## **Quantitative Investment Strategies: QIS** <a name="analytics"></a>
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](#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. [Updates](#updates)
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 = ">=3.8",
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** <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=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)
```
```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=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)
```
```python
# 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 <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.
## **Updates** <a name="updates"></a>
#### 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** <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).
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"description": "\n## **Quantitative Investment Strategies: QIS** <a name=\"analytics\"></a>\n\nqis package implements analytics for visualisation of financial data, performance\nreporting, analysis of quantitative strategies. \n\nqis package is split into 5 main modules with the \ndependecy path increasing sequentially as follows.\n\n1. ```qis.utils``` is module containing low level utilities for operations with pandas, numpy, and datetimes.\n\n2. ```qis.perfstats``` is module for computing performance statistics and performance attribution including returns, volatilities, etc.\n\n3. ```qis.plots``` is module for plotting and visualization apis.\n\n4. ```qis.models``` is module containing statistical models including filtering and regressions.\n\n5. ```qis.portfolio``` is high level module for analysis, simulation, backtesting, and reporting of quant strategies.\nFunction ```backtest_model_portfolio()``` in ```qis.portfolio.backtester.py``` takes instrument prices \nand simulated weights from a generic strategy and compute the total return, performance attribution, and risk analysis\n\n```qis.examples``` contains scripts with illustrations of QIS analytics.\n\n```qis.examples.factheets``` contains scripts with examples of factsheets for simulated and actual strategies,\nand cross-sectional analysis of backtests.\n\n\n# Table of contents\n1. [Analytics](#analytics)\n2. [Installation](#installation)\n3. [Examples](#examples)\n 1. [Visualization of price data](#price)\n 2. [Multi assets factsheet](#multiassets)\n 3. [Strategy factsheet](#strategy)\n 4. [Strategy benchmark factsheet](#strategybenchmark)\n 5. [Multi strategy factsheet](#multistrategy)\n 6. [Notebooks](#notebooks)\n4. [Contributions](#contributions)\n5. [Updates](#updates)\n6. [ToDos](#todos)\n7. [Disclaimer](#disclaimer)\n\n\n## **Installation** <a name=\"installation\"></a>\nInstall using\n```python \npip install qis\n```\nUpgrade using\n```python \npip install --upgrade qis\n```\n\nClose using\n```python \ngit clone https://github.com/ArturSepp/QuantInvestStrats.git\n```\n\nCore dependencies:\n python = \">=3.8\",\n numba = \">=0.56.4\",\n numpy = \">=1.22.4\",\n scipy = \">=1.10\",\n statsmodels = \">=0.13.5\",\n pandas = \">=1.5.2\",\n matplotlib = \">=3.2.2\",\n seaborn = \">=0.12.2\"\n\nOptional dependencies:\n yfinance \">=0.1.38\" (for getting test price data),\n pybloqs \">=1.2.13\" (for producing html and pdf factsheets)\n\nTo use pybloqs for pandas > 2.x, locate file \"...