Repair the main

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,3 +1,12 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: bug
+assignees: ''
+
+---
+
 ## Problem
 
 Describe what is broken.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,31 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+## Goal
+
+Describe what should be achieved.
+
+## Why
+
+Explain why this task is useful for the project.
+
+## Scope
+
+- 
+- 
+- 
+
+## Acceptance criteria
+
+- [ ] 
+- [ ] 
+- [ ] 
+
+> [!NOTE]
+> Priority:

.github/workflows/ci.yml

@@ -17,7 +17,7 @@ jobs:
 
     steps:
       - name: Check out repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v7
 
       - name: Set up Python
         uses: actions/setup-python@v5

.github/workflows/commit-message.yml

@@ -15,15 +15,15 @@ jobs:
 
     steps:
       - name: Check commit message
-        uses: actions/checkout@v4
+        uses: actions/checkout@v7
         with:
           fetch-depth: 0
       - name: Fetch base branch
         run: git fetch origin "${{ github.base_ref }}"
       - name: Validate commit message
         shell: bash
         run: |
-          pattern='^((feat|fix|docs|style|refactor|test|chore|ci|build|perf)\(#[0-9]+\): .{1,50}|chore\(deps\): .{1,72})$'
+          pattern='^((feat|fix|docs|style|refactor|test|chore|ci|build|perf)\(#[0-9]+\): .{1,72}|chore\(deps\): .{1,72})$'
           invalid=0
 
           while IFS= read -r commit_message; do
@@ -44,9 +44,14 @@ jobs:
             echo ""
             echo "Expected format:"
             echo "  feat(#1): add feature"
-            echo "  chore(#5): add a file to .gitignore"
+            echo "  chore(#5): add file to .gitignore"
+            echo "  ci(#3): validate commit messages"
+            echo "  chore(deps): update dependencies"
             echo ""
             echo "Allowed types:"
             echo "  feat, fix, docs, style, refactor, test, chore, ci, build, perf"
+            echo ""
+            echo "Special dependency format:"
+            echo "  chore(deps): message"
             exit 1
           fi

.gitignore

@@ -12,4 +12,5 @@ __pycache__
 .ruff_cache
 
 # Virtual environment
-.env
+.env
+.venv

README.md

@@ -13,12 +13,13 @@ ARGUS is a Python-based market analytics project evolving from a small FX conver
 
 ARGUS is currently focused on building a clean local foundation:
 
-- currency conversion using live exchange-rate data
+- currency conversion using live ExchangeRate API data
+- historical market-data retrieval using yfinance
 - calculator and conversion logic
 - input validation and error handling
 - Tkinter GUI prototype
 - legacy CLI/debug interface
-- first pandas/matplotlib-based analytics prototype
+- pandas/matplotlib-based analytics prototype
 - tests and documentation
 
 > [!IMPORTANT]
@@ -66,19 +67,16 @@ Each roadmap phase is treated as a separate development sprint. The roadmap desc
 ## Current Features
 
 - Calculator
-- Currency conversion using live exchange rates
+- Currency conversion using live ExchangeRate API data
+- Historical market-data retrieval using yfinance
 - Input validation and error handling
 - Tkinter GUI prototype
 - Legacy CLI/debug interface
 - Basic pandas-based trend metrics
 - Matplotlib-based trend visualization
-- Mock time-series data for early analytics development
+- Basic client, service and analytics pipeline
 - Basic test suite
 
-> [!CAUTION]
-> Historical market data support is still limited.  
-> The current live exchange-rate client is useful for simple conversion, but future analytics work will require additional data sources such as Frankfurter or yfinance.
-
 ---
 
 ## Project Structure
@@ -115,6 +113,7 @@ README.md
 
 - requests
 - python-dotenv
+- yfinance
 - pandas
 - NumPy
 - matplotlib
@@ -124,6 +123,7 @@ README.md
 ### Current data source
 
 - ExchangeRate API for live currency conversion
+- yfinance for historical market-data retrieval and analytics
 
 ---
 
@@ -136,7 +136,6 @@ Planned or likely future technologies include:
 ### Data sources
 
 - Frankfurter API for historical FX data
-- yfinance for broader market data
 - possible additional market-data APIs later
 
 ### Data processing
@@ -193,7 +192,7 @@ Before running ARGUS locally, make sure you have:
 - Python 3.11 or newer
 - Git
 - pip
-- an ExchangeRate API key for live currency conversion
+- an ExchangeRate API key for live currency conversion. Historical analytics currently use yfinance and do not require an additional API key.
 
