quantstats-lumi 0.3.3

Portfolio analytics for quants

Fork of Original QuantStats by Ran Aroussi

This is a forked version of the original QuantStats library by Ran Aroussi. The original library can be found at https://github.com/ranaroussi/quantstats

This forked version was created because it seems that the original library is no longer being maintained. The original library has a number of issues and pull requests that have been open for a long time and have not been addressed. This forked version aims to address some of these issues and pull requests.

This forked version is created and maintained by the Lumiwealth team. We are a team of data scientists and software engineers who are passionate about quantitative finance and algorithmic trading. We use QuantStats in our daily work with the Lumibot library and we want to make sure that QuantStats is a reliable and well-maintained library.

If you’re interested in learning how to make your own trading algorithms, check out our Lumibot library at https://github.com/Lumiwealth/lumibot and check out our courses at https://lumiwealth.com

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.

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.

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

Quick Start

Install QuantStats Lumi using pip:

$ pip install quantstats-lumi

%matplotlib inline
import quantstats_lumi 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

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’ 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)

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',
 '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

Installation

Install using pip:

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

Install using conda:

$ conda install -c ranaroussi quantstats

Requirements

• 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())

Questions?

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.

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