Quantitative Investment Strategies: QIS

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

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. Function backtest_model_portfolio() in qis.portfolio.backtester.py takes instrument prices and simulated weights from a generic strategy and compute the total return, performance attribution, and risk analysis

qis.examples contains scripts with illustrations of QIS analytics.

qis.examples.factheets contains scripts with examples of factsheets for simulated and actual strategies, and cross-sectional analysis of backtests.

Table of contents

1. Analytics
2. Installation
3. Examples
   1. Visualization of price data
   2. Multi assets factsheet
   3. Strategy factsheet
   4. Strategy benchmark factsheet
   5. Multi strategy factsheet
   6. Notebooks
4. Contributions
5. Updates
6. ToDos
7. Disclaimer

Installation

Install using

pip install qis

Upgrade using

pip install --upgrade qis

Close using

git clone https://github.com/ArturSepp/QuantInvestStrats.git

Core dependencies: python = ">=3.8,<3.11", numba = ">=0.56.4", numpy = ">=1.22.4", scipy = ">=1.10", statsmodels = ">=0.13.5", pandas = ">=1.5.2", matplotlib = ">=3.2.2", seaborn = ">=0.12.2"

Optional dependencies: yfinance ">=0.1.38" (for getting test price data), pybloqs ">=1.2.13" (for producing html and pdf factsheets)

To use pybloqs for pandas > 2.x, locate file "...\Lib\site-packages\pybloqs\jinja\table.html" and change line 44 from {% for col_name, cell in row.iteritems() %} to {% for col_name, cell in row.items() %}

Examples

1. Visualization of price data

The script is located in qis.examples.performances (https://github.com/ArturSepp/QuantInvestStrats/blob/master/qis/examples/performances.py)

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

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

# plotting price data with minimum usage
with sns.axes_style("darkgrid"):
    fig, ax = plt.subplots(1, 1, figsize=(10, 7))
    qis.plot_prices(prices=prices, x_date_freq='YE', ax=ax)

# 2-axis plot with drawdowns using sns styles
with sns.axes_style("darkgrid"):
    fig, axs = plt.subplots(2, 1, figsize=(10, 7), tight_layout=True)
    qis.plot_prices_with_dd(prices=prices, x_date_freq='YE', 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='ME', freq_reg='QE', alpha_an_factor=4.0, rates_data=ust_3m_rate)
# perf_columns is list to display different perfomance metrics from enumeration PerfStat
fig = qis.plot_ra_perf_table(prices=prices,
                             perf_columns=[PerfStat.TOTAL_RETURN, PerfStat.PA_RETURN, PerfStat.PA_EXCESS_RETURN,
                                           PerfStat.VOL, PerfStat.SHARPE_RF0,
                                           PerfStat.SHARPE_EXCESS, PerfStat.SORTINO_RATIO, PerfStat.CALMAR_RATIO,
                                           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)

# add benchmark regression using excess returns for linear beta
# regression frequency is specified using perf_params.freq_reg
# regression alpha is multiplied using perf_params.alpha_an_factor
fig = qis.plot_ra_perf_table_benchmark(prices=prices,
                                       benchmark='SPY',
                                       perf_columns=[PerfStat.TOTAL_RETURN, PerfStat.PA_RETURN, PerfStat.PA_EXCESS_RETURN,
                                                     PerfStat.VOL, PerfStat.SHARPE_RF0,
                                                     PerfStat.SHARPE_EXCESS, PerfStat.SORTINO_RATIO, PerfStat.CALMAR_RATIO,
                                                     PerfStat.MAX_DD, PerfStat.MAX_DD_VOL,
                                                     PerfStat.SKEWNESS, PerfStat.KURTOSIS,
                                                     PerfStat.ALPHA_AN, PerfStat.BETA, PerfStat.R2],
                                       title=f"Risk-adjusted performance: {qis.get_time_period_label(prices, date_separator='-')} benchmarked with SPY",
                                       perf_params=perf_params)

2. Multi assets factsheet

This report is adopted for reporting the risk-adjusted performance of several assets with the goal of cross-sectional comparision

Run example in qis.examples.factsheets.multi_assets.py https://github.com/ArturSepp/QuantInvestStrats/blob/master/qis/examples/factsheets/multi_assets.py

3. Strategy factsheet

This report is adopted for report performance, risk, and trading statistics for either backtested or actual strategy with strategy data passed as PortfolioData object

Run example in qis.examples.factsheets.strategy.py https://github.com/ArturSepp/QuantInvestStrats/blob/master/qis/examples/factsheets/strategy.py

4. Strategy benchmark factsheet

This report is adopted for report performance and marginal comparison of strategy vs a benchmark strategy (data for both are passed using individual PortfolioData object)

Run example in qis.examples.factsheets.strategy_benchmark.py https://github.com/ArturSepp/QuantInvestStrats/blob/master/qis/examples/factsheets/strategy_benchmark.py

Brinson-Fachler performance attribution (https://en.wikipedia.org/wiki/Performance_attribution)

5. Multi strategy factsheet

This report is adopted to examine the sensitivity of backtested strategy to a parameter or set of parameters:

Run example in qis.examples.factsheets.multi_strategy.py https://github.com/ArturSepp/QuantInvestStrats/blob/master/qis/examples/factsheets/multi_strategy.py

6. Notebooks

Recommended package to work with notebooks:

pip install notebook

Starting local server

jupyter notebook

Examples of using qis analytics jupyter notebooks are located here https://github.com/ArturSepp/QuantInvestStrats/blob/master/qis/examples/notebooks

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.

Updates

30 December 2022, Version 1.0.1 released

08 July 2023, Version 2.0.1 released

Core Changes

1. Portfolio optimization (qis.portfolio.optimisation) layer is removed with core functionality moved to a stand-alone Python package: Backtesting Optimal Portfolio (bop)

• This allows to remove the dependency from cvxpy and sklearn packages and thus to simplify the dependency management for qis

2. Added factsheet reporting using pybloqs package https://github.com/man-group/PyBloqs

• Pybloqs is a versatile tool to create customised reporting using Matplotlib figures and table and thus leveraging QIS visualisation analytics

3. New factsheets are added

• Examples are added for the four type of reports:
  1. multi assets: report performance of several assets with goal of cross-sectional comparision: see qis.examples.factsheets.multi_asset.py
  2. strategy: report performance, risk, and trading statictics for either backtested or actual strategy with strategy data passed as PortfolioData object: see qis.examples.factsheets.strategy.py
  3. strategy vs benchmark: report performance and marginal comparison of strategy vs a benchmark strategy (data for both are passed using individual PortfolioData object): see qis.examples.factsheets.strategy_benchmark.py
  4. multi_strategy: report for a list of strategies with individual PortfolioData. This report is useful to examine the sensetivity of backtested strategy to a parameter or set of parameters: see qis.examples.factsheets.multi_strategy

ToDos

1. Enhanced documentation and readme examples.

2. Docstrings for key functions.

3. Reporting analytics and factsheets generation enhancing to matplotlib.

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.