\\Lib\\site-packages\\pybloqs\\jinja\\table.html\" and \nchange line 44 from {% for col_name, cell in row.iteritems() %} to {% for col_name, cell in row.items() %}\n\n\n## **Examples** <a name=\"examples\"></a>\n\n### 1. Visualization of price data <a name=\"price\"></a>\n\nThe script is located in ```qis.examples.performances``` (https://github.com/ArturSepp/QuantInvestStrats/blob/master/qis/examples/performances.py)\n\n```python \nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport yfinance as yf\nimport qis\n\n# define tickers and fetch price data\ntickers = ['SPY', 'QQQ', 'EEM', 'TLT', 'IEF', 'SHY', 'LQD', 'HYG', 'GLD']\nprices = yf.download(tickers, start=None, end=None)['Adj Close'][tickers].dropna()\n\n# plotting price data with minimum usage\nwith sns.axes_style(\"darkgrid\"):\n fig, ax = plt.subplots(1, 1, figsize=(10, 7))\n qis.plot_prices(prices=prices, x_date_freq='YE', ax=ax)\n```\n\n```python \n# 2-axis plot with drawdowns using sns styles\nwith sns.axes_style(\"darkgrid\"):\n fig, axs = plt.subplots(2, 1, figsize=(10, 7), tight_layout=True)\n qis.plot_prices_with_dd(prices=prices, x_date_freq='YE', axs=axs)\n```\n\n\n```python \n# plot risk-adjusted performance table with excess Sharpe ratio\nust_3m_rate = yf.download('^IRX', start=None, end=None)['Adj Close'].dropna() / 100.0\n# set parameters for computing performance stats including returns vols and regressions\nperf_params = qis.PerfParams(freq='ME', freq_reg='QE', alpha_an_factor=4.0, rates_data=ust_3m_rate)\n# perf_columns is list to display different perfomance metrics from enumeration PerfStat\nfig = qis.plot_ra_perf_table(prices=prices,\n perf_columns=[PerfStat.TOTAL_RETURN, PerfStat.PA_RETURN, PerfStat.PA_EXCESS_RETURN,\n PerfStat.VOL, PerfStat.SHARPE_RF0,\n PerfStat.SHARPE_EXCESS, PerfStat.SORTINO_RATIO, PerfStat.CALMAR_RATIO,\n PerfStat.MAX_DD, PerfStat.MAX_DD_VOL,\n PerfStat.SKEWNESS, PerfStat.KURTOSIS],\n title=f\"Risk-adjusted performance: {qis.get_time_period_label(prices, date_separator='-')}\",\n perf_params=perf_params)\n```\n\n\n\n\n```python \n# add benchmark regression using excess returns for linear beta\n# regression frequency is specified using perf_params.freq_reg\n# regression alpha is multiplied using perf_params.alpha_an_factor\nfig = qis.plot_ra_perf_table_benchmark(prices=prices,\n benchmark='SPY',\n perf_columns=[PerfStat.TOTAL_RETURN, PerfStat.PA_RETURN, PerfStat.PA_EXCESS_RETURN,\n PerfStat.VOL, PerfStat.SHARPE_RF0,\n PerfStat.SHARPE_EXCESS, PerfStat.SORTINO_RATIO, PerfStat.CALMAR_RATIO,\n PerfStat.MAX_DD, PerfStat.MAX_DD_VOL,\n PerfStat.SKEWNESS, PerfStat.KURTOSIS,\n PerfStat.ALPHA_AN, PerfStat.BETA, PerfStat.R2],\n title=f\"Risk-adjusted performance: {qis.get_time_period_label(prices, date_separator='-')} benchmarked with SPY\",\n perf_params=perf_params)\n```\n\n\n\n\n### 2. Multi assets factsheet <a name=\"multiassets\"></a>\nThis report is adopted for reporting the risk-adjusted performance \nof several assets with the goal\nof cross-sectional comparision\n\nRun example in ```qis.examples.factsheets.multi_assets.py``` https://github.com/ArturSepp/QuantInvestStrats/blob/master/qis/examples/factsheets/multi_assets.py\n\n\n\n\n### 3. Strategy factsheet <a name=\"strategy\"></a>\nThis report is adopted for report performance, risk, and trading statistics\nfor either backtested or actual strategy\n with strategy data passed as PortfolioData object\n\nRun example in ```qis.examples.factsheets.strategy.py``` https://github.com/ArturSepp/QuantInvestStrats/blob/master/qis/examples/factsheets/strategy.py\n\n\n\n\n\n### 4. Strategy benchmark factsheet <a name=\"strategybenchmark\"></a>\nThis report is adopted for report performance and marginal comparison\n of strategy vs a benchmark strategy \n(data for both are passed using individual PortfolioData object)\n\nRun example in ```qis.examples.factsheets.strategy_benchmark.py``` https://github.com/ArturSepp/QuantInvestStrats/blob/master/qis/examples/factsheets/strategy_benchmark.py\n\n\n\nBrinson-Fachler performance attribution (https://en.wikipedia.org/wiki/Performance_attribution)\n\n\n\n### 