arbitragelab
ArbitrageLab is a python library that enables traders who want to exploit mean-reverting portfolios by providing a complete set of algorithms from the best academic journals.
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Download Latest Version (.zip)- bug_report.md
- docs_suggestion.md
- feature_request.md
- publish-final-dist.yaml
- publish-test-dist.yaml
- python-tests.yaml
- pull_request_template.md
- __init__.py
- codependence_matrix.py
- correlation.py
- gnpr_distance.py
- information.py
- optimal_transport.py
- __init__.py
- base.py
- coint_sim.py
- engle_granger.py
- johansen.py
- minimum_profit.py
- multi_coint.py
- sparse_mr_portfolio.py
- utils.py
- __init__.py
- clayton.py
- frank.py
- gumbel.py
- joe.py
- n13.py
- n14.py
- __init__.py
- gaussian.py
- student.py
- __init__.py
- base.py
- cfg_mix_copula.py
- ctg_mix_copula.py
- __init__.py
- base.py
- copula_calculation.py
- pairs_selection.py
- vine_copula_partner_selection.py
- vine_copula_partner_selection_utils.py
- vinecop_generate.py
- vinecop_strategy.py
- __init__.py
- basic_distance_approach.py
- pearson_distance_approach.py
- __init__.py
- adf_optimal.py
- box_tiao.py
- half_life.py
- johansen.py
- linear.py
- spread_construction.py
- __init__.py
- feature_expander.py
- filters.py
- neural_networks.py
- optics_dbscan_pairs_clustering.py
- regressor_committee.py
- tar.py
- __init__.py
- cir_model.py
- heat_potentials.py
- ou_model.py
- xou_model.py
- __init__.py
- kalman_filter.py
- pca_approach.py
- __init__.py
- base.py
- cointegration.py
- __init__.py
- optimal_convergence.py
- ou_model_jurek.py
- ou_model_mudchanatongsuk.py
- __init__.py
- tearsheet.py
- __init__.py
- arima_predict.py
- h_strategy.py
- ou_optimal_threshold.py
- ou_optimal_threshold_bertram.py
- ou_optimal_threshold_zeng.py
- quantile_time_series.py
- regime_switching_arbitrage_rule.py
- __init__.py
- basic_copula.py
- copula_strategy_mpi.py
- minimum_profit.py
- multi_coint.py
- z_score.py
- __init__.py
- base_futures_roller.py
- data_cursor.py
- data_importer.py
- generate_dataset.py
- indexed_highlight.py
- rollers.py
- spread_modeling_helper.py
- __init__.py
- .gitkeep
- favicon_arbitragelab.png
- ht_logo_black.png
- ht_logo_white.png
- logo_black.png
- logo_white.png
- breadcrumbs.html
- license.rst
- abs.png
- angular_distance.png
- codep_slides.png
- codependence_slides.png
- dependence_copulas.png
- distance_correlation.png
- entropy_relation_diagram.png
- independent.png
- linear.png
- modified_angular_distance.png
- optimal_transport_distance.png
- squared.png
- target_copulas.png
- codependence_marti.rst
- codependence_matrix.rst
- correlation_based_metrics.rst
- information_theory_metrics.rst
- introduction.rst
- optimal_transport.rst
- AME-DOV.png
- cluster.gif
- coint_sim.png
- column_lasso_demo-opt.gif
- cov_select_demo-opt.gif
- engle-granger_portfolio.png
- greedy_demo.gif
- johansen_portfolio.png
- minimum_profit_slides.png
- MR_strength_box_tiao.png
- multitask_lasso_demo-opt.gif
- nile_river_level.png
- sparse_mr_slides.png
- cointegration_tests.rst
- half_life.rst
- introduction.rst
- minimum_profit.rst
- minimum_profit_simulation.rst
