DeepSeek uses the power of cognitive dimensionality and ecological reconstruction to rewrite AI investment strategy: a cognitive revolution driven by data and algorithms, reshaping the underlying logic and decision-making paradigm of AI investment.
Technical implementation: three core strategies reconstruct investment logic
1. Momentum strategy: make trends your friends
DeepSeek builds a dynamic portfolio through dual verification of historical price data and market sentiment analysis. Take a case of an investor as an example: the system screens out stock A with a growth of more than 30% and stock B with a growth of 25% in the past 6 months. After confirming that there is no major negative news in the market through news sentiment analysis, it automatically generates a buy order and sets a 5% stop loss line. This strategy shows strong adaptability in a volatile market, and backtesting shows that the annualized return is 8-12% higher than the traditional momentum strategy.
2. Mean reversion strategy: capture arbitrage opportunities for price deviations
The system calculates the 200-day moving average of stock C and finds that its price deviates from the mean by 20%, and its price-earnings ratio has reached the historical 10% percentile. DeepSeek automatically triggers a buy signal and issues a sell order when the price returns to the moving average + 1 standard deviation. This strategy successfully seized three major valuation repair opportunities in the market volatility in 2024, with a single return of up to 47%.
3. Multi-factor model: building an intelligent stock selection matrix
DeepSeek analyzes 200+ factors and determines the weights through machine learning:
- Value factor (30% weight): low P/E ratio, high dividend yield
- Quality factor (25% weight): ROE>15%, cash flow/net profit>1
- Momentum factor (20% weight): 12-week price strength
- Volatility factor (15% weight): beta coefficient <0.8
- Emotion factor (10% weight): institutional research frequency, stock bar popularity
The model is dynamically adjusted every month, and the 2025 backtest shows that the portfolio Sharpe ratio reached 1.73, far exceeding the CSI 300 Index.
Technical breakthrough:
- MoE architecture: 37 billion parameters are dynamically called from the 671 billion parameter pool, reducing the inference cost by 92%
- Multi-head latent attention mechanism: When processing 100,000+ stock data, the memory usage is reduced by 85%
- Group relative strategy optimization: No need to supervise data, automatic evolution through reinforcement learning
Market impact: from efficiency improvement to ecological reconstruction
1. Digital transformation of financial institutions
- Investment research scenario: After the head brokerage firm accessed DeepSeek, the research report generation speed was shortened from 2 hours to 15 minutes, and the key data verification accuracy was increased from 78% to 95%
- Risk control system: Real-time scanning of 4,000+ stocks in the entire market, and the risk warning response speed in extreme market conditions increased by 3 times
- Asset allocation: A bank wealth management subsidiary used a multi-factor model to reduce the volatility of the FOF portfolio by 18%
2. Changes in the pricing mechanism of the capital market
- Information response speed: In the first quarter of 2025, the price adjustment within 30 minutes after the announcement of the A-share market increased by 40% compared with 2023
- Liquidity stratification: The proportion of algorithmic trading surged from 12% in 2023 to 37% in 2025, and the average daily trading volume increased by 2.1 times
- Valuation system reshaping: The PEG valuation method of technology stocks was replaced by the AI expected cash flow model, and traditional consumer stocks introduced market sentiment factor pricing
3. New paradigm of supervision and compliance
- Intelligent audit: The China Securities Regulatory Commission used DeepSeek to analyze the financial reports of listed companies, and the recognition rate of abnormal related transactions increased from 65% to 89%
- Public opinion monitoring: After the exchange deployed the system, the speed of false information transmission decreased by 70%, and the penalty response cycle was shortened by 60%
Actual case: Reconstruction practice from individual investors to institutions
1. The wealth code of individual investors
- Case 1: Programmer Xiao Li used DeepSeek to build a “sideline kingdom”
- Developing automatic writing tools: Input “Generate in-depth analysis of technology stocks”, the system outputs a three-dimensional report including technical aspects, fundamentals, and market sentiment
