machine-learning-for-trading

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Machine Learning for Trading, 3rd edition의 코드로, 데이터 소싱부터 실시간 실행까지의 전 과정을 다룹니다.

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- - check_import_resolution.py
  - check_notebook_pair_sync.py
  - check_openmp_import_order.py
  - check_verified_notebooks.py
  - check_windows_safe_paths.py
  - notebook_provenance.py
  - render_notebook.py
  - sanitize_notebook_paths.py
  - smoke_lightgbm_openmp.py
  - strip_empty_cell_tags.py
  - strip_papermill_cell_metadata.py
- - container-smoke.yml
  - docker-publish.yml
  - full-test.yml
  - reader-install.yml
  - test.yml
  - weekly-external.yml
- copilot-instructions.md
- dependabot.yml
- factor_regimes.ipynb
- factor_regimes.py
- macro_regimes.ipynb
- macro_regimes.py
- README.md
- 01_us_equities_eda.ipynb
- 01_us_equities_eda.py
- 02_corporate_actions.ipynb
- 02_corporate_actions.py
- 03_etfs_eda.ipynb
- 03_etfs_eda.py
- 04_cme_futures_eda.ipynb
- 04_cme_futures_eda.py
- 05_futures_session_aggregation.ipynb
- 05_futures_session_aggregation.py
- 06_futures_continuous.ipynb
- 06_futures_continuous.py
- 07_sp500_options_eda.ipynb
- 07_sp500_options_eda.py
- 08_options_greeks_computation.ipynb
- 08_options_greeks_computation.py
- 09_options_continuous.ipynb
- 09_options_continuous.py
- 10_crypto_perps_eda.ipynb
- 10_crypto_perps_eda.py
- 11_crypto_premium_analysis.ipynb
- 11_crypto_premium_analysis.py
- 12_fx_pairs_eda.ipynb
- 12_fx_pairs_eda.py
- 13_data_quality_framework.ipynb
- 13_data_quality_framework.py
- 14_point_in_time_validation.ipynb
- 14_point_in_time_validation.py
- 15_survivorship_bias_detection.ipynb
- 15_survivorship_bias_detection.py
- 16_provider_comparison.ipynb
- 16_provider_comparison.py
- 17_complete_pipeline.ipynb
- 17_complete_pipeline.py
- 18_data_management.ipynb
- 18_data_management.py
- 19_incremental_updates.ipynb
- 19_incremental_updates.py
- 20_storage_benchmark_file.ipynb
- 20_storage_benchmark_file.py
- 21_storage_benchmark_database.ipynb
- 21_storage_benchmark_database.py
- 22_pandas_polars_benchmark.ipynb
- 22_pandas_polars_benchmark.py
- README.md
- 01_itch_parser.ipynb
- 01_itch_parser.py
- 02_itch_lob_reconstruction.ipynb
- 02_itch_lob_reconstruction.py
- 03_itch_lob_analysis.ipynb
- 03_itch_lob_analysis.py
- 04_itch_order_lifecycle_analysis.ipynb
- 04_itch_order_lifecycle_analysis.py
- 05_itch_trading_activity.ipynb
- 05_itch_trading_activity.py
- 06_itch_intraday_patterns.ipynb
- 06_itch_intraday_patterns.py
- 07_itch_stylized_facts.ipynb
- 07_itch_stylized_facts.py
- 08_databento_lob_reconstruction.ipynb
- 08_databento_lob_reconstruction.py
- 09_databento_mbo_analysis.ipynb
- 09_databento_mbo_analysis.py
- 10_iex_lob_reconstruction.ipynb
- 10_iex_lob_reconstruction.py
- 11_algoseek_taq_eda.ipynb
- 11_algoseek_taq_eda.py
- 12_algoseek_taq_lob_reconstruction.ipynb
- 12_algoseek_taq_lob_reconstruction.py
- 13_algoseek_minute_bars_eda.ipynb
- 13_algoseek_minute_bars_eda.py
- 14_itch_bar_sampling.ipynb
- 14_itch_bar_sampling.py
- 15_itch_lee_ready.ipynb
- 15_itch_lee_ready.py
- 16_itch_information_bars.ipynb
- 16_itch_information_bars.py
- 17_databento_bar_sampling.ipynb
