金 金融工程点评 [Table_Title] 石油石化指数趋势跟踪模型效果点评 [Table_Author] 证券分析师：刘晓锋 电话：13401163428 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 研究助理：孙弋轩 电话：18910596766 E-MAIL：sunyixuan@tpyzq.com 一般证券业务登记编码：S1190123080008 模型概述 结果评估： 融 工 程 点 评 ◼ 设计原理：模型假定标的价格走势具有很好的局部延续性，标的价格永远处 于某一趋势中，出现反转行情的持续时间明显小于趋势延续的时间，若出现 窄幅盘整的情况，亦假设其延续之前的趋势。当处于大级别的趋势之中时， 给定较短时间的观察窗口，走势将延续观察窗口内的局部趋势。而当趋势发 生反转时，在观察窗口始末位置的价格变动方向会明显超出随机波动造成的 趋势背离范围，从而排除随机波动的影响。虽然指数本身在实际中进行双向 操作有诸多限制，但是为了更加严谨地评估模型效果，我们默认可以对标的 进行多空操作，以更准确评估该策略相对指数本身的回报率是否存在明显的 优势。 ◼ 作用标的：申万 ...

金 金融工程点评 汽车指数趋势跟踪模型效果点评 [Table_Author] 证券分析师：刘晓锋 电话：13401163428 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 研究助理：孙弋轩 电话：18910596766 E-MAIL：sunyixuan@tpyzq.com 一般证券业务登记编码：S1190123080008 模型概述 结果评估： [Table_Message]2025-05-19 区间年化收益：6.26% 波动率（年化）：25.15% 夏普率：0.25 最大回撤：32.95% 指数期间总回报率：30.17% 太 平 洋 证 券 股 份 有 限 公 司 证 券 研 究 [Table_Title] [Table_Summary] 融 工 程 点 评 报 告 ◼ 设计原理：模型假定标的价格走势具有很好的局部延续性，标的价格永远处 于某一趋势中，出现反转行情的持续时间明显小于趋势延续的时间，若出现 窄幅盘整的情况，亦假设其延续之前的趋势。当处于大级别的趋势之中时， 给定较短时间的观察窗口，走势将延续观察窗口内的局部趋势。而当趋势发 生反转时，在观察窗口 ...

金 金融工程点评 [Table_Title] 银行指数趋势跟踪模型效果点评 [Table_Author] 证券分析师：刘晓锋 电话：13401163428 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 [Table_Summary] 融 工 程 点 评 研究助理：孙弋轩 电话：18910596766 E-MAIL：sunyixuan@tpyzq.com 一般证券业务登记编码：S1190123080008 模型概述 结果评估： 区间年化收益：-7.72% 波动率（年化）：17.15% 夏普率：-0.45 最大回撤：28.99% 指数期间总回报率：26.24% [Table_Message]2025-05-16 太 平 洋 证 券 股 份 有 限 公 司 证 券 研 究 报 告 ◼ 设计原理：模型假定标的价格走势具有很好的局部延续性，标的价格永远处 于某一趋势中，出现反转行情的持续时间明显小于趋势延续的时间，若出现 窄幅盘整的情况，亦假设其延续之前的趋势。当处于大级别的趋势之中时， 给定较短时间的观察窗口，走势将延续观察窗口内的局部趋势。而当趋势发 生反转时，在观察 ...

截至2025年5月16日，共544只股票在过去20个交易日间创出250日新高。其中创新高个股数量最多的是基础化工、机械、医药行业，创新高个股数量占 比最高的是银行、交通运输、国防军工行业。按照板块分布来看，本周制造、周期板块创新高股票数量最多；按照指数分布来看，中证2000、中证 1000、中证500、沪深300、创业板指、科创50指数中创新高个股数量占指数成份股个数比例分别为：10.05%、7.80%、7.40%、10.33%、5.00%、 6.00%。 平稳创新高股票跟踪 我们根据分析师关注度、股价相对强弱、趋势延续性、股价路径平稳性、创新高持续性等角度，本周从全市场创新高股票中筛选出了包含双林股份、万辰 集团、中宠股份等47只平稳创新高的股票。按照板块来看，创新高股票数量最多的是制造、消费板块，分别有17、11只入选。其中，制造板块中创新高最 多的是汽车行业；消费板块中创新高最多的是农林牧渔行业。 一 乘势而起：市场新高趋势追踪 报 告 摘 要 乘势而起：市场新高趋势追踪 触及新高的个股、行业和板块可被视为市场的风向标。越来越多的研究表明动量、趋势跟踪策略的有效性。本报告旨在定期跟踪市场中创新高的个股及 ...

