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:

%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:

0.8135304438803402

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

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

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

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

Output will generate something like this:

(view original html file)

[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",
 "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"]

[f for f in dir(qs.plots) if f[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