pfund-plot 0.0.2

A library for financial data visualization

PFund-Plot: Financial Charts in One Line of Code

TL;DR: pfund-plot handles the plotting libraries, so traders just get charts that work

Problem

Traders often need to quickly visualize their data without investing time in learning new tools. For example, plotting a candlestick should be as simple as writing a single line of code.

Solution

We created a high-level financial visualization layer that combines the best features from existing plotting libraries into an easy-to-use interface.

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pfund-plot is a financial visualization layer on top of existing plotting libraries, giving traders a simple, domain-specific interface for plotting and streaming market data.

Core Features

• Multi-Display Mode: support displaying plots in a Jupyter notebook, Marimo notebook, browser and desktop window
• Intuitive & Chainable API: plt.ohlc(df).style(...).control(...).mode(...).show()
• Streaming Plots: support streaming data in real-time
• DataFrame Agnostic: support pandas, polars, and dask
• Financial Plots: plot financial data by just one function call
• Combine multiple plots into a dashboard quickly for visualization

Installation

pip install pfund-plot

Quickstart

1. Plotting in a Jupyter or Marimo Notebook

To use data sources other than Bybit, please go to pfeed and install the corresponding extras. For example: pip install "pfeed[data_source1, data_source2]"

import pfeed as pe
import pfund_plot as plt

feed = pe.Bybit().market_feed
result = feed.download(product='BTC_USDT_PERP', resolution='1h', rollback_period='1d')
df = result.data.collect()

# Notebook mode:
plt.ohlc(df)

2. Plotting in a Python Script

# Browser mode:
plt.ohlc(df).mode('browser').show()

# Desktop mode:
if __name__ == "__main__":  # Required because desktop mode uses multiprocessing.
    plt.ohlc(df).mode('desktop').show()

3. Streaming a Plot

# Streaming requires pipeline_mode=True.
feed = pe.Bybit(pipeline_mode=True).market_feed
feed.stream(product='BTC_USDT_PERP', resolution='1s')

# In a Python script:
plt.ohlc(feed).control(update_interval=1000).mode('browser')  # or mode('desktop') under if __name__ == "__main__"

# In a notebook environment (not recommended to start streaming in a notebook unless you know what you're doing):
# await plt.ohlc(feed).control(update_interval=1000).show_async()

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Tapping into the JavaScript World

Python developers sometimes envy the JavaScript visualization ecosystem for its rich set of interactive charts and dashboards. With pfund-plot, they can still enjoy the benefits of the JavaScript world while keeping the same Python-first API. For example:

plt.ohlc(df).backend('svelte')

This renders the following chart using TradingView's Lightweight Charts:

This is meant as a showcase rather than a core direction: pfund-plot remains Python-first, and JavaScript-backed charts are supported where they fit naturally. If you want to bring more useful JavaScript visualizations into pfund-plot, contributions are welcome.