 Recommended for development:
 
@@ -342,9 +341,9 @@ The project now has a runnable local Python application, a Tkinter GUI prototype
 
 Current focus:
 
-- start Sprint 2 — Market Analytics & Data Source Expansion
-- improve historical exchange-rate data support
+- continue Sprint 2 — Market Analytics & Data Source Expansion
+- extend historical market-data support beyond the first yfinance client
 - add stronger market metrics
 - expand pandas-based analytics workflows
 - improve dashboard usefulness without adding unnecessary chart noise
-- document metric definitions, assumptions and data-source behavior
+- document metric definitions, assumptions and data-source behavior

pyproject.toml

@@ -8,6 +8,7 @@ dependencies = [
     "pandas",
     "numpy",
     "matplotlib",
+    "yfinance",
 ]
 
 [project.optional-dependencies]

src/argus/analytics/charts/trend_chart.py

@@ -1,9 +1,8 @@
 import matplotlib.pyplot as plt
-import pandas as pd
 from argus.services.timeseries_service import prepare_trend_analysis
 
 
-def create_trendchart(curr: str, dates: pd.DataFrame):
+def create_trendchart(curr_symbol: str, start: str, end: str, interval: str):
     """
     Create a trend chart for exchange-rate analysis.
 
@@ -31,8 +30,10 @@ def create_trendchart(curr: str, dates: pd.DataFrame):
         Minimum and maximum exchange-rate values are marked with scatter
         points and annotations.
     """
-    df = pd.DataFrame()
-    df, min_max_rates = prepare_trend_analysis(curr, dates)
+    result = prepare_trend_analysis(curr_symbol, start, end, interval)
+    if result is None:
+        return None
+    df, min_max_rates = result
     min_date = min_max_rates["min_date"][0]
     min_rate = min_max_rates["min_rate"][0]
     max_date = min_max_rates["max_date"][0]

src/argus/clients/yfinance_client.py

@@ -0,0 +1,43 @@
+import yfinance as yf
+import logging
+
+
+def get_timeseries(curr_symbol, start, end, interval):
+    """
+    Fetch historical exchange-rate time series data from Yahoo Finance.
+
+    Args:
+        curr_symbol (str): Currency symbol used by Yahoo Finance, for example
+            "EURUSD=X".
+        start (str): Start date of the requested time range in YYYY-MM-DD format.
+        end (str): End date of the requested time range in YYYY-MM-DD format.
+        interval (str): Data interval supported by Yahoo Finance, for example
+            "1d", "1h", or "15m".
+
+    Returns:
+        pandas.DataFrame | None: A DataFrame containing the columns ``date`` and
+        ``rate`` if data was successfully fetched. Returns ``None`` if the
+        request fails, returns no data, or an exception occurs.
+    """
+    try:
+        yf_logger = logging.getLogger("yfinance")
+        yf_logger.disabled = True
+        data = yf.download(
+            tickers=curr_symbol,
+            start=start,
+            end=end,
+            interval=interval,
+            multi_level_index=False,
+            progress=False,
+        )
+        yf_logger.disabled = False
+        if data is None:
+            return None
+        if data.empty:
+            return None
+        data = data.reset_index()
+        data = data[["Date", "Close"]]
+        data = data.rename(columns={"Date": "date", "Close": "rate"})
+        return data
+    except Exception:
+        return None

src/argus/gui/app.py

@@ -1,5 +1,4 @@
 import tkinter as tk
-import pandas as pd
 from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
 from argus.analytics.charts.trend_chart import create_trendchart
 from argus.services.calculator_service import calc, check_op
@@ -77,27 +76,10 @@ def show_trend() -> None:
     global trend_canvas
     global trend_chart_widget
 
