{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JDT4OWPG5FT3BXT6YUWOWGNE6G","short_pith_number":"pith:JDT4OWPG","schema_version":"1.0","canonical_sha256":"48e7c759e6e967b0de7ec52ceb19a4f195ebb84678d009e83533edc59bc606e6","source":{"kind":"arxiv","id":"2307.05522","version":1},"attestation_state":"computed","paper":{"title":"Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.TR","authors_text":"Stefan Zohren, Stephen Roberts, Tom Liu","submitted_at":"2023-07-07T15:07:07Z","abstract_excerpt":"We introduce Deep Inception Networks (DINs), a family of Deep Learning models that provide a general framework for end-to-end systematic trading strategies. DINs extract time series (TS) and cross sectional (CS) features directly from daily price returns. This removes the need for handcrafted features, and allows the model to learn from TS and CS information simultaneously. DINs benefit from a fully data-driven approach to feature extraction, whilst avoiding overfitting. Extending prior work on Deep Momentum Networks, DIN models directly output position sizes that optimise Sharpe ratio, but fo"},"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":"2307.05522","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.TR","submitted_at":"2023-07-07T15:07:07Z","cross_cats_sorted":[],"title_canon_sha256":"9d7b2ef8af436c0b9e083490e269c2a1abd2c97860326328258905a64833a7e1","abstract_canon_sha256":"3a5d7d8fd6b4c208a105a4c9a2a06dea1c2ef3f70c6993fcdb2ed66709d8444e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:30:09.317229Z","signature_b64":"yYmJi2yzM6ujTC2STQ8zIb6j1ruoaPscqCSk5Zs2By7qS4jtLkjYUI1n4te9lblN7V0vhlpLwsFsAFY+yOTABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48e7c759e6e967b0de7ec52ceb19a4f195ebb84678d009e83533edc59bc606e6","last_reissued_at":"2026-07-05T06:30:09.316867Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:30:09.316867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.TR","authors_text":"Stefan Zohren, Stephen Roberts, Tom Liu","submitted_at":"2023-07-07T15:07:07Z","abstract_excerpt":"We introduce Deep Inception Networks (DINs), a family of Deep Learning models that provide a general framework for end-to-end systematic trading strategies. DINs extract time series (TS) and cross sectional (CS) features directly from daily price returns. This removes the need for handcrafted features, and allows the model to learn from TS and CS information simultaneously. DINs benefit from a fully data-driven approach to feature extraction, whilst avoiding overfitting. Extending prior work on Deep Momentum Networks, DIN models directly output position sizes that optimise Sharpe ratio, but fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.05522","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/2307.05522/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":"2307.05522","created_at":"2026-07-05T06:30:09.316930+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.05522v1","created_at":"2026-07-05T06:30:09.316930+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.05522","created_at":"2026-07-05T06:30:09.316930+00:00"},{"alias_kind":"pith_short_12","alias_value":"JDT4OWPG5FT3","created_at":"2026-07-05T06:30:09.316930+00:00"},{"alias_kind":"pith_short_16","alias_value":"JDT4OWPG5FT3BXT6","created_at":"2026-07-05T06:30:09.316930+00:00"},{"alias_kind":"pith_short_8","alias_value":"JDT4OWPG","created_at":"2026-07-05T06:30:09.316930+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.05107","citing_title":"Painting the market: generative diffusion models for financial limit order book simulation and forecasting","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JDT4OWPG5FT3BXT6YUWOWGNE6G","json":"https://pith.science/pith/JDT4OWPG5FT3BXT6YUWOWGNE6G.json","graph_json":"https://pith.science/api/pith-number/JDT4OWPG5FT3BXT6YUWOWGNE6G/graph.json","events_json":"https://pith.science/api/pith-number/JDT4OWPG5FT3BXT6YUWOWGNE6G/events.json","paper":"https://pith.science/paper/JDT4OWPG"},"agent_actions":{"view_html":"https://pith.science/pith/JDT4OWPG5FT3BXT6YUWOWGNE6G","download_json":"https://pith.science/pith/JDT4OWPG5FT3BXT6YUWOWGNE6G.json","view_paper":"https://pith.science/paper/JDT4OWPG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.05522&json=true","fetch_graph":"https://pith.science/api/pith-number/JDT4OWPG5FT3BXT6YUWOWGNE6G/graph.json","fetch_events":"https://pith.science/api/pith-number/JDT4OWPG5FT3BXT6YUWOWGNE6G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JDT4OWPG5FT3BXT6YUWOWGNE6G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JDT4OWPG5FT3BXT6YUWOWGNE6G/action/storage_attestation","attest_author":"https://pith.science/pith/JDT4OWPG5FT3BXT6YUWOWGNE6G/action/author_attestation","sign_citation":"https://pith.science/pith/JDT4OWPG5FT3BXT6YUWOWGNE6G/action/citation_signature","submit_replication":"https://pith.science/pith/JDT4OWPG5FT3BXT6YUWOWGNE6G/action/replication_record"}},"created_at":"2026-07-05T06:30:09.316930+00:00","updated_at":"2026-07-05T06:30:09.316930+00:00"}