- 17_databento_bar_sampling.py
- 18_algoseek_jump_detection.ipynb
- 18_algoseek_jump_detection.py
- itch_message_specs.py
- limit_orderbook.py
- README.md
- 01_academic_characteristics.ipynb
- 01_academic_characteristics.py
- 02_sec_filing_explorer.ipynb
- 02_sec_filing_explorer.py
- 03_sec_form4_insider_transactions.ipynb
- 03_sec_form4_insider_transactions.py
- 04_sec_xbrl_fundamentals.ipynb
- 04_sec_xbrl_fundamentals.py
- 05_entity_resolution.ipynb
- 05_entity_resolution.py
- 06_fred_macro_eda.ipynb
- 06_fred_macro_eda.py
- 07_macro_data_alignment.ipynb
- 07_macro_data_alignment.py
- 08_futures_positioning.ipynb
- 08_futures_positioning.py
- 09_onchain_fundamentals.ipynb
- 09_onchain_fundamentals.py
- 10_institutional_holdings_13f.ipynb
- 10_institutional_holdings_13f.py
- 11_defi_tvl_evaluation.ipynb
- 11_defi_tvl_evaluation.py
- 12_kalshi_prediction_markets.ipynb
- 12_kalshi_prediction_markets.py
- 13_polymarket_prediction_markets.ipynb
- 13_polymarket_prediction_markets.py
- 14_text_data_extraction.ipynb
- 14_text_data_extraction.py
- README.md
- 00_classical_simulation.ipynb
- 00_classical_simulation.py
- 01_timegan.ipynb
- 01_timegan.py
- 02_tailgan_tail_risk.ipynb
- 02_tailgan_tail_risk.py
- 03_sigcwgan_signatures.ipynb
- 03_sigcwgan_signatures.py
- 04_gtgan_irregular.ipynb
- 04_gtgan_irregular.py
- 05_diffusion_ts.ipynb
- 05_diffusion_ts.py
- 06_llm_tabular_great.ipynb
- 06_llm_tabular_great.py
- 07_dp_gan.ipynb
- 07_dp_gan.py
- README.md
- timegan_metrics.py
- 01_where_ideas_come_from.ipynb
- 01_where_ideas_come_from.py
- 02_cv_foundations.ipynb
- 02_cv_foundations.py
- 03_case_study_overview.ipynb
- 03_case_study_overview.py
- exploration.md
- README.md
- 01_data_quality_diagnostics.ipynb
- 01_data_quality_diagnostics.py
- 02_preprocessing_pipeline.ipynb
- 02_preprocessing_pipeline.py
- 03_label_methods.ipynb
- 03_label_methods.py
- 04_maximum_favorable_adverse_excursion.ipynb
- 04_maximum_favorable_adverse_excursion.py
- 05_signal_evaluation.ipynb
- 05_signal_evaluation.py
- 06_ic_inference.ipynb
- 06_ic_inference.py
- 07_multiple_testing.ipynb
- 07_multiple_testing.py
- 08_causal_sanity_checks.ipynb
- 08_causal_sanity_checks.py
- 10_ml4t_library_ecosystem.ipynb
- 10_ml4t_library_ecosystem.py
- _benchmark_utils.py
- README.md
- 01_price_volume_features.ipynb
- 01_price_volume_features.py
- 02_microstructure_features.ipynb
- 02_microstructure_features.py
- 03_structural_cross_instrument_features.ipynb
- 03_structural_cross_instrument_features.py
- 04_fundamentals_macro_calendar.ipynb
- 04_fundamentals_macro_calendar.py
- 05_feature_selection.ipynb
- 05_feature_selection.py
- 06_robustness_sensitivity.ipynb
- 06_robustness_sensitivity.py
- 07_event_studies.ipynb
- 07_event_studies.py
- case_study_feature_summary.ipynb
- case_study_feature_summary.py
- README.md
- 01_visual_diagnostics.ipynb
- 01_visual_diagnostics.py
- 02_structural_breaks.ipynb
- 02_structural_breaks.py
- 03_fractional_differencing.ipynb
- 03_fractional_differencing.py
- 04_kalman_filter.ipynb
- 04_kalman_filter.py
- 05_spectral_features.ipynb
- 05_spectral_features.py
- 06_path_signatures.ipynb
- 06_path_signatures.py
- 07_arima_features.ipynb
- 07_arima_features.py
