Workflow
量化交易
icon
Search documents
堆量骑点公式调妥,但担心的事还是发生了
猛兽派选股· 2025-08-25 16:01
经过一部分微调之后,6.23日起始点选出股票从43只变为35只,胜率提高到68%,从公式本身来说,完全按照既定思路描述,看上去貌似不错。 接着按照常规回测操作,对以前的行情进行复刻一次。分成七组: 第二问题,需要好好想想。可能是一种特定情形下的偶然,也可能是交易方式发生变化之后的一种现象级涌现,或者是因为短期资金密度太高,同时交易 宽度不足引起的拥挤。 如果仅仅是因为主升浪导致的话,那么以前牛市行情的主升浪也应该出现这种现象,为此我特别圈出了往届主升浪的时间段,并进行筛选,晒出的结果也 在上面数据列表,只能说几乎没有太大的不同。 堆量骑牛的模型,说到底无非就是短期OVS起爆之后,持续高换手推升,OBV表现为持续稳定爬升,然后短暂窄幅横盘,博弈第二波。以前之所以成功 率不高,是因为只要一横盘,多数就滞涨旋即就结束了,就像这样: 第一组:2012.12.06~2015.06.15一轮完整上涨行情; 第二组:2018.10.22~2021.12.01一轮完整上涨行情; 第三组:2021.12.01~2024.02.05一轮完整下跌行情; 第四组:2024.09.24~205.06.23本轮行情到主升之前; 第五组: ...
AI炒股到底靠不靠谱
Group 1 - The core viewpoint of the articles highlights the significant impact of AI on stock trading, with a notable increase in retail investors entering the market, driven by AI tools that promise high returns [1][2] - AI trading, as an extension of quantitative trading, utilizes machine learning and natural language processing to analyze market data and make trading decisions, operating continuously to optimize strategies [2][3] - Major investment institutions have already integrated AI into their decision-making processes, with a consensus emerging on the importance of occupying the AI space in quantitative investment [2][3] Group 2 - Many brokerage platforms are adopting AI functionalities, making AI tools accessible to a wide range of retail investors, indicating a growing penetration of AI in investment practices [3] - Despite the advantages of AI, experts caution that the stock market's complexity and unpredictability mean that human oversight remains essential, and AI should be used as a supplementary tool rather than a standalone solution [3][4] - Legal uncertainties surrounding the use of AI in investment, including issues of compliance and responsibility, remain unresolved, highlighting the need for clarity in the regulatory framework [4] Group 3 - The future of AI in trading is seen as promising, with expectations for further evolution and integration of diverse data types, including social media sentiment analysis [4] - AI trading is not a guaranteed success or a scam; it requires users to have market knowledge and the ability to effectively utilize the tools available [4]
AI炒股到底靠不靠谱
21世纪经济报道· 2025-08-25 05:10
记者丨章驰 编辑丨王俊 8月25日早盘,沪指 突破3858点, 续创10年新高。 新股民跑步进场,老股民翻身解套就在眼 前。 和10年前不同,这一次的市场里,有了AI的科技与狠活! 在各大平台搜索"DeepSeek炒股",不少博主晒出高收益、高回报数据,号称自己利用AI炒股 赚得盆满钵满。很多散户用了AI也发现,相比传统投资顾问,AI确实有优势。因为在进行走 势判断时,投资者会关注四个方面:技术面、基本面、新闻事件、市场情绪。AI就能够7×24 小时不间断工作,给用户提供公司数据、行业报告、新闻资讯等。 其实AI炒股就是用AI做量化,是量化交易的智能化延伸 ,用机器学习盯盘,扫描K线图找规 律,设置买点卖点,用自然语言处理(NLP)24小时爬财报和新闻热搜,一旦发现"暴雷"这 类关键词就秒清仓。这套机制通过强化学习模拟交易环境,不断优化策略,使算法能在短时 间内做出最优决策。 目前,多数券商平台也纷纷接入DeepSeek部署,加码AI功能。股民们用的软件基本都有一个 AI入口,所以AI其实已经渗透到了广大股民的投资过程中。 不过专业人士们都知道,股市是一个复杂且不确定的系统,并不具备规律性,单纯靠AI来战 胜 ...
