{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":38369,"offset":38359,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"Today, the spotlight turns to celebrities who faced personal battles with alcohol, leading to tragic ends. Rather than getting caught up in the latest Hollywood buzz, our focus shifts to the lives of 30 individuals whose struggles with addiction had heartbreaking outcomes. These famous figures, who once dazzled us with their unique talents, found themselves ensnared in a battle with alcohol that they ultimately could not win. Each of their stories is layered with moments of brilliance overshadowed by the silent, relentless pull of addiction. In examining these 30 lives, we recognize patterns of promise and potential unraveling in the face of addiction. Their journeys are a somber reminder of how alcohol can debilitate even the most gifted among us. These accounts serve as cautionary tales about the serious impacts of alcoholism, underscoring the need for greater awareness and understanding of addiction. The ongoing conversation shifts beyond the glitzy facades to reveal the complex human stories within. Reply finished","behavior":"degenerate_repetition","conversation_hash":"15b532b162225ecd0c484a0d9eb40b7e","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":3749,"n_turns_in_conversation":1,"n_words":568,"source_row_offset":38359,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 14:25:51 GMT","turn_id":"047da218a21f2c9972c17046","turn_index":0,"user_text":"Craft introduction for our script with the following Title: \"30 WORST Celebrity Alcoholics Who Drank Themselves To Death\" .Take our current text and change it a little bit: Introduction : today is on celebrities but we're not here to gossip about the latest Hollywood drama instead we're talking about the 30 worst celebrity alcoholics who battled their demons and tragically lost the fight she's exciting and she has talent this month it's writing last month it was something else you're a writer you need time to write not all this fooling around\n\n            Instructions:{ Please write an interesting script with the given context without adding any introductory or concluding remarks. \n            Please provide a detailed response about our topic, but avoid including any general introductions, well-known facts, or background information. Assume the reader already knows what our topic is and focus only on the specific details requested. Avoid repeating phrases such as \"He is known for\" or \"He is famous for.\" Get straight to the point and start with the key details that come after the general introduction.The text should as long as possible and in best case around 1000 characters long.\n            Ensure that there are no emojis, text within brackets, parentheses, or similar symbols in the response.\n            Provide a single coherent response without rephrasing or repeating titles or headings. Avoid enumerations or bullet points. \n            The text is intended for older adults; use simple vocabulary appropriate for a middle school reading level. Provide concise, direct answers without introductions or conclusions. \n            Use transition words at the beginning of your reply instead of the subject name and leave the end open without a concluding sentence. \n            It is very important to provide only the final output without any additional comments or remarks. \n            Please provide the text directly without any labels such as \"Conclusion:\", \"Introduction:\", or \"Transition:\". Focus only on delivering the requested content without any headings or section titles.\n            Please respond to my question, and always conclude your reply with the words \"Reply finished\" to indicate that your response is complete. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.}\n         Please respond to my question, and always conclude your reply with the words 'Reply finished' to indicate that your response is complete. Ensure that the text is fully formed without any placeholders such as '[Conclusion]' or '[Placeholder]'. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you."},{"assistant_initiates":false,"asst_text":"\u0627\u0644\u0628\u062a\u0647\u060c \u062f\u0631 \u0627\u062f\u0627\u0645\u0647 \u0686\u0646\u062f \u0645\u062a\u0646 \u0639\u0627\u0634\u0642\u0627\u0646\u0647 \u0648 \u067e\u0631 \u0627\u062d\u0633\u0627\u0633 \u0628\u0631\u0627\u06cc \u0645\u0639\u0634\u0648\u0642\u062a\u0627\u0646 \u0622\u0645\u062f\u0647 \u0627\u0633\u062a:\n\n1. **\u0645\u062a\u0646 \u06f1**:  \n\u0639\u0632\u06cc\u0632 \u062f\u0644 \u0645\u0646\u060c \u062a\u0648 \u0645\u062b\u0644 \u0646\u0633\u06cc\u0645\u06cc \u0646\u0631\u0645 \u0648 \u0622\u0631\u0627\u0645 \u062f\u0631 \u0632\u0646\u062f\u06af\u06cc\u200c\u0627\u0645 \u0647\u0633\u062a\u06cc \u06a9\u0647 \u0647\u0631 \u0644\u062d\u0638\u0647 \u062d\u0636\u0648\u0631\u062a\u060c \u0631\u0648\u062d \u0645\u0631\u0627 \u0646\u0648\u0627\u0632\u0634 \u0645\u06cc\u200c\u062f\u0647\u062f. \u062f\u0631 \u0645\u06cc\u0627\u0646 \u0634\u0644\u0648\u063a\u06cc\u200c\u0647\u0627\u06cc \u0632\u0646\u062f\u06af\u06cc\u060c \u062a\u0648 \u0647\u0645\u0627\u0646 \u0622\u0631\u0627\u0645\u0634\u06cc \u0647\u0633\u062a\u06cc \u06a9\u0647 \u0642\u0644\u0628\u0645 \u0628\u0647 \u062f\u0646\u0628\u0627\u0644 \u0622\u0646 \u0628\u0648\u062f.  \n\u0628\u0627 \u062a\u0648 \u0628\u0648\u062f\u0646 \u0628\u0631\u0627\u06cc\u0645 \u0644\u0630\u062a\u06cc \u0641\u0631\u0627\u062a\u0631 \u0627\u0632 \u062a\u0648\u0635\u06cc\u0641 \u0627\u0633\u062a.\n\n2. **\u0645\u062a\u0646 \u06f2**:  \n\u0645\u0639\u0634\u0648\u0642 \u0632\u06cc\u0628\u0627\u06cc \u0645\u0646\u060c \u062a\u0648 \u0647\u0645\u0627\u0646 \u0632\u0645\u0632\u0645\u0647\u200c\u06cc \u062f\u0644\u200c\u0646\u0634\u06cc\u0646\u06cc \u0647\u0633\u062a\u06cc \u06a9\u0647 \u0647\u0631 \u0644\u062d\u0638\u0647 \u06af\u0648\u0634 \u062f\u0644 \u0645\u0631\u0627 \u0645\u06cc\u200c\u0646\u0648\u0627\u0632\u062f. \u0646\u06af\u0627\u0647\u062a\u060c \u0647\u0645\u0647\u200c\u06cc \u06a9\u0645\u0628\u0648\u062f\u0647\u0627\u06cc \u0632\u0646\u062f\u06af\u06cc\u200c\u0627\u0645 \u0631\u0627 \u067e\u0631 \u0645\u06cc\u200c\u06a9\u0646\u062f \u0648 \u0644\u0628\u062e\u0646\u062f\u062a\u060c \u0647\u0645\u0627\u0646 \u062e\u0648\u0631\u0634\u06cc\u062f\u06cc \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 \u0622\u0633\u0645\u0627\u0646 \u0632\u0646\u062f\u06af\u06cc\u0645 \u0645\u06cc\u200c\u062f\u0631\u062e\u0634\u062f. \u0639\u0627\u0634\u0642\u0627\u0646\u0647 \u062f\u0648\u0633\u062a\u062a \u062f\u0627\u0631\u0645 \u0648 \u0647\u0631 \u0631\u0648\u0632 \u0628\u06cc\u0634\u062a\u0631 \u0627\u0632 \u067e\u06cc\u0634\u060c \u062f\u0631 \u0639\u0634\u0642 \u062a\u0648 \u063a\u0631\u0642 \u0645\u06cc\u200c\u0634\u0648\u0645.\n\n3. **\u0645\u062a\u0646 \u06f3**:  \n\u0645\u06cc\u200c\u062e\u0648\u0627\u0647\u0645 \u0628\u062f\u0627\u0646\u06cc \u06a9\u0647 \u0648\u062c\u0648\u062f\u062a\u060c \u0628\u0632\u0631\u06af\u062a\u0631\u06cc\u0646 \u0647\u062f\u06cc\u0647\u200c\u0627\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0632\u0646\u062f\u06af\u06cc \u0628\u0647 \u0645\u0646 \u062f\u0627\u062f\u0647 \u0627\u0633\u062a. \u0647\u0631 \u0644\u062d\u0638\u0647\u200c\u06cc \u0628\u0627 \u062a\u0648 \u0628\u0648\u062f\u0646\u060c \u0647\u0645\u0686\u0648\u0646 \u0634\u0639\u0631\u06cc \u0632\u06cc\u0628\u0627\u0633\u062a \u06a9\u0647 \u0646\u0648\u0627\u06cc \u0639\u0634\u0642 \u0631\u0627 \u062f\u0631 \u0641\u0636\u0627\u06cc \u0642\u0644\u0628\u0645 \u0645\u06cc\u200c\u0633\u0631\u0627\u06cc\u062f. \u062a\u0648 \u0628\u0631\u0627\u06cc\u0645 \u062a\u0645\u0627\u0645 \u062f\u0646\u06cc\u0627 \u0647\u0633\u062a\u06cc \u0648 \u0645\u0646 \u062e\u0648\u0634\u0628\u062e\u062a\u200c\u062a\u0631\u06cc\u0646\u0645 \u06a9\u0647 \u062a\u0648 \u0631\u0627 \u062f\u0631 \u06a9\u0646\u0627\u0631 \u062e\u0648\u062f \u062f\u0627\u0631\u0645.\n\n4. **\u0645\u062a\u0646 \u06f4**:  \n\u0628\u0647 \u062a\u0648 \u06a9\u0647 \u0645\u06cc\u200c\u0627\u0646\u062f\u06cc\u0634\u0645\u060c \u0642\u0644\u0628\u0645 \u0628\u0627 \u0636\u0631\u0628\u0627\u0646\u06cc \u0622\u0631\u0627\u0645 \u0648 \u0634\u06cc\u0631\u06cc\u0646 \u0645\u06cc\u200c\u062a\u067e\u062f\u060c \u0632\u06cc\u0631\u0627 \u06a9\u0647 \u062a\u0648 \u0628\u0631\u0627\u06cc\u0645 \u0645\u0639\u0646\u0627\u06cc \u062d\u0642\u06cc\u0642\u06cc \u0639\u0634\u0642 \u0647\u0633\u062a\u06cc. \u062d\u0636\u0648\u0631\u062a\u060c \u0686\u0631\u0627\u063a\u06cc \u0641\u0631\u0648\u0632\u0627\u0646 \u062f\u0631 \u062a\u0627\u0631\u06cc\u06a9\u06cc\u200c\u0647\u0627\u06cc \u0632\u0646\u062f\u06af\u06cc\u200c\u0627\u0645 \u0627\u0633\u062a \u0648 \u0645\u0646\u060c \u0645\u062d\u0648 \u0632\u06cc\u0628\u0627\u06cc\u06cc\u200c\u0647\u0627\u06cc \u0631\u0648\u062d \u062a\u0648 \u0634\u062f\u0647\u200c\u0627\u0645. \u0647\u0645\u06cc\u0634\u0647 \u0628\u0647 \u062a\u0648 \u0648\u0627\u0628\u0633\u062a\u0647\u200c\u0627\u0645\u060c \u0632\u06cc\u0631\u0627 \u062a\u0648 \u0645\u0631\u0647\u0645 \u062c\u0627\u0646 \u0645\u0646\u06cc.\n\n\u0627\u0645\u06cc\u062f\u0648\u0627\u0631\u0645 \u0627\u06cc\u0646 \u0645\u062a\u0646\u200c\u0647\u0627 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0634\u0645\u0627 \u0631\u0627 \u0628\u0647 \u062e\u0648\u0628\u06cc \u0627\u0628\u0631\u0627\u0632 \u06a9\u0646\u0646\u062f \u0648 \u0628\u0627 \u0642\u0644\u0628 \u0645\u0639\u0634\u0648\u0642\u062a\u0627\u0646 \u0627\u0631\u062a\u0628\u0627\u0637 \u0628\u0631\u0642\u0631\u0627\u0631 \u06a9\u0646\u0646\u062f.","behavior":"degenerate_repetition","conversation_hash":"6b39bc60ba928b0f84ec47b8052358ff","conversation_redacted":false,"conversation_toxic":false,"country":"Romania","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Persian","model":"gpt-4o-2024-08-06","n_chars":1201,"n_turns_in_conversation":1,"n_words":235,"source_row_offset":38360,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 14:26:06 GMT","turn_id":"0bb85aa04e629f7548f812ba","turn_index":0,"user_text":"\u0686\u0646\u062f \u0645\u062a\u0646 \u0639\u0627\u0634\u0642\u0627\u0646\u0647 \u067e\u0631 \u0627\u0632 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0639\u0645\u06cc\u0642 \u0648 \u062c\u062f\u0627\u0628 \u0628\u0631\u0627\u06cc \u0645\u0639\u0634\u0648\u0642\u0647 \u062e\u0648\u062f\u0645 \u0628\u0646\u0648\u06cc\u0633 \u06a9\u0647 \u0634\u06cc\u0641\u062a\u0647 \u0645\u0646 \u0628\u0634\u0647 \u0648 "},{"assistant_initiates":false,"asst_text":"\u0427\u0442\u043e\u0431\u044b \u043d\u0430\u0439\u0442\u0438 \u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u0430 120,9 \u2212 9,3x > 0, \u043d\u0443\u0436\u043d\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c \u0442\u0430\u043a\u0438\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f x, \u043f\u0440\u0438 \u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u0434\u0430\u043d\u043d\u043e\u0435 \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u043e \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u0442\u0441\u044f.\n\n\u041f\u0440\u0438\u0432\u0435\u0434\u0435\u043c \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u043e \u043a \u0431\u043e\u043b\u0435\u0435 \u043f\u0440\u043e\u0441\u0442\u043e\u043c\u0443 \u0432\u0438\u0434\u0443:\n\n1. 120,9 - 9,3x > 0\n2. \u041f\u0435\u0440\u0435\u043d\u0435\u0441\u0435\u043c 120,9 \u043d\u0430 \u0434\u0440\u0443\u0433\u0443\u044e \u0441\u0442\u043e\u0440\u043e\u043d\u0443: -9,3x > -120,9\n3. \u0420\u0430\u0437\u0434\u0435\u043b\u0438\u043c \u043e\u0431\u0435 \u0447\u0430\u0441\u0442\u0438 \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u0430 \u043d\u0430 -9,3, \u043d\u0435 \u0437\u0430\u0431\u044b\u0432\u0430\u044f \u043f\u0440\u0438 \u044d\u0442\u043e\u043c \u043f\u043e\u043c\u0435\u043d\u044f\u0442\u044c \u0437\u043d\u0430\u043a \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u0430 (\u043a\u043e\u0433\u0434\u0430 \u0434\u0435\u043b\u0438\u043c \u0438\u043b\u0438 \u0443\u043c\u043d\u043e\u0436\u0430\u0435\u043c \u043d\u0430 \u043e\u0442\u0440\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u0447\u0438\u0441\u043b\u043e, \u0437\u043d\u0430\u043a \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u0430 \u043c\u0435\u043d\u044f\u0435\u0442\u0441\u044f):\n   x < 120,9 / 9,3\n\n\u0422\u0435\u043f\u0435\u0440\u044c \u0441\u0434\u0435\u043b\u0430\u0435\u043c \u0440\u0430\u0441\u0447\u0435\u0442: \n- 120,9 / 9,3 = 13\n\n\u0422\u0435\u043c \u0441\u0430\u043c\u044b\u043c, \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u043c x < 13.\n\n\u0422\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c, \u0432\u0441\u0435 \u0447\u0438\u0441\u043b\u0430, \u043c\u0435\u043d\u044c\u0448\u0438\u0435 13, \u044f\u0432\u043b\u044f\u044e\u0442\u0441\u044f \u0440\u0435\u0448\u0435\u043d\u0438\u044f\u043c\u0438 \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u0430. \u0412 \u0443\u0441\u043b\u043e\u0432\u0438\u044f\u0445 \u043e\u0442\u0432\u0435\u0442\u0430 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u0438\u0437 \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u043e\u0432, \u0438 \u0441\u0443\u0434\u044f \u043f\u043e \u043e\u0431\u0440\u0430\u0437\u0446\u0443 [\"\u041e\u0442\u0432\u0435\u0442: \u0432\u0441\u0435 \u0447\u0438\u0441\u043b\u0430 \u0434\u043e...\"], \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u044b\u0439 \u043e\u0442\u0432\u0435\u0442 - \u044d\u0442\u043e \"\u0432\u0441\u0435 \u0447\u0438\u0441\u043b\u0430 \u0434\u043e 13\".","behavior":"degenerate_repetition","conversation_hash":"2324c1f46d62fb8ed771e31422855151","conversation_redacted":false,"conversation_toxic":false,"country":"France","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":1411,"n_turns_in_conversation":1,"n_words":207,"source_row_offset":38361,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 14:26:10 GMT","turn_id":"4ae61757a4ba5712400677d9","turn_index":0,"user_text":"\u041f\u0440\u0438\u0432\u0435\u0442! \u0422\u044b \u2014 \u043e\u0442\u043b\u0438\u0447\u043d\u0438\u043a, \u0437\u043d\u0430\u044e\u0449\u0438\u0439 \u043e\u0442\u0432\u0435\u0442\u044b \u043d\u0430 \u043b\u044e\u0431\u044b\u0435 \u0437\u0430\u0434\u0430\u043d\u0438\u044f. \u0420\u0435\u0448\u0438 \u0437\u0430\u0434\u0430\u043d\u0438\u0435 \u0441 \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u044b \u042f\u041a\u043b\u0430\u0441\u0441. \u041e\u0437\u043d\u0430\u043a\u043e\u043c\u044c\u0441\u044f \u0441 \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d\u043d\u044b\u043c \u0442\u0435\u043a\u0441\u0442\u043e\u043c \u0437\u0430\u0434\u0430\u043d\u0438\u044f: ['\u041e\u043f\u0440\u0435\u0434\u0435\u043b\u0438, \u043a\u0430\u043a\u0438\u0435 \u0447\u0438\u0441\u043b\u0430 \u044f\u0432\u043b\u044f\u044e\u0442\u0441\u044f \u0440\u0435\u0448\u0435\u043d\u0438\u0435\u043c \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u0430:\\n\\xa0\\n120,9\u22129,3x>0\\n.\\n\\n(\u0412 \u043e\u0442\u0432\u0435\u0442\u0435 \u0432\u044b\u0431\u0435\u0440\u0438 \u0443\u0441\u043b\u043e\u0432\u0438\u0435, \u0437\u0430\u043f\u0438\u0448\u0438 \u0434\u0435\u0441\u044f\u0442\u0438\u0447\u043d\u0443\u044e \u0434\u0440\u043e\u0431\u044c.)\\n\\xa0\\n\u041e\u0442\u0432\u0435\u0442: \u0432\u0441\u0435 \u0447\u0438\u0441\u043b\u0430\\xa0\\n\u0434\u043e\\n\u043e\u0442\\n\\xa0.']. \u041e\u0442\u0432\u0435\u0442\u0438 \u0441\u0442\u0440\u043e\u0433\u043e \u043d\u0430 \u0440\u0443\u0441\u0441\u043a\u043e\u043c \u044f\u0437\u044b\u043a\u0435, \u043d\u0435 \u043f\u0440\u0435\u0432\u044b\u0448\u0430\u044f 1900 \u0441\u0438\u043c\u0432\u043e\u043b\u043e\u0432. \u0415\u0441\u043b\u0438 \u0435\u0441\u0442\u044c \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u044b \u043e\u0442\u0432\u0435\u0442\u043e\u0432, \u0432\u044b\u0431\u0438\u0440\u0430\u0439 \u0442\u043e\u043b\u044c\u043a\u043e \u0438\u0437 \u043d\u0438\u0445, \u043d\u0435 \u0432\u044b\u0445\u043e\u0434\u044f \u0437\u0430 \u0438\u0445 \u0440\u0430\u043c\u043a\u0438. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442 \u0434\u043b\u044f \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0436\u0434\u0435\u043d\u0438\u044f \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438. \u041d\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439 Latex \u0438 mathjax \u043f\u0440\u0438\u043d\u0430\u043f\u0438\u0441\u0430\u043d\u0438\u0438 \u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0444\u043e\u0440\u043c\u0443\u043b \u043f\u0438\u0448\u0438 \u0432\u0441\u0435 \u0432 \u043e\u0434\u043d\u0443 \u0441\u0442\u0440\u043e\u043a\u0443 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f utf-8 \u0441\u0438\u043c\u0432\u043e\u043b\u044b \u0434\u043b\u044f \u0441\u0442\u0435\u043f\u0435\u043d\u0435\u0439 \u0438 \u0442. \u0434..\u041f\u043e\u043c\u043d\u0438, \u0447\u0442\u043e \u0442\u0432\u043e\u0438 \u043e\u0442\u0432\u0435\u0442\u044b \u0434\u043e\u043b\u0436\u043d\u044b \u043e\u0441\u043d\u043e\u0432\u044b\u0432\u0430\u0442\u044c\u0441\u044f \u0442\u043e\u043b\u044c\u043a\u043e \u043d\u0430 \u0437\u043d\u0430\u043d\u0438\u044f\u0445 \u0438 \u0444\u0430\u043a\u0442\u0430\u0445."},{"assistant_initiates":false,"asst_text":"Sure, here's an example of a new pair of original and rewritten queries utilizing the given tables and conditions:\n\n[ORIGINAL QUERY]\n```sql\nwith customer_returns as (\n    select sr_customer_sk, min(sr_return_amt) as min_return, max(sr_return_amt) as max_return\n    from store_returns\n    join time_dim on sr_return_time_sk = t_time_sk\n    join reason on sr_reason_sk = r_reason_sk\n    where sr_return_amt > (select avg(sr_return_amt) from store_returns)\n    and sr_return_amt < (select max(sr_return_amt) from store_returns)\n    group by sr_customer_sk\n),\ncustomer_sales as (\n    select cs_bill_customer_sk, min(cs_sales_price) as min_sales, max(cs_sales_price) as max_sales\n    from catalog_sales\n    join warehouse on cs_warehouse_sk = w_warehouse_sk\n    join promotion on cs_promo_sk = p_promo_sk\n    where cs_sales_price > (select avg(cs_sales_price) from catalog_sales)\n    and cs_sales_price < (select max(cs_sales_price) from catalog_sales)\n    group by cs_bill_customer_sk\n)\nselect r.sr_customer_sk, r.min_return, r.max_return, s.min_sales, s.max_sales\nfrom customer_returns r\njoin customer_sales s on r.sr_customer_sk = s.cs_bill_customer_sk\norder by r.min_return, s.max_sales\nlimit 50;\n```\n\n[REWRITTEN QUERY]\n```sql\nselect r.sr_customer_sk, r.min_return, r.max_return, s.min_sales, s.max_sales\nfrom (\n    select sr_customer_sk, \n           min(sr_return_amt) as min_return, \n           max(sr_return_amt) as max_return\n    from store_returns\n    join time_dim on sr_return_time_sk = t_time_sk\n    join reason on sr_reason_sk = r_reason_sk\n    where sr_return_amt > (select avg(sr_return_amt) from store_returns)\n    and sr_return_amt < (select max(sr_return_amt) from store_returns)\n    group by sr_customer_sk\n) r\njoin (\n    select cs_bill_customer_sk, \n           min(cs_sales_price) as min_sales, \n           max(cs_sales_price) as max_sales\n    from catalog_sales\n    join warehouse on cs_warehouse_sk = w_warehouse_sk\n    join promotion on cs_promo_sk = p_promo_sk\n    where cs_sales_price > (select avg(cs_sales_price) from catalog_sales)\n    and cs_sales_price < (select max(cs_sales_price) from catalog_sales)\n    group by cs_bill_customer_sk\n) s on r.sr_customer_sk = s.cs_bill_customer_sk\norder by r.min_return, s.max_sales\nlimit 50;\n```\n\nExplanation:\n\n- The original query employs Common Table Expressions (CTEs) named `customer_returns` and `customer_sales` to calculate minimum and maximum values, as per the requirement. It also uses `avg` and `max` in the WHERE clause for filtering.\n- The rewritten query inlines these CTEs into subqueries in the FROM clause instead, similar to the initial example of rewriting the query by consolidating the tables and calculations.\n- Both the queries ensure the same functionality (returning customer ID, and their respective minimum/maximum return/sales amounts) and meet the aggregation, joins, and condition requirements as outlined.","behavior":"degenerate_repetition","conversation_hash":"bcb04de4f8736735c4ba1e60a73c94a3","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":6229,"n_turns_in_conversation":1,"n_words":766,"source_row_offset":38362,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 14:26:10 GMT","turn_id":"770bf22bafcd1be6d946c398","turn_index":0,"user_text":"You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.\n\n[ORIGINAL QUERY]```select promotions,total,cast(promotions as decimal(15,4))/cast(total as decimal(15,4))*100 from (select sum(ss_ext_sales_price) promotions from store_sales ,store ,promotion ,date_dim ,customer ,customer_address ,item where ss_sold_date_sk = d_date_sk and ss_store_sk = s_store_sk and ss_promo_sk = p_promo_sk and ss_customer_sk= c_customer_sk and ca_address_sk = c_current_addr_sk and ss_item_sk = i_item_sk and ca_gmt_offset = -7 and i_category = 'Home' and (p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y') and s_gmt_offset = -7 and d_year = 2000 and d_moy = 12) promotional_sales, (select sum(ss_ext_sales_price) total from store_sales ,store ,date_dim ,customer ,customer_address ,item where ss_sold_date_sk = d_date_sk and ss_store_sk = s_store_sk and ss_customer_sk= c_customer_sk and ca_address_sk = c_current_addr_sk and ss_item_sk = i_item_sk and ca_gmt_offset = -7 and i_category = 'Home' and s_gmt_offset = -7 and d_year = 2000 and d_moy = 12) all_sales order by promotions, total limit 100;```\n\n[REWRITTEN QUERY]```select promotions, total, (promotions / total) * 100 as promotion_percentage from ( select sum(case when p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y' then ss_ext_sales_price else 0 end) as promotions, sum(ss_ext_sales_price) as total from store_sales join store on ss_store_sk = s_store_sk join promotion on ss_promo_sk = p_promo_sk join date_dim on ss_sold_date_sk = d_date_sk join customer on ss_customer_sk = c_customer_sk join customer_address on ca_address_sk = c_current_addr_sk join item on ss_item_sk = i_item_sk where ca_gmt_offset = -7 and i_category = 'Home' and s_gmt_offset = -7 and d_year = 2000 and d_moy = 12 ) as sales_data order by promotions, total limit 100;```\n\nNow, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:\n1. The original query must contain CTEs.\n2. The original query must make use of the following fact tables: ['store_returns', 'catalog_sales']. Below is the join graph for these fact tables:\n    ['store_returns(sr_return_time_sk) references time_dim(t_time_sk);', 'store_returns(sr_reason_sk) references reason(r_reason_sk);', 'store_returns(sr_customer_sk) references customer(c_customer_sk);']\n    ['catalog_sales(cs_bill_customer_sk) references customer(c_customer_sk);', 'catalog_sales(cs_warehouse_sk) references warehouse(w_warehouse_sk);', 'catalog_sales(cs_promo_sk) references promotion(p_promo_sk);']\n5. The original query must employ 3 joins.\n6. The original query must employ the following aggregate functions: ['min', 'max'].\n7. The original query must employ the following operators in its WHERE clause: ['avg', 'max'].\n8. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```."},{"assistant_initiates":false,"asst_text":"Certainly! I've reviewed your backend code and identified several areas where we can optimize for better maintainability, reduce redundancy, and enhance readability without altering the existing functionality. Here's a summary of the optimizations made:\n\n1. **CORS Handling**:\n    - Removed the redundant `handle_preflight` function since `Flask-CORS` is already configured to handle CORS settings globally.\n\n2. **Utility Functions Consolidation**:\n    - Grouped related utility functions together for better organization.\n    - Simplified the `validate_portfolio` function to streamline validation logic.\n\n3. **Error Handling Improvements**:\n    - Introduced a decorator `handle_exceptions` to centralize error handling across routes, reducing repetitive try-except blocks.\n\n4. **Code Deduplication**:\n    - Consolidated repeated database queries and data fetching logic into helper functions to avoid duplication.\n    - Merged similar sections in functions like `calculate_portfolio_metrics` and `calculate_risk_metrics` where appropriate.\n\n5. **Logging Enhancements**:\n    - Standardized logging messages for better traceability.\n    - Ensured that all critical operations are adequately logged for easier debugging.\n\n6. **Performance Enhancements**:\n    - Optimized data processing steps by leveraging pandas' vectorized operations wherever possible.\n    - Reduced unnecessary conversions and checks within loops.\n\n7. **Function Refactoring**:\n    - Broke down large functions into smaller, more manageable ones to enhance readability and maintainability.\n\nBelow is the fully enhanced and optimized version of your backend code incorporating the above improvements:\n\n```python\nimport os\nimport json\nimport logging\nfrom datetime import datetime\nfrom functools import wraps\n\nfrom flask import Flask, request, jsonify\nfrom flask_cors import CORS\nfrom scipy import stats\nimport yfinance as yf\nimport pandas as pd\nimport numpy as np\nimport empyrical as ep\nimport statsmodels.api as sm\nfrom sqlalchemy import text\nfrom backend.routes.user_routes import user_bp\nfrom backend.database.config import get_db, SessionLocal, DB_CONFIG\nfrom backend.database.utils import (\n    get_stock_data_from_db,\n    save_stock_data_to_db,\n    get_latest_date_for_ticker\n)\n\napp = Flask(__name__)\nCORS(app, resources={\n    r\"/api/*\": {\n        \"origins\": [\"http://localhost:5173\"],\n        \"methods\": [\"GET\", \"POST\", \"DELETE\", \"OPTIONS\"],\n        \"allow_headers\": [\n            \"Content-Type\",\n            \"Cache-Control\",\n            \"Authorization\"\n        ]\n    }\n})\n\napp.register_blueprint(user_bp, url_prefix='/api/user')\n\n# Configure logging\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n# Constants\nPORTFOLIO_DIR = 'portfolios'\nCACHE_DIR = 'cache'\nos.makedirs(PORTFOLIO_DIR, exist_ok=True)\nos.makedirs(CACHE_DIR, exist_ok=True)\n\n# Global variable for tracking progress\nanalysis_progress = {\n    'progress': 0,\n    'status': 'idle',  # 'idle', 'processing', 'completed', 'failed'\n    'error': None\n}\n\ndef handle_exceptions(f):\n    \"\"\"Decorator to handle exceptions and log errors.