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ENH #267 stc added with refactoring DOC readme updates TST stc added
This commit is contained in:
Kevin Johnson
@@ -41,7 +41,7 @@ _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that lever
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* [Indicators by Category](#indicators-by-category)
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* [Candles](#candles-63)
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* [Cycles](#cycles-1)
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* [Momentum](#momentum-37)
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* [Momentum](#momentum-38)
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* [Overlap](#overlap-31)
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* [Performance](#performance-4)
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* [Statistics](#statistics-9)
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@@ -97,7 +97,7 @@ $ pip install pandas_ta
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Latest Version
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--------------
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Best choice! Version: *0.2.68b*
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Best choice! Version: *0.2.69b*
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```sh
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$ pip install -U git+https://github.com/twopirllc/pandas-ta
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```
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@@ -567,10 +567,6 @@ help(ta.yf)
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# **Indicators** (_by Category_)
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### **Candles** (63)
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_Candle Patterns_: ```ta.cdl_pattern``` or ```ta.cdl```
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Patterns that are **not bold**, require TA-Lib to be installed: ```pip install TA-Lib```
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* 2crows
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@@ -657,12 +653,11 @@ df.ta.cdl(["doji", "inside"], append=True)
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### **Cycles** (1)
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* _Even Better Sinewave_: **ebsw**
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<br/>
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### **Momentum** (37)
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### **Momentum** (38)
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* _Awesome Oscillator_: **ao**
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* _Absolute Price Oscillator_: **apo**
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* _Bias_: **bias**
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@@ -708,6 +703,7 @@ df.ta.cdl(["doji", "inside"], append=True)
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| _Moving Average Convergence Divergence_ (MACD) |
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|:--------:|
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|  |
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<br/>
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### **Overlap** (31)
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@@ -752,8 +748,8 @@ df.ta.cdl(["doji", "inside"], append=True)
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| _Simple Moving Averages_ (SMA) and _Bollinger Bands_ (BBANDS) |
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|:--------:|
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|  |
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<br/>
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<br/>
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### **Performance** (4)
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@@ -811,6 +807,8 @@ Use parameter: cumulative=**True** for cumulative results.
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|:--------:|
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|  |
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<br/>
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### **Utility** (5)
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* _Above_: **above**
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@@ -819,6 +817,8 @@ Use parameter: cumulative=**True** for cumulative results.
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* _Below Value_: **below_value**
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* _Cross_: **cross**
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<br/>
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### **Volatility** (13)
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* _Aberration_: **aberration**
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@@ -839,6 +839,8 @@ Use parameter: cumulative=**True** for cumulative results.
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|:--------:|
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|  |
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<br/>
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### **Volume** (14)
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* _Accumulation/Distribution Index_: **ad**
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@@ -899,12 +901,17 @@ result = ta.cagr(df.close)
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## **Breaking Indicators**
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* _Trend Return_ (**trend_return**) when given a trend Series like ```close > sma(close, 50)``` it now returns by default log and cumulative log returns of the trend as well as the Trends, Trades, Trade Entries and Trade Exits of that trend. Now compatible with [**vectorbt**](https://github.com/polakowo/vectorbt) by setting ```asbool=True``` to get boolean Trade Entries and Exits. See: ```help(ta.trend_return)```
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<br/>
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## **New Indicators**
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* _Arnaud Legoux Moving Average_ (**alma**) uses the curve of the Normal (Gauss) distribution to allow regulating the smoothness and high sensitivity of the indicator. See: ```help(ta.alma)```
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trading account, or fund. See: ```help(ta.drawdown)```
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* _Candle Patterns_ (**cdl_pattern**) If TA Lib is installed, then all those Candle Patterns are available. See the list and examples above on how to call the patterns. See: ```help(ta.cdl_pattern)```
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* _Even Better Sinewave_ (**ebsw**) measures market cycles and uses a low pass filter to remove noise. See: ```help(ta.ebsw)```
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* _Schaff Trend Cycle_ (**stc**) is an evolution of the popular MACD incorportating two
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cascaded stochastic calculations with additional smoothing. See: ```help(ta.stc)```
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* _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)```
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<br/>
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## **Updated Indicators**
