++

prepare_prompt/alpha_prompt.txt

@@ -1,32 +1,24 @@
-名称
-供应商集中度动态调整因子
+跨境技术溢出效应
+
 假设
-供应商集中度（前五大供应商采购额占总采购额比重）的动态变化直接反映企业供应链风险的管控能力。若企业供应商集中度从高位持续回落，意味着其正在主动分散供应链依赖风险，能够有效降低单一供应商违约、提价或断供带来的经营冲击，进而提升盈利稳定性与抗风险能力，这类企业应享有估值溢价，适合建立多头仓位；反之，若供应商集中度从低位持续攀升，企业对少数供应商的依赖度加深，供应链脆弱性上升，经营不确定性增加，适合建立空头仓位。此外，集中度调整的速度与幅度和超额收益呈正相关，快速且合理的分散调整比缓慢调整更具信号价值。
+在全球化产业链中，若一家公司的海外主要客户或供应商拥有强大的技术创新能力（如高研发投入、高专利质量），则该公司可能通过业务关联，获得隐性的知识外溢与技术扩散益处。这种“技术溢出”能提升该公司的运营效率、产品竞争力或降低其研发风险，从而可能在未来转化为超预期的盈利增长与估值提升。市场对这类隐含的、非线性的增长期权可能存在定价不足。
+
 实施方案
-计算核心指标：基于企业采购数据，测算供应商集中度比率（CR5 = 前五大供应商采购金额 / 总采购金额）；
-时序趋势分析：使用时序趋势算子（ts_trend）拟合过去 12 个月 CR5 的变化斜率，区分 “持续下降（斜率为负且绝对值大于阈值）”“持续上升（斜率为正且绝对值大于阈值）”“平稳波动” 三类标的；
-规模与行业校准：将 CR5 除以企业总采购额的对数以消除规模影响，同时计算标的 CR5 与行业均值的偏离度；
-构建多空策略：做多 “CR5 持续下降 + 当前 CR5 低于行业均值” 的标的；做空 “CR5 持续上升 + 当前 CR5 高于行业均值” 的标的；剔除 CR5 平稳波动且偏离行业均值较小的标的以降低噪声。
+构建“技术关联强度”因子。识别公司年报或供应链数据中披露的前五大海外客户/供应商，并获取这些关联实体的公开技术创新指标（如人均专利引用量、研发费用增速）。使用加权平均算子，依据交易金额占比为权重，计算公司所关联的海外实体的整体技术强度。使用时序滞后算子，将技术强度数据滞后6-12个月以匹配技术吸收与转化周期，再通过横截面排名评估公司在全市场中的相对技术关联优势。
+
 阿尔法因子优化建议
-引入行业差异化阈值：不同行业的供应商集中度基准值差异显著（如半导体行业核心物料供应商集中度天然偏高，快消品行业集中度偏低），建议采用行业分位数算子替代固定阈值，在行业内部分层判断集中度调整的合理性；
-叠加供应商质量验证：整合供应商信用评级、合作年限等数据，当集中度下降伴随 “新增供应商信用评级高于原有供应商” 时，强化多头信号权重；若集中度上升源于 “优质供应商排他性合作”，则弱化空头信号；
-事件驱动权重调整：运用事件触发算子，在行业性供应链危机（如原材料涨价潮、地缘政治导致的物料断供）发生时，放大该因子的配置权重，捕捉危机期间供应链稳健企业的超额收益；
-分档加权优化：采用分层算子将集中度调整幅度分为 “大幅调整”“中度调整”“小幅调整” 三档，针对不同档位设置差异化仓位权重，提升策略的风险收益比。
+技术溢出的效果受公司自身“吸收能力”调节。建议引入公司自身的研发团队质量（如技术人员占比）、内部研发投入强度作为调节变量，通过交互项算子或条件分层处理（例如，仅在自身研发投入超过行业平均的公司样本中，技术关联强度因子才被启用），以更精准地捕捉那些既拥有外部技术源头、又有能力内部化的优质标的。
+
+Cross-Border Technology Spillover Effect
 