5. Multi strategy factsheet <a name=\"multistrategy\"></a>\nThis report is adopted to examine the sensitivity of \nbacktested strategy to a parameter or set of parameters:\n\nRun example in ```qis.examples.factsheets.multi_strategy.py``` https://github.com/ArturSepp/QuantInvestStrats/blob/master/qis/examples/factsheets/multi_strategy.py\n\n\n\n\n### 6. Notebooks <a name=\"notebooks\"></a>\n\nRecommended package to work with notebooks: \n```python \npip install notebook\n```\nStarting local server\n```python \njupyter notebook\n```\n\nExamples of using qis analytics jupyter notebooks are located here\nhttps://github.com/ArturSepp/QuantInvestStrats/blob/master/qis/examples/notebooks\n\n\n## **Contributions** <a name=\"contributions\"></a>\nIf you are interested in extending and improving QIS analytics, \nplease consider contributing to the library.\n\nI 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 \nthe focus on developing high level commercial applications.\n\nThere are a number of requirements:\n\n- The code is [Pep 8 compliant](https://peps.python.org/pep-0008/)\n\n- Reliance on common Python data types including numpy arrays, pandas, and dataclasses.\n\n- Transparent naming of functions and data types with enough comments. Type annotations of functions and arguments is a must.\n\n- Each submodule has a unit test for core functions and a localised entry point to core functions.\n\n- Avoid \"super\" pythonic constructions. Readability is the priority.\n\n\n\n## **Updates** <a name=\"updates\"></a>\n\n#### 30 December 2022, Version 1.0.1 released\n\n#### 08 July 2023, Version 2.0.1 released\n\nCore Changes\n\n1. Portfolio optimization (qis.portfolio.optimisation) layer is removed with core\nfunctionality moved to a stand-alone Python package: Backtesting Optimal Portfolio (bop)\n \n* This allows to remove the dependency from cvxpy and sklearn packages and \nthus to simplify the dependency management for qis\n\n2.\tAdded factsheet reporting using pybloqs package https://github.com/man-group/PyBloqs\n* Pybloqs is a versatile tool to create customised reporting using Matplotlib figures and table\nand thus leveraging QIS visualisation analytics\n\n3. New factsheets are added\n* Examples are added for the four type of reports:\n 1. multi assets: report performance of several assets with goal of cross-sectional comparision:\n see qis.examples.factsheets.multi_asset.py\n 2. strategy: report performance, risk, and trading statictics for either backtested or actual strategy\n with strategy data passed as PortfolioData object: see qis.examples.factsheets.strategy.py\n 3. strategy vs benchmark: report performance and marginal comparison\n of strategy vs a benchmark strategy (data for both are passed using individual PortfolioData object): \n see qis.examples.factsheets.strategy_benchmark.py\n 4. multi_strategy: report for a list of strategies with individual PortfolioData. This report is \n useful to examine the sensetivity of backtested strategy to a parameter or set of parameters: \n see qis.examples.factsheets.multi_strategy\n\n\n## **ToDos** <a name=\"todos\"></a>\n\n1. Enhanced documentation and readme examples.\n\n2. Docstrings for key functions.\n\n3. Reporting analytics and factsheets generation enhancing to matplotlib.\n\n\n\n## **Disclaimer** <a name=\"disclaimer\"></a>\n\nQIS package is distributed FREE & WITHOUT ANY WARRANTY under the GNU GENERAL PUBLIC LICENSE.\n\nSee the [LICENSE.txt](https://github.com/ArturSepp/QuantInvestStrats/blob/master/LICENSE.txt) in the release for details.\n\nPlease report any bugs or suggestions by opening an [issue](https://github.com/ArturSepp/QuantInvestStrats/issues).\n\n\n",
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