突破该关键阻力位打破了中期看跌预期，原预期倾向于进一步筑底并在春末及夏季再次回测关键支撑。 相反，当前注意力转向25429-25618点(2024年12月-2025年2月顶部形态低点)、2024年12月25833点 (78.6%回撤位)和2025年4月26800点(底部形态量度目标)，作为下一个潜在阻力区间。其他长期阻力位于 2024年12月27794点周期高点和2025年5月28060点中途缺口目标。 与大盘策略类似，小摩建议转向趋势跟踪策略，将周一22824点跳空缺口作为突破后新入场风险头寸的 止损位。其他支撑位包括50日均线和5月1日22013点缺口。该行预计这轮涨势和七巨头重新确立的领涨 地位将持续至夏季，初步阻力位在25429-26800点，潜在上行目标为27794-28060点阻力区间。 Bloomberg Magnificent 7 Index (BM7P Index), daily bars with momentum divergence signals 智通财经APP获悉，摩根大通近日发布的美股技术策略报告指出，中美贸易战缓和推动标普500指数突 破5750-5785关键阻力位，确认进入低 ...

[Table_Message]2025-05-13 金 金融工程点评 国防军工指数趋势跟踪模型效果点评 [Table_Author] 证券分析师：刘晓锋 电话：13401163428 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 研究助理：孙弋轩 电话：18910596766 E-MAIL：sunyixuan@tpyzq.com 一般证券业务登记编码：S1190123080008 模型概述 结果评估： 区间年化收益：-0.37% 波动率（年化）：29.80% 夏普率：-0.01 最大回撤：29.79% 指数期间总回报率：-3.81% 融 工 程 点 评 太 平 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 2023-03-07 2023-04-12 2023-05-22 2023-06-28 2023-08-02 2023-09-06 2023-10-19 2023-11-23 2023-12-28 2024-02-02 2024-03-18 2024-04-24 2024-06-03 2024-07 ...

- Model Name: Electricity Equipment Index Trend Tracking Model[2] - Model Construction Idea: The model assumes that the price trend of the target has good local continuity, and the target price is always in a certain trend. The duration of the reversal trend is significantly shorter than the trend continuation time. If there is a narrow range consolidation, it is assumed to continue the previous trend[3] - Model Construction Process: - Calculate the difference (del) between the closing price on day T and the closing price on day T-20 - Calculate the volatility (Vol) from day T-20 to day T (excluding) - If the absolute value of del is greater than N times Vol, it is considered that the current price has deviated from the original oscillation range and formed a trend. The trend direction corresponds to the positive or negative of del. If it is less than or equal to N times Vol, it is considered that the current trend continues, and the trend direction is the same as day T-1 - Considering the more intense fluctuations in the stock market compared to the bond market, small wave opportunities are more frequent, so N=1 is used for tracking - The return of both long and short directions of electricity equipment is considered, and the combined result is used as the final evaluation basis[3] - Model Evaluation: The model is not suitable for direct use on the Shenwan First-Level Electricity Equipment Index due to long periods of drawdown and inability to achieve good cumulative returns during certain periods[4] Model Backtest Results - Annualized Return: 13.52%[3] - Annualized Volatility: 29.91%[3] - Sharpe Ratio: 0.45[3] - Maximum Drawdown: 27.32%[3] - Total Return Rate of the Index During the Period: -22.56%[3]

- The model is named "Building Decoration Index Trend Tracking Model" and is designed based on the assumption that the price trend of the target has strong local continuity, with reversal periods being significantly shorter than trend continuation periods. It assumes that narrow-range consolidation will continue the previous trend. When a major trend is present, a short observation window will reflect the local trend, and reversals will show price changes exceeding the range of random fluctuations, thus filtering out random noise[2][3] - The model targets the SW First-Level Building Decoration Index, with raw data retained for preprocessing. It operates on a long-short signal dimension[3] - The specific algorithm involves calculating the difference between the closing price on day T and day T-20 ($del$), as well as the volatility ($Vol$) from day T-20 to day T (excluding T). If the absolute value of $del$ exceeds $N$ times $Vol$, it indicates a trend breakout, with the trend direction determined by the sign of $del$. Otherwise, the trend direction follows that of day T-1. For this model, $N$ is set to 1 to capture smaller opportunities in the more volatile stock market. The model evaluates combined long-short returns as the final performance metric[3] - The model's backtesting period spans from March 7, 2023, to March 18, 2025[3] - The model's performance evaluation indicates that it achieved its highest net value during the period from March 7, 2023, to January 22, 2024. However, from January 22, 2024, to September 26, 2024, the net value declined due to market conditions. Subsequently, the net value returned to near its historical high but entered a period of oscillation. The model demonstrates relatively low annualized returns and prolonged drawdowns in the later stages, making it unsuitable for direct application to the SW First-Level Building Decoration Index[4] - The model's backtesting results include the following metrics: annualized return of 4.39%, annualized volatility of 23.96%, Sharpe ratio of 0.18, maximum drawdown of 22.47%, and total return of -12.25% during the evaluation period[3]

