diff --git a/technical/indicators/overlap_studies.py b/technical/indicators/overlap_studies.py index ec33b3c0..5e53ed4f 100644 --- a/technical/indicators/overlap_studies.py +++ b/technical/indicators/overlap_studies.py @@ -3,7 +3,7 @@ """ import talib.abstract as ta -from numpy import ndarray +from numpy import nan, ndarray from pandas import DataFrame, Series ######################################## @@ -82,6 +82,43 @@ def ema(dataframe: DataFrame, period: int, field="close") -> Series: return ta.EMA(dataframe, timeperiod=period, price=field) +# RMA Running Moving Average (Wilder's Smoothing) +def rma(dataframe: DataFrame, period: int, field="close") -> Series: + """ + Running Moving Average (RMA) — also known as Wilder's Smoothing Average. + + TradingView Pine Script reference: + https://www.tradingview.com/pine-script-reference/v5/#fun_ta.rma + + Equivalent to the following pine script: + + pine_rma(src, length) => + alpha = 1/length + sum = 0.0 + sum := na(sum[1]) ? ta.sma(src, length) : alpha * src + (1 - alpha) * nz(sum[1]) + + The first value is the SMA of the first `period` values; + subsequent values follow: RMA = (prev_RMA * (period - 1) + current) / period. + + :param dataframe: DataFrame with price data + :param period: RMA period (must be >= 1) + :param field: Column name to use (default: "close") + :return: Series containing RMA values + """ + series = dataframe[field].astype("float64") + + if len(series) < period: + return Series(nan, index=series.index, name=field) + + # ewm(adjust=False) would seed the recursion with the first value - seed it with the + # SMA of the first `period` values instead, and blank the warmup ahead of it. + seeded = series.copy() + seeded.iloc[: period - 1] = nan + seeded.iloc[period - 1] = series.iloc[:period].mean() + + return seeded.ewm(alpha=1.0 / period, adjust=False).mean() + + # HT_TRENDLINE Hilbert Transform - Instantaneous Trendline # KAMA Kaufman Adaptive Moving Average # MA Moving average diff --git a/tests/__snapshots__/test_indicators_generic.ambr b/tests/__snapshots__/test_indicators_generic.ambr index 7566a9ad..42af7bf7 100644 --- a/tests/__snapshots__/test_indicators_generic.ambr +++ b/tests/__snapshots__/test_indicators_generic.ambr @@ -23137,6 +23137,1012 @@ ''' # --- +# name: test_indicators_generic_interface[rma-args36-series-None] + ''' + date,open,high,low,close,volume,rma + 2017-11-14 06:06:00+00:00,0.0026029300,0.0026100000,0.0026001100,0.0026100000,511.3714885800, + 2017-11-14 06:07:00+00:00,0.0026271400,0.0026271500,0.0026005200,0.0026113000,639.1654878500, + 2017-11-14 06:08:00+00:00,0.0026205600,0.0026205700,0.0026200000,0.0026205700,707.5670105200, + 2017-11-14 06:09:00+00:00,0.0026205700,0.0026271600,0.0026205700,0.0026271600,1056.4841745600, + 2017-11-14 06:10:00+00:00,0.0026205800,0.0026271600,0.0026205800,0.0026271600,1910.6746252500, + 2017-11-14 06:11:00+00:00,0.0026205800,0.0026271600,0.0026006000,0.0026009400,225.4379632900, + 2017-11-14 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21:14:00+00:00,0.0025758700,0.0025758700,0.0025758700,0.0025758700,1.3932387200,0.0025681035 + 2017-11-14 21:15:00+00:00,0.0025581000,0.0025758200,0.0025581000,0.0025758200,65.8230793300,0.0025686547 + 