- multivariate_cointegration.rst
- sparse_mr_portfolio.rst
- 3d_vinecop_decomposition.png
- AMGN_HD_MixCop.png
- Bollinger_band_example.png
- C_vine_D_vine_structure.png
- CMPI_vs_log_prices.png
- copula_marginal_dist_demo.png
- CumDenN13.png
- Cvine_tuple.png
- densityGaussian.png
- densityGumbel.png
- ecdf_vs_ecdflin.png
- Equity_curve_cvinecop.png
- eucdis_ranked_rho_tau.png.png
- formation_copulas.png
- individual_ranked_rho_tau.png
- positions_log_prices.png
- R_vine_structure.png
- rho_ranked_rho_tau.png
- Rvine_Cvine_Dvine.png
- tau_ranked_rho_tau.png
- top_euc.png
- top_tau.png
- top_tau_quantile.png
- trading_opportunities.png
- workflow_getdata.png
- workflow_select_structure.png
- workflow_vinecop_density.png
- copula_brief_intro.rst
- copula_deeper_intro.rst
- cvine_copula_strategy.rst
- introduction.rst
- partner_selection.rst
- utility_functions.rst
- vine_copula_intro.rst
- back_cont.jpeg
- prices.png
- returns.png
- rolling_intuition.png
- ticker_collection.png
- data_importer.rst
- futures_rollover.rst
- debugging.rst
- distance_approach_pair.png
- distance_approach_portfolio.png
- distance_approach_results_portfolio.png
- pearson_approach_beta_stocks.png
- SSD_distance_example.png
- distance_approach.rst
- introduction.rst
- pearson_approach.rst
- derivative.png
- graph.png
- OLS_vs_TLS.png
- prior.png
- equity_curve_convention.rst
- installation.rst
- research_tools.rst
- hedge_ratios.rst
- 2nd_order_honn.png
- 3d_cluster_optics_plot.png
- confirmation_filter.png
- correlation_filter.png
- correlation_filter_example.png
- crack_spread.png
- example_ml_pair.png
- honn_decision_region_xor.png
- honn_loss_xor.png
- honn_types.png
- knee_plot.png
- leverage_structure.png
- mlp_decision_region_xor.png
- mlp_loss_xor.png
- pairs_selection_rules_diagram.png
- paper_results.png
- pi_sigma_nn.png
- prposed_framework_diagram.png
- rnn_lstm_example.png
- rpnn.png
- threshold_filter_example.png
- vol_filter.png
- xor_boundaries.png
- filters.rst
- introduction.rst
- ml_based_pairs_selection.rst
- neural_networks.rst
- spread_modeling.rst
- threshold_ar.rst
- cir_description.png
- cir_optimal_switching.png
- description_function.png
- fit_check_function.png
- optimal_levels_plot.png
- optimal_switching.png
- xou_vs_ou.png
- cir_model.rst
- heat_potentials.rst
- introduction.rst
- ou_model.rst
- xou_model.rst
- kalman_cumulative_returns.png
- kalman_intercept.png
- kalman_slope.png
- pca_approach_portfolio.png
- pca_approach_s_score.png
- kalman_filter.rst
- pca_approach.rst
- pairs_selection_rules_diagram.png
- cointegration_spread_selection.rst
- jurek_describe.png
- mudchana_describe.png
- oc_delta_neutral_first.png
- oc_delta_neutral_second.png
- oc_describe.png
- oc_optimal_first.png
- oc_optimal_second.png
- oc_spread.png
- oc_wealth_delta_neutral.png
- oc_wealth_optimal.png
- optimal_weights.png
- optimal_weights_fund_flows.png
- stabilization_bound.png
- introduction.rst
- optimal_convergence.rst
- ou_model_jurek.rst
- ou_model_mudchanatongsuk.rst
- auto_arima_prediction.png
- model_diagram.png
- quantile_thresholds.png
- trading_example.png
- h_strategy.rst
- introduction.rst
- ou_optimal_threshold_bertram.rst