-Name
-Dynamic Adjustment Factor of Supplier Concentration
 Hypothesis
-The dynamic change in supplier concentration (measured by the proportion of purchases from the top 5 suppliers to total purchases) directly reflects an enterprise's ability to control supply chain risks. If a company's supplier concentration continues to decline from a high level, it indicates that it is actively diversifying supply chain dependence risks, which can effectively reduce operational shocks caused by default, price increase or supply disruption of a single supplier, thereby improving profit stability and risk resistance. Such enterprises deserve a valuation premium and are suitable for establishing long positions. Conversely, if supplier concentration continues to rise from a low level, the enterprise's dependence on a few suppliers deepens, supply chain vulnerability increases, and operational uncertainty rises, making it suitable for establishing short positions. In addition, the speed and magnitude of concentration adjustment are positively correlated with excess returns—fast and reasonable diversification adjustments have higher signal value than slow adjustments.
+In the global industrial chain, if a company's key overseas customers or suppliers possess strong technological innovation capabilities (e.g., high R&D investment, high patent quality), the company may benefit from implicit knowledge spillover and technology diffusion through these business linkages. This "technology spillover" can enhance the company's operational efficiency, product competitiveness, or reduce its R&D risks, potentially translating into unexpected profit growth and valuation appreciation in the future. The market may underprice this implicit, non-linear growth option.
+
 Implementation Plan
-Calculate core indicators: Based on enterprise procurement data, measure the supplier concentration ratio (CR5 = purchase amount from top 5 suppliers / total purchase amount);
-Time-series trend analysis: Use the time-series trend operator (ts_trend) to fit the change slope of CR5 over the past 12 months, and classify targets into three categories: "continuous decline (negative slope with absolute value greater than threshold)", "continuous rise (positive slope with absolute value greater than threshold)", and "stable fluctuation";
-Scale and industry calibration: Divide CR5 by the logarithm of the enterprise's total purchase amount to eliminate scale effects, and calculate the deviation of the target's CR5 from the industry average;
-Construct long-short strategy: Establish long positions on targets with "continuously declining CR5 + current CR5 below industry average"; establish short positions on targets with "continuously rising CR5 + current CR5 above industry average"; exclude targets with stable CR5 fluctuation and small deviation from industry average to reduce noise.
-Alpha Factor Optimization Suggestions
-Introduce industry-differentiated thresholds: There are significant differences in the benchmark values of supplier concentration across industries (e.g., the concentration of core material suppliers in the semiconductor industry is naturally high, while that in the consumer goods industry is low). It is recommended to use the industry quantile operator instead of fixed thresholds to judge the rationality of concentration adjustment by stratification within the industry;
-Superimpose supplier quality verification: Integrate data such as supplier credit ratings and cooperation years. When the decline in concentration is accompanied by "the credit rating of new suppliers being higher than that of original suppliers", strengthen the weight of long signals; if the rise in concentration stems from "exclusive cooperation with high-quality suppliers", weaken the short signals;
-Event-driven weight adjustment: Use the event trigger operator to increase the allocation weight of this factor during industry-wide supply chain crises (such as raw material price surges, material supply disruptions caused by geopolitics), capturing excess returns of supply chain-robust enterprises during crises;
-Tiered weighting optimization: Use the stratification operator to divide the concentration adjustment range into three tiers: "significant adjustment", "moderate adjustment" and "minor adjustment", and set differentiated position weights for different tiers to improve the risk-return ratio of the strategy.
+Construct a "Technology Linkage Intensity" factor. Identify the top five overseas customers/suppliers disclosed in the company's annual reports or supply chain data, and obtain public technological innovation metrics for these linked entities (e.g., patent citations per capita, R&D expense growth rate). Use a weighted average operator, with transaction amount proportion as weights, to calculate the aggregated technological strength of the overseas entities linked to the company. Apply a time-series lag operator to lag the technology strength data by 6-12 months to account for technology absorption and conversion cycles, then assess the company's relative technological linkage advantage across the market via cross-sectional ranking.
+
+Alpha Factor Optimization Suggestion
+The effect of technology spillover is moderated by the company's own "absorptive capacity." It is suggested to introduce the quality of the company's own R&D team (e.g., proportion of technical staff) and internal R&D intensity as moderating variables. Through interaction term operators or conditional stratification (e.g., enabling the Technology Linkage Intensity factor only within the subsample of companies whose own R&D investment exceeds the industry average), the factor can more precisely identify high-quality targets that possess both external technology sources and the internal capability to assimilate them.
 