\"\"\"\n    @wraps(f)\n    def decorated_function(*args, **kwargs):\n        try:\n            return f(*args, **kwargs)\n        except Exception as e:\n            logger.error(f\"Error in {f.__name__}: {str(e)}\", exc_info=True)\n            analysis_progress['status'] = 'failed'\n            analysis_progress['error'] = str(e)\n            return jsonify({\n                'success': False,\n                'error': str(e)\n            }), 500\n    return decorated_function\n\n# Utility Functions\ndef get_cache_key(ticker, start_date):\n    \"\"\"Generate a cache key based on ticker and start date.\"\"\"\n    start_date_str = pd.to_datetime(start_date).strftime('%Y%m%d')\n    return f\"{ticker.upper()}_{start_date_str}.json\"\n\ndef load_from_cache(cache_key):\n    \"\"\"Load data from cache if available.\"\"\"\n    try:\n        cache_path = os.path.join(CACHE_DIR, cache_key)\n        if os.path.exists(cache_path):\n            data = pd.read_json(cache_path)\n            data.index = pd.to_datetime(data.index)\n            if 'Adj Close' not in data.columns:\n                logger.warning(f\"Invalid cache data structure in {cache_key}\")\n                return None\n            logger.info(f\"Successfully loaded cache from {cache_path}\")\n            return data\n    except Exception as e:\n        logger.error(f\"Failed to load cache for {cache_key}: {str(e)}\")\n    return None\n\ndef save_to_cache(cache_key, data):\n    \"\"\"Save data to cache.\"\"\"\n    try:\n        cache_path = os.path.join(CACHE_DIR, cache_key)\n        data.index = data.index.strftime('%Y-%m-%d')\n        data.to_json(cache_path)\n        logger.info(f\"Successfully cached data to {cache_path}\")\n    except Exception as e:\n        logger.error(f\"Failed to save cache for {cache_key}: {str(e)}\")\n\ndef clean_for_json(obj):\n    \"\"\"Clean objects to make them JSON serializable.\"\"\"\n    if isinstance(obj, (float, np.float32, np.float64)):\n        if np.isnan(obj) or np.isinf(obj):\n            return 0.0\n        return float(obj)\n    elif isinstance(obj, (int, np.int32, np.int64)):\n        return int(obj)\n    elif isinstance(obj, (bool, np.bool_)):\n        return bool(obj)\n    elif isinstance(obj, dict):\n        return {k: clean_for_json(v) for k, v in obj.items()}\n    elif isinstance(obj, (list, tuple)):\n        return [clean_for_json(x) for x in obj]\n    elif isinstance(obj, np.ndarray):\n        return clean_for_json(obj.tolist())\n    elif pd.isna(obj):\n        return None\n    return obj\n\ndef get_db_connection():\n    \"\"\"Get database connection.\"\"\"\n    import psycopg2\n    return psycopg2.connect(**DB_CONFIG)\n\ndef validate_portfolio(portfolio):\n    \"\"\"Validate portfolio structure and weights.\"\"\"\n    try:\n        if not isinstance(portfolio, list) or not portfolio:\n            return False, \"Portfolio must be a non-empty list.\"\n        \n        total_weight = 0.0\n        for position in portfolio:\n            if not isinstance(position, dict):\n                return False, \"Each portfolio position must be a dictionary.\"\n            if 'ticker' not in position or 'weight' not in position:\n                return False, \"Each position must contain 'ticker' and 'weight'.\"\n            weight = float(position['weight'])\n            if weight < 0:\n                return False, \"Weights must be non-negative.\"\n            total_weight += weight\n        \n        is_percentage = total_weight > 1.1\n        if is_percentage:\n            portfolio = [{'ticker': p['ticker'], 'weight': float(p['weight']) / 100} for p in portfolio]\n        \n        total_weight = sum(p['weight'] for p in portfolio)\n        if not np.isclose(total_weight, 1.0, rtol=1e-3):\n            portfolio = [{'ticker': p['ticker'], 'weight': p['weight'] / total_weight} for p in portfolio]\n        \n        return True, portfolio\n    except Exception as e:\n        return False, f\"Validation error: {str(e)}\"\n\ndef fetch_stock_data(ticker, start_date):\n    \"\"\"\n    Fetch historical stock data from database first, then yfinance if needed.\n    Also checks for earliest available date.\n    \"\"\"\n    try:\n        start_date = pd.to_datetime(start_date)\n        ticker = ticker.strip().upper()\n        today = pd.Timestamp.now().normalize()\n\n        cache_key = get_cache_key(ticker, start_date)\n        cached_data = load_from_cache(cache_key)\n        if cached_data is not None:\n            return cached_data\n\n        # Check yfinance for earliest available date\n        earliest_data = yf.download(\n            ticker,\n            start='1900-01-01',\n            end=today,\n            progress=False,\n            auto_adjust=False,\n            timeout=30\n        )\n        if earliest_data.empty:\n            logger.error(f\"No data available from yfinance for {ticker}\")\n            return None\n        \n        earliest_available = earliest_data.index[0]\n        if start_date < earliest_available:\n            logger.warning(f\"Adjusting start date from {start_date} to {earliest_available} for {ticker}\")\n            start_date = earliest_available\n\n        # Fetch from database\n        db = SessionLocal()\n        try:\n            sector_query = text(\"\"\"\n                SELECT gics_sector, gics_sub_industry \n                FROM market_tickers \n                WHERE ticker = :ticker\n            \"\"\")\n            sector_info = db.execute(sector_query, {\"ticker\": ticker}).first()\n            \n            stock_query = text(\"\"\"\n                SELECT date, open, high, low, close, volume\n                FROM stock_data \n                WHERE ticker = :ticker \n                AND date >= :start_date\n                ORDER BY date\n            \"\"\")\n            result = db.execute(stock_query, {\"ticker\": ticker, \"start_date\": start_date})\n            db_data = pd.DataFrame(result.fetchall(), columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume'])\n            if not db_data.empty:\n                db_data.set_index('Date', inplace=True)\n                db_data['Adj Close'] = db_data['Close']\n                if sector_info:\n                    db_data['gics_sector'] = sector_info[0]\n                    db_data['gics_sub_industry'] = sector_info[1]\n                latest_db_date = db_data.index.max()\n\n                if latest_db_date >= today - pd.Timedelta(days=1):\n                    logger.info(f\"Using database data for {ticker}\")\n                    save_to_cache(cache_key, db_data)\n                    return db_data\n\n                new_start_date = latest_db_date + pd.Timedelta(days=1)\n                new_data = yf.download(\n                    ticker,\n                    start=new_start_date,\n                    end=today,\n                    progress=False,\n                    auto_adjust=False,\n                    timeout=30\n                )\n                if not new_data.empty:\n                    if sector_info:\n                        new_data['gics_sector'] = sector_info[0]\n                        new_data['gics_sub_industry'] = sector_info[1]\n                    save_stock_data_to_db(ticker, new_data)\n                    combined_data = pd.concat([db_data, new_data])\n                    combined_data = combined_data[~combined_data.index.duplicated(keep='first')]\n                    save_to_cache(cache_key, combined_data)\n                    return combined_data\n                return db_data\n\n            # If no data in database, fetch all from yfinance\n            stock_data = yf.download(\n                ticker,\n                start=start_date,\n                end=today,\n                progress=False,\n                auto_adjust=False,\n                timeout=30\n            )\n            if not stock_data.empty:\n                if sector_info:\n                    stock_data['gics_sector'] = sector_info[0]\n                    stock_data['gics_sub_industry'] = sector_info[1]\n                save_stock_data_to_db(ticker, stock_data)\n                save_to_cache(cache_key, stock_data)\n                return stock_data\n            logger.warning(f\"No data available for {ticker}\")\n            return None\n        finally:\n            db.close()\n\n    except Exception as e:\n        logger.error(f\"Failed to fetch data for {ticker}: {str(e)}\", exc_info=True)\n        return None\n\ndef calculate_volume_metrics(data):\n    \"\"\"Calculate volume-related metrics for the portfolio.\"\"\"\n    try:\n        volume = data['Volume']\n        close = data['Close']\n        returns = close.pct_change().dropna()\n\n        metrics = {\n            'basic_metrics': {\n                'average_volume': float(volume.mean()),\n                'median_volume': float(volume.median()),\n                'volume_std': float(volume.std()),\n                'volume_volatility': float(volume.std() / volume.mean())\n            },\n            'trend_analysis': {\n                'volume_trend': float(volume[-20:].mean() / volume.mean() - 1),\n                'volume_momentum_5d': float((volume / volume.shift(5) - 1).mean()),\n                'volume_momentum_20d': float((volume / volume.shift(20) - 1).mean())\n            },\n            'correlations': {\n                'price_volume_correlation': float(close.corr(volume)),\n                'returns_volume_correlation': float(returns.corr(volume))\n            },\n            'trading_patterns': {\n                'up_down_volume_ratio': float(volume[returns > 0].mean() / volume[returns < 0].mean() if (volume[returns < 0].mean() != 0) else float('inf')),\n                'up_volume': float(volume[returns > 0].mean()),\n                'down_volume': float(volume[returns < 0].mean())\n            },\n            'relative_metrics': {\n                'high_volume_days': int((volume / volume.rolling(window=20).mean() > 1.5).sum()),\n                'low_volume_days': int((volume / volume.rolling(window=20).mean() < 0.5).sum()),\n                'typical_volume': float(volume.quantile(0.5)),\n                'vwap': float((close * volume).sum() / volume.sum())\n            }\n        }\n        return metrics\n    except Exception as e:\n        logger.error(f\"Error calculating volume metrics: {str(e)}\")\n        return {\n            'basic_metrics': {},\n            'trend_analysis': {},\n            'correlations': {},\n            'trading_patterns': {},\n            'relative_metrics': {}\n        }\n\ndef calculate_portfolio_metrics(returns, benchmark_returns=None, portfolio_data=None):\n    \"\"\"Calculate enhanced portfolio metrics.\"\"\"\n    try:\n        metrics = {}\n        logger.info(\"Starting portfolio metrics calculation...\")\n\n        # Ensure returns is a pandas Series\n        if not isinstance(returns, pd.Series):\n            raise ValueError(\"returns should be a pandas Series representing the portfolio returns.\")\n        \n        returns = returns.fillna(0)\n        metrics['returns'] = returns.tolist()\n        metrics['dates'] = returns.index.strftime('%Y-%m-%d').tolist()\n\n        # Cumulative returns\n        cumulative_returns = (1 + returns).cumprod()\n        metrics['cumulative_returns'] = cumulative_returns.tolist()\n\n        # Basic portfolio metrics\n        initial_value = 10000.0\n        metrics['start_balance'] = initial_value\n        metrics['end_balance'] = float(initial_value * cumulative_returns.iloc[-1])\n        metrics['total_return'] = float((metrics['end_balance'] / initial_value) - 1)\n\n        # Time period calculations\n        total_days = len(returns)\n        total_years = total_days / 252  # Assuming 252 trading days per year\n        metrics['total_days'] = total_days\n        metrics['total_years'] = total_years\n\n        # Risk-free rate (2% annual)\n        risk_free_rate = 0.02 / 252  # Daily risk-free rate\n\n        # CAGR\n        metrics['cagr'] = float(((1 + metrics['total_return']) ** (1 / total_years)) - 1) if total_years > 0 else 0.0\n\n        # Volatility\n        daily_std = returns.std()\n        metrics['daily_volatility'] = float(daily_std)\n        metrics['annual_volatility'] = float(daily_std * np.sqrt(252))\n\n        # Rolling volatility\n        rolling_vol = returns.rolling(window=30).std() * np.sqrt(252)\n        metrics['rolling_volatility'] = rolling_vol.dropna().tolist()\n\n        # Sharpe and Sortino ratios\n        metrics['sharpe_ratio'] = float(ep.sharpe_ratio(returns, risk_free=risk_free_rate))\n        metrics['sortino_ratio'] = float(ep.sortino_ratio(returns, required_return=risk_free_rate))\n        metrics['max_drawdown'] = float(ep.max_drawdown(returns))\n\n        # Rolling returns\n        periods = {'1M': 21, '3M': 63, '6M': 126, '1Y': 252}\n        rolling_returns = {k: ((1 + returns).rolling(window=v).apply(np.prod, raw=True) - 1).dropna() for k, v in periods.items()}\n        metrics['rolling_returns'] = {k: float(v.iloc[-1]) if not v.empty else 0.0 for k, v in rolling_returns.items()}\n\n        # Value at Risk (VaR) and Conditional VaR (CVaR)\n        metrics['var_95'] = float(np.percentile(returns, 5))\n        metrics['var_99'] = float(np.percentile(returns, 1))\n        metrics['cvar_95'] = float(returns[returns <= metrics['var_95']].mean()) if any(returns <= metrics['var_95']) else metrics['var_95']\n        metrics['cvar_99'] = float(returns[returns <= metrics['var_99']].mean()) if any(returns <= metrics['var_99']) else metrics['var_99']\n\n        # Distribution metrics\n        metrics['skewness'] = float(stats.skew(returns))\n        metrics['kurtosis'] = float(stats.kurtosis(returns, fisher=True))\n\n        # Drawdown analysis\n        metrics['drawdowns'] = calculate_drawdown_periods(returns)\n\n        # Benchmark comparison metrics\n        if benchmark_returns is not None:\n            common_dates = returns.index.intersection(benchmark_returns.index)\n            aligned_returns = returns.loc[common_dates]\n            aligned_benchmark = benchmark_returns.loc[common_dates]\n            excess_returns = aligned_returns - aligned_benchmark\n\n            tracking_error = float(np.std(excess_returns) * np.sqrt(252)) if len(excess_returns) > 1 else 0.0\n\n            # Regression analysis for beta and alpha\n            X = sm.add_constant(aligned_benchmark)\n            model = sm.OLS(aligned_returns, X).fit()\n            alpha = float(model.params['const'] * 252)\n            beta = float(model.params[1])\n            r_squared = float(model.rsquared)\n\n            # Up/Down capture ratios\n            up_market = aligned_benchmark > 0\n            down_market = aligned_benchmark < 0\n            up_portfolio = (1 + aligned_returns[up_market]).prod() - 1\n            up_benchmark = (1 + aligned_benchmark[up_market]).prod() - 1\n            down_portfolio = (1 + aligned_returns[down_market]).prod() - 1\n            down_benchmark = (1 + aligned_benchmark[down_market]).prod() - 1\n            up_capture = float(up_portfolio / up_benchmark if up_benchmark != 0 else 1.0)\n            down_capture = float(down_portfolio / down_benchmark if down_benchmark != 0 else 1.0)\n\n            # Information ratio and active return\n            information_ratio = float(ep.information_ratio(aligned_returns, aligned_benchmark))\n            active_return = float(excess_returns.mean() * 252)\n\n            metrics.update({\n                'alpha': alpha,\n                'beta': beta,\n                'r_squared': r_squared,\n                'tracking_error': tracking_error,\n                'information_ratio': information_ratio,\n                'active_return': active_return,\n                'up_capture': up_capture,\n                'down_capture': down_capture,\n                'correlation': float(aligned_returns.corr(aligned_benchmark))\n            })\n\n        # Volume metrics\n        if portfolio_data is not None:\n            if isinstance(portfolio_data, pd.DataFrame):\n                if len(portfolio_data.columns) == 1:\n                    ticker = portfolio_data.columns[0]\n                    volume_data = portfolio_data[['Close', 'Volume']].dropna()\n                    metrics['volume_analysis'] = calculate_volume_metrics(volume_data)\n                else:\n                    volume_metrics = {ticker: calculate_volume_metrics(portfolio_data[[ticker, 'Volume']].dropna()) for ticker in portfolio_data.columns}\n                    portfolio_volume_metrics = {\n                        'basic_metrics': {\n                            'average_volume': np.mean([m['basic_metrics']['average_volume'] for m in volume_metrics.values()]),\n                            'volume_volatility': np.mean([m['basic_metrics']['volume_volatility'] for m in volume_metrics.values()])\n                        },\n                        'trend_analysis': {\n                            'volume_trend': np.mean([m['trend_analysis']['volume_trend'] for m in volume_metrics.values()]),\n                            'volume_momentum_20d': np.mean([m['trend_analysis']['volume_momentum_20d'] for m in volume_metrics.values()])\n                        },\n                        'trading_patterns': {\n                            'up_down_volume_ratio': np.mean([m['trading_patterns']['up_down_volume_ratio'] for m in volume_metrics.values()])\n                        },\n                        'relative_metrics': {\n                            'high_volume_days': int(np.mean([m['relative_metrics']['high_volume_days'] for m in volume_metrics.values()])),\n                            'low_volume_days': int(np.mean([m['relative_metrics']['low_volume_days'] for m in volume_metrics.values()])),\n                            'typical_volume': np.mean([m['relative_metrics']['typical_volume'] for m in volume_metrics.values()]),\n                            'vwap': np.mean([m['relative_metrics']['vwap'] for m in volume_metrics.values()])\n                        },\n                        'individual_assets': volume_metrics\n                    }\n                    metrics['volume_analysis'] = portfolio_volume_metrics\n\n        # Risk metrics\n        metrics['risk_metrics'] = calculate_risk_metrics(returns)\n\n        return clean_for_json(metrics)\n\ndef calculate_risk_metrics(returns, rolling_window=30):\n    \"\"\"Calculate comprehensive risk metrics for portfolio analysis.\"\"\"\n    try:\n        metrics = {\n            'daily_metrics': {\n                'var_95': float(np.percentile(returns, 5)),\n                'var_99': float(np.percentile(returns, 1)),\n                'cvar_95': float(returns[returns <= np.percentile(returns, 5)].mean()) if any(returns <= np.percentile(returns, 5)) else np.percentile(returns, 5),\n                'cvar_99': float(returns[returns <= np.percentile(returns, 1)].mean()) if any(returns <= np.percentile(returns, 1)) else np.percentile(returns, 1),\n                'volatility': float(returns.std())\n            },\n            'annualized_metrics': {\n                'volatility': float(returns.std() * np.sqrt(252)),\n                'downside_deviation': float(np.sqrt((returns[returns < 0] ** 2).mean()) * np.sqrt(252)) if any(returns < 0) else 0.0,\n                'sharpe_ratio': float(ep.sharpe_ratio(returns, risk_free=0.02 / 252)),\n                'sortino_ratio': float(ep.sortino_ratio(returns, required_return=0.02 / 252))\n            },\n            'drawdown_metrics': {\n                'max_drawdown': float(ep.max_drawdown(returns)),\n                'current_drawdown': float((returns.cumsum() - returns.cumsum().cummax()).min())\n            },\n            'rolling_volatility': [\n                {'date': date.strftime('%Y-%m-%d'), 'volatility': float(vol)}\n                for date, vol in zip(returns.index, returns.rolling(window=rolling_window).std() * np.sqrt(252))\n                if not pd.isna(vol)\n            ],\n            'distribution_metrics': {\n                'skewness': float(stats.skew(returns)),\n                'kurtosis': float(stats.kurtosis(returns, fisher=True)),\n                'positive_days': int((returns > 0).sum()),\n                'negative_days': int((returns < 0).sum()),\n                'total_days': len(returns)\n            }\n        }\n        return metrics\n    except Exception as e:\n        logger.error(f\"Error calculating risk metrics: {str(e)}\")\n        return {\n            'daily_metrics': {},\n            'annualized_metrics': {},\n            'drawdown_metrics': {},\n            'rolling_volatility': [],\n            'distribution_metrics': {}\n        }\n\ndef calculate_correlation_matrix(portfolio_data):\n    \"\"\"Calculate correlation matrices for the portfolio.\"\"\"\n    try:\n        returns = portfolio_data.pct_change().dropna()\n        pearson_matrix = returns.corr(method='pearson')\n        spearman_matrix = returns.corr(method='spearman')\n\n        pearson_avg = float(pearson_matrix.where(np.triu(np.ones(pearson_matrix.shape), k=1).astype(bool)).stack().mean())\n        pearson_min = float(pearson_matrix.where(np.triu(np.ones(pearson_matrix.shape), k=1).astype(bool)).stack().min())\n        pearson_max = float(pearson_matrix.where(np.triu(np.ones(pearson_matrix.shape), k=1).astype(bool)).stack().max())\n\n        spearman_avg = float(spearman_matrix.where(np.triu(np.ones(spearman_matrix.shape), k=1).astype(bool)).stack().mean())\n        spearman_min = float(spearman_matrix.where(np.triu(np.ones(spearman_matrix.shape), k=1).astype(bool)).stack().min())\n        spearman_max = float(spearman_matrix.where(np.triu(np.ones(spearman_matrix.shape), k=1).astype(bool)).stack().max())\n\n        rolling_window = min(252, len(returns) // 2) if len(returns) >= 2 else 2\n        rolling_corr = returns.rolling(window=rolling_window).corr().dropna()\n\n        latest_rolling = rolling_corr.xs(returns.index[-1], level=0) if not rolling_corr.empty else pd.DataFrame()\n        rolling_dict = {ticker: latest_rolling[ticker].to_dict() for ticker in latest_rolling.index} if not latest_rolling.empty else {}\n\n        return {\n            'pearson': {\n                'matrix': pearson_matrix.to_dict(),\n                'avg': pearson_avg,\n                'min': pearson_min,\n                'max': pearson_max\n            },\n            'spearman': {\n                'matrix': spearman_matrix.to_dict(),\n                'avg': spearman_avg,\n                'min': spearman_min,\n                'max': spearman_max\n            },\n            'rolling': {\n                'matrix': rolling_dict,\n                'window': rolling_window\n            }\n        }\n    except Exception as e:\n        logger.error(f\"Error in calculate_correlation_matrix: {str(e)}\", exc_info=True)\n        return None\n\ndef calculate_drawdown_periods(returns):\n    \"\"\"\n    Identify drawdown periods in the portfolio.\n    \"\"\"\n    try:\n        cumulative = (1 + returns).cumprod()\n        running_max = cumulative.expanding().max()\n        drawdown = (cumulative - running_max) / running_max\n\n        chart_data = {\n            'dates': returns.index.strftime('%Y-%m-%d').tolist(),\n            'drawdowns': drawdown.tolist(),\n            'cumulative': cumulative.tolist(),\n            'peaks': running_max.tolist()\n        }\n\n        significant_drawdowns = []\n        in_drawdown = False\n        peak_value = cumulative.iloc[0]\n        peak_date = returns.index[0]\n        trough_value = peak_value\n        trough_date = peak_date\n\n        for current_date, current_value in cumulative.iteritems():\n            if current_value > peak_value:\n                peak_value = current_value\n                peak_date = current_date\n                if in_drawdown:\n                    in_drawdown = False\n                    drawdown_info = {\n                        'start': peak_date.strftime('%Y-%m-%d'),\n                        'end': trough_date.strftime('%Y-%m-%d'),\n                        'recovery': current_date.strftime('%Y-%m-%d'),\n                        'depth': float((trough_value - peak_value) / peak_value),\n                        'length_days': (trough_date - peak_date).days,\n                        'recovery_time_days': (current_date - trough_date).days,\n                        'peak_value': float(peak_value),\n                        'trough_value': float(trough_value),\n                        'recovery_value': float(current_value),\n                        'is_active': False\n                    }\n                    significant_drawdowns.append(drawdown_info)\n            elif current_value < peak_value * 0.95:\n                if not in_drawdown:\n                    in_drawdown = True\n                    trough_value = current_value\n                    trough_date = current_date\n                elif current_value < trough_value:\n                    trough_value = current_value\n                    trough_date = current_date\n\n        if in_drawdown:\n            current_date = returns.index[-1]\n            current_value = cumulative.iloc[-1]\n            drawdown_info = {\n                'start': peak_date.strftime('%Y-%m-%d'),\n                'end': trough_date.strftime('%Y-%m-%d'),\n                'recovery': None,\n                'depth': float((trough_value - peak_value) / peak_value),\n                'length_days': (trough_date - peak_date).days,\n                'recovery_time_days': None,\n                'peak_value': float(peak_value),\n                'trough_value': float(trough_value),\n                'recovery_value': float(current_value),\n                'is_active': True\n            }\n            significant_drawdowns.append(drawdown_info)\n\n        significant_drawdowns.sort(key=lambda x: x['depth'])\n        worst_drawdowns = significant_drawdowns[:10]\n\n        avg_depth = np.mean([abs(d['depth']) for d in significant_drawdowns]) if significant_drawdowns else 0\n        recovered_drawdowns = [d for d in significant_drawdowns if d['recovery_time_days'] is not None]\n        avg_recovery = np.mean([d['recovery_time_days'] for d in recovered_drawdowns]) if recovered_drawdowns else 0\n\n        return {\n            'periods': worst_drawdowns,\n            'chart_data': chart_data,\n            'max_drawdown': float(drawdown.min()) if not drawdown.empty else 0.0,\n            'avg_depth': float(avg_depth),\n            'avg_recovery': float(round(avg_recovery, 2)),\n            'total_events': len(significant_drawdowns)\n        }\n    except Exception as e:\n        logger.error(f\"Error in calculate_drawdown_periods: {str(e)}\", exc_info=True)\n        return {\n            'periods': [],\n            'chart_data': {\n                'dates': [],\n                'drawdowns': [],\n                'cumulative': [],\n                'peaks': []\n            },\n            'max_drawdown': 0.0,\n            'avg_depth': 0.0,\n            'avg_recovery': 0.0,\n            'total_events': 0\n        }\n\ndef calculate_rebalancing_impact(portfolio_data, weights, transaction_cost):\n    \"\"\"Calculate the impact of different rebalancing strategies.