What is Time Series Data | Python for Trading | Quantra Free Course

Time series is a collection of the different values that a variable exhibits, known as observations, at different points in time. In other words, it is a series of observations indexed in time order. These observations are recorded at specific and regular intervals in time.

In financial markets, the price of a stock is recorded in a time series format. One of the most important series for short-term trading is the OHLCV time series data, which give us the daily value of the stock’s opening price of the day, day’s high, day’s low, closing price of the day and the day’s volume over a period of time which you choose. The data gathered in such a way, allows an investor to analyse, examine and forecast a stock’s price movement to take his/her financial decisions. However, for intra-day trading, a trader may choose a LTP or Last Traded Price time series on a minute-to-minute basis.

You can choose the granularity of data according to your requirement while selecting a time series to analyse. Granularity is the length of the interval between which the observation is recorded. Granularity in time series can be that of 1 second, 1 minute, 1 hour or 1 day. While analysing any stock price time series, choosing the correct granularity of data for analysis is very important for optimal results. For instance, let us assume you are an intra-day trader. If you analyse your data using Machine Learning techniques, choosing a low granular data like that of 1 second LTP is suitable to train your ML algorithm. However, if you are a Japanese candlestick trader, a 1 minute or 5 minute OHLC data would be more suitable for successful analysis.

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