qis 1.0.9

Implementation of visualisation and reporting analytics for Quantitative Investment Strategies

QIS: Quantitative Investment Strategies

The package implements analytics for visualisation of financial data, performance reporting, analysis of quantitative strategies.

Table of contents

1. Installation

2. Analytics

   1. Disclaimer
   2. Contributions

3. Examples

   1. Visualization of price data

4. ToDos

Installation

pip install qis

pip install qis --upgrade

Core dependencies: Python >= 3.8+, pandas >=1.0.0, numpy >= 1.15.0, numba >= 1.2.0, matplotlib >= 3.0.0, seaborn >= 0.9.0, yfinance >= 0.1.38 (optional for getting test price data)

Analytics

The QIS package is split into 5 main modules with the dependecy path increasing sequentially as follows.

1. qis.utils is module containing low level utilities for operations with pandas, numpy, and datetimes.

2. qis.perfstats is module for computing performance statistics and performance attribution including returns, volatilities, etc.

3. qis.plots is module for plotting and visualization apis.

4. qis.models is module containing statistical models including filtering and regressions.

5. qis.portfolio is high level module for analysis, simulation, backtesting, and reporting of quant strategies.

qis.examples contains scripts with illustrations of QIS analytics.

Disclaimer

QIS package is distributed FREE & WITHOUT ANY WARRANTY under the GNU GENERAL PUBLIC LICENSE.

See the LICENSE.txt in the release for details.

Please report any bugs or suggestions by opening an issue.

Contributions

If you are interested in extending and improving QIS analytics, please consider contributing to the library.

I have found it is a good practice to isolate general purpose and low level analytics and visualizations, which can be outsourced and shared, while keeping the focus on developing high level commercial applications.

There are a number of requirements:

• The code is Pep 8 compliant

• Reliance on common Python data types including numpy arrays, pandas, and dataclasses.

• Transparent naming of functions and data types with enough comments. Type annotations of functions and arguments is a must.

• Each submodule has a unit test for core functions and a localised entry point to core functions.

• Avoid "super" pythonic constructions. Readability is the priority.

Examples

Visualization of price data

The script is located in qis.examples.performances

import matplotlib.pyplot as plt
import seaborn as sns
import yfinance as yf
import qis
from qis import PerfStat

# define tickers and fetch price data
tickers = ['SPY', 'QQQ', 'EEM', 'TLT', 'IEF', 'LQD', 'HYG', 'GLD']
prices = yf.download(tickers, start=None, end=None)['Adj Close'][tickers].dropna()

# plotting price data with minimum usage
fig = qis.plot_prices(prices=prices)

# 2-axis plot with drawdowns using sns styles
with sns.axes_style("darkgrid"):
    fig, axs = plt.subplots(2, 1, figsize=(10, 7))
    qis.plot_prices_with_dd(prices=prices, axs=axs)

# plot risk-adjusted performance table with excess Sharpe ratio
ust_3m_rate = yf.download('^IRX', start=None, end=None)['Adj Close'].dropna() / 100.0
# set parameters for computing performance stats including returns vols and regressions
perf_params = qis.PerfParams(freq='M', freq_reg='Q', rates_data=ust_3m_rate)
fig = qis.plot_ra_perf_table(prices=prices,
                             perf_columns=[PerfStat.TOTAL_RETURN, PerfStat.PA_RETURN, PerfStat.VOL, PerfStat.SHARPE,
                                           PerfStat.SHARPE_EXCESS, PerfStat.MAX_DD, PerfStat.MAX_DD_VOL,
                                           PerfStat.SKEWNESS, PerfStat.KURTOSIS],
                             title=f"Risk-adjusted performance: {qis.get_time_period_label(prices, date_separator='-')}",
                             perf_params=perf_params)

ToDos and Contributions

1. Enhanced documentation and readme examples.

2. Docstrings for key functions.

3. Reporting analytics and factsheets generation enhancing to matplotlib.