diff --git a/README.md b/README.md index d0a7687..7a98ed5 100644 --- a/README.md +++ b/README.md @@ -62,7 +62,7 @@ _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that lever * [Trend](#trend-19) * [Utility](#utility-5) * [Volatility](#volatility-14) - * [Volume](#volume-15) + * [Volume](#volume-16) * [Misc](#misc) * [Backtesting](#backtesting) * [Performance Metrics](#performance-metrics) @@ -118,7 +118,7 @@ $ pip install pandas_ta Latest Version -------------- -Best choice! Version: *0.3.38b* +Best choice! Version: *0.3.41b* * Includes all fixes and updates between **pypi** and what is covered in this README. ```sh $ pip install -U git+https://github.com/twopirllc/pandas-ta @@ -219,7 +219,7 @@ Thanks for using **Pandas TA**! _Thank you for your contributions!_ - +
@@ -898,7 +898,7 @@ Use parameter: cumulative=**True** for cumulative results.
-### **Volume** (15) +### **Volume** (16) * _Accumulation/Distribution Index_: **ad** * _Accumulation/Distribution Oscillator_: **adosc** @@ -915,6 +915,7 @@ Use parameter: cumulative=**True** for cumulative results. * _Price Volume Rank_: **pvr** * _Price Volume Trend_: **pvt** * _Volume Profile_: **vp** +* _Worden Brothers Time Segmented Value_: **wb_tsv** | _On-Balance Volume_ (OBV) | |:--------:| @@ -1025,6 +1026,7 @@ help(ta.sample) ## **Breaking / Depreciated Indicators** * _Arnaud Legoux Moving Average_ (**alma**) Updated accuracy and speed with new default ```length=9``` and argument ```distribution_offset``` renamed to ```dist_offset```. See ```help(ta.alma)```. * _Trend Return_ (**trend_return**) has been removed and replaced with **tsignals**. When given a trend Series like ```close > sma(close, 50)``` it returns the Trend, Trade Entries and Trade Exits of that trend to make it compatible with [**vectorbt**](https://github.com/polakowo/vectorbt) by setting ```asbool=True``` to get boolean Trade Entries and Exits. See ```help(ta.tsignals)``` +* _Volume Profile_ (**vp**) is no longer part of the DataFrame Extension since it does not return a Time Series. * _Zero Lag Moving Average_ (**zlma**) now using available Moving Averages from ```ta.ma```. See ```help(ta.zlma)``` and ```help(ta.ma)```.
@@ -1039,6 +1041,7 @@ help(ta.sample) * _Squeeze Pro_ (**squeeze_pro**) is an extended version of "TTM Squeeze" from John Carter. See ```help(ta.squeeze_pro)``` * _Tom DeMark's Sequential_ (**td_seq**) attempts to identify a price point where an uptrend or a downtrend exhausts itself and reverses. Currently exlcuded from ```df.ta.strategy()``` for performance reasons. See ```help(ta.td_seq)``` * _Think or Swim Standard Deviation All_ (**tos_stdevall**) indicator which returns the standard deviation of data for the entire plot or for the interval of the last bars defined by the length parameter. See ```help(ta.tos_stdevall)``` +* _Worden Brothers Time Segmented Value_ (**wb_tsv**) is an oscillator indicator that attempts to indentify money flow in a stock, similar to On Balance Volume (**obv**). See ```help(ta.wb_tsv)```
diff --git a/pandas_ta/__init__.py b/pandas_ta/__init__.py index 87cd421..11ad7e4 100644 --- a/pandas_ta/__init__.py +++ b/pandas_ta/__init__.py @@ -80,10 +80,11 @@ Category = { "natr", "pdist", "rvi", "thermo", "true_range", "ui" ], - # Volume, "vp" or "Volume Profile" is unique + # Volume. + # Note: "vp" or "Volume Profile" is excluded since it does not return a Time Series "volume": [ "ad", "adosc", "aobv", "cmf", "efi", "eom", "kvo", "mfi", "nvi", "obv", - "pvi", "pvol", "pvr", "pvt" + "pvi", "pvol", "pvr", "pvt", "wb_tsv" ], } diff --git a/pandas_ta/candles/cdl_doji.py b/pandas_ta/candles/cdl_doji.py index 9c053c3..6d41dee 100644 --- a/pandas_ta/candles/cdl_doji.py +++ b/pandas_ta/candles/cdl_doji.py @@ -13,17 +13,6 @@ def cdl_doji(open_, high, low, close, length=None, factor=None, scalar=None, asi Sources: TA-Lib: 96.56% Correlation - Calculation: - Default values: - length=10, percent=10 (0.1), scalar=100 - ABS = Absolute Value - SMA = Simple Moving Average - - BODY = ABS(close - open) - HL_RANGE = ABS(high - low) - - DOJI = scalar IF BODY < 0.01 * percent * SMA(HL_RANGE, length) ELSE 0 - Args: open_ (pd.Series): Series of 'open's high (pd.Series): Series of 'high's diff --git a/pandas_ta/candles/cdl_inside.py b/pandas_ta/candles/cdl_inside.py index 272e85e..1ade4c0 100644 --- a/pandas_ta/candles/cdl_inside.py +++ b/pandas_ta/candles/cdl_inside.py @@ -15,14 +15,6 @@ def cdl_inside(open_, high, low, close, asbool=False, offset=None, **kwargs): Sources: https://www.tradingview.com/script/IyIGN1WO-Inside-Bar/ - Calculation: - Default Inputs: - asbool=False - inside = (high.diff() < 0) & (low.diff() > 0) - - if not asbool: - inside *= candle_color(open_, close) - Args: open_ (pd.Series): Series of 'open's high (pd.Series): Series of 'high's diff --git a/pandas_ta/candles/cdl_z.py b/pandas_ta/candles/cdl_z.py index a003f2b..b15cc4c 100644 --- a/pandas_ta/candles/cdl_z.py +++ b/pandas_ta/candles/cdl_z.py @@ -11,16 +11,6 @@ def cdl_z(open_, high, low, close, length=None, full=None, ddof=None, offset=Non Source: Kevin Johnson - Calculation: - Default values: - length=30, full=False, ddof=1 - Z = ZSCORE - - open = Z( open, length, ddof) - high = Z( high, length, ddof) - low = Z( low, length, ddof) - close = Z(close, length, ddof) - Args: open_ (pd.Series): Series of 'open's high (pd.Series): Series of 'high's diff --git a/pandas_ta/candles/ha.py b/pandas_ta/candles/ha.py index f6b2532..6473a6c 100644 --- a/pandas_ta/candles/ha.py +++ b/pandas_ta/candles/ha.py @@ -19,25 +19,6 @@ def ha(open_, high, low, close, offset=None, **kwargs): Sources: https://www.investopedia.com/terms/h/heikinashi.asp - Calculation: - HA_OPEN[0] = (open[0] + close[0]) / 2 - HA_CLOSE = (open[0] + high[0] + low[0] + close[0]) / 4 - - for i > 1 in df.index: - HA_OPEN = (HA_OPEN[iāˆ’1] + HA_CLOSE[iāˆ’1]) / 2 - - HA_HIGH = MAX(HA_OPEN, HA_HIGH, HA_CLOSE) - HA_LOW = MIN(HA_OPEN, HA_LOW, HA_CLOSE) - - How to Calculate Heikin-Ashi - - Use one period to create the first Heikin-Ashi (HA) candle, using - the formulas. For example use the high, low, open, and close to - create the first HA close price. Use the open and close to create - the first HA open. The high of the period will be the first HA high, - and the low will be the first HA low. With the first HA calculated, - it is now possible to continue computing the HA candles per the formulas. - ā€‹ā€‹ Args: open_ (pd.Series): Series of 'open's high (pd.Series): Series of 'high's diff --git a/pandas_ta/core.py b/pandas_ta/core.py index 1307b36..a1c8beb 100644 --- a/pandas_ta/core.py +++ b/pandas_ta/core.py @@ -667,7 +667,6 @@ class AnalysisIndicators(BasePandasObject): "short_run", "td_seq", # Performance exclusion "tsignals", - "vp", "xsignals", ] @@ -1834,8 +1833,8 @@ class AnalysisIndicators(BasePandasObject): result = pvt(close=close, volume=volume, offset=offset, **kwargs) return self._post_process(result, **kwargs) - def vp(self, width=None, percent=None, **kwargs): + def wb_tsv(self, length=None, signal=None, offset=None, **kwargs): close = self._get_column(kwargs.pop("close", "close")) volume = self._get_column(kwargs.pop("volume", "volume")) - result = vp(close=close, volume=volume, width=width, percent=percent, **kwargs) + result = wb_tsv(close=close, volume=volume, signal=signal, offset=offset, **kwargs) return self._post_process(result, **kwargs) diff --git a/pandas_ta/cycles/ebsw.py b/pandas_ta/cycles/ebsw.py index 5cad4b7..c7ed2a5 100644 --- a/pandas_ta/cycles/ebsw.py +++ b/pandas_ta/cycles/ebsw.py @@ -36,9 +36,6 @@ def ebsw(close, length=None, bars=None, offset=None, initial_version=False, **kw - https://www.prorealcode.com/prorealtime-indicators/even-better-sinewave/ - J.F.Ehlers 'Cycle Analytics for Traders', 2014 - Calculation: - refer to 'sources' or implementation - Args: close (pd.Series): Series of 'close's length (int): It's max cycle/trend period. Values between 40-48 work like diff --git a/pandas_ta/cycles/reflex.py b/pandas_ta/cycles/reflex.py index 63a79c1..c7092e9 100644 --- a/pandas_ta/cycles/reflex.py +++ b/pandas_ta/cycles/reflex.py @@ -24,9 +24,6 @@ def reflex(close, length=None, smooth=None, alpha=None, offset=None, **kwargs): Sources: https://www.prorealcode.com/prorealtime-indicators/reflex-and-trendflex-indicators-john-f-ehlers/ - Calculation: - Refer to provided source or the code above. - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 20 diff --git a/pandas_ta/momentum/ao.py b/pandas_ta/momentum/ao.py index e436e1c..8d3bddc 100644 --- a/pandas_ta/momentum/ao.py +++ b/pandas_ta/momentum/ao.py @@ -13,13 +13,6 @@ def ao(high, low, fast=None, slow=None, offset=None, **kwargs): https://www.tradingview.com/wiki/Awesome_Oscillator_(AO) https://www.ifcm.co.uk/ntx-indicators/awesome-oscillator - Calculation: - Default Inputs: - fast=5, slow=34 - SMA = Simple Moving Average - median = (high + low) / 2 - AO = SMA(median, fast) - SMA(median, slow) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/momentum/apo.py b/pandas_ta/momentum/apo.py index 74f9883..4866da8 100644 --- a/pandas_ta/momentum/apo.py +++ b/pandas_ta/momentum/apo.py @@ -14,12 +14,6 @@ def apo(close, fast=None, slow=None, mamode=None, talib=None, offset=None, **kwa Sources: https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/absolute-price-oscillator-apo/ - Calculation: - Default Inputs: - fast=12, slow=26 - SMA = Simple Moving Average - APO = SMA(close, fast) - SMA(close, slow) - Args: close (pd.Series): Series of 'close's fast (int): The short period. Default: 12 diff --git a/pandas_ta/momentum/bias.py b/pandas_ta/momentum/bias.py index f48bc7e..a70bbc2 100644 --- a/pandas_ta/momentum/bias.py +++ b/pandas_ta/momentum/bias.py @@ -12,13 +12,6 @@ def bias(close, length=None, mamode=None, offset=None, **kwargs): Few internet resources on definitive definition. Request by Github user homily, issue #46 - Calculation: - Default Inputs: - length=26, MA='sma' - - BIAS = (close - MA(close, length)) / MA(close, length) - = (close / MA(close, length)) - 1 - Args: close (pd.Series): Series of 'close's length (int): The period. Default: 26 diff --git a/pandas_ta/momentum/bop.py b/pandas_ta/momentum/bop.py index 224deb8..51ef086 100644 --- a/pandas_ta/momentum/bop.py +++ b/pandas_ta/momentum/bop.py @@ -11,9 +11,6 @@ def bop(open_, high, low, close, scalar=None, talib=None, offset=None, **kwargs) Sources: http://www.worden.com/TeleChartHelp/Content/Indicators/Balance_of_Power.htm - Calculation: - BOP = scalar * (close - open) / (high - low) - Args: open (pd.Series): Series of 'open's high (pd.Series): Series of 'high's diff --git a/pandas_ta/momentum/brar.py b/pandas_ta/momentum/brar.py index ec601c6..eb8d258 100644 --- a/pandas_ta/momentum/brar.py +++ b/pandas_ta/momentum/brar.py @@ -12,20 +12,6 @@ def brar(open_, high, low, close, length=None, scalar=None, drift=None, offset=N No internet resources on definitive definition. Request by Github user homily, issue #46 - Calculation: - Default Inputs: - length=26, scalar=100 - SUM = Sum - - HO_Diff = high - open - OL_Diff = open - low - HCY = high - close[-1] - CYL = close[-1] - low - HCY[HCY < 0] = 0 - CYL[CYL < 0] = 0 - AR = scalar * SUM(HO, length) / SUM(OL, length) - BR = scalar * SUM(HCY, length) / SUM(CYL, length) - Args: open_ (pd.Series): Series of 'open's high (pd.Series): Series of 'high's diff --git a/pandas_ta/momentum/cci.py b/pandas_ta/momentum/cci.py index cf62515..b4f5f1c 100644 --- a/pandas_ta/momentum/cci.py +++ b/pandas_ta/momentum/cci.py @@ -14,16 +14,6 @@ def cci(high, low, close, length=None, c=None, talib=None, offset=None, **kwargs Sources: https://www.tradingview.com/wiki/Commodity_Channel_Index_(CCI) - Calculation: - Default Inputs: - length=14, c=0.015 - SMA = Simple Moving Average - MAD = Mean Absolute Deviation - tp = typical_price = hlc3 = (high + low + close) / 3 - mean_tp = SMA(tp, length) - mad_tp = MAD(tp, length) - CCI = (tp - mean_tp) / (c * mad_tp) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/momentum/cfo.py b/pandas_ta/momentum/cfo.py index 94fe4ce..38b9297 100644 --- a/pandas_ta/momentum/cfo.py +++ b/pandas_ta/momentum/cfo.py @@ -12,13 +12,6 @@ def cfo(close, length=None, scalar=None, drift=None, offset=None, **kwargs): Sources: https://www.fmlabs.com/reference/default.htm?url=ForecastOscillator.htm - Calculation: - Default Inputs: - length=9, drift=1, scalar=100 - LINREG = Linear Regression - - CFO = scalar * (close - LINERREG(length, tdf=True)) / close - Args: close (pd.Series): Series of 'close's length (int): The period. Default: 9 diff --git a/pandas_ta/momentum/cg.py b/pandas_ta/momentum/cg.py index a63abe4..a6544f4 100644 --- a/pandas_ta/momentum/cg.py +++ b/pandas_ta/momentum/cg.py @@ -11,10 +11,6 @@ def cg(close, length=None, offset=None, **kwargs): Sources: http://www.mesasoftware.com/papers/TheCGOscillator.pdf - Calculation: - Default Inputs: - length=10 - Args: close (pd.Series): Series of 'close's length (int): The length of the period. Default: 10 diff --git a/pandas_ta/momentum/cmo.py b/pandas_ta/momentum/cmo.py index 6893e1c..9b425a4 100644 --- a/pandas_ta/momentum/cmo.py +++ b/pandas_ta/momentum/cmo.py @@ -14,13 +14,6 @@ def cmo(close, length=None, scalar=None, talib=None, drift=None, offset=None, ** https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/chande-momentum-oscillator-cmo/ https://www.tradingview.com/script/hdrf0fXV-Variable-Index-Dynamic-Average-VIDYA/ - Calculation: - Default Inputs: - drift=1, scalar=100 - - # Same Calculation as RSI except for this step - CMO = scalar * (PSUM - NSUM) / (PSUM + NSUM) - Args: close (pd.Series): Series of 'close's scalar (float): How much to magnify. Default: 100 diff --git a/pandas_ta/momentum/coppock.py b/pandas_ta/momentum/coppock.py index 6329437..12f8aa7 100644 --- a/pandas_ta/momentum/coppock.py +++ b/pandas_ta/momentum/coppock.py @@ -16,16 +16,6 @@ def coppock(close, length=None, fast=None, slow=None, offset=None, **kwargs): Sources: https://en.wikipedia.org/wiki/Coppock_curve - Calculation: - Default Inputs: - length=10, fast=11, slow=14 - SMA = Simple Moving Average - MAD = Mean Absolute Deviation - tp = typical_price = hlc3 = (high + low + close) / 3 - mean_tp = SMA(tp, length) - mad_tp = MAD(tp, length) - CCI = (tp - mean_tp) / (c * mad_tp) - Args: close (pd.Series): Series of 'close's length (int): WMA period. Default: 10 diff --git a/pandas_ta/momentum/cti.py b/pandas_ta/momentum/cti.py