基本面分析

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黄金又逢震荡趋势何时转变?美联储利率凌晨来袭!TTPS主教练齐上阵,基本面技术面如何看?现在正在直播,立即观看!
news flash· 2025-06-18 12:56
黄金又逢震荡趋势何时转变?美联储利率凌晨来袭!TTPS主教练齐上阵,基本面技术面如何看?现在 正在直播,立即观看! 相关链接 黄金行情讲解中 ...
铝合金期货,短期以何种思路对待?
Sou Hu Cai Jing· 2025-06-11 03:43
Core Viewpoint - The newly launched aluminum alloy futures have attracted significant market attention, with initial trading showing a strong upward trend due to lower listing prices compared to spot prices [2]. Group 1: Market Performance - On the first trading day, aluminum alloy futures saw an overall increase, with the main contract 2511 rising by over 4% [2]. - The weighted average price of aluminum alloy increased by 4.41%, reaching 19,175 [2]. - The main continuous contract rose by 4.49%, with a latest price of 6,161 [2]. Group 2: Technical Analysis - Due to the lack of historical data for the newly listed aluminum alloy futures, traders are advised to focus on smaller time frame charts to capture sufficient volatility details for decision-making [4]. Group 3: Fundamental Analysis - Approximately 70% of the aluminum alloy futures correspond to the transportation sector, including automotive, motorcycle, and electric vehicle industries, while the remaining demand comes from power electronics, home appliances, and machinery manufacturing [7]. - Despite being in a consumption off-season with low purchasing enthusiasm from downstream processing enterprises, the upcoming 618 mid-year consumption event and stable order volumes from automotive profile manufacturers may provide short-term support for aluminum alloy prices [7]. - However, there are concerns about potential oversupply due to relatively low technical barriers in aluminum alloy production, which could lead to high inventory pressure and selling hedging pressure if terminal consumption weakens [9].
风格轮动策略(四):成长、价值轮动的基本面信号
Changjiang Securities· 2025-06-05 11:17
Group 1 - The report attempts to integrate subjective judgment and quantitative analysis to construct a style rotation framework, primarily based on five dimensions to build a core style rotation model, which will eventually be applied to actual investable portfolios [3][8] - The fundamental perspective of growth and value style rotation strategy has shown long-term excess returns compared to its balanced allocation benchmark, although the performance of the strategy is limited due to varying transmission paths and delays of different fundamental indicators under different contexts [3][10] Group 2 - The report reviews the construction of style indices and the style rotation framework, continuing to explore the growth and value style rotation from a fundamental perspective [8][17] - Common fundamental indicators are primarily micro data, but the report adopts a different perspective by observing the overall situation of the equity market or specific styles, reflecting the specific conditions of certain groups [8][30] Group 3 - The analysis of fundamental factors is conducted from five angles: growth, profitability, financial health and solvency, operational efficiency, and valuation levels, with growth, profitability, and valuation signals being relatively stable and accurate [9][31] - The overall turnover rate of the growth and value style rotation strategy is low, generally favoring long-term holdings of growth or value stocks, with an average monthly win rate of approximately 60.91% and an average annualized return of about 15.26% from January 1, 2005, to April 29, 2025 [10][31] Group 4 - The growth style index and value style index are constructed based on similar logic, with the main difference being the sorting of constituent stocks using growth and value factors respectively [18][21] - The report outlines the style rotation framework, which is expected to be based on five major dimensions to construct the core style rotation model, focusing on the fundamental dimension of growth and value style rotation [27][30] Group 5 - The report categorizes fundamental indicators into two main types: market overall indicators and style difference indicators, further divided into growth indicators, profitability indicators, financial health and solvency indicators, operational efficiency indicators, and valuation indicators [30][31] - The financial health and solvency indicators focus on the reasonableness of capital structure and short-term liquidity, with asset-liability ratio and current ratio being particularly effective in the context of growth and value style rotation [57][65]
结合基本面和量价特征的GRU模型
China Post Securities· 2025-06-05 07:20
