问题描述
我一直试图从交易视图 RMA 方法中获得相同的结果，但我不知道如何实现
在他们的页面中，RMA 计算如下：
plot(rma(close,15))
//the same on pine
pine_rma(src,length) =>
alpha = 1/length
sum = 0.0
sum := na(sum[1]) ? sma(src,length) : alpha * src + (1 - alpha) * nz(sum[1])
plot(pine_rma(close,15))
为了测试我使用的输入及其结果，这是输入列和应用交易视图 rma(input,14) 后的相同输入：
data = [[588.0,519.9035093599585],[361.98999999999984,508.62397297710436],[412.52000000000055,501.7594034787397],[197.60000000000042,480.0337318016869],[208.71999999999932,460.6541795301378],[380.1100000000006,454.90102384941366],[537.6599999999999,460.8123792887413],[323.5600000000013,451.0086379109742],[431.78000000000077,449.6351637744761],[299.6299999999992,438.9205092191563],[225.1900000000005,423.65404427493087],[292.42000000000013,414.28018396957873],[357.64999999999964,410.23517082889435],[692.5100000000003,430.3976586268306],[219.70999999999916,415.34854015348543],[400.32999999999987,414.2757872853794],[604.3099999999995,427.849659622138],[204.29000000000087,411.8811125062711],[176.26000000000022,395.0510330415374],[204.1800000000003,381.41738782428473],[324.0,377.3161458368358],[231.67000000000007,366.91284970563316],[184.21000000000092,353.8626461552309],[483.0,363.08674285842864],[290.6399999999994,357.911975511398],[107.10000000000036,339.996834403441],[179.0,328.49706051748086],[182.36000000000058,318.05869905194663],[275.0,314.98307769109323],[135.70000000000073,302.17714357030087],[419.59000000000015,310.56377617242225],[275.6399999999994,308.06922073153487],[440.48999999999984,317.5278478221396],[224.0,310.8472872634153],[548.0100000000001,327.78748103031415],[257.0,322.73123238529183],[267.97999999999956,318.82043007205664],[366.51000000000016,322.2268279240526],[341.14999999999964,323.57848307233456],[147.4200000000001,310.9957342814536],[158.78000000000063,300.12318183277836],[416.03000000000077,308.4022402732943],[360.78999999999917,312.14422311091613],[1330.7299999999996,384.90035003156487],[506.92000000000013,393.61603931502464],[307.6100000000006,387.4727507925229],[296.7299999999996,380.991125735914],[462.0,386.7774738976345],[473.8099999999995,392.9940829049463],[769.4200000000002,419.88164841173585],[971.4799999999997,459.2815306680404],[722.1399999999994,478.0571356203232],[554.9799999999996,483.5516259331572],[688.5,498.19079550936027],[292.0,483.462881544406],[634.9500000000007,494.2833900055199]]
# Create the pandas DataFrame
dfRMA = pd.DataFrame(data,columns = ['input','wantedrma'])
dfRMA['try1'] = dfRMA['input'].ewm( alpha=1/14,adjust=False).mean()
dfRMA['try2'] = numpy_ewma_vectorized(dfRMA['input'],14)
dfRMA
ewm 没有给我相同的结果，所以我搜索并找到了这个，但我只是复制了 ewma
def numpy_ewma_vectorized(data,window):
alpha = 1/window
alpha_rev = 1-alpha
scale = 1/alpha_rev
n = data.shape[0]
r = np.arange(n)
scale_arr = scale**r
offset = data[0]*alpha_rev**(r+1)
pw0 = alpha*alpha_rev**(n-1)
mult = data*pw0*scale_arr
cumsums = mult.cumsum()
out = offset + cumsums*scale_arr[::-1]
return out
我得到了这些结果
你知道如何在 Pandas 中翻译 pinescript rma 方法吗？
我意识到使用 pandas ewm 似乎收敛，最后一行越来越接近该值，这是正确的吗？
...
解决方法
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