index 4d74c01..a55bb28 100644 --- a/pandas_ta/momentum/cti.py +++ b/pandas_ta/momentum/cti.py @@ -17,6 +17,10 @@ def cti(close, length=None, offset=None, **kwargs) -> Series: length (int): It's period. Default: 12 offset (int): How many periods to offset the result. Default: 0 + Kwargs: + fillna (value, optional): pd.DataFrame.fillna(value) + fill_method (value, optional): Type of fill method + Returns: pd.Series: Series of the CTI values for the given period. """ diff --git a/pandas_ta/momentum/dm.py b/pandas_ta/momentum/dm.py index 0ff2701..a3fb986 100644 --- a/pandas_ta/momentum/dm.py +++ b/pandas_ta/momentum/dm.py @@ -16,22 +16,6 @@ def dm(high, low, length=None, mamode=None, talib=None, drift=None, offset=None, https://www.tradingview.com/pine-script-reference/#fun_dmi https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=24&Name=Directional_Movement_Index - Calculation: - Default Inputs: - length=14, mamode="rma", drift=1 - up = high - high.shift(drift) - dn = low.shift(drift) - low - - pos_ = ((up > dn) & (up > 0)) * up - neg_ = ((dn > up) & (dn > 0)) * dn - - pos_ = pos_.apply(zero) - neg_ = neg_.apply(zero) - - # Not the same values as TA Lib's -+DM - pos = ma(mamode, pos_, length=length) - neg = ma(mamode, neg_, length=length) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's @@ -41,6 +25,10 @@ def dm(high, low, length=None, mamode=None, talib=None, drift=None, offset=None, drift (int): The difference period. Default: 1 offset (int): How many periods to offset the result. Default: 0 + Kwargs: + fillna (value, optional): pd.DataFrame.fillna(value) + fill_method (value, optional): Type of fill method + Returns: pd.DataFrame: DMP (+DM) and DMN (-DM) columns. """ diff --git a/pandas_ta/momentum/er.py b/pandas_ta/momentum/er.py index 79e9c12..e6bf876 100644 --- a/pandas_ta/momentum/er.py +++ b/pandas_ta/momentum/er.py @@ -13,16 +13,6 @@ def er(close, length=None, drift=None, offset=None, **kwargs): Sources: https://help.tc2000.com/m/69404/l/749623-kaufman-efficiency-ratio - Calculation: - Default Inputs: - length=10 - ABS = Absolute Value - EMA = Exponential Moving Average - - abs_diff = ABS(close.diff(length)) - volatility = ABS(close.diff(1)) - ER = abs_diff / SUM(volatility, length) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 1 diff --git a/pandas_ta/momentum/eri.py b/pandas_ta/momentum/eri.py index 2570ca0..46f3e52 100644 --- a/pandas_ta/momentum/eri.py +++ b/pandas_ta/momentum/eri.py @@ -20,14 +20,6 @@ def eri(high, low, close, length=None, offset=None, **kwargs): Sources: https://admiralmarkets.com/education/articles/forex-indicators/bears-and-bulls-power-indicator - Calculation: - Default Inputs: - length=13 - EMA = Exponential Moving Average - - BULLPOWER = high - EMA(close, length) - BEARPOWER = low - EMA(close, length) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/momentum/fisher.py b/pandas_ta/momentum/fisher.py index bd765a2..e6d3e73 100644 --- a/pandas_ta/momentum/fisher.py +++ b/pandas_ta/momentum/fisher.py @@ -16,29 +16,6 @@ def fisher(high, low, length=None, signal=None, offset=None, **kwargs): Sources: TradingView (Correlation >99%) - Calculation: - Default Inputs: - length=9, signal=1 - HL2 = hl2(high, low) - HHL2 = HL2.rolling(length).max() - LHL2 = HL2.rolling(length).min() - - HLR = HHL2 - LHL2 - HLR[HLR < 0.001] = 0.001 - - position = ((HL2 - LHL2) / HLR) - 0.5 - - v = 0 - m = high.size - FISHER = [npNaN for _ in range(0, length - 1)] + [0] - for i in range(length, m): - v = 0.66 * position[i] + 0.67 * v - if v < -0.99: v = -0.999 - if v > 0.99: v = 0.999 - FISHER.append(0.5 * (nplog((1 + v) / (1 - v)) + FISHER[i - 1])) - - SIGNAL = FISHER.shift(signal) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/momentum/inertia.py b/pandas_ta/momentum/inertia.py index 2387ce0..372f480 100644 --- a/pandas_ta/momentum/inertia.py +++ b/pandas_ta/momentum/inertia.py @@ -15,13 +15,6 @@ def inertia(close=None, high=None, low=None, length=None, rvi_length=None, scala Sources: https://www.investopedia.com/terms/r/relative_vigor_index.asp - Calculation: - Default Inputs: - length=14, ma_length=20 - LSQRMA = Least Squares Moving Average - - INERTIA = LSQRMA(RVI(length), ma_length) - Args: open_ (pd.Series): Series of 'open's high (pd.Series): Series of 'high's diff --git a/pandas_ta/momentum/kdj.py b/pandas_ta/momentum/kdj.py index 991572c..f48ae4a 100644 --- a/pandas_ta/momentum/kdj.py +++ b/pandas_ta/momentum/kdj.py @@ -16,18 +16,6 @@ def kdj(high=None, low=None, close=None, length=None, signal=None, offset=None, https://www.prorealcode.com/prorealtime-indicators/kdj/ https://docs.anychart.com/Stock_Charts/Technical_Indicators/Mathematical_Description#kdj - Calculation: - Default Inputs: - length=9, signal=3 - LL = low for last 9 periods - HH = high for last 9 periods - - FAST_K = 100 * (close - LL) / (HH - LL) - - K = RMA(FAST_K, signal) - D = RMA(K, signal) - J = 3K - 2D - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/momentum/kst.py b/pandas_ta/momentum/kst.py index 7a3985e..f38a298 100644 --- a/pandas_ta/momentum/kst.py +++ b/pandas_ta/momentum/kst.py @@ -13,20 +13,6 @@ def kst(close, roc1=None, roc2=None, roc3=None, roc4=None, sma1=None, sma2=None, https://www.tradingview.com/wiki/Know_Sure_Thing_(KST) https://www.incrediblecharts.com/indicators/kst.php - Calculation: - Default Inputs: - roc1=10, roc2=15, roc3=20, roc4=30, - sma1=10, sma2=10, sma3=10, sma4=15, signal=9, drift=1 - ROC = Rate of Change - SMA = Simple Moving Average - rocsma1 = SMA(ROC(close, roc1), sma1) - rocsma2 = SMA(ROC(close, roc2), sma2) - rocsma3 = SMA(ROC(close, roc3), sma3) - rocsma4 = SMA(ROC(close, roc4), sma4) - - KST = 100 * (rocsma1 + 2 * rocsma2 + 3 * rocsma3 + 4 * rocsma4) - KST_Signal = SMA(KST, signal) - Args: close (pd.Series): Series of 'close's roc1 (int): ROC 1 period. Default: 10 diff --git a/pandas_ta/momentum/macd.py b/pandas_ta/momentum/macd.py index c3fff77..5fb2295 100644 --- a/pandas_ta/momentum/macd.py +++ b/pandas_ta/momentum/macd.py @@ -17,19 +17,6 @@ def macd(close, fast=None, slow=None, signal=None, talib=None, offset=None, **kw https://www.tradingview.com/wiki/MACD_(Moving_Average_Convergence/Divergence) AS Mode: https://tr.tradingview.com/script/YFlKXHnP/ - Calculation: - Default Inputs: - fast=12, slow=26, signal=9 - EMA = Exponential Moving Average - MACD = EMA(close, fast) - EMA(close, slow) - Signal = EMA(MACD, signal) - Histogram = MACD - Signal - - if asmode: - MACD = MACD - Signal - Signal = EMA(MACD, signal) - Histogram = MACD - Signal - Args: close (pd.Series): Series of 'close's fast (int): The short period. Default: 12 diff --git a/pandas_ta/momentum/mom.py b/pandas_ta/momentum/mom.py index 39fd3fd..3d5d502 100644 --- a/pandas_ta/momentum/mom.py +++ b/pandas_ta/momentum/mom.py @@ -12,11 +12,6 @@ def mom(close, length=None, talib=None, offset=None, **kwargs): Sources: http://www.onlinetradingconcepts.com/TechnicalAnalysis/Momentum.html - Calculation: - Default Inputs: - length=1 - MOM = close.diff(length) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 1 diff --git a/pandas_ta/momentum/pgo.py b/pandas_ta/momentum/pgo.py index 1b04840..882b53b 100644 --- a/pandas_ta/momentum/pgo.py +++ b/pandas_ta/momentum/pgo.py @@ -14,15 +14,6 @@ def pgo(high, low, close, length=None, offset=None, **kwargs): Sources: https://library.tradingtechnologies.com/trade/chrt-ti-pretty-good-oscillator.html - Calculation: - Default Inputs: - length=14 - ATR = Average True Range - SMA = Simple Moving Average - EMA = Exponential Moving Average - - PGO = (close - SMA(close, length)) / EMA(ATR(high, low, close, length), length) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/momentum/ppo.py b/pandas_ta/momentum/ppo.py index 28baf0c..c363812 100644 --- a/pandas_ta/momentum/ppo.py +++ b/pandas_ta/momentum/ppo.py @@ -14,17 +14,6 @@ def ppo(close, fast=None, slow=None, signal=None, scalar=None, mamode=None, tali Sources: https://www.tradingview.com/wiki/MACD_(Moving_Average_Convergence/Divergence) - Calculation: - Default Inputs: - fast=12, slow=26 - SMA = Simple Moving Average - EMA = Exponential Moving Average - fast_sma = SMA(close, fast) - slow_sma = SMA(close, slow) - PPO = 100 * (fast_sma - slow_sma) / slow_sma - Signal = EMA(PPO, signal) - Histogram = PPO - Signal - Args: close(pandas.Series): Series of 'close's fast(int): The short period. Default: 12 diff --git a/pandas_ta/momentum/psl.py b/pandas_ta/momentum/psl.py index 82f40d9..d732d31 100644 --- a/pandas_ta/momentum/psl.py +++ b/pandas_ta/momentum/psl.py @@ -14,20 +14,6 @@ def psl(close, open_=None, length=None, scalar=None, drift=None, offset=None, ** Sources: https://www.quantshare.com/item-851-psychological-line - Calculation: - Default Inputs: - length=12, scalar=100, drift=1 - - IF NOT open: - DIFF = SIGN(close - close[drift]) - ELSE: - DIFF = SIGN(close - open) - - DIFF.fillna(0) - DIFF[DIFF <= 0] = 0 - - PSL = scalar * SUM(DIFF, length) / length - Args: close (pd.Series): Series of 'close's open_ (pd.Series, optional): Series of 'open's diff --git a/pandas_ta/momentum/pvo.py b/pandas_ta/momentum/pvo.py index 751b4d5..1239128 100644 --- a/pandas_ta/momentum/pvo.py +++ b/pandas_ta/momentum/pvo.py @@ -12,15 +12,6 @@ def pvo(volume, fast=None, slow=None, signal=None, scalar=None, offset=None, **k Sources: https://www.fmlabs.com/reference/default.htm?url=PVO.htm - Calculation: - Default Inputs: - fast=12, slow=26, signal=9 - EMA = Exponential Moving Average - - PVO = (EMA(volume, fast) - EMA(volume, slow)) / EMA(volume, slow) - Signal = EMA(PVO, signal) - Histogram = PVO - Signal - Args: volume (pd.Series): Series of 'volume's fast (int): The short period. Default: 12 diff --git a/pandas_ta/momentum/qqe.py b/pandas_ta/momentum/qqe.py index 82ac2e4..b730351 100644 --- a/pandas_ta/momentum/qqe.py +++ b/pandas_ta/momentum/qqe.py @@ -21,10 +21,6 @@ def qqe(close, length=None, smooth=None, factor=None, mamode=None, drift=None, o https://www.tradingpedia.com/forex-trading-indicators/quantitative-qualitative-estimation https://www.prorealcode.com/prorealtime-indicators/qqe-quantitative-qualitative-estimation/ - Calculation: - Default Inputs: - length=14, smooth=5, factor=4.236, mamode="ema", drift=1 - Args: close (pd.Series): Series of 'close's length (int): RSI period. Default: 14 diff --git a/pandas_ta/momentum/roc.py b/pandas_ta/momentum/roc.py index ac847bf..18b1ae8 100644 --- a/pandas_ta/momentum/roc.py +++ b/pandas_ta/momentum/roc.py @@ -14,12 +14,6 @@ def roc(close, length=None, scalar=None, talib=None, offset=None, **kwargs): Sources: https://www.tradingview.com/wiki/Rate_of_Change_(ROC) - Calculation: - Default Inputs: - length=1 - MOM = Momentum - ROC = 100 * MOM(close, length) / close.shift(length) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 1 diff --git a/pandas_ta/momentum/rsi.py b/pandas_ta/momentum/rsi.py index 7bf49b0..8a4d208 100644 --- a/pandas_ta/momentum/rsi.py +++ b/pandas_ta/momentum/rsi.py @@ -14,21 +14,6 @@ def rsi(close, length=None, scalar=None, talib=None, drift=None, offset=None, ** Sources: https://www.tradingview.com/wiki/Relative_Strength_Index_(RSI) - Calculation: - Default Inputs: - length=14, scalar=100, drift=1 - ABS = Absolute Value - RMA = Rolling Moving Average - - diff = close.diff(drift) - positive = diff if diff > 0 else 0 - negative = diff if diff < 0 else 0 - - pos_avg = RMA(positive, length) - neg_avg = ABS(RMA(negative, length)) - - RSI = scalar * pos_avg / (pos_avg + neg_avg) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 14 diff --git a/pandas_ta/momentum/rsx.py b/pandas_ta/momentum/rsx.py index d735fff..58834b5 100644 --- a/pandas_ta/momentum/rsx.py +++ b/pandas_ta/momentum/rsx.py @@ -17,9 +17,6 @@ def rsx(close, length=None, drift=None, offset=None, **kwargs): http://www.jurikres.com/catalog1/ms_rsx.htm https://www.prorealcode.com/prorealtime-indicators/jurik-rsx/ - Calculation: - Refer to the sources above for information as well as code example. - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 14 diff --git a/pandas_ta/momentum/rvgi.py b/pandas_ta/momentum/rvgi.py index a903a93..064d2a8 100644 --- a/pandas_ta/momentum/rvgi.py +++ b/pandas_ta/momentum/rvgi.py @@ -15,14 +15,6 @@ def rvgi(open_, high, low, close, length=None, swma_length=None, offset=None, ** Sources: https://www.investopedia.com/terms/r/relative_vigor_index.asp - Calculation: - Default Inputs: - length=14, swma_length=4 - SWMA = Symmetrically Weighted Moving Average - numerator = SUM(SWMA(close - open, swma_length), length) - denominator = SUM(SWMA(high - low, swma_length), length) - RVGI = numerator / denominator - Args: open_ (pd.Series): Series of 'open's high (pd.Series): Series of 'high's diff --git a/pandas_ta/momentum/slope.py b/pandas_ta/momentum/slope.py index 731ca44..b88525a 100644 --- a/pandas_ta/momentum/slope.py +++ b/pandas_ta/momentum/slope.py @@ -24,12 +24,12 @@ def slope( close, length=None, as_angle=None, to_degrees=None, vertical=None, of Args: close (pd.Series): Series of 'close's - length (int): It's period. Default: 1 + length (int): It's period. Default: 1 + as_angle (value, optional): Converts slope to an angle. Default: False + to_degrees (value, optional): Converts slope angle to degrees. Default: False offset (int): How many periods to offset the result. Default: 0 Kwargs: - as_angle (value, optional): Converts slope to an angle. Default: False - to_degrees (value, optional): Converts slope angle to degrees. Default: False fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method diff --git a/pandas_ta/momentum/smi.py b/pandas_ta/momentum/smi.py index ebfa9c5..9e13695 100644 --- a/pandas_ta/momentum/smi.py +++ b/pandas_ta/momentum/smi.py @@ -21,16 +21,6 @@ def smi(close, fast=None, slow=None, signal=None, scalar=None, offset=None, **kw https://www.tradingview.com/script/Xh5Q0une-SMI-Ergodic-Oscillator/ https://www.tradingview.com/script/cwrgy4fw-SMIIO/ - Calculation: - Default Inputs: - fast=5, slow=20, signal=5 - TSI = True Strength Index - EMA = Exponential Moving Average - - ERG = TSI(close, fast, slow) - Signal = EMA(ERG, signal) - OSC = ERG - Signal - Args: close (pd.Series): Series of 'close's fast (int): The short period. Default: 5 diff --git a/pandas_ta/momentum/squeeze.py b/pandas_ta/momentum/squeeze.py index 1f56126..e764b1f 100644 --- a/pandas_ta/momentum/squeeze.py +++ b/pandas_ta/momentum/squeeze.py @@ -24,37 +24,6 @@ def