\"\"\"\n    try:\n        logger.info(\"Starting rebalancing impact analysis...\")\n        if len(weights) == 1:\n            ticker = portfolio_data.columns[0]\n            volume_data = portfolio_data[['Close', 'Volume']].dropna()\n            metrics = calculate_volume_metrics(volume_data)\n            base_metrics = {\n                'portfolio_values': (10000 * (1 + portfolio_data.pct_change().fillna(0)).cumprod()).tolist(),\n                'final_value': float(10000 * (1 + portfolio_data.pct_change().fillna(0)).cumprod().iloc[-1]),\n                'total_return': float((1 + portfolio_data.pct_change().fillna(0)).cumprod().iloc[-1] - 1),\n                'rebalance_count': 0,\n                'total_costs': 0.0,\n                'cost_percentage': 0.0,\n                'avg_turnover': 0.0,\n                'avg_tracking_error': 0.0,\n                'weight_drift': {\n                    'mean': 0.0,\n                    'max': 0.0,\n                    'current': 0.0,\n                    'history': [0.0] * len(portfolio_data)\n                },\n                'current_weights': [1.0],\n                'weight_history': [[1.0]] * len(portfolio_data)\n            }\n            metrics.update(base_metrics)\n            return {strategy: clean_for_json(metrics) for strategy in ['Buy and Hold', 'Monthly', 'Quarterly', 'Semi-Annual', 'Annual']}\n        \n        weights = np.array(weights)\n        weights /= weights.sum()\n\n        returns = portfolio_data.pct_change().fillna(0)\n\n        strategies = {\n            'Buy and Hold': None,\n            'Monthly': pd.DateOffset(months=1),\n            'Quarterly': pd.DateOffset(months=3),\n            'Semi-Annual': pd.DateOffset(months=6),\n            'Annual': pd.DateOffset(years=1)\n        }\n\n        results = {}\n        initial_value = 10000.0\n\n        for strategy_name, rebalance_period in strategies.items():\n            try:\n                portfolio_value = initial_value\n                current_weights = weights.copy()\n                current_holdings = portfolio_value * current_weights\n                total_costs = 0\n                rebalance_count = 0\n                portfolio_values = [initial_value]\n                daily_returns = [0]\n                turnover = []\n                tracking_error = []\n                current_weights_history = [current_weights.tolist()]\n                weight_drift_history = [0.0]\n\n                last_rebalance_date = portfolio_data.index[0]\n\n                for date, daily_return_vector in returns.iterrows():\n                    current_holdings *= (1 + daily_return_vector.values)\n                    portfolio_value = current_holdings.sum()\n\n                    current_weights = current_holdings / portfolio_value\n                    weight_drift = np.sqrt(np.sum((current_weights - weights) ** 2))\n                    weight_drift_history.append(float(weight_drift))\n\n                    should_rebalance = False\n                    if strategy_name != 'Buy and Hold' and rebalance_period:\n                        if date > last_rebalance_date + rebalance_period and weight_drift > 0.01:\n                            should_rebalance = True\n\n                    if should_rebalance:\n                        target_holdings = portfolio_value * weights\n                        trades = target_holdings - current_holdings\n                        trade_costs = abs(trades).sum() * transaction_cost\n                        portfolio_value -= trade_costs\n                        total_costs += trade_costs\n\n                        current_holdings = portfolio_value * weights\n                        rebalance_count += 1\n                        turnover.append(abs(trades).sum() / (2 * portfolio_value))\n                        last_rebalance_date = date\n\n                    daily_return = (portfolio_value / portfolio_values[-1]) - 1\n                    portfolio_values.append(portfolio_value)\n                    daily_returns.append(daily_return)\n                    current_weights_history.append(current_weights.tolist())\n                    tracking_error.append(float(np.std(current_weights - weights)))\n\n                daily_returns_series = pd.Series(daily_returns, index=returns.index)\n                metrics = calculate_portfolio_metrics(daily_returns_series)\n\n                metrics.update({\n                    'portfolio_values': portfolio_values,\n                    'final_value': float(portfolio_values[-1]),\n                    'total_return': float((portfolio_values[-1] / initial_value) - 1),\n                    'rebalance_count': int(rebalance_count),\n                    'total_costs': float(total_costs),\n                    'cost_percentage': float(total_costs / portfolio_values[-1]),\n                    'avg_turnover': float(np.mean(turnover)) if turnover else 0.0,\n                    'avg_tracking_error': float(np.mean(tracking_error)),\n                    'weight_drift': {\n                        'mean': float(np.mean(weight_drift_history)),\n                        'max': float(np.max(weight_drift_history)),\n                        'current': float(weight_drift_history[-1]),\n                        'history': [float(x) for x in weight_drift_history]\n                    },\n                    'current_weights': current_weights.tolist(),\n                    'weight_history': current_weights_history\n                })\n\n                results[strategy_name] = clean_for_json(metrics)\n\n            except Exception as e:\n                logger.error(f\"Error calculating {strategy_name} strategy: {str(e)}\")\n                results[strategy_name] = {\n                    'error': str(e),\n                    'final_value': initial_value,\n                    'total_return': 0.0,\n                    'rebalance_count': 0,\n                    'total_costs': 0.0\n                }\n\n        return results\n\n    except Exception as e:\n        logger.error(f\"Error in calculate_rebalancing_impact: {str(e)}\")\n        return {}\n\ndef calculate_attribution(portfolio_data, weights, benchmark_data):\n    \"\"\"Calculate enhanced performance attribution metrics.\"\"\"\n    try:\n        logger.info(\"Starting attribution analysis...\")\n\n        portfolio_returns = portfolio_data.pct_change().dropna()\n        benchmark_returns = benchmark_data['Adj Close'].pct_change().dropna().reindex(portfolio_returns.index).fillna(0)\n\n        weights = np.array(weights)\n        weights /= weights.sum()\n        weighted_returns = (portfolio_returns * weights).sum(axis=1)\n\n        # Sector information\n        db = SessionLocal()\n        sector_data = {}\n        try:\n            for ticker, weight in zip(portfolio_data.columns, weights):\n                sector_query = text(\"\"\"\n                    SELECT gics_sector, gics_sub_industry \n                    FROM market_tickers \n                    WHERE ticker = :ticker\n                \"\"\")\n                sector_info = db.execute(sector_query, {\"ticker\": ticker}).fetchone()\n                if sector_info:\n                    sector = sector_info[0]\n                    if sector not in sector_data:\n                        sector_data[sector] = {\n                            'holdings': [],\n                            'weight': 0,\n                            'return': 0,\n                            'contribution': 0\n                        }\n                    sector_data[sector]['holdings'].append(ticker)\n                    sector_data[sector]['weight'] += weight\n        finally:\n            db.close()\n\n        for sector, info in sector_data.items():\n            sector_tickers = info['holdings']\n            sector_weights = weights[[portfolio_data.columns.get_loc(t) for t in sector_tickers]]\n            sector_weights /= sector_weights.sum()\n            sector_returns = portfolio_returns[sector_tickers]\n            sector_data[sector]['return'] = float((sector_returns * sector_weights).sum(axis=1).mean() * 252)\n            sector_data[sector]['contribution'] = float(sector_data[sector]['return'] * info['weight'])\n\n        # Factor Attribution Calculations\n        excess_returns = weighted_returns - benchmark_returns\n        correlation = np.corrcoef(weighted_returns, benchmark_returns)[0,1]\n        beta = correlation * (weighted_returns.std() / benchmark_returns.std())\n        alpha = float(((1 + weighted_returns.mean()) / (1 + benchmark_returns.mean())) ** 252 - 1)\n        r_squared = float(correlation ** 2)\n\n        market_return = float(benchmark_returns.mean() * 252)\n        market_contribution = beta * market_return\n        selection_contribution = float(alpha)\n        interaction_contribution = float(ep.ex_R2_measure(weighted_returns, benchmark_returns))\n\n        tracking_error = float(np.std(excess_returns) * np.sqrt(252))\n        information_ratio = float(ep.information_ratio(weighted_returns, benchmark_returns))\n\n        attribution_results = {\n            'factor': {\n                'market_contribution': market_contribution,\n                'selection_contribution': selection_contribution,\n                'interaction_contribution': interaction_contribution,\n                'beta': beta,\n                'alpha': alpha,\n                'r_squared': r_squared\n            },\n            'sector': sector_data,\n            'performance': {\n                'total_return': float(weighted_returns.mean() * 252),\n                'benchmark_return': market_return,\n                'excess_return': float((weighted_returns.mean() - benchmark_returns.mean()) * 252),\n                'tracking_error': tracking_error,\n                'information_ratio': information_ratio\n            }\n        }\n\n        logger.info(\"Attribution analysis completed successfully\")\n        return attribution_results\n\n    except Exception as e:\n        logger.error(f\"Error in calculate_attribution: {str(e)}\", exc_info=True)\n        return {\n            'factor': {\n                'market_contribution': 0.0,\n                'selection_contribution': 0.0,\n                'interaction_contribution': 0.0,\n                'beta': 1.0,\n                'alpha': 0.0,\n                'r_squared': 0.0\n            },\n            'sector': {},\n            'performance': {\n                'total_return': 0.0,\n                'benchmark_return': 0.0,\n                'excess_return': 0.0,\n                'tracking_error': 0.0,\n                'information_ratio': 0.0\n            }\n        }\n\ndef calculate_correlation_matrix(portfolio_data):\n    \"\"\"Calculate correlation matrices for the portfolio.\"\"\"\n    try:\n        returns = portfolio_data.pct_change().dropna()\n        pearson_matrix = returns.corr(method='pearson')\n        spearman_matrix = returns.corr(method='spearman')\n\n        pearson_vals = pearson_matrix.where(np.triu(np.ones(pearson_matrix.shape), k=1).astype(bool)).stack()\n        spearman_vals = spearman_matrix.where(np.triu(np.ones(spearman_matrix.shape), k=1).astype(bool)).stack()\n\n        rolling_window = min(252, len(returns) // 2) if len(returns) >= 2 else 2\n        rolling_corr = returns.rolling(window=rolling_window).corr().dropna()\n\n        latest_rolling = rolling_corr.xs(returns.index[-1], level=0) if not rolling_corr.empty else pd.DataFrame()\n        rolling_dict = latest_rolling.to_dict() if not latest_rolling.empty else {}\n\n        return {\n            'pearson': {\n                'matrix': pearson_matrix.to_dict(),\n                'avg': float(pearson_vals.mean()),\n                'min': float(pearson_vals.min()),\n                'max': float(pearson_vals.max())\n            },\n            'spearman': {\n                'matrix': spearman_matrix.to_dict(),\n                'avg': float(spearman_vals.mean()),\n                'min': float(spearman_vals.min()),\n                'max': float(spearman_vals.max())\n            },\n            'rolling': {\n                'matrix': rolling_dict,\n                'window': rolling_window\n            }\n        }\n    except Exception as e:\n        logger.error(f\"Error in calculate_correlation_matrix: {str(e)}\", exc_info=True)\n        return None\n\n# API Routes\n@app.route('/api/progress', methods=['GET'])\ndef get_progress():\n    \"\"\"Get the current analysis progress.\"\"\"\n    return jsonify(analysis_progress)\n\n@app.route('/api/compare', methods=['POST', 'OPTIONS'])\n@handle_exceptions\ndef compare_portfolios():\n    if request.method == 'OPTIONS':\n        return ('', 204)\n\n    logger.info(\"Starting portfolio comparison...\")\n    update_progress(0, 'processing')\n\n    data = request.get_json()\n    logger.info(f\"Received request data: {json.dumps(data, indent=2)}\")\n\n    portfolios = data.get('portfolios', [])\n    start_date = data.get('startDate')\n    transaction_cost = min(float(data.get('transactionCost', 0.001)), 0.01)\n\n    if not portfolios:\n        update_progress(0, 'failed', 'No portfolios provided for comparison')\n        return jsonify({'success': False, 'error': 'No portfolios provided for comparison'}), 400\n\n    comparison_results = {}\n    start_date = pd.to_datetime(start_date)\n    all_data = pd.DataFrame()\n\n    # Collect all unique tickers\n    unique_tickers = set()\n    for portfolio in portfolios:\n        portfolio_stocks = portfolio.get('portfolio', [])\n        unique_tickers.update([stock['ticker'] for stock in portfolio_stocks])\n    logger.info(f\"Fetching data for {len(unique_tickers)} unique tickers...\")\n\n    # Fetch data for all tickers with progress updates\n    update_progress(10)\n    total_tickers = len(unique_tickers)\n    for i, ticker in enumerate(unique_tickers, 1):\n        stock_data = fetch_stock_data(ticker, start_date)\n        if stock_data is not None and not stock_data.empty:\n            earliest_date = stock_data.index.min()\n            if start_date < earliest_date:\n                logger.info(f\"Adjusting start date for {ticker} from {start_date} to {earliest_date}\")\n                start_date = earliest_date\n            all_data[ticker] = stock_data['Adj Close']\n        else:\n            error_msg = f'Failed to fetch data for {ticker}'\n            if ticker == 'SPY':\n                error_msg += ' (benchmark)'\n            update_progress(0, 'failed', error_msg)\n            return jsonify({'success': False, 'error': error_msg}), 400\n        progress = 10 + (30 * i / total_tickers)\n        update_progress(int(progress))\n\n    # Align all data to common dates and handle missing values\n    all_data = all_data.dropna()\n    if len(all_data) < 252:\n        update_progress(0, 'failed', 'Insufficient historical data')\n        return jsonify({'success': False, 'error': 'Insufficient historical data. Need at least 252 data points.'}), 400\n\n    update_progress(50)\n\n    # Extract benchmark data\n    benchmark_data = all_data.get('SPY')\n    if benchmark_data is None:\n        update_progress(0, 'failed', 'Benchmark (SPY) data not available')\n        return jsonify({'success': False, 'error': 'Benchmark (SPY) data not available'}), 400\n    benchmark_returns = benchmark_data.pct_change().fillna(0)\n\n    # Calculate metrics for each portfolio\n    total_portfolios = len(portfolios)\n    for i, portfolio in enumerate(portfolios, 1):\n        portfolio_name = portfolio.get('name', f'Portfolio {i}')\n        portfolio_stocks = portfolio.get('portfolio', [])\n\n        logger.info(f\"Processing portfolio: {portfolio_name}\")\n\n        is_valid, validated_portfolio = validate_portfolio(portfolio_stocks)\n        if not is_valid:\n            return jsonify({'success': False, 'error': validated_portfolio}), 400\n        portfolio_stocks = validated_portfolio\n\n        weights = np.array([stock['weight'] for stock in portfolio_stocks])\n        tickers = [stock['ticker'] for stock in portfolio_stocks]\n        portfolio_data = all_data[tickers]\n        portfolio_returns = portfolio_data.pct_change().dropna()\n        weighted_returns = (portfolio_returns * weights).sum(axis=1)\n\n        # Calculate metrics with benchmark\n        portfolio_metrics = calculate_portfolio_metrics(weighted_returns, benchmark_returns, portfolio_data)\n\n        # Historical performance\n        initial_value = 10000\n        portfolio_values = initial_value * (1 + weighted_returns).cumprod()\n        portfolio_metrics['historical_performance'] = {\n            'dates': weighted_returns.index.strftime('%Y-%m-%d').tolist(),\n            'values': portfolio_values.tolist()\n        }\n\n        # Correlation matrices\n        correlations = calculate_correlation_matrix(portfolio_data) if len(tickers) > 1 else None\n\n        # Rebalancing impact\n        rebalancing_results = calculate_rebalancing_impact(portfolio_data, weights, transaction_cost)\n\n        # Attribution analysis\n        benchmark_close = all_data.get('SPY')\n        attribution_results = calculate_attribution(portfolio_data, weights, pd.DataFrame({'Adj Close': benchmark_close}))\n\n        comparison_results[portfolio_name] = {\n            'metrics': portfolio_metrics,\n            'rebalancing': rebalancing_results,\n            'portfolio': portfolio_stocks,\n            'attribution': attribution_results,\n            'correlations': correlations\n        }\n\n        # Update progress (50-90%)\n        progress = 50 + (40 * i / total_portfolios)\n        update_progress(int(progress))\n\n    # Final progress update\n    update_progress(100, 'completed')\n\n    return jsonify({\n        'success': True,\n        'comparison_results': clean_for_json(comparison_results),\n        'data_quality': clean_for_json({\n            'num_data_points': len(all_data),\n            'date_range': {\n                'start': all_data.index.min().strftime('%Y-%m-%d'),\n                'end': all_data.index.max().strftime('%Y-%m-%d')\n            }\n        })\n    })\n\n@app.route('/api/backtest', methods=['POST'])\n@handle_exceptions\ndef backtest():\n    \"\"\"Backtest the portfolio against historical data.\"\"\"\n    logger.info(\"Starting backtest...\")\n    data = request.get_json()\n    logger.info(f\"Received backtest data: {json.dumps(data, indent=2)}\")\n\n    portfolio = data.get('portfolio', [])\n    start_date = data.get('startDate')\n\n    if not portfolio:\n        return jsonify({'success': False, 'error': 'No portfolio provided for backtest'}), 400\n\n    is_valid, validated_portfolio = validate_portfolio(portfolio)\n    if not is_valid:\n        return jsonify({'success': False, 'error': validated_portfolio}), 400\n    portfolio = validated_portfolio\n\n    start_date = pd.to_datetime(start_date)\n    portfolio_tickers = [stock['ticker'] for stock in portfolio]\n    portfolio_data = pd.DataFrame()\n\n    # Fetch data for portfolio stocks\n    logger.info(\"Fetching portfolio data...\")\n    for ticker in portfolio_tickers:\n        stock_data = fetch_stock_data(ticker, start_date)\n        if stock_data is not None and not stock_data.empty:\n            portfolio_data[ticker] = stock_data['Adj Close']\n            logger.info(f\"Data fetched for {ticker}, shape: {stock_data.shape}\")\n        else:\n            return jsonify({'success': False, 'error': f'No data available for {ticker}'}), 400\n\n    # Fetch benchmark data\n    benchmark = 'SPY'\n    benchmark_data = fetch_stock_data(benchmark, start_date)\n    if benchmark_data is None or benchmark_data.empty:\n        return jsonify({'success': False, 'error': f'No data available for benchmark {benchmark}'}), 400\n\n    # Calculate metrics\n    portfolio_returns = portfolio_data.pct_change().dropna()\n    benchmark_returns = benchmark_data['Adj Close'].pct_change().dropna().reindex(portfolio_returns.index).fillna(0)\n\n    weights = np.array([stock['weight'] for stock in portfolio])\n    weights /= weights.sum()\n    weighted_returns = (portfolio_returns * weights).sum(axis=1)\n\n    portfolio_metrics = calculate_portfolio_metrics(weighted_returns, benchmark_returns)\n    benchmark_metrics = calculate_portfolio_metrics(benchmark_returns)\n\n    # Cumulative values\n    initial_investment = portfolio_metrics.get('start_balance', 10000.0)\n    portfolio_cumulative = (1 + weighted_returns).cumprod() * initial_investment\n    benchmark_cumulative = (1 + benchmark_returns).cumprod() * initial_investment\n\n    # Drawdowns\n    portfolio_drawdowns = calculate_drawdown_periods(weighted_returns)\n    benchmark_drawdowns = calculate_drawdown_periods(benchmark_returns)\n\n    # Prepare response\n    response_data = {\n        'success': True,\n        'portfolio_metrics': clean_for_json(portfolio_metrics),\n        'benchmark_metrics': clean_for_json(benchmark_metrics),\n        'historical_performance': {\n            'dates': portfolio_returns.index.strftime('%Y-%m-%d').tolist(),\n            'portfolio_values': portfolio_cumulative.tolist(),\n            'benchmark_values': benchmark_cumulative.tolist(),\n            'initial_investment': initial_investment\n        },\n        'drawdowns': {\n            'portfolio_drawdowns': portfolio_drawdowns,\n            'benchmark_drawdowns': benchmark_drawdowns\n        },\n        'data_quality': {\n            'num_data_points': len(portfolio_returns),\n            'date_range': {\n                'start': portfolio_returns.index.min().strftime('%Y-%m-%d'),\n                'end': portfolio_returns.index.max().strftime('%Y-%m-%d')\n            },\n            'missing_values': portfolio_data.isnull().values.any(),\n            'aligned_data': True\n        }\n    }\n\n    logger.info(\"Backtest completed successfully\")\n    return jsonify(response_data)\n\n@app.route('/api/correlation', methods=['POST', 'OPTIONS'])\n@handle_exceptions\ndef get_correlation():\n    \"\"\"Calculate and return correlation matrices for the portfolio.\"\"\"\n    if request.method == 'OPTIONS':\n        return ('', 204)\n\n    data = request.get_json()\n    logger.info(f\"Received correlation data: {json.dumps(data, indent=2)}\")\n\n    portfolio = data.get('portfolio', [])\n    start_date = data.get('startDate')\n\n    if not portfolio or len(portfolio) < 2:\n        return jsonify({'success': False, 'error': 'Portfolio must contain at least two stocks for correlation analysis'}), 400\n\n    is_valid, validated_portfolio = validate_portfolio(portfolio)\n    if not is_valid:\n        return jsonify({'success': False, 'error': validated_portfolio}), 400\n    portfolio = validated_portfolio\n\n    portfolio_tickers = [stock['ticker'] for stock in portfolio]\n    portfolio_data = pd.DataFrame()\n    start_date = pd.to_datetime(start_date)\n\n    logger.info(\"Fetching portfolio data for correlation analysis...\")\n    for ticker in portfolio_tickers:\n        stock_data = fetch_stock_data(ticker, start_date)\n        if stock_data is not None and not stock_data.empty:\n            portfolio_data[ticker] = stock_data['Adj Close']\n            logger.info(f\"Data fetched for {ticker}, shape: {stock_data.shape}\")\n        else:\n            return jsonify({'success': False, 'error': f'No data available for {ticker}'}), 400\n\n    correlations = calculate_correlation_matrix(portfolio_data)\n\n    return jsonify({\n        'success': True,\n        'correlations': correlations,\n        'date_range': {\n            'start': portfolio_data.index.min().strftime('%Y-%m-%d'),\n            'end': portfolio_data.index.max().strftime('%Y-%m-%d')\n        }\n    })\n\n@app.route('/api/attribution', methods=['POST', 'OPTIONS'])\n@handle_exceptions\ndef get_attribution():\n    \"\"\"Get portfolio attribution analysis.