A股火爆 开户升温
Market Overview - The A-share market is experiencing a significant upward trend, with the Shanghai Composite Index closing at 3771.1 points, up 0.13% [1] - Recent market activity has led to a notable increase in stock account openings, with some brokerage firms reporting a month-on-month growth of over 300% to 400% [2][3] - However, the overall account opening volume remains significantly lower than the levels seen during the "9.24" market surge last year [2][4] Account Opening Trends - The current increase in stock account openings is primarily a month-on-month comparison, and the total volume is still below the peak levels of early this year [2][4] - Margin trading account openings are also on the rise, but the numbers are only about half of what was seen during the "9.24" market [3][4] Investor Behavior - Investors are showing a preference for ETFs and index products to mitigate the challenges of stock selection, especially in a market characterized by mixed individual stock performances [1][14] - The increase in the number of listed companies has made stock selection more difficult, leading to a rise in the popularity of ETFs among retail investors [13][14] Brokerage Strategies - Leading brokerages are shifting their focus from acquiring new accounts to activating dormant clients, recognizing the potential revenue from re-engaging existing customers [6][7] - Strategies to attract high-net-worth clients include offering algorithmic trading and customized investment advisory services, which are becoming standard offerings among top brokerages [9][10] Investment Opportunities - Analysts suggest focusing on four key investment directions: high-margin assets with low valuations, technology growth sectors, consumer sectors boosted by policy support, and companies with long-term competitive advantages [16][17][18] - The technology sector, particularly in areas like artificial intelligence and biotechnology, is highlighted as having significant investment potential [16]
量化工具“下沉” 二十余家券商已推出T0算法服务
Zheng Quan Ri Bao· 2025-08-15 16:54
Core Viewpoint - The introduction of T0 algorithm services by multiple brokerage firms reflects a shift in quantitative trading from institutional exclusivity to retail market accessibility, potentially becoming a standard tool for brokers to attract clients [1][2] Group 1: Industry Trends - Numerous brokerage firms have launched T0 algorithm services this year, integrating AI technology and quantitative trading capabilities into their apps, which enhances financial technology accessibility for individual investors and creates new revenue growth opportunities for the firms [1] - The number of brokerage firms supporting T0 algorithm services has been increasing, with over twenty firms, including Guangfa Securities and Dongfang Securities, now offering such services [1] - The T0 algorithm is an automated trading tool based on AI quantitative models, designed to analyze market data in real-time and execute high-frequency trades to capture intraday price fluctuations while maintaining the investor's end-of-day net position [1] Group 2: Market Dynamics - The rise of T0 algorithm services is a response to the declining commission rates in the brokerage industry and the need for firms to explore new revenue streams amid a favorable A-share market environment [2] - The introduction of T0 services is expected to enhance trading volume and commission income, increase customer loyalty, and promote the development of related services such as intelligent investment advisory [2] - The advancement of financial technology has reduced the technical costs for brokerage firms, facilitating the promotion of specialized trading tools [2] Group 3: Investor Considerations - T0 algorithm services require a certain level of expertise from investors, making them suitable for those with trading experience and a basic understanding of technical analysis or quantitative models [3] - Investors must assess the compatibility of the T0 tool with their investment philosophy and strategy, and be aware that using automated trading tools does not guarantee profits [3] - The T0 algorithm service is not recommended for novice investors or those with low-risk tolerance, as it is more suited for medium to long-term investors [3][4] Group 4: Access Requirements - Most brokerage firms have set entry thresholds for T0 algorithm services, such as requiring a minimum average asset of 500,000 yuan in the investor's account over the first ten trading days [4] - The T0 trading tool is designed for clients classified as C4 (active) or higher [4]
DeepSeek母公司亿元腐败案背后
虎嗅APP· 2025-08-13 13:35
