{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:V5CC6W72V64ASQO7G73M3FT6FC","short_pith_number":"pith:V5CC6W72","schema_version":"1.0","canonical_sha256":"af442f5bfaafb80941df37f6cd967e288d90ded52a6767ce804ec1725c84759d","source":{"kind":"arxiv","id":"1911.10107","version":1},"attestation_state":"computed","paper":{"title":"Deep Reinforcement Learning for Trading","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-fin.TR"],"primary_cat":"q-fin.CP","authors_text":"Stefan Zohren, Stephen Roberts, Zihao Zhang","submitted_at":"2019-11-22T16:10:45Z","abstract_excerpt":"We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts. Both discrete and continuous action spaces are considered and volatility scaling is incorporated to create reward functions which scale trade positions based on market volatility. We test our algorithms on the 50 most liquid futures contracts from 2011 to 2019, and investigate how performance varies across different asset classes including commodities, equity indices, fixed income and FX markets. We compare our algorithms against classical time series momentum strategies, and show tha"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1911.10107","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2019-11-22T16:10:45Z","cross_cats_sorted":["cs.LG","q-fin.TR"],"title_canon_sha256":"24df374442acba98edbce8c77808333cd998144f3babbab36e44f98f7aa83220","abstract_canon_sha256":"9ad27f4976fbcc23f18307e545c9eb15e04da5a045f0caf2e008edf64d9c8f8b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:21:20.625859Z","signature_b64":"J2s64/3J7zZzsk4zTvbm80zHSSSneF2em+wg0iOFsqcnAM29SFShi3WC7+BmyWZiz+15SAj17qmLuIFmmlACAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af442f5bfaafb80941df37f6cd967e288d90ded52a6767ce804ec1725c84759d","last_reissued_at":"2026-07-05T00:21:20.625406Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:21:20.625406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Reinforcement Learning for Trading","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-fin.TR"],"primary_cat":"q-fin.CP","authors_text":"Stefan Zohren, Stephen Roberts, Zihao Zhang","submitted_at":"2019-11-22T16:10:45Z","abstract_excerpt":"We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts. Both discrete and continuous action spaces are considered and volatility scaling is incorporated to create reward functions which scale trade positions based on market volatility. We test our algorithms on the 50 most liquid futures contracts from 2011 to 2019, and investigate how performance varies across different asset classes including commodities, equity indices, fixed income and FX markets. We compare our algorithms against classical time series momentum strategies, and show tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.10107","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1911.10107/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1911.10107","created_at":"2026-07-05T00:21:20.625466+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.10107v1","created_at":"2026-07-05T00:21:20.625466+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.10107","created_at":"2026-07-05T00:21:20.625466+00:00"},{"alias_kind":"pith_short_12","alias_value":"V5CC6W72V64A","created_at":"2026-07-05T00:21:20.625466+00:00"},{"alias_kind":"pith_short_16","alias_value":"V5CC6W72V64ASQO7","created_at":"2026-07-05T00:21:20.625466+00:00"},{"alias_kind":"pith_short_8","alias_value":"V5CC6W72","created_at":"2026-07-05T00:21:20.625466+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20624","citing_title":"In LLM Reasoning, there is Irrationality on top of Value Misalignment","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2502.17011","citing_title":"Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20348","citing_title":"Memory-Induced Supra-Competitive Outcomes Between Deep Reinforcement Learning Agents in Optimal Trade Execution","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V5CC6W72V64ASQO7G73M3FT6FC","json":"https://pith.science/pith/V5CC6W72V64ASQO7G73M3FT6FC.json","graph_json":"https://pith.science/api/pith-number/V5CC6W72V64ASQO7G73M3FT6FC/graph.json","events_json":"https://pith.science/api/pith-number/V5CC6W72V64ASQO7G73M3FT6FC/events.json","paper":"https://pith.science/paper/V5CC6W72"},"agent_actions":{"view_html":"https://pith.science/pith/V5CC6W72V64ASQO7G73M3FT6FC","download_json":"https://pith.science/pith/V5CC6W72V64ASQO7G73M3FT6FC.json","view_paper":"https://pith.science/paper/V5CC6W72","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.10107&json=true","fetch_graph":"https://pith.science/api/pith-number/V5CC6W72V64ASQO7G73M3FT6FC/graph.json","fetch_events":"https://pith.science/api/pith-number/V5CC6W72V64ASQO7G73M3FT6FC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V5CC6W72V64ASQO7G73M3FT6FC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V5CC6W72V64ASQO7G73M3FT6FC/action/storage_attestation","attest_author":"https://pith.science/pith/V5CC6W72V64ASQO7G73M3FT6FC/action/author_attestation","sign_citation":"https://pith.science/pith/V5CC6W72V64ASQO7G73M3FT6FC/action/citation_signature","submit_replication":"https://pith.science/pith/V5CC6W72V64ASQO7G73M3FT6FC/action/replication_record"}},"created_at":"2026-07-05T00:21:20.625466+00:00","updated_at":"2026-07-05T00:21:20.625466+00:00"}