AI is completely reshaping the stock market ecosystem, with quantitative trading accounting for 60% -70%. Retail investors can only survive in the algorithm dominated market by abandoning short-term games and turning to long-term value investing, utilizing “quantitative blind spots” and tool assistance. The future belongs to “human-machine collaboration” investors who can integrate human insights and AI efficiency. At present, AI is rapidly developing at a speed beyond people’s imagination and deeply intervening in the stock market, and this trend is irreversible. The trading ecosystem of A-shares has undergone fundamental changes, with the penetration rate of AI in the stock market increasing. Quantitative trading, algorithmic strategies, and high-frequency games have profoundly changed the microstructure of the market. AI and quantification are reshaping the market 1. The proportion of quantitative trading is constantly increasing, but it has not monopolized Quantitative trading (including high-frequency, statistical arbitrage, passive indexes, etc.) accounts for about 60-70% of the US stock market, while programmatic trading accounts for about 20-30% of the A-share market (including institutional quantification). There are still a large amount of funds (social security, foreign investment, speculative capital, retail investors) trading based on non quantitative logic such as fundamentals, emotions, and policies. The behavior patterns of different funds will influence each other, and the market is not a closed game between pure quantification. According to the current trend, the proportion of quantitative trading in A-shares will continue to increase, eventually approaching that of the US stock market, reaching 60-70%. 2. Quantitative strategies have crowding and failure cycles When a large number of quantitative strategies converge (such as momentum or mean regression), it can trigger stampede under specific conditions (such as the 2020 US stock quantitative crash and the liquidity crisis of A-share micro cap stocks). AI models can also overfit and encounter style switching. Therefore, there is also a “mutual hunting” and strategy iteration between quantifications, which is not a stable convergence equilibrium. 3. The regulatory environment is constantly adjusting Regulatory authorities in various countries have continuous constraints on programmatic trading, securities lending, high-frequency reporting and cancellation of orders (such as the quantitative trading reporting system and differentiated fees for A-shares). The existence of policy variables makes it impossible for pure quantitative games to completely override rule changes. Quantitative trading has changed the characteristics of market transactions The new characteristics of the current market can be summarized as: algorithm dominance, ecological upheaval 1. Extremely fast rotation: Plate rotation compresses from weekly to intraday, with a pulse like surge followed by a rapid decline. 2. Hotspot short-lived: Consistent expectations often suffer from reverse harvesting, with board height decreasing from 7-8 boards to 2-3 boards. 3. Intensifying volatility: Quantifying the concentration of small and micro market inflows and outflows, amplifying the sharp rise and fall, and creating “bait fluctuations”. 4. Retail investors are losing money, transforming or exiting, and their short-term game winning rate continues to decline. 4、 Faced with changes in the stock market ecosystem, individual investors should keep up with the times and change their investment methods and trading strategies Change investment methods 1. Long term investment Completely abandon high-frequency short-term games and turn to long-term value investments centered on in-depth industry research. By avoiding emotional trading and hot topic chasing, funds are allocated to hard technology tracks or high dividend assets with clear industry trends, thereby building defensive barriers in algorithm dominated markets and achieving stable asset appreciation. 2. Shift from individual stock game to index investment By using tools such as fixed investment broad-based ETFs, we eliminate the disadvantages of stock selection and short trading frequency, transfer short-term volatility returns in exchange for average market returns, and achieve long-term wealth accumulation through rational allocation in the liquidity cycle of quantitative fund competition. 3. Using data models to optimize stock selection and risk control Faced with the changing times, retail investors need to seize the opportunity, actively embrace technological revolution, and deeply integrate subjective investment logic with quantitative tools. By utilizing data models to optimize the scope of stock selection and risk control system, a differentiated strategy of “subjective judgment+quantitative execution” is adopted to compensate for the disadvantage of computing power, improve decision-making efficiency in complex market environments, and achieve the transformation from traditional traders to composite investors. 4. Indirect investment through public funds, private equity products, or professional wealth management channels Retail investors can choose to gradually withdraw from direct trading and turn to indirect investment through public funds, private equity products, or professional wealth management channels. Relying on the professional investment research capabilities and systematic risk control system of institutions, individual decision-making risks are dispersed, and in the quantitatively reshaped market pattern, wealth preservation and growth are achieved through the logic of asset allocation and professional division of labor. Change trading strategy Faced with aggressive high-frequency quantitative trading, retail investors need to reposition their role in the market ecosystem. 