- 08_garch_volatility.ipynb
- 08_garch_volatility.py
- 09_har_rough_volatility.ipynb
- 09_har_rough_volatility.py
- 10_uncertainty_features.ipynb
- 10_uncertainty_features.py
- 11_hmm_regimes.ipynb
- 11_hmm_regimes.py
- 12_wasserstein_regimes.ipynb
- 12_wasserstein_regimes.py
- 13_regime_as_feature.ipynb
- 13_regime_as_feature.py
- 14_panel_features.ipynb
- 14_panel_features.py
- case_study_temporal_summary.ipynb
- case_study_temporal_summary.py
- README.md
- 01_word2vec_training.ipynb
- 01_word2vec_training.py
- 02_asset_embeddings.ipynb
- 02_asset_embeddings.py
- 03_sentiment_evolution.ipynb
- 03_sentiment_evolution.py
- 04_bert_finetuning.ipynb
- 04_bert_finetuning.py
- 05_financial_ner_finetuning.ipynb
- 05_financial_ner_finetuning.py
- 06_finbert_cross_dataset.ipynb
- 06_finbert_cross_dataset.py
- 07_news_return_signals.ipynb
- 07_news_return_signals.py
- 08_text_feature_evaluation.ipynb
- 08_text_feature_evaluation.py
- 09_filing_text_signals.ipynb
- 09_filing_text_signals.py
- README.md
- 01_ols_inference.ipynb
- 01_ols_inference.py
- 02_regularization_paths.ipynb
- 02_regularization_paths.py
- 03_logistic_classification.ipynb
- 03_logistic_classification.py
- 04_nested_cv_hpo.ipynb
- 04_nested_cv_hpo.py
- 05_shap_analysis.ipynb
- 05_shap_analysis.py
- 06_conformal_prediction.ipynb
- 06_conformal_prediction.py
- 07_case_study_insights.ipynb
- 07_case_study_insights.py
- 08_ml_backtest_intro.ipynb
- 08_ml_backtest_intro.py
- README.md
- 01_ensemble_foundations.ipynb
- 01_ensemble_foundations.py
- 02_gbm_comparison.ipynb
- 02_gbm_comparison.py
- 03_dl_vs_gbm.ipynb
- 03_dl_vs_gbm.py
- 04_optuna_tuning.ipynb
- 04_optuna_tuning.py
- 05_cross_library_hpo.ipynb
- 05_cross_library_hpo.py
- 06_optuna_multi_asset.ipynb
- 06_optuna_multi_asset.py
- 07_hpo_comparison.ipynb
- 07_hpo_comparison.py
- 08_shap_analysis.ipynb
- 08_shap_analysis.py
- 09_xai_limitations.ipynb
- 09_xai_limitations.py
- 10_shap_nlp_sentiment.ipynb
- 10_shap_nlp_sentiment.py
- 11_conformal_gbm.ipynb
- 11_conformal_gbm.py
- 12_case_study_insights.ipynb
- 12_case_study_insights.py
- README.md
- 01_core_architectures.ipynb
- 01_core_architectures.py
- 02_nbeats_interpretable.ipynb
- 02_nbeats_interpretable.py
- 03_great_debate.ipynb
- 03_great_debate.py
- 04_transformers.ipynb
- 04_transformers.py
- 05_tcn.ipynb
- 05_tcn.py
- 06_tsmixer.ipynb
- 06_tsmixer.py
- 07_mamba_ssm.ipynb
- 07_mamba_ssm.py
- 08_cnn_image_encoding.ipynb
- 08_cnn_image_encoding.py
- 09_foundation_models.ipynb
- 09_foundation_models.py
- 10_uncertainty.ipynb
- 10_uncertainty.py
- 11_library_landscape.ipynb
- 11_library_landscape.py
- 12_case_study_insights.ipynb
- 12_case_study_insights.py
- dl_sequences.py
- README.md
- 01_pca_equity_sectors.ipynb
- 01_pca_equity_sectors.py
- 02_eigenportfolios.ipynb
- 02_eigenportfolios.py
- 03_yield_curve_decomposition.ipynb
- 03_yield_curve_decomposition.py
- 04_ipca.ipynb
- 04_ipca.py
- 05_rp_pca.ipynb
- 05_rp_pca.py
- 06_conditional_autoencoder.ipynb
- 06_conditional_autoencoder.py
- 07_stochastic_discount_factor.ipynb
- 07_stochastic_discount_factor.py
- 08_supervised_autoencoder.ipynb
- 08_supervised_autoencoder.py
- 09_case_study_insights.ipynb
- 09_case_study_insights.py
