Relative Strength Index in python pandas

Question

I am new to pandas. What is the best way to calculate the relative strength part in the RSI indicator in pandas? So far I got the following:

from pylab impor         


        

Answer 1

I too had this question and was working down the rolling_apply path that Jev took. However, when I tested my results, they didn't match up against the commercial stock charting programs I use, such as StockCharts.com or thinkorswim. So I did some digging and discovered that when Welles Wilder created the RSI, he used a smoothing technique now referred to as Wilder Smoothing. The commercial services above use Wilder Smoothing rather than a simple moving average to calculate the average gains and losses.

I'm new to Python (and Pandas), so I'm wondering if there's some brilliant way to refactor out the for loop below to make it faster. Maybe someone else can comment on that possibility.

I hope you find this useful.

More info here.

def get_rsi_timeseries(prices, n=14):
    # RSI = 100 - (100 / (1 + RS))
    # where RS = (Wilder-smoothed n-period average of gains / Wilder-smoothed n-period average of -losses)
    # Note that losses above should be positive values
    # Wilder-smoothing = ((previous smoothed avg * (n-1)) + current value to average) / n
    # For the very first "previous smoothed avg" (aka the seed value), we start with a straight average.
    # Therefore, our first RSI value will be for the n+2nd period:
    #     0: first delta is nan
    #     1:
    #     ...
    #     n: lookback period for first Wilder smoothing seed value
    #     n+1: first RSI

    # First, calculate the gain or loss from one price to the next. The first value is nan so replace with 0.
    deltas = (prices-prices.shift(1)).fillna(0)

    # Calculate the straight average seed values.
    # The first delta is always zero, so we will use a slice of the first n deltas starting at 1,
    # and filter only deltas > 0 to get gains and deltas < 0 to get losses
    avg_of_gains = deltas[1:n+1][deltas > 0].sum() / n
    avg_of_losses = -deltas[1:n+1][deltas < 0].sum() / n

    # Set up pd.Series container for RSI values
    rsi_series = pd.Series(0.0, deltas.index)

    # Now calculate RSI using the Wilder smoothing method, starting with n+1 delta.
    up = lambda x: x if x > 0 else 0
    down = lambda x: -x if x < 0 else 0
    i = n+1
    for d in deltas[n+1:]:
        avg_of_gains = ((avg_of_gains * (n-1)) + up(d)) / n
        avg_of_losses = ((avg_of_losses * (n-1)) + down(d)) / n
        if avg_of_losses != 0:
            rs = avg_of_gains / avg_of_losses
            rsi_series[i] = 100 - (100 / (1 + rs))
        else:
            rsi_series[i] = 100
        i += 1

    return rsi_series