++

prepare_prompt/alpha_prompt.txt

@@ -1,11 +1,13 @@
-任务指令: 你需要根据以下这个金融逻辑, 组合创新 alpha
-数字化客户粘性溢价
-假设
-在数字经济时代，拥有高度数字化客户互动渠道和强大用户粘性的公司（如高频使用的移动应用、高活跃度的在线社区、高转化率的私域流量），能够以更低的边际成本获取更高质量的用户数据和现金流。这种数字化粘性构筑了强大的竞争护城河，其价值尚未被传统财务指标充分定价，有望在未来产生持续的超额收益。
-实施方案
-整合月活跃用户(MAU)增长率、用户日均使用时长、用户生命周期价值(LTV)与获客成本(CAC)的比率等数字化运营数据字段。使用时序动量算子（如ts_rank）对过去一年内“用户时长增长率”与“LTV/CAC比率”的综合得分进行排序。对排名持续提升且处于前30%的公司赋予正向阿尔法权重，并结合横截面标准化确保信号在同类商业模式公司（如消费互联网、金融科技）内进行比较。
-阿尔法因子优化建议
-数字化粘性的价值在不同增长阶段（导入期、成长期、成熟期）差异显著。建议引入“增长阶段分档”算子，根据用户规模增速和营收增速对公司进行动态分类，在各自的阶段组内计算粘性指标的相对强弱，以避免对处于不同生命周期公司的误判。此外，可叠加“变现效率变化”指标（如每用户平均收入(ARPU)的环比加速度），在粘性提升的同时，筛选出变现能力同步增强的标的，以增强信号的纯度。
+Liquidity Stratification Pricing Factor
+
+Hypothesis
+During periods of liquidity tightening or market stress, investors' preference for asset liquidity increases sharply, leading to an intensified phenomenon of liquidity stratification. High-liquidity stocks experience relatively smaller declines due to a "liquidity premium," while low-liquidity stocks are sold off significantly due to a "liquidity discount." When market stress eases and the liquidity environment shifts towards easing, previously overly discounted low-liquidity stocks often exhibit stronger mean reversion and generate more substantial rebounds. Conversely, in the initial phase of a shift from loose to tight market liquidity, high-liquidity stocks may demonstrate stronger defensive characteristics.
+
+Implementation Plan
+Construct a comprehensive liquidity measurement indicator incorporating trading volume, turnover rate, bid-ask spread, and the Amihud illiquidity ratio. Use a time-series market regime identification operator (e.g., based on thresholds for market breadth, monetary conditions index, or volatility index VIX) to determine the prevailing "liquidity regime" of the market. During phases identified as a transition from "tight" to "easy," apply positive weights to the portfolio of stocks with the worst prior liquidity (bottom 20% based on the composite indicator). In the initial phase of liquidity "tightening," apply positive weights to the portfolio of stocks with the best liquidity (top 20%). Utilize cross-sectional ranking and group assignment to construct the investment portfolio.
+
+Alpha Factor Optimization Suggestions
+Pure liquidity metrics can be strongly influenced by firm market capitalization and industry attributes. A two-step neutralization process is recommended: First, perform liquidity ranking within industries to eliminate differences in industry trading characteristics. Second, after factor construction, control for exposure to the size factor. Furthermore, introducing the "duration of liquidity shock" as a weighting parameter could help refine the strategy. Stocks that have endured liquidity pressure for a longer period could be assigned higher weights during the reversal phase to more precisely capture the rebound momentum following suppression.
 
 *=========================================================================================*
 

prepare_prompt/keys_text.txt

@@ -1,11 +1,12 @@
-Name
-Supply Chain Financial Resilience Factor
+Liquidity Stratification Pricing Factor
 
 Hypothesis
-Supply chain financial resilience reflects the stability and risk resistance of capital flow between a company and its upstream and downstream partners. During periods of macroeconomic tightening or industry credit events, companies with fragile supply chain financial networks (e.g., over-reliance on a single financing channel, highly concentrated accounts receivable, or abnormally extended payment terms) are more prone to liquidity crises, leading to stock price declines. Conversely, companies with strong supply chain financial resilience (e.g., diversified financing channels, healthy accounts payable structure, stable capital flow synergy with core partners) can better withstand shocks and may even leverage crises to increase market share, potentially resulting in more resilient stock price performance or alpha generation.
+During periods of liquidity tightening or market stress, investors' preference for asset liquidity increases sharply, leading to an intensified phenomenon of liquidity stratification. High-liquidity stocks experience relatively smaller declines due to a "liquidity premium," while low-liquidity stocks are sold off significantly due to a "liquidity discount." When market stress eases and the liquidity environment shifts towards easing, previously overly discounted low-liquidity stocks often exhibit stronger mean reversion and generate more substantial rebounds. Conversely, in the initial phase of a shift from loose to tight market liquidity, high-liquidity stocks may demonstrate stronger defensive characteristics.
 
 Implementation Plan
-Integrate data fields such as accounts receivable concentration (top five customers' share), distribution of accounts payable days, proportion of commercial paper financing, and whether the company is connected to a core enterprise credit circulation platform to construct a "Supply Chain Financial Resilience Score." Use a time-series stability operator (e.g., ts_stddev) to calculate the volatility of this score during the past three periods of widening credit spreads—lower volatility indicates stronger resilience. Employ a cross-sectional bucketing operator to group securities by their industrial chain and compute the percentile rank of the resilience score within each group. Assign positive alpha weights to securities with consistently improving ranks or those stably in the top 30%.
+Construct a comprehensive liquidity measurement indicator incorporating trading volume, turnover rate, bid-ask spread, and the Amihud illiquidity ratio. Use a time-series market regime identification operator (e.g., based on thresholds for market breadth, monetary conditions index, or volatility index VIX) to determine the prevailing "liquidity regime" of the market. During phases identified as a transition from "tight" to "easy," apply positive weights to the portfolio of stocks with the worst prior liquidity (bottom 20% based on the composite indicator). In the initial phase of liquidity "tightening," apply positive weights to the portfolio of stocks with the best liquidity (top 20%). Utilize cross-sectional ranking and group assignment to construct the investment portfolio.
 
 Alpha Factor Optimization Suggestions
-The effectiveness of this factor may be influenced by the overall monetary policy cycle. It is recommended to introduce a macro-state identification operator (e.g., defining "loose" and "tight" states based on treasury term spreads or credit spreads) to dynamically adjust the factor's weight or neutralization method under different states. During tight cycles, the allocation weight of this factor can be amplified; during loose cycles, consider reducing its weight or applying a composite neutralization approach incorporating industry and market cap neutralization to strip out the beta returns driven by systemic liquidity abundance.
+Pure liquidity metrics can be strongly influenced by firm market capitalization and industry attributes. A two-step neutralization process is recommended: First, perform liquidity ranking within industries to eliminate differences in industry trading characteristics. Second, after factor construction, control for exposure to the size factor. Furthermore, introducing the "duration of liquidity shock" as a weighting parameter could help refine the strategy. Stocks that have endured liquidity pressure for a longer period could be assigned higher weights during the reversal phase to more precisely capture the rebound momentum following suppression.
+
+"volume", "turnover", "spread", "illiquidity", "amihud", "bid", "ask", "vix", "liquidity", "tight", "easy", "monetary", "condition", "stress", "market", "breadth", "regime", "duration"