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+2
-2
@@ -1039,9 +1039,9 @@ class AnalysisIndicators(BasePandasObject):
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result = squeeze(high=high, low=low, close=close, bb_length=bb_length, bb_std=bb_std, kc_length=kc_length, kc_scalar=kc_scalar, mom_length=mom_length, mom_smooth=mom_smooth, use_tr=use_tr, offset=offset, **kwargs)
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return self._post_process(result, **kwargs)
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def stc(self, ma1=None, ma2=None, osc=None, tclen=None, fast=None, slow=None, factor=None, offset=None, **kwargs):
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def stc(self, ma1=None, ma2=None, osc=None, tclength=None, fast=None, slow=None, factor=None, offset=None, **kwargs):
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close = self._get_column(kwargs.pop("close", "close"))
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result = stc(close=close, ma1=ma1, ma2=ma2, osc=osc, tclen=tclen, fast=fast, slow=slow, factor=factor, offset=offset, **kwargs)
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result = stc(close=close, ma1=ma1, ma2=ma2, osc=osc, tclength=tclength, fast=fast, slow=slow, factor=factor, offset=offset, **kwargs)
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return self._post_process(result, **kwargs)
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def stoch(self, fast_k=None, slow_k=None, slow_d=None, offset=None, **kwargs):
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+83
-75
@@ -1,96 +1,64 @@
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# -*- coding: utf-8 -*-
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from pandas import DataFrame, Series, concat
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from pandas import DataFrame, Series
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from pandas_ta.overlap import ema
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from pandas_ta.utils import get_offset, verify_series, signals
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from pandas_ta.utils import get_offset, non_zero_range, verify_series
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def schaff_tc(close, XMAC, tclen, factor):
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# ACTUAL Calculation part, which is shared between operation modes
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# 1St : Stochastic of MACD
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Value1 = XMAC.rolling(tclen).min() # min value in interval tclen
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Value2 = XMAC.rolling(tclen).max() - Value1 # max value in interval tclen
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# ... : %Fast K of MACD
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Frac1 = list(XMAC)
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Frac1[0] = 0
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PF = list(XMAC)
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PF[0] = 0
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for i in range(1, len(XMAC)):
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if Value1[i] > 0:
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Frac1[i] = ((XMAC[i] - Value1[i]) / Value2[i]) * 100
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else:
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Frac1[i] = Frac1[i - 1]
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# Smoothed Calculation for % Fast D of MACD
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PF[i] = round(PF[i - 1] + (factor * (Frac1[i] - PF[i - 1])), 8)
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PF = Series(PF, index=close.index)
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# 2nd : Stochastic of smoothed Percent Fast D, 'PF', above
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Value3 = PF.rolling(tclen).min() # min value in interval tclen
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Value4 = PF.rolling(tclen).max() - Value3 # max value in interval tclen
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# ... : % of Fast K of PF
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Frac2 = list(XMAC)
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Frac2[0] = 0
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PFF = list(XMAC)
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PFF[0] = 0
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for i in range(1, len(XMAC)):
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if Value4[i] > 0:
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Frac2[i] = ((PF[i] - Value3[i]) / Value4[i]) * 100
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else:
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Frac2[i] = Frac2[i - 1]
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# Smoothed Calculation for % Fast D of MACD
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PFF[i] = round(PFF[i - 1] + (factor * (Frac2[i] - PFF[i - 1])), 8)
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return [PFF, PF]
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def stc(close, tclen=None, fast=None, slow=None, factor=None, offset=None, **kwargs):
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def stc(close, tclength=None, fast=None, slow=None, factor=None, offset=None, **kwargs):
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"""Indicator: Schaff Trend Cycle (STC)"""
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# Validate arguments
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close = verify_series(close) # close
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tclen = int(tclen) if tclen and tclen > 0 else 10
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tclength = int(tclength) if tclength and tclength > 0 else 10
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fast = int(fast) if fast and fast > 0 else 12
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slow = int(slow) if slow and slow > 0 else 26
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factor = float(factor) if factor and factor > 0 else 0.5
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if slow < fast: # mandatory condition, but might be confusing
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if slow < fast: # mandatory condition, but might be confusing
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fast, slow = slow, fast
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_length = max(tclength, fast, slow)
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close = verify_series(close, _length)
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offset = get_offset(offset)
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# kwargs allows for three more series (ma1, ma2 and osc) which can be passed here
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# ma1 and ma2 input negate internal ema calculations, osc substitutes both ma's.
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if close is None: return
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# kwargs allows for three more series (ma1, ma2 and osc) which can be passed
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# here ma1 and ma2 input negate internal ema calculations, osc substitutes
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# both ma's.
