finol 0.1.19

An open financial platform for facilitating data-driven OLPS research

FinOL: Towards Open Benchmarking for Data-Driven Online Portfolio Selection

 

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FinOL represents a pioneering open database for facilitating data-driven OLPS research. As an ambitious project, it collects and organizes extensive assets from global markets over half a century, it provides a long-awaited unified platform to advance data-driven OLPS research.

:star: What's NEW!

Update	Status	Label
New release! FinOL is going to release the official website	Coming soon......	Release
New metric! FinOL is going to support the t-test metric	Coming soon......	Enhancement
New model! FinOL is going to support the AlphaStock model	Coming soon......	Enhancement
New release! FinOL now releases the official docs	Released on 22 July 2024	Release
New feature! FinOL now supports the Auto Hyperparameter Tuning feature	Supported since 22 July 2024	Enhancement
New feature! FinOL now supports the Radar Chart evaluation	Supported since 10 June 2024	Enhancement
New model! FinOL now supports the AlphaPortfolio model	Supported since 06 June 2024	Enhancement
New feature! FinOL now supports the Economic Distillation feature	Supported since 15 May 2024	Enhancement
New feature! FinOL now supports the Interpretability Analysis feature	Supported since 16 April 2024	Enhancement
New feature! FinOL now supports the Winner Assets Selection feature	Supported since 14 April 2024	Enhancement
Release FinOL tutorials	Released on 22 March 2024	Release
Release FinOL v0.0.1	Released on 21 March 2024	Release

Outline

• FinOL: Towards Open Benchmarking for Data-Driven Online Portfolio Selection
  • Outline
  • About
  • Why should I use FinOL?
  • Installation
  • Examples and Tutorials
  • Using FinOL
  • File Structure
  • Supported Datasets
  • Supported Baselines
  • Supported Metrics
  • License
  • Contact Us

About

Online portfolio selection (OLPS) is an important issue in operations research community that studies how to dynamically adjust portfolios according to market changes. In the past, OLPS research relied on a general database called OLPS containing price relatives data of financial assets across different markets. However, with the widespread adoption of data-driven technologies like machine learning in finance, OLPS can no longer meet the needs of OLPS research due to the lack of support for high-dimensional feature spaces. To solve this problem, we propose FinOL, an open financial platform for advancing research in data-driven OLPS. FinOL expands and enriches the previous OLPS database, containing 9 benchmark financial datasets from 1962 to present across global markets. To promote fair comparisons, we evaluate a large number of past classic OLPS methods on FinOL, providing reusable benchmark results for future FinOL users and effectively supporting OLPS research. More importantly, to lower the barriers to research, FinOL provides a complete data-training-testing suite with just three lines of command. We are also committed to regularly updating FinOL with new data and benchmark results reflecting the latest developments and trends in the field. This ensures FinOL remains a valuable resource as data-driven OLPS methods continue evolving.

Overall Framework of FinOL

Why should I use FinOL?

1. FinOL contributes comprehensive datasets spanning diverse market conditions and asset classes to enable large-scale empirical validation;
2. FinOL contributes the most extensive benchmark results to date for portfolio selection methods, providing the academic community an unbiased performance assessment;
3. FinOL contributes a user-friendly Python library for data-driven OLPS research, providing a comprehensive toolkit for academics to develop, test, and validate new OLPS methods.

Installation

Installing via PIP

FinOL is available on PyPI, we recommend to install FinOL via pip:

> pip install --upgrade finol

You can also install the development version of FinOL from master branch of Git repository:

> pip install git+https://github.com/jiahaoli57/finol.git

Examples and Tutorials

You can find useful tutorials on how to use FinOL in the tutorials folder.

Here we show a simple application (taken from tutorial_2): we transform asset "AA" into a richer representation.

Visualization of Train Normalization Data for Asset "AA"

Using FinOL

To lower the barriers for the research community, FinOL provides a complete data-training-testing suite with just three lines of command.

from finol.data_layer.dataset_loader import DatasetLoader
from finol.optimization_layer.model_trainer import ModelTrainer
from finol.evaluation_layer.model_evaluator import ModelEvaluator


load_dataset_output = DatasetLoader().load_dataset()
train_model_output = ModelTrainer(load_dataset_output).train_model()
evaluate_model_output = ModelEvaluator(load_dataset_output, train_model_output).evaluate_model()

