🚀 Quantitative Investment Strategies: QIS

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

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Quantitative Investment Strategies: QIS <a name="analytics"></a>

The package is split into 5 main modules with the dependency 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 <a name="installation"></a>

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", numba = ">=0.56.4", numpy = ">=1.22.4", scipy = ">=1.10", statsmodels = ">=0.13.5", pandas = ">=2.2.0", 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)

Examples <a name="examples"></a>

1. Visualization of price data <a name="price"></a>

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="2003-12-31", end=None, ignore_tz=True, auto_adjust=True)['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="2003-12-31", end=None, ignore_tz=True, auto_adjust=True)['Close'].dropna() / 100.0
# set parameters for computing performance stats including returns vols and regressions
perf_params = qis.PerfParams(freq='ME', freq_reg='QE', 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 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 <a name="multiassets"></a>

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 <a name="strategy"></a>

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 <a name="strategybenchmark"></a>

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 <a name="multistrategy"></a>

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 <a name="notebooks"></a>

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 <a name="contributions"></a>

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.