feat(altair): implement indicator-rsi (#6941)

## Implementation: `indicator-rsi` - python/altair

Implements the **python/altair** version of `indicator-rsi`.

**File:** `plots/indicator-rsi/implementations/python/altair.py`

**Parent Issue:** #3229

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/25967325906)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>

Author: github-actions[bot]

plots/indicator-rsi/implementations/python/altair.py

@@ -1,23 +1,37 @@
-""" pyplots.ai
+""" anyplot.ai
 indicator-rsi: RSI Technical Indicator Chart
-Library: altair 6.0.0 | Python 3.13.11
-Quality: 91/100 | Created: 2026-01-07
+Library: altair 6.1.0 | Python 3.13.13
+Quality: 88/100 | Updated: 2026-05-16
 """
 
-import altair as alt
+import os
+import sys
+from importlib.machinery import SourceFileLoader
+
 import numpy as np
 import pandas as pd
 
 
-# Data - Generate realistic RSI data from simulated price movements
+venv_path = sys.executable
+site_packages = os.path.join(os.path.dirname(venv_path), "..", "lib", "python3.13", "site-packages")
+altair_init = os.path.join(site_packages, "altair", "__init__.py")
+
+loader = SourceFileLoader("altair", altair_init)
+alt = loader.load_module()
+
+
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
+
 np.random.seed(42)
-n_periods = 120
+n_periods = 140
 dates = pd.date_range(start="2024-01-01", periods=n_periods, freq="D")
 
-# Simulate price changes to calculate RSI
-price_changes = np.random.randn(n_periods) * 2
+price_changes = np.random.randn(n_periods) * 4
 
-# Calculate RSI using standard 14-period lookback
 lookback = 14
 gains = np.zeros(n_periods)
 losses = np.zeros(n_periods)
@@ -29,7 +43,6 @@
     else:
         losses[i] = abs(change)
 
-# Smoothed averages using exponential moving average style
 avg_gain = np.zeros(n_periods)
 avg_loss = np.zeros(n_periods)
 avg_gain[lookback] = np.mean(gains[1 : lookback + 1])
@@ -39,52 +52,55 @@
     avg_gain[i] = (avg_gain[i - 1] * (lookback - 1) + gains[i]) / lookback
     avg_loss[i] = (avg_loss[i - 1] * (lookback - 1) + losses[i]) / lookback
 
-# Calculate RSI
 with np.errstate(divide="ignore", invalid="ignore"):
     rs = np.where(avg_loss != 0, avg_gain / avg_loss, 0)
     rsi = np.where(avg_loss != 0, 100 - (100 / (1 + rs)), 100)
-rsi[:lookback] = 50  # Fill initial values with neutral
+rsi[:lookback] = 50
 
 df = pd.DataFrame({"date": dates, "rsi": rsi})
 
-# Overbought zone (70-100)
 overbought_df = pd.DataFrame({"y": [70], "y2": [100]})
-
-# Oversold zone (0-30)
 oversold_df = pd.DataFrame({"y": [0], "y2": [30]})
 
-# Overbought zone shading (red/orange tint)
 overbought_zone = (
-    alt.Chart(overbought_df).mark_rect(opacity=0.15, color="#E74C3C").encode(y=alt.Y("y:Q"), y2=alt.Y2("y2:Q"))
+    alt.Chart(overbought_df)
+    .mark_rect(opacity=0.12, color="#E74C3C")
+    .encode(y=alt.Y("y:Q", scale=alt.Scale(domain=[0, 100])), y2=alt.Y2("y2:Q"))
 )
 
-# Oversold zone shading (green tint)
 oversold_zone = (
-    alt.Chart(oversold_df).mark_rect(opacity=0.15, color="#27AE60").encode(y=alt.Y("y:Q"), y2=alt.Y2("y2:Q"))
+    alt.Chart(oversold_df)
+    .mark_rect(opacity=0.12, color="#27AE60")
+    .encode(y=alt.Y("y:Q", scale=alt.Scale(domain=[0, 100])), y2=alt.Y2("y2:Q"))
 )
 
-# Horizontal threshold lines
 threshold_df = pd.DataFrame({"y": [30, 50, 70], "label": ["Oversold (30)", "Neutral (50)", "Overbought (70)"]})
 
 threshold_lines = (
     alt.Chart(threshold_df)
     .mark_rule(strokeDash=[8, 4], strokeWidth=2)
     .encode(
-        y=alt.Y("y:Q"),
+        y=alt.Y("y:Q", scale=alt.Scale(domain=[0, 100])),
         color=alt.Color(
             "label:N",
             scale=alt.Scale(
-                domain=["Oversold (30)", "Neutral (50)", "Overbought (70)"], range=["#27AE60", "#95A5A6", "#E74C3C"]
+                domain=["Oversold (30)", "Neutral (50)", "Overbought (70)"], range=["#27AE60", INK_SOFT, "#E74C3C"]
+            ),
+            legend=alt.Legend(
+                title="Thresholds",
+                orient="bottom-left",
+                titleFontSize=16,
+                labelFontSize=14,
+                fillColor=ELEVATED_BG,
+                strokeColor=INK_SOFT,
             ),
-            legend=alt.Legend(title="Threshold Lines", orient="top-right", titleFontSize=16, labelFontSize=14),
         ),
     )
 )
 
-# RSI line chart
 rsi_line = (
     alt.Chart(df)
-    .mark_line(strokeWidth=3, color="#306998")
+    .mark_line(strokeWidth=3, color="#0072B2")
     .encode(
         x=alt.X("date:T", title="Date", axis=alt.Axis(format="%b %Y")),
         y=alt.Y("rsi:Q", title="RSI Value", scale=alt.Scale(domain=[0, 100])),
@@ -95,24 +111,34 @@
     )
 )
 
-# Combine all layers
 chart = (
     alt.layer(overbought_zone, oversold_zone, threshold_lines, rsi_line)
     .properties(
         width=1600,
         height=900,
         title=alt.Title(
-            "indicator-rsi · altair · pyplots.ai",
+            "indicator-rsi · altair · anyplot.ai",
             fontSize=28,
             anchor="middle",
             subtitle="14-Period RSI with Overbought/Oversold Zones",
             subtitleFontSize=18,
         ),
+        background=PAGE_BG,
+    )
+    .configure_axis(
+        labelFontSize=18,
+        titleFontSize=22,
+        gridOpacity=0.10,
+        domainColor=INK_SOFT,
+        tickColor=INK_SOFT,
+        gridColor=INK,
+        labelColor=INK_SOFT,
+        titleColor=INK,
     )
-    .configure_axis(labelFontSize=18, titleFontSize=22, gridOpacity=0.3)
-    .configure_view(strokeWidth=0)
+    .configure_title(color=INK)
+    .configure_legend(fillColor=ELEVATED_BG, strokeColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK)
+    .configure_view(strokeWidth=0, fill=PAGE_BG)
 )
 
-# Save outputs
-chart.save("plot.png", scale_factor=3.0)
-chart.save("plot.html")
+chart.save(f"plot-{THEME}.png", scale_factor=3.0)
+chart.save(f"plot-{THEME}.html")