The only god in the trading field is not Livermore, who has gone bankrupt multiple times, but James Simmons. He constructs a high-frequency quantitative trading system through mathematics and models, which has never experienced a significant pullback, let alone bankruptcy. The fund curve is always steadily rising, so stable that it looks like a fraudster, treating the financial market as an ATM. Let's talk about his trading strategy 1/Gecko style investment method: Simmons' core trading philosophy Simmons' trading method is known as the "gecko style investment method," and he describes it as follows: "Trading should be like a gecko, lying motionless on the wall, quickly eaten by mosquitoes once they appear, then calm down and wait for the next opportunity The core of this investment philosophy lies in: 1. Use mathematical functions in advance to determine whether the market is weak or strong 2. Only take proactive actions in strong markets (bull markets or volatile markets) 3. Maintain silence at other times and patiently wait for the next opportunity Simmons does not pursue huge profits from every trade, but rather accumulates profits by capturing a large number of abnormal moments and small profit opportunities in the market, while trading a large number of varieties and relying on a large number of trades completed in a short period of time. From a mathematical genius to a master of quantitative trading James Harris Simmons is not a finance professional with a formal education, but a mathematical genius. After obtaining his doctoral degree at the age of 23, he rose to prominence in the academic community and served as one of the youngest professors in Harvard's mathematics department. Later, he revitalized the mathematics program at Stony Brook University in New York State. In 1974, he co authored a paper with Chen Shengshen to establish the famous "Chen Simmons Theory", and in 1976, he won the highest honor in the American mathematical community - the Veblen Prize. Simmons' investment career began in 1978 when he left academia and founded the private investment fund Limroy. At first, he used traditional fundamental analysis methods, but soon found that this approach was too subjective and difficult to explain the rate of return mathematically. As a mathematical genius, Simmons firmly believed in the existence of patterns and laws that could be discovered in the market. Therefore, in March 1988, he closed the Limroy Fund and founded the Medallion Fund, which was entirely based on mathematics. Grand Medal Fund: An Unprecedented Investment Legend The performance of the Renaissance Medal Fund shocked Wall Street. Since its establishment in 1988, the fund has achieved an annualized return rate of 66.1%, even after charging a 5% management fee and a 44% performance commission, it still maintains an annualized return rate of 39.1%. This astonishing performance far exceeds Buffett's 20.5% return rate during the same period. Even more astonishing is that the Grand Medal Fund has performed exceptionally well during market crises: -In the 1994 'Bond Massacre', a 70% return rate was achieved -During the Internet foam in 2000, 98.5% of the net return was obtained -During the 2008 financial crisis, it achieved a super high return of 98.2% -In the bear market triggered by the pandemic in 2020, it achieved a 24% return in just the first four months The ability to achieve ultra-high returns without a pullback during significant market fluctuations has made the Grand Medal Fund a true legend in the investment industry. The Evolution and Core Strategies of Quantitative Models The strategy of the Grand Medal Fund has undergone multiple evolutions and optimizations. At the beginning of its establishment, the performance of the fund was not satisfactory, and even suffered losses in 1989. The key turning point occurred in 1989, when Simmons and Princeton mathematician Henry Larufer jointly redeveloped trading strategies, completely shifting investment direction from fundamental analysis to quantitative analysis. Mean Regression Strategy One of the core strategies of the Renaissance was the Mean Regression strategy. When the price deviates too much from the long-term mean, the model expects the price to return to the mean level. This strategy performs particularly well in the foreign exchange and commodity futures markets. Mean regression is reflected in Simmons' market overreaction strategy: if the price of a futures is much higher at the opening than the previous day's closing price, Medals will short the futures; On the contrary, if the opening price is much lower than the closing price of the previous day, the Medal will be bought. This strategy utilizes the market's short-term overreaction, waiting for prices to return to reasonable levels. High frequency trading and market neutral strategy Simmons adopts a high-frequency trading strategy to capture small profit opportunities in the market. The holding time for this trading method is extremely short, ranging from a few milliseconds to several days, and usually does not leave overnight positions. Due to the short time of market entry, the risk of market fluctuations borne is relatively low. At the same time, the Grand Medal Fund also adopts a Market Neutral Strategy to reduce the impact of overall market volatility on the investment portfolio. This enables the fund to maintain stable profitability in various market environments. Multi factor nonlinear model To cope with complex market dynamics, the Simmons team developed high-dimensional nonlinear models. They realized that asset prices are not simply linear relationships, but rather complex nonlinear relationships and high-dimensional feature spaces. The team extracts hidden patterns from massive historical data using non-linear kernel methods and early machine learning techniques. These models can capture price patterns that are difficult to discover through traditional technical analysis, providing more accurate guidance for trading decisions. Data driven decision-making system The Renaissance placed great emphasis on the collection and processing of data. As early as the early 1980s, when the Internet was not yet born, Simmons had begun to collect a large number of pricing data of stocks, bulk commodities and other assets and digitize them, many of which even date back to 1700. Their emphasis on data far exceeded that of other investment institutions at the time. The Simmons team not only analyzes traditional financial data, but also includes unstructured data such as economic indicators, news sentiment, and even weather changes. After strict cleaning and screening, these data become the foundation for model construction.