- Quantitative trading system: Set the condition of “P/E ratio <15 and institutional holdings increase”, automatically buy 10 stocks, and earn 87% in 3 months
- Code outsourcing service: Develop AI customer service system for small and medium-sized enterprises, with a single-day income of over 10,000
- Case 2: Baoma batch operation of goods-carrying accounts
- Copywriting generation: Input “maternal and child products + promotion nodes”, the system generates 50 differentiated copywriting, with a click-through rate 40% higher than that of peers
- Product selection strategy: Analyze social media data, recommend “high-value + pragmatic” products, and increase conversion rate by 25%
2. Strategic transformation of institutional investors
- Tencent “Multi-mode Toys” project:
- Customer service scenario: DeepSeek-R1 handles 85% of routine consultations, and the human intervention rate decreases by 60%
- Game NPC: V3 model increases the naturalness of character dialogue by 47% and increases user retention by 18%
- Advertising system: Distill model builds user portraits, with 32% higher accuracy than traditional methods
- Postal Savings Bank Smart Investment Advisor:
- Asset allocation: Generate 12 types of portfolios based on risk preferences, and the drawdown control is 15% better than the traditional model
- Rebalancing strategy: When market volatility triggers adjustments, the execution speed is increased by 5 times
3. Exponential growth of startups
- Xueyi Technology:
- Intelligent education applications: After accessing the V3 model, the accuracy of math problem answers is 92%, and essay corrections include logical structure analysis
- User growth: 500,000 customers in 3 months, 12% paid conversion rate, ARPU value of 280 yuan
- Cost structure: R&D expenditures are reduced by 70%, and server costs are reduced by 85% through model compression
Competitive advantage: dual barriers of open source ecology and vertical scenarios
1. Technology generation gap
- Chinese processing: On the CLUE benchmark test, DeepSeek’s F1 value is 13% higher than GPT-4
- Inference cost: Generate research reports of the same quality at only 1/7 of the cost of overseas models
- Iteration speed: Models are updated weekly, and the expansion speed of financial-specific dictionaries is 3 times faster than that of competitors
2. Ecological layout
- Developer community: 120,000 developers have been attracted and 4,000+ industry models have been submitted
- Hardware collaboration: Adapted with Huawei Ascend and Feiteng CPUs, the inference speed has increased by 60%
- Data Alliance: Access to real-time data streams from the Shanghai Stock Exchange and Shenzhen Stock Exchange to build a full market monitoring network
3. Compliance advantages
- Data security: Full-link localization, passed the third-level security certification
- Policy adaptation: Automatically generate investment advisory reports that comply with the “Generative AI Management Measures”
Future Outlook: Evolution from tools to ecology
1. Short-term (1-2 years)
- Agent-based investment research assistant: Embedded in Wind and Tonghuashun terminals, automatically complete the entire process of data collection-analysis-reporting
- Smart order system: Combined with VWAP algorithm, the transaction impact cost is reduced by 40%
- Cross-border investment model: Develop A-share-Hong Kong-US stock linkage strategy to capture the market price difference among the three places
2. Medium-term (3-5 years)
- Cognitive intelligence upgrade: Introduce embodied intelligence and build a personalized interactive interface for virtual traders
- Market prediction engine: Integrate satellite data and IoT information to predict industry turning points 3 months in advance
- Regulatory technology sandbox: Cooperate with the central bank to pilot AI-driven macro-prudential supervision tools
3. Long-term (more than 5 years)
- Economic simulation system: Build a digital twin market and simulate policy shocks in real time
- AI asset manager: Intelligent entities that pass the Turing test directly manage tens of billions of funds
- Global computing power network: Build distributed training nodes with “Belt and Road” countries to break through chip blockades
This investment strategy revolution initiated by DeepSeek is reshaping the genes of the financial market.
When algorithms begin to understand market sentiment and models can predict human predictions, investment is evolving from a game of strategy to an evolutionary competition of data and cognition.
In this competition, DeepSeek not only provides tools, but also defines the rules – a new era for smart investors has arrived.
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