- README.md
- 01_library_overview.ipynb
- 01_library_overview.py
- 02_dowhy_causal_graph.ipynb
- 02_dowhy_causal_graph.py
- 03_econml_dml.ipynb
- 03_econml_dml.py
- 04_dml_crypto_regime.ipynb
- 04_dml_crypto_regime.py
- 05_momentum_causal_trading.ipynb
- 05_momentum_causal_trading.py
- 06_fed_announcement_bsts.ipynb
- 06_fed_announcement_bsts.py
- 07_tigramite_time_series.ipynb
- 07_tigramite_time_series.py
- 08_neural_causal_discovery.ipynb
- 08_neural_causal_discovery.py
- 09_adia_causal_benchmark.ipynb
- 09_adia_causal_benchmark.py
- 10_case_study_insights.ipynb
- 10_case_study_insights.py
- 11_factor_zoo_validation.ipynb
- 11_factor_zoo_validation.py
- README.md
- - backtrader_zipline_parity_results.json
  - case_study_lean_parity_results.json
  - lean_parity_results.json
  - vectorbt_parity_results.json
- - __init__.py
  - _library_validation.py
  - result.py
  - weights.py
- 01_backtest_first_principles.ipynb
- 01_backtest_first_principles.py
- 02_futures_backtesting.ipynb
- 02_futures_backtesting.py
- 03_single_asset_vectorbt.ipynb
- 03_single_asset_vectorbt.py
- 04_single_asset_ml4t_backtest.ipynb
- 04_single_asset_ml4t_backtest.py
- 05_stateful_strategies.ipynb
- 05_stateful_strategies.py
- 06_framework_parity.ipynb
- 06_framework_parity.py
- 07_engine_divergence_anatomy.ipynb
- 07_engine_divergence_anatomy.py
- 08_signal_method_comparison.ipynb
- 08_signal_method_comparison.py
- 09_performance_reporting.ipynb
- 09_performance_reporting.py
- 10_regime_backtest_analysis.ipynb
- 10_regime_backtest_analysis.py
- 11_sharpe_ratio_inference.ipynb
- 11_sharpe_ratio_inference.py
- 12_dsr_validation.ipynb
- 12_dsr_validation.py
- 13_ras_protocol.ipynb
- 13_ras_protocol.py
- 14_cost_sensitivity.ipynb
- 14_cost_sensitivity.py
- 15_lean_engine_parity.ipynb
- 15_lean_engine_parity.py
- 16_case_study_lean_parity.ipynb
- 16_case_study_lean_parity.py
- 17_backtrader_zipline_engine_parity.ipynb
- 17_backtrader_zipline_engine_parity.py
- 18_vectorbt_engine_parity.ipynb
- 18_vectorbt_engine_parity.py
- _etf_baseline.py
- README.md
- - __init__.py
  - configs.py
  - dataset.py
  - features.py
  - graph.py
  - inference.py
  - losses.py
  - model.py
  - train.py
  - utils.py
- 01_portfolio_metrics.ipynb
- 01_portfolio_metrics.py
- 02_mean_variance_optimization.ipynb
- 02_mean_variance_optimization.py
- 03_robust_optimization.ipynb
- 03_robust_optimization.py
- 04_kelly_criterion.ipynb
- 04_kelly_criterion.py
- 05_factor_allocation_evidence.ipynb
- 05_factor_allocation_evidence.py
- 06_hierarchical_risk_parity.ipynb
- 06_hierarchical_risk_parity.py
- 07_conformal_position_sizing.ipynb
- 07_conformal_position_sizing.py
- 08_library_comparison.ipynb
- 08_library_comparison.py
- 09_allocator_comparison.ipynb
- 09_allocator_comparison.py
- 11_dl_portfolio_allocation.ipynb
- 11_dl_portfolio_allocation.py
- 12_vlstm_portfolio.ipynb
- 12_vlstm_portfolio.py
- 13_deepm_regime_robust.ipynb
- 13_deepm_regime_robust.py
- README.md
- 01_cost_taxonomy.ipynb
- 01_cost_taxonomy.py
- 02_spread_estimation.ipynb
- 02_spread_estimation.py
- 03_market_impact_calibration.ipynb
- 03_market_impact_calibration.py
- 04_vwap_twap_execution.ipynb
- 04_vwap_twap_execution.py
