RSI calculation to match Tradingview

rsi.py

import pandas as pd


def rsi(ohlc: pd.DataFrame, period: int = 14) -> pd.Series:
    """See source https://github.com/peerchemist/finta
    and fix https://www.tradingview.com/wiki/Talk:Relative_Strength_Index_(RSI)

    Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements.
    RSI oscillates between zero and 100. Traditionally, and according to Wilder, RSI is considered overbought when above 70 and oversold when below 30.
    Signals can also be generated by looking for divergences, failure swings and centerline crossovers.
    RSI can also be used to identify the general trend."""

    delta = ohlc["close"].diff()

    up, down = delta.copy(), delta.copy()
    up[up < 0] = 0
    down[down > 0] = 0

    _gain = up.ewm(com=(period - 1), min_periods=period).mean()
    _loss = down.abs().ewm(com=(period - 1), min_periods=period).mean()

    RS = _gain / _loss
    return pd.Series(100 - (100 / (1 + RS)), name="RSI")

here is an implementation in Python that is equivalent to the current one in TradingView. Happy trading.

https://github.com/lukaszbinden/rsi_tradingview

here is an implementation in Python that is equivalent to the current one in TradingView. Happy trading.

https://github.com/lukaszbinden/rsi_tradingview

Hello. I just want to know value of rsi. Can i do this by using this?

here is an implementation in Python that is equivalent to the current one in TradingView. Happy trading.

https://github.com/lukaszbinden/rsi_tradingview

I implemented your implemenation with data from yahoo finance. The vast majority of them work, But I found that they differ on AAPL, MSFT and WMT. Do you know why this is?

@lukaszbinden many thanks chief

pine_sma(x, y) =>
    sum = 0.0
    for i = 0 to y - 1
        sum := sum + x[i] / y
    sum
pine_rma(src, length) =>
  alpha = 1/length
  sum = 0.0
  sum := na(sum[1]) ? 
    pine_sma(src, length) : 
    alpha * src + (1 - alpha) * nz(sum[1])
  sum
pine_rsi(x, y) =>
    u = math.max(x - x[1], 0) // upward ta.change
    d = math.max(x[1] - x, 0) // downward ta.change
    rs = pine_rma(u, y) / pine_rma(d, y)
    res = 100 - 100 / (1 + rs)
    res