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 »

QuantStats is comprised of 3 main modules:

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.

NEW! Monte Carlo Simulations

Run probabilistic risk analysis with built-in Monte Carlo simulations:

mc = qs.stats.montecarlo(returns, sims=1000, bust=-0.20, goal=0.50)
print(f"Bust probability: {mc.bust_probability:.1%}")
print(f"Goal probability: {mc.goal_probability:.1%}")
mc.plot()

Full Monte Carlo documentation »

Quick Start

%matplotlib inline
import quantstats as qs

# extend pandas functionality with metrics, etc.
qs.extend_pandas()

# fetch the daily returns for a stock
stock = qs.utils.download_returns('META')

# show sharpe ratio
qs.stats.sharpe(stock)

# or using extend_pandas() :)
stock.sharpe()

Output:

Visualize stock performance

qs.plots.snapshot(stock, title='Facebook Performance', show=True)

# can also be called via:
# stock.plot_snapshot(title='Facebook Performance', show=True)

Output:

Creating a report

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's create an html tearsheet:

# benchmark can be a pandas Series or ticker
qs.reports.html(stock, "SPY")

Output will generate something like this:

View original html file

Available methods

To view a complete list of available methods, run:

[f for f in dir(qs.stats) if f[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',
 'montecarlo',
 'montecarlo_cagr',
 'montecarlo_drawdown',
 'montecarlo_sharpe',
 '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',
 'value_at_risk',
 'var',
 'volatility',
 'win_loss_ratio',
 'win_rate',
 'worst']

[f for f in dir(qs.plots) if f[0] != '_']

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

*** Full documentation coming soon ***

Important: Period-Based vs Trade-Based Metrics

QuantStats analyzes return series (daily, weekly, monthly returns), not discrete trade data. This means:

• Win Rate = percentage of periods with positive returns
• Consecutive Wins/Losses = consecutive positive/negative return periods
• Payoff Ratio = average winning period return / average losing period return
• Profit Factor = sum of positive returns / sum of negative returns

These metrics are valid and useful for:

• Systematic/algorithmic strategies with regular rebalancing
• Analyzing return-series behavior over time
• Comparing strategies on a period-by-period basis

For discretionary traders with multi-day trades, these period-based metrics may differ from trade-level statistics. A single 5-day trade might span 3 positive days and 2 negative days - QuantStats would count these as 3 "wins" and 2 "losses" at the daily level.

This is consistent with how all return-based analytics work (Sharpe ratio, Sortino ratio, drawdown analysis, etc.) - they operate on return periods, not discrete trade entries/exits.

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)
    calculates the conditional daily value-at-risk (aka expected shortfall)
    quantifies the amount of tail risk an investment

Installation

Install using pip:

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

Install using conda:

$ conda install -c ranaroussi quantstats

Requirements

• Python >= 3.10
• pandas >= 1.5.0
• numpy >= 1.24.0
• scipy >= 1.11.0
• matplotlib >= 3.7.0
• seaborn >= 0.13.0
• tabulate >= 0.9.0
• yfinance >= 0.2.40
• plotly >= 5.0.0 (optional, for using plots.to_plotly())

Questions?

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

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

Known Issues

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.

Legal Stuff

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

P.S.

Please drop me a note with any feedback you have.

Ran Aroussi