1. Abandoning and quantifying competition in “high-frequency, short-term, and hot topics” Not chasing the limit up board, not frequently trading within the day, and not participating in theme pulses without fundamental support. Avoid impulsive trading 30 minutes before opening and 15 minutes before closing (quantifying active periods). 2. Turning to areas where quantification is not proficient High quality companies with low attention, low popularity, and moderate market value: Quantify and prefer targets with high liquidity, high volatility, and high correlation. Companies with stable cash flow but no one interested have low quantitative participation. Reverse layout in event driven: When a group of quantitative risk control rules collectively sell a certain sector (such as liquidity shocks or factor failures), it is actually an opportunity for individual investors to intervene in batches. Long term holding (3-5 years): Most quantitative models have holding periods ranging from a few seconds to several weeks, and are rarely priced on an annual basis. Utilize industry cycles and company growth to avoid short-term statistical arbitrage interference. 3. Profit from Quantitative ‘Predictable Behavior’ When a certain type of factor (such as low volatility, low turnover) fails periodically and is quantitatively sold off, it is actually a source of excess returns. Pay attention to quantitative congestion indicators (such as abnormal securities lending balance and concentrated selling of quantitative seats on the Dragon and Tiger List) as short-term risk signals. 4. Build your own trading discipline, partially ‘quantified’ Set clear buy trigger conditions (such as PE<historical 30% percentile+dividend yield>4%), sell conditions (such as gains exceeding 50% or deteriorating fundamentals), and strictly enforce them. Avoid emotional decision-making: Do not place an order using ‘I feel like it’s going up’, instead use ‘If X occurs, execute Y’. 5. Using tools instead of adversarial tools Using AI to assist rather than replace judgment: such as backtesting simple strategies with Wind, iFind, or free tools (such as TradingView, Li Almond), and quickly sorting out financial report controversies with large models. Participating in index based investment: For most retail investors with limited time, they can directly allocate low rate index funds (CSI 300/CSI 500/S&P 500), which essentially utilizes the liquidity provided by quantification in index constituent stocks, and the fees are low and worry free. 5、 Future prospects 1. What would happen to the market if all funds were used in quantitative games? If almost all the funds in the market are using quantitative trading, there will be a state of ‘everyone is looking in the rearview mirror and driving at the same time’, and strategy homogenization will lead to a rare but severe collapse (liquidity halt). At that time, non quantitative funds (such as value investors, industrial capital, and contrarian retail investors) will instead become market stabilizers and earn excess returns. It can be seen that a completely and thoroughly quantified game is an unstable extreme, and there will always be space for multiple participants in reality. With the deep involvement of AI in the stock market, the market will present a new normal of human-machine collaboration. The future market will indeed simplify into a game between quantification and quantification, but this does not mean a complete withdrawal of human investors. 2. Investor Transformation The tedious tasks of data cleaning and basic organization are being replaced by AI, and the value of human investors is increasing. The core competitiveness of the future lies in defining problem frameworks, calibrating AI results, assuming ultimate decision-making responsibility, and conducting top-level design that requires cross domain insights. 3. Human machine collaboration will become mainstream The institutional investment research team regards AI as a “super researcher” rather than a substitute, and the strategy iteration cycle has been shortened from months to weeks. In fact, AI technology itself is becoming an “equal rights tool” for retail investors. Through AI assisted analysis of financial reports, generation of quantitative strategies, and optimization of trading timing, some retail investors with learning abilities have begun to build a “human-machine collaborative” investment model. 4. Supervision gradually improves With the increasing proportion of quantitative trading, regulatory authorities are also continuously deepening their supervision of high-frequency quantitative trading, clearly defining standards and risk control requirements to maintain market fairness and stability. Regulatory measures will tighten algorithmic trading: high-frequency reporting and cancellation restrictions, algorithmic filing systems, and abnormal trading monitoring will gradually be improved to curb systemic risks. At this crossroads of wealth reshuffling, the best choice for individual investors is not to leave, but to evolve. Reduce transaction frequency, improve transaction quality, and respond to the short-term high-frequency and cold calculation of machines with human long termism and deep thinking.