A Relative Strength Index (RSI) chart displaying the momentum oscillator on a 0-100 scale with horizontal threshold lines at 70 (overbought) and 30 (oversold). The RSI measures the speed and magnitude of recent price changes to evaluate overbought or oversold conditions. This is a fundamental momentum indicator in technical analysis, helping traders identify potential reversal points when the market reaches extreme conditions.
""" anyplot.ai
indicator-rsi: RSI Technical Indicator Chart
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 80/100 | Updated: 2026-05-16
"""
import numpy as np
import pandas as pd
from bokeh.io import export_png, save
from bokeh.models import BoxAnnotation, ColumnDataSource, Legend, LegendItem, Span
from bokeh.plotting import figure
from bokeh.resources import CDN
# Data - Generate realistic stock price data and calculate RSI
np.random.seed(42)
n_days = 120
# Generate price data with realistic movements
dates = pd.date_range(start="2024-01-01", periods=n_days, freq="B")
returns = np.random.normal(0.0005, 0.02, n_days)
returns[30:40] = np.random.normal(0.02, 0.01, 10) # Strong uptrend (will push RSI high)
returns[60:75] = np.random.normal(-0.015, 0.01, 15) # Downtrend (will push RSI low)
returns[95:105] = np.random.normal(0.018, 0.01, 10) # Another uptrend
price = 100 * np.exp(np.cumsum(returns))
# Calculate RSI with 14-period lookback
period = 14
delta = np.diff(price)
gains = np.where(delta > 0, delta, 0)
losses = np.where(delta < 0, -delta, 0)
# Calculate initial average gain/loss
avg_gain = np.zeros(len(price))
avg_loss = np.zeros(len(price))
avg_gain[period] = np.mean(gains[:period])
avg_loss[period] = np.mean(losses[:period])
# Smoothed RSI calculation
for i in range(period + 1, len(price)):
avg_gain[i] = (avg_gain[i - 1] * (period - 1) + gains[i - 1]) / period
avg_loss[i] = (avg_loss[i - 1] * (period - 1) + losses[i - 1]) / period
rs = np.divide(avg_gain, avg_loss, out=np.zeros_like(avg_gain), where=avg_loss != 0)
rsi = 100 - (100 / (1 + rs))
rsi[:period] = np.nan # RSI not valid for first 'period' values
# Prepare data
df = pd.DataFrame({"date": dates, "rsi": rsi})
df = df.dropna()
source = ColumnDataSource(df)
# Create figure
p = figure(
width=4800,
height=2700,
title="indicator-rsi · bokeh · pyplots.ai",
x_axis_label="Date",
y_axis_label="RSI (14-Period)",
x_axis_type="datetime",
y_range=(0, 100),
)
# Styling
p.title.text_font_size = "32pt"
p.xaxis.axis_label_text_font_size = "24pt"
p.yaxis.axis_label_text_font_size = "24pt"
p.xaxis.major_label_text_font_size = "18pt"
p.yaxis.major_label_text_font_size = "18pt"
# Add shaded zones for overbought and oversold
overbought_zone = BoxAnnotation(bottom=70, top=100, fill_alpha=0.15, fill_color="#FF6B6B", level="underlay")
oversold_zone = BoxAnnotation(bottom=0, top=30, fill_alpha=0.15, fill_color="#4ECDC4", level="underlay")
p.add_layout(overbought_zone)
p.add_layout(oversold_zone)
# Add threshold lines
overbought_line = Span(location=70, dimension="width", line_color="#E74C3C", line_width=3, line_dash="dashed")
oversold_line = Span(location=30, dimension="width", line_color="#27AE60", line_width=3, line_dash="dashed")
centerline = Span(location=50, dimension="width", line_color="#95A5A6", line_width=2, line_dash="dotted")
p.add_layout(overbought_line)
p.add_layout(oversold_line)
p.add_layout(centerline)
# Plot RSI line
rsi_line = p.line(x="date", y="rsi", source=source, line_width=4, line_color="#306998", alpha=0.9)
# Add scatter points at extremes for emphasis
extreme_high = df[df["rsi"] >= 70].copy()
extreme_low = df[df["rsi"] <= 30].copy()
if not extreme_high.empty:
source_high = ColumnDataSource(extreme_high)
p.scatter(x="date", y="rsi", source=source_high, size=18, color="#E74C3C", alpha=0.8)
if not extreme_low.empty:
source_low = ColumnDataSource(extreme_low)
p.scatter(x="date", y="rsi", source=source_low, size=18, color="#27AE60", alpha=0.8)
# Create legend manually
legend_items = [LegendItem(label="RSI (14)", renderers=[rsi_line])]
legend = Legend(items=legend_items, location="top_right", label_text_font_size="18pt")
p.add_layout(legend)
# Grid styling
p.grid.grid_line_alpha = 0.3
p.grid.grid_line_dash = [6, 4]
# Background
p.background_fill_color = "#FAFAFA"
# Save
export_png(p, filename="plot.png")
save(p, filename="plot.html", resources=CDN, title="indicator-rsi · bokeh · pyplots.ai")
Part of RSI Technical Indicator Chart on anyplot.ai.