- 05_almgren_chriss_optimal_execution.ipynb
- 05_almgren_chriss_optimal_execution.py
- 06_ml4t_execution_demo.ipynb
- 06_ml4t_execution_demo.py
- 07_ml4t_volume_participation.ipynb
- 07_ml4t_volume_participation.py
- 08_ml_dynamic_execution.ipynb
- 08_ml_dynamic_execution.py
- 09_frequency_tradeoff.ipynb
- 09_frequency_tradeoff.py
- 10_gross_vs_net_performance.ipynb
- 10_gross_vs_net_performance.py
- 11_cost_cliff.ipynb
- 11_cost_cliff.py
- 12_commission_slippage_comparison.ipynb
- 12_commission_slippage_comparison.py
- _cost_analysis.py
- README.md
- 01_var_cvar.ipynb
- 01_var_cvar.py
- 02_exit_strategies.ipynb
- 02_exit_strategies.py
- 03_position_sizing_mae_mfe.ipynb
- 03_position_sizing_mae_mfe.py
- 04_factor_exposure.ipynb
- 04_factor_exposure.py
- 05_trade_shap_diagnostics.ipynb
- 05_trade_shap_diagnostics.py
- 06_stress_testing.ipynb
- 06_stress_testing.py
- 07_drift_detection.ipynb
- 07_drift_detection.py
- 08_ml_exit_signals.ipynb
- 08_ml_exit_signals.py
- 09_deep_hedging.ipynb
- 09_deep_hedging.py
- 10_ml4t_backtest_risk_demo.ipynb
- 10_ml4t_backtest_risk_demo.py
- 11_systematic_risk_sweep.ipynb
- 11_systematic_risk_sweep.py
- README.md
- 00_holdout_predictions.ipynb
- 00_holdout_predictions.py
- 01_aggregate_synthesis.ipynb
- 01_aggregate_synthesis.py
- 02_feature_evaluation.ipynb
- 02_feature_evaluation.py
- 03_signal_quality.ipynb
- 03_signal_quality.py
- 04_signal_to_strategy.ipynb
- 04_signal_to_strategy.py
- 05_portfolio_allocation.ipynb
- 05_portfolio_allocation.py
- 06_cost_survival.ipynb
- 06_cost_survival.py
- 07_regime_risk.ipynb
- 07_regime_risk.py
- 08_recommendations.ipynb
- 08_recommendations.py
- holdout.py
- README.md
- 01_algorithms_comparison.ipynb
- 01_algorithms_comparison.py
- 02_optimal_execution_ppo.ipynb
- 02_optimal_execution_ppo.py
- 03_market_making_ppo.ipynb
- 03_market_making_ppo.py
- 04_crypto_execution_rl.ipynb
- 04_crypto_execution_rl.py
- 05_deep_hedging_pfhedge.ipynb
- 05_deep_hedging_pfhedge.py
- 06_inverse_reinforcement_learning.ipynb
- 06_inverse_reinforcement_learning.py
- 07_backtest_with_impact.ipynb
- 07_backtest_with_impact.py
- crypto_execution_env.py
- market_making_env.py
- README.md
- rl_calibration.py
- rl_environments.py
- 01_sec_filing_pipeline.ipynb
- 01_sec_filing_pipeline.py
- 02_domain_embeddings_comparison.ipynb
- 02_domain_embeddings_comparison.py
- 03_hybrid_retrieval.ipynb
- 03_hybrid_retrieval.py
- 04_ragas_evaluation.ipynb
- 04_ragas_evaluation.py
- 05_10k_rag_assistant.ipynb
- 05_10k_rag_assistant.py
- 06_esg_rag_vs_finetune.ipynb
- 06_esg_rag_vs_finetune.py
- 07_institutional_holdings_graph.ipynb
- 07_institutional_holdings_graph.py
- 08_rag_security.ipynb
- 08_rag_security.py
- README.md
- - figure_23_3_supply_chain_network.pdf
  - figure_23_3_supply_chain_network.png
  - supply_chain_d3.html
  - supply_chain_d3.json
  - supply_chain_interactive.html
- 01_sp100_sec_download.ipynb
- 01_sp100_sec_download.py
- 02_supply_chain_kg_construction.ipynb
- 02_supply_chain_kg_construction.py
- 02_supply_chain_kg_construction_qwen25_rerun.ipynb
- 02_supply_chain_kg_construction_qwen3.ipynb
- 03_graph_rag_qa.ipynb
- 03_graph_rag_qa.py
- 04_rag_comparison_benchmark.ipynb
