![]() ![]() Any indicator type (oscillator, trend, volume, etc.) or a combination of them can operate on multiple timeframes. The main difference from the classical indicators is that MTF indicators can process generalized information from all time intervals or financial instruments and then pass data to the current chart. In addition to basic information, these indicators can display the real state of the market trend and recommend further trading activities. The solution to this problem is an indicator which receives information from different timeframes or multiple trading symbols and displays the comprehensive data on the screen, so that the user can efficiently assess the market state. In these situations, the user can only rely on their experience or information accessing speed. This may lead to messy decisions, early closing of positions or missing of market reversal points. When performing trades on lower TFs, one can miss profitable signals on higher periods. Thus traders using these strategies are often in the state of mental strain. This is especially important for MTF strategies based on the simultaneous evaluation of different TFs, such as the Wolfe Waves or the Elder's Three Screens. Moreover, the data should be available right here and now, not on another tab or anywhere else. While this information can sometimes be vital. However, there is no time for checking required information form higher periods when working on the M1-M15 timeframes. What to do next? Does one have to ignore older timeframes or keep switching between windows and periods? When the working timeframe is Н1 or above, the user has time for careful evaluation of the general situation. Thus the preliminary analysis is performed. To perform multi-timeframe analysis, users often have to open several windows or switch between chart periods if the same set of instruments is used. This analysis method seems to be a mandatory part of professional approach for successful trading. The analysis is performed downwards to lower timeframes until the one, at which deals are performed. So if anyone knows of a good way to normalize track gain in an iTunes library that consists of a combination of burned songs and iTunes-purchased songs, I'd really appreciate it.Most of traders agree that the current market state analysis starts with the evaluation of higher chart timeframes. I've tried programs that apply the replaygain algorithm to the Sound Check values (such as MP3tag, BeaTunes, and iVolume) without any luck. And I do have Sound Check checked in Itunes and on my iPhone. I've used Sound Check alone and the results haven't been satisfactory (if it does anything at all). ![]() I mostly play songs on my iPhone using my car stereo. I like to play my music in playlists that I've made, not album by album, and the differences in song volume leads to me having to adjust the volume knob all the time. I have songs from basically 2 different sources in my iTunes library: songs burned from CDs and songs purchased from iTunes. I am looking for a way to normalize the volume of my iTunes songs. I imagine this question has been answered elsewhere but for the life of me I cannot find a satisfactory answer by searching. ![]()
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