https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html
This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.
Historical daily stock prices (open, high, low, close, volume)
Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)
Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)
Feature engineering based on financial data and technical indicators
Sentiment analysis data from social media and news articles
Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
Researchers investigating the effectiveness of machine learning in stock market prediction
Analysts developing quantitative trading Buy/Sell strategies
Individuals interested in building their own stock market prediction models
Students learning about machine learning and financial applications
The dataset may include different levels of granularity (e.g., daily, hourly)
Data cleaning and preprocessing are essential before model training
Regular updates are recommended to maintain the accuracy and relevance of the data
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Indonesia Consumer Price Index (CPI): 2022=100: Weights: Food, Beverage and Tobacco: Food: Dragon Fruit data was reported at 0.034 % in Mar 2025. This stayed constant from the previous number of 0.034 % for Feb 2025. Indonesia Consumer Price Index (CPI): 2022=100: Weights: Food, Beverage and Tobacco: Food: Dragon Fruit data is updated monthly, averaging 0.034 % from Jan 2022 (Median) to Mar 2025, with 39 observations. The data reached an all-time high of 0.034 % in Mar 2025 and a record low of 0.034 % in Mar 2025. Indonesia Consumer Price Index (CPI): 2022=100: Weights: Food, Beverage and Tobacco: Food: Dragon Fruit data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Inflation – Table ID.IA010: Consumer Price Index: 2022=100: Weights.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The movement of toys not only captures attention of children but also informs the design of soft robotics. Pneumatic actuators are a key area of research in soft robotics due to their lightweight and easily controllable characteristic. This study presents the design of a novel pneumatic soft gripper that integrates a soft, minimally stretchable inflatable tube with a pre-deformed stiffener. The motion of the gripper is inspired by the inflation and deflation process of the blowing dragon toys. The gripper’s bending performance and load capacity are analyzed through theoretical force analysis and finite element simulations. Its grasping performance and versatility were evaluated across a range of applications in experiments. Results indicate a consistent trend between theoretical force analysis, finite element simulations, and experimental outcomes. The proposed soft gripper is capable of grasping objects weighing up to 23.32 g and with a diameter of up to 50 mm, achieving a weight-to-grip ratio of approximately 28.38 times its own weight (0.82 g). Compared with conventional lightweight pneumatic soft grippers, the proposed design exhibits superior load capacity. Furthermore, it demonstrates practical applications in tasks such as catching thumbtacks, collecting items, and cleaning pipes due to its excellent bending performance.
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https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html
This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.
Historical daily stock prices (open, high, low, close, volume)
Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)
Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)
Feature engineering based on financial data and technical indicators
Sentiment analysis data from social media and news articles
Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
Researchers investigating the effectiveness of machine learning in stock market prediction
Analysts developing quantitative trading Buy/Sell strategies
Individuals interested in building their own stock market prediction models
Students learning about machine learning and financial applications
The dataset may include different levels of granularity (e.g., daily, hourly)
Data cleaning and preprocessing are essential before model training
Regular updates are recommended to maintain the accuracy and relevance of the data