金 金融工程点评 [Table_Title] 综合指数趋势跟踪模型效果点评 [Table_Author] 证券分析师：刘晓锋 电话：13401163428 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 研究助理：孙弋轩 电话：18910596766 E-MAIL：sunyixuan@tpyzq.com 一般证券业务登记编码：S1190123080008 模型概述 结果评估： 区间年化收益：13.05% 波动率（年化）：26.25% 夏普率：0.50 最大回撤：27.97% 指数期间总回报率：-6.43% [Table_Summary] 融 工 程 点 评 ◼ 设计原理：模型假定标的价格走势具有很好的局部延续性，标的价格永远处 于某一趋势中，出现反转行情的持续时间明显小于趋势延续的时间，若出现 窄幅盘整的情况，亦假设其延续之前的趋势。当处于大级别的趋势之中时， 给定较短时间的观察窗口，走势将延续观察窗口内的局部趋势。而当趋势发 生反转时，在观察窗口始末位置的价格变动方向会明显超出随机波动造成的 趋势背离范围，从而排除随机波动的影响。虽然指数本身在实际中进行双向 操 ...

Core Viewpoint - Establishing a systematic investment thinking framework for gold futures requires integrating macroeconomic logic, commodity attributes, market sentiment, and trading strategies to form a comprehensive understanding of gold price fluctuations [1]. Group 1: Understanding the Gold Market - Gold's intrinsic properties include being a safe-haven asset that attracts risk-averse funds during geopolitical conflicts and economic crises, as seen during the 2022 Russia-Ukraine conflict [1]. - Gold futures have unique characteristics, such as leveraged trading through standardized contracts (e.g., COMEX gold at 100 ounces per contract) and a margin system that amplifies risk and returns [2]. Group 2: Analytical Framework Construction - Geopolitical events and black swan occurrences, like wars and sovereign credit crises (e.g., the 2011 European debt crisis), can trigger safe-haven buying, but caution is needed for profit-taking after events settle [3]. - Technical analysis involves assessing long-term trends through weekly/monthly charts (e.g., a decade-long bull market from 2001-2011) and capturing short-term fluctuations via hourly charts [4]. - Historical price points, such as the peak of $2075 per ounce in August 2020 and key psychological levels (e.g., $1800, $1900), are critical for analysis [5]. Group 3: Fundamental Analysis - Key macroeconomic indicators include the U.S. CPI and non-farm payroll data, which influence inflation and employment, subsequently affecting Federal Reserve policies and gold prices through real interest rates [6]. - The 10-year TIPS yield (real interest rate) shows a significant negative correlation with gold prices [6]. - Central bank policies, particularly during the initial phase of a rate hike cycle, can suppress gold prices, but expectations of economic recession may lead to a reversal in gold's favor [6]. - Global central bank gold purchases provide long-term support for gold prices [6]. Group 4: Trading Strategies - Trend-following strategies are suitable during rising recession expectations and ongoing central bank easing [12]. - Mean reversion strategies apply when gold prices deviate from implied values based on real interest rates or when overbought/oversold indicators signal a reversal [15]. - Event-driven strategies involve adjusting positions before key data releases (e.g., non-farm payrolls, CPI) and entering trades based on market reactions [17]. Group 5: Risk Management - Leverage control is essential due to gold futures' high volatility (daily fluctuations of 1-3%), recommending a maximum risk of 2% of the trading capital per trade [19]. - Dynamic stop-loss strategies can be based on support/resistance levels or volatility measures like the Average True Range (ATR) [21][22]. - Hedging strategies may involve inverse positions in the U.S. dollar index or balancing with equity assets [23]. Group 6: Trading Psychology and Cognitive Upgrades - Overcoming cognitive biases, such as anchoring effects and overtrading, is crucial for successful trading in gold [24][25]. - Recognizing the "inflation-recession" cycle of gold can help traders adapt their strategies accordingly [26]. - Continuous review and iteration of trading logic and strategy performance are necessary for improvement [29]. Group 7: Common Misconceptions and Responses - Misconception 1: Viewing gold solely as an inflation hedge; real interest rates must be negative for gold to be truly bullish [31]. - Misconception 2: Ignoring liquidity risks, especially during significant market events that may lead to liquidity shortages [32]. - Misconception 3: Confusing futures with physical gold, as futures contracts incur time costs and potential roll-over losses [33]. Summary of the Gold Investment Framework - The core of the gold investment thinking system is a triadic driving model comprising real interest rates (fundamentals), dollar cycles (monetary attributes), and risk-averse sentiment (emotional factors) [35]. - Strategies should align with market conditions, utilizing trend strategies in trending markets and mean-reversion strategies in sideways markets [36]. - Prioritizing risk management is vital due to gold's volatility, emphasizing survival over profit [37].