QuantStats Python library that performs portfolio profiling, allowing quants and portfolio managers to understand their performance better by providing them with in-depth analytics and risk metrics.

Changelog »

1. quantstats.stats - for calculating various performance metrics, like Sharpe ratio, Win rate, Volatility, etc.
2. quantstats.plots - for visualizing performance, drawdowns, rolling statistics, monthly returns, etc.
3. quantstats.reports - for generating metrics reports, batch plotting, and creating tear sheets that can be saved as an HTML file.

Here's an example of a simple tear sheet analyzing a strategy:

%matplotlibinlineimportquantstatsasqs# extend pandas functionality with metrics, etc.qs.extend_pandas() # fetch the daily returns for a stockstock=qs.utils.download_returns('FB') # show sharpe ratioqs.stats.sharpe(stock) # or using extend_pandas() :)stock.sharpe()

Output:

0.8135304438803402 

qs.plots.snapshot(stock, title='Facebook Performance') # can also be called via:# stock.plot_snapshot(title='Facebook Performance')

Output:

You can create 7 different report tearsheets:

1. qs.reports.metrics(mode='basic|full", ...) - shows basic/full metrics
2. qs.reports.plots(mode='basic|full", ...) - shows basic/full plots
3. qs.reports.basic(...) - shows basic metrics and plots
4. qs.reports.full(...) - shows full metrics and plots
5. qs.reports.html(...) - generates a complete report as html

Let' create an html tearsheet

(benchmarkcanbeapandasSeriesorticker) qs.reports.html(stock, "SPY")

Output will generate something like this:

(view original html file)

[fforfindir(qs.stats) iff[0] !='_']

['avg_loss', 'avg_return', 'avg_win', 'best', 'cagr', 'calmar', 'common_sense_ratio', 'comp', 'compare', 'compsum', 'conditional_value_at_risk', 'consecutive_losses', 'consecutive_wins', 'cpc_index', 'cvar', 'drawdown_details', 'expected_return', 'expected_shortfall', 'exposure', 'gain_to_pain_ratio', 'geometric_mean', 'ghpr', 'greeks', 'implied_volatility', 'information_ratio', 'kelly_criterion', 'kurtosis', 'max_drawdown', 'monthly_returns', 'outlier_loss_ratio', 'outlier_win_ratio', 'outliers', 'payoff_ratio', 'profit_factor', 'profit_ratio', 'r2', 'r_squared', 'rar', 'recovery_factor', 'remove_outliers', 'risk_of_ruin', 'risk_return_ratio', 'rolling_greeks', 'ror', 'sharpe', 'skew', 'sortino', 'adjusted_sortino', 'tail_ratio', 'to_drawdown_series', 'ulcer_index', 'ulcer_performance_index', 'upi', 'utils', 'value_at_risk', 'var', 'volatility', 'win_loss_ratio', 'win_rate', 'worst'] 

[fforfindir(qs.plots) iff[0] !='_']

['daily_returns', 'distribution', 'drawdown', 'drawdowns_periods', 'earnings', 'histogram', 'log_returns', 'monthly_heatmap', 'returns', 'rolling_beta', 'rolling_sharpe', 'rolling_sortino', 'rolling_volatility', 'snapshot', 'yearly_returns'] 

*** Full documenttion coming soon ***

In the meantime, you can get insights as to optional parameters for each method, by using Python's help method:

help(qs.stats.conditional_value_at_risk)

Help on function conditional_value_at_risk in module quantstats.stats: conditional_value_at_risk(returns, sigma=1, confidence=0.99) calculats the conditional daily value-at-risk (aka expected shortfall) quantifies the amount of tail risk an investment 

Install using pip:

$ pip install quantstats --upgrade --no-cache-dir

Install using conda:

$ conda install -c ranaroussi quantstats

• Python >= 3.5+
• pandas (tested to work with >=0.24.0)
• numpy >= 1.15.0
• scipy >= 1.2.0
• matplotlib >= 3.0.0
• seaborn >= 0.9.0
• tabulate >= 0.8.0
• yfinance >= 0.1.38
• plotly >= 3.4.1 (optional, for using plots.to_plotly())

This is a new library... If you find a bug, please open an issue in this repository.

If you'd like to contribute, a great place to look is the issues marked with help-wanted.

For some reason, I couldn't find a way to tell seaborn not to return the monthly returns heatmap when instructed to save - so even if you save the plot (by passing savefig={...}) it will still show the plot.

QuantStats is distributed under the Apache Software License. See the LICENSE.txt file in the release for details.

Please drop me a note with any feedback you have.

Ran Aroussi