Remove TA-Lib installation and configuration files

- Delete .travis.yml configuration
- Remove TA-Lib installation script and source files from Docker build helpers
- Update Dockerfile to remove TA-Lib installation steps
- Remove TA-Lib installation steps from GitHub Actions workflow
- Minor comment update in linearreg_intercept indicator

.github/workflows/codeql-analysis.yml

@@ -46,22 +46,6 @@ jobs:
         key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
         restore-keys: |
          ${{ runner.os }}-pip-
-    - name: Install ta-lib
-      run: |
-        if ([ "$RUNNER_OS" = "macOS" ]); then
-          brew install ta-lib
-        fi
-        if ([ "$RUNNER_OS" = "Linux" ]); then
-          if [ ! -f "$GITHUB_WORKSPACE/ta-lib/src" ]; then wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz -q && tar -xzf ta-lib-0.4.0-src.tar.gz; fi
-          cd ta-lib/
-          ./configure --prefix=/usr
-          if [ ! -f "$HOME/ta-lib/src" ]; then make; fi
-          sudo make install
-          cd
-        fi
-        if ([ "$RUNNER_OS" = "Windows" ]); then
-          curl -sL http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-msvc.zip -o $GITHUB_WORKSPACE/ta-lib.zip --create-dirs && 7z x $GITHUB_WORKSPACE/ta-lib.zip -o/c/ta-lib && mv /c/ta-lib/ta-lib/* /c/ta-lib/ && rm -rf /c/ta-lib/ta-lib && cd /c/ta-lib/c/make/cdr/win32/msvc && nmake
-        fi
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

Dockerfile

@@ -13,11 +13,6 @@ RUN pip3 install Cython numpy
 RUN mkdir /jesse-docker
 WORKDIR /jesse-docker
 
-# Install TA-lib
-COPY docker_build_helpers/* /tmp/
-RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
-ENV LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
-
 # Install dependencies
 COPY requirements.txt /jesse-docker
 RUN pip3 install -r requirements.txt

jesse/indicators/linearreg_intercept.py

@@ -23,7 +23,7 @@ def linearreg_intercept(candles: np.ndarray, period: int = 14, source_type: str
         candles = slice_candles(candles, sequential)
         source = get_candle_source(candles, source_type=source_type)
 
-    # Compute linear regression intercept using vectorized operations (no TA-Lib required)
+    # Compute linear regression intercept using vectorized operations 
     if len(source) < period:
         if sequential:
             return np.full_like(source, np.nan, dtype=float)