Core Insights - The article emphasizes the importance of utilizing data to focus on key investment opportunities and risks, particularly in the context of geopolitical tensions and trade dynamics [3][5]. Group 1: Fund Risk Exposure - The article discusses a new method for quantifying fund risk exposure by combining industry classification data with fund holding data, allowing for a more precise assessment of actual risk exposure across various sectors [3][5]. - A comparison is made between traditional methods and the new business classification method, highlighting that the latter provides a more balanced view of a fund's industry exposure [3][5]. - The analysis identifies the top 10 exchange-traded products (ETPs) with the highest tariff risk exposure in North America, with the Simplify Volt TSLA Revolution ETF showing a sensitivity of 22.1 [5]. Group 2: News Sentiment Analysis - The article introduces a natural language processing (NLP) approach to quantify news sentiment and its correlation with asset performance, particularly focusing on crude oil futures [7][9]. - The methodology involves generating sentiment scores from news headlines and using z-scores to identify significant deviations from historical norms, which can indicate potential price movements [7][9]. - The analysis reveals that negative sentiment often correlates with supply disruptions, which historically lead to price increases in the crude oil market [9]. Group 3: Enhanced OHLC Data - The article presents enhanced OHLC (Open, High, Low, Close) data that includes precise timestamps for price movements, allowing for improved trading strategies [12][15]. - It categorizes OHLC bars into "trend bars" and "range bars" based on the sequence of high and low points, which can provide insights into market behavior [12][15]. - The article suggests that the type of OHLC bar may influence the likelihood of price continuation, which can be critical for traders [18].