Supported Datasets

Name	Market	Country/Region	Data Frequency	# of Assets	Data Range	# of Periods
NYSE(O)	Stock	United States	Daily	26	03/July./1962 - 31/Dec./1984	5,651: 3,390/1,130/1,131
NYSE(N)	Stock	United States	Daily	403	02/Jan./1985 - 30/June./2010	6,430: 3,858/1,286/1,286
DJIA	Stock	United States	Daily	28	14/Jan./2001 - 14/Jan./2003	500: 300/100/100
SP500	Stock	United States	Daily	339	02/Jan./1998 - 31/Jan./2003	1,268: 756/256/256
TSE	Stock	Canada	Daily	48	12/Jan./1995 - 31/Dec./1998	1,001: 600/200/200
SSE	Stock	China	Weekly	30	05/July./2010 - 26/June./2023	678: 406/136/136
HSI	Stock	Hong Kong, China	Weekly	53	05/July./2010 - 26/June./2023	678: 406/136/136
CMEG	Futures	United States	Weekly	25	05/July./2010 - 26/June./2023	678: 406/136/136
CRYPTO	Cryptocurrency	World	Daily	43	09/Nov./2017 - 01/Mar./2024	2,305: 1,421/442/442

Supported Baselines

Name	Category	Source	Journal/Conference
Market	Classic OLPS: Benchmark baseline	--	--
Best	Classic OLPS: Benchmark baseline	--	--
UCRP	Classic OLPS: Benchmark baseline	Kelly 1956; Cover 1991	The Bell System Technical Journal; Mathematical Finance
BCRP	Classic OLPS: Benchmark baseline	Cover 1991	Mathematical Finance
UP	Classic OLPS: Follow-the-winner	Cover 1991	Mathematical Finance
EG	Classic OLPS: Follow-the-winner	Helmbold et al. 1998	Mathematical Finance
SCRP	Classic OLPS: Follow-the-winner	Gaivoronski and Stella 2000	Annals of Operations Research
PPT	Classic OLPS: Follow-the-winner	Lai et al. 2018	IEEE Transactions on Neural Networks and Learning Systems
SSPO	Classic OLPS: Follow-the-winner	Lai et al. 2018	The Journal of Machine Learning Research
ANTI1	Classic OLPS: Follow-the-loser	Borodin et al. 2004	Advances in Neural Information Processing Systems
ANTI2	Classic OLPS: Follow-the-loser	Borodin et al. 2004	Advances in Neural Information Processing Systems
PAMR	Classic OLPS: Follow-the-loser	Li et al. 2012	Machine Learning
CWMR-Var	Classic OLPS: Follow-the-loser	Li et al. 2013	ACM Transactions on Knowledge Discovery from Data
CWMR-Stdev	Classic OLPS: Follow-the-loser	Li et al. 2013	ACM Transactions on Knowledge Discovery from Data
OLMAR-S	Classic OLPS: Follow-the-loser	Li et al. 2015	Artificial Intelligence
OLMAR-E	Classic OLPS: Follow-the-loser	Li et al. 2015	Artificial Intelligence
RMR	Classic OLPS: Follow-the-loser	Huang et al. 2016	IEEE Transactions on Knowledge and Data Engineering
RPRT	Classic OLPS: Follow-the-loser	Lai et al. 2020	IEEE Transactions on Systems, Man, and Cybernetics: Systems
AICTR	Classic OLPS: Pattern-matching	Lai et al. 2018	IEEE Transactions on Neural Networks and Learning Systems
KTPT	Classic OLPS: Pattern-matching	Lai et al. 2018	Data Mining and Knowledge Discovery
SP	Classic OLPS: Meta-learning	Singer 1997	International Journal of Neural Systems
ONS	Classic OLPS: Meta-learning	Agarwal et al. 2006	International Conference on Machine Learning
GRW	Classic OLPS: Meta-learning	Levina and Shafer 2008	International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
WAAS	Classic OLPS: Meta-learning	Zhang and Yang 2017	Computational Economics
CW-OGD	Classic OLPS: Meta-learning	Zhang et al. 2021	Knowledge-Based Systems

Supported Metrics

Name	Abbreviation	Category
Cumulative Wealth	CW	Profit Metric
Annualized Percentage Yield	APY	Profit Metric
Sharpe Ratio	SR	Profit Metric
Volatility Risk	VR	Risk Metric
Maximum DrawDown	MDD	Risk Metric
Average Turnover	ATO	Practical Metric
Transaction Costs-Adjusted Cumulative Wealth	TCW	Practical Metric
Running Time	RT	Practical Metric

