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https://github.com/ArturSepp/QuantInvestStrats
Quantitative Investment Strategies (QIS) package implements Python analytics for visualisation of financial data, performance reporting, analysis of quantitative strategies.
https://github.com/ArturSepp/QuantInvestStrats
asset-management data-analysis data-visualization investment-analysis performance-attribution portfolio-optimization portfolio-risk-management python quantitative-finance
Last synced: 3 months ago
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Quantitative Investment Strategies (QIS) package implements Python analytics for visualisation of financial data, performance reporting, analysis of quantitative strategies.
- Host: GitHub
- URL: https://github.com/ArturSepp/QuantInvestStrats
- Owner: ArturSepp
- License: gpl-3.0
- Created: 2022-12-30T16:57:52.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2024-08-15T07:59:32.000Z (5 months ago)
- Last Synced: 2024-08-16T09:18:56.283Z (5 months ago)
- Topics: asset-management, data-analysis, data-visualization, investment-analysis, performance-attribution, portfolio-optimization, portfolio-risk-management, python, quantitative-finance
- Language: Python
- Homepage: https://github.com/ArturSepp/QuantInvestStrats
- Size: 105 MB
- Stars: 122
- Watchers: 5
- Forks: 21
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
- trackawesomelist - QuantInvestStrats (⭐169) - Quantitative Investment Strategies (QIS) package implements Python analytics for visualisation of financial data, performance reporting, analysis of quantitative strategies. (Recently Updated / [Oct 14, 2024](/content/2024/10/14/README.md))
- awesome-quant - QuantInvestStrats - Quantitative Investment Strategies (QIS) package implements Python analytics for visualisation of financial data, performance reporting, analysis of quantitative strategies. (Python / Visualization)
README
## **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](#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**
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,<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() %}
### 1. Visualization of price data
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
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
Recommended package to work with notebooks:
```python
pip install notebook
```
Starting local server
```python
jupyter notebook
```
## **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](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.
#### 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
1. Enhanced documentation and readme examples.
2. Docstrings for key functions.
3. Reporting analytics and factsheets generation enhancing to matplotlib.
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).