squeeze(high, low, close, bb_length=None, bb_std=None, kc_length=None, kc_sc https://www.tradingview.com/scripts/lazybear/ https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/T-U/TTM-Squeeze - Calculation: - Default Inputs: - bb_length=20, bb_std=2, kc_length=20, kc_scalar=1.5, mom_length=12, - mom_smooth=12, tr=True, lazybear=False, - BB = Bollinger Bands - KC = Keltner Channels - MOM = Momentum - SMA = Simple Moving Average - EMA = Exponential Moving Average - TR = True Range - - RANGE = TR(high, low, close) if using_tr else high - low - BB_LOW, BB_MID, BB_HIGH = BB(close, bb_length, std=bb_std) - KC_LOW, KC_MID, KC_HIGH = KC(high, low, close, kc_length, kc_scalar, TR) - - if lazybear: - HH = high.rolling(kc_length).max() - LL = low.rolling(kc_length).min() - AVG = 0.25 * (HH + LL) + 0.5 * KC_MID - SQZ = linreg(close - AVG, kc_length) - else: - MOMO = MOM(close, mom_length) - if mamode == "ema": - SQZ = EMA(MOMO, mom_smooth) - else: - SQZ = EMA(momo, mom_smooth) - - SQZ_ON = (BB_LOW > KC_LOW) and (BB_HIGH < KC_HIGH) - SQZ_OFF = (BB_LOW < KC_LOW) and (BB_HIGH > KC_HIGH) - NO_SQZ = !SQZ_ON and !SQZ_OFF - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/momentum/squeeze_pro.py b/pandas_ta/momentum/squeeze_pro.py index 2899996..b8f5612 100644 --- a/pandas_ta/momentum/squeeze_pro.py +++ b/pandas_ta/momentum/squeeze_pro.py @@ -24,36 +24,6 @@ def squeeze_pro(high, low, close, bb_length=None, bb_std=None, kc_length=None, k https://usethinkscript.com/threads/john-carters-squeeze-pro-indicator-for-thinkorswim-free.4021/ https://www.tradingview.com/script/TAAt6eRX-Squeeze-PRO-Indicator-Makit0/ - Calculation: - Default Inputs: - bb_length=20, bb_std=2, kc_length=20, kc_scalar_wide=2, - kc_scalar_normal=1.5, kc_scalar_narrow=1, mom_length=12, - mom_smooth=6, tr=True, - BB = Bollinger Bands - KC = Keltner Channels - MOM = Momentum - SMA = Simple Moving Average - EMA = Exponential Moving Average - TR = True Range - - RANGE = TR(high, low, close) if using_tr else high - low - BB_LOW, BB_MID, BB_HIGH = BB(close, bb_length, std=bb_std) - KC_LOW_WIDE, KC_MID_WIDE, KC_HIGH_WIDE = KC(high, low, close, kc_length, kc_scalar_wide, TR) - KC_LOW_NORMAL, KC_MID_NORMAL, KC_HIGH_NORMAL = KC(high, low, close, kc_length, kc_scalar_normal, TR) - KC_LOW_NARROW, KC_MID_NARROW, KC_HIGH_NARROW = KC(high, low, close, kc_length, kc_scalar_narrow, TR) - - MOMO = MOM(close, mom_length) - if mamode == "ema": - SQZPRO = EMA(MOMO, mom_smooth) - else: - SQZPRO = EMA(momo, mom_smooth) - - SQZPRO_ON_WIDE = (BB_LOW > KC_LOW_WIDE) and (BB_HIGH < KC_HIGH_WIDE) - SQZPRO_ON_NORMAL = (BB_LOW > KC_LOW_NORMAL) and (BB_HIGH < KC_HIGH_NORMAL) - SQZPRO_ON_NARROW = (BB_LOW > KC_LOW_NARROW) and (BB_HIGH < KC_HIGH_NARROW) - SQZPRO_OFF_WIDE = (BB_LOW < KC_LOW_WIDE) and (BB_HIGH > KC_HIGH_WIDE) - SQZPRO_NO = !SQZ_ON_WIDE and !SQZ_OFF_WIDE - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/momentum/stc.py b/pandas_ta/momentum/stc.py index 5ec975a..0a0d6b1 100644 --- a/pandas_ta/momentum/stc.py +++ b/pandas_ta/momentum/stc.py @@ -26,17 +26,10 @@ def stc(close, tclength=None, fast=None, slow=None, factor=None, offset=None, ** The same goes for osc=, which allows the input of an externally calculated oscillator, overriding ma1 & ma2. - Sources: Implemented by rengel8 based on work found here: https://www.prorealcode.com/prorealtime-indicators/schaff-trend-cycle2/ - Calculation: - STCmacd = Moving Average Convergance/Divergance or Oscillator - STCstoch = Intermediate Stochastic of MACD/Osc. - 2nd Stochastic including filtering with results in the - STC = Schaff Trend Cycle - Args: close (pd.Series): Series of 'close's, used for indexing Series, mandatory tclen (int): SchaffTC Signal-Line length. Default: 10 (adjust to the half of cycle) diff --git a/pandas_ta/momentum/stoch.py b/pandas_ta/momentum/stoch.py index c20df25..e83537d 100644 --- a/pandas_ta/momentum/stoch.py +++ b/pandas_ta/momentum/stoch.py @@ -1,10 +1,11 @@ # -*- coding: utf-8 -*- from pandas import DataFrame +from pandas_ta import Imports from pandas_ta.overlap import ma -from pandas_ta.utils import get_offset, non_zero_range, verify_series +from pandas_ta.utils import get_offset, non_zero_range, tal_ma, verify_series -def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, offset=None, **kwargs): +def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, talib=None, offset=None, **kwargs): """Stochastic (STOCH) The Stochastic Oscillator (STOCH) was developed by George Lane in the 1950's. @@ -20,17 +21,6 @@ def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, offset=N https://www.tradingview.com/wiki/Stochastic_(STOCH) https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=332&Name=KD_-_Slow - Calculation: - Default Inputs: - k=14, d=3, smooth_k=3 - SMA = Simple Moving Average - LL = low for last k periods - HH = high for last k periods - - STOCH = 100 * (close - LL) / (HH - LL) - STOCHk = SMA(STOCH, smooth_k) - STOCHd = SMA(FASTK, d) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's @@ -39,6 +29,8 @@ def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, offset=N d (int): The Slow %D period. Default: 3 smooth_k (int): The Slow %K period. Default: 3 mamode (str): See ```help(ta.ma)```. Default: 'sma' + talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib + version. Default: True offset (int): How many periods to offset the result. Default: 0 Kwargs: @@ -58,18 +50,24 @@ def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, offset=N close = verify_series(close, _length) offset = get_offset(offset) mamode = mamode if isinstance(mamode, str) else "sma" + mode_tal = bool(talib) if isinstance(talib, bool) else True if high is None or low is None or close is None: return # Calculate Result - lowest_low = low.rolling(k).min() - highest_high = high.rolling(k).max() + if Imports["talib"] and mode_tal: + from talib import STOCH + stoch_ = STOCH(high, low, close, k, d, tal_ma(mamode), d, tal_ma(mamode)) + stoch_k, stoch_d = stoch_[0], stoch_[1] + else: + lowest_low = low.rolling(k).min() + highest_high = high.rolling(k).max() - stoch = 100 * (close - lowest_low) - stoch /= non_zero_range(highest_high, lowest_low) + stoch = 100 * (close - lowest_low) + stoch /= non_zero_range(highest_high, lowest_low) - stoch_k = ma(mamode, stoch.loc[stoch.first_valid_index():,], length=smooth_k) - stoch_d = ma(mamode, stoch_k.loc[stoch_k.first_valid_index():,], length=d) + stoch_k = ma(mamode, stoch.loc[stoch.first_valid_index():,], length=smooth_k) + stoch_d = ma(mamode, stoch_k.loc[stoch_k.first_valid_index():,], length=d) # Offset if offset != 0: diff --git a/pandas_ta/momentum/stochf.py b/pandas_ta/momentum/stochf.py index 40cdbd9..f5e536e 100644 --- a/pandas_ta/momentum/stochf.py +++ b/pandas_ta/momentum/stochf.py @@ -16,16 +16,6 @@ def stochf(high, low, close, k=None, d=None, mamode=None, talib=None, offset=Non https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=333&Name=KD_-_Fast https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/fast-stochastic-indicator/ - Calculation: - Default Inputs: - k=14, d=3, mamode="sma", - SMA = Simple Moving Average - LL = low for last k periods - HH = high for last k periods - - STOCHFk = 100 * (close - LL) / (HH - LL) - STOCHFd = MA(SMA, STOCH, d) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/momentum/stochrsi.py b/pandas_ta/momentum/stochrsi.py index c5fe145..eb545a3 100644 --- a/pandas_ta/momentum/stochrsi.py +++ b/pandas_ta/momentum/stochrsi.py @@ -18,20 +18,6 @@ def stochrsi(close, length=None, rsi_length=None, k=None, d=None, mamode=None, o Sources: https://www.tradingview.com/wiki/Stochastic_(STOCH) - Calculation: - Default Inputs: - length=14, rsi_length=14, k=3, d=3 - RSI = Relative Strength Index - SMA = Simple Moving Average - - RSI = RSI(high, low, close, rsi_length) - LL = lowest RSI for last rsi_length periods - HH = highest RSI for last rsi_length periods - - STOCHRSI = 100 * (RSI - LL) / (HH - LL) - STOCHRSIk = SMA(STOCHRSI, k) - STOCHRSId = SMA(STOCHRSIk, d) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/momentum/td_seq.py b/pandas_ta/momentum/td_seq.py index 0c0d9bc..de1babe 100644 --- a/pandas_ta/momentum/td_seq.py +++ b/pandas_ta/momentum/td_seq.py @@ -14,11 +14,6 @@ def td_seq(close, asint=None, offset=None, **kwargs): Sources: https://tradetrekker.wordpress.com/tdsequential/ - Calculation: - Compare current close price with 4 days ago price, up to 13 days. For the - consecutive ascending or descending price sequence, display 6th to 9th day - value. - Args: close (pd.Series): Series of 'close's asint (bool): If True, fillnas with 0 and change type to int. Default: False diff --git a/pandas_ta/momentum/trix.py b/pandas_ta/momentum/trix.py index 90d7978..e4a0dbe 100644 --- a/pandas_ta/momentum/trix.py +++ b/pandas_ta/momentum/trix.py @@ -12,16 +12,6 @@ def trix(close, length=None, signal=None, scalar=None, drift=None, offset=None, Sources: https://www.tradingview.com/wiki/TRIX - Calculation: - Default Inputs: - length=18, drift=1 - EMA = Exponential Moving Average - ROC = Rate of Change - ema1 = EMA(close, length) - ema2 = EMA(ema1, length) - ema3 = EMA(ema2, length) - TRIX = 100 * ROC(ema3, drift) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 18 diff --git a/pandas_ta/momentum/tsi.py b/pandas_ta/momentum/tsi.py index 8d7c366..46fddeb 100644 --- a/pandas_ta/momentum/tsi.py +++ b/pandas_ta/momentum/tsi.py @@ -14,21 +14,6 @@ def tsi(close, fast=None, slow=None, signal=None, scalar=None, mamode=None, drif Sources: https://www.investopedia.com/terms/t/tsi.asp - Calculation: - Default Inputs: - fast=13, slow=25, signal=13, scalar=100, drift=1 - EMA = Exponential Moving Average - diff = close.diff(drift) - - slow_ema = EMA(diff, slow) - fast_slow_ema = EMA(slow_ema, slow) - - abs_diff_slow_ema = absolute_diff_ema = EMA(ABS(diff), slow) - abema = abs_diff_fast_slow_ema = EMA(abs_diff_slow_ema, fast) - - TSI = scalar * fast_slow_ema / abema - Signal = EMA(TSI, signal) - Args: close (pd.Series): Series of 'close's fast (int): The short period. Default: 13 diff --git a/pandas_ta/momentum/uo.py b/pandas_ta/momentum/uo.py index f224d71..9b9fbbd 100644 --- a/pandas_ta/momentum/uo.py +++ b/pandas_ta/momentum/uo.py @@ -13,24 +13,6 @@ def uo(high, low, close, fast=None, medium=None, slow=None, fast_w=None, medium_ Sources: https://www.tradingview.com/wiki/Ultimate_Oscillator_(UO) - Calculation: - Default Inputs: - fast=7, medium=14, slow=28, - fast_w=4.0, medium_w=2.0, slow_w=1.0, drift=1 - min_low_or_pc = close.shift(drift).combine(low, min) - max_high_or_pc = close.shift(drift).combine(high, max) - - bp = buying pressure = close - min_low_or_pc - tr = true range = max_high_or_pc - min_low_or_pc - - fast_avg = SUM(bp, fast) / SUM(tr, fast) - medium_avg = SUM(bp, medium) / SUM(tr, medium) - slow_avg = SUM(bp, slow) / SUM(tr, slow) - - total_weight = fast_w + medium_w + slow_w - weights = (fast_w * fast_avg) + (medium_w * medium_avg) + (slow_w * slow_avg) - UO = 100 * weights / total_weight - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/momentum/willr.py b/pandas_ta/momentum/willr.py index 98f2140..38eaa57 100644 --- a/pandas_ta/momentum/willr.py +++ b/pandas_ta/momentum/willr.py @@ -12,14 +12,6 @@ def willr(high, low, close, length=None, talib=None, offset=None, **kwargs): Sources: https://www.tradingview.com/wiki/Williams_%25R_(%25R) - Calculation: - Default Inputs: - length=20 - LL = low.rolling(length).min() - HH = high.rolling(length).max() - - WILLR = 100 * ((close - LL) / (HH - LL) - 1) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/overlap/alligator.py b/pandas_ta/overlap/alligator.py index 5a14e9c..2ed3d4e 100644 --- a/pandas_ta/overlap/alligator.py +++ b/pandas_ta/overlap/alligator.py @@ -20,15 +20,6 @@ def alligator(close, jaw=None, teeth=None, lips=None, talib=None, offset=None, * https://www.tradingview.com/scripts/alligator/ https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=175&Name=Bill_Williams_Alligator - Calculation: - Default Inputs: - jaw=13, teeth=8, lips=5, mamode="sma" - SMMA = SMoothed Moving Average - - JAW = SMMA(close, jaw) - TEETH = SMMA(close, teeth) - LIPS = SMMA(close, lips) - Args: close (pd.Series): Series of 'close's jaw (int): The Jaw period. Default: 13 diff --git a/pandas_ta/overlap/alma.py b/pandas_ta/overlap/alma.py index 9a8dc24..72690c1 100644 --- a/pandas_ta/overlap/alma.py +++ b/pandas_ta/overlap/alma.py @@ -24,14 +24,6 @@ def alma(close, length=None, sigma=None, dist_offset=None, offset=None, **kwargs https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=475&Name=Moving_Average_-_Arnaud_Legoux https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/ - Calculation: - length=9, sigma=6.0, dist_offset=0.85 - - X = [0, 1, ..., length - 1] - WEIGHTS = e^(-0.5 * (X - FLOOR(dist_offset(length - 1)))^2 / (length / sigma)^2) - - ALMA = close.rolling(length).apply(WEIGHTS) - Args: close (pd.Series): Series of 'close's length (int): It's period, window size. Default: 9 diff --git a/pandas_ta/overlap/dema.py b/pandas_ta/overlap/dema.py index fa45a62..149e8f1 100644 --- a/pandas_ta/overlap/dema.py +++ b/pandas_ta/overlap/dema.py @@ -13,15 +13,6 @@ def dema(close, length=None, talib=None, offset=None, **kwargs): Sources: https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/double-exponential-moving-average-dema/ - Calculation: - Default Inputs: - length=10 - EMA = Exponential Moving Average - ema1 = EMA(close, length) - ema2 = EMA(ema1, length) - - DEMA = 2 * ema1 - ema2 - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/ema.py b/pandas_ta/overlap/ema.py index 4ba812d..70682ef 100644 --- a/pandas_ta/overlap/ema.py +++ b/pandas_ta/overlap/ema.py @@ -17,15 +17,6 @@ def ema(close, length=None, talib=None, offset=None, **kwargs): https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp - Calculation: - Default Inputs: - length=10, adjust=False, sma=True - if sma: - sma_nth = close[0:length].sum() / length - close[:length - 1] = np.NaN - close.iloc[length - 1] = sma_nth - EMA = close.ewm(span=length, adjust=adjust).mean() - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/fwma.py b/pandas_ta/overlap/fwma.py index 161b0cb..cdd7733 100644 --- a/pandas_ta/overlap/fwma.py +++ b/pandas_ta/overlap/fwma.py @@ -10,18 +10,6 @@ def fwma(close, length=None, asc=None, offset=None, **kwargs): Source: Kevin Johnson - Calculation: - Default Inputs: - length=10, - - def weights(w): - def _compute(x): - return np.dot(w * x) - return _compute - - fibs = utils.fibonacci(length - 1) - FWMA = close.rolling(length)_.apply(weights(fibs), raw=True) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/hilo.py b/pandas_ta/overlap/hilo.py index 5bfc7da..c467442 100644 --- a/pandas_ta/overlap/hilo.py +++ b/pandas_ta/overlap/hilo.py @@ -23,33 +23,6 @@ def hilo(high, low, close, high_length=None, low_length=None, mamode=None, offse https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/ https://www.tradingview.com/script/XNQSLIYb-Gann-High-Low/ - Calculation: - Default Inputs: - high_length=13, low_length=21, mamode="sma" - EMA = Exponential Moving Average - HMA = Hull Moving Average - SMA = Simple Moving Average # Default - - if "ema": - high_ma = EMA(high, high_length) - low_ma = EMA(low, low_length) - elif "hma": - high_ma = HMA(high, high_length) - low_ma = HMA(low, low_length) - else: # "sma" - high_ma = SMA(high, high_length) - low_ma = SMA(low, low_length) - - # Similar to Supertrend MA selection - hilo = Series(npNaN, index=close.index) - for i in range(1, m): - if close.iloc[i] > high_ma.iloc[i - 1]: - hilo.iloc[i] = low_ma.iloc[i] - elif close.iloc[i] < low_ma.iloc[i - 1]: - hilo.iloc[i] = high_ma.iloc[i] - else: - hilo.iloc[i] = hilo.iloc[i - 1] - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/overlap/hl2.py b/pandas_ta/overlap/hl2.py index d6383c9..a5236e9 100644 --- a/pandas_ta/overlap/hl2.py +++ b/pandas_ta/overlap/hl2.py @@ -5,12 +5,12 @@ from pandas_ta.utils import get_offset, verify_series def hl2(high, low, offset=None, **kwargs): """HL2 - Calculation: - HL2 = 0.5 * (high + low) + HL2 is the midpoint/average of high and low. Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's + offset (int): How many periods to offset the result. Default: 0 Returns: pd.Series: New feature generated. diff --git a/pandas_ta/overlap/hlc3.py b/pandas_ta/overlap/hlc3.py index 20558f1..4c3b241 100644 --- a/pandas_ta/overlap/hlc3.py +++ b/pandas_ta/overlap/hlc3.py @@ -6,13 +6,13 @@ from pandas_ta.utils import get_offset, verify_series def hlc3(high, low, close, talib=None, offset=None, **kwargs): """HLC3 - Calculation: - HLC3 = (high + low + close) / 3.0 + HLC3 is the average of high, low and close. Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's close (pd.Series): Series of 'close's + offset (int): How many periods to offset the result. Default: 0 Returns: pd.Series: New feature generated. diff --git a/pandas_ta/overlap/hma.py b/pandas_ta/overlap/hma.py index 0b7f8c5..05413bd 100644 --- a/pandas_ta/overlap/hma.py +++ b/pandas_ta/overlap/hma.py @@ -13,17 +13,6 @@ def hma(close, length=None, offset=None, **kwargs): Sources: https://alanhull.com/hull-moving-average - Calculation: - Default Inputs: - length=10 - WMA = Weighted Moving Average - half_length = int(0.5 * length) - sqrt_length = int(sqrt(length)) - - wmaf = WMA(close, half_length) - wmas = WMA(close, length) - HMA = WMA(2 * wmaf - wmas, sqrt_length) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/hwma.py b/pandas_ta/overlap/hwma.py index 0616c19..a03f769 100644 --- a/pandas_ta/overlap/hwma.py +++ b/pandas_ta/overlap/hwma.py @@ -16,19 +16,12 @@ def hwma(close, na=None, nb=None, nc=None, offset=None, **kwargs): Sources: https://www.mql5.com/en/code/20856 - Calculation: - HWMA[i] = F[i] + V[i] + 0.5 * A[i] - where.. - F[i] = (1-na) * (F[i-1] + V[i-1] + 0.5 * A[i-1]) + na * Price[i] - V[i] = (1-nb) * (V[i-1] + A[i-1]) + nb * (F[i] - F[i-1]) - A[i] = (1-nc) * A[i-1] + nc * (V[i] - V[i-1]) - Args: close (pd.Series): Series of 'close's na (float): Smoothed series parameter (from 0 to 1). Default: 0.2 nb (float): Trend parameter (from 0 to 1). Default: 0.1 nc (float): Seasonality parameter (from 0 to 1). Default: 0.1 - close (pd.Series): Series of 'close's + offset (int): How many periods to offset the result. Default: 0 Kwargs: fillna (value, optional): pd.DataFrame.fillna(value) diff --git a/pandas_ta/overlap/ichimoku.py b/pandas_ta/overlap/ichimoku.py index 2aec18c..f0cc4c1 100644 --- a/pandas_ta/overlap/ichimoku.py +++ b/pandas_ta/overlap/ichimoku.py @@ -12,20 +12,6 @@ def ichimoku(high, low, close, tenkan=None, kijun=None, senkou=None, include_chi Sources: https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/ichimoku-ich/ - Calculation: - Default Inputs: - tenkan=9, kijun=26, senkou=52 - MIDPRICE = Midprice - TENKAN_SEN = MIDPRICE(high, low, close, length=tenkan) - KIJUN_SEN = MIDPRICE(high, low, close, length=kijun) - CHIKOU_SPAN = close.shift(-kijun) - - SPAN_A = 0.5 * (TENKAN_SEN + KIJUN_SEN) - SPAN_A = SPAN_A.shift(kijun) - - SPAN_B = MIDPRICE(high, low, close, length=senkou) - SPAN_B = SPAN_B.shift(kijun) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/overlap/jma.py b/pandas_ta/overlap/jma.py index e20f8f5..ebd851b 100644 --- a/pandas_ta/overlap/jma.py +++ b/pandas_ta/overlap/jma.py @@ -20,10 +20,6 @@ def jma(close, length=None, phase=None, offset=None, **kwargs): https://c.mql5.com/forextsd/forum/164/jurik_1.pdf https://www.prorealcode.com/prorealtime-indicators/jurik-volatility-bands/ - Calculation: - Default Inputs: - length=7, phase=0 - Args: close (pd.Series): Series of 'close's length (int): Period of calculation. Default: 7 @@ -99,8 +95,8 @@ def jma(close, length=None, phase=None, offset=None, **kwargs): jma[i] = jma[i-1] + det1 # Remove initial lookback data and convert to pandas frame - jma[0:_length - 1] = npNaN jma = Series(jma, index=close.index) + jma.iloc[0:_length - 1] = npNaN # Offset if offset != 0: diff --git a/pandas_ta/overlap/kama.py b/pandas_ta/overlap/kama.py index 009616d..aa75b40 100644 --- a/pandas_ta/overlap/kama.py +++ b/pandas_ta/overlap/kama.py @@ -18,10 +18,6 @@ def kama(close, length=None, fast=None, slow=None, mamode=None, drift=None, offs https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:kaufman_s_adaptive_moving_average https://www.tradingview.com/script/wZGOIz9r-REPOST-Indicators-3-Different-Adaptive-Moving-Averages/ - Calculation: - Default Inputs: - length=10 - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/linreg.py b/pandas_ta/overlap/linreg.py index 58e61ad..f673ef2 100644 --- a/pandas_ta/overlap/linreg.py +++ b/pandas_ta/overlap/linreg.py @@ -18,25 +18,6 @@ def linreg(close, length=None, talib=None, offset=None, **kwargs): Source: TA Lib - Calculation: - Default Inputs: - length=14 - x = [1, 2, ..., n] - x_sum = 0.5 * length * (length + 1) - x2_sum = length * (length + 1) * (2 * length + 1) / 6 - divisor = length * x2_sum - x_sum * x_sum - - lr(series): - y_sum = series.sum() - y2_sum = (series* series).sum() - xy_sum = (x * series).sum() - - m = (length * xy_sum - x_sum * y_sum) / divisor - b = (y_sum * x2_sum - x_sum * xy_sum) / divisor - return m * (length - 1) + b - - linreg = close.rolling(length).apply(lr) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/mcgd.py b/pandas_ta/overlap/mcgd.py index 54f63f6..8607314 100644 --- a/pandas_ta/overlap/mcgd.py +++ b/pandas_ta/overlap/mcgd.py @@ -16,24 +16,11 @@ def mcgd(close, length=None, offset=None, c=None, **kwargs): Sources: https://www.investopedia.com/articles/forex/09/mcginley-dynamic-indicator.asp - Calculation: - Default Inputs: - length=10 - offset=0 - c=1 - - def mcg_(series): - denom = (constant * length * (series.iloc[1] / series.iloc[0]) ** 4) - series.iloc[1] = (series.iloc[0] + ((series.iloc[1] - series.iloc[0]) / denom)) - return series.iloc[1] - mcg_cell = close[0:].rolling(2, min_periods=2).apply(mcg_, raw=False) - mcg_ds = close[:1].append(mcg_cell[1:]) - Args: close (pd.Series): Series of 'close's length (int): Indicator's period. Default: 10 - offset (int): Number of periods to offset the result. Default: 0 c (float): Multiplier for the denominator, sometimes set to 0.6. Default: 1 + offset (int): Number of periods to offset the result. Default: 0 Kwargs: fillna (value, optional): pd.DataFrame.fillna(value) diff --git a/pandas_ta/overlap/midpoint.py b/pandas_ta/overlap/midpoint.py index f432f8b..dd678d0 100644 --- a/pandas_ta/overlap/midpoint.py +++ b/pandas_ta/overlap/midpoint.py @@ -8,15 +8,6 @@ def midpoint(close, length=None, talib=None, offset=None, **kwargs): The Midpoint is the average of the rolling high and low of period length. - Sources: - - Calculation: - Default Inputs: - length=2 - HC = close.rolling(length).max() - LC = close.rolling(length).min() - MID = 0.5 * (HC + LC) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 2 diff --git a/pandas_ta/overlap/midprice.py b/pandas_ta/overlap/midprice.py index 4531b5c..08d1aff 100644 --- a/pandas_ta/overlap/midprice.py +++ b/pandas_ta/overlap/midprice.py @@ -8,15 +8,6 @@ def midprice(high, low, length=None, talib=None, offset=None, **kwargs): The Midprice is the average of the rolling high and low of period length. - Sources: - - Calculation: - Default Inputs: - length=2 - HH = high.rolling(length).max() - LL = low.rolling(length).min() - MID = 0.5 * (HH + LL) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/overlap/ohlc4.py b/pandas_ta/overlap/ohlc4.py index 75ca8fc..02495aa 100644 --- a/pandas_ta/overlap/ohlc4.py +++ b/pandas_ta/overlap/ohlc4.py @@ -5,14 +5,14 @@ from pandas_ta.utils import get_offset, verify_series def ohlc4(open_, high, low, close, offset=None, **kwargs): """OHLC4 - Calculation: - OHLC4 = 0.25 * (open + high + low + close) + OHLC4 is the average of open, high, low and close. Args: open_ (pd.Series): Series of 'open's high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's close (pd.Series): Series of 'close's + offset (int): How many periods to offset the result. Default: 0 Returns: pd.Series: New feature generated. diff --git a/pandas_ta/overlap/pwma.py b/pandas_ta/overlap/pwma.py index 6f33786..c0fe60e 100644 --- a/pandas_ta/overlap/pwma.py +++ b/pandas_ta/overlap/pwma.py @@ -10,18 +10,6 @@ def pwma(close, length=None, asc=None, offset=None, **kwargs): Source: Kevin Johnson - Calculation: - Default Inputs: - length=10 - - def weights(w): - def _compute(x): - return np.dot(w * x) - return _compute - - triangle = utils.pascals_triangle(length + 1) - PWMA = close.rolling(length)_.apply(weights(triangle), raw=True) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/rma.py b/pandas_ta/overlap/rma.py index 1cdeb0f..0e1e5fb 100644 --- a/pandas_ta/overlap/rma.py +++ b/pandas_ta/overlap/rma.py @@ -12,13 +12,6 @@ def rma(close, length=None, offset=None, **kwargs): https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/V-Z/WildersSmoothing https://www.incrediblecharts.com/indicators/wilder_moving_average.php - Calculation: - Default Inputs: - length=10 - EMA = Exponential Moving Average - alpha = 1 / length - RMA = EMA(close, alpha=alpha) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/sinwma.py b/pandas_ta/overlap/sinwma.py index c8ee00a..f7a50bd 100644 --- a/pandas_ta/overlap/sinwma.py +++ b/pandas_ta/overlap/sinwma.py @@ -15,19 +15,6 @@ def sinwma(close, length=None, offset=None, **kwargs): https://www.tradingview.com/script/6MWFvnPO-Sine-Weighted-Moving-Average/ Author: Everget (https://www.tradingview.com/u/everget/) - Calculation: - Default Inputs: - length=10 - - def weights(w): - def _compute(x): - return np.dot(w * x) - return _compute - - sines = Series([sin((i + 1) * pi / (length + 1)) for i in range(0, length)]) - w = sines / sines.sum() - SINWMA = close.rolling(length, min_periods=length).apply(weights(w), raw=True) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/sma.py b/pandas_ta/overlap/sma.py index a6a9dcc..59381f7 100644 --- a/pandas_ta/overlap/sma.py +++ b/pandas_ta/overlap/sma.py @@ -12,11 +12,6 @@ def sma(close, length=None, talib=None, offset=None, **kwargs): Sources: https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/ - Calculation: - Default Inputs: - length=10 - SMA = SUM(close, length) / length - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/smma.py b/pandas_ta/overlap/smma.py index 9f37804..1c6f3cc 100644 --- a/pandas_ta/overlap/smma.py +++ b/pandas_ta/overlap/smma.py @@ -20,14 +20,6 @@ def smma(close, length=None, mamode=None, talib=None, offset=None, **kwargs): https://www.tradingview.com/scripts/smma/ https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=173&Name=Moving_Average_-_Smoothed - Calculation: - Default Inputs: - length=10, mamode="sma" - MA = Moving Average - - SMMA[0:length] = MA(mamode, close, length) - SMMA[:length] = ((length - 1) * SMMA[i] + close[i]) / length - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 @@ -57,7 +49,7 @@ def smma(close, length=None, mamode=None, talib=None, offset=None, **kwargs): m = close.size smma = close.copy() smma[:length - 1] = npNaN - smma.iloc[length - 1] = ma(mamode, close[0:length], length=length, talib=mode_tal)[-1] + smma.iloc[length - 1] = ma(mamode, close[0:length], length=length, talib=mode_tal).iloc[-1] for i in range(length, m): smma.iloc[i] = ((length - 1) * smma.iloc[i - 1] + smma.iloc[i]) / length diff --git a/pandas_ta/overlap/ssf.py b/pandas_ta/overlap/ssf.py index e13995e..5707b74 100644 --- a/pandas_ta/overlap/ssf.py +++ b/pandas_ta/overlap/ssf.py @@ -24,12 +24,6 @@ def ssf(close, length=None, poles=None, offset=None, **kwargs): https://www.mql5.com/en/code/588 https://www.mql5.com/en/code/589 - Calculation: - Default Inputs: - length=10, poles=[2, 3] - - See the source code or Sources listed above. - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/supertrend.py b/pandas_ta/overlap/supertrend.py index 5be59f5..d192eca 100644 --- a/pandas_ta/overlap/supertrend.py +++ b/pandas_ta/overlap/supertrend.py @@ -16,31 +16,6 @@ def supertrend(high, low, close, length=None, multiplier=None, offset=None, **kw Sources: http://www.freebsensetips.com/blog/detail/7/What-is-supertrend-indicator-its-calculation - Calculation: - Default Inputs: - length=7, multiplier=3.0 - Default Direction: - Set to +1 or bullish trend at start - - MID = multiplier * ATR - LOWERBAND = HL2 - MID - UPPERBAND = HL2 + MID - - if UPPERBAND[i] < FINAL_UPPERBAND[i-1] and close[i-1] > FINAL_UPPERBAND[i-1]: - FINAL_UPPERBAND[i] = UPPERBAND[i] - else: - FINAL_UPPERBAND[i] = FINAL_UPPERBAND[i-1]) - - if LOWERBAND[i] > FINAL_LOWERBAND[i-1] and close[i-1] < FINAL_LOWERBAND[i-1]: - FINAL_LOWERBAND[i] = LOWERBAND[i] - else: - FINAL_LOWERBAND[i] = FINAL_LOWERBAND[i-1]) - - if close[i] <= FINAL_UPPERBAND[i]: - SUPERTREND[i] = FINAL_UPPERBAND[i] - else: - SUPERTREND[i] = FINAL_LOWERBAND[i] - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/overlap/swma.py