Supported Visualization

The FInOL project provides a suite of visualization tools to facilitate a comprehensive evaluation of data-driven OLPS research. Here are some examples:

1. Dynamic Training/Validation Loss Visualization

2. Daily Cumulative Wealth Visualization

3. Daily DrawDown

4. Transaction Cost-Adjusted Cumulative Wealth

5. Comprehensive Radar Chart

File Structure

FinOL
├─ LICENSE
├─ MANIFEST.in
├─ README.md
├─ TA_Lib-0.4.24-cp39-cp39-win_amd64.whl
├─ figure
│    ├─ FinOL.png
│    ├─ logo.png
│    └─ tutorial_2.png
├─ finol
│    ├─ __init__.py
│    ├─ config.py
│    ├─ data
│    │    ├─ benchmark_results
│    │    │    ├─ __init__.py
│    │    │    ├─ other
│    │    │    │    └─ price_relative
│    │    │    │           ├─ price_relative_CMEG.mat
│    │    │    │           ├─ price_relative_CRYPTO.mat
│    │    │    │           ├─ price_relative_DJIA.mat
│    │    │    │           ├─ price_relative_HSI.mat
│    │    │    │           ├─ price_relative_NYSE(N).mat
│    │    │    │           ├─ price_relative_NYSE(O).mat
│    │    │    │           ├─ price_relative_SP500.mat
│    │    │    │           ├─ price_relative_SSE.mat
│    │    │    │           └─ price_relative_TSE.mat
│    │    │    ├─ practical_metrics
│    │    │    │    ├─ CMEG
│    │    │    │    │    ├─ final_practical_result.xlsx
│    │    │    │    │    └─ transaction_costs_adjusted_cumulative_wealth.xlsx
│    │    │    │    ├─ CRYPTO
│    │    │    │    │    ├─ final_practical_result.xlsx
│    │    │    │    │    └─ transaction_costs_adjusted_cumulative_wealth.xlsx
│    │    │    │    ├─ DJIA
│    │    │    │    │    ├─ final_practical_result.xlsx
│    │    │    │    │    └─ transaction_costs_adjusted_cumulative_wealth.xlsx
│    │    │    │    ├─ HSI
│    │    │    │    │    ├─ final_practical_result.xlsx
│    │    │    │    │    └─ transaction_costs_adjusted_cumulative_wealth.xlsx
│    │    │    │    ├─ NYSE(N)
│    │    │    │    │    ├─ final_practical_result.xlsx
│    │    │    │    │    └─ transaction_costs_adjusted_cumulative_wealth.xlsx
│    │    │    │    ├─ NYSE(O)
│    │    │    │    │    ├─ final_practical_result.xlsx
│    │    │    │    │    └─ transaction_costs_adjusted_cumulative_wealth.xlsx
│    │    │    │    ├─ SP500
│    │    │    │    │    ├─ final_practical_result.xlsx
│    │    │    │    │    └─ transaction_costs_adjusted_cumulative_wealth.xlsx
│    │    │    │    ├─ SSE
│    │    │    │    │    ├─ final_practical_result.xlsx
│    │    │    │    │    └─ transaction_costs_adjusted_cumulative_wealth.xlsx
│    │    │    │    └─ TSE
│    │    │    │           ├─ final_practical_result.xlsx
│    │    │    │           └─ transaction_costs_adjusted_cumulative_wealth.xlsx
│    │    │    ├─ profit_metrics
│    │    │    │    ├─ CMEG
│    │    │    │    │    ├─ daily_cumulative_wealth.xlsx
│    │    │    │    │    ├─ daily_return.xlsx
│    │    │    │    │    └─ final_profit_result.xlsx
│    │    │    │    ├─ CRYPTO
│    │    │    │    │    ├─ daily_cumulative_wealth.xlsx
│    │    │    │    │    ├─ daily_return.xlsx
│    │    │    │    │    └─ final_profit_result.xlsx
│    │    │    │    ├─ DJIA
│    │    │    │    │    ├─ daily_cumulative_wealth.xlsx
│    │    │    │    │    ├─ daily_return.xlsx
│    │    │    │    │    └─ final_profit_result.xlsx
│    │    │    │    ├─ HSI
│    │    │    │    │    ├─ daily_cumulative_wealth.xlsx
│    │    │    │    │    ├─ daily_return.xlsx
│    │    │    │    │    └─ final_profit_result.xlsx
│    │    │    │    ├─ NYSE(N)