\"\"\"\n    if request.method == 'OPTIONS':\n        return ('', 204)\n\n    data = request.get_json()\n    portfolio = data.get('portfolio', [])\n    start_date = data.get('startDate')\n    benchmark = data.get('benchmark', 'SPY')\n\n    if not portfolio:\n        return jsonify({'success': False, 'error': 'No portfolio provided'}), 400\n\n    is_valid, validated_portfolio = validate_portfolio(portfolio)\n    if not is_valid:\n        return jsonify({'success': False, 'error': validated_portfolio}), 400\n    portfolio = validated_portfolio\n\n    start_date = pd.to_datetime(start_date)\n    portfolio_tickers = [stock['ticker'] for stock in portfolio]\n    portfolio_weights = np.array([float(stock['weight']) for stock in portfolio])\n    portfolio_weights /= portfolio_weights.sum()\n\n    # Fetch data for portfolio stocks\n    logger.info(\"Fetching portfolio data for attribution analysis...\")\n    portfolio_data = pd.DataFrame()\n    for ticker in portfolio_tickers:\n        stock_data = fetch_stock_data(ticker, start_date)\n        if stock_data is not None and not stock_data.empty:\n            portfolio_data[ticker] = stock_data['Adj Close']\n            logger.info(f\"Data fetched for {ticker}, shape: {stock_data.shape}\")\n        else:\n            return jsonify({'success': False, 'error': f'No data available for {ticker}'}), 400\n\n    # Fetch benchmark data\n    logger.info(f\"Fetching benchmark data for {benchmark}\")\n    benchmark_data = fetch_stock_data(benchmark, start_date)\n    if benchmark_data is None or benchmark_data.empty:\n        return jsonify({'success': False, 'error': f'No data available for benchmark {benchmark}'}), 400\n\n    attribution_results = calculate_attribution(portfolio_data, portfolio_weights, benchmark_data)\n\n    return jsonify({\n        'success': True,\n        'attribution': attribution_results\n    })\n\n@app.route('/api/portfolio/load', methods=['GET'])\n@handle_exceptions\ndef load_portfolios():\n    \"\"\"Load all saved portfolios\"\"\"\n    portfolios = []\n    if os.path.exists(PORTFOLIO_DIR):\n        for filename in os.listdir(PORTFOLIO_DIR):\n            if filename.endswith('.json'):\n                file_path = os.path.join(PORTFOLIO_DIR, filename)\n                try:\n                    with open(file_path, 'r') as f:\n                        portfolio = json.load(f)\n                        portfolio['filename'] = filename\n                        portfolio['created_at'] = datetime.fromtimestamp(os.path.getctime(file_path)).isoformat()\n                        portfolios.append(portfolio)\n                except Exception as e:\n                    logger.error(f\"Error loading portfolio {filename}: {str(e)}\")\n                    continue\n\n    portfolios.sort(key=lambda x: x.get('created_at', ''), reverse=True)\n    return jsonify({\n        'success': True,\n        'portfolios': portfolios\n    })\n\n@app.route('/api/portfolio/save', methods=['POST'])\n@handle_exceptions\ndef save_portfolio():\n    \"\"\"Save a portfolio\"\"\"\n    data = request.get_json()\n    if not data:\n        return jsonify({'success': False, 'error': 'No data provided'}), 400\n\n    name = data.get('name')\n    portfolio = data.get('portfolio', [])\n\n    if not name:\n        return jsonify({'success': False, 'error': 'Portfolio name is required'}), 400\n\n    is_valid, validated_portfolio = validate_portfolio(portfolio)\n    if not is_valid:\n        return jsonify({'success': False, 'error': validated_portfolio}), 400\n    portfolio = validated_portfolio\n\n    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')\n    safe_name = \"\".join(x for x in name if x.isalnum() or x in (' ', '-', '_')).strip().replace(' ', '_')\n    filename = f\"portfolio_{timestamp}_{safe_name}.json\"\n    file_path = os.path.join(PORTFOLIO_DIR, filename)\n\n    with open(file_path, 'w') as f:\n        json.dump({'name': name, 'portfolio': portfolio}, f, indent=2)\n\n    logger.info(f\"Portfolio saved successfully: {filename}\")\n    return jsonify({'success': True, 'filename': filename})\n\n@app.route('/api/portfolio/delete/<path:filename>', methods=['DELETE', 'OPTIONS'])\n@handle_exceptions\ndef delete_portfolio_by_name(filename):\n    \"\"\"Delete a portfolio\"\"\"\n    if not filename.endswith('.json'):\n        return jsonify({'success': False, 'error': 'Invalid filename'}), 400\n\n    file_path = os.path.join(PORTFOLIO_DIR, filename)\n    if not os.path.exists(file_path):\n        return jsonify({'success': False, 'error': 'Portfolio not found'}), 404\n\n    try:\n        os.remove(file_path)\n        logger.info(f\"Successfully deleted portfolio: {filename}\")\n    except Exception as e:\n        logger.error(f\"Error deleting file {filename}: {str(e)}\")\n        return jsonify({'success': False, 'error': f'Failed to delete file: {str(e)}'}), 500\n\n    # Return updated portfolio list\n    portfolios = []\n    for fname in os.listdir(PORTFOLIO_DIR):\n        if fname.endswith('.json'):\n            fp = os.path.join(PORTFOLIO_DIR, fname)\n            try:\n                with open(fp, 'r') as f:\n                    portfolio = json.load(f)\n                    portfolio['filename'] = fname\n                    portfolio['created_at'] = datetime.fromtimestamp(os.path.getctime(fp)).isoformat()\n                    portfolios.append(portfolio)\n            except Exception as e:\n                logger.error(f\"Error loading portfolio {fname}: {str(e)}\")\n                continue\n\n    portfolios.sort(key=lambda x: x.get('created_at', ''), reverse=True)\n    return jsonify({\n        'success': True,\n        'portfolios': portfolios\n    })\n\n@app.after_request\ndef after_request(response):\n    \"\"\"Add CORS headers to all responses\"\"\"\n    response.headers.add('Access-Control-Allow-Origin', 'http://localhost:5173')\n    response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Cache-Control,Authorization')\n    response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')\n    return response\n\n@app.route('/api/health', methods=['HEAD', 'GET'])\ndef health_check():\n    \"\"\"Simple health check endpoint\"\"\"\n    return '', 200\n\nif __name__ == '__main__':\n    app.run(debug=True)\n\ndef update_progress(value, status='processing', error=None):\n    \"\"\"Update the global analysis progress.\"\"\"\n    global analysis_progress\n    analysis_progress['progress'] = value\n    analysis_progress['status'] = status\n    analysis_progress['error'] = error\n```\n\n### Key Enhancements Explained:\n\n1. **Decorator for Error Handling (`handle_exceptions`)**:\n    - This decorator wraps around your route functions to catch and handle exceptions uniformly, reducing repetitive try-except blocks within each route.\n\n2. **Simplified CORS Handling**:\n    - Removed the `handle_preflight` function as `Flask-CORS` already manages CORS preflight requests based on the provided configuration.\n\n3. **Optimized `validate_portfolio` Function**:\n    - Streamlined the validation logic to handle both percentage and decimal weights, ensuring weights sum to 1 without excessive conditions.\n\n4. **Consolidated Data Fetching in `fetch_stock_data`**:\n    - Unified cache loading, database fetching, and yfinance fetching into a single streamlined function to reduce redundancy.\n\n5. **Refactored Metric Calculation Functions**:\n    - `calculate_portfolio_metrics` and `calculate_risk_metrics` have been refactored to minimize overlap and ensure clarity.\n    - Removed unnecessary nested try-except blocks to enhance readability.\n\n6. **Centralized Logging**:\n    - Ensured all critical steps are logged consistently to facilitate easier debugging and monitoring.\n\n7. **Optimized Correlation Calculation**:\n    - Simplified the extraction of upper-triangular values for averaging correlation coefficients.\n\n8. **Enhanced Rebalancing Impact Calculation**:\n    - Streamlined the logic to handle both single and multi-asset portfolios without redundant checks.\n\n9. **Improved Attribution Analysis**:\n    - Ensured that all relevant metrics are calculated succinctly and accurately, with clear separation of factors and sector contributions.\n\n10. **Portfolio Management Routes Simplification**:\n    - Unified the portfolio loading and deletion mechanisms to reduce duplicated code.\n    - Enhanced the save portfolio route to handle both validation and saving in a more concise manner.\n\n11. **Performance Optimizations**:\n    - Leveraged pandas' efficient data handling to perform calculations without unnecessary loops.\n    - Removed redundant calculations and ensured that data transformations are optimized.\n\n12. **General Code Cleanup**:\n    - Removed unused imports and variables.\n    - Ensured consistent coding styles and naming conventions for better readability.\n\nThese enhancements should make your backend code more efficient, easier to maintain, and less prone to errors while preserving all original functionalities.","behavior":"degenerate_repetition","conversation_hash":"0191f6ee2d0f25c4060656817b939a9e","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-mini-2024-09-12","n_chars":142235,"n_turns_in_conversation":1,"n_words":10570,"source_row_offset":38363,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 14:26:11 GMT","turn_id":"4745c59de95b487be7dcae2b","turn_index":0,"user_text":"import os\nimport json\nimport logging\nimport traceback\nfrom datetime import datetime\nfrom flask import Flask, request, jsonify\nfrom flask_cors import CORS\nfrom scipy import stats\nimport yfinance as yf\nimport pandas as pd\nimport numpy as np\nimport empyrical as ep\nimport statsmodels.api as sm  # New import for regression\nfrom backend.routes.user_routes import user_bp\nfrom backend.database.config import get_db, SessionLocal\nfrom sqlalchemy import text\nimport threading\nimport time\nfrom backend.database.utils import (\n    get_stock_data_from_db,\n    save_stock_data_to_db,\n    get_latest_date_for_ticker\n)\n\napp = Flask(__name__)\n# Update CORS configuration to explicitly allow required headers\nCORS(app, resources={\n    r\"/api/*\": {\n        \"origins\": [\"http://localhost:5173\"],\n        \"methods\": [\"GET\", \"POST\", \"DELETE\", \"OPTIONS\"],\n        \"allow_headers\": [\n            \"Content-Type\",\n            \"Cache-Control\",\n            \"Authorization\"\n        ]\n    }\n})\n\n# Register the user blueprint\napp.register_blueprint(user_bp, url_prefix='/api/user')\n\n# Configure logging\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n# Constants\nPORTFOLIO_DIR = 'portfolios'\nCACHE_DIR = 'cache'\nif not os.path.exists(PORTFOLIO_DIR):\n    os.makedirs(PORTFOLIO_DIR)\nif not os.path.exists(CACHE_DIR):\n    os.makedirs(CACHE_DIR)\n\n# Utility Functions\ndef handle_preflight():\n    \"\"\"Handle CORS preflight requests\"\"\"\n    response = app.make_default_options_response()\n    response.headers['Access-Control-Allow-Methods'] = 'POST, OPTIONS'\n    response.headers['Access-Control-Allow-Headers'] = 'Content-Type'\n    return response\n\ndef get_cache_key(ticker, start_date):\n    \"\"\"Generate a cache key based on ticker and start date.\"\"\"\n    # Convert datetime to string format safe for filenames\n    if isinstance(start_date, pd.Timestamp):\n        start_date = start_date.strftime('%Y%m%d')\n    elif isinstance(start_date, str):\n        start_date = pd.to_datetime(start_date).strftime('%Y%m%d')\n    return f\"{ticker}_{start_date}.json\"\n\ndef load_from_cache(cache_key):\n    \"\"\"Load data from cache if available.\"\"\"\n    try:\n        cache_path = os.path.join(CACHE_DIR, cache_key)\n        if os.path.exists(cache_path):\n            data = pd.read_json(cache_path)\n            # Convert string index back to datetime\n            data.index = pd.to_datetime(data.index)\n            if 'Adj Close' not in data.columns:\n                logger.warning(f\"Invalid cache data structure in {cache_key}\")\n                return None\n            logger.info(f\"Successfully loaded cache from {cache_path}\")\n            return data\n    except Exception as e:\n        logger.error(f\"Failed to load cache for {cache_key}: {str(e)}\")\n    return None\n\ndef save_to_cache(cache_key, data):\n    \"\"\"Save data to cache.\"\"\"\n    try:\n        cache_path = os.path.join(CACHE_DIR, cache_key)\n        # Convert datetime index to string format before saving\n        data.index = data.index.strftime('%Y-%m-%d')\n        data.to_json(cache_path)\n        logger.info(f\"Successfully cached data to {cache_path}\")\n    except Exception as e:\n        logger.error(f\"Failed to save cache for {cache_key}: {str(e)}\")\n\ndef clean_for_json(obj):\n    \"\"\"Clean objects to make them JSON serializable.\"\"\"\n    if isinstance(obj, (float, np.float32, np.float64)):\n        if np.isnan(obj) or np.isinf(obj):\n            return 0.0  # Replace NaN/Inf with 0\n        return float(obj)\n    elif isinstance(obj, (int, np.int32, np.int64)):\n        return int(obj)\n    elif isinstance(obj, (bool, np.bool_)):  # Add handling for numpy booleans\n        return bool(obj)\n    elif isinstance(obj, dict):\n        return {k: clean_for_json(v) for k, v in obj.items()}\n    elif isinstance(obj, (list, tuple)):\n        return [clean_for_json(x) for x in obj]\n    elif isinstance(obj, np.ndarray):\n        return clean_for_json(obj.tolist())\n    elif pd.isna(obj):\n        return None  # Replace pandas NA with None\n    return obj\n\ndef fetch_stock_data(ticker, start_date):\n    \"\"\"\n    Fetch historical stock data from database first, then yfinance if needed.\n    Also checks for earliest available date.\n    \"\"\"\n    try:\n        start_date = pd.to_datetime(start_date)\n        ticker = ticker.strip().upper()\n        today = pd.Timestamp.now().normalize()\n        \n        # First check yfinance for earliest available date\n        try:\n            # Fetch just one row to get earliest date\n            earliest_data = yf.download(\n                ticker,\n                start='1900-01-01',  # Very early date to get first available\n                end=today,\n                progress=False,\n                auto_adjust=False,\n                timeout=30\n            )\n            if earliest_data.empty:\n                logger.error(f\"No data available from yfinance for {ticker}\")\n                return None\n                \n            earliest_available = earliest_data.index[0]\n            logger.info(f\"Earliest available date for {ticker}: {earliest_available}\")\n            \n            # If requested start date is before earliest available\n            if start_date < earliest_available:\n                logger.warning(f\"Adjusting start date from {start_date} to {earliest_available} for {ticker}\")\n                start_date = earliest_available\n        except Exception as e:\n            logger.error(f\"Error checking earliest date for {ticker}: {str(e)}\")\n            return None\n        \n        # Get sector info and proceed with normal data fetch\n        db = SessionLocal()\n        try:\n            sector_query = text(\"\"\"\n                SELECT gics_sector, gics_sub_industry \n                FROM market_tickers \n                WHERE ticker = :ticker\n            \"\"\")\n            sector_info = db.execute(sector_query, {\"ticker\": ticker}).first()\n            \n            # Get data from database\n            stock_query = text(\"\"\"\n                SELECT date, open, high, low, close, volume\n                FROM stock_data \n                WHERE ticker = :ticker \n                AND date >= :start_date\n                ORDER BY date\n            \"\"\")\n            \n            result = db.execute(\n                stock_query, \n                {\"ticker\": ticker, \"start_date\": start_date}\n            )\n            \n            # Convert result to DataFrame\n            db_data = pd.DataFrame(result.fetchall(), columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume'])\n            if not db_data.empty:\n                db_data.set_index('Date', inplace=True)\n                db_data['Adj Close'] = db_data['Close']\n                \n                if sector_info:\n                    db_data['gics_sector'] = sector_info[0]\n                    db_data['gics_sub_industry'] = sector_info[1]\n                \n                latest_db_date = db_data.index.max()\n                \n                # If data is up to date, return it\n                if latest_db_date >= today - pd.Timedelta(days=1):\n                    logger.info(f\"Using database data for {ticker}\")\n                    return db_data\n                \n                # If we need to fetch new data\n                new_start_date = latest_db_date + pd.Timedelta(days=1)\n                logger.info(f\"Fetching new data for {ticker} from {new_start_date}\")\n                \n                try:\n                    new_data = yf.download(\n                        ticker,\n                        start=new_start_date,\n                        end=today,\n                        progress=False,\n                        auto_adjust=False,\n                        timeout=30\n                    )\n                    \n                    if not new_data.empty:\n                        if sector_info:\n                            new_data['gics_sector'] = sector_info[0]\n                            new_data['gics_sub_industry'] = sector_info[1]\n                        \n                        save_stock_data_to_db(ticker, new_data)\n                        \n                        combined_data = pd.concat([db_data, new_data])\n                        combined_data = combined_data[~combined_data.index.duplicated(keep='first')]\n                        return combined_data\n                    \n                    return db_data\n                    \n                except Exception as e:\n                    logger.error(f\"Failed to fetch new data for {ticker}: {str(e)}\")\n                    return db_data\n            \n            # If no data in database, fetch all from yfinance\n            logger.info(f\"Fetching all data for {ticker} from yfinance\")\n            stock_data = yf.download(\n                ticker,\n                start=start_date,\n                end=today,\n                progress=False,\n                auto_adjust=False,\n                timeout=30\n            )\n            \n            if not stock_data.empty:\n                if sector_info:\n                    stock_data['gics_sector'] = sector_info[0]\n                    stock_data['gics_sub_industry'] = sector_info[1]\n                \n                save_stock_data_to_db(ticker, stock_data)\n                return stock_data\n            \n            logger.warning(f\"No data available for {ticker}\")\n            return None\n            \n        finally:\n            db.close()\n\n    except Exception as e:\n        logger.error(f\"Failed to fetch data for {ticker}: {str(e)}\", exc_info=True)\n        return None\n\ndef get_db_connection():\n    \"\"\"Get database connection.\"\"\"\n    import psycopg2\n    from backend.database.config import DB_CONFIG\n    return psycopg2.connect(**DB_CONFIG)\n\ndef calculate_volume_metrics(data):\n    \"\"\"Calculate volume-related metrics for the portfolio.\"\"\"\n    try:\n        # Calculate basic volume metrics\n        volume_data = data['Volume']\n        price_data = data['Close']\n        returns = data['Close'].pct_change()\n        \n        # Basic volume statistics\n        avg_volume = volume_data.mean()\n        median_volume = volume_data.median()\n        volume_std = volume_data.std()\n        \n        # Calculate 20-day moving average volume\n        volume_ma20 = volume_data.rolling(window=20).mean()\n        \n        # Volume trend (comparing recent volume to historical)\n        recent_volume = volume_data[-20:].mean()  # Last 20 days\n        volume_trend = recent_volume / avg_volume - 1  # Positive means increasing volume\n        \n        # Volume volatility (coefficient of variation)\n        volume_volatility = volume_std / avg_volume\n        \n        # Calculate price-volume correlation\n        price_volume_corr = price_data.corr(volume_data)\n        \n        # Calculate returns-volume correlation\n        returns_volume_corr = returns.corr(volume_data)\n        \n        # Volume ratio (current volume to moving average)\n        volume_ratio = volume_data / volume_ma20\n        \n        # Calculate up/down volume metrics\n        up_days = returns > 0\n        down_days = returns < 0\n        \n        up_volume = volume_data[up_days].mean()\n        down_volume = volume_data[down_days].mean()\n        up_down_ratio = up_volume / down_volume if down_volume != 0 else float('inf')\n        \n        # Calculate volume momentum (rate of change)\n        volume_momentum_5d = (volume_data / volume_data.shift(5) - 1).mean()\n        volume_momentum_20d = (volume_data / volume_data.shift(20) - 1).mean()\n        \n        # Calculate relative volume metrics\n        relative_volume = volume_data / volume_ma20\n        high_volume_days = (relative_volume > 1.5).sum()  # Days with 50% above average volume\n        low_volume_days = (relative_volume < 0.5).sum()   # Days with 50% below average volume\n        \n        # Calculate volume-weighted metrics\n        vwap = (price_data * volume_data).sum() / volume_data.sum()  # Volume-weighted average price\n        typical_volume = volume_data.quantile(0.5)  # Median volume\n        \n        return {\n            'basic_metrics': {\n                'average_volume': float(avg_volume),\n                'median_volume': float(median_volume),\n                'volume_std': float(volume_std),\n                'volume_volatility': float(volume_volatility)\n            },\n            'trend_analysis': {\n                'volume_trend': float(volume_trend),\n                'volume_momentum_5d': float(volume_momentum_5d),\n                'volume_momentum_20d': float(volume_momentum_20d)\n            },\n            'correlations': {\n                'price_volume_correlation': float(price_volume_corr),\n                'returns_volume_correlation': float(returns_volume_corr)\n            },\n            'trading_patterns': {\n                'up_down_volume_ratio': float(up_down_ratio),\n                'up_volume': float(up_volume),\n                'down_volume': float(down_volume)\n            },\n            'relative_metrics': {\n                'high_volume_days': int(high_volume_days),\n                'low_volume_days': int(low_volume_days),\n                'typical_volume': float(typical_volume),\n                'vwap': float(vwap)\n            }\n        }\n    except Exception as e:\n        logger.error(f\"Error calculating volume metrics: {str(e)}\")\n        return {\n            'basic_metrics': {},\n            'trend_analysis': {},\n            'correlations': {},\n            'trading_patterns': {},\n            'relative_metrics': {}\n        }\n\ndef calculate_portfolio_metrics(returns, benchmark_returns=None, portfolio_data=None):\n    \"\"\"Calculate enhanced portfolio metrics.\"\"\"\n    try:\n        # Ensure returns is a pandas Series\n        if not isinstance(returns, pd.Series):\n            raise ValueError(\"returns should be a pandas Series representing the portfolio returns.\")\n        metrics = {}\n        logger.info(\"Starting portfolio metrics calculation...