b/pandas_ta/overlap/swma.py index f0ebf23..91d32bb 100644 --- a/pandas_ta/overlap/swma.py +++ b/pandas_ta/overlap/swma.py @@ -13,18 +13,6 @@ def swma(close, length=None, asc=None, offset=None, **kwargs): Source: https://www.tradingview.com/study-script-reference/#fun_swma - Calculation: - Default Inputs: - length=10 - - def weights(w): - def _compute(x): - return np.dot(w * x) - return _compute - - triangle = utils.symmetric_triangle(length - 1) - SWMA = close.rolling(length)_.apply(weights(triangle), raw=True) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/t3.py b/pandas_ta/overlap/t3.py index c16c170..e37f157 100644 --- a/pandas_ta/overlap/t3.py +++ b/pandas_ta/overlap/t3.py @@ -13,22 +13,6 @@ def t3(close, length=None, a=None, talib=None, offset=None, **kwargs): Sources: http://www.binarytribune.com/forex-trading-indicators/t3-moving-average-indicator/ - Calculation: - Default Inputs: - length=10, a=0.7 - c1 = -a^3 - c2 = 3a^2 + 3a^3 = 3a^2 * (1 + a) - c3 = -6a^2 - 3a - 3a^3 - c4 = a^3 + 3a^2 + 3a + 1 - - ema1 = EMA(close, length) - ema2 = EMA(ema1, length) - ema3 = EMA(ema2, length) - ema4 = EMA(ema3, length) - ema5 = EMA(ema4, length) - ema6 = EMA(ema5, length) - T3 = c1 * ema6 + c2 * ema5 + c3 * ema4 + c4 * ema3 - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/tema.py b/pandas_ta/overlap/tema.py index d963e08..10b3096 100644 --- a/pandas_ta/overlap/tema.py +++ b/pandas_ta/overlap/tema.py @@ -12,15 +12,6 @@ def tema(close, length=None, talib=None, offset=None, **kwargs): Sources: https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triple-exponential-moving-average-tema/ - Calculation: - Default Inputs: - length=10 - EMA = Exponential Moving Average - ema1 = EMA(close, length) - ema2 = EMA(ema1, length) - ema3 = EMA(ema2, length) - TEMA = 3 * (ema1 - ema2) + ema3 - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/trima.py b/pandas_ta/overlap/trima.py index b364c9a..76f0bdc 100644 --- a/pandas_ta/overlap/trima.py +++ b/pandas_ta/overlap/trima.py @@ -15,14 +15,6 @@ def trima(close, length=None, talib=None, offset=None, **kwargs): tma = sma(sma(src, ceil(length / 2)), floor(length / 2) + 1) # Tradingview trima = sma(sma(x, n), n) # Tradingview - Calculation: - Default Inputs: - length=10 - SMA = Simple Moving Average - half_length = round(0.5 * (length + 1)) - SMA1 = SMA(close, half_length) - TRIMA = SMA(SMA1, half_length) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/vidya.py b/pandas_ta/overlap/vidya.py index b529614..0c0273a 100644 --- a/pandas_ta/overlap/vidya.py +++ b/pandas_ta/overlap/vidya.py @@ -17,15 +17,6 @@ def vidya(close, length=None, drift=None, offset=None, **kwargs): https://www.tradingview.com/script/hdrf0fXV-Variable-Index-Dynamic-Average-VIDYA/ https://www.perfecttrendsystem.com/blog_mt4_2/en/vidya-indicator-for-mt4 - Calculation: - Default Inputs: - length=10, adjust=False, sma=True - if sma: - sma_nth = close[0:length].sum() / length - close[:length - 1] = np.NaN - close.iloc[length - 1] = sma_nth - EMA = close.ewm(span=length, adjust=adjust).mean() - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 14 diff --git a/pandas_ta/overlap/vwap.py b/pandas_ta/overlap/vwap.py index cce8d14..591a378 100644 --- a/pandas_ta/overlap/vwap.py +++ b/pandas_ta/overlap/vwap.py @@ -14,11 +14,6 @@ def vwap(high, low, close, volume, anchor=None, offset=None, **kwargs): https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/volume-weighted-average-price-vwap/ https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vwap_intraday - Calculation: - tp = typical_price = hlc3(high, low, close) - tpv = tp * volume - VWAP = tpv.cumsum() / volume.cumsum() - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/overlap/vwma.py b/pandas_ta/overlap/vwma.py index 7da8115..205fa7c 100644 --- a/pandas_ta/overlap/vwma.py +++ b/pandas_ta/overlap/vwma.py @@ -11,13 +11,6 @@ def vwma(close, volume, length=None, offset=None, **kwargs): Sources: https://www.motivewave.com/studies/volume_weighted_moving_average.htm - Calculation: - Default Inputs: - length=10 - SMA = Simple Moving Average - pv = close * volume - VWMA = SMA(pv, length) / SMA(volume, length) - Args: close (pd.Series): Series of 'close's volume (pd.Series): Series of 'volume's diff --git a/pandas_ta/overlap/wcp.py b/pandas_ta/overlap/wcp.py index 72ba149..a234eeb 100644 --- a/pandas_ta/overlap/wcp.py +++ b/pandas_ta/overlap/wcp.py @@ -12,9 +12,6 @@ def wcp(high, low, close, talib=None, offset=None, **kwargs): Sources: https://www.fmlabs.com/reference/default.htm?url=WeightedCloses.htm - Calculation: - WCP = (2 * close + high + low) / 4 - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/overlap/wma.py b/pandas_ta/overlap/wma.py index 2bf00ba..b77a70a 100644 --- a/pandas_ta/overlap/wma.py +++ b/pandas_ta/overlap/wma.py @@ -13,20 +13,6 @@ def wma(close, length=None, asc=None, talib=None, offset=None, **kwargs): Sources: https://en.wikipedia.org/wiki/Moving_average#Weighted_moving_average - Calculation: - Default Inputs: - length=10, asc=True - total_weight = 0.5 * length * (length + 1) - weights_ = [1, 2, ..., length + 1] # Ascending - weights = weights if asc else weights[::-1] - - def linear_weights(w): - def _compute(x): - return (w * x).sum() / total_weight - return _compute - - WMA = close.rolling(length)_.apply(linear_weights(weights), raw=True) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/overlap/zlma.py b/pandas_ta/overlap/zlma.py index a370d13..a2a0c83 100644 --- a/pandas_ta/overlap/zlma.py +++ b/pandas_ta/overlap/zlma.py @@ -15,15 +15,6 @@ def zlma(close, length=None, mamode=None, offset=None, **kwargs): Sources: https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average - Calculation: - Default Inputs: - length=10, mamode=EMA - EMA = Exponential Moving Average - lag = int(0.5 * (length - 1)) - - SOURCE = 2 * close - close.shift(lag) - ZLMA = MA(kind=mamode, SOURCE, length) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/performance/drawdown.py b/pandas_ta/performance/drawdown.py index ef6b385..021da08 100644 --- a/pandas_ta/performance/drawdown.py +++ b/pandas_ta/performance/drawdown.py @@ -15,12 +15,6 @@ def drawdown(close, offset=None, **kwargs) -> DataFrame: Sources: https://www.investopedia.com/terms/d/drawdown.asp - Calculation: - PEAKDD = close.cummax() - DD = PEAKDD - close - DD% = 1 - (close / PEAKDD) - DDlog = log(PEAKDD / close) - Args: close (pd.Series): Series of 'close's. offset (int): How many periods to offset the result. Default: 0 diff --git a/pandas_ta/performance/log_return.py b/pandas_ta/performance/log_return.py index a2b9fe4..9c125d7 100644 --- a/pandas_ta/performance/log_return.py +++ b/pandas_ta/performance/log_return.py @@ -12,12 +12,6 @@ def log_return(close, length=None, cumulative=None, offset=None, **kwargs): Sources: https://stackoverflow.com/questions/31287552/logarithmic-returns-in-pandas-dataframe - Calculation: - Default Inputs: - length=1, cumulative=False - LOGRET = log( close.diff(periods=length) ) - CUMLOGRET = LOGRET.cumsum() if cumulative - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 20 diff --git a/pandas_ta/performance/percent_return.py b/pandas_ta/performance/percent_return.py index 0ab8369..864a909 100644 --- a/pandas_ta/performance/percent_return.py +++ b/pandas_ta/performance/percent_return.py @@ -11,12 +11,6 @@ def percent_return(close, length=None, cumulative=None, offset=None, **kwargs): Sources: https://stackoverflow.com/questions/31287552/logarithmic-returns-in-pandas-dataframe - Calculation: - Default Inputs: - length=1, cumulative=False - PCTRET = close.pct_change(length) - CUMPCTRET = PCTRET.cumsum() if cumulative - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 20 diff --git a/pandas_ta/statistics/entropy.py b/pandas_ta/statistics/entropy.py index 11c74a0..0fa515a 100644 --- a/pandas_ta/statistics/entropy.py +++ b/pandas_ta/statistics/entropy.py @@ -13,13 +13,6 @@ def entropy(close, length=None, base=None, offset=None, **kwargs): Sources: https://en.wikipedia.org/wiki/Entropy_(information_theory) - Calculation: - Default Inputs: - length=10, base=2 - - P = close / SUM(close, length) - E = SUM(-P * npLog(P) / npLog(base), length) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 diff --git a/pandas_ta/statistics/kurtosis.py b/pandas_ta/statistics/kurtosis.py index d5641f8..a269c46 100644 --- a/pandas_ta/statistics/kurtosis.py +++ b/pandas_ta/statistics/kurtosis.py @@ -7,11 +7,6 @@ def kurtosis(close, length=None, offset=None, **kwargs): Calculates the Kurtosis over a rolling period. - Calculation: - Default Inputs: - length=30 - KURTOSIS = close.rolling(length).kurt() - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 30 diff --git a/pandas_ta/statistics/mad.py b/pandas_ta/statistics/mad.py index b23462e..57c9035 100644 --- a/pandas_ta/statistics/mad.py +++ b/pandas_ta/statistics/mad.py @@ -8,11 +8,6 @@ def mad(close, length=None, offset=None, **kwargs): Calculates the Mean Absolute Deviation over a rolling period. - Calculation: - Default Inputs: - length=30 - mad = close.rolling(length).mad() - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 30 diff --git a/pandas_ta/statistics/median.py b/pandas_ta/statistics/median.py index e650bfd..f9757ba 100644 --- a/pandas_ta/statistics/median.py +++ b/pandas_ta/statistics/median.py @@ -10,11 +10,6 @@ def median(close, length=None, offset=None, **kwargs): Sources: https://www.incrediblecharts.com/indicators/median_price.php - Calculation: - Default Inputs: - length=30 - MEDIAN = close.rolling(length).median() - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 30 diff --git a/pandas_ta/statistics/quantile.py b/pandas_ta/statistics/quantile.py index fd01af2..3d11ad7 100644 --- a/pandas_ta/statistics/quantile.py +++ b/pandas_ta/statistics/quantile.py @@ -7,11 +7,6 @@ def quantile(close, length=None, q=None, offset=None, **kwargs): Calculates the Quantile over a rolling period. - Calculation: - Default Inputs: - length=30, q=0.5 - QUANTILE = close.rolling(length).quantile(q) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 30 diff --git a/pandas_ta/statistics/skew.py b/pandas_ta/statistics/skew.py index 4af7b37..f89e50f 100644 --- a/pandas_ta/statistics/skew.py +++ b/pandas_ta/statistics/skew.py @@ -7,11 +7,6 @@ def skew(close, length=None, offset=None, **kwargs): Calculates the Skew over a rolling period. - Calculation: - Default Inputs: - length=30 - SKEW = close.rolling(length).skew() - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 30 diff --git a/pandas_ta/statistics/stdev.py b/pandas_ta/statistics/stdev.py index 013144a..1bc1d52 100644 --- a/pandas_ta/statistics/stdev.py +++ b/pandas_ta/statistics/stdev.py @@ -10,12 +10,6 @@ def stdev(close, length=None, ddof=None, talib=None, offset=None, **kwargs): Calculates the Standard Deviation over a rolling period. - Calculation: - Default Inputs: - length=30 - VAR = Variance - STDEV = variance(close, length).apply(np.sqrt) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 30 diff --git a/pandas_ta/statistics/tos_stdevall.py b/pandas_ta/statistics/tos_stdevall.py index 796e729..64432f6 100644 --- a/pandas_ta/statistics/tos_stdevall.py +++ b/pandas_ta/statistics/tos_stdevall.py @@ -17,18 +17,6 @@ def tos_stdevall(close, length=None, stds=None, ddof=None, offset=None, **kwargs Sources: https://tlc.thinkorswim.com/center/reference/thinkScript/Functions/Statistical/StDevAll - Calculation: - Default Inputs: - length=None (All), stds=[1, 2, 3], ddof=1 - LR = Linear Regression - STDEV = Standard Deviation - - LR = LR(close, length) - STDEV = STDEV(close, length, ddof) - for level in stds: - LOWER = LR - level * STDEV - UPPER = LR + level * STDEV - Args: close (pd.Series): Series of 'close's length (int): Bars from current bar. Default: None diff --git a/pandas_ta/statistics/variance.py b/pandas_ta/statistics/variance.py index 1216f2d..c4443e6 100644 --- a/pandas_ta/statistics/variance.py +++ b/pandas_ta/statistics/variance.py @@ -8,11 +8,6 @@ def variance(close, length=None, ddof=None, talib=None, offset=None, **kwargs): Calculates the Variance over a rolling period. - Calculation: - Default Inputs: - length=30 - VARIANCE = close.rolling(length).var() - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 30 diff --git a/pandas_ta/statistics/zscore.py b/pandas_ta/statistics/zscore.py index 9dbb9c2..c432a29 100644 --- a/pandas_ta/statistics/zscore.py +++ b/pandas_ta/statistics/zscore.py @@ -9,15 +9,6 @@ def zscore(close, length=None, std=None, offset=None, **kwargs): Calculates the Z Score over a rolling period. - Calculation: - Default Inputs: - length=30, std=1 - SMA = Simple Moving Average - STDEV = Standard Deviation - std = std * STDEV(close, length) - mean = SMA(close, length) - ZSCORE = (close - mean) / std - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 30 diff --git a/pandas_ta/trend/adx.py b/pandas_ta/trend/adx.py index ee78eaa..df0943c 100644 --- a/pandas_ta/trend/adx.py +++ b/pandas_ta/trend/adx.py @@ -12,58 +12,8 @@ def adx(high, low, close, length=None, lensig=None, scalar=None, mamode=None, dr the amount of movement in a single direction. Sources: - https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/average-directional-movement-adx/ TA Lib Correlation: >99% - - Calculation: - DMI ADX TREND 2.0 by @TraderR0BERT, NETWORTHIE.COM - //Created by @TraderR0BERT, NETWORTHIE.COM, last updated 01/26/2016 - //DMI Indicator - //Resolution input option for higher/lower time frames - study(title="DMI ADX TREND 2.0", shorttitle="ADX TREND 2.0") - - adxlen = input(14, title="ADX Smoothing") - dilen = input(14, title="DI Length") - thold = input(20, title="Threshold") - - threshold = thold - - //Script for Indicator - dirmov(len) => - up = change(high) - down = -change(low) - truerange = rma(tr, len) - plus = fixnan(100 * rma(up > down and up > 0 ? up : 0, len) / truerange) - minus = fixnan(100 * rma(down > up and down > 0 ? down : 0, len) / truerange) - [plus, minus] - - adx(dilen, adxlen) => - [plus, minus] = dirmov(dilen) - sum = plus + minus - adx = 100 * rma(abs(plus - minus) / (sum == 0 ? 