Conditional Statistics combines two essential ideas from data analysis: Statistics and Conditions. Let’s explore each component and how they apply to trading.

Statistics

Statistics summarize and describe the characteristics of a dataset. They help us understand and interpret large amounts of data by providing key insights. Some popular statistical measures include:

• Average: The sum of all values divided by the number of values.
• Median: The middle value when all values are sorted in ascending order.
• Standard Deviation: the amount of variation or dispersion in a set of values.
• Range: The difference between the highest and lowest values in a dataset.

Conditional

Statistics can be calculated based on the entire sample of data, but they can also be dependent on specific conditions. These conditions can be categorical (e.g. day of the week, pre/post holidays) or numerical (e.g. stock price change, prior day’s volume). By applying conditions, we can isolate specific scenarios and gain deeper insights.

Conditional Statistics in Trading

In the stock market, Conditional Statistics™ are statistics about stock price changes under certain conditions. These conditions can vary widely, including technical indicators (e.g. moving averages, RSI levels), fundamental indicators (e.g. P/E ratio, debt to equity) and more.

Conditional Statistics™ help identify patterns where certain indicators have historically shown strong positive or negative returns. While not all conditions are causal, recognizing these patterns can be valuable. Traders can use this information to make more informed decisions when similar conditions arise in the future, potentially enhancing their trading strategies.

By understanding Conditional Statistics™, you gain a powerful tool to analyze market data and uncover insights that can guide your trading decisions. Welcome to a deeper level of market analysis with our web app!