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10 lines
14 KiB
10 lines
14 KiB
{
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"success": true,
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"alpha_id": "MP5gWQva",
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"stdout": "✓ 已加载模板总结文件: /Users/jack/source/mySpace/mycode/my_project/py/alpha_odoo/alpha_tools/simple72/Tranformer/template_summary.md\n✓ 已从命令行参数加载配置: /Users/jack/source/mySpace/mycode/my_project/py/alpha_odoo/alpha_tools/simple72/Tranformer/config_51a505a0-322f-45f2-9180-98c25b2e731c.json\n✓ 使用内置模板总结\n--- 正在启动 BRAIN 会话... ---\nNew session created (ID: 12659217744) with authentication response: 201, {'user': {'id': 'YC93384'}, 'token': {'expiry': 14400.0}, 'permissions': ['BEFORE_AND_AFTER_PERFORMANCE_V2', 'BRAIN_LABS', 'BRAIN_LABS_JUPYTER_LAB', 'CONSULTANT', 'MULTI_SIMULATION', 'PROD_ALPHAS', 'REFERRAL', 'VISUALIZATION', 'WORKDAY']} (新会话已创建)\n--- 正在认证 LLM Gateway... ---\n✓ LLM Gateway 认证成功\n\n--- 正在获取 Alpha ID: MP5gWQva 的详情... ---\nNew session created (ID: 12659217744) with authentication response: 201, {'user': {'id': 'YC93384'}, 'token': {'expiry': 14400.0}, 'permissions': ['BEFORE_AND_AFTER_PERFORMANCE_V2', 'BRAIN_LABS', 'BRAIN_LABS_JUPYTER_LAB', 'CONSULTANT', 'MULTI_SIMULATION', 'PROD_ALPHAS', 'REFERRAL', 'VISUALIZATION', 'WORKDAY']} (新会话已创建)\nstatus_code 429, sleep 3 seconds\nLLM Gateway Authentication successful. (LLM网关认证成功)\n--- Calling LLM to propose templates... (正在调用LLM生成模板...) ---\nLLM Gateway Authentication successful. (LLM网关认证成功)\n--- Calling LLM to propose templates... (正在调用LLM生成模板...) ---\nAlpha MP5gWQva description updated on platform. (Alpha描述已在平台更新)\nNew session created (ID: 12659217744) with authentication response: 201, {'user': {'id': 'YC93384'}, 'token': {'expiry': 14400.0}, 'permissions': ['BEFORE_AND_AFTER_PERFORMANCE_V2', 'BRAIN_LABS', 'BRAIN_LABS_JUPYTER_LAB', 'CONSULTANT', 'MULTI_SIMULATION', 'PROD_ALPHAS', 'REFERRAL', 'VISUALIZATION', 'WORKDAY']} (新会话已创建)\n✓ LLM Gateway 认证成功\nAlpha Details Retrieved (已获取Alpha详情):\n{\n \"settings\": {\n \"instrumentType\": \"EQUITY\",\n \"region\": \"IND\",\n \"universe\": \"TOP500\",\n \"delay\": 1,\n \"decay\": 12,\n \"neutralization\": \"SLOW_AND_FAST\",\n \"truncation\": 0.02,\n \"pasteurization\": \"ON\",\n \"unitHandling\": \"VERIFY\",\n \"nanHandling\": \"ON\",\n \"maxTrade\": \"OFF\",\n \"maxPosition\": \"OFF\",\n \"language\": \"FASTEXPR\",\n \"visualization\": false,\n \"startDate\": \"2014-01-01\",\n \"endDate\": \"2023-12-31\"\n },\n \"expression\": {\n \"code\": \"divide(avg_pct_change_estimate_next_year_earnings_7d, add(analysts_count_revising_up_quarter2_earnings_30d, 0.0001))\",\n \"description\": \"{\\n \\\"text\\\": \\\"<think>\\\\nWe need to generate a new, improved description for the alpha code.\\\\n\\\\nThe code:\\\\n\\\\ndivide(avg_pct_change_estimate_next_year_earnings_7d, add(analysts_count_revising_up_quarter2_earnings_30d, 0.0001))\\\\n\\\\nSo the alpha is dividing the average percent change in next-year earnings estimates over the past 7 days by the number of analysts revising up Q2 earnings over the last 30 days plus a small constant.\\\\n\\\\nWe need to produce an improved description: explain investment idea, rationale for data used, rationale for operators used.\\\\n\\\\nWe need to format as:\\\\n\\\\n\\\\\\\"Idea: xxxxx\\\\\\\\nRationale for data used: xxxxx\\\\\\\\nRationale for operators used: xxxxx\\\\\\\"\\\\n\\\\nWe should produce a description that clarifies the alpha: The alpha tries to measure the momentum in earnings estimate revisions relative to the breadth of analyst revisions, possibly indicating the strength of upward sentiment. By dividing the short-term (7d) average percentage change in next-year earnings estimates by the count of analysts revising up Q2 earnings (with a small floor), it normalizes the magnitude of estimate changes by the number of analysts, adjusting for market breadth. The small constant avoids division by zero.\\\\n\\\\nRationale for data used: avg_pct_change_estimate_next_year_earnings_7d captures recent changes in forward earnings expectations; analysts_count_revising_up_quarter2_earnings_30d captures recent positive revisions for near-term quarter; using next-year vs quarter provides longer horizon vs near-term; combining them reflects both magnitude and breadth.\\\\n\\\\nRationale for operators: divide normalizes the magnitude by breadth; add ensures non-zero denominator; maybe we can mention the constant 0.0001 is to avoid division by zero.\\\\n\\\\nThe description should be clear and concise, suitable for a quantitative alpha description.