- 04_rag_comparison_benchmark.py
- 05_institutional_holdings_kg.ipynb
- 05_institutional_holdings_kg.py
- 06_gnn_feature_engineering.ipynb
- 06_gnn_feature_engineering.py
- 07_dynamic_kg_temporal.ipynb
- 07_dynamic_kg_temporal.py
- 08_8k_event_extraction.ipynb
- 08_8k_event_extraction.py
- 08_8k_event_extraction_qwen3.ipynb
- 09_knowledge_graph_features.ipynb
- 09_knowledge_graph_features.py
- 10_network_portfolio_construction.ipynb
- 10_network_portfolio_construction.py
- README.md
- - 01_react_reasoning_20260615T191047Z_04eb6e7c603d.json
  - 04_research_agent_20260609T141730Z_b694ab4d0453.json
  - 06_multi_agent_research_20260609T141413Z_9fc4a2655471.json
  - 07_adversarial_debate_20260609T141631Z_cf1ad76cf379.json
  - 08_forecasting_pipeline_20260609T141954Z_ef5bca7b95bd.json
  - 08_forecasting_pipeline_20260609T142158Z_24e083e7fe54.json
  - 09_evaluation_and_governance_20260615T191431Z_d5030378899c.json
  - 10_framework_comparison_20260609T150909Z_ba1855847f4c.json
  - 10_framework_comparison_20260609T151202Z_c8cba3007562.json
- - run_etfs_20260504T223150.json
  - run_us_firm_characteristics_20260504T225521.json
- 01_react_reasoning.ipynb
- 01_react_reasoning.py
- 02_tool_contracts.ipynb
- 02_tool_contracts.py
- 03_state_and_memory.ipynb
- 03_state_and_memory.py
- 04_research_agent.ipynb
- 04_research_agent.py
- 05_aggregation_math.ipynb
- 05_aggregation_math.py
- 06_multi_agent_research.ipynb
- 06_multi_agent_research.py
- 07_adversarial_debate.ipynb
- 07_adversarial_debate.py
- 08_forecasting_pipeline.ipynb
- 08_forecasting_pipeline.py
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- test_sample_registry_downstream_surface.py
- test_sample_registry_for_tests.py
- test_sdf_macro_context.py
- test_seed_prediction_fixtures.py
- test_seeded_output_dir.py
- test_sequence_dataset.py
- test_signals.py
- test_sitecustomize_data_root.py
- test_skip_recent_momentum_construction.py
- test_slot_strategy.py
- test_sp500_options_evaluation_fold_alignment.py
- test_sp500_options_feasibility_rv.py
- test_sp500_options_financial_features.py
- test_sp500_options_garch_pit.py
- test_sp500_options_gbm_contract.py
- test_sp500_options_label_price_basis.py
- test_sp500_options_linear_fold_alignment.py
- test_sp500_options_research.py
- test_sp500_options_straddle_moves.py
- test_sp500_options_underlying_returns.py
- test_strategy_gates.py
- test_style.py
- test_sweep_config_seam.py
- test_tabular_dl.py
- test_tabular_dl_adapter.py
- test_tabular_dl_runtime.py
- test_temporal.py
- test_temporal_artifact_is_read_by_fold.py
- test_toolchain_pins.py
- test_uncertainty_cscv.py
- test_us_equities_panel_feature_ordering.py
- test_us_firm_cost_selection.py
- test_us_firm_risk_selection.py
- test_variant_fold_geometry.py
- test_verified_notebooks_guard.py
- test_warmup_periods_for.py
- __init__.py
- artifact_specs.py
- config.py
- cv_splits.py
- data_quality.py
- downloading.py
- modeling.py
- paths.py
- predictions_cache.py
- README.md
- reproducibility.py
- storage_benchmarks.py
- style.py
.env.example
.gitignore
.pre-commit-config.yaml
.verified-notebooks.tsv
docker-compose.yml
LICENSE
matplotlibrc
pyproject.toml
README.md
sitecustomize.py
uv.lock