│    │    │    │    │    ├─ daily_cumulative_wealth.xlsx
│    │    │    │    │    ├─ daily_return.xlsx
│    │    │    │    │    └─ final_profit_result.xlsx
│    │    │    │    ├─ NYSE(O)
│    │    │    │    │    ├─ daily_cumulative_wealth.xlsx
│    │    │    │    │    ├─ daily_return.xlsx
│    │    │    │    │    └─ final_profit_result.xlsx
│    │    │    │    ├─ SP500
│    │    │    │    │    ├─ daily_cumulative_wealth.xlsx
│    │    │    │    │    ├─ daily_return.xlsx
│    │    │    │    │    └─ final_profit_result.xlsx
│    │    │    │    ├─ SSE
│    │    │    │    │    ├─ daily_cumulative_wealth.xlsx
│    │    │    │    │    ├─ daily_return.xlsx
│    │    │    │    │    └─ final_profit_result.xlsx
│    │    │    │    └─ TSE
│    │    │    │           ├─ daily_cumulative_wealth.xlsx
│    │    │    │           ├─ daily_return.xlsx
│    │    │    │           └─ final_profit_result.xlsx
│    │    │    └─ risk_metrics
│    │    │           ├─ CMEG
│    │    │           │    ├─ daily_drawdown.xlsx
│    │    │           │    └─ final_risk_result.xlsx
│    │    │           ├─ CRYPTO
│    │    │           │    ├─ daily_drawdown.xlsx
│    │    │           │    └─ final_risk_result.xlsx
│    │    │           ├─ DJIA
│    │    │           │    ├─ daily_drawdown.xlsx
│    │    │           │    └─ final_risk_result.xlsx
│    │    │           ├─ HSI
│    │    │           │    ├─ daily_drawdown.xlsx
│    │    │           │    └─ final_risk_result.xlsx
│    │    │           ├─ NYSE(N)
│    │    │           │    ├─ daily_drawdown.xlsx
│    │    │           │    └─ final_risk_result.xlsx
│    │    │           ├─ NYSE(O)
│    │    │           │    ├─ daily_drawdown.xlsx
│    │    │           │    └─ final_risk_result.xlsx
│    │    │           ├─ SP500
│    │    │           │    ├─ daily_drawdown.xlsx
│    │    │           │    └─ final_risk_result.xlsx
│    │    │           ├─ SSE
│    │    │           │    ├─ daily_drawdown.xlsx
│    │    │           │    └─ final_risk_result.xlsx
│    │    │           └─ TSE
│    │    │                  ├─ daily_drawdown.xlsx
│    │    │                  └─ final_risk_result.xlsx
│    │    └─ datasets
│    │           ├─ CMEG
│    │           ├─ CRYPTO
│    │           ├─ DJIA
│    │           ├─ HSI
│    │           ├─ NYSE(N)
│    │           ├─ NYSE(O)
│    │           ├─ SP500
│    │           ├─ SSE
│    │           └─ TSE
│    ├─ data_layer
│    │    ├─ __init__.py
│    │    ├─ data_loader.py
│    │    └─ scaler_selector.py
│    ├─ evaluation_layer
│    │    ├─ __init__.py
│    │    ├─ benchmark_loader.py
│    │    ├─ metric_caculator.py
│    │    └─ model_evaluator.py
│    ├─ main
│    │    ├─ main.ipynb
│    │    └─ main.py
│    ├─ model_layer
│    │    ├─ __init__.py
│    │    ├─ CNN.py
│    │    ├─ DNN.py
│    │    ├─ LSRE_CAAN.py
│    │    ├─ LSTM.py
│    │    ├─ RNN.py
│    │    ├─ Transformer.py
│    │    └─ model_selector.py
│    ├─ optimization_layer
│    │    ├─ __init__.py
│    │    ├─ criterion_selector.py
│    │    ├─ model_trainer.py
│    │    └─ optimizer_selector.py
│    ├─ setup.py
│    ├─ tutorials
│    │    ├─ README.md
│    │    ├─ _.ipynb
│    │    ├─ tutorial_1.ipynb
│    │    ├─ tutorial_2.ipynb
│    │    ├─ tutorial_3.ipynb
│    │    └─ tutorial_4.ipynb
│    ├─ update
│    │    └─ __init__.py
│    └─ utils.py
├─ logdir
├─ requirements.txt
└─ setup.py

License

Released under the MIT License.

Contact Us

For inquiries, please get in touch with us at [e-mail] (Monday to Friday, 9:00 AM to 6:00 PM)