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@@ -52,7 +44,7 @@ Tiered weighting optimization: Use the stratification operator to divide the con
 =================================================================
 
 重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。
-以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 30 个alpha：
+以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 100 个 alpha：
 不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
 
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prepare_prompt/keys_text.txt

@@ -1,14 +1 @@
-Name
-Dynamic Adjustment Factor of Supplier Concentration
-Hypothesis
-The dynamic change in supplier concentration (measured by the proportion of purchases from the top 5 suppliers to total purchases) directly reflects an enterprise's ability to control supply chain risks. If a company's supplier concentration continues to decline from a high level, it indicates that it is actively diversifying supply chain dependence risks, which can effectively reduce operational shocks caused by default, price increase or supply disruption of a single supplier, thereby improving profit stability and risk resistance. Such enterprises deserve a valuation premium and are suitable for establishing long positions. Conversely, if supplier concentration continues to rise from a low level, the enterprise's dependence on a few suppliers deepens, supply chain vulnerability increases, and operational uncertainty rises, making it suitable for establishing short positions. In addition, the speed and magnitude of concentration adjustment are positively correlated with excess returns—fast and reasonable diversification adjustments have higher signal value than slow adjustments.
-Implementation Plan
-Calculate core indicators: Based on enterprise procurement data, measure the supplier concentration ratio (CR5 = purchase amount from top 5 suppliers / total purchase amount);
-Time-series trend analysis: Use the time-series trend operator (ts_trend) to fit the change slope of CR5 over the past 12 months, and classify targets into three categories: "continuous decline (negative slope with absolute value greater than threshold)", "continuous rise (positive slope with absolute value greater than threshold)", and "stable fluctuation";
-Scale and industry calibration: Divide CR5 by the logarithm of the enterprise's total purchase amount to eliminate scale effects, and calculate the deviation of the target's CR5 from the industry average;
-Construct long-short strategy: Establish long positions on targets with "continuously declining CR5 + current CR5 below industry average"; establish short positions on targets with "continuously rising CR5 + current CR5 above industry average"; exclude targets with stable CR5 fluctuation and small deviation from industry average to reduce noise.
-Alpha Factor Optimization Suggestions
-Introduce industry-differentiated thresholds: There are significant differences in the benchmark values of supplier concentration across industries (e.g., the concentration of core material suppliers in the semiconductor industry is naturally high, while that in the consumer goods industry is low). It is recommended to use the industry quantile operator instead of fixed thresholds to judge the rationality of concentration adjustment by stratification within the industry;
-Superimpose supplier quality verification: Integrate data such as supplier credit ratings and cooperation years. When the decline in concentration is accompanied by "the credit rating of new suppliers being higher than that of original suppliers", strengthen the weight of long signals; if the rise in concentration stems from "exclusive cooperation with high-quality suppliers", weaken the short signals;
-Event-driven weight adjustment: Use the event trigger operator to increase the allocation weight of this factor during industry-wide supply chain crises (such as raw material price surges, material supply disruptions caused by geopolitics), capturing excess returns of supply chain-robust enterprises during crises;
-Tiered weighting optimization: Use the stratification operator to divide the concentration adjustment range into three tiers: "significant adjustment", "moderate adjustment" and "minor adjustment", and set differentiated position weights for different tiers to improve the risk-return ratio of the strategy.
+["customer", "supplier", "overseas", "foreign", "revenue", "patent", "citation", "rnd", "research", "development", "innovation", "tech", "technical", "linkage", "export", "import", "intellectual", "property", "license", "royalty", "collaboration", "partner", "affiliate", "segment", "geographic", "region", "country", "transaction", "sale", "purchase", "expenditure", "spending", "intensity", "employee", "staff", "scientist", "engineer", "personnel", "capability", "absorptive", "rank", "score", "strength", "quality", "weight", "share", "percentage", "ratio", "growth", "lag", "delay", "temporal"]