Abstract
Technical analysis includes objectivity technical indicators and subjectivity chart recognition etc. Different technical indicators tend to delivery different trading signals; similarly, observing the stock patterns from different time scales also often have different conclusions. To solve the puzzle on the trading decisions, this article attempts to using the underlying historical datas to train supervised machine learning algorithms model, in order to construct the quantifying contact between technical indicators and trading signals, and between trading patterns and the signals, then predicting trading signals based on this quantifying contact. Under such an algorithm model, we constructed a low-frequency trading strategies to test the historial data of Japan, Taiwan, South Korea, and the United States. When the input data are Technical indicators, the Mainland and Taiwan markets have substantial revenue; when the input data are patterns, the Mainland and Korean markets have considerable benefits. It also builds a 5-minute intraday high frequency trading strategies on the Mainland stock index futures and main commodity futures. The test result presents high volatility futures products have satisfying performance.