Update to most recent develop version

src/main_markdown.py

@@ -7,77 +7,114 @@
 
 
 def get_stock_data(ticker, start, end):
-    ticker_data = yf.download(ticker, start, end)  # downloading the stock data from START to TODAY
+    ticker_data = yf.download(
+        ticker, start, end
+    )  # downloading the stock data from START to TODAY
     ticker_data.reset_index(inplace=True)  # put date in the first column
-    ticker_data['Date'] = pd.to_datetime(ticker_data['Date']).dt.tz_localize(None)
+    ticker_data["Date"] = pd.to_datetime(ticker_data["Date"]).dt.tz_localize(None)
     return ticker_data
 
 
 def get_data_from_range(state):
     print("GENERATING HIST DATA")
-    start_date = state.start_date if type(state.start_date)==str else state.start_date.strftime("%Y-%m-%d")
-    end_date = state.end_date if type(state.end_date)==str else state.end_date.strftime("%Y-%m-%d")
+    start_date = (
+        state.start_date
+        if type(state.start_date) == str
+        else state.start_date.strftime("%Y-%m-%d")
+    )
+    end_date = (
+        state.end_date
+        if type(state.end_date) == str
+        else state.end_date.strftime("%Y-%m-%d")
+    )
 
     state.data = get_stock_data(state.selected_stock, start_date, end_date)
     if len(state.data) == 0:
-        notify(state, "error", f"Not able to download data {state.selected_stock} from {start_date} to {end_date}")
+        notify(
+            state,
+            "error",
+            f"Not able to download data {state.selected_stock} from {start_date} to {end_date}",
+        )
         return
-    notify(state, 's', 'Historical data has been updated!')
-    notify(state, 'w', 'Deleting previous predictions...')
-    state.forecast = pd.DataFrame(columns=['Date', 'Lower', 'Upper'])
+    notify(state, "s", "Historical data has been updated!")
+    notify(state, "w", "Deleting previous predictions...")
+    state.forecast = pd.DataFrame(columns=["Date", "Lower", "Upper"])
 
 
 def create_candlestick_chart(data):
     fig = go.Figure()
-    fig.add_trace(go.Candlestick(x=data['Date'],
-                                 open=data['Open'],
-                                 high=data['High'],
-                                 low=data['Low'],
-                                 close=data['Close'],
-                                 name='Candlestick'))
-    fig.update_layout(margin=dict(l=30, r=30, b=30, t=30), xaxis_rangeslider_visible=False)
+    fig.add_trace(
+        go.Candlestick(
+            x=data["Date"],
+            open=data["Open"],
+            high=data["High"],
+            low=data["Low"],
+            close=data["Close"],
+            name="Candlestick",
+        )
+    )
+    fig.update_layout(
+        margin=dict(l=30, r=30, b=30, t=30), xaxis_rangeslider_visible=False
+    )
     return fig
 
 
 def generate_forecast_data(data, n_years):
     # FORECASTING
-    df_train = data[['Date', 'Close']]
-    df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})  # This is the format that Prophet accepts
+    df_train = data[["Date", "Close"]]
+    df_train = df_train.rename(
+        columns={"Date": "ds", "Close": "y"}
+    )  # This is the format that Prophet accepts
 
     m = Prophet()
     m.fit(df_train)
     future = m.make_future_dataframe(periods=n_years * 365)
-    fc = m.predict(future)[['ds', 'yhat_lower', 'yhat_upper']].rename(columns={"ds": "Date", "yhat_lower": "Lower", "yhat_upper": "Upper"})
+    fc = m.predict(future)[["ds", "yhat_lower", "yhat_upper"]].rename(
+        columns={"ds": "Date", "yhat_lower": "Lower", "yhat_upper": "Upper"}
+    )
     print("Process Completed!")
     return fc
 
 
 def forecast_display(state):
-    notify(state, 'i', 'Predicting...')
+    notify(state, "i", "Predicting...")
     state.forecast = generate_forecast_data(state.data, state.n_years)
-    notify(state, 's', 'Prediction done! Forecast data has been updated!')
+    notify(state, "s", "Prediction done! Forecast data has been updated!")
 
     if len(forecast) == 0 or len(data) == 0:
         return -1
-    return int((forecast.loc[len(forecast)-1, 'Lower'] - forecast.loc[len(data), 'Lower'])/forecast.loc[len(data), 'Lower']*100)
+    return int(
+        (forecast.loc[len(forecast) - 1, "Lower"] - forecast.loc[len(data), "Lower"])
+        / forecast.loc[len(data), "Lower"]
+        * 100
+    )
 
 
 def pessimistic_forecast_display(forecast, data):
     if len(forecast) == 0 or len(data) == 0:
         return -1
-    return int((forecast.loc[len(forecast)-1, 'Lower'] - forecast.loc[len(data), 'Lower'])/forecast.loc[len(data), 'Lower']*100)
+    return int(
+        (forecast.loc[len(forecast) - 1, "Lower"] - forecast.loc[len(data), "Lower"])
+        / forecast.loc[len(data), "Lower"]
+        * 100
+    )
 
 
 def optimistic_forecast_display(forecast, data):
     if len(forecast) == 0 or len(data) == 0:
         return -1
-    return int((forecast.loc[len(forecast)-1, 'Upper'] - forecast.loc[len(data), 'Upper'])/forecast.loc[len(data), 'Upper']*100)
+    return int(
+        (forecast.loc[len(forecast) - 1, "Upper"] - forecast.loc[len(data), "Upper"])
+        / forecast.loc[len(data), "Upper"]
+        * 100
+    )
+
 
 if __name__ == "__main__":
     # Parameters for retrieving the stock data
     start_date = "2015-01-01"
     end_date = date.today().strftime("%Y-%m-%d")
-    selected_stock = 'AAPL'
+    selected_stock = "AAPL"
     n_years = 1
 
     #### Getting the data, make initial forcast and build a front end web-app with Taipy GUI
@@ -89,7 +126,9 @@ def optimistic_forecast_display(forecast, data):
     partial_md = "<|{forecast}|table|>"
     dialog_md = "<|{show_dialog}|dialog|partial={partial}|title=Forecast Data|on_action={lambda state: state.assign('show_dialog', False)}|>"
 
-    page = dialog_md + """<|toggle|theme|>
+    page = (
+        dialog_md
+        + """<|toggle|theme|>
 <|container|
 # Stock Price **Analysis**{: .color-primary} Dashboard
 
@@ -170,6 +209,7 @@ def optimistic_forecast_display(forecast, data):
 
 <br/>
     """
+    )
 
     # Run Taipy GUI
     gui = Gui(page)

src/requirements.txt

@@ -1,5 +1,6 @@
-taipy==3.1
-yfinance==0.1.96
+numpy==1.24.2
+taipy==4.0.0.dev6
+yfinance==0.2.41
 holidays==0.24
 prophet==1.1.2
 plotly