Core Viewpoint - The article discusses a corruption case involving DeepSeek's parent company, Huanfang Quantitative, highlighting the alleged misconduct of former market director Li Cheng and his connections with China Merchants Securities, which has raised concerns about the company's internal controls and its relationship with the AI subsidiary DeepSeek [4][19]. Group 1: Corruption Case Details - Li Cheng, the former market director of Huanfang Quantitative, is accused of colluding with a China Merchants Securities employee to siphon off 118 million yuan in trading commissions from 2018 to 2023 [4][7]. - The investigation revealed that Li Cheng and Meng Pengfei, a former manager at China Merchants Securities, arranged for Meng's relatives to act as "exclusive brokers" to facilitate the illicit commission extraction [7][8]. - Following the exposure of the case, several individuals involved, including Li Cheng and Meng Pengfei, have been handed over to judicial authorities for further action [8]. Group 2: Huanfang Quantitative's Background - Huanfang Quantitative was founded in 2015 by Liang Wenfeng and has since grown to manage over 60 billion yuan in assets, becoming one of China's top quantitative private equity firms [10][17]. - The firm has achieved a cumulative return of 124% from 2017 to 2022, significantly outperforming traditional investment strategies [17]. - Liang Wenfeng's vision for Huanfang was to leverage mathematics and AI for quantitative trading, marking a shift from traditional investment methods [14][15]. Group 3: Impact on DeepSeek - Despite the corruption scandal, DeepSeek, the AI subsidiary of Huanfang, appears to be insulated from the fallout due to its operational independence and focus on AI development rather than quantitative trading [19][20]. - However, DeepSeek faces challenges, including a significant drop in monthly downloads by 72.2% in Q2 2025, raising concerns about its market position and competition [21]. - The ongoing scrutiny of Huanfang's internal controls may affect investor confidence and regulatory oversight in the industry, potentially impacting DeepSeek's future operations [19].
OpenAI重走“幻方”路,硅谷与华尔街战争一触即发
Tai Mei Ti A P P· 2025-08-13 00:48
Core Insights - The article discusses the increasing competition between AI companies and traditional financial firms, particularly in the recruitment of talent from quantitative finance backgrounds [1][2][3][4][5] Group 1: AI Companies' Recruitment Strategies - AI companies like Anthropic are actively recruiting quantitative researchers, indicating a shift in focus towards Wall Street talent [1][2] - OpenAI and Perplexity AI have also engaged in similar recruitment efforts, highlighting a trend among leading AI firms to attract talent from the finance sector [2] - The financial incentives in AI, including higher salaries and equity compensation, are drawing talent away from traditional finance roles [2][3] Group 2: Talent Competition Dynamics - The competition for quantitative talent has intensified, with AI companies increasing their hiring by 12-18% over the past 12-18 months [3] - Entry-level quantitative professionals on Wall Street can earn up to $300,000 in base salary, excluding bonuses, while AI firms offer comparable salaries supported by equity [3] - Notable quantitative firms like Jane Street are losing their appeal to top talent, who are more excited about contributing to groundbreaking AI projects [3][4] Group 3: Skills Overlap and Industry Trends - The skills required in quantitative trading, such as analyzing large datasets and reducing algorithmic latency, are highly relevant to AI development [4] - Anthropic emphasizes the importance of rigorous analytical thinking and empirical research methods, which align with the challenges of developing advanced AI systems [4][5] - The ongoing recruitment of finance professionals by AI companies suggests a potential future where these firms may expand into financial services products [5]
DeepSeek母公司亿元腐败案背后:当事人曾是招商证券员工
凤凰网财经· 2025-08-12 14:47
Core Viewpoint - The corruption case involving Huanfang Quantitative and its market director Li Cheng has raised significant concerns about the company's internal controls and its relationship with the AI subsidiary DeepSeek, which has been performing well in the AI sector [1][13][15]. Group 1: Corruption Case Details - Li Cheng, the former market director of Huanfang Quantitative, is accused of colluding with a staff member from China Merchants Securities to embezzle 118 million yuan in trading commissions from 2018 to 2023 [1][5]. - The investigation revealed that Li Cheng and Meng Pengfei, a former manager at China Merchants Securities, arranged for Meng's relatives to act as "exclusive brokers" for Huanfang, allowing them to funnel commissions into personal accounts [5][6]. - Following the exposure of the case, several individuals involved, including Li Cheng and Meng Pengfei, have been handed over to judicial authorities for further action [6]. Group 2: Company Background and Growth - Huanfang Quantitative was founded in 2015 by Liang Wenfeng and has since grown to manage over 60 billion yuan in assets, becoming one of China's top four quantitative private equity firms [7][11]. - The company has achieved a cumulative return of 124% from 2017 to 2022, significantly outperforming traditional investment benchmarks [11][12]. - Liang Wenfeng's vision for Huanfang was to leverage mathematics and AI for quantitative trading, marking a shift from traditional investment strategies [9][11]. Group 3: Impact on DeepSeek - Despite the corruption scandal, DeepSeek, Huanfang's AI subsidiary, appears to be insulated from the fallout due to its operational independence and focus on AI development rather than quantitative trading [13][15]. - However, DeepSeek has faced challenges, including a significant drop in monthly downloads, indicating a potential decline in market interest [15].