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ma1 = kwargs.pop("ma1", False)
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ma2 = kwargs.pop("ma2", False)
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osc = kwargs.pop("osc", False)
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# 3 different modes of calculation..
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if isinstance(ma1, Series) and isinstance(ma2, Series) and not osc:
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ma1 = verify_series(ma1)
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ma2 = verify_series(ma2)
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ma1 = verify_series(ma1, _length)
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ma2 = verify_series(ma2, _length)
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if ma1 is None or ma2 is None: return
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# Calculate Result based on external feeded series
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XMAC = ma1 - ma2
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xmacd = ma1 - ma2
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# invoke shared calculation
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collect = schaff_tc(close, XMAC, tclen, factor)
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pff, pf = schaff_tc(close, xmacd, tclength, factor)
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elif isinstance(osc, Series):
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osc = verify_series(osc)
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# Calculate Result based on feeded oscillator (should be ranging around 0 x-axis)
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XMAC = osc
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osc = verify_series(osc, _length)
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if osc is None: return
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# Calculate Result based on feeded oscillator
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# (should be ranging around 0 x-axis)
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xmacd = osc
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# invoke shared calculation
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collect = schaff_tc(close, XMAC, tclen, factor)
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pff, pf = schaff_tc(close, xmacd, tclength, factor)
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else:
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# Calculate Result .. (traditionel/full)
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# MACD line
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fastma = ema(close, length=fast)
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slowma = ema(close, length=slow)
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XMAC = fastma - slowma
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xmacd = fastma - slowma
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# invoke shared calculation
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collect = schaff_tc(close, XMAC, tclen, factor)
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pff, pf = schaff_tc(close, xmacd, tclength, factor)
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# Resulting Series
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stc = Series(collect[0], index=close.index)
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macd = Series(XMAC, index=close.index)
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stoch = Series(collect[1], index=close.index)
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stc = Series(pff, index=close.index)
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macd = Series(xmacd, index=close.index)
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stoch = Series(pf, index=close.index)
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# Offset
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if offset != 0:
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@@ -109,7 +77,7 @@ def stc(close, tclen=None, fast=None, slow=None, factor=None, offset=None, **kwa
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stoch.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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_props = f"_{tclen}_{fast}_{slow}_{factor}"
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_props = f"_{tclength}_{fast}_{slow}_{factor}"
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stc.name = f"STC{_props}"
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macd.name = f"STCmacd{_props}"
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stoch.name = f"STCstoch{_props}"
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@@ -127,15 +95,17 @@ def stc(close, tclen=None, fast=None, slow=None, factor=None, offset=None, **kwa
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stc.__doc__ = \
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"""Schaff Trend Cycle (STC)
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The Schaff Trend Cycle is an evolution of the popular MACD incorportating two cascaded
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stochastic calculations with additional smoothing.
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The STC returns also the beginning MACD result as well as the result after the first stochastic
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including its smoothing. This implementation has been extended for Pandas TA to
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also allow for separatly feeding any other two moving Averages (as ma1 and ma2) or to skip this
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to feed an oscillator (osc), based on which the Schaff Trend Cycle should be calculated.
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The Schaff Trend Cycle is an evolution of the popular MACD incorportating two
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cascaded stochastic calculations with additional smoothing.
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The STC returns also the beginning MACD result as well as the result after the
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first stochastic including its smoothing. This implementation has been extended
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for Pandas TA to also allow for separatly feeding any other two moving Averages
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(as ma1 and ma2) or to skip this to feed an oscillator (osc), based on which the
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Schaff Trend Cycle should be calculated.
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Feed external moving averages:
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Internally calculation..
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Internally calculation..
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stc = ta.stc(close=df["close"], tclen=stc_tclen, fast=ma1_interval, slow=ma2_interval, factor=stc_factor)
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becomes..
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extMa1 = df.ta.zlma(close=df["close"], length=ma1_interval, append=True)
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@@ -146,14 +116,14 @@ The same goes for osc=, which allows the input of an externally calculated oscil
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Sources:
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Implemented by rengel8 based on work found here:
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Implemented by rengel8 based on work found here:
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https://www.prorealcode.com/prorealtime-indicators/schaff-trend-cycle2/
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Calculation:
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STCmacd = Moving Average Convergance/Divergance or Oscillator
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Calculation:
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STCmacd = Moving Average Convergance/Divergance or Oscillator
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STCstoch = Intermediate Stochastic of MACD/Osc.