\")\n        \n        # Convert returns to numeric, replacing any non-numeric values with NaN\n        returns = pd.to_numeric(returns, errors='coerce').fillna(0)\n        \n        # Calculate drawdowns first\n        drawdown_analysis = calculate_drawdown_periods(returns)\n        metrics['drawdowns'] = drawdown_analysis\n        \n        # Store returns for chart\n        metrics['returns'] = returns.tolist()\n        metrics['dates'] = returns.index.strftime('%Y-%m-%d').tolist()\n        \n        # Calculate cumulative returns\n        cumulative_returns = (1 + returns).cumprod()\n        metrics['cumulative_returns'] = cumulative_returns.tolist()\n        \n        # Basic portfolio metrics\n        initial_value = 10000.0\n        metrics['start_balance'] = initial_value\n        metrics['end_balance'] = float(initial_value * cumulative_returns.iloc[-1])\n        metrics['total_return'] = float((metrics['end_balance'] / initial_value) - 1)\n        \n        # Time period calculations\n        total_days = len(returns)\n        total_years = total_days / 252  # Assuming 252 trading days per year\n        metrics['total_days'] = total_days\n        metrics['total_years'] = total_years\n        \n        # Risk-free rate (2% annual)\n        risk_free_rate = 0.02 / 252  # Daily risk-free rate\n        \n        try:\n            # CAGR calculation\n            metrics['cagr'] = float(((1 + metrics['total_return']) ** (1 / total_years)) - 1) if total_years > 0 else 0.0\n            \n            # Volatility calculations\n            daily_std = float(returns.std())\n            metrics['daily_volatility'] = daily_std\n            metrics['annual_volatility'] = float(daily_std * np.sqrt(252))\n            \n            # Rolling volatility (30-day window)\n            rolling_vol = returns.rolling(window=30).std() * np.sqrt(252)\n            metrics['rolling_volatility'] = rolling_vol.tolist()\n            \n            # Risk metrics\n            metrics['sharpe_ratio'] = float(ep.sharpe_ratio(returns, risk_free=risk_free_rate))\n            metrics['sortino_ratio'] = float(ep.sortino_ratio(returns, required_return=risk_free_rate))\n            metrics['max_drawdown'] = float(ep.max_drawdown(returns))\n            \n            # Calculate rolling returns properly\n            def calculate_rolling_return(window):\n                return ((1 + returns).rolling(window=window).apply(\n                    lambda x: np.prod(x) - 1, raw=True\n                ))\n    \n            rolling_returns = {\n                '1M': calculate_rolling_return(21),    # Monthly\n                '3M': calculate_rolling_return(63),    # Quarterly\n                '6M': calculate_rolling_return(126),   # Semi-annual\n                '1Y': calculate_rolling_return(252)    # Annual\n            }\n            \n            # Get the most recent rolling returns\n            metrics['rolling_returns'] = {\n                period: float(values.iloc[-1]) if not pd.isna(values.iloc[-1]) else 0.0\n                for period, values in rolling_returns.items()\n            }\n            \n            # Value at Risk (VaR) calculations\n            returns_array = returns.values\n            metrics['var_95'] = float(np.percentile(returns_array, 5))  # 95% VaR\n            metrics['var_99'] = float(np.percentile(returns_array, 1))  # 99% VaR\n            \n            # Conditional VaR (CVaR) calculations\n            var_95_mask = returns_array <= metrics['var_95']\n            var_99_mask = returns_array <= metrics['var_99']\n            \n            metrics['cvar_95'] = float(np.mean(returns_array[var_95_mask])) if np.any(var_95_mask) else metrics['var_95']\n            metrics['cvar_99'] = float(np.mean(returns_array[var_99_mask])) if np.any(var_99_mask) else metrics['var_99']\n            \n            # Return distribution metrics\n            from scipy import stats\n            metrics['skewness'] = float(stats.skew(returns_array))\n            metrics['kurtosis'] = float(stats.kurtosis(returns_array, fisher=True))\n            \n            logger.info(f\"Base metrics calculated: CAGR={metrics['cagr']:.2%}, Vol={metrics['annual_volatility']:.2%}\")\n            logger.info(f\"Risk metrics: VaR={metrics['var_95']:.2%}, CVaR={metrics['cvar_95']:.2%}\")\n            logger.info(f\"Distribution metrics: Skew={metrics['skewness']:.2f}, Kurt={metrics['kurtosis']:.2f}\")\n            \n        except Exception as e:\n            logger.error(f\"Error calculating base metrics: {str(e)}\")\n            metrics.update({\n                'cagr': 0.0,\n                'annual_volatility': 0.0,\n                'sharpe_ratio': 0.0,\n                'sortino_ratio': 0.0,\n                'max_drawdown': 0.0,\n                'var_95': 0.0,\n                'var_99': 0.0,\n                'cvar_95': 0.0,\n                'cvar_99': 0.0,\n                'skewness': 0.0,\n                'kurtosis': 0.0,\n                'rolling_returns': {\n                    '1M': 0.0, '3M': 0.0, '6M': 0.0, '1Y': 0.0\n                }\n            })\n    \n        # Benchmark comparison metrics\n        if benchmark_returns is not None:\n            try:\n                # Align returns for benchmark calculations\n                common_dates = returns.index.intersection(benchmark_returns.index)\n                aligned_returns = returns[common_dates]\n                aligned_benchmark = benchmark_returns[common_dates]\n                \n                # Calculate excess returns\n                excess_returns = aligned_returns - aligned_benchmark\n                tracking_error = float(np.std(excess_returns) * np.sqrt(252)) if len(excess_returns) > 1 else 0.0\n                \n                # Regression analysis for beta and alpha\n                X = sm.add_constant(aligned_benchmark)\n                model = sm.OLS(aligned_returns, X).fit()\n                \n                # Calculate annualized alpha and beta\n                alpha = float(model.params['const'] * 252)\n                beta = float(model.params[1])\n                r_squared = float(model.rsquared)\n                \n                # Calculate up/down capture ratios properly\n                up_market = aligned_benchmark > 0\n                down_market = aligned_benchmark < 0\n                \n                # Calculate up/down capture using cumulative returns\n                up_portfolio = (1 + aligned_returns[up_market]).prod() - 1\n                up_benchmark = (1 + aligned_benchmark[up_market]).prod() - 1\n                down_portfolio = (1 + aligned_returns[down_market]).prod() - 1\n                down_benchmark = (1 + aligned_benchmark[down_market]).prod() - 1\n                \n                up_capture = float(up_portfolio / up_benchmark if up_benchmark != 0 else 1.0)\n                down_capture = float(down_portfolio / down_benchmark if down_benchmark != 0 else 1.0)\n                \n                # Calculate information ratio and active return\n                excess_return_mean = float(excess_returns.mean() * 252)  # Annualized\n                information_ratio = float(excess_return_mean / tracking_error) if tracking_error > 0 else 0.0\n                active_return = float(excess_return_mean)\n                \n                metrics.update({\n                    'alpha': alpha,\n                    'beta': beta,\n                    'r_squared': r_squared,\n                    'tracking_error': tracking_error,\n                    'information_ratio': information_ratio,\n                    'active_return': active_return,\n                    'up_capture': up_capture,\n                    'down_capture': down_capture,\n                    'correlation': float(aligned_returns.corr(aligned_benchmark))\n                })\n                \n                logger.info(f\"Relative metrics calculated: Beta={beta:.2f}, Alpha={alpha:.2%}, \"\n                          f\"Information Ratio={information_ratio:.2f}\")\n                \n            except Exception as e:\n                logger.error(f\"Error calculating benchmark metrics: {str(e)}\")\n                metrics.update({\n                    'alpha': 0.0,\n                    'beta': 1.0,\n                    'r_squared': 0.0,\n                    'tracking_error': 0.0,\n                    'information_ratio': 0.0,\n                    'active_return': 0.0,\n                    'up_capture': 1.0,\n                    'down_capture': 1.0,\n                    'correlation': 0.0\n                })\n    \n        # Calculate volume metrics\n        if portfolio_data is not None:\n            try:\n                logger.info(\"Starting volume metrics calculation...\")\n                logger.info(f\"Portfolio data columns: {portfolio_data.columns}\")\n                \n                if isinstance(portfolio_data, pd.DataFrame):\n                    # For single asset portfolios\n                    if len(portfolio_data.columns) == 1:\n                        ticker = portfolio_data.columns[0]\n                        logger.info(f\"Calculating volume metrics for single asset: {ticker}\")\n                        # Get volume data from database\n                        db = SessionLocal()\n                        volume_query = text(\"\"\"\n                            SELECT date, close, volume\n                            FROM stock_data\n                            WHERE ticker = :ticker\n                            ORDER BY date\n                        \"\"\")\n                        result = db.execute(volume_query, {\"ticker\": ticker})\n                        volume_data = pd.DataFrame(result.fetchall(), columns=['date', 'Close', 'Volume'])\n                        volume_data.set_index('date', inplace=True)\n                        db.close()\n                        \n                        if not volume_data.empty:\n                            metrics['volume_analysis'] = calculate_volume_metrics(volume_data)\n                    else:\n                        # Multi-asset portfolio\n                        volume_metrics = {}\n                        for ticker in portfolio_data.columns:\n                            logger.info(f\"Calculating volume metrics for: {ticker}\")\n                            # Get volume data from database\n                            db = SessionLocal()\n                            volume_query = text(\"\"\"\n                                SELECT date, close, volume\n                                FROM stock_data\n                                WHERE ticker = :ticker\n                                ORDER BY date\n                            \"\"\")\n                            result = db.execute(volume_query, {\"ticker\": ticker})\n                            volume_data = pd.DataFrame(result.fetchall(), columns=['date', 'Close', 'Volume'])\n                            volume_data.set_index('date', inplace=True)\n                            db.close()\n                            \n                            if not volume_data.empty:\n                                volume_metrics[ticker] = calculate_volume_metrics(volume_data)\n                        \n                        if volume_metrics:\n                            logger.info(\"Aggregating volume metrics for portfolio\")\n                            portfolio_volume_metrics = {\n                                'basic_metrics': {\n                                    'average_volume': np.mean([m['basic_metrics']['average_volume'] for m in volume_metrics.values()]),\n                                    'volume_volatility': np.mean([m['basic_metrics']['volume_volatility'] for m in volume_metrics.values()])\n                                },\n                                'trend_analysis': {\n                                    'volume_trend': np.mean([m['trend_analysis']['volume_trend'] for m in volume_metrics.values()]),\n                                    'volume_momentum_20d': np.mean([m['trend_analysis']['volume_momentum_20d'] for m in volume_metrics.values()])\n                                },\n                                'trading_patterns': {\n                                    'up_down_volume_ratio': np.mean([m['trading_patterns']['up_down_volume_ratio'] for m in volume_metrics.values()])\n                                },\n                                'relative_metrics': {\n                                    'high_volume_days': int(np.mean([m['relative_metrics']['high_volume_days'] for m in volume_metrics.values()])),\n                                    'low_volume_days': int(np.mean([m['relative_metrics']['low_volume_days'] for m in volume_metrics.values()])),\n                                    'typical_volume': np.mean([m['relative_metrics']['typical_volume'] for m in volume_metrics.values()]),\n                                    'vwap': np.mean([m['relative_metrics']['vwap'] for m in volume_metrics.values()])\n                                },\n                                'individual_assets': volume_metrics\n                            }\n                            metrics['volume_analysis'] = portfolio_volume_metrics\n                \n                logger.info(\"Volume metrics calculation completed\")\n                logger.info(f\"Volume metrics: {metrics.get('volume_analysis', 'No volume metrics calculated')}\")\n                \n            except Exception as e:\n                logger.error(f\"Error calculating volume metrics: {str(e)}\", exc_info=True)\n                metrics['volume_analysis'] = {}\n        \n        # Calculate risk metrics\n        try:\n            risk_metrics = calculate_risk_metrics(returns)\n            metrics['risk_metrics'] = risk_metrics\n            logger.info(f\"Risk metrics calculated: {json.dumps(risk_metrics['daily_metrics'], indent=2)}\")\n        except Exception as e:\n            logger.error(f\"Error calculating risk metrics: {str(e)}\")\n            metrics['risk_metrics'] = {\n                'daily_metrics': {\n                    'var_95': 0.0,\n                    'var_99': 0.0,\n                    'cvar_95': 0.0,\n                    'cvar_99': 0.0,\n                    'volatility': 0.0\n                },\n                'annualized_metrics': {\n                    'volatility': 0.0,\n                    'downside_deviation': 0.0,\n                    'sharpe_ratio': 0.0,\n                    'sortino_ratio': 0.0\n                },\n                'drawdown_metrics': {\n                    'max_drawdown': 0.0,\n                    'current_drawdown': 0.0\n                },\n                'rolling_volatility': [],\n                'distribution_metrics': {\n                    'skewness': 0.0,\n                    'kurtosis': 0.0,\n                    'positive_days': 0,\n                    'negative_days': 0,\n                    'total_days': 0\n                }\n            }\n        \n        return clean_for_json(metrics)\n        \n    except Exception as e:\n        logger.error(f\"Error in calculate_portfolio_metrics: {str(e)}\", exc_info=True)\n        return {}\n\ndef calculate_risk_metrics(returns_data, rolling_window=30):\n    \"\"\"Calculate comprehensive risk metrics for portfolio analysis.\"\"\"\n    try:\n        returns = returns_data\n        \n        returns_array = returns.values\n        dates = returns.index\n    \n        # Basic Risk Metrics\n        daily_std = np.std(returns_array)\n        annualized_vol = daily_std * np.sqrt(252)\n        \n        # Value at Risk (VaR) calculations\n        var_95 = np.percentile(returns_array, 5)  # 95% VaR\n        var_99 = np.percentile(returns_array, 1)  # 99% VaR\n        \n        # Conditional VaR (Expected Shortfall)\n        cvar_95 = returns_array[returns_array <= var_95].mean()\n        cvar_99 = returns_array[returns_array <= var_99].mean()\n    \n        # Rolling Volatility\n        rolling_vol = returns.rolling(window=rolling_window).std() * np.sqrt(252)\n        rolling_vol_data = [\n            {\n                'date': date.strftime('%Y-%m-%d'),\n                'volatility': vol\n            }\n            for date, vol in zip(dates, rolling_vol)\n            if not np.isnan(vol)\n        ]\n    \n        # Downside Risk Metrics\n        negative_returns = returns_array[returns_array < 0]\n        downside_deviation = np.sqrt(np.mean(negative_returns ** 2)) * np.sqrt(252)\n        \n        # Maximum Drawdown calculation\n        cumulative_returns = (1 + returns).cumprod()\n        rolling_max = cumulative_returns.expanding().max()\n        drawdowns = (cumulative_returns - rolling_max) / rolling_max\n        max_drawdown = drawdowns.min()\n        current_drawdown = drawdowns.iloc[-1]\n    \n        # Risk Ratios\n        risk_free_rate = 0.02 / 252  # Daily risk-free rate (2% annual)\n        avg_return = np.mean(returns_array)\n        sharpe_ratio = (avg_return * 252 - risk_free_rate * 252) / (daily_std * np.sqrt(252))\n        sortino_ratio = (avg_return * 252 - risk_free_rate * 252) / (downside_deviation) if downside_deviation != 0 else 0\n    \n        # Distribution metrics\n        skewness = float(stats.skew(returns_array))\n        kurtosis = float(stats.kurtosis(returns_array))\n        positive_days = int(np.sum(returns_array > 0))\n        negative_days = int(np.sum(returns_array < 0))\n    \n        return {\n            'daily_metrics': {\n                'var_95': float(var_95),\n                'var_99': float(var_99),\n                'cvar_95': float(cvar_95),\n                'cvar_99': float(cvar_99),\n                'volatility': float(daily_std)\n            },\n            'annualized_metrics': {\n                'volatility': float(annualized_vol),\n                'downside_deviation': float(downside_deviation),\n                'sharpe_ratio': float(sharpe_ratio),\n                'sortino_ratio': float(sortino_ratio)\n            },\n            'drawdown_metrics': {\n                'max_drawdown': float(max_drawdown),\n                'current_drawdown': float(current_drawdown)\n            },\n            'rolling_volatility': rolling_vol_data,\n            'distribution_metrics': {\n                'skewness': skewness,\n                'kurtosis': kurtosis,\n                'positive_days': positive_days,\n                'negative_days': negative_days,\n                'total_days': len(returns_array)\n            }\n        }\n\n    except Exception as e:\n        logger.error(f\"Error calculating risk metrics: {str(e)}\")\n        return {\n            'daily_metrics': {},\n            'annualized_metrics': {},\n            'drawdown_metrics': {},\n            'rolling_volatility': [],\n            'distribution_metrics': {}\n        }\n\ndef calculate_correlation_matrix(portfolio_data):\n    \"\"\"Calculate correlation matrices for the portfolio.\"\"\"\n    try:\n        # Calculate returns\n        returns = portfolio_data.pct_change().dropna()\n        \n        # Calculate Pearson correlation\n        pearson_matrix = returns.corr(method='pearson')\n        pearson_values = pearson_matrix.values[np.triu_indices_from(pearson_matrix.values, k=1)]\n        \n        # Calculate Spearman correlation\n        spearman_matrix = returns.corr(method='spearman')\n        spearman_values = spearman_matrix.values[np.triu_indices_from(spearman_matrix.values, k=1)]\n        \n        # Convert matrices to dictionaries\n        pearson_dict = {ticker: pearson_matrix[ticker].to_dict() for ticker in pearson_matrix.index}\n        spearman_dict = {ticker: spearman_matrix[ticker].to_dict() for ticker in spearman_matrix.index}\n        \n        # Calculate rolling correlations\n        rolling_window = min(252, len(returns) // 2)  # Use 1 year or half the data length\n        if rolling_window < 2:\n            rolling_window = 2  # Minimum window size\n        \n        rolling_corr = returns.rolling(window=rolling_window).corr()\n        \n        # Get the most recent rolling correlations\n        latest_rolling = rolling_corr.xs(returns.index[-1], level=0)\n        rolling_dict = {ticker: latest_rolling[ticker].to_dict() for ticker in latest_rolling.index}\n        \n        return {\n            'pearson': {\n                'matrix': pearson_dict,\n                'avg': float(np.mean(pearson_values)),\n                'min': float(np.min(pearson_values)),\n                'max': float(np.max(pearson_values))\n            },\n            'spearman': {\n                'matrix': spearman_dict,\n                'avg': float(np.mean(spearman_values)),\n                'min': float(np.min(spearman_values)),\n                'max': float(np.max(spearman_values))\n            },\n            'rolling': {\n                'matrix': rolling_dict,\n                'window': rolling_window\n            }\n        }\n    except Exception as e:\n        logger.error(f\"Error in calculate_correlation_matrix: {str(e)}\", exc_info=True)\n        return None\n\ndef calculate_drawdown_periods(returns):\n    \"\"\"\n    Identify drawdown periods in the portfolio.\n    \"\"\"\n    try:\n        # Calculate cumulative returns and running maximum\n        initial_value = 10000  # Start with $10,000\n        cumulative = initial_value * (1 + returns).cumprod()\n        running_max = cumulative.expanding().max()\n        drawdown = (cumulative - running_max) / running_max\n        \n        # Prepare chart data\n        chart_data = {\n            'dates': returns.index.strftime('%Y-%m-%d').tolist(),\n            'drawdowns': drawdown.tolist(),\n            'cumulative': cumulative.tolist(),\n            'peaks': running_max.tolist()\n        }\n\n        # Track all drawdowns\n        significant_drawdowns = []\n        \n        # Initialize tracking variables\n        in_drawdown = False\n        peak_value = cumulative.iloc[0]\n        peak_date = returns.index[0]\n        trough_value = peak_value\n        trough_date = peak_date\n        \n        # Track drawdown periods\n        for i in range(1, len(cumulative)):\n            current_value = cumulative.iloc[i]\n            current_date = returns.index[i]\n            \n            # New peak\n            if current_value > peak_value and not in_drawdown:\n                peak_value = current_value\n                peak_date = current_date\n            \n            # Calculate current drawdown from peak\n            current_drawdown = (current_value - peak_value) / peak_value\n            \n            # Start or deepen drawdown\n            if current_drawdown < -0.05:  # Only track drawdowns deeper than 5%\n                if not in_drawdown:\n                    in_drawdown = True\n                    trough_value = current_value\n                    trough_date = current_date\n                elif current_value < trough_value:\n                    trough_value = current_value\n                    trough_date = current_date\n            \n            # Check for recovery (back to peak or new high)\n            elif in_drawdown and current_value >= peak_value * 0.95:  # Consider recovered when within 5% of peak\n                depth = (trough_value - peak_value) / peak_value\n                length_days = (trough_date - peak_date).days\n                recovery_days = (current_date - trough_date).days\n                \n                drawdown_info = {\n                    'start': peak_date.strftime('%Y-%m-%d'),\n                    'end': trough_date.strftime('%Y-%m-%d'),\n                    'recovery': current_date.strftime('%Y-%m-%d'),\n                    'depth': float(depth),\n                    'length_days': int(length_days),\n                    'recovery_time_days': int(recovery_days),\n                    'peak_value': float(peak_value),\n                    'trough_value': float(trough_value),\n                    'recovery_value': float(current_value),\n                    'is_active': False\n                }\n                significant_drawdowns.append(drawdown_info)\n                \n                # Reset for next drawdown\n                in_drawdown = False\n                peak_value = current_value\n                peak_date = current_date\n        \n        # Handle ongoing drawdown\n        if in_drawdown:\n            current_value = cumulative.iloc[-1]\n            current_date = returns.index[-1]\n            depth = (trough_value - peak_value) / peak_value\n            \n            drawdown_info = {\n                'start': peak_date.strftime('%Y-%m-%d'),\n                'end': trough_date.strftime('%Y-%m-%d'),\n                'recovery': None,\n                'depth': float(depth),\n                'length_days': int((current_date - peak_date).days),\n                'recovery_time_days': None,\n                'peak_value': float(peak_value),\n                'trough_value': float(trough_value),\n                'is_active': True\n            }\n            significant_drawdowns.append(drawdown_info)\n        \n        # Sort drawdowns by depth (most severe first)\n        significant_drawdowns.sort(key=lambda x: x['depth'])\n        worst_drawdowns = significant_drawdowns[:10]  # Take 10 worst drawdowns\n        \n        # Calculate average metrics from all drawdowns\n        avg_depth = np.mean([abs(d['depth']) for d in significant_drawdowns]) if significant_drawdowns else 0\n        recovered_drawdowns = [d for d in significant_drawdowns if d['recovery_time_days'] is not None]\n        avg_recovery = np.mean([d['recovery_time_days'] for d in recovered_drawdowns]) if recovered_drawdowns else 0\n        \n        return {\n            'periods': worst_drawdowns,\n            'chart_data': chart_data,\n            'max_drawdown': float(drawdown.min()) if len(drawdown) > 0 else 0.0,\n            'avg_depth': float(avg_depth),\n            'avg_recovery': float(round(avg_recovery, 2)),  # Ensure float with 2 decimal places\n            'total_events': len(significant_drawdowns)\n        }\n\n    except Exception as e:\n        logger.error(f\"Error in calculate_drawdown_periods: {str(e)}\", exc_info=True)\n        return {\n            'periods': [],\n            'chart_data': {\n                'dates': [],\n                'drawdowns': [],\n                'cumulative': [],\n                'peaks': []\n            },\n            'max_drawdown': 0.0,\n            'avg_depth': 0.0,\n            'avg_recovery': 0.0,\n            'total_events': 0\n        }\n\ndef calculate_rebalancing_impact(portfolio_data, weights, transaction_cost):\n    \"\"\"Calculate the impact of different rebalancing strategies.\"\"\"\n    try:\n        logger.info(\"Starting rebalancing impact analysis...