-    mock_dates = {
-        "date": [
-            "2026-06-01",
-            "2026-06-02",
-            "2026-06-03",
-            "2026-06-04",
-            "2026-06-05",
-            "2026-06-06",
-            "2026-06-07",
-            "2026-06-08",
-            "2026-06-09",
-            "2026-06-10",
-            "2026-06-11",
-            "2026-06-12",
-            "2026-06-13",
-            "2026-06-14",
-            "2026-06-15",
-        ]
-    }
-    mock_dates = pd.DataFrame(mock_dates)
-    mock_curr = "USD"
+    curr_symbol = "EURUSD=X"
+    start = "2024-01-01"
+    end = "2025-01-01"
+    interval = "1d"
 
     calc_frame.pack_forget()
     conv_frame.pack_forget()
@@ -108,14 +90,16 @@ def show_trend() -> None:
     content.pack(side="top", fill=tk.BOTH, expand=True)
 
     if trend_canvas is None:
-        fig = create_trendchart(mock_curr, mock_dates)
+        fig = create_trendchart(curr_symbol, start, end, interval)
+        if fig is None:
+            return None
         fig.set_size_inches(7, 4)
 
         trend_canvas = FigureCanvasTkAgg(fig, master=content)
         trend_chart_widget = trend_canvas.get_tk_widget()
 
     if trend_chart_widget is None:
-        return
+        return None
     trend_canvas.draw()
     trend_chart_widget.pack(fill=tk.BOTH, expand=True)
 

src/argus/services/timeseries_service.py

@@ -1,5 +1,5 @@
 import pandas as pd
-from argus.clients.mock_client import get_mock_timeseries
+from argus.clients.yfinance_client import get_timeseries
 from argus.analytics.metrics.trend_metrics import (
     add_rolling_average,
     add_daily_percentage_change,
@@ -8,24 +8,33 @@
 
 
 def prepare_trend_analysis(
-    mock_curr: str, df: pd.DataFrame
-) -> tuple[pd.DataFrame, dict]:
+    curr_symbol: str, start: str, end: str, intervall: str
+) -> tuple[pd.DataFrame, dict] | None:
     """
-    Prepares the data for trend analysis by adding conversion rates, daily percentage change, and rolling average.
+    Prepare time-series data for trend analysis.
 
-    Arg1: mock_curr: str - the currency code for which the trend analysis is to
-    be performed
-    Arg2: df: pd.DataFrame - the DataFrame containing the dates for which the
-    conversion rates are to be added
+    Fetches historical exchange-rate data for the given currency symbol and
+    enriches it with daily percentage changes and a rolling average. It also
+    calculates the minimum and maximum exchange rates for the resulting time
+    series.
 
-    Return: tuple[pd.DataFrame, dict] - a tuple containing the updated DataFrame with conversion rates,
-    daily percentage change, and rolling average, and a dictionary with the minimum and maximum rates
+    Args:
+        curr_symbol (str): Currency symbol used by Yahoo Finance, for example
+            "EURUSD=X".
+        start (str): Start date of the requested time range in YYYY-MM-DD format.
+        end (str): End date of the requested time range in YYYY-MM-DD format.
+        intervall (str): Data interval supported by Yahoo Finance, for example
+            "1d", "1h", or "15m".
+
+    Returns:
+        tuple[pd.DataFrame, dict] | None: A tuple containing the prepared
+        DataFrame and a dictionary with minimum and maximum rates. Returns
+        ``None`` if no time-series data could be fetched.
     """
-    df["rate"] = 0.0
-    # For each date one API call to get the rate
-    for i in range(len(df)):
-        date = str(df.loc[i, "date"])
-        df.loc[i, "rate"] = get_mock_timeseries(mock_curr, date)
+
+    df = get_timeseries(curr_symbol, start, end, intervall)
+    if df is None:
+        return None
     df = add_daily_percentage_change(df)
     df = add_rolling_average(df)
     min_max_rates = get_min_max_rates(df)