - if column_name == "": - column_name = '{}_{}_trix'.format(column, windows) - - window = self.get_int_positive(windows) - - single = self.ema(self[column], window) + window = meta.int + single = self.ema(self[meta.column], window) double = self.ema(single, window) triple = self.ema(double, window) prev_triple = self._shift(triple, -1) triple_change = self._delta(triple, -1) - self[column_name] = triple_change * 100 / prev_triple + self[meta.name] = triple_change * 100 / prev_triple - def _get_tema(self, column=None, windows=None): + def _get_tema(self, meta: _Meta): """ Another implementation for triple ema Check the algorithm described below: https://www.forextraders.com/forex-education/forex-technical-analysis/triple-exponential-moving-average-the-tema-indicator/ - - :param column: column to calculate ema - :param windows: window of the calculation - :return: result series """ - column_name = "" - if column is None and windows is None: - column_name = 'tema' - if column is None: - column = 'close' - if windows is None: - windows = self.TEMA_EMA_WINDOW - if column_name == "": - column_name = '{}_{}_tema'.format(column, windows) - - window = self.get_int_positive(windows) - - single = self.ema(self[column], window) + window = meta.int + single = self.ema(self[meta.column], window) double = self.ema(single, window) triple = self.ema(double, window) - self[column_name] = 3 * single - 3 * double + triple + self[meta.name] = 3.0 * single - 3.0 * double + triple - def _get_wr(self, window=None): + def _get_wr(self, meta: _Meta): """ Williams Overbought/Oversold Index Definition: https://www.investopedia.com/terms/w/williamsr.asp @@ -480,23 +532,13 @@ def _get_wr(self, window=None): Ct - the close price Hn - N periods high Ln - N periods low - - :param window: number of periods - :return: None """ - if window is None: - window = self.WR - column_name = 'wr' - else: - column_name = 'wr_{}'.format(window) - - window = self.get_int_positive(window) + window = meta.int ln = self.mov_min(self['low'], window) - hn = self.mov_max(self['high'], window) - self[column_name] = (hn - self['close']) / (hn - ln) * -100 + self[meta.name] = (hn - self['close']) / (hn - ln) * -100 - def _get_cci(self, window=None): + def _get_cci(self, meta: _Meta): """ Commodity Channel Index CCI = (Typical Price - 20-period SMA of TP) / (.015 x Mean Deviation) @@ -505,21 +547,12 @@ def _get_cci(self, window=None): * when amount is available: Typical Price (TP) = Amount / Volume TP is also implemented as 'middle'. - - :param window: number of periods - :return: None """ - if window is None: - window = self.CCI - column_name = 'cci' - else: - column_name = 'cci_{}'.format(window) - window = self.get_int_positive(window) - + window = meta.int tp = self._tp() tp_sma = self.sma(tp, window) mad = self._mad(tp, window) - self[column_name] = (tp - tp_sma) / (.015 * mad) + self[meta.name] = (tp - tp_sma) / (.015 * mad) def _tr(self): prev_close = self._shift(self['close'], -1) @@ -530,7 +563,7 @@ def _tr(self): c3 = (low - prev_close).abs() return pd.concat((c1, c2, c3), axis=1).max(axis=1) - def _get_tr(self): + def _get_tr(self, meta: _Meta): """ True Range of the trading TR is a measure of volatility of a High-Low-Close series @@ -539,22 +572,16 @@ def _get_tr(self): :return: None """ - self['tr'] = self._tr() + self[meta.name] = self._tr() - def _get_supertrend(self, window=None): + def _get_supertrend(self, meta: _Meta): """ Supertrend Supertrend indicator shows trend direction. It provides buy or sell indicators. https://medium.com/codex/step-by-step-implementation-of-the-supertrend-indicator-in-python-656aa678c111 - - :param window: number of periods - :return: None """ - if window is None: - window = self.SUPERTREND_WINDOW - window = self.get_int_positive(window) - + window = meta.int high = self['high'] low = self['low'] close = self['close'] @@ -613,11 +640,11 @@ def _get_supertrend(self, window=None): else: st[i] = ub[i] - self['supertrend_ub'] = ub - self['supertrend_lb'] = lb - self['supertrend'] = st + self[f'{meta.name}_ub'] = ub + self[f'{meta.name}_lb'] = lb + self[f'{meta.name}'] = st - def _get_aroon(self, window=None): + def _get_aroon(self, meta: _Meta): """ Aroon Oscillator The Aroon Oscillator measures the strength of a trend and @@ -630,12 +657,7 @@ def _get_aroon(self, window=None): * Aroon Down = 100 * (n - periods since n-period low) / n * n = window size """ - if window is None: - window = 25 - column_name = 'aroon' - else: - window = self.get_int_positive(window) - column_name = 'aroon_{}'.format(window) + window = meta.int def _window_pct(s): n = float(window) @@ -648,9 +670,9 @@ def _window_pct(s): aroon_up = _window_pct(high_since) aroon_down = _window_pct(low_since) - self[column_name] = aroon_up - aroon_down + self[meta.name] = aroon_up - aroon_down - def _get_z(self, column, window): + def _get_z(self, meta: _Meta): """ Z score Z-score is a statistical measurement that describes a value's @@ -668,45 +690,40 @@ def _get_z(self, column, window): * μ = the mean * σ = the standard deviation """ - window = self.get_int_positive(window) - column_name = '{}_{}_z'.format(column, window) - col = self[column] + window = meta.int + col = self[meta.column] mean = self.sma(col, window) std = self.mov_std(col, window) - self[column_name] = ((col - mean) / std).fillna(0.0) + self[meta.name] = ((col - mean) / std).fillna(0.0) def _atr(self, window): tr = self._tr() return self.smma(tr, window) - def _get_atr(self, window=None): + def _get_atr(self, meta: _Meta): """ Average True Range The average true range is an N-day smoothed moving average (SMMA) of the true range values. Default to 14 periods. https://en.wikipedia.org/wiki/Average_true_range - - :param window: number of periods - :return: None """ - if window is None: - window = self.ATR_SMMA - column_name = 'atr' - else: - column_name = 'atr_{}'.format(window) - window = self.get_int_positive(window) - self[column_name] = self._atr(window) + window = meta.int + self[meta.name] = self._atr(window) - def _get_dma(self): + def _get_dma(self, meta: _Meta): """ Difference of Moving Average default to 10 and 50. :return: None """ - self['dma'] = self['close_10_sma'] - self['close_50_sma'] + fast = meta.int0 + slow = meta.int1 + col = self[meta.column] + diff = self.sma(col, fast) - self.sma(col, slow) + self[meta.name] = diff - def _get_dmi(self): + def _get_dmi(self, _: _Meta): """ get the default setting for DMI including: @@ -717,9 +734,7 @@ def _get_dmi(self): :return: """ - self['pdi'] = self._get_pdi(self.PDI_SMMA) - self['mdi'] = self._get_mdi(self.MDI_SMMA) - self['dx'] = self._get_dx(self.DX_SMMA) + self['dx'] = self._dx(self.DX_SMMA) self['adx'] = self.ema(self['dx'], self.ADX_EMA) self['adxr'] = self.ema(self['adx'], self.ADXR_EMA) @@ -741,44 +756,23 @@ def _ndm(self, window): _, ret = self._get_pdm_ndm(window) return ret - def _get_pdm(self, windows): + def _get_pdm(self, meta: _Meta): """ +DM, positive directional moving If window is not 1, calculate the SMMA of +DM - - :param windows: range - :return: """ - window = self.get_int_positive(windows) - if window > 1: - column_name = 'pdm_{}'.format(window) - else: - column_name = 'pdm' - self[column_name] = self._pdm(window) + self[meta.name] = self._pdm(meta.int) - def _get_ndm(self, windows): + def _get_ndm(self, meta: _Meta): """ -DM, negative directional moving accumulation If window is not 1, return the SMA of -DM. - - :param windows: range - :return: """ - window = self.get_int_positive(windows) - if window > 1: - column_name = 'ndm_{}'.format(window) - else: - column_name = 'ndm' - self[column_name] = self._ndm(window) - - def _get_vr(self, windows=None): - if windows is None: - window = self.VR - column_name = 'vr' - else: - window = self.get_int_positive(windows) - column_name = 'vr_{}'.format(window) + self[meta.name] = self._ndm(meta.int) + def _get_vr(self, meta: _Meta): + """ VR - Volume Variation Index """ + window = meta.int idx = self.index gt_zero = np.where(self['change'] > 0, self['volume'], 0) av = pd.Series(gt_zero, index=idx) @@ -792,7 +786,7 @@ def _get_vr(self, windows=None): cv = pd.Series(eq_zero, index=idx) cvs = self.mov_sum(cv, window) - self[column_name] = (avs + cvs / 2) / (bvs + cvs / 2) * 100 + self[meta.name] = (avs + cvs / 2) / (bvs + cvs / 2) * 100 def _get_pdi_ndi(self, window): pdm, ndm = self._get_pdm_ndm(window) @@ -801,57 +795,34 @@ def _get_pdi_ndi(self, window): ndi = ndm / atr * 100 return pdi, ndi - def _get_pdi(self, windows): - """ +DI, positive directional moving index + def _get_pdi(self, meta: _Meta): + """ +DI, positive directional moving index """ + pdi, _ = self._get_pdi_ndi(meta.int) + self[meta.name] = pdi + return pdi - :param windows: range - :return: - """ - window = self.get_int_positive(windows) - pdi, _ = self._get_pdi_ndi(window) - pdi_column = 'pdi_{}'.format(window) - self[pdi_column] = pdi - return self[pdi_column] - - def _get_mdi(self, windows): - window = self.get_int_positive(windows) - _, ndi = self._get_pdi_ndi(window) - mdi_column = 'mdi_{}'.format(window) - self[mdi_column] = ndi - return self[mdi_column] - - def _get_dx(self, windows): - window = self.get_int_positive(windows) - dx_column = 'dx_{}'.format(window) - pdi, mdi = self._get_pdi_ndi(window) - self[dx_column] = abs(pdi - mdi) / (pdi + mdi) * 100 - return self[dx_column] + def _get_ndi(self, meta: _Meta): + """ -DI, negative directional moving index """ + _, ndi = self._get_pdi_ndi(meta.int) + self[meta.name] = ndi + return ndi - def _get_kdj_default(self): - """ default KDJ, 9 periods + def _dx(self, window): + pdi, mdi = self._get_pdi_ndi(window) + return abs(pdi - mdi) / (pdi + mdi) * 100 - :return: None - """ - self['kdjk'] = self['kdjk_{}'.format(self.KDJ_WINDOW)] - self['kdjd'] = self['kdjd_{}'.format(self.KDJ_WINDOW)] - self['kdjj'] = self['kdjj_{}'.format(self.KDJ_WINDOW)] + def _get_dx(self, meta: _Meta): + self[meta.name] = self._dx(meta.int) - def _get_cr(self, windows=None): + def _get_cr(self, meta: _Meta): """ Energy Index (Intermediate Willingness Index) https://support.futunn.com/en/topic167/?lang=en-us Use the relationship between the highest price, the lowest price and yesterday's middle price to reflect the market's willingness to buy and sell. - - :param windows: window of the moving sum - :return: None """ - if windows is None: - window = 26 - else: - window = self.get_int_positive(windows) - + window = meta.int middle = self._tp() last_middle = self._shift(middle, -1) ym = self._shift(middle, -1) @@ -862,21 +833,11 @@ def _get_cr(self, windows=None): p1 = self.mov_sum(high - p1_m, window) p2 = self.mov_sum(ym - p2_m, window) - if windows is None: - cr = 'cr' - cr_ma1 = 'cr-ma1' - cr_ma2 = 'cr-ma2' - cr_ma3 = 'cr-ma3' - else: - cr = 'cr_{}'.format(window) - cr_ma1 = 'cr_{}-ma1'.format(window) - cr_ma2 = 'cr_{}-ma2'.format(window) - cr_ma3 = 'cr_{}-ma3'.format(window) - - self[cr] = cr = p1 / p2 * 100 - self[cr_ma1] = self._shifted_cr_sma(cr, self.CR_MA1) - self[cr_ma2] = self._shifted_cr_sma(cr, self.CR_MA2) - self[cr_ma3] = self._shifted_cr_sma(cr, self.CR_MA3) + name = meta.name + self[name] = cr = p1 / p2 * 100 + self[f'{name}-ma1'] = self._shifted_cr_sma(cr, self.CR_MA[0]) + self[f'{name}-ma2'] = self._shifted_cr_sma(cr, self.CR_MA[1]) + self[f'{name}-ma3'] = self._shifted_cr_sma(cr, self.CR_MA[2]) def _shifted_cr_sma(self, cr, window): cr_sma = self.sma(cr, window) @@ -887,11 +848,11 @@ def _tp(self): return self['amount'] / self['volume'] return (self['close'] + self['high'] + self['low']).divide(3.0) - def _get_tp(self): - self['tp'] = self._tp() + def _get_tp(self, meta: _Meta): + self[meta.name] = self._tp() - def _get_middle(self): - self['middle'] = self._tp() + def _get_middle(self, meta: _Meta): + self[meta.name] = self._tp() def _calc_kd(self, column): param0, param1 = self.KDJ_PARAM @@ -901,51 +862,40 @@ def _calc_kd(self, column): k = param0 * k + i yield k - def _get_kdjk(self, window): + def _get_kdjk(self, meta: _Meta): """ Get the K of KDJ K ＝ 2/3 × (prev. K) +1/3 × (curr. RSV) 2/3 and 1/3 are the smooth parameters. - :param window: number of periods - :return: None """ - rsv_column = 'rsv_{}'.format(window) - k_column = 'kdjk_{}'.format(window) - self[k_column] = list(self._calc_kd(self.get(rsv_column))) + window = meta.int + rsv = self._rsv(window) + self[meta.name] = list(self._calc_kd(rsv)) - def _get_kdjd(self, window): + def _get_kdjd(self, meta: _Meta): """ Get the D of KDJ D = 2/3 × (prev. D) +1/3 × (curr. K) 2/3 and 1/3 are the smooth parameters. - :param window: number of periods - :return: None """ - k_column = 'kdjk_{}'.format(window) - d_column = 'kdjd_{}'.format(window) - self[d_column] = list(self._calc_kd(self.get(k_column))) + k_column = meta.name.replace('kdjd', 'kdjk') + self[meta.name] = list(self._calc_kd(self.get(k_column))) - def _get_kdjj(self, window): + def _get_kdjj(self, meta: _Meta): """ Get the J of KDJ J = 3K-2D - :param self: data - :param window: number of periods - :return: None """ - k_column = 'kdjk_{}'.format(window) - d_column = 'kdjd_{}'.format(window) - j_column = 'kdjj_{}'.format(window) - self[j_column] = 3 * self[k_column] - 2 * self[d_column] + k_column = meta.name.replace('kdjj', 'kdjk') + d_column = meta.name.replace('kdjj', 'kdjd') + self[meta.name] = 3 * self[k_column] - 2 * self[d_column] @staticmethod def _delta(series, window): return series.diff(-window).fillna(0.0) - def _get_d(self, column, shifts): - shift = self.to_int(shifts) - column_name = '{}_{}_d'.format(column, shift) - self[column_name] = self._delta(self[column], shift) + def _get_d(self, meta: _Meta): + self[meta.name] = self._delta(self[meta.column], meta.int) @classmethod def mov_min(cls, series, size): @@ -966,7 +916,10 @@ def sma(cls, series, size): @staticmethod def roc(series, size): ret = series.diff(size) / series.shift(size) - ret.iloc[:size] = 0 + if size < 0: + ret.iloc[size:] = 0 + else: + ret.iloc[:size] = 0 return ret * 100 @classmethod @@ -983,40 +936,22 @@ def f(x): return cls._rolling(series, window).apply(f, raw=True) - def _get_mad(self, column, window): - """ get mean absolute deviation - - :param column: column to calculate - :param window: number of periods - :return: None - """ - window = self.get_int_positive(window) - column_name = '{}_{}_mad'.format(column, window) - self[column_name] = self._mad(self[column], window) - - def _get_sma(self, column, windows): - """ get simple moving average + def _get_mad(self, meta: _Meta): + """ get mean absolute deviation """ + window = meta.int + self[meta.name] = self._mad(self[meta.column], window) - :param column: column to calculate - :param windows: window of simple moving average - :return: None - """ - window = self.get_int_positive(windows) - column_name = '{}_{}_sma'.format(column, window) - self[column_name] = self.sma(self[column], window) + def _get_sma(self, meta: _Meta): + """ get simple moving average """ + window = meta.int + self[meta.name] = self.sma(self[meta.column], window) - def _get_lrma(self, column, window): - """ get linear regression moving average + def _get_lrma(self, meta: _Meta): + """ get linear regression moving average """ + window = meta.int + self[meta.name] = self.linear_reg(self[meta.column], window) - :param column: column to calculate - :param window: window size - :return: None - """ - window = self.get_int_positive(window) - column_name = '{}_{}_lrma'.format(column, window) - self[column_name] = self.linear_reg(self[column], window) - - def _get_roc(self, column, window): + def _get_roc(self, meta: _Meta): """get Rate of Change (ROC) of a column The Price Rate of Change (ROC) is a momentum-based technical indicator @@ -1032,14 +967,8 @@ def _get_roc(self, column, window): Where: * PriceP: the price of the current period * PricePn: the price of the n periods ago - - :param column: column to calculate - :param window: window of Rate of Change (ROC) - :return: None """ - window = self.get_int_positive(window) - column_name = '{}_{}_roc'.format(column, window) - self[column_name] = self.roc(self[column], window) + self[meta.name] = self.roc(self[meta.column], meta.int) @staticmethod def ema(series, window, *, adjust=True): @@ -1050,7 +979,7 @@ def ema(series, window, *, adjust=True): adjust=adjust).mean() @staticmethod - def _rolling(series, window): + def _rolling(series: pd.Series, window: int): return series.rolling(window, min_periods=1, center=False) @classmethod @@ -1082,7 +1011,7 @@ def linear_reg(cls, x2_sum = x_sum * (2 * window + 1) / 3 divisor = window * x2_sum - x_sum * x_sum - def linear_regression(s): + def linear_regression(s: pd.Series): y_sum = s.sum() xy_sum = (x * s).sum() @@ -1107,44 +1036,22 @@ def rolling(arr): index=series.index) return ret - def _get_cti(self, column=None, window=None): + def _get_cti(self, meta: _Meta): """ get correlation trend indicator Correlation Trend Indicator is a study that estimates the current direction and strength of a trend. https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/C-D/CorrelationTrendIndicator - - :param column: column to calculate, default to 'close' - :param window: window of Correlation Trend Indicator """ - if column is None and window is None: - column_name = 'cti' - else: - column_name = '{}_{}_cti'.format(column, window) - - if column is None: - column = 'close' - if window is None: - window = self.CTI - else: - window = self.get_int_positive(window) - value = self.linear_reg( - self[column], window, correlation=True) - self[column_name] = value - - def _get_ema(self, column, windows): - """ get exponential moving average + self[meta.column], meta.int, correlation=True) + self[meta.name] = value - :param column: column to calculate - :param windows: collection of window of exponential moving average - :return: None - """ - window = self.get_int_positive(windows) - column_name = '{}_{}_ema'.format(column, window) - self[column_name] = self.ema(self[column], window) + def _get_ema(self, meta: _Meta): + """ get exponential moving average """ + self[meta.name] = self.ema(self[meta.column], meta.int) - def _get_boll(self, window=None): + def _get_boll(self, meta: _Meta): """ Get Bollinger bands. boll_ub means the upper band of the Bollinger bands @@ -1155,16 +1062,10 @@ def _get_boll(self, window=None): K = BOLL_STD_TIMES :return: None """ - if window is None: - n = self.BOLL_PERIOD - boll = 'boll' - boll_ub = 'boll_ub' - boll_lb = 'boll_lb' - else: - n = self.get_int_positive(window) - boll = 'boll_{}'.format(n) - boll_ub = 'boll_ub_{}'.format(n) - boll_lb = 'boll_lb_{}'.format(n) + n = meta.int + boll = meta.name + boll_ub = meta.name_ex('_ub') + boll_lb = meta.name_ex('_lb') moving_avg = self.sma(self['close'], n) moving_std = self.mov_std(self['close'], n) @@ -1173,7 +1074,7 @@ def _get_boll(self, window=None): self[boll_ub] = moving_avg + width self[boll_lb] = moving_avg - width - def _get_macd(self): + def _get_macd(self, meta: _Meta): """ Moving Average Convergence Divergence This function will initialize all following columns. @@ -1181,17 +1082,19 @@ def _get_macd(self): MACD Line (macd): (12-day EMA - 26-day EMA) Signal Line (macds): 9-day EMA of MACD Line MACD Histogram (macdh): MACD Line - Signal Line - - :return: None """ close = self['close'] - ema_short = self.ema(close, self.MACD_EMA_SHORT) - ema_long = self.ema(close, self.MACD_EMA_LONG) - self['macd'] = ema_short - ema_long - self['macds'] = self.ema(self['macd'], self.MACD_EMA_SIGNAL) - self['macdh'] = self['macd'] - self['macds'] - - def _get_ppo(self): + short_w, long_w, signal_w = meta.int0, meta.int1, meta.int2 + ema_short = self.ema(close, short_w) + ema_long = self.ema(close, long_w) + macd = meta.name + macds = meta.name_ex('s') + macdh = meta.name_ex('h') + self[macd] = ema_short - ema_long + self[macds] = self.ema(self[macd], signal_w) + self[macdh] = self[macd] - self[macds] + + def _get_ppo(self, meta: _Meta): """ Percentage Price Oscillator https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:price_oscillators_ppo @@ -1202,24 +1105,35 @@ def _get_ppo(self): Signal Line: 9-day EMA of PPO PPO Histogram: PPO - Signal Line - - :return: None """ close = self['close'] - ppo_short = self.ema(close, self.PPO_EMA_SHORT) - ppo_long = self.ema(close, self.PPO_EMA_LONG) + short_w, long_w, signal_w = meta.int0, meta.int1, meta.int2 + ppo_short = self.ema(close, short_w) + ppo_long = self.ema(close, long_w) self['ppo'] = (ppo_short - ppo_long) / ppo_long * 100 - self['ppos'] = self.ema(self['ppo'], self.PPO_EMA_SIGNAL) + self['ppos'] = self.ema(self['ppo'], signal_w) self['ppoh'] = self['ppo'] - self['ppos'] - def _get_eribull(self, windows=None): - return self._get_eri(windows) + def _eri(self, window): + ema = self.ema(self['close'], window, adjust=False) + bull = self['high'] - ema + bear = self['low'] - ema + return bull, bear - def _get_eribear(self, windows=None): - return self._get_eri(windows) + def _get_eribull(self, meta: _Meta): + """ The bull line of Elder-Ray Index """ + bull, _ = self._eri(meta.int) + self[meta.name] = bull + return bull - def _get_eri(self, windows=None): - """ The bull line of Elder-Ray Index + def _get_eribear(self, meta: _Meta): + """ The bear line of Elder-Ray Index """ + _, bear = self._eri(meta.int) + self[meta.name] = bear + return bear + + def _get_eri(self, meta: _Meta): + """ The Elder-Ray Index The Elder-Ray Index contains the bull and the bear power. Both are calculated based on the EMA of the close price. @@ -1233,45 +1147,23 @@ def _get_eri(self, windows=None): * Bears Power = Low - EMA * EMA is exponential moving average of close of N periods """ - if windows is None: - window = self.ERI - bull_name = 'eribull' - bear_name = 'eribear' - else: - window = self.get_int_positive(windows) - bull_name = 'eribull_{}'.format(window) - bear_name = 'eribear_{}'.format(window) - ema = self.ema(self['close'], window, adjust=False) - self[bull_name] = self['high'] - ema - self[bear_name] = self['low'] - ema + bull, bear = self._eri(meta.int) + self[meta.name_ex('bull')] = bull + self[meta.name_ex('bear')] = bear - def _get_coppock(self, windows=None): + def _get_coppock(self, meta: _Meta): """ Get Coppock Curve Coppock Curve is a momentum indicator that signals long-term trend reversals. https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:coppock_curve - - :param windows: collection of window of Coppock Curve - :return: None """ - if windows is None: - window = self.COPPOCK[0] - fast = self.COPPOCK[1] - slow = self.COPPOCK[2] - column_name = 'coppock' - else: - periods = self.to_ints(windows) - window = periods[0] - fast = periods[1] - slow = periods[2] - column_name = 'coppock_{}'.format(windows) - + window, fast, slow = meta.int0, meta.int1, meta.int2 fast_roc = self.roc(self['close'], fast) slow_roc = self.roc(self['close'], slow) roc_ema = self.linear_wma(fast_roc + slow_roc, window) - self[column_name] = roc_ema + self[meta.name] = roc_ema @classmethod def get_int_positive(cls, windows): @@ -1288,7 +1180,7 @@ def _hl_mid(self, period): pl = self.mov_min(self['low'], period) return (ph + pl) * 0.5 - def _get_ichimoku(self, windows=None): + def _get_ichimoku(self, meta: _Meta): """ get Ichimoku Cloud The Ichimoku Cloud is a collection of technical indicators @@ -1317,18 +1209,7 @@ def _get_ichimoku(self, windows=None): * PL = Period Low """ - if windows is None: - conv = self.ICHIMOKU[0] - base = self.ICHIMOKU[1] - lead = self.ICHIMOKU[2] - column_name = 'ichimoku' - else: - periods = self.to_ints(windows) - conv = periods[0] - base = periods[1] - lead = periods[2] - column_name = 'ichimoku_{}'.format(windows) - + conv, base, lead = meta.int0, meta.int1, meta.int2 conv_line = self._hl_mid(conv) base_line = self._hl_mid(base) lead_a = (conv_line + base_line) * 0.5 @@ -1336,60 +1217,37 @@ def _get_ichimoku(self, windows=None): lead_a_s = lead_a.shift(base, fill_value=lead_a.iloc[0]) lead_b_s = lead_b.shift(base, fill_value=lead_b.iloc[0]) - self[column_name] = lead_a_s - lead_b_s + self[meta.name] = lead_a_s - lead_b_s @classmethod def mov_std(cls, series, window): return cls._rolling(series, window).std() - def _get_mstd(self, column, windows): - """ get moving standard deviation - - :param column: column to calculate - :param windows: collection of window of moving standard deviation - :return: None - """ - window = self.get_int_positive(windows) - column_name = '{}_{}_mstd'.format(column, window) - self[column_name] = self.mov_std(self[column], window) + def _get_mstd(self, meta: _Meta): + """ get moving standard deviation """ + self[meta.name] = self.mov_std(self[meta.column], meta.int) @classmethod def mov_var(cls, series, window): return cls._rolling(series, window).var() - def _get_mvar(self, column, windows): - """ get moving variance + def _get_mvar(self, meta: _Meta): + """ get moving variance """ + self[meta.name] = self.mov_var(self[meta.column], meta.int) - :param column: column to calculate - :param windows: collection of window of moving variance - :return: None - """ - window = self.get_int_positive(windows) - column_name = '{}_{}_mvar'.format(column, window) - self[column_name] = self.mov_var(self[column], window) - - def _get_vwma(self, window=None): + def _get_vwma(self, meta: _Meta): """ get Volume Weighted Moving Average The definition is available at: https://www.investopedia.com/articles/trading/11/trading-with-vwap-mvwap.asp - - :param window: number of periods relevant for the indicator - :return: None """ - if window is None: - window = self.VWMA - column_name = 'vwma' - else: - column_name = 'vwma_{}'.format(window) - window = self.get_int_positive(window) - + window = meta.int tpv = self['volume'] * self._tp() rolling_tpv = self.mov_sum(tpv, window) rolling_vol = self.mov_sum(self['volume'], window) - self[column_name] = rolling_tpv / rolling_vol + self[meta.name] = rolling_tpv / rolling_vol - def _get_chop(self, window=None): + def _get_chop(self, meta: _Meta): """ get Choppiness Index (CHOP) See the definition of the index here: @@ -1403,16 +1261,8 @@ def _get_chop(self, window=None): ATR(1) = Average True Range (Period of 1) SUM(ATR(1), n) = Sum of the Average True Range over past n bars MaxHi(n) = The highest high over past n bars - - :param window: number of periods relevant for the indicator - :return: None """ - if window is None: - window = self.CHOP - column_name = 'chop' - else: - column_name = 'chop_{}'.format(window) - window = self.get_int_positive(window) + window = meta.int atr = self._atr(1) atr_sum = self.mov_sum(atr, window) high = self.mov_max(self['high'], window) @@ -1420,23 +1270,15 @@ def _get_chop(self, window=None): choppy = atr_sum / (high - low) numerator = np.log10(choppy) * 100 denominator = np.log10(window) - self[column_name] = numerator / denominator + self[meta.name] = numerator / denominator - def _get_mfi(self, window=None): + def _get_mfi(self, meta: _Meta): """ get money flow index The definition of money flow index is available at: https://www.investopedia.com/terms/m/mfi.asp - - :param window: number of periods relevant for the indicator - :return: None """ - if window is None: - window = self.MFI - column_name = 'mfi' - else: - column_name = 'mfi_{}'.format(window) - window = self.get_int_positive(window) + window = meta.int middle = self._tp() money_flow = (middle * self["volume"]).fillna(0.0) shifted = self._shift(middle, -1) @@ -1448,9 +1290,9 @@ def _get_mfi(self, window=None): money_flow_ratio = rolling_pos_flow / (rolling_neg_flow + 1e-12) mfi = (1.0 - 1.0 / (1 + money_flow_ratio)) mfi.iloc[:window] = 0.5 - self[column_name] = mfi + self[meta.name] = mfi - def _get_ao(self, windows=None): + def _get_ao(self, meta: _Meta): """ get awesome oscillator The AO indicator is a good indicator for measuring the market dynamics, @@ -1465,21 +1307,13 @@ def _get_ao(self, windows=None): https://www.ifcm.co.uk/ntx-indicators/awesome-oscillator """ - if windows is None: - fast = self.AO_FAST - slow = self.AO_SLOW - column_name = 'ao' - else: - n0, n1 = self.to_ints(windows) - fast = min(n0, n1) - slow = max(n0, n1) - column_name = 'ao_{},{}'.format(fast, slow) - + fast = meta.int0 + slow = meta.int1 median_price = (self['high'] + self['low']) * 0.5 ao = self.sma(median_price, fast) - self.sma(median_price, slow) - self[column_name] = ao + self[meta.name] = ao - def _get_bop(self): + def _get_bop(self, meta: _Meta): """ get balance of power The Balance of Power indicator measures the strength of the bulls. @@ -1489,9 +1323,9 @@ def _get_bop(self): """ dividend = self['close'] - self['open'] divisor = self['high'] - self['low'] - self['bop'] = dividend / divisor + self[meta.name] = dividend / divisor - def _get_cmo(self, window=None): + def _get_cmo(self, meta: _Meta): """ get Chande Momentum Oscillator The Chande Momentum Oscillator (CMO) is a technical momentum @@ -1504,14 +1338,8 @@ def _get_cmo(self, window=None): * sH=the sum of higher closes over N periods * sL=the sum of lower closes of N periods """ - if window is None: - window = self.CMO - column_name = 'cmo' - else: - window = self.get_int_positive(window) - column_name = 'cmo_{}'.format(window) - - close_diff = self['close'].diff() + window = meta.int + close_diff = self._col_diff('close') up = close_diff.clip(lower=0) down = close_diff.clip(upper=0).abs() sum_up = self.mov_sum(up, window) @@ -1519,8 +1347,8 @@ def _get_cmo(self, window=None): dividend = sum_up - sum_down divisor = sum_up + sum_down res = 100 * dividend / divisor - res.iloc[0] = 0 - self[column_name] = res + res.iloc[0] = 0.0 + self[meta.name] = res def ker(self, column, window): col = self[column] @@ -1532,7 +1360,7 @@ def ker(self, column, window): ret.iloc[0] = 0 return ret - def _get_ker(self, column=None, window=None): + def _get_ker(self, meta: _Meta): """ get Kaufman's efficiency ratio The Efficiency Ratio (ER) is calculated by @@ -1554,38 +1382,15 @@ def _get_ker(self, column=None, window=None): volatility = moving sum of last_change in n KER = window_change / volatility """ - if column is None and window is None: - column = 'close' - window = self.ER - column_name = 'ker' - else: - window = self.get_int_positive(window) - column_name = '{}_{}_ker'.format(column, window) + self[meta.name] = self.ker(meta.column, meta.int) - self[column_name] = self.ker(column, window) - - def _get_kama(self, column, windows, fasts=None, slows=None): + def _get_kama(self, meta: _Meta): """ get Kaufman's Adaptive Moving Average. Implemented after https://school.stockcharts.com/doku.php?id=technical_indicators:kaufman_s_adaptive_moving_average - - :param column: column to calculate - :param windows: collection of window of exponential moving average - :param fasts: faster EMA constant - :param slows: slower EMA constant - :return: None """ - window = self.get_int_positive(windows) - if slows is None or fasts is None: - slow, fast = self.KAMA_SLOW, self.KAMA_FAST - column_name = "{}_{}_kama".format(column, window) - else: - slow = self.get_int_positive(slows) - fast = self.get_int_positive(fasts) - column_name = '{}_{}_kama_{}_{}'.format(column, window, fast, slow) - - col = self[column] - efficiency_ratio = self.ker(column, window) + window, fast, slow = meta.int0, meta.int1, meta.int2 + efficiency_ratio = self.ker(meta.column, window) fast_ema_smoothing = 2.0 / (fast + 1) slow_ema_smoothing = 2.0 / (slow + 1) smoothing_2 = fast_ema_smoothing - slow_ema_smoothing @@ -1593,17 +1398,19 @@ def _get_kama(self, column, windows, fasts=None, slows=None): smoothing = list(2 * (efficient_smoothing + slow_ema_smoothing)) # start with simple moving average + col = self[meta.column] kama = list(self.sma(col, window)) - col_list = list(col) if len(kama) >= window: last_kama = kama[window - 1] else: last_kama = 0.0 + + col_list = list(col) for i in range(window, len(kama)): cur = smoothing[i] * (col_list[i] - last_kama) + last_kama kama[i] = cur last_kama = cur - self[column_name] = kama + self[meta.name] = kama @staticmethod def parse_column_name(name): @@ -1615,11 +1422,6 @@ def parse_column_name(name): ret = m.group(1, 2) else: ret = m.group(1, 2, 3) - if any(map(lambda i: i in ret[0], - StockDataFrame.MULTI_SPLIT_INDICATORS)): - m_prev = re.match(r'(.*)_([\d\-+~,.]+)_(\w+)', ret[0]) - if m_prev is not None: - ret = m_prev.group(1, 2, 3) + ret[1:] return ret CROSS_COLUMN_MATCH_STR = '(.+)_(x|xu|xd)_(.+)' @@ -1636,11 +1438,8 @@ def parse_cross_column(cls, name): ret = m.group(1, 2, 3) return ret - def _get_rate(self): - """ same as percent - - :return: None - """ + def _get_rate(self, _: _Meta): + """ same as percent """ self['rate'] = self['close'].pct_change() * 100 def _col_diff(self, col): @@ -1673,7 +1472,7 @@ def _get_cross(self, key): def init_all(self): """ initialize all stats. in the handler """ for handler in self.handler.values(): - handler() + _call_handler(handler) def drop_column(self, names=None, inplace=False): """ drop column by the name @@ -1722,12 +1521,17 @@ def handler(self): ('boll', 'boll_ub', 'boll_lb'): self._get_boll, ('macd', 'macds', 'macdh'): self._get_macd, ('ppo', 'ppos', 'ppoh'): self._get_ppo, - ('kdjk', 'kdjd', 'kdjj'): self._get_kdj_default, + ('kdjk',): self._get_kdjk, + ('kdjd',): self._get_kdjd, + ('kdjj',): self._get_kdjj, + ('rsv',): self._get_rsv, ('cr', 'cr-ma1', 'cr-ma2', 'cr-ma3'): self._get_cr, ('cci',): self._get_cci, ('tr',): self._get_tr, ('atr',): self._get_atr, - ('pdi', 'mdi', 'dx', 'adx', 'adxr'): self._get_dmi, + ('pdi',): self._get_pdi, + ('ndi',): self._get_ndi, + ('dx', 'adx', 'adxr'): self._get_dmi, ('trix',): self._get_trix, ('tema',): self._get_tema, ('vr',): self._get_vr, @@ -1736,7 +1540,7 @@ def handler(self): ('chop',): self._get_chop, ('log-ret',): self._get_log_ret, ('mfi',): self._get_mfi, - ('wt1', 'wt2'): self._get_wave_trend, + ('wt1', 'wt2'): self._get_wt, ('wr',): self._get_wr, ('supertrend', 'supertrend_lb', @@ -1755,8 +1559,7 @@ def handler(self): def __init_not_exist_column(self, key): for names, handler in self.handler.items(): if key in names: - handler() - return + return _call_handler(handler) if key.endswith('_delta'): self._get_delta(key) @@ -1764,22 +1567,16 @@ def __init_not_exist_column(self, key): self._get_cross(key) else: ret = self.parse_column_name(key) - if len(ret) == 5: - c, r, t, s, f = ret - func_name = '_get_{}'.format(t) - getattr(self, func_name)(c, r, s, f) - elif len(ret) == 3: - c, r, t = ret - func_name = '_get_{}'.format(t) - getattr(self, func_name)(c, r) + if len(ret) == 3: + col, n, name = ret elif len(ret) == 2: - c, r = ret - func_name = '_get_{}'.format(c) - getattr(self, func_name)(r) + name, n = ret + col = None else: raise UserWarning("Invalid number of return arguments " - "after parsing column name: '{}'" - .format(key)) + f"after parsing column name: '{key}'") + meta = _Meta(name, windows=n, column=col) + getattr(self, f'_get_{name}')(meta) def __init_column(self, key): if key not in self: @@ -1812,6 +1609,7 @@ def start_from(self, start_date): def within(self, start_date, end_date): return