活动回顾 | 探索量化交易新机遇,AI驱动金融创新
Refinitiv路孚特· 2025-08-11 08:54
Core Insights - The event hosted by LSEG focused on exploring new opportunities in quantitative trading within the foreign exchange, fixed income, and precious metals sectors, emphasizing the integration of AI technologies [1][8] - LSEG views the Chinese market as a core component of its global strategy, significantly increasing its investment in local innovation, particularly in quantitative trading [3] - The financial quantitative innovation competition showcased China's leading practices in AI and quantitative strategy integration, highlighting deep collaboration between LSEG and both domestic and foreign financial institutions [3][8] Group 1 - LSEG's Asia-Pacific Front Office Solutions Director, Arman Sahovic, emphasized the importance of data and solutions in empowering strategy development and risk analysis [1] - The competition attracted 14 major domestic and foreign banks, resulting in over 30 submissions across various asset classes, generating significant interest in the financial industry [8] - The event featured a roundtable discussion where experts shared experiences in strategy design, data application, and AI technology practices, focusing on AI-driven changes in quantitative trading [11] Group 2 - Professor Zhou Hongsong discussed the fundamental principles and development trends of quantitative investment and trading, highlighting the integration of machine learning and deep learning in quantitative processes [5] - LSEG's global client technology director introduced the competition's design, focusing on price prediction as a core capability, and emphasized the support provided by LSEG's data and tools [13] - Future collaboration suggestions included establishing a "data application experimental environment" to enhance interaction and growth between data providers and financial institutions [14]
量化私募佣金调查:“万1.2”成行业基准
Mei Ri Jing Ji Xin Wen· 2025-08-10 12:42
近期,量化私募行业热度再起,交易佣金费率与返佣问题再度成为焦点。 《每日经济新闻》记者调查发现,量化私募与券商的佣金议价空间较大,普遍费率在万1至万1.5之间, 但不同机构因规模、策略及业务关系差异显著。与此同时,佣金返还作为监管明令禁止的行为,其合规 风险持续受到关注。 交易佣金包含多项支出 券商向量化私募基金收取的交易佣金费率,究竟有多高? "在开户阶段,由于量化机构通常具备较高的资金规模和交易频率,具备一定议价能力,往往能获得更 低的基础交易费率,常见在万1至万2之间。同时,对于需要借助算法拆单等服务的股票策略,费用也会 通过券商端接入第三方系统支付,并一并计入交易成本。"某顶级量化私募人士表示。 记者从某百亿私募基金人士处了解到,目前,由于量化交易频率高,佣金费率普遍在万1到万1.2之间, 且免收最低5元手续费。中小量化私募基金公司虽然管理规模小,但其交易频率高,交易量并不小。 上海某券商相关人士进一步剖析道:"一般量化私募基金把交易放到券商,交易佣金费率基本上是万1.5 起谈,万1.2比较常见,议价能力再强一点,万1也是有的。若费率超过万1.8,往往是因为券商需额外承 担产品销售对价,也就是券商还要 ...