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/stefan-jansen/machine-learning-for-trading

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd machine-learning-for-trading

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. 공식 설치 스크립트

쉬움 추천

curl -LsSf https://astral.sh/uv/install.sh | sh

공식 설치 스크립트를 다운로드해서 바로 실행합니다. 이 한 줄로 필요한 게 자동으로 설치됩니다.

✅ 설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Docker

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Docker Desktop 컨테이너를 빌드하고 실행하려면 필요합니다. 설치 후 실행해서 백그라운드에 켜두세요.

docker compose pull ml4t # Option A — Docker (recommended)

compose 설정 파일에 정의된 서비스들을 대상으로 명령을 실행합니다.

docker compose up -d ml4t # Docker: same address

정의된 모든 컨테이너(서버, DB 등)를 한 번에 빌드하고 백그라운드에서 실행합니다.

├── docker-compose.yml all Docker services

compose 설정 파일에 정의된 서비스들을 대상으로 명령을 실행합니다.

✅ 터미널에 docker compose ps 를 입력해 컨테이너들이 Up 상태인지 확인하세요. README에 포트 번호가 적혀있다면 브라우저에서 http://localhost:포트번호 로 접속해보세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

4. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

ML4T_DATA_PATH="${ML4T_DATA_PATH:-$PWD/data}" uv run jupyter lab # local: open the URL it prints

이 명령어를 터미널에 그대로 입력해 실행하세요.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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