TIA VertexFX Indicator

The Trend Interruption Average (TIA) is a powerful VertexFX client-side indicator based on Moving Average useful in identifying price swings and strength of a trend.


In an uptrending market we see consecutive positive closes. Likewise, in a downtrending market we see consecutive negative closes. The TIA indicator uses the positive / negative consecutive closes to calculate the strength of the trend and potential reversal. The TIA comprises of three components, namely the TIA-Up (GREEN) line, the TIA-Down (RED) line, and the TIA-Difference (BLUE) line.
In the first step, we calculate the cumulative sum of positive closes. If the current close is above previous close, it is assigned a value of 1. If next candle closes above current close, the cumulative count is increment by 1, otherwise it is reset to 0. For example, if there were three consecutive positive closes, the value of cumulative count will be 1, 2 and 3 for the recent three candles. Similarly, the opposite logic is employed for negative closes - we calculate the cumulative sum of the negative closes separately.
In the next step, we calculate the moving average of the cumulative positive closes and negative closes using the MA_PERIOD. The moving average of the cumulative positive closes is the TIA-Up (GREEN) line, and that of the cumulative negative closes is the TIA-Down (RED) line. The TIA-Difference (BLUE) line is the difference between the TIA-Up and TIA-Down.
When the TIA-Up is rising it implies a bullish trend, whereas when the TIA-Down is rising it implies a bearish trend.
BUY / EXIT SHORT - Enter LONG (or exit SHORT) at the close of the candle when the TIA-Up (GREEN) line closes above the TIA-Down (RED) line. The stop-loss can be placed below the nearest Swing Low.

SHORT / EXIT LONG - Enter SHORT (or exit LONG) at the close of the candle when the TIA-Down (RED) line closes above the TIA-Up (GREEN) line. The stop-loss can be placed above the nearest Swing High.

vBulletin [video]

deviating from the mean price. Higher the standard deviation, greater is the volality. In the first step, we calculate the mean and standard deviation of the recent VAR_PERIOD candles. In the next step, we calculate the variance by subtracting the price from the standard deviation. Finally, we smooth the variance, using the co-efficient, which is reciprocal of (1 + VAR_PERIOD). When the variance increases, the probabilty of fast./,