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2nd Stochastic including filtering with results in the
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STC = Schaff Trend Cycle
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2nd Stochastic including filtering with results in the
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STC = Schaff Trend Cycle
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Args:
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close (pd.Series): Series of 'close's, used for indexing Series, mandatory
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@@ -173,3 +143,41 @@ Kwargs:
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Returns:
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pd.DataFrame: stc, macd, stoch
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"""
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def schaff_tc(close, xmacd, tclength, factor):
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# ACTUAL Calculation part, which is shared between operation modes
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# 1St : Stochastic of MACD
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lowest_xmacd = xmacd.rolling(tclength).min() # min value in interval tclen
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xmacd_range = non_zero_range(xmacd.rolling(tclength).max(), lowest_xmacd)
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m = len(xmacd)
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# %Fast K of MACD
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stoch1, pf = list(xmacd), list(xmacd)
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stoch1[0], pf[0] = 0, 0
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for i in range(1, m):
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if lowest_xmacd[i] > 0:
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stoch1[i] = 100 * ((xmacd[i] - lowest_xmacd[i]) / xmacd_range[i])
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else:
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stoch1[i] = stoch1[i - 1]
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# Smoothed Calculation for % Fast D of MACD
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pf[i] = round(pf[i - 1] + (factor * (stoch1[i] - pf[i - 1])), 8)
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pf = Series(pf, index=close.index)
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# 2nd : Stochastic of smoothed Percent Fast D, 'PF', above
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lowest_pf = pf.rolling(tclength).min()
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pf_range = non_zero_range(pf.rolling(tclength).max(), lowest_pf)
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# % of Fast K of PF
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stoch2, pff = list(xmacd), list(xmacd)
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stoch2[0], pff[0] = 0, 0
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for i in range(1, m):
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if pf_range[i] > 0:
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stoch2[i] = 100 * ((pf[i] - lowest_pf[i]) / pf_range[i])
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else:
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stoch2[i] = stoch2[i - 1]
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# Smoothed Calculation for % Fast D of PF
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pff[i] = round(pff[i - 1] + (factor * (stoch2[i] - pff[i - 1])), 8)
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return [pff, pf]
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+27
-12
@@ -32,7 +32,8 @@ def calmar_ratio(close: Series, method: str = "percent", years: int = 3) -> floa
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Args:
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close (pd.Series): Series of 'close's
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method (str): Max DD calculation options: 'dollar', 'percent', 'log'. Default: 'dollar'
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method (str): Max DD calculation options: 'dollar', 'percent', 'log'.
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Default: 'dollar'
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years (int): The positive number of years to use. Default: 3
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>>> result = ta.calmar_ratio(close, method="percent", years=3)
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@@ -56,7 +57,8 @@ def downside_deviation(returns: Series, benchmark_rate: float = 0.0, tf: str = "
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Args:
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close (pd.Series): Series of 'close's
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benchmark_rate (float): Benchmark Rate to use. Default: 0.0
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tf (str): Time Frame options: 'days', 'weeks', 'months', and 'years'. Default: 'years'
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tf (str): Time Frame options: 'days', 'weeks', 'months', and 'years'.
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Default: 'years'
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>>> result = ta.downside_deviation(returns, benchmark_rate=0.0, tf="years")
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"""
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@@ -106,8 +108,10 @@ def max_drawdown(close: Series, method:str = None, all:bool = False) -> float:
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Args:
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close (pd.Series): Series of 'close's
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method (str): Max DD calculation options: 'dollar', 'percent', 'log'. Default: 'dollar'
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all (bool): If True, it returns all three methods as a dict. Default: False
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method (str): Max DD calculation options: 'dollar', 'percent', 'log'.
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Default: 'dollar'
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all (bool): If True, it returns all three methods as a dict.
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Default: False
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>>> result = ta.max_drawdown(close, method="dollar", all=False)
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"""
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@@ -136,8 +140,11 @@ def optimal_leverage(
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Args:
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close (pd.Series): Series of 'close's
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benchmark_rate (float): Benchmark Rate to use. Default: 0.0
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period (int, float): Period to use to calculate Mean Annual Return and Annual Standard Deviation. Default: None or the default sharpe_ratio.period()
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log (bool): If True, calculates log_return. Otherwise it returns percent_return. Default: False
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period (int, float): Period to use to calculate Mean Annual Return and
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Annual Standard Deviation.
