PySharpe

PySharpe is a portfolio research toolkit that wraps data collection, return analytics, optimisation, and simulation workflows into a single Python package. The repository ships a Streamlit dashboard for exploratory analysis, a CLI for scripted runs, and a modular library for notebooks or downstream automation.

Features

• Download and clean market data from Yahoo Finance or local CSV files.
• Compute annualised return, volatility, and Sharpe ratio statistics.
• Optimise weights with pypfopt using an Efficient Frontier model.
• Run dollar-cost averaging (DCA) projections and export the results.
• Interact through an opinionated Streamlit UI or the command-line utilities.

Installation

python3 -m venv .venv
source .venv/bin/activate
pip install -e .[dev]

The editable install keeps the CLI, dashboard, and library in sync. The dev extras bring in pytest, coverage, and linting tools that match the CI configuration.

Usage

Streamlit Dashboard

streamlit run app.py

Upload your own CSV or enter a comma-separated list of tickers. The dashboard will download prices, compute metrics, optimise weights, and plot DCA projections with downloadable CSV exports.

CLI Example

pysharpe optimise \
  --portfolio tests/data/sample_portfolio.csv \
  --export-dir outputs/ \
  --risk-free-rate 0.02

The command writes optimised weights, summary metrics, and diagnostic plots to the specified export directory. Run pysharpe --help for the full list of subcommands and options.

Library Example

import pandas as pd
from pysharpe import metrics
from pysharpe.optimization import estimate_max_sharpe_weights

prices = pd.read_csv("tests/data/sample_prices.csv", index_col=0, parse_dates=True)
returns = prices.pct_change().dropna()
mu = metrics.expected_return(returns)
cov = returns.cov() * 252
weights = estimate_max_sharpe_weights(mu=mu, cov_matrix=cov)
print("Optimised allocations:", weights.clean_weights())

All analytics functions accept pandas Series/DataFrames and return aligned structures so they compose naturally with your existing research workflow.

Contributing

1. Create an isolated environment and install the project with dev extras: pip install -e .[dev].
2. Format and lint prior to committing: ruff format . && ruff check ..
3. Write or update tests alongside any behavioural change.
4. Document new public APIs or workflows in docstrings and, when appropriate, in the README.

Testing

pytest

The suite includes unit tests for metrics, optimisation, data collation, and workflow helpers. Add regression tests whenever you touch analytics code, and prefer fixtures over network requests.

License

PySharpe is distributed under the MIT License. See LICENSE for details.