- ou_optimal_threshold_zeng.rst
- quantile_time_series_strategy.rst
- regime_switching_arbitrage_rule.rst
- AME-DOV.png
- formation_copulas.png
- mpi_flags_positions.png
- mpi_normalized_prices.png
- mpi_units.png
- positions_log_prices.png
- returns_and_samples.png
- trading_opportunities.png
- basic_copula.rst
- minimum_profit.rst
- mispricing_index_strategy.rst
- multi_coint.rst
- z_score.rst
- coint_eg.png
- coint_jh.png
- ou_tearsheet.png
- tearsheet.rst
- changelog.rst
- conf.py
- index.rst
- make.bat
- Makefile
- ANZ-ADB.csv
- b0.csv
- BKD_ESC_2009_2011.csv
- BKD_ESC_unittest_positions.csv
- cl.csv
- CL=F_NG=F_data.csv
- Country_ETF.csv
- eh1.csv
- eh2.csv
- gld_gdx_data.csv
- incorrect_data.csv
- multi_coint.csv
- nbp.csv
- NonNegative_CL_forward_roll.csv
- NonNegative_nRB_forward_roll.csv
- prices_10y_SP500.csv
- rb.csv
- s.csv
- shell-rdp-close_USD.csv
- sp100_prices.csv
- sp500_2016_test.csv
- sp500_constituents-detailed.csv
- stock_prices.csv
- XLF-XLK.csv
- __init__.py
- test_auto_arima.py
- test_basic_distance_approach.py
- test_cir_model.py
- test_codependence.py
- test_coint_sim.py
- test_copula_generate_mixedcopula.py
- test_copula_pairs_selection.py
- test_copulas.py
- test_data_importer.py
- test_feature_expander.py
- test_filters.py
- test_futures_roller.py
- test_h_strategy.py
- test_heat_potentials.py
- test_hedge_ratios.py
- test_hedge_ratios_spread_construction.py
- test_indexed_highlight.py
- test_kalman_filter.py
- test_mean_reversion.py
- test_minimum_profit.py
- test_mixed_copula.py
- test_multi_coint.py
- test_neural_networks.py
- test_optics_dbscan_pairs_clustering.py
- test_optimal_convergence.py
- test_ou_model.py
- test_ou_model_jurek.py
- test_ou_model_mudchanatongsuk.py
- test_ou_optimal_threshold.py
- test_ou_optimal_threshold_bertram.py
- test_ou_optimal_threshold_zeng.py
- test_partner_selection.py
- test_pca_approach.py
- test_pearson_distance_approach.py
- test_quantile_time_series.py
- test_regime_switching_arbitrage_rule.py
- test_regressor_committee.py
- test_sparse_mr_portfolio.py
- test_spread_modeling_helper.py
- test_spread_selection_cointegration.py
- test_tar.py
- test_tearsheet.py
- test_trading_basic_copula.py
- test_trading_copula_strategy_mpi.py
- test_trading_minimum_profit.py
- test_trading_multi_coint.py
- test_trading_z_score.py
- test_vinecop_generate_strategy.py
- test_xou_model.py
- .bumpversion.cfg
- .coveragerc
- .gitignore
- .pylintrc
- .readthedocs.yml
- CODE_OF_CONDUCT.md
- CONTRIBUTING.md
- coverage
- DEV_NOTE
- LICENSE.txt
- MANIFEST.in
- pylint
- pyproject.toml
- README.md
# Installation Guide
1. Get the code
git clone https://github.com/hudson-and-thames/arbitragelab
Downloads the entire project code from GitHub to your computer.
cd arbitragelab
Moves into the project folder you just downloaded.
2. Python
Easy RecommendedPrerequisites
pip install .
Installs the package published on PyPI directly β no need to clone the source.
python <μ€νν νμΌλͺ
>.py # READMEμμ μ νν μ€ν νμΌλͺ
μ νμΈνμΈμ
Runs the Python script (or module).
If it runs without errors and prints output in the terminal, it worked.
// repository documentation
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