2017-11-14 21:16:00+00:00,0.0025757800,0.0025757800,0.0025757800,0.0025757800,1.1041492000,0.0025691637 + 2017-11-14 21:17:00+00:00,0.0025758600,0.0025850000,0.0025758600,0.0025839000,345.6402000000,0.0025702163 + 2017-11-14 21:18:00+00:00,0.0025758600,0.0025838800,0.0025758600,0.0025838800,100.0000000000,0.0025711922 + 2017-11-14 21:19:00+00:00,0.0025837800,0.0025837800,0.0025758600,0.0025758600,621.7982544200,0.0025715257 + 2017-11-14 21:20:00+00:00,0.0025586200,0.0025586200,0.0025580900,0.0025586200,13.9598612700,0.0025706038 + 2017-11-14 21:21:00+00:00,0.0025754500,0.0025755100,0.0025586000,0.0025755100,61.6413561200,0.0025709543 + 2017-11-14 21:22:00+00:00,0.0025586200,0.0025586200,0.0025586200,0.0025586200,1.1600000000,0.0025700732 + 2017-11-14 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21:43:00+00:00,0.0025839000,0.0025839100,0.0025839000,0.0025839100,13.9334259900,0.0025777488 + 2017-11-14 21:44:00+00:00,0.0025839000,0.0025839000,0.0025839000,0.0025839000,105.0890612300,0.0025781882 + 2017-11-14 21:45:00+00:00,0.0025839000,0.0025839000,0.0025839000,0.0025839000,319.1290561400,0.0025785961 + 2017-11-14 21:46:00+00:00,0.0025839000,0.0025916000,0.0025839000,0.0025839500,154.2388763700,0.0025789786 + 2017-11-14 21:47:00+00:00,0.0025839500,0.0025916100,0.0025839500,0.0025916100,37.8359243700,0.0025798808 + 2017-11-14 21:48:00+00:00,0.0025928900,0.0025928900,0.0025916100,0.0025916100,160.1640756200,0.0025807186 + 2017-11-14 21:49:00+00:00,0.0025928900,0.0025928900,0.0025928900,0.0025928900,4.5922512900,0.0025815880 + 2017-11-14 21:50:00+00:00,0.0025928900,0.0025928900,0.0025928900,0.0025928900,363.4800589000,0.0025823953 + 2017-11-14 21:51:00+00:00,0.0025916100,0.0025916100,0.0025754900,0.0025916100,14.6527135900,0.0025830535 + 2017-11-14 21:52:00+00:00,0.0025755100,0.0025916000,0.0025755100,0.0025906000,90.8074259900,0.0025835925 + 2017-11-14 21:53:00+00:00,0.0025906000,0.0025916000,0.0025906000,0.0025906000,4.0972289800,0.0025840930 + 2017-11-14 21:54:00+00:00,0.0025906000,0.0025906000,0.0025755100,0.0025906000,732.3770814600,0.0025845578 + 2017-11-14 21:55:00+00:00,0.0025906000,0.0025929000,0.0025906000,0.0025929000,152.8710911000,0.0025851537 + 2017-11-14 21:56:00+00:00,0.0025929000,0.0025930000,0.0025929000,0.0025930000,80.8589929400,0.0025857141 + 2017-11-14 21:57:00+00:00,0.0025931700,0.0026060000,0.0025931700,0.0026000000,188.4100435500,0.0025867346 + 2017-11-14 21:58:00+00:00,0.0026000000,0.0026000000,0.0025930300,0.0025930300,51.9538992800,0.0025871842 + 2017-11-14 22:00:00+00:00,0.0025930400,0.0026099700,0.0025930400,0.0026065400,595.3250010500,0.0025885668 + 2017-11-14 22:01:00+00:00,0.0026065400,0.0026099700,0.0026000000,0.0026000000,604.1831553800,0.0025893835 + 2017-11-14 22:02:00+00:00,0.0026081000,0.0026099700,0.0026000000,0.0026099600,131.1813932200,0.0025908532 + 2017-11-14 22:03:00+00:00,0.0026099700,0.0026111600,0.0026099700,0.0026106200,149.3799592900,0.0025922651 + 2017-11-14 22:04:00+00:00,0.0026111500,0.0026111700,0.0026106200,0.0026111500,166.0781005400,0.0025936140 + 2017-11-14 22:05:00+00:00,0.0026111700,0.0026124800,0.0026111500,0.0026124800,176.9348918900,0.0025949616 + 2017-11-14 22:06:00+00:00,0.0026132400,0.0026132800,0.0026132400,0.0026132800,13.8207480800,0.0025962701 + 2017-11-14 22:07:00+00:00,0.0026136700,0.0026151600,0.0026136700,0.0026136700,14.5711335600,0.0025975129 + 2017-11-14 22:08:00+00:00,0.0026136700,0.0026154900,0.0026136700,0.0026154900,595.9211210100,0.0025987970 + 2017-11-14 22:09:00+00:00,0.0026154900,0.0026193600,0.0026154900,0.0026154900,6.1264344500,0.0025999894 + 2017-11-14 22:10:00+00:00,0.0026193600,0.0026249000,0.0026193600,0.0026249000,1072.9558417400,0.0026017687 + 2017-11-14 22:11:00+00:00,0.0026249000,0.0026300000,0.0026249000,0.0026300000,312.7960254600,0.0026037852 + 2017-11-14 22:12:00+00:00,0.0026292000,0.0026379600,0.0026292000,0.0026350100,530.0172577700,0.0026060155 + 2017-11-14 22:13:00+00:00,0.0026379500,0.0026431100,0.0026301000,0.0026400000,644.7171792800,0.0026084430 + 2017-11-14 22:14:00+00:00,0.0026400000,0.0026725200,0.0026400000,0.0026610000,2602.3379435800,0.0026121971 + 2017-11-14 22:15:00+00:00,0.0026610000,0.0026723800,0.0026491300,0.0026491300,500.9336085500,0.0026148351 + 2017-11-14 22:16:00+00:00,0.0026491500,0.0026610000,0.0026491300,0.0026600000,279.5038927100,0.0026180612 + 2017-11-14 22:17:00+00:00,0.0026600000,0.0026610000,0.0026491700,0.0026610000,145.4705300000,0.0026211283 + 2017-11-14 22:18:00+00:00,0.0026491700,0.0026610000,0.0026491700,0.0026610000,472.2342414200,0.0026239762 + 2017-11-14 22:19:00+00:00,0.0026610000,0.0026722100,0.0026610000,0.0026722100,106.8181160500,0.0026274215 + 2017-11-14 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22:29:00+00:00,0.0026699900,0.0026725000,0.0026611000,0.0026700000,1393.9620410300,0.0026482755 + 2017-11-14 22:30:00+00:00,0.0026700000,0.0026790000,0.0026700000,0.0026700000,403.0006069000,0.0026498272 + 2017-11-14 22:31:00+00:00,0.0026730000,0.0026769400,0.0026700000,0.0026769400,479.9121817600,0.0026517638 + 2017-11-14 22:32:00+00:00,0.0026769400,0.0026769400,0.0026610100,0.0026610100,266.2023893100,0.0026524243 + 2017-11-14 22:33:00+00:00,0.0026610900,0.0026804000,0.0026610000,0.0026610900,897.2849340300,0.0026530433 + 2017-11-14 22:34:00+00:00,0.0026610900,0.0026799600,0.0026610900,0.0026799600,17.2538836900,0.0026549659 + 2017-11-14 22:35:00+00:00,0.0026611100,0.0026611500,0.0026611100,0.0026611500,18.2609408700,0.0026554076 + 2017-11-14 22:36:00+00:00,0.0026700000,0.0026700000,0.0026610900,0.0026699900,742.4665012300,0.0026564492 + 2017-11-14 22:37:00+00:00,0.0026610900,0.0026699900,0.0026610900,0.0026610900,124.8619154800,0.0026567807 + 2017-11-14 22:38:00+00:00,0.0026620000,0.0026650000,0.0026620000,0.0026620000,71.7856914700,0.0026571535 + 2017-11-14 22:39:00+00:00,0.0026620000,0.0026804000,0.0026610900,0.0026699900,195.4475005500,0.0026580704 + 2017-11-14 22:40:00+00:00,0.0026803800,0.0026809900,0.0026700000,0.0026803900,244.7614640500,0.0026596647 + 2017-11-14 22:41:00+00:00,0.0026803900,0.0026900000,0.0026803800,0.0026803800,330.6928683500,0.0026611443 + 2017-11-14 22:42:00+00:00,0.0026803800,0.0026820000,0.0026803800,0.0026820000,69.1816233600,0.0026626340 + 2017-11-14 22:43:00+00:00,0.0026900000,0.0026900000,0.0026847700,0.0026847700,178.4715637600,0.0026642152 + 2017-11-14 22:44:00+00:00,0.0026847700,0.0026900000,0.0026847700,0.0026900000,601.0077789200,0.0026660569 + 2017-11-14 22:45:00+00:00,0.0026910000,0.0026980000,0.0026910000,0.0026940000,325.6839922000,0.0026680529 + 2017-11-14 22:46:00+00:00,0.0026939000,0.0027000000,0.0026939000,0.0026999900,869.6750294400,0.0026703341 + 2017-11-14 22:47:00+00:00,0.0026999900,0.0027012300,0.0026928700,0.0027000000,792.9708715800,0.0026724531 + 2017-11-14 22:48:00+00:00,0.0027000000,0.0027000000,0.0026803700,0.0026843800,929.6372329800,0.0026733050 + 2017-11-14 22:49:00+00:00,0.0026978000,0.0026999400,0.0026843800,0.0026843800,138.1803110400,0.0026740961 + 2017-11-14 22:50:00+00:00,0.0026843800,0.0026843800,0.0026843800,0.0026843800,4.4406761300,0.0026748306 + 2017-11-14 22:51:00+00:00,0.0026803700,0.0026935700,0.0026788700,0.0026935700,124.6814707500,0.0026761692 + 2017-11-14 22:52:00+00:00,0.0026935800,0.0026935800,0.0026900000,0.0026900000,82.7751912200,0.0026771571 + 2017-11-14 22:53:00+00:00,0.0026900000,0.0026900000,0.0026899900,0.0026899900,800.4023675400,0.0026780737 + 2017-11-14 22:54:00+00:00,0.0026900000,0.0026935700,0.0026899900,0.0026935700,168.6562155500,0.0026791806 + 2017-11-14 22:55:00+00:00,0.0026990000,0.0027000000,0.0026935700,0.0027000000,37.8821572200,0.0026806677 + 2017-11-14 22:56:00+00:00,0.0027000000,0.0027026400,0.0027000000,0.0027026400,107.1748727900,0.0026822371 + 2017-11-14 22:57:00+00:00,0.0027036600,0.0027100100,0.0027000000,0.0027020000,581.6329675300,0.0026836488 + 2017-11-14 22:58:00+00:00,0.0027000000,0.0027110000,0.0027000000,0.0027110000,224.8463798000,0.0026856024 + + ''' +# --- # name: test_indicators_generic_interface[sma-args22-series-None] ''' date,open,high,low,close,volume,sma diff --git a/tests/test_indicators.py b/tests/test_indicators.py index 62d81294..ab807d1e 100644 --- a/tests/test_indicators.py +++ b/tests/test_indicators.py @@ -82,3 +82,31 @@ def test_return_on_investment(): assert (dataframe["roi"] >= 0).all() assert (dataframe.loc[dataframe["buy"] == 1, "roi"] == 0).all() assert numpy.allclose(numpy.array(dataframe["roi"]), roi) + + +def test_rma(): + from pandas import DataFrame + + from technical.indicators import rma + + close = numpy.array([6, -1, 5, 4, 12, 5, 11, 10, 3, 13], dtype=float) + dataframe = DataFrame({"high": close}) + + # Seeded with the SMA of the first 5 values, then (prev * 4 + current) / 5. + expected = [numpy.nan] * 4 + [5.2, 5.16, 6.328, 7.0624, 6.249920, 7.599936] + + result = rma(dataframe, 5, field="high") + + assert numpy.allclose(result, expected, equal_nan=True) + # Warmup is period - 1 candles, matching talib's lookback for a period-length seed + assert result.isna().sum() == 4 + + +def test_rma_short_dataframe(): + from pandas import DataFrame + + from technical.indicators import rma + + dataframe = DataFrame({"close": numpy.array([1.0, 2.0, 3.0])}) + + assert rma(dataframe, 5).isna().all() diff --git a/tests/test_indicators_generic.py b/tests/test_indicators_generic.py index 219b8aa2..f1b2d518 100644 --- a/tests/test_indicators_generic.py +++ b/tests/test_indicators_generic.py @@ -45,6 +45,7 @@ (ti.vwmacd, [], "df", ["vwmacd", "signal", "hist"]), (ti.williams_percent, [], "series", None), (ti.supertrend, [], "tuple", None), + (ti.rma, [14], "series", None), ], ) def test_indicators_generic_interface(