Implementing TradingView's Stochastic RSI Indicator in Python

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The Stochastic RSI (StochRSI) is a powerful technical analysis tool that combines the momentum sensitivity of the Relative Strength Index (RSI) with the normalization and oscillation mechanics of the Stochastic Oscillator. For algorithmic traders, understanding and implementing this indicator in Python enables precise identification of short-term overbought and oversold conditions, potential trend reversals, and high-probability entry points. This guide walks through a complete, TradingView-accurate implementation using pandas, NumPy, and yfinance—ideal for backtesting, live trading systems, or custom dashboards.

What Is the Stochastic RSI Indicator?

The Stochastic RSI is a momentum-of-momentum indicator, meaning it measures the velocity of changes in the RSI itself rather than price directly. This dual-layer approach makes it significantly more sensitive than standard RSI, allowing traders to detect subtle shifts in market dynamics before they appear in price action.

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At its core:

This creates a magnified view of RSI behavior, highlighting when momentum is peaking or bottoming out—often before price confirms the move.

Key Benefits of StochRSI

How Is Stochastic RSI Calculated?

Implementing StochRSI accurately requires replicating TradingView’s specific calculation logic. The process unfolds in three clear stages.

Step 1: Compute the Standard RSI

RSI calculation follows J. Welles Wilder’s original method, which uses exponential smoothing for average gain and loss:

  1. Calculate daily price changes (delta)
  2. Separate positive gains (ups) and absolute losses (downs)
  3. Seed initial values with simple averages over the first period (typically 14)
  4. Apply exponential moving averages (EWM) to both series
  5. Derive RSI:
    RSI = 100 - (100 / (1 + (avg_gain / avg_loss)))

This ensures alignment with TradingView’s backend calculations.

Step 2: Apply Stochastic Formula to RSI

Once RSI values are computed, apply min-max normalization over a rolling window:

StochRSI = (Current RSI - Lowest RSI) / (Highest RSI - Lowest RSI)

Where:

This scales all values between 0 and 1, showing where the current RSI stands relative to its recent range.

Step 3: Smooth the Signal with K and D Lines

Raw StochRSI is highly volatile. To generate actionable signals:

These smoothed lines reduce noise and create crossover signals:

Interpreting Stochastic RSI Signals

Understanding signal thresholds improves decision-making:

When price is near key support/resistance levels and StochRSI hits extreme zones, confluence increases the probability of a reversal.

Core Python Implementation

To replicate TradingView’s output precisely, we define a clean, reusable function using pandas and NumPy.

from dataclasses import dataclass
import pandas as pd
import numpy as np

@dataclass(frozen=True)
class StochasticRSIComputation:
    stoch_rsi: pd.Series
    k_line: pd.Series
    d_line: pd.Series

def stochastic_rsi(
    series: pd.Series,
    period: int = 14,
    k_smooth: int = 3,
    d_smooth: int = 3
) -> StochasticRSIComputation:
    
    if period <= 0 or period >= len(series):
        raise ValueError("Period must be valid and less than data length")

    delta = series.diff().dropna()
    ups = pd.Series(np.zeros(len(delta)), index=delta.index)
    downs = ups.copy()

    ups[delta > 0] = delta[delta > 0]
    downs[delta < 0] = np.abs(delta[delta < 0])

    # Seed initial values using simple average
    ups.iloc[period - 1] = ups.iloc[:period].mean()
    downs.iloc[period - 1] = downs.iloc[:period].mean()

    ups = ups.drop(ups.index[:period - 1])
    downs = downs.drop(downs.index[:period - 1])

    # Exponential moving averages (Wilder's method)
    alpha = 1.0 / period
    ups_ewm = ups.ewm(alpha=alpha, adjust=False).mean()
    downs_ewm = downs.ewm(alpha=alpha, adjust=False).mean()

    rs = ups_ewm / downs_ewm
    rsi = 100 - (100 / (1 + rs))

    # Min-max scaling over rolling window
    rsi_min = rsi.rolling(period).min()
    rsi_max = rsi.rolling(period).max()
    stoch_rsi = (rsi - rsi_min) / (rsi_max - rsi_min)

    # Smoothing: K-line and D-line
    k_line = stoch_rsi.rolling(k_smooth).mean()
    d_line = k_line.rolling(d_smooth).mean()

    return StochasticRSIComputation(stoch_rsi, k_line, d_line)

Applying Stochastic RSI to Real Market Data

Use yfinance to fetch historical OHLCV data:

import yfinance as yf
from datetime import datetime, timedelta

ticker = "RSI"
end_date = datetime(2025, 3, 1)
start_date = end_date - timedelta(days=250)

df = yf.download(ticker, start=start_date, end=end_date, interval="1d")
df.columns = ["Open", "High", "Low", "Close", "Volume"]

Compute indicators:

result = stochastic_rsi(df["Close"])
df["KLine"] = result.k_line
df["DLine"] = result.d_line
df["50MA"] = df["Close"].rolling(50).mean()
df.dropna(inplace=True)

Visualizing Results with mplfinance

Create professional-grade financial charts:

import mplfinance as mpf

add_plots = [
    mpf.make_addplot(df["50MA"], color="blue", width=1.5, label="50MA"),
    mpf.make_addplot(df["KLine"], panel=1, color="blue"),
    mpf.make_addplot(df["DLine"], panel=1, color="orange"),
    mpf.make_addplot([0.2]*len(df), panel=1, color="red", linestyle="--"),
    mpf.make_addplot([0.8]*len(df), panel=1, color="green", linestyle="--"),
]

mpf.plot(
    df,
    type="candle",
    style="yahoo",
    addplot=add_plots,
    figsize=(14, 7),
    title=f"{ticker} - Stochastic RSI [Daily]"
)

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Frequently Asked Questions

Q: Why does my StochRSI differ from TradingView?
A: Differences often stem from using simple vs. exponential moving averages in RSI calculation. This implementation follows Wilder’s EWM method used by TradingView.

Q: Can Stochastic RSI be used alone for trading decisions?
A: While insightful, it should be combined with trend filters (like moving averages) and volume analysis to avoid false signals in choppy markets.

Q: What are optimal settings for day trading?
A: Shorter periods like period=9, k_smooth=2, d_smooth=2 increase sensitivity for intraday strategies.

Q: How do I handle NaN values during calculation?
A: Drop initial rows after computation—rolling windows require warm-up periods before valid outputs appear.

Q: Is StochRSI effective in trending markets?
A: In strong trends, it may remain overbought/oversold for extended periods. Use alongside trend-following tools to avoid premature reversals.

Q: Can I automate trade signals from crossovers?
A: Yes—programmatically detect K/D line crossovers and filter by overbought/oversold thresholds for systematic entries.

Final Thoughts

Implementing TradingView’s Stochastic RSI in Python empowers traders with full control over signal generation, customization, and integration into automated systems. By accurately replicating the underlying math—especially Wilder’s smoothing method—you ensure consistency across platforms.

When combined with moving averages and volume confirmation, StochRSI becomes a robust tool for identifying high-probability turning points. Always validate strategies through backtesting and practice sound risk management.

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