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Default: None or the default sharpe_ratio.period()
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log (bool): If True, calculates log_return. Otherwise it returns
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percent_return. Default: False
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>>> result = ta.optimal_leverage(close, benchmark_rate=0.0, log=False)
|
||||
"""
|
||||
@@ -181,9 +188,12 @@ def sharpe_ratio(close: Series, benchmark_rate: float = 0.0, log: bool = False,
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
benchmark_rate (float): Benchmark Rate to use. Default: 0.0
|
||||
log (bool): If True, calculates log_return. Otherwise it returns percent_return. Default: False
|
||||
log (bool): If True, calculates log_return. Otherwise it returns
|
||||
percent_return. Default: False
|
||||
use_cagr (bool): Use cagr - benchmark_rate instead. Default: False
|
||||
period (int, float): Period to use to calculate Mean Annual Return and Annual Standard Deviation. Default: RATE["TRADING_DAYS_PER_YEAR"] (currently 252)
|
||||
period (int, float): Period to use to calculate Mean Annual Return and
|
||||
Annual Standard Deviation.
|
||||
Default: RATE["TRADING_DAYS_PER_YEAR"] (currently 252)
|
||||
|
||||
>>> result = ta.sharpe_ratio(close, benchmark_rate=0.0, log=False)
|
||||
"""
|
||||
@@ -204,7 +214,8 @@ def sortino_ratio(close: Series, benchmark_rate: float = 0.0, log: bool = False)
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
benchmark_rate (float): Benchmark Rate to use. Default: 0.0
|
||||
log (bool): If True, calculates log_return. Otherwise it returns percent_return. Default: False
|
||||
log (bool): If True, calculates log_return. Otherwise it returns
|
||||
percent_return. Default: False
|
||||
|
||||
>>> result = ta.sortino_ratio(close, benchmark_rate=0.0, log=False)
|
||||
"""
|
||||
@@ -221,9 +232,13 @@ def volatility(close: Series, tf: str = "years", returns: bool = False, log: boo
|
||||
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
tf (str): Time Frame options: 'days', 'weeks', 'months', and 'years'. Default: 'years'
|
||||
returns (bool): If True, then it replace the close Series with the user defined Series; typically user generated returns or percent returns or log returns. Default: False
|
||||
log (bool): If True, calculates log_return. Otherwise it calculates percent_return. Default: False
|
||||
tf (str): Time Frame options: 'days', 'weeks', 'months', and 'years'.
|
||||
Default: 'years'
|
||||
returns (bool): If True, then it replace the close Series with the user
|
||||
defined Series; typically user generated returns or percent returns
|
||||
or log returns. Default: False
|
||||
log (bool): If True, calculates log_return. Otherwise it calculates
|
||||
percent_return. Default: False
|
||||
|
||||
>>> result = ta.volatility(close, tf="years", returns=False, log=False, **kwargs)
|
||||
"""
|
||||
|
||||
@@ -18,7 +18,7 @@ setup(
|
||||
"pandas_ta.volatility",
|
||||
"pandas_ta.volume"
|
||||
],
|
||||
version=".".join(("0", "2", "68b")),
|
||||
version=".".join(("0", "2", "69b")),
|
||||
description=long_description,
|
||||
long_description=long_description,
|
||||
author="Kevin Johnson",
|
||||
|
||||
@@ -197,6 +197,11 @@ class TestMomentumExtension(TestCase):
|
||||
["SQZ_ON", "SQZ_OFF", "SQZ_NO", "SQZhlr_20_2.0_20_1.5"]
|
||||
)
|
||||
|
||||
def test_stc_ext(self):
|
||||
self.data.ta.stc(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(list(self.data.columns[-3:]), ["STC_10_12_26_0.5", "STCmacd_10_12_26_0.5", "STCstoch_10_12_26_0.5"])
|
||||
|
||||
def test_stoch_ext(self):
|
||||
self.data.ta.stoch(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
|
||||
@@ -349,6 +349,11 @@ class TestMomentum(TestCase):
|
||||
self.assertIsInstance(result, DataFrame)
|
||||
self.assertEqual(result.name, "SQZhlr_20_2.0_20_1.5_LB")
|
||||
|
||||
def test_stc(self):
|
||||
result = pandas_ta.stc(self.close)
|
||||
self.assertIsInstance(result, DataFrame)
|
||||
self.assertEqual(result.name, "STC_10_12_26_0.5")
|
||||
|
||||
# @skip
|
||||
def test_stoch(self):
|
||||
# TV Correlation
|
||||
|
||||
Reference in New Issue
Block a user