\\\\n\\\\nPotential nuance: The alpha may be interpreted as a measure of how large the per-analyst estimate change is, relative to the number of analysts revising up; if many analysts revise up but the average change is small, the ratio may be low; if a few analysts raise estimates dramatically, the ratio may be high. This can signal either concentrated strong conviction or broad moderate revisions.\\\\n\\\\nWe might also mention that the 0.0001 constant ensures stability and prevents division by zero while having negligible effect on scaling.\\\\n\\\\nWe need to incorporate the idea that the alpha uses a small denominator offset to avoid extreme values.\\\\n\\\\nThus the description will be something like:\\\\n\\\\nIdea: The alpha captures the intensity of recent upward revisions in earnings estimates by dividing the short\\\\u2011term percentage change in next\\\\u2011year consensus earnings by the count of analysts raising Q2 earnings. It thus reflects the average magnitude of upward revisions per revising analyst, combining the speed of estimate moves with the breadth of positive revisions.\\\\n\\\\nRationale for data used: ... etc.\\\\n\\\\nRationale for operators used: ... etc.\\\\n\\\\nLet's produce that. Use proper punctuation.\\\\n\\\\nMake sure to mention the constant 0.0001 for stability.\\\\n\\\\nOk.\\\\n</think>\\\\n\\\\nIdea: The alpha measures the intensity of recent upward earnings revisions by taking the short\\\\u2011term percentage change in consensus estimates for the next fiscal year and scaling it by the breadth of analysts that have raised their second\\\\u2011quarter estimates. In other words, it reflects how large the average upward revision is per revising analyst, combining the momentum of estimate changes with the number of analysts turning bullish.\\\\n\\\\nRationale for data used: \\\\n- **avg_pct_change_estimate_next_year_earnings_7d** captures the most recent (7\\\\u2011day) directional momentum in forward\\\\u2011year earnings expectations, giving a timely signal of how fast analysts are revising their views. \\\\n- **analysts_count_revising_up_quarter2_earnings_30d** measures the breadth of positive sentiment over a slightly longer (30\\\\u2011day) window for the nearer\\\\u2011term quarter, indicating how many analysts are turning optimistic. By pairing a long\\\\u2011horizon momentum metric with a near\\\\u2011term breadth metric, the alpha blends the strength of the revision trend with the consensus behind it.\\\\n\\\\nRationale for operators used: \\\\n- **divide** normalizes the magnitude of the estimate change by the count of revising analysts, producing a \\\\u201cper\\\\u2011analyst\\\\u201d revision intensity that is comparable across stocks regardless of how many analysts cover them. \\\\n- **add(..., 0.0001)** introduces a tiny constant to the denominator to avoid division\\\\u2011by\\\\u2011zero when no analysts have revised up, ensuring numerical stability without materially affecting the ratio\\\\u2019s scaling. The small offset is negligible in normal conditions but prevents extreme values or errors in thin\\\\u2011coverage names.\\\"\\n}\",\n \"operatorCount\": 2\n }\n}\n\n============================================================\n[Step 2/5] 正在生成 Alpha 模板提议...\n============================================================\ncurrent seed alpha detail (当前种子Alpha详情): {'code': 'divide(avg_pct_change_estimate_next_year_earnings_7d, add(analysts_count_revising_up_quarter2_earnings_30d, 0.0001))', 'description': '{\\n \"text\": \"<think>\\\\nWe need to generate a new, improved description for the alpha code.\\\\n\\\\nThe code:\\\\n\\\\ndivide(avg_pct_change_estimate_next_year_earnings_7d, add(analysts_count_revising_up_quarter2_earnings_30d, 0.0001))\\\\n\\\\nSo the alpha is dividing the average percent change in next-year earnings estimates over the past 7 days by the number of analysts revising up Q2 earnings over the last 30 days plus a small constant.\\\\n\\\\nWe need to produce an improved description: explain investment idea, rationale for data used, rationale for operators used.\\\\n\\\\nWe need to format as:\\\\n\\\\n\\\\\"Idea: xxxxx\\\\\\\\nRationale for data used: xxxxx\\\\\\\\nRationale for operators used: xxxxx\\\\\"\\\\n\\\\nWe should produce a description that clarifies the alpha: The alpha tries to measure the momentum in earnings estimate revisions relative to the breadth of analyst revisions, possibly indicating the strength of upward sentiment. By dividing the short-term (7d) average percentage change in next-year earnings estimates by the count of analysts revising up Q2 earnings (with a small floor), it normalizes the magnitude of estimate changes by the number of analysts, adjusting for market breadth. The small constant avoids division by zero.