2024-04-26 Silver News
2024-04-25
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Analysis
By observing the dataset provided, we can derive numerous insights. Following is the comprehensive analysis of this financial dataset, listed below for each of the stated objectives:
1. Understanding the overall trend of the exchange rates
Based on the comprehensive analysis of the provided data, the overall trend of the exchange rates for XAG seems to be highly volatile but with a general upward growth. Specifically, the dataset starts with a value of 37.12228, achieving a peak value of 37.72588, before finally ending at 37.63927. Given that these are the extremum values, it suggests an upward movement. However, it's important to note that values within the timeframe fluctuate, suggesting varying degrees of volatility in the exchange rates.
2. Identifying any seasonality or recurring patterns in the changes of exchange rates
From the given dataset, it's difficult to ascertain any evident seasonality or recurring patterns due to the general volatility pattern and the short window of time the data captures. While there are periods of consistent growth and periods of consistent falls, these don’t appear to be predictable in a seasonal or cyclical manner based solely on the provided data. It would be helpful to analyse a larger dataset over different periods throughout the years to ascertain any potential seasonal or recurring patterns.
3. Noting any outliers, or instances where the exchange rate differs significantly from what would be expected based on the trend or seasonality
Regarding outliers, there appear to be occasional instances where the exchange rate changes dramatically within a short time span, which could be considered out of the ordinary considering the overall trend. For example, the sharp rise from 37.27171 to 37.69460 over less than an hour, and thereafter the fall to 37.39296 in the next half an hour. These are considerable perturbations in the dataset that might be attributed to extraordinary events—internal or external to the particular financial instruments being traded. However, without additional context, it's challenging to know what specifically caused these fluctuations. It may be valuable for further analysis to identify these instances and investigate their potential causes.