Category: 4 SPL Big Data Computing

Usually, the rise of stock market will create an upward trend. If one is able to capture the trend, they will get handsome benefits. This essay will introduce the commonly used Trend Trading Strategy. First, let’s look at some related concepts before introducing the trading strategy: 1. The latest trough: the stock price lower than that over the N trading days before and after the date when it occurs. 2. The crest before the trough: The stock price that is higher than that over the N trading days before and after the day and that appears before the latest trough.

In the stock market, there may be a linear relationship between the stock price on the current day and stock prices in several previous trading days. According to this concept, we design the following strategy: 1. Use data of the 100 trading days before the current day as the training data; 2. Among the training data, set closing price on each day’s next day as the target variable; 3. Among the training data, use the opening price, the highest price, the lowest price and the closing price of each day’s recent M days as the metrics; 4. Perform Principal Component

The trading strategy is like this: 1. Set “up or not” as the target (target). If the closing price is higher than that of the previous date, assign 1 as the target value, otherwise assign -1 to it. 2. Add two metrics – highest closing price minus lowest closing price (max_dif) and closing price minus opening price (co_dif). 3. Suppose we need to predict the stock prices in the year 2023. Compute distance between the two metrics (max_dif and co_dif) on the current date and those on each of the previous 660 trading dates, find the 95 dates with the

The MACD (Moving Average Convergence Divergence), derived from the Exponential Moving Average (EMA), is highly effective for capturing trending market conditions. Its top (bearish) and bottom (bullish) divergences serve as a proven method for “buying the dip and selling the peak”, making it a key consideration for many medium- to long-term investors in real-world trading. In this essay, we will implement the MACD divergence strategy using SPL (Structured Process Language) and conduct backtesting to evaluate its performance. Before offering the SPL code, we need to look at how to compute MACD: 1. Short-term EMA: The short-term exponential moving average. The

The specific rule of Turtle Trading Strategy is like this: buy a stock when the price breaks above the last N-day (usually N is 20) high and sell it when the price falls below the last N-day low. These peaks and lows construct a channel that called Donchian Channel. Generate buy/sell signals (signal) in this manner: set buy signal (1) when the price exceeds the Donchian Channel upper band, sell signal (-1) when it is below the lower band, and no action (0) at all other times. Place a buy order if there is a buy signal and no positions

The TPC official website provides a C language generation program, which can be downloaded, compiled, and executed to generate TPCH data. However, many people are not familiar with the C language and its environment, and are stuck at this step. According to official rules, a SPL script has been written here to generate TPCH data. This way, as long as esProc is installed, data can be easily generated. TPCH has 8 tables, with 5 fixed records in the region table and 25 fixed records in the nation table. The remaining 6 tables are generated based on the Scale Factor (SF).

Course data download Background The sources and storage forms and types of data in an application are diverse, including not only the traditional relational databases, but also more data sources such as NoSQL databases, cloud storage, APIs, file systems, etc. Therefore, combining and analyzing data originating these different sources constitutes the problem of mixed data source computations. Logical data warehouses can facilitate mixed-source computations to some extent, because most of them are SQL-based and can access RDB data sources through table mapping. But it is difficult for them to access other types of data sources. Even with the help of

Background Applications may need to work based on different databases. Although the SQL syntax for various databases is generally consistent, differences still exist, which necessitates modifications to related SQL statements. Such modifications often require manual adjustments, which involve heavy workload and are error-prone. Fully automating the SQL transformation is nearly impossible due to the varying functionalities of different databases. However, upon closer examination, it becomes clear that most issues stem from differences in the syntax of SQL functions. Especially for functions related to dates and strings, there isn’t a standard in the field, and each database has its own approach.

Download Test Data Structured Text Computing Requirements Structured text files are a common data storage method, such as this score.txt file, which records the scores of students in all classes. The first row is the column name, and each subsequent row is the data of a student, separated by tab within each row. This structured text also has various computational and processing requirements. For example, selecting the girls with the English score of 90 or above, to calculate the total score of each student and sort them in descending order, to calculate the average Chinese language score for each class.

Overview Data maintenance routine can implement regular maintenance and update of data. For real-time hot data, however, they can only be read instantly during query and then returned after being merged with historical data. Based on this situation, the ability to quickly return results when querying real-time hot data and to handle frequent concurrent access is required, but this will impose a significant burden on the business system. If the real-time hot data can be stored separately in memory and read and returned directly from memory during query, it can greatly speed up the query speed and, it can also