Revert "Update the github action for commit messages"

README.md

@@ -13,13 +13,12 @@ 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 ExchangeRate API data
-- historical market-data retrieval using yfinance
+- currency conversion using live exchange-rate data
 - calculator and conversion logic
 - input validation and error handling
 - Tkinter GUI prototype
 - legacy CLI/debug interface
-- pandas/matplotlib-based analytics prototype
+- first pandas/matplotlib-based analytics prototype
 - tests and documentation
 
 > [!IMPORTANT]
@@ -67,16 +66,19 @@ Each roadmap phase is treated as a separate development sprint. The roadmap desc
 ## Current Features
 
 - Calculator
-- Currency conversion using live ExchangeRate API data
-- Historical market-data retrieval using yfinance
+- Currency conversion using live exchange rates
 - Input validation and error handling
 - Tkinter GUI prototype
 - Legacy CLI/debug interface
 - Basic pandas-based trend metrics
 - Matplotlib-based trend visualization
-- Basic client, service and analytics pipeline
+- Mock time-series data for early analytics development
 - 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
@@ -113,7 +115,6 @@ README.md
 
 - requests
 - python-dotenv
-- yfinance
 - pandas
 - NumPy
 - matplotlib
@@ -123,7 +124,6 @@ README.md
 ### Current data source
 
 - ExchangeRate API for live currency conversion
-- yfinance for historical market-data retrieval and analytics
 
 ---
 
@@ -136,6 +136,7 @@ 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
@@ -192,7 +193,7 @@ Before running ARGUS locally, make sure you have:
 - Python 3.11 or newer
 - Git
 - pip
-- an ExchangeRate API key for live currency conversion. Historical analytics currently use yfinance and do not require an additional API key.
+- an ExchangeRate API key for live currency conversion
 
 Recommended for development:
 
@@ -341,9 +342,9 @@ The project now has a runnable local Python application, a Tkinter GUI prototype
 
 Current focus:
 
-- continue Sprint 2 — Market Analytics & Data Source Expansion
-- extend historical market-data support beyond the first yfinance client
+- start Sprint 2 — Market Analytics & Data Source Expansion
+- improve historical exchange-rate data support
 - 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

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

src/argus/analytics/charts/trend_chart.py

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

@@ -1,4 +1,5 @@
 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
@@ -76,10 +77,27 @@ def show_trend() -> None:
     global trend_canvas
     global trend_chart_widget
 
-    curr_symbol = "EURUSD=X"
-    start = "2024-01-01"
-    end = "2025-01-01"
-    interval = "1d"
+    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"
 
     calc_frame.pack_forget()
     conv_frame.pack_forget()
@@ -90,16 +108,14 @@ def show_trend() -> None:
     content.pack(side="top", fill=tk.BOTH, expand=True)
 
     if trend_canvas is None:
-        fig = create_trendchart(curr_symbol, start, end, interval)
-        if fig is None:
-            return None
+        fig = create_trendchart(mock_curr, mock_dates)
         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 None
+        return
     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.yfinance_client import get_timeseries
+from argus.clients.mock_client import get_mock_timeseries
 from argus.analytics.metrics.trend_metrics import (
     add_rolling_average,
     add_daily_percentage_change,
@@ -8,33 +8,24 @@
 
 
 def prepare_trend_analysis(
-    curr_symbol: str, start: str, end: str, intervall: str
-) -> tuple[pd.DataFrame, dict] | None:
+    mock_curr: str, df: pd.DataFrame
+) -> tuple[pd.DataFrame, dict]:
     """
-    Prepare time-series data for trend analysis.
+    Prepares the data for trend analysis by adding conversion rates, daily percentage change, and rolling average.
 
-    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.
+    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
 
-    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.
+    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
     """
-
-    df = get_timeseries(curr_symbol, start, end, intervall)
-    if df is None:
-        return None
+    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 = add_daily_percentage_change(df)
     df = add_rolling_average(df)
     min_max_rates = get_min_max_rates(df)