1 : sum), adxlen) - [adx, plus, minus] - - [sig, up, down] = adx(dilen, adxlen) - osob=input(40,title="Exhaustion Level for ADX, default = 40") - col = sig >= sig[1] ? green : sig <= sig[1] ? red : gray - - //Plot Definitions Current Timeframe - p1 = plot(sig, color=col, linewidth = 3, title="ADX") - p2 = plot(sig, color=col, style=circles, linewidth=3, title="ADX") - p3 = plot(up, color=blue, linewidth = 3, title="+DI") - p4 = plot(up, color=blue, style=circles, linewidth=3, title="+DI") - p5 = plot(down, color=fuchsia, linewidth = 3, title="-DI") - p6 = plot(down, color=fuchsia, style=circles, linewidth=3, title="-DI") - h1 = plot(threshold, color=black, linewidth =3, title="Threshold") - - trender = (sig >= up or sig >= down) ? 1 : 0 - bgcolor(trender>0?black:gray, transp=85) - - //Alert Function for ADX crossing Threshold - Up_Cross = crossover(up, threshold) - alertcondition(Up_Cross, title="DMI+ cross", message="DMI+ Crossing Threshold") - Down_Cross = crossover(down, threshold) - alertcondition(Down_Cross, title="DMI- cross", message="DMI- Crossing Threshold") + https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/average-directional-movement-adx/ Args: high (pd.Series): Series of 'high's diff --git a/pandas_ta/trend/amat.py b/pandas_ta/trend/amat.py index fb040e8..77c33e4 100644 --- a/pandas_ta/trend/amat.py +++ b/pandas_ta/trend/amat.py @@ -18,19 +18,6 @@ def amat(close=None, fast=None, slow=None, lookback=None, mamode=None, offset=No Sources: https://www.tradingview.com/script/Z2mq63fE-Trade-Archer-Moving-Averages-v1-4F/ - Calculation: - Default Inputs: - fast=8, slow=21, mamode="ema", lookback=2 - OBV = On Balance Volume - LR = Long Run Trend - SR = Short Run Trend - - FMA = ma(close, mamode, fast) - SMA = ma(close, mamode, slow) - - AMAT_LR = LR(FMA, SMA, lookback) - AMAT_SR = SR(FMA, SMA, lookback) - Args: close (pd.Series): Series of 'close's fast (int): The period of the fast moving average. Default: 8 diff --git a/pandas_ta/trend/aroon.py b/pandas_ta/trend/aroon.py index 4265583..28066b3 100644 --- a/pandas_ta/trend/aroon.py +++ b/pandas_ta/trend/aroon.py @@ -14,21 +14,6 @@ def aroon(high, low, length=None, scalar=None, talib=None, offset=None, **kwargs https://www.tradingview.com/wiki/Aroon https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/aroon-ar/ - Calculation: - Default Inputs: - length=1, scalar=100 - - recent_maximum_index(x): return int(np.argmax(x[::-1])) - recent_minimum_index(x): return int(np.argmin(x[::-1])) - - periods_from_hh = high.rolling(length + 1).apply(recent_maximum_index, raw=True) - AROON_UP = scalar * (1 - (periods_from_hh / length)) - - periods_from_ll = low.rolling(length + 1).apply(recent_minimum_index, raw=True) - AROON_DN = scalar * (1 - (periods_from_ll / length)) - - AROON_OSC = AROON_UP - AROON_DN - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 14 diff --git a/pandas_ta/trend/chop.py b/pandas_ta/trend/chop.py index db7fd48..6cc0798 100644 --- a/pandas_ta/trend/chop.py +++ b/pandas_ta/trend/chop.py @@ -18,16 +18,6 @@ def chop(high, low, close, length=None, atr_length=None, ln=None, scalar=None, d https://www.tradingview.com/scripts/choppinessindex/ https://www.motivewave.com/studies/choppiness_index.htm - Calculation: - Default Inputs: - length=14, scalar=100, drift=1 - HH = high.rolling(length).max() - LL = low.rolling(length).min() - - ATR_SUM = SUM(ATR(drift), length) - CHOP = scalar * (LOG10(ATR_SUM) - LOG10(HH - LL)) - CHOP /= LOG10(length) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/trend/cksp.py b/pandas_ta/trend/cksp.py index ec19ac0..65de6a7 100644 --- a/pandas_ta/trend/cksp.py +++ b/pandas_ta/trend/cksp.py @@ -16,21 +16,14 @@ def cksp(high, low, close, p=None, x=None, q=None, tvmode=None, offset=None, **k view implementation uses the Welles Wilder moving average, the book uses a simple moving average. + Defaults: + Book: p=10, x=3, q=20 + Trading View: p=10, x=1, q=9 + Sources: https://www.multicharts.com/discussion/viewtopic.php?t=48914 "The New Technical Trader", Wikey 1st ed. ISBN 9780471597803, page 95 - Calculation: - Default Inputs: - p=10, x=1, q=9, tvmode=True - ATR = Average True Range - - LS0 = high.rolling(p).max() - x * ATR(length=p) - LS = LS0.rolling(q).max() - - SS0 = high.rolling(p).min() + x * ATR(length=p) - SS = SS0.rolling(q).min() - Args: close (pd.Series): Series of 'close's p (int): ATR and first stop period. Default: 10 in both modes @@ -47,7 +40,6 @@ def cksp(high, low, close, p=None, x=None, q=None, tvmode=None, offset=None, **k pd.DataFrame: long and short columns. """ # Validate Arguments - # TV defaults=(10,1,9), book defaults = (10,3,20) p = int(p) if p and p > 0 else 10 x = float(x) if x and x > 0 else 1 if tvmode is True else 3 q = int(q) if q and q > 0 else 9 if tvmode is True else 20 diff --git a/pandas_ta/trend/decay.py b/pandas_ta/trend/decay.py index 8191f5b..fe20e7e 100644 --- a/pandas_ta/trend/decay.py +++ b/pandas_ta/trend/decay.py @@ -13,15 +13,6 @@ def decay(close, kind=None, length=None, mode=None, offset=None, **kwargs): Sources: https://tulipindicators.org/decay - Calculation: - Default Inputs: - length=5, mode=None - - if mode == "exponential" or mode == "exp": - max(close, close[-1] - exp(-length), 0) - else: - max(close, close[-1] - (1 / length), 0) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 1 diff --git a/pandas_ta/trend/decreasing.py b/pandas_ta/trend/decreasing.py index 5860d74..b9a1b4c 100644 --- a/pandas_ta/trend/decreasing.py +++ b/pandas_ta/trend/decreasing.py @@ -9,15 +9,6 @@ def decreasing(close, length=None, strict=None, asint=None, percent=None, drift= over the period. When using the kwarg 'asint', then it returns 1 for True or 0 for False. - Calculation: - if strict: - decreasing = all(i > j for i, j in zip(close[-length:], close[1:])) - else: - decreasing = close.diff(length) < 0 - - if asint: - decreasing = decreasing.astype(int) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 1 diff --git a/pandas_ta/trend/dpo.py b/pandas_ta/trend/dpo.py index 2a06673..1a9b106 100644 --- a/pandas_ta/trend/dpo.py +++ b/pandas_ta/trend/dpo.py @@ -14,16 +14,6 @@ def dpo(close, length=None, centered=True, offset=None, **kwargs): https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/dpo http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:detrended_price_osci - Calculation: - Default Inputs: - length=20, centered=True - SMA = Simple Moving Average - t = int(0.5 * length) + 1 - - DPO = close.shift(t) - SMA(close, length) - if centered: - DPO = DPO.shift(-t) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 1 diff --git a/pandas_ta/trend/increasing.py b/pandas_ta/trend/increasing.py index d4aa916..bbbb691 100644 --- a/pandas_ta/trend/increasing.py +++ b/pandas_ta/trend/increasing.py @@ -9,15 +9,6 @@ def increasing(close, length=None, strict=None, asint=None, percent=None, drift= over the period. When using the kwarg 'asint', then it returns 1 for True or 0 for False. - Calculation: - if strict: - increasing = all(i < j for i, j in zip(close[-length:], close[1:])) - else: - increasing = close.diff(length) > 0 - - if asint: - increasing = increasing.astype(int) - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 1 diff --git a/pandas_ta/trend/long_run.py b/pandas_ta/trend/long_run.py index 96798c9..9e13343 100644 --- a/pandas_ta/trend/long_run.py +++ b/pandas_ta/trend/long_run.py @@ -21,18 +21,6 @@ def long_run(fast, slow, length=None, offset=None, **kwargs): It is part of the Converging and Diverging Conditional logic in: https://www.tradingview.com/script/Z2mq63fE-Trade-Archer-Moving-Averages-v1-4F/ - Calculation: - Default Inputs: - length=2 - INC = increasing - DEC = decreasing - BINC = Both increasing - PBOT = Potential Bottom - - BINC = INC(fast, length) & INC(slow, length) - PBOT = INC(fast, length) & DEC(slow, length) - LR = BINC | PBOT - Args: fast (pd.Series): Series of 'fast' values. slow (pd.Series): Series of 'slow' values. diff --git a/pandas_ta/trend/psar.py b/pandas_ta/trend/psar.py index e14c191..f617038 100644 --- a/pandas_ta/trend/psar.py +++ b/pandas_ta/trend/psar.py @@ -21,12 +21,6 @@ def psar(high, low, close=None, af0=None, af=None, max_af=None, offset=None, **k https://www.tradingview.com/pine-script-reference/#fun_sar https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=66&Name=Parabolic - Calculation: - Default Inputs: - af0=0.02, af=0.02, max_af=0.2 - - See Source links - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/trend/qstick.py b/pandas_ta/trend/qstick.py index 8d2f970..37cd46b 100644 --- a/pandas_ta/trend/qstick.py +++ b/pandas_ta/trend/qstick.py @@ -12,12 +12,6 @@ def qstick(open_, close, length=None, offset=None, **kwargs): Sources: https://library.tradingtechnologies.com/trade/chrt-ti-qstick.html - Calculation: - Default Inputs: - length=10 - xMA is one of: sma (default), dema, ema, hma, rma - qstick = xMA(close - open, length) - Args: open (pd.Series): Series of 'open's close (pd.Series): Series of 'close's diff --git a/pandas_ta/trend/short_run.py b/pandas_ta/trend/short_run.py index 2330d15..8d87788 100644 --- a/pandas_ta/trend/short_run.py +++ b/pandas_ta/trend/short_run.py @@ -21,18 +21,6 @@ def short_run(fast, slow, length=None, offset=None, **kwargs): It is part of the Converging and Diverging Conditional logic in: https://www.tradingview.com/script/Z2mq63fE-Trade-Archer-Moving-Averages-v1-4F/ - Calculation: - Default Inputs: - length=2 - INC = increasing - DEC = decreasing - BDEC = Both decreasing - PTOP = Potential Top - - BDEC = DEC(fast, length) & DEC(slow, length) - PTOP = DEC(fast, length) & INC(slow, length) - SR = BDEC | PTOP - Args: fast (pd.Series): Series of 'fast' values. slow (pd.Series): Series of 'slow' values. diff --git a/pandas_ta/trend/trendflex.py b/pandas_ta/trend/trendflex.py index 8ae0bb9..9aee235 100644 --- a/pandas_ta/trend/trendflex.py +++ b/pandas_ta/trend/trendflex.py @@ -25,9 +25,6 @@ def trendflex(close, length=None, smooth=None, alpha=None, offset=None, **kwargs Sources: https://www.prorealcode.com/prorealtime-indicators/reflex-and-trendflex-indicators-john-f-ehlers/ - Calculation: - Refer to provided source or the code above. - Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 20 diff --git a/pandas_ta/trend/tsignals.py b/pandas_ta/trend/tsignals.py index c8e2bb7..91075ef 100644 --- a/pandas_ta/trend/tsignals.py +++ b/pandas_ta/trend/tsignals.py @@ -20,14 +20,6 @@ def tsignals(trend, asbool=None, trend_reset=0, trade_offset=None, drift=None, o Source: Kevin Johnson - Calculation: - Default Inputs: - asbool=False, trend_reset=0, trade_offset=0, drift=1 - - trades = trends.diff().shift(trade_offset).fillna(0).astype(int) - entries = (trades > 0).astype(int) - exits = (trades < 0).abs().astype(int) - Args: trend (pd.Series): Series of 'trend's. The trend can be either a boolean or integer series of '0's and '1's diff --git a/pandas_ta/trend/ttm_trend.py b/pandas_ta/trend/ttm_trend.py index c6cd8ac..d755d21 100644 --- a/pandas_ta/trend/ttm_trend.py +++ b/pandas_ta/trend/ttm_trend.py @@ -15,26 +15,17 @@ def ttm_trend(high, low, close, length=None, offset=None, **kwargs): Sources: https://www.prorealcode.com/prorealtime-indicators/ttm-trend-price/ - Calculation: - Default Inputs: - length=6 - averageprice = (((high[5]+low[5])/2)+((high[4]+low[4])/2)+((high[3]+low[3])/2)+((high[2]+low[2])/2)+((high[1]+low[1])/2)+((high[6]+low[6])/2)) / 6 - - if close > averageprice: - drawcandle(open,high,low,close) coloured(0,255,0) - - if close < averageprice: - drawcandle(open,high,low,close) coloured(255,0,0) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's close (pd.Series): Series of 'close's length (int): It's period. Default: 6 offset (int): How many periods to offset the result. Default: 0 + Kwargs: fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method + Returns: pd.DataFrame: ttm_trend. """ diff --git a/pandas_ta/trend/vhf.py b/pandas_ta/trend/vhf.py index e5b3b88..d461433 100644 --- a/pandas_ta/trend/vhf.py +++ b/pandas_ta/trend/vhf.py @@ -11,14 +11,6 @@ def vhf(close, length=None, drift=None, offset=None, **kwargs): Sources: https://www.incrediblecharts.com/indicators/vertical_horizontal_filter.php - Calculation: - Default Inputs: - length = 28 - HCP = Highest Close Price in Period - LCP = Lowest Close Price in Period - Change = abs(Ct - Ct-1) - VHF = (HCP - LCP) / RollingSum[length] of Change - Args: source (pd.Series): Series of prices (usually close). length (int): The period length. Default: 28 diff --git a/pandas_ta/trend/vortex.py b/pandas_ta/trend/vortex.py index a1af6f1..929a817 100644 --- a/pandas_ta/trend/vortex.py +++ b/pandas_ta/trend/vortex.py @@ -12,20 +12,6 @@ def vortex(high, low, close, length=None, drift=None, offset=None, **kwargs): Sources: https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator - Calculation: - Default Inputs: - length=14, drift=1 - TR = True Range - SMA = Simple Moving Average - tr = TR(high, low, close) - tr_sum = tr.rolling(length).sum() - - vmp = (high - low.shift(drift)).abs() - vmn = (low - high.shift(drift)).abs() - - VIP = vmp.rolling(length).sum() / tr_sum - VIM = vmn.rolling(length).sum() / tr_sum - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/trend/xsignals.py b/pandas_ta/trend/xsignals.py index 28d3dad..6962b1e 100644 --- a/pandas_ta/trend/xsignals.py +++ b/pandas_ta/trend/xsignals.py @@ -34,14 +34,6 @@ def xsignals(signal, xa, xb, above:bool=True, long:bool=True, asbool:bool=None, Source: Kevin Johnson - Calculation: - Default Inputs: - asbool=False, trend_reset=0, trade_offset=0, drift=1 - - trades = trends.diff().shift(trade_offset).fillna(0).astype(int) - entries = (trades > 0).astype(int) - exits = (trades < 0).abs().astype(int) - Args: signal (pd.Series): The Signal to compare from. Commonly the 'close'. xa (pd.Series): The Series the Signal crosses above if 'above=True'. diff --git a/pandas_ta/volatility/accbands.py b/pandas_ta/volatility/accbands.py index de21502..28b7184 100644 --- a/pandas_ta/volatility/accbands.py +++ b/pandas_ta/volatility/accbands.py @@ -13,24 +13,6 @@ def accbands(high, low, close, length=None, c=None, drift=None, mamode=None, off Sources: https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/acceleration-bands-abands/ - Calculation: - Default Inputs: - length=10, c=4 - EMA = Exponential Moving Average - SMA = Simple Moving Average - HL_RATIO = c * (high - low) / (high + low) - LOW = low * (1 - HL_RATIO) - HIGH = high * (1 + HL_RATIO) - - if 'ema': - LOWER = EMA(LOW, length) - MID = EMA(close, length) - UPPER = EMA(HIGH, length) - else: - LOWER = SMA(LOW, length) - MID = SMA(close, length) - UPPER = SMA(HIGH, length) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volatility/atr.py b/pandas_ta/volatility/atr.py