Add README and initial Python directory for project setup

README.md

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+# Lagged Oil vs. Unemployment Charting
+
+This repository provides two Python scripts to fetch the latest US Unemployment Rate (UNRATE) and WTI crude oil price (DCOILWTICO) from FRED, apply a configurable lag or lead to the oil series, and generate polished dual‑axis charts.
+
+## Files
+
+* **`lagged_oil_unrate_chart.py`**
+  A lightweight script that:
+
+  1. Downloads monthly UNRATE and averages daily WTI to monthly.
+  2. Shifts the oil series by `N` months (default = 18).
+  3. Plots both series on a dual‑axis Matplotlib chart.
+
+* **`lagged_oil_unrate_chart_styled.py`**
+  A more polished version that adds:
+
+  * Title, subtitle, footnote with date range and source.
+  * Clean dual‑axis styling with percentage and dollar‑format ticks.
+  * Customizable date range and extended x‑axis (e.g., 1973–2028).
+  * CLI flags for offset, start/end dates, and axis extension.
+
+* **`requirements.txt`**
+  Lists all Python dependencies for both scripts.
+
+## Prerequisites
+
+* Python 3.7 or later
+* Internet connection (to fetch data from FRED)
+
+## Setup
+
+Clone or download this repository:
+
+```bash
+git clone https://your-repo-url.git
+cd your-repo-folder
+```
+
+### Create a Virtual Environment & Install Dependencies
+
+#### On Linux / macOS
+
+```bash
+# 1. Create a venv
+python3 -m venv venv
+
+# 2. Activate it
+source venv/bin/activate
+
+# 3. Upgrade pip and install requirements
+pip install --upgrade pip
+pip install -r requirements.txt
+```
+
+#### On Windows (PowerShell)
+
+```powershell
+# 1. Create a venv
+python -m venv venv
+
+# 2. Activate it
+venv\Scripts\Activate.ps1
+
+# 3. Upgrade pip and install requirements
+python -m pip install --upgrade pip
+pip install -r requirements.txt
+```
+
+> **Note:** If you get execution policy errors on Windows, you may need to run:
+>
+> ```powershell
+> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
+> ```
+
+## Usage
+
+Once the virtual environment is active and dependencies are installed, run either script from the project root.
+
+### 1. Basic Lag Script
+
+```bash
+# Default: oil lags unemployment by 18 months (~1.5 years)
+python lagged_oil_unrate_chart.py
+
+# Other offsets (in months)
+python lagged_oil_unrate_chart.py --offset 6    # 6‑month lag
+python lagged_oil_unrate_chart.py --offset -12  # 12‑month lead
+```
+
+### 2. Styled & Configurable Chart
+
+```bash
+python lagged_oil_unrate_chart_styled.py \
+    --offset 18 \
+    --start 1973-01-01 \
+    --end 2025-05-31 \
+    --extend-years 3
+```
+
+| Flag               | Description                                                    |
+| ------------------ | -------------------------------------------------------------- |
+| `--offset N`       | Lag (positive) or lead (negative) in months (default = 18)     |
+| `--start DATE`     | Series start date (`YYYY-MM-DD`, default = `1973-01-01`)       |
+| `--end DATE`       | Series end date (`YYYY-MM-DD`, default = today)                |
+| `--extend-years M` | How many years beyond `end` to extend the x‑axis (default = 3) |
+
+## Customization
+
+* **Normalization**: Insert a normalization step (e.g., percent change from a base date).
+* **Export**: Modify the script to save figures with `plt.savefig('chart.png', dpi=300)`.
+* **Statistics**: Add `ta.correl()` or use Pandas to compute rolling correlations.
+
+## Troubleshooting
+
+* **"ModuleNotFoundError"**: Ensure your venv is activated, then reinstall dependencies.
+* **SSL/DataReader errors**: Upgrade `pandas_datareader` or switch to an alternate data source.
+
+## License
+
+This project is released under the MIT License.

