Master

requirements.txt

@@ -1,3 +1,4 @@
-altair
 pandas
-streamlit
+yfinance
+prophet
+plotly

streamlit_app.py

@@ -1,40 +1,65 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 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_objects as go
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+
+# เปลี่ยนแปลงพอร์ตเซิร์ฟเวอร์เมื่อมีการรัน Streamlit app
+port = 8502
+START = "2019-01-01"
+TODAY = date.today().strftime("%Y-%m-%d")
+
+st.title("Stock Prediction App")
+
+stocks = ("ADVANC.bk", "AOT.bk", "AWC.bk", "BANPU.bk", "BBL.bk", "BDMS.bk", "BEM.bk", "BGRIM.bk", "BH.bk", "BTS.bk",
+    "CBG.bk", "CENTEL.bk", "COM7.bk", "CPALL.bk", "CPF.bk", "CPN.bk", "CRC.bk", "DELTA.bk", "EA.bk", "EGCO.bk",
+    "GLOBAL.bk", "GPSC.bk", "GULF.bk", "HMPRO.bk", "INTUCH.bk", "IVL.bk", "KBANK.bk", "KCE.bk", "KTB.bk", "KTC.bk",
+    "LH.bk", "MINT.bk", "MTC.bk", "OR.bk", "OSP.bk", "PTT.bk", "PTTEP.bk", "PTTGC.bk", "RATCH.bk", "SAWAD.bk",
+    "SCB.bk", "SCC.bk", "SCGP.bk", "TISCO.bk", "TOP.bk", "TTB.bk", "TU.bk", "WHA.bk")
+
+selected_stocks = st.selectbox("Select Symbol for prediction",stocks)
+
+n_years = st.slider("Year 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("Load data....")
+data = load_data(selected_stocks)
+data_load_state.text("Loading data...done")
+
+
+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()
+
+#Forecasting
+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)
+
+st.subheader('Forecast data')
+st.write(forecast.tail())
+
+st.write('forecast data')
+fig1 = plot_plotly(m,forecast)
+st.plotly_chart(fig1)
+
+st.write('forecast components')
+fig2 = m.plot_components(forecast)
+st.write(fig2)