QuantInvestStrats
# 🚀 **Quantitative Investment Strategies: QIS** > qis package implements analytics for visualisation of financial data, performance reporting, factsheets and analysis of quantitative strategies. --- | 📊 Metric | 🔢 Value | |-----------|----------| | PyPI Version |  | | Python Versions |  | | License | | | CI Status | [](https://github.com/ArturSepp/QuantInvestStrats/actions) | ### 📈 Package Statistics | 📊 Metric | 🔢 Value | |-----------|----------| | Total Downloads | [](https://pepy.tech/project/qis) | | Monthly |  | | Weekly |  | | GitHub Stars |  | | GitHub Forks |  | ## **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 runnable scripts showcasing the analytics, organised by sub-package: * ```qis.examples.perfstats``` — performance metrics on price series: quickstart usage, Sharpe vs Sortino across return frequencies, rolling performance, bond-ETF risk/return frontier, multi-figure performance reports, miss-best-worst-days impact, infrequent-returns interpolation, and an end-to-end de-levering / unsmoothing walkthrough on a bundled BDC vs private-credit dataset. * ```qis.examples.models``` — numba-vs-pandas EWM kernel benchmarks, multivariate EWM linear factor models, multivariate OLS, EWM correlation tables, OHLC realised-volatility estimators, intraday/overnight return decomposition, rolling correlations, and block bootstrap of price paths. * ```qis.examples.regimes``` — regime-conditional analytics: bull/bear/normal Sharpe attribution, conditional return boxplots by VIX regime, calendar-month seasonality, US election regime study. * ```qis.examples.portfolios``` — backtests using ```backtest_model_portfolio```: balanced 60/40 with and without a BTC sleeve, constant-notional short, leveraged-ETF combinations, long/short pairs, and vol-target / trend-following parameter sweeps. * ```qis.examples.factsheets``` — full multi-page factsheets for simulated and actual strategies, cross-sectional asset-class comparisons, multi-strategy parameter sweeps, and optional pybloqs-rendered variants. * ```qis.examples.plots``` — plotting primitives showcase: dual-axis figures, scatter with regression diagnostics. * ```qis.examples.utils``` — date schedules and rolling calendars: option / futures roll generation via ```generate_fixed_maturity_rolls```. * ```qis.examples.case_studies``` — cross-cutting domain studies: VIX beta to equities and bonds, VIX term-structure correlation with SPX, conditional returns on the front-month short-VIX strategy, credit-spread regression vs equity / rates. A README inside ```qis/examples/``` lists every script with a one-line description; examples that need a Bloomberg terminal are flagged inline. # Table of contents 1. [Analytics](#analytics) 2. [Installation](#installation) 3. [Examples](#examples) 1. [Visualization of price data](#price) 2. [Multi assets factsheet](#multiassets) 3. [Strategy factsheet](#strategy) 4. [Strategy benchmark factsheet](#strategybenchmark) 5. [Multi strategy factsheet](#multistrategy) 6. [Notebooks](#notebooks) 4. [Contributions](#contributions) 5. [Changelog](#changelog) 6. [ToDos](#todos) 7. [Disclaimer](#disclaimer) ## **Installation** <a name="installation"></a> Install using ```python pip install qis ``` Upgrade using ```python pip install --upgrade qis ``` Close using ```python git clone https://github.com/ArturSepp/QuantInvestStrats.git ``` Core dependencies: python = ">=3.10", numba = ">=0.63.0", numpy = ">=2.0", scipy = ">=1.12.0", statsmodels = ">=0.14.0", pandas = ">=2.2.0", matplotlib = ">=3.8.0", seaborn = ">=0.13.0", openpyxl = ">=3.1.0", PyYAML = ">=6.0", yfinance = ">=0.2.40", pandas-datareader = ">=0.10.0" Python 3.14 is supported (numba 0.63+ ships cp314 wheels). Optional dependencies: pybloqs ">=1.2.13" (for producing html and pdf factsheets — install with `pip install qis[reports]`), bbg-fetch ">=2.0.0" (third-party; for examples that pull data from a Bloomberg terminal) See `pyproject.toml` for the full list of optional extras (`reports`, `visualization`, `io`, `database`, `jupyter`, `dev`, `all`). ## **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) ```python 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) ```  ```python # 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) ```  ```python # 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) ```  ```python # 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: ```python pip install notebook ``` Starting local server ```python 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](https://peps.python.org/pep-0008/) - 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. ## **Changelog** <a name="changelog"></a> Release history is maintained in [CHANGELOG.md](CHANGELOG.md). ## **ToDos** <a name="todos"></a> 1. Enhanced documentation and readme examples. 2. Docstrings for key functions. 3. Reporting analytics and factsheets generation enhancing to matplotlib. ## **Disclaimer** <a name="disclaimer"></a> QIS package is distributed FREE & WITHOUT ANY WARRANTY under the GNU GENERAL PUBLIC LICENSE. See the [LICENSE.txt](https://github.com/ArturSepp/QuantInvestStrats/blob/master/LICENSE.txt) in the release for details. Please report any bugs or suggestions by opening an [issue](https://github.com/ArturSepp/QuantInvestStrats/issues). . ## BibTeX Citation If you use BloombergFetch in your research, please cite it as: ```bibtex @software{sepp2024qis, author={Sepp, Artur}, title={Qua: A Python Package for Bloomberg Terminal Data Access}, year={2024}, url={https://github.com/ArturSepp/BloombergFetch}, version={1.0.27} } ``` ## BibTeX Citations for QIS (Quantitative Investment Strategies) Package If you use QIS in your research, please cite it as: ```bibtex @software{sepp2024qis, title={qis: Implementation of visualisation and reporting analytics for Quantitative Investment Strategies}, author={Sepp, Artur}, year={2024}, url={https://github.com/ArturSepp/QuantInvestStrats} } ```