Quantitative Models and Construction Methods GRU Model - **Model Name**: GRU - **Model Construction Idea**: The GRU model is used to mine volume and price information, and this report explores its ability to incorporate financial information[2][14]. - **Model Construction Process**: - **Data Range**: 20130101-20250430, all market stocks (excluding Beijing Stock Exchange)[16] - **Input**: Each stock has one sample at the end of each month, containing volume and price information for the past 240 trading days, including 7 fields: opening price, highest price, lowest price, closing price, trading volume, trading amount, and turnover rate. Each field is standardized using z-score for 240 values[16]. - **Prediction Target**: Next month's return rate standardized by cross-section (opening price at the beginning of the month to closing price at the end of the month)[16]. - **Training Set**: Samples from the past 6 years, divided into training and validation sets in a 4:1 ratio according to time sequence[16]. - **Training Method**: Rolling training every month, early stopping if the loss function does not decrease for 10 consecutive rounds[16]. - **Model Evaluation**: The GRU model can simultaneously mine volume and price information and financial information. The high-frequency processing of financial information improves the model results to some extent[2][18]. - **Model Testing Results**: - **Annualized Excess Return**: 8.75% - **IR**: 2.25 - **Maximum Drawdown**: 4.71%[3][19][23] GRU Model with Financial Information - **Model Name**: GRU with Financial Information - **Model Construction Idea**: Incorporating financial information into the GRU model to improve its performance[4][24]. - **Model Construction Process**: - **Simple Splicing of Financial Information**: Financial data is calculated as TTM value according to the latest available quarterly report for each trading day, then spliced into new columns. The matrix containing volume and price information and fundamental information is standardized and input into the GRU network[25]. - **Adjusted Financial Information**: Assuming the TTM value of financial indicators grows steadily at the quarterly growth rate, the daily adjustment formula for TTM values is: $$ \mathrm{DFTTM}_{\mathrm{q1}}={\frac{\mathrm{FactorTTM}_{\mathrm{q1}}-\mathrm{FactorTTM}_{\mathrm{q0}}}{a b s\big(\mathrm{FactorTTM}_{\mathrm{q0}}\big)}} $$ $$ \mathrm{Factort} = \mathrm{FactorTTMq} + \mathrm{abs(FactorTTMq)} \times \left(\frac{90}{1}\right) $$ where t is the trading day, q is the financial report period (March 31, June 30, September 30, December 31)[36][38]. - **Model Evaluation**: Incorporating financial information improves the overall performance of the baseline model, especially before 2022. However, after 2023, the improvement is weaker or even negative[4][35][42]. - **Model Testing Results**: - **Annualized Excess Return**: 7.76% - **IR**: 1.65 - **Maximum Drawdown**: 5.40%[41][44] GRU Model with Simplified Financial Information - **Model Name**: GRU with Simplified Financial Information - **Model Construction Idea**: Simplifying the financial indicators to only include important ones like net profit TTM and market value[45]. - **Model Construction Process**: - **Simplified Financial Information**: Only retaining important indicators like net profit TTM and market value, and incorporating them into the GRU model[45]. - **Model Evaluation**: Simplifying the financial indicators improves the overall performance of the model, especially before 2022. After 2023, the improvement is weaker but still positive[45][55]. - **Model Testing Results**: - **Annualized Excess Return**: 9.97% - **IR**: 1.93 - **Maximum Drawdown**: 5.70%[51][52] Mixed Frequency Model - **Model Name**: Mixed Frequency Model (barra5d + daily GRU) - **Model Construction Idea**: Combining long-term and short-term prediction capabilities by integrating barra5d and daily GRU models[56][65]. - **Model Construction Process**: - **Input**: Combining the daily GRU model with the barra5d model, which is trained on 240-minute intraday data to predict the next 1-5 days' returns[56][65]. - **Model Evaluation**: The mixed frequency model significantly improves the performance of the barra5d model, especially after October 2024. Adding fundamental information further stabilizes the annual excess performance[65][72][80]. - **Model Testing Results**: - **Annualized Excess Return**: 11.82% - **IR**: 2.39 - **Maximum Drawdown**: 5.70%[77][78] Model Backtesting Results GRU Model - **Annualized Excess Return**: 8.75% - **IR**: 2.25 - **Maximum Drawdown**: 4.71%[3][19][23] GRU Model with Financial Information - **Annualized Excess Return**: 7.76% - **IR**: 1.65 - **Maximum Drawdown**: 5.40%[41][44] GRU Model with Simplified Financial Information - **Annualized Excess Return**: 9.97% - **IR**: 1.93 - **Maximum Drawdown**: 5.70%[51][52] Mixed Frequency Model (barra5d + daily GRU) - **Annualized Excess Return**: 11.82% - **IR**: 2.39 - **Maximum Drawdown**: 5.70%[77][78]
