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requirements.txt

@@ -1,3 +1,4 @@
-altair
-pandas
-streamlit
+plotly==6.0.0
+prophet==1.1.6
+streamlit==1.42.0
+yfinance==0.2.55

yahoo.py

@@ -0,0 +1,60 @@
+import streamlit as st
+from datetime import date
+import yfinance as yf
+from prophet import Prophet
+from prophet.plot import plot_plotly
+from plotly import graph_objs as go
+
+START = "2015-01-01"
+TODAY = date.today().strftime("%Y-%m-%d")
+st.title("Stock Price Prediction App")
+
+stocks = ("AAPL", "GOOG", "MSFT", "AMZN")
+selected_stock = st.selectbox("Select stock for prediction", stocks)
+n_years = st.slider("Years of prediction", 1, 4)
+period = n_years * 365
+
+@st.cache_data
+def load_data(ticker):
+    data = yf.download(ticker, START, TODAY)
+    data.reset_index(inplace=True)
+    return data
+
+data_load_state = st.text("Loading data...")
+data = load_data(selected_stock)
+data_load_state.text("Loading data...done!")
+st.subheader("Raw data")
+data.columns = data.columns.droplevel(1)
+st.write(data.tail())
+
+#PLOT Raw Data
+def plot_raw_data():
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=data["Date"], y=data["Open"], 
+        name="Stock Open"))
+    fig.add_trace(go.Scatter(x=data["Date"], y=data["Close"], 
+        name="Stock Close"))
+    fig.layout.update(title_text="Time Series Data", 
+        xaxis_rangeslider_visible=True)
+    st.plotly_chart(fig)
+plot_raw_data()
+
+#PREDICTION AVEC PROPHET
+df_train = data[["Date", "Close"]]
+df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})
+m = Prophet()
+m.fit(df_train)
+future = m.make_future_dataframe(periods=period)
+forecast = m.predict(future)
+#Show and plot forecast
+st.subheader("Forecast data")
+st.write(forecast.tail())
+st.write(f"Forecast plot for {n_years} years")
+fig1 = plot_plotly(m, forecast)
+st.plotly_chart(fig1)
+
+st.write("Forecast components")
+fig2 = m.plot_components(forecast)
+st.write(fig2)
+
+