Determining if a stock is at an all-time high price is a simple exercise for companies that don’t split their stocks or pay dividends. Support and resistance levels on a chart are a graphic illustration of crowd psychology and are observable in all financial markets. Let’s not forget that a company’s stock price is just as much dependent on investor demand as it is how the company is actually doing. For the comparison, I was using historical data from previous projects as supplied by TickData and CQG.
You are strictly looking at the security’s past trading date to determine where you think the security might go in the future (albeit in an hour, tomorrow, in a week, in a year, etc.). One can look at a stock’s past trading data via chart patterns, technical indicators, and a few others. Prices moves in trends is pretty self explanatory; once a trend has been established, the future price movement has a better chance of following in that same direction.
As you most likely already know, a stock opens at a price every morning, fluctuates throughout the day, then closes every evening at a certain price. When looking at a standard (line) chart, if you are looking at it in a time period of over a week then the point on the graph is representative of what the stock closed at for that day. When on a finance site like Google Finance, you can make the time scale of the graph be anywhere from less than one day to the entire duration of the stock being traded publicly. Fun fact, a chart with a time scale of less than a day is considered an intraday chart.
Of the sources listed, I narrowed down to 3 intraday discount vendors: Price Data/Grain Market Research ( -/ ), Pi Trading ( ), and Kibot ( ). I wish I could afford to look at Tick Data ( ), but the prices are ridiculous; they even have a minimum order amount. Marcos, likely a paid affiliate, has posted this same message/link over 17 times (google search count) on various trading forums in the month of April.
After taking a day or so for the DataClient to realize I had purchased the 18 months of AMEX data, a day or so to download the data (probs with that too) and then I find out it’s not adjusted for splits. Performing a statistical analysis of the -H-L-C prices between four vendors, I compared each price field individually and only used regular session history. Again, I had to compare unadjusted prices only as the QuantQuote method had too many errors to reliability align the data between sources. I plotted and analyzed my results in Excel but could not find how to post the charts in this forum. I am after weekly open and close date and monthly open and close data from yahoo.