Added additional CLI usage informations

README.md

@@ -56,3 +56,48 @@ Resamples the extracted signals to a weekly frequency and performs quantitative
 Install the required quantitative stack:
 ```bash
 pip install pytrends pandas numpy scikit-learn yfinance matplotlib
+```
+
+### Command Line Interface (CLI) Usage
+The script features a fully parameterized CLI. You can customize the geographic scope, validation benchmark, time horizons, Google properties, and the anchor term itself.
+
+```bash
+# Display the full help menu and options
+python google_trends_sentiment_prototype.py -h
+```
+
+### Advanced CLI Examples:
+```bash
+# 1. Standard global run validating against the MSCI World ETF (URTH)
+python google_trends_sentiment_prototype.py --geo GLOBAL --ticker URTH
+
+# 2. Localized analysis for Germany validating against the DAX over a specific 5-year period
+python google_trends_sentiment_prototype.py --geo DE --ticker ^GDAXI --timeframe "today 5-y"
+
+# 3. Target Google News specifically rather than general web search, disabling visual plots
+python google_trends_sentiment_prototype.py --gprop news --no-plot
+
+# 4. Change the anchor term to a custom high-volume baseline
+python google_trends_sentiment_prototype.py --anchor "news" --ticker URTH
+```
+
+### Full CLI Arguments Reference:
+**Flag**,**Type**,**Default**,**Description**
+```bash
+"-h, --help",Flag,-,Show the help message and exit.
+--geo GEO,String,'GLOBAL',"Geographic region code (ISO 3166-1 alpha-2). E.g., 'US', 'DE'."
+--ticker TICKER,String,None,"Yahoo Finance ticker symbol for validation (e.g., '^GSPC', 'URTH')."
+--timeframe TIMEFRAME,String,'today 5-y',"Data duration. Supports 'today 12-m', 'today 5-y', or specific 'YYYY-MM-DD YYYY-MM-DD'."
+--gprop GPROP,String,'' (Web),"Google property filter. Supports '' (Web Search), 'news', 'images', 'froogle', or 'youtube'."
+--anchor ANCHOR,String,'weather',Reference term used to rescale and link multiple keyword batches together.
+--no-plot,Flag,False,Disables visual plot generation (.png) and only serializes the computed CSV data.
+```
+
+### Generated Outputs
+he pipeline structures and serializes its findings into the following artifacts (where <geo> represents your chosen region code):
+
+- `trends_raw_<geo>.csv`: The compiled, raw Google Trends search intensities.
+- `trends_features_<geo>.csv`: The engineered feature set post-Z-score transformation.
+- `sentiment_data_<geo>.csv`: The final calculated Sentiment Spread and Macro PCA Factor.
+- `sentiment_plot_<geo>.png` / `combined_sentiment_analysis_<geo>.png`: Visual correlation plots mapping the extracted sentiment against your chosen financial benchmark.
+