QuantStats: Portfolio analytics for quants
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.
QuantStats is comprised of 3 main modules:
quantstats.stats- for calculating various performance metrics, like Sharpe ratio, Win rate, Volatility, etc.quantstats.plots- for visualizing performance, drawdowns, rolling statistics, monthly returns, etc.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:
0.7604779884378278
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:
qs.reports.metrics(mode='basic|full", ...)- shows basic/full metricsqs.reports.plots(mode='basic|full", ...)- shows basic/full plotsqs.reports.basic(...)- shows basic metrics and plotsqs.reports.full(...)- shows full metrics and plotsqs.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:
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=0.95, prepare_returns=True, method='parametric')
Calculate the Conditional Value at Risk (CVaR), also known as Expected Shortfall.
CVaR measures the expected loss given that a loss exceeds the VaR threshold.
It quantifies the amount of tail risk an investment faces, providing a more
comprehensive risk measure than VaR alone.
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
1 maintainer
Metadata
1 maintainer
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