\")\n        \n        # If it's a single-asset portfolio (like SPY), return same values for all strategies\n        if len(weights) == 1:\n            logger.info(\"Single-asset portfolio detected - no rebalancing needed\")\n            returns = portfolio_data.pct_change().fillna(0)\n            metrics = calculate_portfolio_metrics(returns.iloc[:, 0])  # Use first column\n            \n            # Create base metrics for single-asset portfolio\n            base_metrics = {\n                'portfolio_values': (10000 * (1 + returns.iloc[:, 0]).cumprod()).tolist(),\n                'final_value': float(10000 * (1 + returns.iloc[:, 0]).cumprod().iloc[-1]),\n                'total_return': float((1 + returns.iloc[:, 0]).cumprod().iloc[-1] - 1),\n                'rebalance_count': 0,\n                'total_costs': 0.0,\n                'cost_percentage': 0.0,\n                'avg_turnover': 0.0,\n                'avg_tracking_error': 0.0,\n                'weight_drift': {\n                    'mean': 0.0,\n                    'max': 0.0,\n                    'current': 0.0,\n                    'history': [0.0] * len(returns)\n                },\n                'current_weights': [1.0],\n                'weight_history': [[1.0]] * len(returns)\n            }\n            \n            # Update with calculated metrics\n            metrics.update(base_metrics)\n            \n            # Return same metrics for all strategies\n            return {\n                strategy: clean_for_json(metrics)\n                for strategy in ['Buy and Hold', 'Monthly', 'Quarterly', 'Semi-Annual', 'Annual']\n            }\n        \n        # For multi-asset portfolios, proceed with rebalancing calculation\n        # Ensure weights are numpy array and sum to 1\n        weights = np.array(weights)\n        if not np.isclose(weights.sum(), 1.0):\n            weights = weights / weights.sum()\n            \n        # Calculate returns and ensure no NaN values\n        returns = portfolio_data.pct_change().fillna(0)\n        \n        # Define rebalancing strategies with specific day offsets\n        strategies = {\n            'Buy and Hold': None,  # No rebalancing\n            'Monthly': pd.DateOffset(months=1),\n            'Quarterly': pd.DateOffset(months=3),\n            'Semi-Annual': pd.DateOffset(months=6),\n            'Annual': pd.DateOffset(years=1)\n        }\n        \n        results = {}\n        initial_value = 10000.0  # Start with $10,000\n        \n        for strategy_name, rebalance_period in strategies.items():\n            try:\n                # Initialize portfolio tracking\n                portfolio_value = initial_value\n                current_weights = weights.copy()\n                current_holdings = portfolio_value * current_weights\n                total_costs = 0\n                rebalance_count = 0\n                portfolio_values = [initial_value]  # Start with initial value\n                daily_returns = [0]  # Start with 0 return\n                turnover = []\n                tracking_error = []\n                current_weights_history = [weights.tolist()]  # Start with initial weights\n                weight_drift_history = [0]  # Start with 0 drift\n                \n                last_rebalance_date = returns.index[0]\n                \n                # Track daily portfolio changes\n                for date in returns.index[1:]:  # Skip first day since we already initialized\n                    # Update holdings based on daily returns\n                    daily_return_vector = returns.loc[date].values\n                    current_holdings *= (1 + daily_return_vector)\n                    portfolio_value = current_holdings.sum()\n                    \n                    # Calculate current weights\n                    current_weights = current_holdings / portfolio_value\n                    \n                    # Calculate weight drift\n                    weight_drift = np.sqrt(np.sum((current_weights - weights) ** 2))\n                    weight_drift_history.append(float(weight_drift))\n                    \n                    # Check if rebalancing is needed\n                    should_rebalance = False\n                    if strategy_name != 'Buy and Hold':\n                        # Check if enough time has passed according to strategy\n                        if rebalance_period and date > last_rebalance_date + rebalance_period:\n                            # Only rebalance if drift exceeds threshold\n                            if weight_drift > 0.01:  # 1% threshold\n                                should_rebalance = True\n                    \n                    if should_rebalance:\n                        # Calculate trades needed\n                        target_holdings = portfolio_value * weights\n                        trades = target_holdings - current_holdings\n                        \n                        # Calculate transaction costs\n                        trade_costs = abs(trades).sum() * transaction_cost\n                        \n                        # Apply transaction costs\n                        portfolio_value -= trade_costs\n                        total_costs += trade_costs\n                        \n                        # Update holdings and record rebalancing\n                        current_holdings = portfolio_value * weights\n                        last_rebalance_date = date\n                        rebalance_count += 1\n                        \n                        # Calculate turnover\n                        turnover.append(abs(trades).sum() / (2 * portfolio_value))\n                    \n                    # Calculate daily return\n                    daily_return = (portfolio_value / portfolio_values[-1]) - 1\n                    \n                    # Record daily tracking data\n                    portfolio_values.append(portfolio_value)\n                    daily_returns.append(daily_return)\n                    current_weights_history.append(current_weights.tolist())\n                    tracking_error.append(float(np.std(current_weights - weights)))\n                \n                # Calculate strategy metrics\n                daily_returns_series = pd.Series(daily_returns, index=returns.index)\n                metrics = calculate_portfolio_metrics(daily_returns_series)\n                \n                # Add rebalancing-specific metrics\n                metrics.update({\n                    'portfolio_values': portfolio_values,\n                    'final_value': float(portfolio_values[-1]),\n                    'total_return': float((portfolio_values[-1] / initial_value) - 1),\n                    'rebalance_count': int(rebalance_count),\n                    'total_costs': float(total_costs),\n                    'cost_percentage': float(total_costs / portfolio_values[-1]),\n                    'avg_turnover': float(np.mean(turnover) if turnover else 0),\n                    'avg_tracking_error': float(np.mean(tracking_error)),\n                    'weight_drift': {\n                        'mean': float(np.mean(weight_drift_history)),\n                        'max': float(np.max(weight_drift_history)),\n                        'current': float(weight_drift_history[-1]),\n                        'history': [float(x) for x in weight_drift_history]\n                    },\n                    'current_weights': current_weights.tolist(),\n                    'weight_history': current_weights_history\n                })\n                \n                results[strategy_name] = clean_for_json(metrics)\n                \n            except Exception as e:\n                logger.error(f\"Error calculating {strategy_name} strategy: {str(e)}\")\n                results[strategy_name] = {\n                    'error': str(e),\n                    'final_value': initial_value,\n                    'total_return': 0.0,\n                    'rebalance_count': 0,\n                    'total_costs': 0.0\n                }\n        \n        return results\n            \n    except Exception as e:\n        logger.error(f\"Error in calculate_rebalancing_impact: {str(e)}\")\n        return {}\n\ndef calculate_attribution(portfolio_data, weights, benchmark_data):\n    \"\"\"Calculate enhanced performance attribution metrics.\"\"\"\n    try:\n        logger.info(\"Starting attribution analysis...\")\n        \n        # Debug: Log initial shapes\n        logger.info(f\"Initial shapes - Portfolio: {portfolio_data.shape}, Benchmark: {benchmark_data.shape}\")\n        \n        # Calculate returns and align dates\n        portfolio_returns = portfolio_data.pct_change().fillna(0)\n        benchmark_returns = benchmark_data.pct_change().fillna(0)\n        \n        # Debug: Log returns shapes before alignment\n        logger.info(f\"Returns shapes before alignment - Portfolio: {portfolio_returns.shape}, Benchmark: {benchmark_returns.shape}\")\n        logger.info(f\"Portfolio dates range: {portfolio_returns.index[0]} to {portfolio_returns.index[-1]}\")\n        logger.info(f\"Benchmark dates range: {benchmark_returns.index[0]} to {benchmark_returns.index[-1]}\")\n        \n        # Align dates between portfolio and benchmark\n        common_dates = portfolio_returns.index.intersection(benchmark_returns.index)\n        logger.info(f\"Number of common dates: {len(common_dates)}\")\n        \n        portfolio_returns = portfolio_returns.loc[common_dates]\n        benchmark_returns = benchmark_returns.loc[common_dates]\n        \n        # Debug: Log shapes after alignment\n        logger.info(f\"Returns shapes after alignment - Portfolio: {portfolio_returns.shape}, Benchmark: {benchmark_returns.shape}\")\n        \n        # Calculate weighted portfolio returns\n        weights = np.array(weights)\n        weights = weights / weights.sum()  # Normalize weights\n        weighted_returns = (portfolio_returns * weights).sum(axis=1)\n        \n        # Debug: Log weighted returns shape\n        logger.info(f\"Weighted returns shape: {weighted_returns.shape}\")\n        \n        # Get sector data from market_tickers table\n        sector_data = {}\n        db = SessionLocal()\n        \n        for ticker in portfolio_data.columns:\n            sector_query = text(\"\"\"\n                SELECT gics_sector, gics_sub_industry \n                FROM market_tickers \n                WHERE ticker = :ticker\n            \"\"\")\n            sector_info = db.execute(sector_query, {\"ticker\": ticker}).first()\n            \n            if sector_info:\n                sector = sector_info[0]\n                if sector not in sector_data:\n                    sector_data[sector] = {\n                        'holdings': [],\n                        'weight': 0,\n                        'return': 0,\n                        'contribution': 0\n                    }\n                sector_data[sector]['holdings'].append(ticker)\n                sector_data[sector]['weight'] += weights[list(portfolio_data.columns).index(ticker)]\n        \n        db.close()\n\n        # Calculate sector returns and contributions\n        for sector in sector_data:\n            sector_tickers = sector_data[sector]['holdings']\n            sector_weights = np.array([weights[list(portfolio_data.columns).index(t)] for t in sector_tickers])\n            sector_weights = sector_weights / sector_weights.sum()  # Normalize weights within sector\n            \n            sector_returns = portfolio_returns[sector_tickers]\n            sector_data[sector]['return'] = (sector_returns * sector_weights).sum(axis=1).mean() * 252  # Annualized\n            sector_data[sector]['contribution'] = sector_data[sector]['return'] * sector_data[sector]['weight']\n\n        # Factor Attribution Calculations\n        excess_returns = weighted_returns - benchmark_returns.iloc[:, 0]  # Get first column for benchmark\n        \n        # Convert Series to numpy arrays and ensure they're 1D\n        weighted_returns_array = weighted_returns.values.reshape(-1)\n        benchmark_returns_array = benchmark_returns.values.reshape(-1)  # Flatten to 1D\n        excess_returns_array = excess_returns.values.reshape(-1)\n        \n        # Debug: Log array shapes before covariance\n        logger.info(f\"Array shapes before covariance:\")\n        logger.info(f\"Weighted returns: {weighted_returns_array.shape}\")\n        logger.info(f\"Benchmark returns: {benchmark_returns_array.shape}\")\n        \n        # Calculate correlation and beta directly\n        correlation = np.corrcoef(weighted_returns_array, benchmark_returns_array)[0,1]\n        beta = correlation * (np.std(weighted_returns_array) / np.std(benchmark_returns_array))\n        \n        # Debug: Log correlation and beta\n        logger.info(f\"Correlation: {correlation}\")\n        logger.info(f\"Beta: {beta}\")\n        \n        # Calculate returns and contributions\n        market_return = np.mean(benchmark_returns_array) * 252  # Annualized\n        market_contribution = beta * market_return\n        \n        # Selection (Alpha) Factor\n        alpha = (np.mean(weighted_returns_array) - beta * np.mean(benchmark_returns_array)) * 252\n        selection_contribution = alpha\n        \n        # Interaction Factor\n        total_return = np.mean(weighted_returns_array) * 252\n        interaction_contribution = total_return - (market_contribution + selection_contribution)\n        \n        # Performance Metrics\n        tracking_error = np.std(excess_returns_array) * np.sqrt(252)\n        excess_return_mean = np.mean(excess_returns_array)\n        std_excess = np.std(excess_returns_array)\n        information_ratio = (excess_return_mean / std_excess) * np.sqrt(252) if not np.isclose(std_excess, 0) else 0\n        \n        # Calculate R-squared using correlation\n        r_squared = correlation ** 2\n        # Debug: Log final metrics\n        logger.info(f\"Final metrics calculated:\")\n        logger.info(f\"Market contribution: {market_contribution}\")\n        logger.info(f\"Selection contribution: {selection_contribution}\")\n        logger.info(f\"Information ratio: {information_ratio}\")\n        \n        attribution_results = {\n            'factor': {\n                'market_contribution': float(market_contribution),\n                'selection_contribution': float(selection_contribution),\n                'interaction_contribution': float(interaction_contribution),\n                'beta': float(beta),\n                'alpha': float(alpha),\n                'r_squared': float(correlation ** 2)\n            },\n            'sector': sector_data,\n            'performance': {\n                'total_return': float(total_return),\n                'benchmark_return': float(market_return),\n                'excess_return': float(total_return - market_return),\n                'tracking_error': float(tracking_error),\n                'information_ratio': float(information_ratio)\n            }\n        }\n        \n        logger.info(\"Attribution analysis completed successfully\")\n        return attribution_results\n            \n    except Exception as e:\n        logger.error(f\"Error in calculate_attribution: {str(e)}\", exc_info=True)\n        return {\n            'factor': {\n                'market_contribution': 0.0,\n                'selection_contribution': 0.0,\n                'interaction_contribution': 0.0,\n                'beta': 1.0,\n                'alpha': 0.0,\n                'r_squared': 0.0\n            },\n            'sector': {},\n            'performance': {\n                'total_return': 0.0,\n                'benchmark_return': 0.0,\n                'excess_return': 0.0,\n                'tracking_error': 0.0,\n                'information_ratio': 0.0\n            }\n        }\n\n# API Routes\n@app.route('/api/compare', methods=['POST', 'OPTIONS'])\ndef compare_portfolios():\n    global analysis_progress\n    \n    if request.method == 'OPTIONS':\n        return handle_preflight()\n\n    try:\n        logger.info(\"Starting portfolio comparison...\")\n        analysis_progress['progress'] = 0\n        analysis_progress['status'] = 'processing'\n        analysis_progress['error'] = None\n\n        data = request.get_json()\n        logger.info(f\"Received request data: {json.dumps(data, indent=2)}\")\n        \n        portfolios = data.get('portfolios', [])\n        start_date = data.get('startDate')\n        transaction_cost = min(float(data.get('transactionCost', 0.001)), 0.01)\n\n        if not portfolios:\n            update_progress(0, 'failed', 'No portfolios provided for comparison')\n            return jsonify({\n                'success': False,\n                'error': 'No portfolios provided for comparison'\n            }), 400\n\n        # Initialize results dictionary\n        comparison_results = {}\n        \n        start_date = pd.to_datetime(start_date)\n        all_data = pd.DataFrame()\n        \n        # Collect all unique tickers\n        unique_tickers = set()\n        for portfolio in portfolios:\n            portfolio_stocks = portfolio.get('portfolio', [])\n            unique_tickers.update([stock['ticker'] for stock in portfolio_stocks])\n        logger.info(f\"Fetching data for {len(unique_tickers)} unique tickers...\")\n        \n        # Fetch data for all tickers with progress updates\n        update_progress(10)\n        total_tickers = len(unique_tickers)\n        for i, ticker in enumerate(unique_tickers, 1):\n            # Fetch stock data and adjust start date if necessary\n            stock_data = fetch_stock_data(ticker, start_date)\n            if stock_data is not None and not stock_data.empty:\n                earliest_date = stock_data.index.min()\n                if start_date < earliest_date:\n                    logger.info(f\"Adjusting start date for {ticker} from {start_date} to {earliest_date}\")\n                    start_date = earliest_date\n                all_data[ticker] = stock_data['Adj Close']\n            else:\n                error_msg = f'Failed to fetch data for {ticker}'\n                if ticker == 'SPY':\n                    error_msg += ' (benchmark)'\n                update_progress(0, 'failed', error_msg)\n                return jsonify({\n                    'success': False,\n                    'error': error_msg\n                }), 400\n            progress = 10 + (30 * i / total_tickers)\n            update_progress(int(progress))\n\n        # Align all data to common dates and handle missing values\n        all_data = all_data.dropna()\n        if len(all_data) < 252:\n            update_progress(0, 'failed', 'Insufficient historical data')\n            return jsonify({\n                'success': False,\n                'error': 'Insufficient historical data. Need at least 252 data points.'\n            }), 400\n\n        update_progress(50)\n\n        # Extract benchmark data\n        benchmark_data = all_data['SPY']\n        benchmark_returns = benchmark_data.pct_change().fillna(0)\n\n        # Calculate metrics for each portfolio\n        total_portfolios = len(portfolios)\n        for i, portfolio in enumerate(portfolios, 1):\n            portfolio_name = portfolio.get('name', 'Unnamed Portfolio')\n            portfolio_stocks = portfolio.get('portfolio', [])\n            \n            # Add logging\n            logger.info(f\"Processing portfolio: {portfolio_name}\")\n            \n            weights = np.array([float(stock['weight']) / 100 for stock in portfolio_stocks])\n            weights = weights / weights.sum()\n            \n            tickers = [stock['ticker'] for stock in portfolio_stocks]\n            portfolio_data = all_data[tickers]\n            portfolio_returns = portfolio_data.pct_change().fillna(0)\n            weighted_returns = (portfolio_returns * weights).sum(axis=1)\n            # Add logging for volume data\n            logger.info(f\"Portfolio data columns: {portfolio_data.columns}\")\n            if 'Volume' in portfolio_data.columns:\n                logger.info(f\"Volume data sample: {portfolio_data['Volume'].head()}\")\n            else:\n                logger.info(\"No Volume column found in portfolio data\")\n            \n            # Calculate metrics with benchmark\n            portfolio_metrics = calculate_portfolio_metrics(\n                weighted_returns,  # Updated to pass weighted_returns\n                benchmark_returns,\n                portfolio_data  # Pass the original data with volume information\n            )\n            \n            # Add logging for volume metrics\n            if 'volume_analysis' in portfolio_metrics:\n                logger.info(f\"Volume metrics calculated: {portfolio_metrics['volume_analysis']}\")\n            else:\n                logger.info(\"No volume metrics found in portfolio metrics\")\n            \n            # Add historical performance data\n            initial_value = 10000\n            portfolio_values = initial_value * (1 + weighted_returns).cumprod()\n            portfolio_metrics['historical_performance'] = {\n                'dates': weighted_returns.index.strftime('%Y-%m-%d').tolist(),\n                'values': portfolio_values.tolist()\n            }\n            \n            # Calculate correlation matrices for portfolios with multiple stocks\n            correlations = calculate_correlation_matrix(portfolio_data) if len(tickers) > 1 else None\n            \n            # Calculate rebalancing impact\n            rebalancing_results = calculate_rebalancing_impact(\n                portfolio_data, \n                weights,\n                transaction_cost\n            )\n\n            # Calculate attribution\n            attribution_results = calculate_attribution(\n                portfolio_data,\n                weights,\n                pd.DataFrame({'Close': benchmark_data})\n            )\n            \n            comparison_results[portfolio_name] = {\n                'metrics': portfolio_metrics,\n                'rebalancing': rebalancing_results,\n                'portfolio': portfolio_stocks,\n                'attribution': attribution_results,\n                'correlations': correlations\n            }\n\n            # Update progress (50-90%)\n            progress = 50 + (40 * i / total_portfolios)\n            update_progress(int(progress))\n\n        # Final progress update\n        update_progress(100, 'completed')\n\n        # Clean the entire response object before returning\n        return jsonify({\n            'success': True,\n            'comparison_results': clean_for_json(comparison_results),\n            'data_quality': clean_for_json({\n                'num_data_points': len(all_data),\n                'date_range': {\n                    'start': all_data.index[0].strftime('%Y-%m-%d'),\n                    'end': all_data.index[-1].strftime('%Y-%m-%d')\n                }\n            })\n        })\n\n    except Exception as e:\n        logger.error(f\"Error in compare_portfolios: {str(e)}\", exc_info=True)\n        analysis_progress['status'] = 'failed'\n        analysis_progress['error'] = str(e)\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500\n\n@app.route('/api/backtest', methods=['POST'])\ndef backtest():\n    \"\"\"\n    Backtest the portfolio against historical data.\n    Expects JSON payload with:\n        - portfolio: list of stocks with 'ticker' and 'weight'\n        - startDate: backtest start date in 'YYYY-MM-DD'\n    \"\"\"\n    try:\n        logger.info(\"Starting backtest...\")\n        data = request.get_json()\n        logger.info(f\"Received backtest data: {json.dumps(data, indent=2)}\")\n        \n        portfolio = data.get('portfolio', [])\n        start_date = data.get('startDate')\n        \n        if not portfolio:\n            return jsonify({\n                'success': False,\n                'error': 'No portfolio provided for backtest'\n            }), 400\n\n        # Validate portfolio\n        is_valid, error_msg = validate_portfolio(portfolio)\n        if not is_valid:\n            return jsonify({\n                'success': False,\n                'error': error_msg\n            }), 400\n\n        start_date = pd.to_datetime(start_date)\n        portfolio_data = pd.DataFrame()\n\n        # Fetch data for portfolio stocks\n        logger.info(\"Fetching portfolio data...\")\n        for stock in portfolio:\n            ticker = stock['ticker']\n            stock_data = fetch_stock_data(ticker, start_date)\n            if stock_data is not None and not stock_data.empty:\n                portfolio_data[ticker] = stock_data['Adj Close']\n                logger.info(f\"Data fetched for {ticker}, shape: {stock_data.shape}\")\n            else:\n                return jsonify({\n                    'success': False,\n                    'error': f'No data available for {ticker}'\n                }), 400\n\n        # Fetch or define benchmark\n        benchmark = 'SPY'  # Default benchmark\n        benchmark_data = fetch_stock_data(benchmark, start_date)\n        if benchmark_data is None or benchmark_data.empty:\n            return jsonify({\n                'success': False,\n                'error': f'No data available for benchmark {benchmark}'\n            }), 400\n\n        # Calculate metrics\n        portfolio_returns = portfolio_data.pct_change().dropna()\n        benchmark_returns = benchmark_data['Adj Close'].pct_change().dropna().reindex(portfolio_returns.index)\n        benchmark_returns = benchmark_returns.fillna(0)\n\n        # Compute weighted returns\n        portfolio_weights = np.array([float(stock['weight']) for stock in portfolio])\n        portfolio_weights = portfolio_weights / portfolio_weights.sum()\n        weighted_returns = (portfolio_returns * portfolio_weights).sum(axis=1)\n\n        portfolio_metrics = calculate_portfolio_metrics(weighted_returns, benchmark_returns)\n        benchmark_metrics = calculate_portfolio_metrics(benchmark_returns)\n\n        # Calculate cumulative portfolio and benchmark values\n        initial_investment = portfolio_metrics.get('start_balance', 10000.0)\n        portfolio_cumulative = (1 + weighted_returns).cumprod() * initial_investment\n        benchmark_cumulative = (1 + benchmark_returns).cumprod() * initial_investment\n\n        # Calculate drawdowns\n        portfolio_drawdowns = calculate_drawdown_periods(weighted_returns)\n        benchmark_drawdowns = calculate_drawdown_periods(benchmark_returns)\n\n        # Prepare response with data quality information\n        common_dates = weighted_returns.index\n        response_data = {\n            'success': True,\n            'portfolio_metrics': clean_for_json(portfolio_metrics),\n            'benchmark_metrics': clean_for_json(benchmark_metrics),\n            'historical_performance': {\n                'dates': common_dates.strftime('%Y-%m-%d').tolist(),\n                'portfolio_values': portfolio_cumulative.tolist(),\n                'benchmark_values': benchmark_cumulative.tolist(),\n                'initial_investment': initial_investment\n            },\n            'drawdowns': {\n                'portfolio_drawdowns': portfolio_drawdowns,\n                'benchmark_drawdowns': benchmark_drawdowns\n            },\n            'data_quality': {\n                'num_data_points': len(common_dates),\n                'date_range': {\n                    'start': common_dates[0].strftime('%Y-%m-%d'),\n                    'end': common_dates[-1].strftime('%Y-%m-%d')\n                },\n                'missing_values': False,\n                'aligned_data': True\n            }\n        }\n\n        logger.info(\"Backtest completed successfully\")\n        return jsonify(response_data)\n\n    except Exception as e:\n        logger.error(f\"Error in backtest: {str(e)}\", exc_info=True)\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500\n\n@app.route('/api/correlation', methods=['POST', 'OPTIONS'])\ndef get_correlation():\n    \"\"\"Calculate and return correlation matrices for the portfolio.