It is an open secret that some big players do not pay much attention to last numbers of fractional parts and place orders on whole numbers. And it is more likely that these levels might serve as support or resistance. Applying this idea as basic in your trading will not give much advantage as this is the comfort zone of large HFT traders. In our case this information can help us save some bucks over a long term by placing entry, take profit and stop loss orders on these levels. The above charts show frequency distribution of fractional prices on daily reversal points. It is evident that the closer a price to a whole number, the larger odds of a bounce from there. This confirms the âtheoryâ of big players and whole numbers. In this topic I would like to share my thoughts regarding this and similar subjects. You are welcome to participate in the discussion as our joint efforts might help us to be more efficient in our trading. We need to share the knowledge and benefit from that as it is getting more and more difficult to make money on the market with every year

I've always payed attention to whole numbers but I've never done a study on it. Nice work. From your graphs it also seems multiples of 0.25 get some attention to.

A nice quote from your link .... "Macroeconomic data Similarly, the macroeconomic data the Greek government reported to the European Union before entering the Euro Zone was shown to be probably fraudulent using Benford's law, albeit years after the country joined."

Another quote... Distributions that would not be expected to obey Benford's law .... Where numbers influenced by human thought: e.g., prices set by psychological thresholds ($1.99)...

Yes, that's exactly why you have to normalize it. Benford gives you a baseline of expected distribution of digits. After accounting for it, you see what's 'abnormal'.

It's easy to show that frequency distributions for the third significant digit are not greater than several percents. Considerable influence of Benford's law would be reflected by a dependence 1/x which is evidently absent on the charts. If I understood your comment incorrectly, could you please clarify it for me.

Closing Point during Different Market Phases This diagram shows where the index S&P500 (SPY) closed on average during the last twenty years. The closing is expressed as Normalized Close Point (NCP), ranging from 0 to 1. NCP is calculated by the formula: NCP= (Close-Low)/(High-Low) If the index closes closer to High, the NCP value is closer to 1, if the index closes closer to Low, the NCP value is closer to 0. The diagram shows that the index closes closer to High than to Low on average. It is no wonder taking into account that the markets have been growing on average for the last 200 years. What is interesting is overfrequency of closing at either High or Low. Almost every tenth day SPY&500 closes exactly at High. This diagram shows the same data as the previous one but with smaller NCP intervals. It demonstrates the overfrequency of extreme closings of the index more prominently. We can also see that closings in the neutral range (0.35-0.65) occur a bit rarer than directed ones.

The below chart shows the index from 1993 until now. Let us divide it into several parts according to the trend direction and consider in detail how the close NCP changes during different market phases. S&P 500 Uptrend, 1993-1999 Downtrend, 2000-2002