Intelligent investment system neural network upgrade

TSAI

The TSAI team has integrated more than 500 unique market characteristic variables, which cover macroeconomic data, micro-corporate financial indicators, industry dynamics, market trading sentiment and other levels, comprehensively and meticulously reflecting the complex ecology of the financial market. By using advanced deep reinforcement learning algorithms, the system conducts in-depth training and optimization of massive financial market historical data from around the world in the past 20 years. The large scale of data and the complexity of processing are both at the leading level in the industry.

During rigorous internal testing and verification by authoritative third-party institutions, the TSAI intelligent investment system has demonstrated excellent improvement in predictive capabilities. In terms of predicting short-term (within a week) price trends in the stock market, the accuracy rate has achieved a qualitative leap, significantly improving from the original 60% to 90%. This means that investors will have a more accurate and reliable basis when making short-term trading decisions, greatly reducing the risks caused by incorrect predictions of price fluctuations. For example, in a recent simulated test of high-frequency short-term trading scenarios, the accuracy of the trading strategy based on the new system was nearly three times higher than that of the old system, and the transaction success rate climbed from less than 30% to about 85%, effectively reducing errors. Losses from trading decisions.

When predicting market trends in the medium to long term (three months to one year), the accuracy rate also improved impressively, by more than 30% to around 80%. Take the trend prediction of the world's major stock indexes as an example. In the simulated tracking predictions of the S&P 500, FTSE 100, Nikkei 225 and other indexes in the past year, the probability that the new system can accurately predict the direction of the index three months in advance is relatively high. The old system improved by 35% to over 78%. This provides solid technical support for investors to formulate mid- and long-term investment plans, allowing them to more accurately grasp the general direction of the market, rationally allocate assets, and maximize potential returns.

Data shows that in tests of simulated investment portfolios with different risk preferences, portfolios managed by intelligent investment systems using the new neural network architecture improved risk-adjusted returns by 40% - 60% compared to the old system. For the conservative investment portfolio, the annualized volatility is reduced by 15% - 20%, while the income stability is increased by 25% - 30%; for the aggressive investment portfolio, the annualized return rate is increased by 18% while bearing the same risk level. % - 25%. These data fully demonstrate the significant positive impact of this upgrade on investment performance.