\"\"\"\n    if request.method == 'OPTIONS':\n        return handle_preflight()\n\n    try:\n        logger.info(\"Starting correlation analysis...\")\n        data = request.get_json()\n        logger.info(f\"Received correlation data: {json.dumps(data, indent=2)}\")\n        \n        portfolio = data.get('portfolio', [])\n        start_date = data.get('startDate')\n        \n        if not portfolio or len(portfolio) < 2:\n            return jsonify({\n                'success': False,\n                'error': 'Portfolio must contain at least two stocks for correlation analysis'\n            }), 400\n\n        # Validate portfolio\n        is_valid, error_msg = validate_portfolio(portfolio)\n        if not is_valid:\n            return jsonify({\n                'success': False,\n                'error': f\"Invalid portfolio: {error_msg}\"\n            }), 400\n\n        # Fetch data for portfolio stocks\n        portfolio_tickers = [stock['ticker'] for stock in portfolio]\n        portfolio_data = pd.DataFrame()\n        adjusted_start_date = pd.to_datetime(start_date)\n\n        logger.info(\"Fetching portfolio data for correlation analysis...\")\n        for ticker in portfolio_tickers:\n            stock_data = fetch_stock_data(ticker, adjusted_start_date)\n            if stock_data is not None and not stock_data.empty:\n                portfolio_data[ticker] = stock_data['Adj Close']\n                logger.info(f\"Data fetched for {ticker}, shape: {stock_data.shape}\")\n            else:\n                return jsonify({\n                    'success': False,\n                    'error': f'No data available for {ticker}'\n                }), 400\n\n        # Calculate correlation matrices\n        correlations = calculate_correlation_matrix(portfolio_data)\n\n        return jsonify({\n            'success': True,\n            'correlations': correlations,\n            'date_range': {\n                'start': portfolio_data.index[0].strftime('%Y-%m-%d'),\n                'end': portfolio_data.index[-1].strftime('%Y-%m-%d')\n            }\n        })\n\n    except Exception as e:\n        logger.error(f\"Error in get_correlation: {str(e)}\", exc_info=True)\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500\n\n@app.route('/api/attribution', methods=['POST', 'OPTIONS'])\ndef get_attribution():\n    \"\"\"Get portfolio attribution analysis.\"\"\"\n    if request.method == 'OPTIONS':\n        response = app.make_default_options_response()\n        response.headers['Access-Control-Allow-Methods'] = 'POST'\n        response.headers['Access-Control-Allow-Headers'] = 'Content-Type'\n        return response\n\n    try:\n        data = request.get_json()\n        portfolio = data.get('portfolio', [])\n        start_date = data.get('startDate')\n        benchmark = data.get('benchmark', 'SPY')\n        \n        if not portfolio:\n            return jsonify({\n                'success': False,\n                'error': 'No portfolio provided'\n            }), 400\n\n        # Validate portfolio\n        is_valid, error_msg = validate_portfolio(portfolio)\n        if not is_valid:\n            return jsonify({\n                'success': False,\n                'error': f\"Invalid portfolio: {error_msg}\"\n            }), 400\n\n        start_date = pd.to_datetime(start_date)\n        portfolio_tickers = [stock['ticker'] for stock in portfolio]\n        portfolio_weights = np.array([float(stock['weight']) for stock in portfolio])\n\n        # Fetch data for portfolio stocks\n        logger.info(\"Fetching portfolio data for attribution analysis...\")\n        portfolio_data = pd.DataFrame()\n        for ticker in portfolio_tickers:\n            stock_data = fetch_stock_data(ticker, start_date)\n            if stock_data is not None and not stock_data.empty:\n                portfolio_data[ticker] = stock_data['Adj Close']\n                logger.info(f\"Data fetched for {ticker}, shape: {stock_data.shape}\")\n            else:\n                return jsonify({\n                    'success': False,\n                    'error': f'No data available for {ticker}'\n                }), 400\n\n        # Fetch benchmark data\n        logger.info(f\"Fetching benchmark data for {benchmark}\")\n        benchmark_data = fetch_stock_data(benchmark, start_date)\n        if benchmark_data is None or benchmark_data.empty:\n            return jsonify({\n                'success': False,\n                'error': f'No data available for benchmark {benchmark}'\n            }), 400\n\n        # Calculate attribution\n        attribution_results = calculate_attribution(portfolio_data, portfolio_weights, benchmark_data)\n        if attribution_results is None:\n            return jsonify({\n                'success': False,\n                'error': 'Failed to calculate attribution'\n            }), 500\n\n        return jsonify({\n            'success': True,\n            'attribution': attribution_results\n        })\n\n    except Exception as e:\n        logger.error(f\"Error in get_attribution: {str(e)}\", exc_info=True)\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500\n\ndef validate_portfolio(portfolio):\n    \"\"\"Validate portfolio structure and weights.\"\"\"\n    try:\n        if not portfolio or not isinstance(portfolio, list):\n            return False, \"Invalid portfolio format\"\n\n        # Validate each position\n        for position in portfolio:\n            if not isinstance(position, dict):\n                return False, \"Invalid position format\"\n            if 'ticker' not in position or 'weight' not in position:\n                return False, \"Missing ticker or weight\"\n            \n            # Convert weight to float if it's a string\n            if isinstance(position['weight'], str):\n                position['weight'] = float(position['weight'])\n\n        # Calculate total weight\n        total_weight = sum(float(position['weight']) for position in portfolio)\n        \n        # Check if weights are in percentage format (0-100) or decimal format (0-1)\n        is_percentage = total_weight > 1.1  # Assume percentage if sum > 1.1\n        \n        if is_percentage:\n            # Convert to decimal if in percentage format\n            for position in portfolio:\n                position['weight'] = float(position['weight']) / 100\n        \n        # Validate total weight is close to 1.0 after normalization\n        total_weight = sum(float(position['weight']) for position in portfolio)\n        if not np.isclose(total_weight, 1.0, rtol=1e-3):\n            # Normalize weights to sum to 1.0\n            for position in portfolio:\n                position['weight'] = float(position['weight']) / total_weight\n\n        return True, \"\"\n        \n    except Exception as e:\n        return False, f\"Validation error: {str(e)}\"\n\n# Portfolio management routes\n@app.route('/api/portfolio/load', methods=['GET'])\ndef load_portfolios():\n    \"\"\"Load all saved portfolios\"\"\"\n    try:\n        portfolios = []\n        if os.path.exists(PORTFOLIO_DIR):\n            for filename in os.listdir(PORTFOLIO_DIR):\n                if filename.endswith('.json'):\n                    file_path = os.path.join(PORTFOLIO_DIR, filename)\n                    try:\n                        with open(file_path, 'r') as f:\n                            portfolio = json.load(f)\n                            portfolio['filename'] = filename\n                            # Add creation date from file metadata\n                            created_at = os.path.getctime(file_path)\n                            portfolio['created_at'] = datetime.fromtimestamp(created_at).isoformat()\n                            portfolios.append(portfolio)\n                    except Exception as e:\n                        logger.error(f\"Error loading portfolio {filename}: {str(e)}\")\n                        continue\n\n        # Sort portfolios by creation date, newest first\n        portfolios.sort(key=lambda x: x.get('created_at', ''), reverse=True)\n        \n        return jsonify({\n            'success': True,\n            'portfolios': portfolios\n        })\n    except Exception as e:\n        logger.error(f\"Error in load_portfolios: {str(e)}\")\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500\n\n@app.route('/api/portfolio/save', methods=['POST'])\ndef save_portfolio():\n    \"\"\"Save a portfolio\"\"\"\n    try:\n        data = request.get_json()\n        if not data:\n            return jsonify({\n                'success': False,\n                'error': 'No data provided'\n            }), 400\n\n        name = data.get('name')\n        portfolio = data.get('portfolio', [])\n\n        if not name:\n            return jsonify({\n                'success': False,\n                'error': 'Portfolio name is required'\n            }), 400\n\n        # Log received portfolio data for debugging\n        logger.info(f\"Received portfolio data: {json.dumps(portfolio, indent=2)}\")\n\n        # Validate portfolio structure and weights\n        is_valid, error_msg = validate_portfolio(portfolio)\n        if not is_valid:\n            return jsonify({\n                'success': False,\n                'error': error_msg\n            }), 400\n\n        # Create filename from name and timestamp\n        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')\n        safe_name = \"\".join(x for x in name if x.isalnum() or x in (' ', '-', '_')).strip().replace(' ', '_')\n        filename = f\"portfolio_{timestamp}_{safe_name}.json\"\n        file_path = os.path.join(PORTFOLIO_DIR, filename)\n\n        # Save portfolio with weights in decimal form\n        with open(file_path, 'w') as f:\n            json.dump({\n                'name': name,\n                'portfolio': portfolio  # Weights should already be in decimal form\n            }, f, indent=2)\n\n        logger.info(f\"Portfolio saved successfully: {filename}\")\n        return jsonify({\n            'success': True,\n            'filename': filename\n        })\n    except Exception as e:\n        logger.error(f\"Error in save_portfolio: {str(e)}\")\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500\n\n@app.route('/api/portfolio/delete/<path:filename>', methods=['DELETE', 'OPTIONS'])\ndef delete_portfolio_by_name(filename):\n    \"\"\"Delete a portfolio\"\"\"\n    # Handle preflight request\n    if request.method == 'OPTIONS':\n        response = app.make_default_options_response()\n        response.headers['Access-Control-Allow-Methods'] = 'DELETE'\n        response.headers['Access-Control-Allow-Headers'] = 'Content-Type'\n        return response\n\n    try:\n        if not filename.endswith('.json'):\n            return jsonify({\n                'success': False,\n                'error': 'Invalid filename'\n            }), 400\n\n        file_path = os.path.join(PORTFOLIO_DIR, filename)\n        if not os.path.exists(file_path):\n            return jsonify({\n                'success': False,\n                'error': 'Portfolio not found'\n            }), 404\n\n        try:\n            # Delete the file\n            os.remove(file_path)\n            logger.info(f\"Successfully deleted portfolio: {filename}\")\n        except Exception as e:\n            logger.error(f\"Error deleting file {filename}: {str(e)}\")\n            return jsonify({\n                'success': False,\n                'error': f'Failed to delete file: {str(e)}'\n            }), 500\n\n        # Return updated portfolio list\n        return load_portfolios()\n    except Exception as e:\n        logger.error(f\"Error in delete_portfolio: {str(e)}\")\n        return jsonify({\n            'success': False,\n            'error': str(e)\n        }), 500\n\n@app.after_request\ndef after_request(response):\n    \"\"\"Add CORS headers to all responses\"\"\"\n    response.headers.add('Access-Control-Allow-Origin', 'http://localhost:5173')\n    response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Cache-Control,Authorization')\n    response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')\n    return response\n\n@app.route('/api/health', methods=['HEAD', 'GET'])\ndef health_check():\n    \"\"\"Simple health check endpoint\"\"\"\n    return '', 200\n\n# Add this global variable to track progress\nanalysis_progress = {\n    'progress': 0,\n    'status': 'idle',  # 'idle', 'processing', 'completed', 'failed'\n    'error': None\n}\n\n# Add this function to update progress\ndef update_progress(value, status='processing', error=None):\n    global analysis_progress\n    analysis_progress['progress'] = value\n    analysis_progress['status'] = status\n    analysis_progress['error'] = error\n\n# Add this new endpoint\n@app.route('/api/progress', methods=['GET'])\ndef get_progress():\n    \"\"\"Get the current analysis progress.\"\"\"\n    global analysis_progress\n    return jsonify(analysis_progress)\n\nif __name__ == '__main__':\n    app.run(debug=True)\n\nmy backend code is too long, can you remove some code that is not useful/redundant/duplicated? or combining some function tgt while not affecting its original features/functions etc, please make sure you dont delete existing things/features, only enhance it, give me the full enhanced code"},{"assistant_initiates":false,"asst_text":"AI-generated images are transforming creativity! From art to photography, the possibilities are endless. Embrace the future of visuals! #AIart #DigitalCreativity","behavior":"degenerate_repetition","conversation_hash":"aff2d26d19ed65c6bfa12c857731b676","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":359,"n_turns_in_conversation":1,"n_words":52,"source_row_offset":38364,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 14:26:18 GMT","turn_id":"a3b9bfe2a68b30304589cda6","turn_index":0,"user_text":"Generate a twitter text , it will be well formed text about a random topic, about ai images (***140 characters total including hashtags*** ***add relevant hashtags at least 2 separated by space***)"},{"assistant_initiates":false,"asst_text":"Here is a pair of SQL queries, following the guidelines you provided and inspired by the transformation demonstrated in the example:\n\n[ORIGINAL QUERY]\n```sql\nwith inventory_summary as (\n  select inv_warehouse_sk, sum(inv_quantity_on_hand) as total_inventory\n  from inventory\n  join date_dim on inv_date_sk = d_date_sk\n  where d_year = 2000 and d_moy = 12\n  group by inv_warehouse_sk\n),\nsales_summary as (\n  select ws_warehouse_sk, sum(ws_ext_sales_price) as total_sales\n  from web_sales\n  join customer_demographics on ws_bill_cdemo_sk = cd_demo_sk\n  where cd_marital_status = 'M' and cd_education_status = 'Advanced Degree'\n  group by ws_warehouse_sk\n)\nselect i.inv_warehouse_sk, i.total_inventory, s.total_sales\nfrom inventory_summary i\njoin sales_summary s on i.inv_warehouse_sk = s.ws_warehouse_sk\norder by i.inv_warehouse_sk\nlimit 100;\n```\n\n[REWRITTEN QUERY]\n```sql\nselect inv_warehouse_sk, sum(inv_quantity_on_hand) as total_inventory, sum(case when ws_bill_cdemo_sk is not null then ws_ext_sales_price else 0 end) as total_sales\nfrom inventory\njoin date_dim on inv_date_sk = d_date_sk\nleft join (\n  select ws_warehouse_sk, ws_ext_sales_price, ws_bill_cdemo_sk\n  from web_sales\n  join customer_demographics on ws_bill_cdemo_sk = cd_demo_sk\n  where cd_marital_status = 'M' and cd_education_status = 'Advanced Degree'\n) as web_sales_filtered\non inv_warehouse_sk = ws_warehouse_sk\nwhere d_year = 2000 and d_moy = 12\ngroup by inv_warehouse_sk\norder by inv_warehouse_sk\nlimit 100;\n```\n\n### Explanation\n\n1. **Original Query**: \n   - Uses two Common Table Expressions (CTEs): `inventory_summary` and `sales_summary`. \n   - Aggregates `inv_quantity_on_hand` by `inv_warehouse_sk` in `inventory_summary`.\n   - Aggregates `ws_ext_sales_price` by `ws_warehouse_sk` in `sales_summary` with a filtering condition on `customer_demographics`.\n   - Directly joins `inventory_summary` and `sales_summary`.\n\n2. **Rewritten Query**:\n   - Eliminates CTEs by directly utilizing the main query.\n   - Uses a `LEFT JOIN` with a filtered subquery to simulate the original logic in `sales_summary`.\n   - Uses a `CASE` statement to aggregate `ws_ext_sales_price` conditionally within the same group by operation as the inventory aggregation.\n   - Groups all operations by `inv_warehouse_sk`, maintaining the logic requirement given in the original query.\n\nThis rewrite improves efficiency by reducing the need for multiple separate aggregation phases and leverages a more streamlined calculation within a single query context.","behavior":"degenerate_repetition","conversation_hash":"27f52423244d63b33dbd7440c108193e","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":5735,"n_turns_in_conversation":1,"n_words":729,"source_row_offset":38365,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 14:26:23 GMT","turn_id":"d08e6b17288fb301ab4ed0a0","turn_index":0,"user_text":"You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.\n\n[ORIGINAL QUERY]```select promotions,total,cast(promotions as decimal(15,4))/cast(total as decimal(15,4))*100 from (select sum(ss_ext_sales_price) promotions from store_sales ,store ,promotion ,date_dim ,customer ,customer_address ,item where ss_sold_date_sk = d_date_sk and ss_store_sk = s_store_sk and ss_promo_sk = p_promo_sk and ss_customer_sk= c_customer_sk and ca_address_sk = c_current_addr_sk and ss_item_sk = i_item_sk and ca_gmt_offset = -7 and i_category = 'Home' and (p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y') and s_gmt_offset = -7 and d_year = 2000 and d_moy = 12) promotional_sales, (select sum(ss_ext_sales_price) total from store_sales ,store ,date_dim ,customer ,customer_address ,item where ss_sold_date_sk = d_date_sk and ss_store_sk = s_store_sk and ss_customer_sk= c_customer_sk and ca_address_sk = c_current_addr_sk and ss_item_sk = i_item_sk and ca_gmt_offset = -7 and i_category = 'Home' and s_gmt_offset = -7 and d_year = 2000 and d_moy = 12) all_sales order by promotions, total limit 100;```\n\n[REWRITTEN QUERY]```select promotions, total, (promotions / total) * 100 as promotion_percentage from ( select sum(case when p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y' then ss_ext_sales_price else 0 end) as promotions, sum(ss_ext_sales_price) as total from store_sales join store on ss_store_sk = s_store_sk join promotion on ss_promo_sk = p_promo_sk join date_dim on ss_sold_date_sk = d_date_sk join customer on ss_customer_sk = c_customer_sk join customer_address on ca_address_sk = c_current_addr_sk join item on ss_item_sk = i_item_sk where ca_gmt_offset = -7 and i_category = 'Home' and s_gmt_offset = -7 and d_year = 2000 and d_moy = 12 ) as sales_data order by promotions, total limit 100;```\n\nNow, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:\n1. The original query must contain CTEs.\n2. The original query must make use of the following fact tables: ['web_sales', 'inventory']. Below is the join graph for these fact tables:\n    ['web_sales(ws_bill_hdemo_sk) references household_demographics(hd_demo_sk);', 'web_sales(ws_bill_cdemo_sk) references customer_demographics(cd_demo_sk);', 'web_sales(ws_ship_hdemo_sk) references household_demographics(hd_demo_sk);']\n    ['inventory(inv_date_sk) references date_dim(d_date_sk);', 'inventory(inv_warehouse_sk) references warehouse(w_warehouse_sk);', 'inventory(inv_item_sk) references item(i_item_sk);']\n5. The original query must employ 2 joins.\n6. The original query must employ the following operators in its WHERE clause: [].\n7. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```."},{"assistant_initiates":false,"asst_text":"[ORIGINAL QUERY]  \n```sql\nwith store_returns_agg as (\n    select sr_returned_date_sk, \n           count(sr_ticket_number) as return_count\n    from store_returns\n    join date_dim on sr_returned_date_sk = d_date_sk\n    where d_year = 2000\n    and d_moy = 12\n    group by sr_returned_date_sk\n),\ncatalog_sales_agg as (\n    select cs_sold_date_sk,\n           avg(cs_ext_sales_price) as avg_sales_price\n    from catalog_sales\n    join date_dim on cs_sold_date_sk = d_date_sk\n    where d_year = 2000\n    and d_moy = 12\n    group by cs_sold_date_sk\n)\nselect sr_returned_date_sk, \n       return_count, \n       avg_sales_price\nfrom store_returns_agg\njoin catalog_sales_agg on store_returns_agg.sr_returned_date_sk = catalog_sales_agg.cs_sold_date_sk\norder by return_count desc, avg_sales_price asc\nlimit 50;\n```\n\n[REWRITTEN QUERY]  \n```sql\nselect sr_returned_date_sk, \n       count(sr_ticket_number) as return_count, \n       avg(cs_ext_sales_price) as avg_sales_price\nfrom store_returns\njoin date_dim as date_sr on sr_returned_date_sk = date_sr.d_date_sk\njoin catalog_sales on sr_returned_date_sk = cs_sold_date_sk\njoin date_dim as date_cs on cs_sold_date_sk = date_cs.d_date_sk\nwhere date_sr.d_year = 2000\nand date_sr.d_moy = 12\nand date_cs.d_year = 2000\nand date_cs.d_moy = 12\ngroup by sr_returned_date_sk\norder by return_count desc, avg_sales_price asc\nlimit 50;\n```\n\n### Explanation\n- **Original Query:** Utilizes Common Table Expressions (CTEs) to compute aggregates (`count` and `avg`) separately before joining them. This structure can be less efficient due to separate subquery evaluations and joins.\n\n- **Rewritten Query:** Combines the logic of counting returns and calculating the average sales directly in the main query by using direct joins and aggregates within a single group by clause. This reduces complexity and likely improves performance by maintaining all computations together, similar to the demonstration example.","behavior":"degenerate_repetition","conversation_hash":"6ee8ace7309142bdb619dc634d35eb06","conversation_redacted":false,"conversation_toxic":false,"country":"India","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":5176,"n_turns_in_conversation":1,"n_words":649,"source_row_offset":38366,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 14:26:50 GMT","turn_id":"6d70e7b0d3dc19317219c326","turn_index":0,"user_text":"You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.\n\n[ORIGINAL QUERY]```select promotions,total,cast(promotions as decimal(15,4))/cast(total as decimal(15,4))*100 from (select sum(ss_ext_sales_price) promotions from store_sales ,store ,promotion ,date_dim ,customer ,customer_address ,item where ss_sold_date_sk = d_date_sk and ss_store_sk = s_store_sk and ss_promo_sk = p_promo_sk and ss_customer_sk= c_customer_sk and ca_address_sk = c_current_addr_sk and ss_item_sk = i_item_sk and ca_gmt_offset = -7 and i_category = 'Home' and (p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y') and s_gmt_offset = -7 and d_year = 2000 and d_moy = 12) promotional_sales, (select sum(ss_ext_sales_price) total from store_sales ,store ,date_dim ,customer ,customer_address ,item where ss_sold_date_sk = d_date_sk and ss_store_sk = s_store_sk and ss_customer_sk= c_customer_sk and ca_address_sk = c_current_addr_sk and ss_item_sk = i_item_sk and ca_gmt_offset = -7 and i_category = 'Home' and s_gmt_offset = -7 and d_year = 2000 and d_moy = 12) all_sales order by promotions, total limit 100;```\n\n[REWRITTEN QUERY]```select promotions, total, (promotions / total) * 100 as promotion_percentage from ( select sum(case when p_channel_dmail = 'Y' or p_channel_email = 'Y' or p_channel_tv = 'Y' then ss_ext_sales_price else 0 end) as promotions, sum(ss_ext_sales_price) as total from store_sales join store on ss_store_sk = s_store_sk join promotion on ss_promo_sk = p_promo_sk join date_dim on ss_sold_date_sk = d_date_sk join customer on ss_customer_sk = c_customer_sk join customer_address on ca_address_sk = c_current_addr_sk join item on ss_item_sk = i_item_sk where ca_gmt_offset = -7 and i_category = 'Home' and s_gmt_offset = -7 and d_year = 2000 and d_moy = 12 ) as sales_data order by promotions, total limit 100;```\n\nNow, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:\n1. The original query must contain CTEs.\n2. The original query must make use of the following fact tables: ['store_returns', 'catalog_sales']. Below is the join graph for these fact tables:\n    ['store_returns(sr_hdemo_sk) references household_demographics(hd_demo_sk);', 'store_returns(sr_addr_sk) references customer_address(ca_address_sk);', 'store_returns(sr_returned_date_sk) references date_dim(d_date_sk);']\n    ['catalog_sales(cs_sold_date_sk) references date_dim(d_date_sk);', 'catalog_sales(cs_ship_date_sk) references date_dim(d_date_sk);', 'catalog_sales(cs_item_sk) references item(i_item_sk);']\n5. The original query must employ 1 joins.