self.start_from(start_date).till(end_date) + # noinspection PyFinal def copy(self, deep=True): return wrap(super(StockDataFrame, self).copy(deep)) diff --git a/test.py b/test.py index c566365..88a1105 100644 --- a/test.py +++ b/test.py @@ -34,6 +34,7 @@ not_, has_item, has_length from numpy import isnan +import stockstats from stockstats import StockDataFrame as Sdf, StockDataFrame from stockstats import wrap, unwrap @@ -135,8 +136,15 @@ def test_column_rate_plus2(self): def test_change(self): stock = self.get_stock_20days() change = stock['change'] + assert_that(change.loc[20110104], equal_to(0)) + assert_that(change.loc[20110105], near_to(0.793)) assert_that(change.loc[20110107], near_to(4.4198)) + change = stock['change_2'] + assert_that(change.loc[20110104], equal_to(0)) + assert_that(change.loc[20110105], equal_to(0)) + assert_that(change.loc[20110106], near_to(0.476)) + def test_middle(self): stock = self.get_stock_20days() middle = stock['middle'] @@ -216,6 +224,22 @@ def test_column_rsv(self): rsv_3 = stock['rsv_3'] assert_that(rsv_3.loc[20110106], near_to(60.6557)) + def test_change_single_default_window(self): + stock = self.get_stock_20days() + rsv = stock['rsv'] + rsv_9 = stock['rsv_9'] + rsv_5 = stock['rsv_5'] + idx = 20110114 + assert_that(rsv[idx], equal_to(rsv_9[idx])) + assert_that(rsv[idx], not_(equal_to(rsv_5[idx]))) + + orig = stockstats.set_dft_window('rsv', 5) + assert_that(orig, equal_to(9)) + stock.drop_column('rsv', inplace=True) + rsv = stock['rsv'] + assert_that(rsv[idx], equal_to(rsv_5[idx])) + stockstats.set_dft_window('rsv', orig) + def test_column_kdj_default(self): stock = self.get_stock_20days() assert_that(stock['kdjk'].loc[20110104], near_to(60.5263)) @@ -472,8 +496,7 @@ def test_get_log_ret(self): @staticmethod def test_rsv_nan_value(): - s = wrap(pd.read_csv(get_file('asml.as.csv'))) - df = wrap(s) + df = wrap(pd.read_csv(get_file('asml.as.csv'))) assert_that(df['rsv_9'][0], equal_to(0.0)) def test_unwrap(self): @@ -543,6 +566,12 @@ def test_get_dma(self): assert_that(c.loc[20160816], near_to(2.15)) assert_that(c.loc[20160815], near_to(2.2743)) + c = self._supor.get('close_10,50_dma') + assert_that(c.loc[20160817], near_to(2.078)) + + c = self._supor.get('high_5,10_dma') + assert_that(c.loc[20160817], near_to(0.174)) + def test_pdm_ndm(self): c = self.get_stock_90days() @@ -561,7 +590,7 @@ def test_get_pdi(self): assert_that(c.loc[20160815], near_to(24.646)) def test_get_mdi(self): - c = self._supor.get('mdi') + c = self._supor.get('ndi') assert_that(c.loc[20160817], near_to(16.195)) assert_that(c.loc[20160816], near_to(17.579)) assert_that(c.loc[20160815], near_to(19.542)) @@ -590,6 +619,12 @@ def test_trix_default(self): assert_that(c.loc[20160816], near_to(0.2135)) assert_that(c.loc[20160815], near_to(0.24)) + c = self._supor.get('close_12_trix') + assert_that(c.loc[20160815], near_to(0.24)) + + c = self._supor.get('high_12_trix') + assert_that(c.loc[20160815], near_to(0.235)) + def test_tema_default(self): c = self._supor.get('tema') a = self._supor.get('close_5_tema') @@ -598,6 +633,9 @@ def test_tema_default(self): assert_that(c.loc[20160816], near_to(39.6371)) assert_that(c.loc[20160815], near_to(39.3778)) + c = self._supor.get('high_3_tema') + assert_that(c.loc[20160815], near_to(39.7315)) + def test_trix_ma(self): c = self._supor.get('trix_9_sma') assert_that(c.loc[20160817], near_to(0.34)) @@ -655,11 +693,13 @@ def test_ker(self): def test_column_kama(self): stock = self.get_stock_90days() - idx = 20110331 - kama_10 = stock['close_10_kama_2_30'] - assert_that(kama_10.loc[idx], near_to(13.6648)) + kama_10 = stock['close_10,2,30_kama'] + assert_that(kama_10.loc[20110331], near_to(13.6648)) + + def test_kama_with_default_fast_slow(self): + stock = self.get_stock_90days() kama_2 = stock['close_2_kama'] - assert_that(kama_2.loc[idx], near_to(13.7326)) + assert_that(kama_2.loc[20110331], near_to(13.7326)) def test_vwma(self): stock = self.get_stock_90days() @@ -696,6 +736,10 @@ def test_wave_trend(self): assert_that(wt1.loc[idx], near_to(38.9610)) assert_that(wt2.loc[idx], near_to(31.6997)) + wt1, wt2 = stock['wt1_10,21'], stock['wt2_10,21'] + assert_that(wt1.loc[idx], near_to(38.9610)) + assert_that(wt2.loc[idx], near_to(31.6997)) + def test_init_all(self): stock = self.get_stock_90days() stock.init_all() @@ -921,3 +965,19 @@ def test_cti(self): assert_that(cti[20110118], near_to(-0.006)) assert_that(cti[20110131], near_to(-0.043)) assert_that(cti[20110215], near_to(0.5006)) + + def test_change_group_window_defaults(self): + stock = self.get_stock_90days() + macd = stock['macd'] + ref = stock['macd_12,26,9'] + i = 20110225 + assert_that(macd[i], equal_to(ref[i])) + + orig = stockstats.set_dft_window('macd', (10, 20, 5)) + assert_that(orig, contains_exactly(12, 26, 9)) + stock.drop_column(['macd', 'macdh', 'macds'], inplace=True) + macd = stock['macd'] + ref = stock['macd_10,20,5'] + assert_that(macd[i], equal_to(ref[i])) + + stockstats.set_dft_window('macd', orig)