index 3ab80a1..0c8f60f 100644 --- a/pandas_ta/volatility/atr.py +++ b/pandas_ta/volatility/atr.py @@ -14,28 +14,6 @@ def atr(high, low, close, length=None, mamode=None, talib=None, drift=None, offs Sources: https://www.tradingview.com/wiki/Average_True_Range_(ATR) - Calculation: - Default Inputs: - length=14, drift=1, percent=False - EMA = Exponential Moving Average - SMA = Simple Moving Average - WMA = Weighted Moving Average - RMA = WildeR's Moving Average - TR = True Range - - tr = TR(high, low, close, drift) - if 'ema': - ATR = EMA(tr, length) - elif 'sma': - ATR = SMA(tr, length) - elif 'wma': - ATR = WMA(tr, length) - else: - ATR = RMA(tr, length) - - if percent: - ATR *= 100 / close - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volatility/bbands.py b/pandas_ta/volatility/bbands.py index b068c9a..c06db7d 100644 --- a/pandas_ta/volatility/bbands.py +++ b/pandas_ta/volatility/bbands.py @@ -14,24 +14,6 @@ def bbands(close, length=None, std=None, ddof=0, mamode=None, talib=None, offset Sources: https://www.tradingview.com/wiki/Bollinger_Bands_(BB) - Calculation: - Default Inputs: - length=5, std=2, mamode="sma", ddof=0 - EMA = Exponential Moving Average - SMA = Simple Moving Average - STDEV = Standard Deviation - stdev = STDEV(close, length, ddof) - if "ema": - MID = EMA(close, length) - else: - MID = SMA(close, length) - - LOWER = MID - std * stdev - UPPER = MID + std * stdev - - BANDWIDTH = 100 * (UPPER - LOWER) / MID - PERCENT = (close - LOWER) / (UPPER - LOWER) - Args: close (pd.Series): Series of 'close's length (int): The short period. Default: 5 diff --git a/pandas_ta/volatility/donchian.py b/pandas_ta/volatility/donchian.py index 761c9c3..9b098eb 100644 --- a/pandas_ta/volatility/donchian.py +++ b/pandas_ta/volatility/donchian.py @@ -12,13 +12,6 @@ def donchian(high, low, lower_length=None, upper_length=None, offset=None, **kwa Sources: https://www.tradingview.com/wiki/Donchian_Channels_(DC) - Calculation: - Default Inputs: - lower_length=upper_length=20 - LOWER = low.rolling(lower_length).min() - UPPER = high.rolling(upper_length).max() - MID = 0.5 * (LOWER + UPPER) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volatility/hwc.py b/pandas_ta/volatility/hwc.py index 5bcc16e..9bd08f6 100644 --- a/pandas_ta/volatility/hwc.py +++ b/pandas_ta/volatility/hwc.py @@ -17,31 +17,21 @@ def hwc(close, na=None, nb=None, nc=None, nd=None, scalar=None, channel_eval=Non Sources: https://www.mql5.com/en/code/20857 - Calculation: - HWMA[i] = F[i] + V[i] + 0.5 * A[i] - where.. - F[i] = (1-na) * (F[i-1] + V[i-1] + 0.5 * A[i-1]) + na * Price[i] - V[i] = (1-nb) * (V[i-1] + A[i-1]) + nb * (F[i] - F[i-1]) - A[i] = (1-nc) * A[i-1] + nc * (V[i] - V[i-1]) - - Top = HWMA + Multiplier * StDt - Bottom = HWMA - Multiplier * StDt - where.. - StDt[i] = Sqrt(Var[i-1]) - Var[i] = (1-d) * Var[i-1] + nD * (Price[i-1] - HWMA[i-1]) * (Price[i-1] - HWMA[i-1]) - Args: - na - parameter of the equation that describes a smoothed series (from 0 to 1) - nb - parameter of the equation to assess the trend (from 0 to 1) - nc - parameter of the equation to assess seasonality (from 0 to 1) - nd - parameter of the channel equation (from 0 to 1) - scaler - multiplier for the width of the channel calculated - channel_eval - boolean to return width and percentage price position against price close (pd.Series): Series of 'close's + na (float): Smoothed series (from 0 to 1). Default: 0.2 + nb (float): Trend value (from 0 to 1). Default: 0.1 + nc (float): Seasonality value (from 0 to 1). Default: 0.1 + nd (float): Channel value (from 0 to 1). Default: 0.1 + scaler (float): Width multiplier of the channel. Default: 1 + channel_eval (bool): Return width and percentage price position against + price. Default: False + offset (int): How many periods to offset the result. Default: 0 Kwargs: fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method + Returns: pd.DataFrame: HWM (Mid), HWU (Upper), HWL (Lower) columns. """ diff --git a/pandas_ta/volatility/kc.py b/pandas_ta/volatility/kc.py index 4e5274a..176bb73 100644 --- a/pandas_ta/volatility/kc.py +++ b/pandas_ta/volatility/kc.py @@ -14,28 +14,6 @@ def kc(high, low, close, length=None, scalar=None, mamode=None, offset=None, **k Sources: https://www.tradingview.com/wiki/Keltner_Channels_(KC) - Calculation: - Default Inputs: - length=20, scalar=2, mamode=None, tr=True - TR = True Range - SMA = Simple Moving Average - EMA = Exponential Moving Average - - if tr: - RANGE = TR(high, low, close) - else: - RANGE = high - low - - if mamode == "ema": - BASIS = sma(close, length) - BAND = sma(RANGE, length) - elif mamode == "sma": - BASIS = sma(close, length) - BAND = sma(RANGE, length) - - LOWER = BASIS - scalar * BAND - UPPER = BASIS + scalar * BAND - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volatility/massi.py b/pandas_ta/volatility/massi.py index 1aa24c1..da3c951 100644 --- a/pandas_ta/volatility/massi.py +++ b/pandas_ta/volatility/massi.py @@ -11,17 +11,6 @@ def massi(high, low, fast=None, slow=None, offset=None, **kwargs): Sources: https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index - mi = sum(ema(high - low, 9) / ema(ema(high - low, 9), 9), length) - - Calculation: - Default Inputs: - fast: 9, slow: 25 - EMA = Exponential Moving Average - hl = high - low - hl_ema1 = EMA(hl, fast) - hl_ema2 = EMA(hl_ema1, fast) - hl_ratio = hl_ema1 / hl_ema2 - MASSI = SUM(hl_ratio, slow) Args: high (pd.Series): Series of 'high's diff --git a/pandas_ta/volatility/natr.py b/pandas_ta/volatility/natr.py index dca5890..46fc339 100644 --- a/pandas_ta/volatility/natr.py +++ b/pandas_ta/volatility/natr.py @@ -12,12 +12,6 @@ def natr(high, low, close, length=None, scalar=None, mamode=None, talib=None, dr Sources: https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/normalized-average-true-range-natr/ - Calculation: - Default Inputs: - length=20 - ATR = Average True Range - NATR = (100 / close) * ATR(high, low, close) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volatility/pdist.py b/pandas_ta/volatility/pdist.py index ae3ce9e..24c1d4c 100644 --- a/pandas_ta/volatility/pdist.py +++ b/pandas_ta/volatility/pdist.py @@ -10,12 +10,6 @@ def pdist(open_, high, low, close, drift=None, offset=None, **kwargs): Sources: https://www.prorealcode.com/prorealtime-indicators/pricedistance/ - Calculation: - Default Inputs: - drift=1 - - PDIST = 2(high - low) - ABS(close - open) + ABS(open - close[drift]) - Args: open_ (pd.Series): Series of 'opens's high (pd.Series): Series of 'high's diff --git a/pandas_ta/volatility/rvi.py b/pandas_ta/volatility/rvi.py index abd7712..857f5c9 100644 --- a/pandas_ta/volatility/rvi.py +++ b/pandas_ta/volatility/rvi.py @@ -15,20 +15,6 @@ def rvi(close, high=None, low=None, length=None, scalar=None, refined=None, thir Sources: https://www.tradingview.com/wiki/Keltner_Channels_(KC) - Calculation: - Default Inputs: - length=14, scalar=100, refined=None, thirds=None - EMA = Exponential Moving Average - STDEV = Standard Deviation - - UP = STDEV(src, length) IF src.diff() > 0 ELSE 0 - DOWN = STDEV(src, length) IF src.diff() <= 0 ELSE 0 - - UPSUM = EMA(UP, length) - DOWNSUM = EMA(DOWN, length - - RVI = scalar * (UPSUM / (UPSUM + DOWNSUM)) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volatility/thermo.py b/pandas_ta/volatility/thermo.py index 457671e..7a4b20d 100644 --- a/pandas_ta/volatility/thermo.py +++ b/pandas_ta/volatility/thermo.py @@ -13,22 +13,6 @@ def thermo(high, low, length=None, long=None, short=None, mamode=None, drift=Non https://www.motivewave.com/studies/elders_thermometer.htm https://www.tradingview.com/script/HqvTuEMW-Elder-s-Market-Thermometer-LazyBear/ - Calculation: - Default Inputs: - length=20, drift=1, mamode=EMA, long=2, short=0.5 - EMA = Exponential Moving Average - - thermoL = (low.shift(drift) - low).abs() - thermoH = (high - high.shift(drift)).abs() - - thermo = np.where(thermoH > thermoL, thermoH, thermoL) - thermo_ma = ema(thermo, length) - - thermo_long = thermo < (thermo_ma * long) - thermo_short = thermo > (thermo_ma * short) - thermo_long = thermo_long.astype(int) - thermo_short = thermo_short.astype(int) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volatility/true_range.py b/pandas_ta/volatility/true_range.py index e73f905..9504d95 100644 --- a/pandas_ta/volatility/true_range.py +++ b/pandas_ta/volatility/true_range.py @@ -14,13 +14,6 @@ def true_range(high, low, close, talib=None, drift=None, offset=None, **kwargs): Sources: https://www.macroption.com/true-range/ - Calculation: - Default Inputs: - drift=1 - ABS = Absolute Value - prev_close = close.shift(drift) - TRUE_RANGE = ABS([high - low, high - prev_close, low - prev_close]) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volatility/ui.py b/pandas_ta/volatility/ui.py index 428c4b0..d10f390 100644 --- a/pandas_ta/volatility/ui.py +++ b/pandas_ta/volatility/ui.py @@ -15,19 +15,6 @@ def ui(close, length=None, scalar=None, offset=None, **kwargs): https://en.wikipedia.org/wiki/Ulcer_index http://www.tangotools.com/ui/ui.htm - Calculation: - Default Inputs: - length=14, scalar=100 - HC = Highest Close - SMA = Simple Moving Average - - HCN = HC(close, length) - DOWNSIDE = scalar * (close - HCN) / HCN - if kwargs["everget"]: - UI = SQRT(SMA(DOWNSIDE^2, length) / length) - else: - UI = SQRT(SUM(DOWNSIDE^2, length) / length) - Args: high (pd.Series): Series of 'high's close (pd.Series): Series of 'close's diff --git a/pandas_ta/volume/__init__.py b/pandas_ta/volume/__init__.py index 1539555..70a5030 100644 --- a/pandas_ta/volume/__init__.py +++ b/pandas_ta/volume/__init__.py @@ -13,5 +13,5 @@ from .pvi import pvi from .pvol import pvol from .pvr import pvr from .pvt import pvt -from .tv_tsv import tv_tsv from .vp import vp +from .wb_tsv import wb_tsv diff --git a/pandas_ta/volume/ad.py b/pandas_ta/volume/ad.py index 53f81f3..4911380 100644 --- a/pandas_ta/volume/ad.py +++ b/pandas_ta/volume/ad.py @@ -12,17 +12,6 @@ def ad(high, low, close, volume, open_=None, talib=None, offset=None, **kwargs): Sources: https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/accumulationdistribution-ad/ - Calculation: - CUM = Cumulative Sum - if 'open': - AD = close - open - else: - AD = 2 * close - high - low - - hl_range = high - low - AD = AD * volume / hl_range - AD = CUM(AD) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volume/adosc.py b/pandas_ta/volume/adosc.py index b488b0e..fb93ba8 100644 --- a/pandas_ta/volume/adosc.py +++ b/pandas_ta/volume/adosc.py @@ -15,15 +15,6 @@ def adosc(high, low, close, volume, open_=None, fast=None, slow=None, talib=None Sources: https://www.investopedia.com/articles/active-trading/031914/understanding-chaikin-oscillator.asp - Calculation: - Default Inputs: - fast=12, slow=26 - AD = Accum/Dist - ad = AD(high, low, close, open) - fast_ad = EMA(ad, fast) - slow_ad = EMA(ad, slow) - ADOSC = fast_ad - slow_ad - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volume/aobv.py b/pandas_ta/volume/aobv.py index cdb8d22..3b8bc90 100644 --- a/pandas_ta/volume/aobv.py +++ b/pandas_ta/volume/aobv.py @@ -18,23 +18,6 @@ def aobv(close, volume, fast=None, slow=None, max_lookback=None, min_lookback=No Sources: https://www.tradingview.com/script/Co1ksara-Trade-Archer-On-balance-Volume-Moving-Averages-v1/ - Calculation: - Default Inputs: - fast=4, slow=12, max_lookback=2, min_lookback=2, mamode="ema", - run_length=2 - OBV = On Balance Volume - LR = Long Run Trend - SR = Short Run Trend - - OBV_FMA = ma(OBV, mamode, fast) - OBV_SMA = ma(OBV, mamode, slow) - - OBV_LR = LR(OBV_FMA, OBV_SMA, run_length) - OBV_SR = SR(OBV_FMA, OBV_SMA, run_length) - - OBV_MAX = OBV.rolling(max_lookback).max() - OBV_MIN = OBV.rolling(min_lookback).min() - Args: close (pd.Series): Series of 'close's volume (pd.Series): Series of 'volume's diff --git a/pandas_ta/volume/cmf.py b/pandas_ta/volume/cmf.py index ff01bb8..c625c76 100644 --- a/pandas_ta/volume/cmf.py +++ b/pandas_ta/volume/cmf.py @@ -12,18 +12,6 @@ def cmf(high, low, close, volume, open_=None, length=None, offset=None, **kwargs https://www.tradingview.com/wiki/Chaikin_Money_Flow_(CMF) https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf - Calculation: - Default Inputs: - length=20 - if 'open': - ad = close - open - else: - ad = 2 * close - high - low - - hl_range = high - low - ad = ad * volume / hl_range - CMF = SUM(ad, length) / SUM(volume, length) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volume/efi.py b/pandas_ta/volume/efi.py index 923389d..394efc7 100644 --- a/pandas_ta/volume/efi.py +++ b/pandas_ta/volume/efi.py @@ -13,18 +13,6 @@ def efi(close, volume, length=None, mamode=None, drift=None, offset=None, **kwar https://www.tradingview.com/wiki/Elder%27s_Force_Index_(EFI) https://www.motivewave.com/studies/elders_force_index.htm - Calculation: - Default Inputs: - length=20, drift=1, mamode=None - EMA = Exponential Moving Average - SMA = Simple Moving Average - - pv_diff = close.diff(drift) * volume - if mamode == 'sma': - EFI = SMA(pv_diff, length) - else: - EFI = EMA(pv_diff, length) - Args: close (pd.Series): Series of 'close's volume (pd.Series): Series of 'volume's diff --git a/pandas_ta/volume/eom.py b/pandas_ta/volume/eom.py index 3405b78..24ee77d 100644 --- a/pandas_ta/volume/eom.py +++ b/pandas_ta/volume/eom.py @@ -14,16 +14,6 @@ def eom(high, low, close, volume, length=None, divisor=None, drift=None, offset= https://www.motivewave.com/studies/ease_of_movement.htm https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ease_of_movement_emv - Calculation: - Default Inputs: - length=14, divisor=100000000, drift=1 - SMA = Simple Moving Average - hl_range = high - low - distance = 0.5 * (high - high.shift(drift) + low - low.shift(drift)) - box_ratio = (volume / divisor) / hl_range - eom = distance / box_ratio - EOM = SMA(eom, length) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volume/kvo.py b/pandas_ta/volume/kvo.py index 38fbb30..6b256d9 100644 --- a/pandas_ta/volume/kvo.py +++ b/pandas_ta/volume/kvo.py @@ -14,15 +14,6 @@ def kvo(high, low, close, volume, fast=None, slow=None, signal=None, mamode=None https://www.investopedia.com/terms/k/klingeroscillator.asp https://www.daytrading.com/klinger-volume-oscillator - Calculation: - Default Inputs: - fast=34, slow=55, signal=13, drift=1 - EMA = Exponential Moving Average - - SV = volume * signed_series(HLC3, 1) - KVO = EMA(SV, fast) - EMA(SV, slow) - Signal = EMA(KVO, signal) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volume/mfi.py b/pandas_ta/volume/mfi.py index 8252e68..c6ea9e5 100644 --- a/pandas_ta/volume/mfi.py +++ b/pandas_ta/volume/mfi.py @@ -14,18 +14,6 @@ def mfi(high, low, close, volume, length=None, talib=None, drift=None, offset=No Sources: https://www.tradingview.com/wiki/Money_Flow_(MFI) - Calculation: - Default Inputs: - length=14, drift=1 - tp = typical_price = hlc3 = (high + low + close) / 3 - rmf = raw_money_flow = tp * volume - - pmf = pos_money_flow = SUM(rmf, length) if tp.diff(drift) > 0 else 0 - nmf = neg_money_flow = SUM(rmf, length) if tp.diff(drift) < 0 else 0 - - MFR = money_flow_ratio = pmf / nmf - MFI = money_flow_index = 100 * pmf / (pmf + nmf) - Args: high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's diff --git a/pandas_ta/volume/nvi.py b/pandas_ta/volume/nvi.py index 634d872..6c324d1 100644 --- a/pandas_ta/volume/nvi.py +++ b/pandas_ta/volume/nvi.py @@ -13,18 +13,6 @@ def nvi(close, volume, length=None, initial=None, offset=None, **kwargs): https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:negative_volume_inde https://www.motivewave.com/studies/negative_volume_index.htm - Calculation: - Default Inputs: - length=1, initial=1000 - ROC = Rate of Change - - roc = ROC(close, length) - signed_volume = signed_series(volume, initial=1) - nvi = signed_volume[signed_volume < 0].abs() * roc_ - nvi.fillna(0, inplace=True) - nvi.iloc[0]= initial - nvi = nvi.cumsum() - Args: close (pd.Series): Series of 'close's volume (pd.Series): Series of 'volume's diff --git a/pandas_ta/volume/obv.py b/pandas_ta/volume/obv.py index a079125..0a19f7e 100644 --- a/pandas_ta/volume/obv.py +++ b/pandas_ta/volume/obv.py @@ -14,10 +14,6 @@ def obv(close, volume, talib=None, offset=None, **kwargs): https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/on-balance-volume-obv/ https://www.motivewave.com/studies/on_balance_volume.htm - Calculation: - signed_volume = signed_series(close, initial=1) * volume - obv = signed_volume.cumsum() - Args: close (pd.Series): Series of 'close's volume (pd.Series): Series of 'volume's diff --git a/pandas_ta/volume/pvi.py b/pandas_ta/volume/pvi.py index 9174cd9..f2d4fce 100644 --- a/pandas_ta/volume/pvi.py +++ b/pandas_ta/volume/pvi.py @@ -13,18 +13,6 @@ def pvi(close, volume, length=None, initial=None, offset=None, **kwargs): Sources: https://www.investopedia.com/terms/p/pvi.asp - Calculation: - Default Inputs: - length=1, initial=1000 - ROC = Rate of Change - - roc = ROC(close, length) - signed_volume = signed_series(volume, initial=1) - pvi = signed_volume[signed_volume > 0].abs() * roc_ - pvi.fillna(0, inplace=True) - pvi.iloc[0]= initial - pvi = pvi.cumsum() - Args: close (pd.Series): Series of 'close's volume (pd.Series): Series of 'volume's diff --git a/pandas_ta/volume/pvol.py b/pandas_ta/volume/pvol.py index 99ed75d..a3cbe05 100644 --- a/pandas_ta/volume/pvol.py +++ b/pandas_ta/volume/pvol.py @@ -7,12 +7,6 @@ def pvol(close, volume, offset=None, **kwargs): Returns a series of the product of price and volume. - Calculation: - if signed: - pvol = signed_series(close, 1) * close * volume - else: - pvol = close * volume - Args: close (pd.Series): Series of 'close's volume (pd.Series): Series of 'volume's diff --git a/pandas_ta/volume/pvr.py b/pandas_ta/volume/pvr.py index 19a13d9..7c9455e 100644 --- a/pandas_ta/volume/pvr.py +++ b/pandas_ta/volume/pvr.py @@ -16,12 +16,6 @@ def pvr(close, volume): Sources: https://www.fmlabs.com/reference/default.htm?url=PVrank.htm - Calculation: - return 1 if 'close change' >= 0 and 'volume change' >= 0 - return 2 if 'close change' >= 0 and 'volume change' < 0 - return 3 if 'close change' < 0 and 'volume change' >= 0 - return 4 if 'close change' < 0 and 'volume change' < 0 - Args: close (pd.Series): Series of 'close's volume (pd.Series): Series of 'volume's diff --git a/pandas_ta/volume/pvt.py b/pandas_ta/volume/pvt.py index 0b98cf1..10530ca 100644 --- a/pandas_ta/volume/pvt.py +++ b/pandas_ta/volume/pvt.py @@ -12,13 +12,6 @@ def pvt(close, volume, drift=None, offset=None, **kwargs): Sources: https://www.tradingview.com/wiki/Price_Volume_Trend_(PVT) - Calculation: - Default Inputs: - drift=1 - ROC = Rate of Change - pv = ROC(close, drift) * volume - PVT = pv.cumsum() - Args: close (pd.Series): Series of 'close's volume (pd.Series): Series of 'volume's diff --git a/pandas_ta/volume/tv_tsv.py b/pandas_ta/volume/tv_tsv.py deleted file mode 100644 index 6521a52..0000000 --- a/pandas_ta/volume/tv_tsv.py +++ /dev/null @@ -1,96 +0,0 @@ -# -*- coding: utf-8 -*- -import pandas as pd -from pandas import DataFrame, Series -from pandas_ta.utils import verify_series, signed_series -import statistics -import time -from datetime import date, datetime, timedelta, timezone - -def tv_tsv(close=None, volume=None, length=None, signal=None, mamode=None, **kwargs): - """Indicator: Time Segmented Value (TSV)""" - # Validate Arguments - drift = kwargs.pop("drift", 1) - length = int(length) if length and length > 0 else 18 - signal = int(signal) if signal and signal > 0 else 10 - mamode = mamode if isinstance(mamode, str) else "sma" - - # Trading View - # https://www.tradingview.com/script/6GR4ht9X-Time-Segmented-Volume/ - # t = sum( close > close[1] ? volume*(close-close[1]) : close < close[1] ? volume*(close-close[1]) : 0,l) - # m = sma(t ,l_ma ) - - # Calculate Result - signed_volume = volume * ta.signed_series(close, 1) # > 0 - signed_volume[signed_volume < 0 ] = -signed_volume # < 0 - signed_volume.apply(ta.zero) # ~ 0 - - cvd = (close.diff(drift) * signed_volume).fillna(0) - - tsv = (cvd.rolling(length).sum()).fillna(0) - signal = (ta.ma(mamode, tsv, length=signal)).fillna(0) - tsv_ratio = (tsv / signal).fillna(0) - - # Handle fills - if "fillna" in kwargs: - tsv.fillna(kwargs["fillna"], inplace=True) - signal.fillna(kwargs["fillna"], inplace=True) - tsv_ratio.fillna(kwargs["fillna"], inplace=True) - if "fill_method" in kwargs: - tsv.fillna(method=kwargs["fill_method"], inplace=True) - signal.fillna(method=kwargs["fill_method"], inplace=True) - tsv_ratio.fillna(method=kwargs["fill_method"], inplace=True) - - - - # Name & Category - _props = f"_{length}_{signal}" - tv_tsv.name = f"TV_TSV{_props}" - tv_tsv.category = "volume" - - #tsv.name = "tsv" - - - df = pd.DataFrame({ - "close": close, "volume": volume, - "signed_volume": signed_volume, "cvd": cvd, - "tsv": tsv, "signal": signal, "tsv_ratio": tsv_ratio - }) - - return df - - - -tv_tsv.__doc__ = \ -"""Time Segmented Value (TSV) - -TSV is a proprietary technical indicator developed by Worden Brothers Inc., classified as an oscillator. -It is calculated by comparing various time segments of both price and volume. -TSV essentially measures the amount of money flowing in or out of a particular stock. -The baseline represents the zero line. - -TSV is a leading indicator because its movement is based on both the stock's price fluctuation and volume. -Ideal entry and exit points are commonly found as the stock moves across the baseline level. -This indicator is similar to on-balance volume (OBV) because it measures the amount of money flowing in or out of a particular stock. - -Sources: - https://www.investopedia.com/terms/t/tsv.asp - -Calculation: - Default Inputs: - length=18, signal=10 - - -Args: - close (pd.Series): Series of 'close's - volume (pd.Series): Series of 'volume's - length (int): It's period. Default: 18 - signal (int): It's avg period. Default: 10 - mamode (str): See ```help(ta.ma)```. Default: 'sma' - -Kwargs: - fillna (value, optional): pd.DataFrame.fillna(value) - fill_method (value, optional): Type of fill method - -Returns: - pd.DataFrame: tsv, signal, tsv_ratio -""" diff --git a/pandas_ta/volume/vp.py b/pandas_ta/volume/vp.py index c1d8a42..6dd88ed 100644 --- a/pandas_ta/volume/vp.py +++ b/pandas_ta/volume/vp.py @@ -17,20 +17,6 @@ def vp(close, volume, width=None, **kwargs): http://www.ranchodinero.com/volume-tpo-essentials/ https://www.tradingtechnologies.com/blog/2013/05/15/volume-at-price/ - Calculation: - Default Inputs: - width=10 - - vp = pd.concat([close, pos_volume, neg_volume], axis=1) - if sort_close: - vp_ranges = cut(vp[close_col], width) - result = ({range_left, mean_close, range_right, pos_volume, neg_volume} foreach range in vp_ranges - else: - vp_ranges = np.array_split(vp, width) - result = ({low_close, mean_close, high_close, pos_volume, neg_volume} foreach range in vp_ranges - vpdf = pd.DataFrame(result) - vpdf['total_volume'] = vpdf['pos_volume'] + vpdf['neg_volume'] - Args: close (pd.Series): Series of 'close's volume (pd.Series): Series of 'volume's diff --git a/pandas_ta/volume/wb_tsv.py b/pandas_ta/volume/wb_tsv.py new file mode 100644 index 0000000..bae6726 --- /dev/null +++ b/pandas_ta/volume/wb_tsv.py @@ -0,0 +1,84 @@ +# -*- coding: utf-8 -*- +from pandas import DataFrame, Series +from pandas_ta.overlap import ma +from pandas_ta.utils import get_drift, get_offset, verify_series, signed_series, zero + + +def wb_tsv(close=None, volume=None, length=None, signal=None, mamode=None, drift=None, offset=None, **kwargs): + """Time Segmented Value (TSV) + + TSV is a proprietary technical indicator developed by Worden Brothers Inc., + classified as an oscillator. It compares various time segments of both price + and volume. It measures the amount money flowing at various time segments + for price and time; similar to On Balance Volume. The zero line is called + the baseline. Entry and exit points are commonly determined when crossing + the baseline. + + Sources: + https://www.tradingview.com/script/6GR4ht9X-Time-Segmented-Volume/ + https://help.tc2000.com/m/69404/l/747088-time-segmented-volume + https://usethinkscript.com/threads/time-segmented-volume-for-thinkorswim.519/ + + Args: + close (pd.Series): Series of 'close's + volume (pd.Series): Series of 'volume's + length (int): It's period. Default: 18 + signal (int): It's avg period. Default: 10 + mamode (str): See ```help(ta.ma)```. Default: 'sma' + drift (int): The difference period. Default: 1 + offset (int): How many periods to offset the result. Default: 0 + + Kwargs: + fillna (value, optional): pd.DataFrame.fillna(value) + fill_method (value, optional): Type of fill method + + Returns: + pd.DataFrame: tsv, signal, ratio + """ + # Validate Arguments + length = int(length) if length and length > 0 else 18 + signal = int(signal) if signal and signal > 0 else 10 + mamode = mamode if isinstance(mamode, str) else "sma" + drift = get_drift(drift) + offset = get_offset(offset) + + # Calculate Result + signed_volume = volume * signed_series(close, 1) # > 0 + signed_volume[signed_volume < 0 ] = -signed_volume # < 0 + signed_volume.apply(zero) # ~ 0 + cvd = signed_volume * close.diff(drift) + + tsv = cvd.rolling(length).sum() + signal_ = ma(mamode, tsv, length=signal) + ratio = tsv / signal_ + + # Offset + if offset != 0: + tsv = tsv.shift(offset) + signal_ = signal.shift(offset) + ratio = ratio.shift(offset) + + # Handle fills + if "fillna" in kwargs: + tsv.fillna(kwargs["fillna"], inplace=True) + signal_.fillna(kwargs["fillna"], inplace=True) + ratio.fillna(kwargs["fillna"], inplace=True) + if "fill_method" in kwargs: + tsv.fillna(method=kwargs["fill_method"], inplace=True) + signal_.fillna(method=kwargs["fill_method"], inplace=True) + ratio.fillna(method=kwargs["fill_method"], inplace=True) + + # Name and Categorize + _props = f"_{length}_{signal}" + tsv.name = f"TSV{_props}" + signal_.name = f"TSVs{_props}" + ratio.name = f"TSVr{_props}" + tsv.category = signal_.category = ratio.category = "volume" + + # Prepare DataFrame to return + data = {tsv.name: tsv, signal_.name: signal_, ratio.name: ratio} + df = DataFrame(data) + df.name = f"TSV{_props}" + df.category = tsv.category + + return df \ No newline at end of file diff --git a/setup.py b/setup.py index 5394b2f..0789f02 100644 --- a/setup.py +++ b/setup.py @@ -19,7 +19,7 @@ setup( "pandas_ta.volatility", "pandas_ta.volume" ], - version=".".join(("0", "3", "38b")), + version=".".join(("0", "3", "41b")), description=long_description, long_description=long_description, author="Kevin Johnson", diff --git a/tests/test_ext_indicator_volume.py b/tests/test_ext_indicator_volume.py index 1ca417e..70da6f2 100644 --- a/tests/test_ext_indicator_volume.py +++ b/tests/test_ext_indicator_volume.py @@ -1,3 +1,4 @@ +from unittest.case import skip from .config import sample_data from .context import pandas_ta @@ -100,7 +101,13 @@ class TestVolumeExtension(TestCase): self.assertIsInstance(self.data, DataFrame) self.assertEqual(self.data.columns[-1], "PVT") + @skip("\nVP does not return a Time Series") def test_vp_ext(self): result = self.data.ta.vp() self.assertIsInstance(result, DataFrame) self.assertEqual(result.name, "VP_10") + + def test_wb_tsv_ext(self): + self.data.ta.wb_tsv(append=True) + self.assertIsInstance(self.data, DataFrame) + self.assertEqual(list(self.data.columns[-3:]), ["TSV_18_10", "TSVs_18_10", "TSVr_18_10"]) diff --git a/tests/test_indicator_momentum.py b/tests/test_indicator_momentum.py index aa467e5..b7f691d 100644 --- a/tests/test_indicator_momentum.py +++ b/tests/test_indicator_momentum.py @@ -446,7 +446,7 @@ class TestMomentum(TestCase): def test_stoch(self): # TV Correlation - result = pandas_ta.stoch(self.high, self.low, self.close) + result = pandas_ta.stoch(self.high, self.low, self.close, talib=False) self.assertIsInstance(result, DataFrame) self.assertEqual(result.name, "STOCH_14_3_3") @@ -467,6 +467,10 @@ class TestMomentum(TestCase): except Exception as ex: error_analysis(result.iloc[:, 1], CORRELATION, ex, newline=False) + result = pandas_ta.stoch(self.high, self.low, self.close) + self.assertIsInstance(result, DataFrame) + self.assertEqual(result.name, "STOCH_14_3_3") + def test_stochf(self): # TV Correlation result = pandas_ta.stochf(self.high, self.low, self.close, talib=False) diff --git a/tests/test_indicator_volume.py b/tests/test_indicator_volume.py index dae1f54..518f7c1 100644 --- a/tests/test_indicator_volume.py +++ b/tests/test_indicator_volume.py @@ -174,3 +174,8 @@ class TestVolume(TestCase): result = pandas_ta.vp(self.close, self.volume_) self.assertIsInstance(result, DataFrame) self.assertEqual(result.name, "VP_10") + + def test_wb_tsv(self): + result = pandas_ta.wb_tsv(self.close, self.volume_) + self.assertIsInstance(result, DataFrame) + self.assertEqual(result.name, "TSV_18_10")