金工专题报告:结合基本面和量价特征的GRU模型
China Post Securities· 2025-06-05 06:23
Quantitative Models and Construction GRU Model - **Model Name**: GRU baseline model [2][3][14] - **Model Construction Idea**: The GRU model is designed to extract information from historical price and volume data to predict future returns. It serves as a baseline to evaluate the impact of adding financial data [14][15]. - **Model Construction Process**: - **Data Range**: All A-share stocks (excluding Beijing Stock Exchange) from 2013-01-01 to 2025-04-30 [16]. - **Input Features**: Past 240 trading days' price and volume data, including open price, high price, low price, close price, trading volume, turnover, and turnover rate. Each feature is standardized using z-score [16]. - **Prediction Target**: Next month's standardized return (from the opening price at the beginning of the month to the closing price at the end of the month) [16]. - **Training**: Rolling training with a 4:1 split between training and validation sets over the past six years. Early stopping is applied if the loss function does not decrease for 10 consecutive iterations [16]. - **Portfolio Construction**: Enhanced portfolio based on the CSI 1000 index, with constraints on stock weight deviation (1%), style deviation (within 0.1 standard deviation), and industry deviation (1%). Monthly rebalancing with a turnover rate of 50% per side [18]. - **Model Evaluation**: The GRU model demonstrates stable performance in extracting price-volume information, achieving consistent excess returns across years [19]. GRU Model with Financial Data - **Model Name**: GRU with financial data [4][24][25] - **Model Construction Idea**: Incorporates financial data into the GRU model to enhance its ability to predict future returns by combining price-volume and fundamental information [14][24]. - **Model Construction Process**: - **Financial Data**: Includes 20 fields from income statements, such as revenue, cost of goods sold, management expenses, R&D costs, and net profit. Data is converted to TTM (trailing twelve months) values [24][25]. - **Integration**: Financial data is appended to the price-volume matrix, standardized, and input into the GRU model [25]. - **Adjustment**: To address frequency mismatches, financial data is adjusted daily based on the assumption of stable TTM growth rates. The adjustment formula is: $$ \text{Factor}_{t} = \text{Factor}_{\text{TTM}_{q}} + \text{abs}(\text{Factor}_{\text{TTM}_{q}}) \cdot \frac{90}{\text{days in quarter}} $$ where \( t \) is the trading day and \( q \) is the financial reporting quarter [36][38]. - **Model Evaluation**: Adding financial data improves performance before 2023 but weakens it afterward. Adjusting financial data enhances overall performance, especially in earlier years [42][45]. Mixed-Frequency GRU Model - **Model Name**: Mixed-frequency GRU model (barra5d + daily GRU) [5][56][65] - **Model Construction Idea**: Combines long-term and short-term prediction capabilities by integrating daily and intraday GRU models [56][65]. - **Model Construction Process**: - **Daily GRU**: Trained on 240 trading days of daily data to predict monthly returns [16]. - **Intraday GRU (barra5d)**: Trained on 240 minutes of intraday data to predict 5-day returns, neutralized for Barra style factors [56]. - **Integration**: The two models are combined to leverage their complementary strengths [65]. - **Model Evaluation**: The mixed-frequency model significantly improves stability and excess returns, addressing weaknesses in individual models [67][68]. Mixed-Frequency GRU with Financial Data - **Model Name**: Mixed-frequency GRU with financial data (barra5d + daily GRU + financial data) [5][73][74] - **Model Construction Idea**: Enhances the mixed-frequency model by incorporating selected financial data to improve stability and performance across years [73][74]. - **Model Construction Process**: - **Financial Data Selection**: Only key financial indicators, such as net profit TTM and market capitalization, are retained to avoid redundancy [45]. - **Integration**: Financial data is appended to the mixed-frequency model, following the same adjustment process as the GRU with financial data model [36][38]. - **Model Evaluation**: The addition of financial data further stabilizes annual excess returns and improves overall performance metrics [77][80]. --- Model Backtesting Results GRU Baseline Model - **Excess Annualized Return**: 8.75% [19][23] - **IR**: 2.25 [19][23] - **Maximum Drawdown**: 4.71% [19][23] GRU with Financial Data - **Excess Annualized Return**: 6.86% [32][33] - **IR**: 1.46 [32][34] - **Maximum Drawdown**: 6.14% [32][34] GRU with Adjusted Financial Data - **Excess Annualized Return**: 7.76% [41][44] - **IR**: 1.65 [41][44] - **Maximum Drawdown**: 5.40% [41][44] GRU with Selected Financial Data - **Excess Annualized Return**: 9.97% [51][52] - **IR**: 1.93 [51][52] - **Maximum Drawdown**: 5.70% [51][52] Mixed-Frequency GRU Model - **Excess Annualized Return**: 11.32% [68][69] - **IR**: 2.42 [68][69] - **Maximum Drawdown**: 8.19% [68][69] Mixed-Frequency GRU with Financial Data - **Excess Annualized Return**: 11.82% [77][78] - **IR**: 2.39 [77][78] - **Maximum Drawdown**: 5.70% [77][78]
