这里有一些链接，我用来拿出代码
https://www.tradingview.com/pine-script-reference/v5/#fun_ta.rma
所以我一直在搜索，找出如何创建rsi像tradingview和后，看了一吨的stackoverview问题，我能够找到一个真正帮助我找出问题是什么.这是答案在这里Pandas.Series.ewm for calculating a RSI Indicator, with Wilders Moving Average
事实上，第一个平均值是使用过去rsi长度的平均值计算的.像这样

avg_gain[period] = gain[1 : period + 1].mean()

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然后，在您这样做之后，您应用rma tradingview计算https://www.tradingview.com/pine-script-reference/v5/#fun_ta.rma

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])

型
所以我想出的公式是这样的，我检查了一下，所有的东西都是98%的准确率......通常它是由.1或.05 ......但仍然超级超级接近

import numpy as np
def qf_tv_calc_rsi_wilder(prices: np.array, period: int):
    prices_shift = np.roll(prices, 1)
    prices_shift[0] = np.nan
    pchg = (prices - prices_shift) / prices_shift
    alpha = 1 / period
    gain = np.where(pchg > 0, pchg, 0)
    avg_gain = np.full_like(gain, np.nan)
    loss = np.where(pchg < 0, abs(pchg), 0)
    avg_loss = np.full_like(loss, np.nan)
    avg_gain[period] = gain[1 : period + 1].mean()
    avg_loss[period] = loss[1 : period + 1].mean()
    for i in range(period + 1, gain.size):
        avg_gain[i] = alpha * gain[i] + (1 - alpha) * avg_gain[i - 1]
        avg_loss[i] = alpha * loss[i] + (1 - alpha) * avg_loss[i - 1]
    rs = avg_gain / avg_loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

型
this is my rsi的
this is tradingviews rsi ... toward the start it gets a little crazy but that is because i only downloaded 200 candles while tradingview is using like 3000的
所以我的问题是在for循环中，我试图向量化它，但它不起作用

avg_gain[period + 1 :] = alpha * gain[period + 1 :] + (1 - alpha) * avg_gain[period]
avg_loss[period + 1 :] = alpha * loss[period + 1 :] + (1 - alpha) * avg_loss[period]

型
它会吐出一些疯狂的数字.有谁知道我怎么才能摆脱for循环？