Refactor documentation for various filters and indicators to enhance clarity and consistency

- Updated Bessel, Bilateral, Blma, Butter, Conv, Ema, Kama, LSMA, MAMA, MGDI, SSF, USF, ATR, ADL, and ADOSC documentation to use bullet points for key concepts and features.
- Added a new Qodana configuration file for code analysis.
- Removed coverage configuration from Quantower.Tests.csproj to streamline testing setup.

lib/trends/bilateral/Bilateral.md

@@ -17,8 +17,8 @@ The filter operates in two domains simultaneously:
 
 This dual-weighting mechanism ensures that:
 
-- Nearby prices with similar values have high influence (smoothing).
-- Distant prices or prices with very different values have low influence (edge preservation).
+* Nearby prices with similar values have high influence (smoothing).
+* Distant prices or prices with very different values have low influence (edge preservation).
 
 ### Complexity
 
@@ -32,18 +32,18 @@ $$ BF = \frac{\sum_{i=0}^{L-1} W_s(i) \cdot W_r(i) \cdot P_i}{\sum_{i=0}^{L-1} W
 
 Where:
 
-- $L$ is the length (period).
-- $P_i$ is the price at index $i$ (0 is current).
-- $W_s(i)$ is the spatial weight:
+* $L$ is the length (period).
+* $P_i$ is the price at index $i$ (0 is current).
+* $W_s(i)$ is the spatial weight:
     $$ W_s(i) = \exp\left(-\frac{i^2}{2\sigma_s^2}\right) $$
 
-- $W_r(i)$ is the range weight:
+* $W_r(i)$ is the range weight:
     $$ W_r(i) = \exp\left(-\frac{(P_0 - P_i)^2}{2\sigma_r^2}\right) $$
 
 Parameters:
 
-- $\sigma_s = \max(L \cdot \text{ratio}, 10^{-10})$
-- $\sigma_r = \max(\text{StDev}(P, L) \cdot \text{mult}, 10^{-10})$
+* $\sigma_s = \max(L \cdot \text{ratio}, 10^{-10})$
+* $\sigma_r = \max(\text{StDev}(P, L) \cdot \text{mult}, 10^{-10})$
 
 ## Performance Profile