Quantitative Trading Strategy Based on Neural Network

As a trading method, quantitative investment has been widely used for more than 30 years, and its investment performance is stable. As the scale of the international financial market continues to expand, more and more investors have been recognized. Therefore, using quantitative decisions to trade financial products is the mainstream direction in the future. Taking gold and bitcoin, for example, we first establish a prediction model. Since there are limited historical prices to refer to at the early stage of trading, we adopt a robust regression strategy of moving averages to buy stocks. When we have commodity prices for more than 200 trading days, we use BP neural network to predict the price of the next trading day. This prevents the data from being too far back in time and affecting our current price trend. We then use polynomials to fit our predicted product prices and compare them to the actual values to evaluate the prediction model. Finally, with our quantitative decision model, our assets increased from $1,000 in September 2016 to approximately $192,922 in September 2021, which can be proven to be an excellent strategy. For Question 2, we believe that investors have the highest probability of profiting from this investment if we accurately judge future commodity price movements. We use a polynomial to fit the scatter plot of the product price predicted by the BP neural network. We perform the KS test, reliability analysis, and correlation analysis on the fitted curves and the actual prices of the products. It is found that our prediction curve is well-fitted. For sensitivity analysis, we change the transaction cost, finding that the transaction cost is negatively correlated with our income using the previously constructed prediction and decision model. When transaction costs appear, we should adjust our investment strategy in time to avoid frequent trading. An increase in transaction costs results in a small decrease in revenue; therefore, commissions are not sensitive to the final revenue. In conclusion, we provide a memorandum to help traders better understand and apply our quantitative trading strategy.