【UNFX课堂】如何获取外汇交易基本面数据
Sou Hu Cai Jing· 2025-05-28 07:03
Group 1: Core Views - The acquisition of fundamental forex trading data is crucial for trading decisions, primarily through economic calendars and news websites that track economic indicators, policy changes, and geopolitical events [1] Group 2: Economic Calendar Tools - Economic calendars are essential for forex traders to obtain macroeconomic data release times and expected values, with several commonly used platforms available [2] - "Forex Bang" economic calendar synchronizes with data, covering key metrics like non-farm payrolls and central bank interest rate decisions, suitable for domestic investors due to its Chinese interface [3] - "Forex Factory" offers rapid data updates and strong community interaction, although it is only available in English, making it suitable for English-speaking traders [4] - "Invezz" provides real-time quotes for 200,000 financial products and a comprehensive economic calendar, including Fed rate hike predictions and earnings calendars, supporting multiple languages and free access [4] - "Securities Star" organizes economic data and political events by date, facilitating quick browsing of important indicators for the day [5] - "Forex Tianyan" integrates global economic indicators, bond auctions, and central bank officials' speeches, with clear data categorization, but is positioned as a third-party information query service [6] Group 3: News and Data Websites - "Jin Shi Data" offers instant financial news, data alerts, and event interpretations, excelling in rapid reporting of unexpected events and supporting multi-terminal access [7] - Reuters and Bloomberg are international authoritative media covering global macroeconomic data, central bank policies, and geopolitical analysis, suitable for in-depth reporting and professional commentary [8] - The Wall Street Journal focuses on U.S. economic policies and market dynamics, providing exclusive interpretations of Fed decisions and trade policies [9] - "Invezz" also provides real-time news, analyst opinions, and market sentiment indicators, suitable for comprehensive assessments [10] - "Finance Network" specializes in forex market news and analysis, covering central bank officials' speeches, policy expectations, and strategies combining technical and fundamental analysis [11] Group 4: Usage Tips and Considerations - Prioritize high-impact data such as interest rate decisions, non-farm employment, GDP, and CPI, as these have the most significant effect on exchange rates, with Fed rate decisions typically causing sharp fluctuations in the dollar index [13] - Compare expected values with actual values, as discrepancies often drive short-term market movements, for instance, if U.S. non-farm employment data significantly exceeds expectations, the dollar may strengthen rapidly [14] - Analyze central bank policy trends in conjunction with inflation and employment data for comprehensive judgment, exemplified by the Fed pausing rate cuts in 2025 due to tariff-induced inflation, leading to increased dollar volatility [15] - Utilize tools to assist decision-making, such as combining technical analysis to predict market reactions before data releases and employing hedging tools like forex options or futures to lock in exchange rate risks, especially before major events like elections [16] Group 5: Summary - The acquisition of forex fundamental data relies on professional economic calendars and real-time news platforms, with a focus on high-impact events and data discrepancies. Investors are advised to combine technical analysis and risk management strategies to enhance the accuracy of trading decisions [17]
《特殊商品》日报-20250526
Guang Fa Qi Huo· 2025-05-26 03:48
| 业期现日报 | | | | | | | --- | --- | --- | --- | --- | --- | | [2011 ] 1292号 2025年5月26日 | | | | 纪元菲 | Z0013180 | | 现货价格及主力合约基差 | | | | | | | 品种 | 5月23日 | 5月22日 | 涨跌 | 涨跌幅 | 单位 | | 华东通氧SI5530工业硅 | 8650 | 8650 | 0 | 0.00% | | | 基差(通氧SI5530基准) | 735 | 770 | -35 | -4.55% | | | 华东SI4210工业硅 | a500 | 9500 | 0 | 0.00% | 元/吨 | | 基差(SI4210基准) | 785 | 820 | -35 | -4.27% | | | 新疆99硅 | 8050 | 8050 | 0 | 0.00% | | | 墓差(新疆) | රි35 | 970 | -35 | -3.61% | | | 月间价差 | | | | | | | 合约 | 5月23日 | 5月22日 | 涨跌 | 涨跌幅 | 单位 | | 2506-2507 ...
