33 replace mock new data client

.gitignore

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

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)

src/legacy/analytics/charts/trend_chart.py

@@ -0,0 +1,72 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+from legacy.services.timeseries_service import prepare_trend_analysis
+
+
+def create_trendchart(curr: str, dates: pd.DataFrame):
+    """
+    Create a trend chart for exchange-rate analysis.
+
+    Builds a Matplotlib figure showing the exchange rate, its rolling
+    average, and the daily percentage change for a selected currency.
+    The minimum and maximum exchange-rate values are highlighted in the
+    chart.
+
+    Args:
+        curr (str): Currency code or currency pair identifier used for
+            the trend analysis.
+        dates (pd.DataFrame): DataFrame containing the date information
+            used to prepare the time-series analysis.
+
+    Returns:
+        matplotlib.figure.Figure: Matplotlib figure containing the trend
+        chart.
+
+    Notes:
+        The chart uses two y-axes:
+
+            - The left y-axis displays the exchange rate and rolling average.
+            - The right y-axis displays the daily percentage change.
+
+        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)
+    min_date = min_max_rates["min_date"][0]
+    min_rate = min_max_rates["min_rate"][0]
+    max_date = min_max_rates["max_date"][0]
+    max_rate = min_max_rates["max_rate"][0]
+
+    # Rate and Rolling Average needs seperat x-Achse von Daily Percentage Chnage erhalten
+    fig, ax1 = plt.subplots(figsize=(5, 3.5), dpi=100)
+
+    # Subplot 1
+    ax1.plot(df["date"], df["rate"], color="black", label="Exchange Rate")
+    ax1.plot(df["date"], df["roll_avg"], color="blue", label="Rolling Average")
+
+    # Scatter and Annote Min/Max Rate
+    ax1.scatter(min_date, min_rate, color="red")
+    ax1.scatter(max_date, max_rate, color="green")
+    ax1.annotate("Min", (min_date, min_rate))
+    ax1.annotate("Max", (max_date, max_rate))
+
+    # Rotate date values for better visibillity
+    ax1.tick_params(axis="x", rotation=45)
+
+    # Subplot 2
+    ax2 = ax1.twinx()
+    bar_colors = ["green" if x >= 0 else "red" for x in df["daily_pct_change"]]
+    ax2.bar(
+        df["date"],
+        df["daily_pct_change"],
+        color=bar_colors,
+        alpha=0.4,
+        label="Daily Change",
+    )
+    ax2.legend(loc="upper left")
+    ax2.set_ylabel("Percentage Scale")
+
+    # Adjust the layout
+    fig.tight_layout()
+    return fig

src/legacy/analytics/metrics/trend_metrics.py

@@ -0,0 +1,75 @@
+import pandas as pd
+
+
+def add_daily_percentage_change(df: pd.DataFrame) -> pd.DataFrame:
+    """
+        Add the daily percentage change of the exchange rate.
+
+    Calculates the percentage change between each rate value and the
+    previous rate value. The result is added as a new column named
+    ``daily_pct_change``.
+
+    Args:
+        df (pd.DataFrame): DataFrame containing at least a ``rate`` column.
+
+    Returns:
+        pd.DataFrame: A copy of the input DataFrame with an added
+        ``daily_pct_change`` column.
+
+    Notes:
+        The first row will contain ``NaN`` because there is no previous
+        rate value to compare against.
+    """
+    result = df.copy()
+    result["daily_pct_change"] = result["rate"].pct_change() * 100
+    return result
+
+
+def add_rolling_average(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Add a rolling average of the exchange rate.
+
+    Calculates a rolling mean over the ``rate`` column using a fixed
+    window size of 3 rows. The result is added as a new column named
+    ``roll_avg``.
+
+    Args:
+        df (pd.DataFrame): DataFrame containing at least a ``rate`` column.
+
+    Returns:
+        pd.DataFrame: A copy of the input DataFrame with an added
+        ``roll_avg`` column.
+    """
+    result = df.copy()
+    result["roll_avg"] = result["rate"].rolling(window=3, min_periods=1).mean()
+    return result
+
+
+def get_min_max_rates(df: pd.DataFrame) -> dict:
+    """
+    Get the minimum and maximum exchange-rate values.
+
+    Finds the rows with the lowest and highest values in the ``rate``
+    column and returns their dates and rates in a dictionary.
+
+    Args:
+        df (pd.DataFrame): DataFrame containing at least ``date`` and
+            ``rate`` columns.
+
+    Returns:
+        dict: Dictionary containing the minimum and maximum rate data with
+        the following keys:
+
+            - ``min_date``: Date of the lowest exchange rate.
+            - ``min_rate``: Lowest exchange-rate value.
+            - ``max_date``: Date of the highest exchange rate.
+            - ``max_rate``: Highest exchange-rate value.
+    """
+    min_max = {"min_date": [], "min_rate": [], "max_date": [], "max_rate": []}
+    min_id = df["rate"].idxmin()
+    max_id = df["rate"].idxmax()
+    min_max["min_date"].append(df.loc[min_id, "date"])
+    min_max["min_rate"].append(df.loc[min_id, "rate"])
+    min_max["max_date"].append(df.loc[max_id, "date"])
+    min_max["max_rate"].append(df.loc[max_id, "rate"])
+    return min_max