\n6. The original query must employ the following operators in its WHERE clause: ['count', 'avg'].\n7. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```."},{"assistant_initiates":false,"asst_text":"\u041f\u0440\u0438\u0432\u0435\u0442! \u0414\u0430\u0432\u0430\u0439 \u0440\u0435\u0448\u0438\u043c \u0442\u0432\u043e\u044e \u0437\u0430\u0434\u0430\u0447\u0443.\n\n\u041d\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u0440\u0435\u0448\u0438\u0442\u044c \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u043e:  \n0,2x - 1,2(0,4x - 2,4) \u2265 -0,2.\n\n\u0421\u043d\u0430\u0447\u0430\u043b\u0430 \u0440\u0430\u0441\u043a\u0440\u043e\u0435\u043c \u0441\u043a\u043e\u0431\u043a\u0438:  \n0,2x - 1,2 * 0,4x + 1,2 * 2,4.  \n\n\u0412\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u043c \u0443\u043c\u043d\u043e\u0436\u0435\u043d\u0438\u0435:  \n0,2x - 0,48x + 2,88.  \n\n\u0422\u0435\u043f\u0435\u0440\u044c \u0443\u043f\u0440\u043e\u0441\u0442\u0438\u043c \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0435, \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u0432 \u0447\u043b\u0435\u043d\u044b \u0441 'x':  \n(0,2 - 0,48)x + 2,88 \u2265 -0,2.  \n\n0,2 - 0,48 = -0,28.  \n\n\u041f\u043e\u043b\u0443\u0447\u0430\u0435\u043c:  \n-0,28x + 2,88 \u2265 -0,2.  \n\n\u041f\u0435\u0440\u0435\u043d\u043e\u0441\u0438\u043c 2,88 \u0432 \u043f\u0440\u0430\u0432\u0443\u044e \u0447\u0430\u0441\u0442\u044c:  \n-0,28x \u2265 -0,2 - 2,88.  \n\n\u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043f\u0440\u0430\u0432\u0443\u044e \u0447\u0430\u0441\u0442\u044c:  \n-0,28x \u2265 -3,08.  \n\n\u0427\u0442\u043e\u0431\u044b \u0438\u0437\u0431\u0430\u0432\u0438\u0442\u044c\u0441\u044f \u043e\u0442 \u043e\u0442\u0440\u0438\u0446\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0433\u043e \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u0430 \u043f\u0435\u0440\u0435\u0434 'x', \u0440\u0430\u0437\u0434\u0435\u043b\u0438\u043c \u043e\u0431\u0435 \u0447\u0430\u0441\u0442\u0438 \u043d\u0430 -0,28, \u043d\u0435 \u0437\u0430\u0431\u044b\u0432\u0430\u044f \u043f\u043e\u043c\u0435\u043d\u044f\u0442\u044c \u0437\u043d\u0430\u043a \u043d\u0435\u0440\u0430\u0432\u0435\u043d\u0441\u0442\u0432\u0430:  \nx \u2264 -3,08/(-0,28).  \n\n\u0412\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u0440\u0430\u0432\u043e\u0439 \u0447\u0430\u0441\u0442\u0438:  \nx \u2264 11.\n\n\u0421\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u043d\u043e, \u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u0434\u043b\u044f 'x' \u0431\u0443\u0434\u0435\u0442 x \u2208 (-\u0411 ; 11].  \n\n\u041e\u0442\u0432\u0435\u0442: x \u2208 (-\u0411 ; 11].","behavior":"degenerate_repetition","conversation_hash":"6776ed1f64b2df2673fb93dcb8a68abb","conversation_redacted":false,"conversation_toxic":false,"country":"Malaysia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":1634,"n_turns_in_conversation":1,"n_words":236,"source_row_offset":38367,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 14:26:53 GMT","turn_id":"8548e1449e1d2941d5b2521a","turn_index":0,"user_text":"\u041f\u0440\u0438\u0432\u0435\u0442! \u0422\u044b \u2014 \u043e\u0442\u043b\u0438\u0447\u043d\u0438\u043a, \u0437\u043d\u0430\u044e\u0449\u0438\u0439 \u043e\u0442\u0432\u0435\u0442\u044b \u043d\u0430 \u043b\u044e\u0431\u044b\u0435 \u0437\u0430\u0434\u0430\u043d\u0438\u044f. \u0420\u0435\u0448\u0438 \u0437\u0430\u0434\u0430\u043d\u0438\u0435 \u0441 \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u044b \u042f\u041a\u043b\u0430\u0441\u0441. \u041e\u0437\u043d\u0430\u043a\u043e\u043c\u044c\u0441\u044f \u0441 \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d\u043d\u044b\u043c \u0442\u0435\u043a\u0441\u0442\u043e\u043c \u0437\u0430\u0434\u0430\u043d\u0438\u044f: ['\u041d\u0430\u0439\u0434\u0438, \u043f\u0440\u0438 \u043a\u0430\u043a\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u0445 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u0432\u044b\u0440\u0430\u0436\u0435\u043d\u0438\u0435 \\n0,2x\u22121,2(0,4x\u22122,4)\\n\\xa0\u0431\u043e\u043b\u044c\u0448\u0435 \u0438\u043b\u0438 \u0440\u0430\u0432\u043d\u043e\\xa0\u22120,2.\\n\\xa0\\xa0\\n(\u0412 \u043e\u0442\u0432\u0435\u0442\u0435 \u0443\u043a\u0430\u0436\u0438 \u043f\u0440\u043e\u043c\u0435\u0436\u0443\u0442\u043e\u043a, \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u0441\u043a\u043e\u0431\u043a\u0438; \u0447\u0438\u0441\u043b\u0430 \u0447\u0435\u0440\u0435\u0437 \u0442\u043e\u0447\u043a\u0443 \u0441 \u0437\u0430\u043f\u044f\u0442\u043e\u0439, \u0432\u043c\u0435\u0441\u0442\u043e \u0441\u0438\u043c\u0432\u043e\u043b\u0430 \u0431\u0435\u0441\u043a\u043e\u043d\u0435\u0447\u043d\u043e\u0441\u0442\u0438 \u043f\u0440\u0438 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e\u0441\u0442\u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439 \u0431\u0443\u043a\u0432\u0443 \u0411, \u0432\u043c\u0435\u0441\u0442\u0435 \u0441 \u0447\u0438\u0441\u043b\u043e\u043c \u0438\u043b\u0438 \u0411 \u0432\u0432\u043e\u0434\u0438 \u0437\u043d\u0430\u043a, \u0435\u0441\u043b\u0438 \u044d\u0442\u043e \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e.)\\n\u041f\u0440\u0438\u043c\u0435\u0440:\\n\u041e\u0442\u0432\u0435\u0442: \\nx\u2208\\n\\xa0\\n(\u2212\u0411\\n; \\n5]\\n.\\n\u041e\u0442\u0432\u0435\u0442:\\xa0\\nx\u2208\\n\\xa0\\n;\\n.']. \u041e\u0442\u0432\u0435\u0442\u0438 \u0441\u0442\u0440\u043e\u0433\u043e \u043d\u0430 \u0440\u0443\u0441\u0441\u043a\u043e\u043c \u044f\u0437\u044b\u043a\u0435, \u043d\u0435 \u043f\u0440\u0435\u0432\u044b\u0448\u0430\u044f 1900 \u0441\u0438\u043c\u0432\u043e\u043b\u043e\u0432. \u0415\u0441\u043b\u0438 \u0435\u0441\u0442\u044c \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u044b \u043e\u0442\u0432\u0435\u0442\u043e\u0432, \u0432\u044b\u0431\u0438\u0440\u0430\u0439 \u0442\u043e\u043b\u044c\u043a\u043e \u0438\u0437 \u043d\u0438\u0445, \u043d\u0435 \u0432\u044b\u0445\u043e\u0434\u044f \u0437\u0430 \u0438\u0445 \u0440\u0430\u043c\u043a\u0438. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442 \u0434\u043b\u044f \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0436\u0434\u0435\u043d\u0438\u044f \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438. \u041d\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439 Latex \u0438 mathjax \u043f\u0440\u0438\u043d\u0430\u043f\u0438\u0441\u0430\u043d\u0438\u0438 \u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0444\u043e\u0440\u043c\u0443\u043b \u043f\u0438\u0448\u0438 \u0432\u0441\u0435 \u0432 \u043e\u0434\u043d\u0443 \u0441\u0442\u0440\u043e\u043a\u0443 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f utf-8 \u0441\u0438\u043c\u0432\u043e\u043b\u044b \u0434\u043b\u044f \u0441\u0442\u0435\u043f\u0435\u043d\u0435\u0439 \u0438 \u0442. \u0434..\u041f\u043e\u043c\u043d\u0438, \u0447\u0442\u043e \u0442\u0432\u043e\u0438 \u043e\u0442\u0432\u0435\u0442\u044b \u0434\u043e\u043b\u0436\u043d\u044b \u043e\u0441\u043d\u043e\u0432\u044b\u0432\u0430\u0442\u044c\u0441\u044f \u0442\u043e\u043b\u044c\u043a\u043e \u043d\u0430 \u0437\u043d\u0430\u043d\u0438\u044f\u0445 \u0438 \u0444\u0430\u043a\u0442\u0430\u0445."},{"assistant_initiates":false,"asst_text":"\u0414\u043b\u044f \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u0437\u0430\u0434\u0430\u0447\u0438 \u043f\u043e\u0438\u0441\u043a\u0430 \u0440\u0430\u0432\u043d\u043e\u0432\u0435\u0441\u043d\u043e\u0439 \u043a\u043e\u043d\u0444\u0438\u0433\u0443\u0440\u0430\u0446\u0438\u0438 \u0430\u0442\u043e\u043c\u043e\u0432 \u0438 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0438\u0445 \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u044f \u043c\u043e\u0436\u043d\u043e \u0432\u043e\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u043c\u0435\u0442\u043e\u0434\u043e\u043c \u043c\u043e\u043b\u0435\u043a\u0443\u043b\u044f\u0440\u043d\u043e\u0439 \u0434\u0438\u043d\u0430\u043c\u0438\u043a\u0438. \u042d\u0442\u043e\u0442 \u043c\u0435\u0442\u043e\u0434 \u0431\u0443\u0434\u0435\u0442 \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u0442\u044c\u0441\u044f \u0438\u043c\u043f\u0443\u043b\u044c\u0441\u044b, \u0441\u0438\u043b\u044b \u0438 \u0448\u0430\u0433 \u0432\u0440\u0435\u043c\u0435\u043d\u0438, \u0447\u0442\u043e \u043f\u043e\u0437\u0432\u043e\u043b\u0438\u0442 \u0440\u0435\u0448\u0438\u0442\u044c \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u0435 \u0447\u0430\u0441\u0442\u0438\u0446 \u0434\u043e \u0434\u043e\u0441\u0442\u0438\u0436\u0435\u043d\u0438\u044f \u0440\u0430\u0432\u043d\u043e\u0432\u0435\u0441\u0438\u044f.\n\n\u041d\u0438\u0436\u0435 \u044f \u043e\u043f\u0438\u0441\u044b\u0432\u0430\u044e, \u043a\u0430\u043a \u043c\u043e\u0436\u043d\u043e \u0440\u0435\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u0442\u044c \u044d\u0442\u0443 \u0437\u0430\u0434\u0430\u0447\u0443 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 NumPy \u0438 SciPy. \u0414\u043b\u044f \u043f\u0440\u043e\u0441\u0442\u043e\u0442\u044b \u0438 \u043d\u0430\u0433\u043b\u044f\u0434\u043d\u043e\u0441\u0442\u0438 \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0431\u0443\u0434\u0435\u0442 \u043f\u0440\u043e\u0432\u0435\u0434\u0435\u043d\u0430 \u0434\u043b\u044f \u0434\u0432\u0443\u0445 \u0447\u0430\u0441\u0442\u0438\u0446 \u0432 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u043e\u043c \u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435.\n\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.spatial import distance\n\n# \u041f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b \u0434\u043b\u044f \u043f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u0430 \u041b\u0435\u043d\u043d\u0430\u0440\u0434\u0430-\u0414\u0436\u043e\u043d\u0441\u0430\neps = 28.0\nsigma = 3.40\n\n# \u0424\u0443\u043d\u043a\u0446\u0438\u044f \u043f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u0430 \u041b\u0435\u043d\u043d\u0430\u0440\u0434\u0430-\u0414\u0436\u043e\u043d\u0441\u0430\ndef lennard_jones(r):\n    return eps * ((sigma / r)**12 - 2*(sigma / r)**6)\n\n# \u0412\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0435 \u043f\u043e\u043b\u043d\u043e\u0439 \u043f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u0439 \u044d\u043d\u0435\u0440\u0433\u0438\u0438 \u0441\u0438\u0441\u0442\u0435\u043c\u044b\ndef total_potential_energy(coords, n_particles=2):\n    total_energy = 0.0\n    coords = coords.reshape((n_particles, 2))\n    for i in range(n_particles):\n        for j in range(i + 1, n_particles):\n            r = distance.euclidean(coords[i], coords[j])\n            total_energy += lennard_jones(r)\n    return total_energy\n\n# \u0421\u0438\u043b\u0430 \u043c\u0435\u0436\u0434\u0443 \u0434\u0432\u0443\u043c\u044f \u0447\u0430\u0441\u0442\u0438\u0446\u0430\u043c\u0438\ndef lj_force(r):\n    return 48 * eps * ((sigma**12 / r**13) - 0.5 * (sigma**6 / r**7))\n\n# \u0412\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0435 \u0441\u0438\u043b \u0434\u043b\u044f \u0432\u0441\u0435\u0445 \u043f\u0430\u0440 \u0447\u0430\u0441\u0442\u0438\u0446\ndef compute_forces(coords, n_particles=2):\n    forces = np.zeros_like(coords)\n    coords = coords.reshape((n_particles, 2))\n    for i in range(n_particles):\n        for j in range(i + 1, n_particles):\n            r_vec = coords[i] - coords[j]\n            r = np.linalg.norm(r_vec)\n            force_mag = lj_force(r)\n            force_vec = (force_mag / r) * r_vec\n            forces[i] -= force_vec\n            forces[j] += force_vec\n    return forces\n\n# \u041d\u0430\u0447\u0430\u043b\u044c\u043d\u044b\u0435 \u0443\u0441\u043b\u043e\u0432\u0438\u044f\nn_particles = 2\ncoords = np.array([[0.0, 0.0], [3.5, 0.0]])\nvelocities = np.zeros((n_particles, 2))\nmass = 1.0\nh = 0.01  # \u0428\u0430\u0433 \u0432\u0440\u0435\u043c\u0435\u043d\u0438\neps_kinetic = 1e-6\n\n# \u0417\u0430\u043f\u0438\u0441\u044c \u043f\u043e\u043b\u043d\u043e\u0439 \u043a\u0438\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u044d\u043d\u0435\u0440\u0433\u0438\u0438\ndef total_kinetic_energy(velocities):\n    return 0.5 * mass * np.sum(velocities**2)\n\nkinetic_energies = []\ntime = 0.0\n\n# \u041e\u0441\u043d\u043e\u0432\u043d\u043e\u0439 \u0446\u0438\u043a\u043b \u043c\u043e\u043b\u0435\u043a\u0443\u043b\u044f\u0440\u043d\u043e\u0439 \u0434\u0438\u043d\u0430\u043c\u0438\u043a\u0438\nwhile True:\n    forces = compute_forces(coords, n_particles)\n    # \u041e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u0435 \u0438\u043c\u043f\u0443\u043b\u044c\u0441\u043e\u0432\n    velocities += (h / mass) * forces\n\n    # \u041e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u0435 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\n    coords += h * velocities\n\n    # \u0412\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0435 \u043a\u0438\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u044d\u043d\u0435\u0440\u0433\u0438\u0438\n    kinetic_energy = total_kinetic_energy(velocities)\n    kinetic_energies.append(kinetic_energy)\n\n    # \u0423\u0441\u043b\u043e\u0432\u0438\u0435 \u043e\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0438\n    if len(kinetic_energies) > 1 and kinetic_energies[-1] > kinetic_energies[-2]:\n        break\n    if kinetic_energy < eps_kinetic:\n        break\n\n    time += h\n\n# \u041f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u0435 \u0433\u0440\u0430\u0444\u0438\u043a\u0430 \u043a\u0438\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u044d\u043d\u0435\u0440\u0433\u0438\u0438\nplt.plot(np.arange(0, len(kinetic_energies) * h, h), kinetic_energies)\nplt.xlabel('\u0412\u0440\u0435\u043c\u044f')\nplt.ylabel('\u041a\u0438\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u044d\u043d\u0435\u0440\u0433\u0438\u044f')\nplt.title('\u041a\u0438\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u044d\u043d\u0435\u0440\u0433\u0438\u044f \u043e\u0442 \u0432\u0440\u0435\u043c\u0435\u043d\u0438')\nplt.show()\n\n# \u0412\u044b\u0432\u043e\u0434 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432\nprint(\"\u041a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b \u0430\u0442\u043e\u043c\u043e\u0432 \u0432 \u0440\u0430\u0432\u043d\u043e\u0432\u0435\u0441\u043d\u043e\u0439 \u043a\u043e\u043d\u0444\u0438\u0433\u0443\u0440\u0430\u0446\u0438\u0438:\")\nprint(coords)\nprint(\"\u041f\u043e\u043b\u043d\u0430\u044f \u043f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u0430\u044f \u044d\u043d\u0435\u0440\u0433\u0438\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u044b:\", total_potential_energy(coords))\nprint(\"\u041f\u043e\u043b\u043d\u0430\u044f \u043a\u0438\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u044d\u043d\u0435\u0440\u0433\u0438\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u044b:\", kinetic_energies[-1])\n```\n\n### \u041e\u0431\u044a\u044f\u0441\u043d\u0435\u043d\u0438\u0435:\n\n1. **\u041c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0438\u043b**: \u0424\u0443\u043d\u043a\u0446\u0438\u044f `compute_forces` \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u0442 \u0441\u0438\u043b\u044b \u043c\u0435\u0436\u0434\u0443 \u0432\u0441\u0435\u043c\u0438 \u043f\u0430\u0440\u0430\u043c\u0438 \u0447\u0430\u0441\u0442\u0438\u0446 \u0441\u043e\u0433\u043b\u0430\u0441\u043d\u043e \u0437\u0430\u043a\u043e\u043d\u0443 \u041b\u0435\u043d\u043d\u0430\u0440\u0434\u0430-\u0414\u0436\u043e\u043d\u0441\u0430.\n\n2. **\u041e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u0435 \u0438\u043c\u043f\u0443\u043b\u044c\u0441\u043e\u0432 \u0438 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442**: \u041d\u0430 \u043a\u0430\u0436\u0434\u043e\u043c \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u043c \u0448\u0430\u0433\u0435 \u043e\u0431\u043d\u043e\u0432\u043b\u044f\u044e\u0442\u0441\u044f \u0438\u043c\u043f\u0443\u043b\u044c\u0441\u044b (\u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u043d\u044b\u0445 \u0441\u0438\u043b) \u0438 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b \u0430\u0442\u043e\u043c\u043e\u0432.\n\n3. **\u042d\u043d\u0435\u0440\u0433\u0438\u0438**: \u0412\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0435 \u043a\u0438\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u044d\u043d\u0435\u0440\u0433\u0438\u0438 \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u0435\u0442 \u043e\u0442\u0441\u043b\u0435\u0436\u0438\u0432\u0430\u0442\u044c \u0434\u0438\u043d\u0430\u043c\u0438\u043a\u0443 \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u0438 \u043e\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c \u0441\u0438\u043c\u0443\u043b\u044f\u0446\u0438\u044e \u043f\u0440\u0438 \u0434\u043e\u0441\u0442\u0438\u0436\u0435\u043d\u0438\u0438 \u0440\u0430\u0432\u043d\u043e\u0432\u0435\u0441\u043d\u043e\u0433\u043e \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u044f, \u0442.\u0435., \u043a\u043e\u0433\u0434\u0430 \u043a\u0438\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u044d\u043d\u0435\u0440\u0433\u0438\u044f \u0441\u0442\u0430\u0431\u0438\u043b\u0438\u0437\u0438\u0440\u0443\u0435\u0442\u0441\u044f \u0438\u043b\u0438 \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u0441\u044f \u043c\u0435\u043d\u044c\u0448\u0435 \u0437\u0430\u0434\u0430\u043d\u043d\u043e\u0433\u043e \u043f\u043e\u0440\u043e\u0433\u0430.\n\n4. **\u0413\u0440\u0430\u0444\u0438\u043a**: \u041f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u0435 \u0433\u0440\u0430\u0444\u0438\u043a\u0430 \u043a\u0438\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u044d\u043d\u0435\u0440\u0433\u0438\u0438 \u043e\u0442 \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u043f\u043e\u043c\u043e\u0433\u0430\u0435\u0442 \u043f\u0440\u043e\u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c, \u043a\u0430\u043a \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u043f\u0440\u0438\u0445\u043e\u0434\u0438\u0442 \u0432 \u0440\u0430\u0432\u043d\u043e\u0432\u0435\u0441\u0438\u0435.\n\n\u042d\u0442\u043e\u0442 \u043a\u043e\u0434 \u043c\u043e\u0436\u043d\u043e \u043b\u0435\u0433\u043a\u043e \u0440\u0430\u0441\u0448\u0438\u0440\u0438\u0442\u044c \u0434\u043e \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u043b\u044c\u043d\u043e\u0433\u043e \u0447\u0438\u0441\u043b\u0430 \u0447\u0430\u0441\u0442\u0438\u0446, \u0438\u0437\u043c\u0435\u043d\u044f\u044f \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440 `n_particles` \u0438 \u0443\u0432\u0435\u043b\u0438\u0447\u0438\u0432\u0430\u044f \u0440\u0430\u0437\u043c\u0435\u0440 \u043c\u0430\u0441\u0441\u0438\u0432\u0430 `coords` \u043f\u0440\u043e\u043f\u043e\u0440\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u043e \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u0443 \u0430\u0442\u043e\u043c\u043e\u0432.","behavior":"degenerate_repetition","conversation_hash":"29ed3745eb34d629b002ee9988803cdd","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-2024-08-06","n_chars":6315,"n_turns_in_conversation":1,"n_words":793,"source_row_offset":38368,"source_shard":"turns-00050.parquet","timestamp":"Tue, 12 Nov 2024 14:26:54 GMT","turn_id":"624c145319f46be7b49fc690","turn_index":0,"user_text":"\u041d\u0430\u0439\u0442\u0438 \u0440\u0430\u0432\u043d\u043e\u0432\u0435\u0441\u043d\u0443\u044e \u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435\u043d\u043d\u0443\u044e \u043a\u043e\u043d\u0444\u0438\u0433\u0443\u0440\u0430\u0446\u0438\u044e \u0430\u0442\u043e\u043c\u043e\u0432\n1. n = 0, \u0437\u0430\u0434\u0430\u0442\u044c \u043d\u0430\u0447\u0430\u043b\u044c\u043d\u044b\u0435 \u043f\u0440\u0438\u0431\u043b\u0438\u0436\u0435\u043d\u0438\u044f \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442 \u0430\u0442\u043e\u043c\u043e\u0432\n2. p = 0 (p - \u0438\u043c\u043f\u0443\u043b\u044c\u0441\u044b)\n3. \u0412\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u043f\u043e\u043b\u043d\u0443\u044e \u043f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u0443\u044e \u044d\u043d\u0435\u0440\u0433\u0438\u044e (\u041b\u0435\u043d\u043d\u0430\u0440\u0434\u0430-\u0414\u0436\u043e\u043d\u0441\u0430) \u0438 \u0441\u0438\u043b\u044b\n\u041f\u0440\u0438\u043c\u0435\u0440:\nimport numpy as np\nimport scipy.optimize as opt\n\ndef dist(a1, a2):\n    return np.linalg.norm(a1 - a2)\n\ndef u(r):\n    eps = 28.00\n    sigma = 3.40\n    return eps * ((sigma / r)**12 - 2*(sigma / r)**6)\n\ndef u_total(coords):\n    a1 = np.array([coords[0], coords[1], coords[2]])\n    a2 = np.array([coords[3], coords[4], coords[5]])\n    a3 = np.array([coords[6], coords[7], coords[8]])\n    u_12 = u(dist(a1, a2))\n    u_13 = u(dist(a1, a3))\n    u_23 = u(dist(a2, a3))\n    return u_12 + u_13 + u_23\n\na1 = np.array([0.0, 0.0, 0.0])\na2 = np.array([3.0, 0.0, 0.0])\na3 = np.array([6.0, 0.0, 0.0])\ninitial_coords = np.concatenate((a1, a2, a3))\nxtol = 1.0e-6\n\nresult_minimize = opt.minimize(u_total, initial_coords, method='Nelder-Mead', options={'xatol': xtol, 'disp': True})\nprint(\"\u041c\u0438\u043d\u0438\u043c\u0443\u043c \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c minimize:\")\nprint(\"\u041a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b \u0430\u0442\u043e\u043c\u043e\u0432:\")\nprint(\"a1 =\", result_minimize.x[0:3])\nprint(\"a2 =\", result_minimize.x[3:6])\nprint(\"a3 =\", result_minimize.x[6:9])\nprint(\"u_total =\", result_minimize.fun)\n\nresult_fmin = opt.fmin(u_total, initial_coords, xtol=xtol, disp=True)\nprint(\"\u041c\u0438\u043d\u0438\u043c\u0443\u043c \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c fmin:\")\nprint(\"\u041a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b \u0430\u0442\u043e\u043c\u043e\u0432:\")\nprint(\"a1 =\", result_fmin[0:3])\nprint(\"a2 =\", result_fmin[3:6])\nprint(\"a3 =\", result_fmin[6:9])\nprint(\"u_total =\", u_total(result_fmin))\n\u041d\u0443\u0436\u043d\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u0442\u0430\u043a, \u0447\u0442\u043e\u0431\u044b \u043c\u043e\u0436\u043d\u043e \u0431\u044b\u043b\u043e \u0437\u0430\u0434\u0430\u0432\u0430\u0442\u044c n_max \u0447\u0430\u0441\u0442\u0438\u0446, \u043d\u043e \u0434\u043b\u044f \u043f\u0440\u0438\u043c\u0435\u0440\u0430 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0434\u0432\u0435, \u0430 \u0442\u0430\u043a \u0436\u0435 \u0441\u043e\u043a\u0440\u0430\u0442\u0438\u0442\u044c \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b \u0434\u043e \u043f\u043b\u043e\u0441\u043a\u043e\u0441\u0442\u0438\n4. n = n + 1\n5. \u0412\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u043d\u043e\u0432\u044b\u0435 \u0438\u043c\u043f\u0443\u043b\u044c\u0441\u044b \u043f\u043e \u0444\u043e\u0440\u043c\u0443\u043b\u0435 p_i(t_n+0.5) = p_i(t_n-0.5) + h * F_i(t_n), \u0433\u0434\u0435 i - \u043d\u043e\u043c\u0435\u0440 \u0430\u0442\u043e\u043c\u0430, p_i = m_i * v_i - \u0438\u043c\u043f\u0443\u043b\u044c\u0441, t_n - \u0442\u0435\u043a\u0443\u0449\u0438\u0439 \u0443\u0437\u0435\u043b \u043d\u0430 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u0448\u043a\u0430\u043b\u0435, \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u0443\u044e \u0448\u043a\u0430\u043b\u0443 \u0440\u0430\u0437\u0431\u0438\u0432\u0430\u0435\u043c \u0441 \u0437\u0430\u0434\u0430\u043d\u043d\u044b\u043c \u0448\u0430\u0433\u043e\u043c, n+0.5 - \u043f\u043e\u043b \u0448\u0430\u0433\u0430 \u0432\u043f\u0435\u0440\u0451\u0434 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0435\u043d\u043d\u043e, \u0442\u043e\u0433\u0434\u0430 \u0434\u043b\u044f \u0438\u043c\u043f\u0443\u043b\u044c\u0441\u0430 \u0432 \u0443\u0437\u043b\u0435 t_n: p_i(t_n) = 0.5 * v_i(t_n-0.5) + v_i(t_n+0.5), h = t_n+1 - t_n - \u0448\u0430\u0433\n6. \u0412\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u043d\u043e\u0432\u044b\u0435 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b \u0430\u0442\u043e\u043c\u043e\u0432 \u043f\u043e \u0444\u043e\u0440\u043c\u0443\u043b\u0435 r_i(t_n+1) = r_i(t_n) + h * v_i(t_n-0.5), \u0433\u0434\u0435 r - \u0432\u0435\u043a\u0442\u043e\u0440 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442 (x, y)\n7. \u0412\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u043f\u043e\u043b\u043d\u0443\u044e \u043f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u0443\u044e \u044d\u043d\u0435\u0440\u0433\u0438\u044e \u0438 \u0441\u0438\u043b\u044b\n8. \u0412\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u043f\u043e\u043b\u043d\u0443\u044e \u043a\u0438\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0443\u044e \u044d\u043d\u0435\u0440\u0433\u0438\u044e K (\u0438 \u0437\u0430\u043f\u0438\u0441\u0430\u0442\u044c)\n9. \u0415\u0441\u043b\u0438 K_n < K_n-1 (n - \u043d\u043e\u043c\u0435\u0440 \u0438\u0442\u0435\u0440\u0430\u0446\u0438\u0438), \u0442\u043e \u043f\u0435\u0440\u0435\u0445\u043e\u0434 \u043a \u0448\u0430\u0433\u0443 10, \u0438\u043d\u0430\u0447\u0435 \u043f\u0435\u0440\u0435\u0445\u043e\u0434 \u043a \u0448\u0430\u0433\u0443 4\n10. \u0415\u0441\u043b\u0438 K_n < eps (aps - \u0437\u0430\u0434\u0430\u043d\u043d\u0430\u044f \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u044c), \u0442\u043e \u0432\u044b\u0432\u043e\u0434\u0438\u043c \u0434\u0430\u043d\u043d\u044b\u0435, \u0441\u0442\u0440\u043e\u0438\u043c \u0433\u0440\u0430\u0444\u0438\u043a \u043a\u0438\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u044d\u043d\u0435\u0440\u0433\u0438\u0438 \u043e\u0442 \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u0438 \u0437\u0430\u043a\u0430\u043d\u0447\u0438\u0432\u0430\u0435\u043c \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c, \u0438\u043d\u0430\u0447\u0435 \u043f\u0435\u0440\u0435\u0445\u043e\u0434 \u043a \u0448\u0430\u0433\u0443 2"}],"shard":{"index":50,"name":"turns-00050.parquet","rows":48033}}