一图看懂华安合鑫:十年专注能力圈,以安全边际创长期价值
私募排排网· 2025-05-26 02:32
华安合鑫简介 深圳市华安合鑫私募证券基金管理有限公司 成立于2015年(协会备案编码:P1062311),中国证券投资基金业协会观察会员,专注于二级市 场股票投资。公司的企业愿景是: "力求为投资者提供长期可持续的合理回报,成为受人尊敬的资产管理公司。" 公司核心投研成员具有清华大学、北京大学等国内一流名校背景,且拥有国内大型公募基金、私募基金公开产品管理经验。其中,公司创始人 袁巍拥有3年IT行业、16年基金行业投研工作经验。他们研究功底深厚,坚持基本面分析,致力于追求长期绝对收益。同时,公司拥有专业、全 面的营运团队,核心运营人员有国内大型券商运营经验。他们以严谨、细致的工作作风,制定了严格的后台运作管理制度和严密的风险管理体 系,并利用先进可靠的信息技术系统,保证了各项投资、研究活动的高效运行。 公司信奉 "专注企业核心价值" 的投资哲学,坚持 "以安全边际为前提,以基本面分析为工具,坚守能力圈,适度逆向投资" 的投资理念,努力 在不确定性的投资游戏里,获得相对确定的回报。凭借优异的历史业绩,公司已屡获"金牛奖"、"金阳光奖"、"金长江奖"、"英华奖"等多个权威 奖项。 (点此查看 华安合鑫旗下产品收益 ...
黄金,晚间会突破阻力迎来大涨吗?
Sou Hu Cai Jing· 2025-05-19 13:09
横批:止损无条件! 止损,永远是对的,错了也对! 死扛,永远是错的,对了也错! 如果没有交易原则,那么,一切技术等于零! 周末我们全面分析了基本面和技术面,整体倾向于看涨黄金,行情分析继续参考:黄金,重磅突发!美国评级遭下调,黄金要暴涨吗? 今天黄金小幅高开于3210一线。早盘拉高3249附近并迎来回落,亚盘我们提示看好回落再涨,最终行情下跌3207一线再度走强,行情完全在我们预期之内! 操作上,上周五3155-52多单持有中,早盘3248-50空3220及下方出局了;晚间突破3250-55前不追涨可短空,下来依托支撑低多为主,突破3250-55顺势跟 多,目标3270--93先看,波段3315--3330上方,目标位也是阻力位,注意冲高回落! 美股期货,符合预期,目前在向历史新高靠近,以防获利回吐,上周五已经做空,美国评级遭下调利空美股。所以,持有上周空单,接下来继续高空为主! 标普阻力5950区域,然后6020区域和新高区域! 美原油,55双底大涨63以上后,短线如期回落,接下来维持观点不变,关注二次上涨!支撑60关口,然后58及55区域!上方阻力依然65区域,突破则打开上 涨空间! 上述个人观点,仅供参 ...