py/lagged_oil_unrate_chart_styled.py

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+#!/usr/bin/env python3
+"""
+lagged_oil_unrate_chart_styled.py
+---------------------------------
+Fetch and plot UNRATE vs. WTI oil price with a configurable lag,
+date range, and polished dual-axis styling.
+
+Usage:
+    python lagged_oil_unrate_chart_styled.py \
+        --offset 18 \
+        --start 1973-01-01 \
+        --end 2025-05-31 \
+        --extend-years 3
+
+Dependencies:
+    pip install pandas pandas_datareader matplotlib python-dateutil
+"""
+import argparse
+from datetime import datetime
+from dateutil.relativedelta import relativedelta
+
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+import pandas as pd
+import pandas_datareader.data as web
+
+
+def fetch_series(start, end):
+    # FRED series
+    unrate = web.DataReader("UNRATE", "fred", start, end)
+    oil   = web.DataReader("DCOILWTICO", "fred", start, end)
+
+    # Resample WTI to month-end average
+    oil_monthly = oil.resample("M").mean()
+    # Ensure UNRATE is at month-end timestamps, too
+    unrate.index = unrate.index.to_period("M").to_timestamp("M")
+    return unrate, oil_monthly
+
+
+def plot_lagged(
+    unrate: pd.DataFrame,
+    oil: pd.DataFrame,
+    offset_months: int,
+    start_date: datetime,
+    end_date: datetime,
+    extend_years: int
+):
+    # Shift oil by offset: positive = lag; negative = lead
+    oil_shifted = oil.shift(offset_months)
+
+    # Build figure
+    fig, ax1 = plt.subplots(figsize=(14, 7))
+    plt.title("Unemployment Rate and US Oil Price", fontsize=20, weight="bold")
+    plt.suptitle(
+        "Civilian Unemployment Rate and WTI Crude Oil Price",
+        fontsize=14,
+        y=0.93
+    )
+
+    # Plot UNRATE on left axis
+    ax1.plot(unrate.index, unrate["UNRATE"], 
+             label="UNRATE (LHS)", linewidth=2.5, color="#1f77b4")
+    ax1.set_ylabel("Unemployment rate (%)", fontsize=12)
+    ax1.set_ylim(0, unrate["UNRATE"].max() * 1.1)
+    ax1.yaxis.set_major_formatter("{x:.0f}%")
+    ax1.grid(axis="y", linestyle="--", alpha=0.4)
+
+    # Plot shifted oil on right axis
+    ax2 = ax1.twinx()
+    ax2.plot(oil_shifted.index, oil_shifted["DCOILWTICO"],
+             label="USOIL (RHS)", linewidth=2, linestyle="-", alpha=0.8, color="#FF9900")
+    ax2.set_ylabel("Oil price (USD)", fontsize=12)
+    ax2.set_ylim(0, oil_shifted["DCOILWTICO"].max() * 1.1)
+    ax2.yaxis.set_major_formatter("${x:,.0f}")
+
+    # X-axis formatting
+    ax1.set_xlim(start_date, end_date + relativedelta(years=extend_years))
+    ax1.xaxis.set_major_locator(mdates.YearLocator(base=5))
+    ax1.xaxis.set_minor_locator(mdates.YearLocator(1))
+    ax1.xaxis.set_major_formatter(mdates.DateFormatter("%Y"))
+    plt.setp(ax1.get_xticklabels(), rotation=0, fontsize=11)
+
+    # Combine legends
+    lines, labels = ax1.get_legend_handles_labels()
+    l2, l2l = ax2.get_legend_handles_labels()
+    ax1.legend(lines + l2, labels + l2l, loc="upper left", frameon=False, fontsize=12)
+
+    # Footnotes
+    foot = (
+        f"Dates: {start_date.strftime('%Y')} through {unrate.index[-1].strftime('%d %b %Y')}.\n"
+        "Source: FRED, pandas_datareader."
+    )
+    plt.annotate(
+        foot,
+        (0,0), (0, -40), 
+        xycoords="axes fraction", textcoords="offset points",
+        va="top", fontsize=10, color="gray"
+    )
+
+    fig.tight_layout(rect=[0, 0.03, 1, 0.95])
+    plt.show()
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser()
+    p.add_argument("-o", "--offset", type=int, default=18,
+                   help="lag in months (positive=lag, negative=lead)")
+    p.add_argument("--start", type=str, default="1973-01-01",
+                   help="series start date (YYYY-MM-DD)")
+    p.add_argument("--end", type=str, default=datetime.today().strftime("%Y-%m-%d"),
+                   help="series end date (YYYY-MM-DD)")
+    p.add_argument("--extend-years", type=int, default=3,
+                   help="how many years beyond end_date to show on the x-axis")
+    args = p.parse_args()
+
+    start_dt = datetime.fromisoformat(args.start)
+    end_dt   = datetime.fromisoformat(args.end)
+
+    unrate, oil = fetch_series(start_dt, end_dt)
+    plot_lagged(unrate, oil, args.offset, start_dt, end_dt, args.extend_years)