\\\\n\\\\nRationale for data used: avg_pct_change_estimate_next_year_earnings_7d captures recent changes in forward earnings expectations; analysts_count_revising_up_quarter2_earnings_30d captures recent positive revisions for near-term quarter; using next-year vs quarter provides longer horizon vs near-term; combining them reflects both magnitude and breadth.\\\\n\\\\nRationale for operators: divide normalizes the magnitude by breadth; add ensures non-zero denominator; maybe we can mention the constant 0.0001 is to avoid division by zero.\\\\n\\\\nThe description should be clear and concise, suitable for a quantitative alpha description.\\\\n\\\\nPotential nuance: The alpha may be interpreted as a measure of how large the per-analyst estimate change is, relative to the number of analysts revising up; if many analysts revise up but the average change is small, the ratio may be low; if a few analysts raise estimates dramatically, the ratio may be high. This can signal either concentrated strong conviction or broad moderate revisions.\\\\n\\\\nWe might also mention that the 0.0001 constant ensures stability and prevents division by zero while having negligible effect on scaling.\\\\n\\\\nWe need to incorporate the idea that the alpha uses a small denominator offset to avoid extreme values.\\\\n\\\\nThus the description will be something like:\\\\n\\\\nIdea: The alpha captures the intensity of recent upward revisions in earnings estimates by dividing the short\\\\u2011term percentage change in next\\\\u2011year consensus earnings by the count of analysts raising Q2 earnings. It thus reflects the average magnitude of upward revisions per revising analyst, combining the speed of estimate moves with the breadth of positive revisions.\\\\n\\\\nRationale for data used: ... etc.\\\\n\\\\nRationale for operators used: ... etc.\\\\n\\\\nLet\\'s produce that. Use proper punctuation.\\\\n\\\\nMake sure to mention the constant 0.0001 for stability.\\\\n\\\\nOk.\\\\n</think>\\\\n\\\\nIdea: The alpha measures the intensity of recent upward earnings revisions by taking the short\\\\u2011term percentage change in consensus estimates for the next fiscal year and scaling it by the breadth of analysts that have raised their second\\\\u2011quarter estimates. In other words, it reflects how large the average upward revision is per revising analyst, combining the momentum of estimate changes with the number of analysts turning bullish.\\\\n\\\\nRationale for data used: \\\\n- **avg_pct_change_estimate_next_year_earnings_7d** captures the most recent (7\\\\u2011day) directional momentum in forward\\\\u2011year earnings expectations, giving a timely signal of how fast analysts are revising their views. \\\\n- **analysts_count_revising_up_quarter2_earnings_30d** measures the breadth of positive sentiment over a slightly longer (30\\\\u2011day) window for the nearer\\\\u2011term quarter, indicating how many analysts are turning optimistic. By pairing a long\\\\u2011horizon momentum metric with a near\\\\u2011term breadth metric, the alpha blends the strength of the revision trend with the consensus behind it.\\\\n\\\\nRationale for operators used: \\\\n- **divide** normalizes the magnitude of the estimate change by the count of revising analysts, producing a \\\\u201cper\\\\u2011analyst\\\\u201d revision intensity that is comparable across stocks regardless of how many analysts cover them. \\\\n- **add(..., 0.0001)** introduces a tiny constant to the denominator to avoid division\\\\u2011by\\\\u2011zero when no analysts have revised up, ensuring numerical stability without materially affecting the ratio\\\\u2019s scaling. The small offset is negligible in normal conditions but prevents extreme values or errors in thin\\\\u2011coverage names.\"\\n}', 'operatorCount': 2}\n\n[Step 1/5] 正在调用 LLM 生成 Alpha 模板...\n - 模型: MiniMax-M2.7\n - 数据类型: MATRIX\nAn error occurred while calling the LLM (调用LLM时发生错误): unhashable type: 'slice'\nFailed to generate proposed alpha templates. (生成提议模板失败)\n",
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"stderr": "",
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"return_code": 1,
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"expressions_success": [],
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"candidates": [],
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"expressions_error": []
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} |