新能源及有色金属日报:基本面偏弱,工业硅盘面偏弱震荡-20250514
Hua Tai Qi Huo· 2025-05-14 03:34
Report Industry Investment Rating No relevant content provided. Core Viewpoints - The overall fundamentals of the industrial silicon industry are weak. Although there has been some production reduction on the supply side, the approaching wet season in the southwest region is expected to increase supply. The falling prices of silicon coal and electricity during the wet season have weakened cost support. On the consumption side, performance is weak, with the possibility of further production cuts [2]. - The futures market for polysilicon has been volatile recently. Downstream production scheduling has decreased month-on-month. News of joint production cuts by silicon material factories has had a significant impact on the market. Attention should be paid to changes in the number of warehouse receipts and the impact of position reduction on the market [6]. Market Analysis Industrial Silicon - On May 13, 2025, the industrial silicon futures price fluctuated weakly. The main contract 2506 opened at 8,320 yuan/ton and closed at 8,230 yuan/ton, a change of -50 yuan/ton (-0.60%) from the previous settlement. As of the close, the main contract 2505 had a position of 162,299 lots, and on May 14, 2025, the total number of warehouse receipts was 66,494 lots, a change of -603 lots from the previous day [1]. - Industrial silicon spot prices remained stable. According to SMM data, the price of oxygenated 553 silicon in East China was 9,000 - 9,200 yuan/ton; 421 silicon was 9,700 - 10,300 yuan/ton; the price of oxygenated 553 silicon in Xinjiang was 8,200 - 8,400 yuan/ton; and 99 silicon was 8,200 - 8,400 yuan/ton. In recent days, downstream alloy users have placed orders, and some traders reported improved trading volumes compared to last week. Sellers' quotes remained stable, but downstream users still had a tendency to bargain [1]. - According to SMM statistics, the quoted price of organic silicon DMC was 11,300 - 11,600 yuan/ton. Domestic organic silicon DMC enterprises maintained stable quotes, with local transaction prices slightly decreasing. The overall transaction range was 11,300 - 11,600 yuan/ton, but market transaction expectations were not strong. Downstream enterprises mainly replenished inventory as needed. It is expected that after May 20, downstream enterprises' raw material inventories will be depleted, which may drive market trading volumes [1]. Polysilicon - On May 13, 2025, the main polysilicon futures contract 2507 rose significantly and then declined. It opened at 38,230 yuan/ton and closed at 38,270 yuan/ton, a 0.91% change from the previous trading day. The main contract had a position of 52,252 lots (69,417 lots the previous day) and a trading volume of 321,982 lots [4]. - Polysilicon spot prices remained stable. According to SMM statistics, the quoted price of polysilicon reclaimed material was 35.00 - 36.00 yuan/kg; dense polysilicon was 34.00 - 35.00 yuan/kg; cauliflower polysilicon was 31.00 - 32.00 yuan/kg; granular silicon was 33.00 - 34.00 yuan/kg; N-type material was 37.00 - 44.00 yuan/kg; and N-type granular silicon was 35.00 - 36.00 yuan/kg. Polysilicon manufacturers' inventories decreased, as did silicon wafer inventories. The latest statistics showed polysilicon inventory at 25.70 (a month-on-month change of -1.90%), silicon wafer inventory at 18.13GW (a month-on-month change of -12.08%), weekly polysilicon production at 21,400.00 tons (a month-on-month change of -4.46%), and silicon wafer production at 12.35GW (a month-on-month change of -7.07%) [4][5]. - For silicon wafers, the price of domestic N-type 18Xmm silicon wafers was 0.98 yuan/piece, N-type 210mm was 1.30 yuan/piece, and N-type 210R silicon wafers were 1.10 yuan/piece. For battery cells, the price of high-efficiency PERC182 battery cells was 0.29 yuan/W; PERC210 battery cells were about 0.28 yuan/W; Topcon M10 battery cells were about 0.27 yuan/W; Topcon G12 battery cells were 0.28 yuan/W; Topcon 210RN battery cells were 0.27 yuan/W; and HJT210 half-cell batteries were 0.37 yuan/W. For components, the mainstream transaction price of PERC182mm was 0.67 - 0.74 yuan/W, PERC210mm was 0.69 - 0.73 yuan/W, N-type 182mm was 0.69 - 0.70 yuan/W, and N-type 210mm was 0.69 - 0.70 yuan/W [5]. Strategies Industrial Silicon - Unilateral: Mainly conduct range operations. Upstream enterprises should sell on rallies for hedging [3]. - Inter - delivery spread: None [3]. - Cross - variety: None [3]. - Spot - futures: None [3]. - Options: None [3]. Polysilicon - Unilateral: Be cautiously bullish on the 2506 contract [7]. - Inter - delivery spread: None [7]. - Cross - variety: None [7]. - Spot - futures: None [7]. - Options: None [7].