Algorithmic Trading & Indicators Beginner

Automated RSI Calculation & Overbought/Oversold Reversal Signals in Python

Calculate Wilder's Relative Strength Index (RSI) in Python Pandas and detect automated bullish and bearish reversal crossovers at 30 and 70 thresholds.

#pandas#numpy

Problem Statement

Using simple rolling averages for RSI produces inaccurate readings during sharp momentum spikes. This script implements Wilder's exact reciprocal smoothing and flags fresh exits from oversold (<30) and overbought (>70) zones.

Formula / Core Logic

RS = Wilder_EMA(Gains, 14) / Wilder_EMA(Losses, 14) RSI = 100 - [100 / (1 + RS)]

Installation

pip install pandas numpy

rsi-divergence-detection-script-python.py

import pandas as pd
import numpy as np

def calculate_rsi_signals(df, period=14):
    df = df.copy()
    delta = df['Close'].diff()
    gain = delta.clip(lower=0)
    loss = -1 * delta.clip(upper=0)
    avg_gain = gain.ewm(alpha=1/period, min_periods=period, adjust=False).mean()
    avg_loss = loss.ewm(alpha=1/period, min_periods=period, adjust=False).mean()
    rs = avg_gain / avg_loss
    df['RSI'] = 100 - (100 / (1 + rs))
    # Signal 1 when RSI crosses back above 30, -1 when RSI crosses below 70
    df['Signal'] = np.where((df['RSI'] > 30) & (df['RSI'].shift(1) <= 30), 1, np.where((df['RSI'] < 70) & (df['RSI'].shift(1) >= 70), -1, 0))
    return df

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Frequently Asked Questions

Q: Why wait for RSI to cross back above 30 instead of buying below 30?

In strong downtrends, RSI can stay below 30 for dozens of candles. Waiting for the cross back above 30 confirms momentum inflection.