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Tim Olson e99ef5d2dd backend redesign

2026-03-11 18:47:11 -04:00

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Chart Utilities - Standard OHLC Plotting

Overview

The chart_utils.py module provides convenience functions for creating beautiful, professional OHLC candlestick charts with a consistent look and feel. This is designed to be used by the LLM in analyze_chart_data scripts, eliminating the need to write custom matplotlib code for every chart.

Key Features

Beautiful by default: Uses mplfinance with seaborn-inspired aesthetics
Consistent styling: Professional color scheme (teal green up, coral red down)
Easy to use: Simple function calls instead of complex matplotlib code
Customizable: Supports all mplfinance options via kwargs
Volume integration: Optional volume subplot

Installation

The required package mplfinance has been added to requirements.txt:

pip install mplfinance

Available Functions

1. `plot_ohlc(df, title=None, volume=True, figsize=(14, 8), **kwargs)`

Main function for creating standard OHLC candlestick charts.

Parameters:

df: pandas DataFrame with DatetimeIndex and OHLCV columns
title: Optional chart title
volume: Whether to include volume subplot (default: True)
figsize: Figure size in inches (default: (14, 8))
**kwargs: Additional mplfinance.plot() arguments

Example:

fig = plot_ohlc(df, title='BTC/USDT 15min', volume=True)

2. `add_indicators_to_plot(df, indicators, **plot_kwargs)`

Creates OHLC chart with technical indicators overlaid.

Parameters:

df: DataFrame with OHLCV data and indicator columns
indicators: Dict mapping indicator column names to display parameters
**plot_kwargs: Additional arguments for plot_ohlc()

Example:

df['SMA_20'] = df['close'].rolling(20).mean()
df['SMA_50'] = df['close'].rolling(50).mean()

fig = add_indicators_to_plot(
    df,
    indicators={
        'SMA_20': {'color': 'blue', 'width': 1.5},
        'SMA_50': {'color': 'red', 'width': 1.5}
    },
    title='Price with Moving Averages'
)

3. Preset Functions

plot_price_volume(df, title=None) - Standard price + volume chart
plot_price_only(df, title=None) - Candlesticks without volume

Integration with analyze_chart_data

These functions are automatically available in the analyze_chart_data tool's script environment:

# In an analyze_chart_data script:
# df is already provided

# Simple usage
fig = plot_ohlc(df, title='Price Action')

# With indicators
df['SMA'] = df['close'].rolling(20).mean()
fig = add_indicators_to_plot(
    df,
    indicators={'SMA': {'color': 'blue', 'width': 1.5}},
    title='Price with SMA'
)

# Return data for the assistant
df[['close', 'SMA']].tail(10)

Styling

The default style includes:

Up candles: Teal green (#26a69a)
Down candles: Coral red (#ef5350)
Background: Light gray with white axes
Grid: Subtle dashed lines with 30% alpha
Professional fonts: Clean, readable sizes

Why This Matters

Before:

# LLM had to write this every time
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(df.index, df['close'], label='Close')
# ... lots more code for styling, colors, etc.

After:

# LLM can now just do this
fig = plot_ohlc(df, title='BTC/USDT')

Benefits:

✅ Less code to generate → faster response
✅ Consistent appearance across all charts
✅ Professional look out of the box
✅ Easier to maintain and customize
✅ Better use of mplfinance's candlestick rendering

Example Output

See chart_utils_example.py for runnable examples demonstrating:

Basic OHLC chart with volume
OHLC chart with multiple indicators
Price-only chart
Custom styling options

File Locations

Main module: backend/src/agent/tools/chart_utils.py
Integration: backend/src/agent/tools/chart_tools.py (lines 306-328)
Examples: backend/src/agent/tools/chart_utils_example.py
Dependency: backend/requirements.txt (mplfinance added)

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