Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,238 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import datetime
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import plotly.express as px
+
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+st.set_page_config(layout="wide")
+
+
+@st.cache_data
+def create_datafram(start_date, num_prices, base_price, price_ratio):
+
+    # Generate timestamps (assuming daily data)
+    dates = pd.date_range(start=start_date, periods=num_prices)
+
+    # Generate random prices 
+    prices = [base_price]  # Initial value
+    for _ in range(num_prices - 1):
+        new_value = prices[-1] + (np.random.randn()* price_ratio)  # AR(1) process
+        prices.append(new_value)
+
+    # Create the DataFrame
+    data = {
+        'timestamp': dates,
+        'price': prices
+    }
+
+    return pd.DataFrame(data)
+
+
+
+
+
+st.header('Create Price Data', divider=True)
+
+col1, col2, col3, col4 = st.columns(4)
+
+with col1:
+    date = st.date_input("Date Start", datetime.date(2019, 7, 6))
+    
+with col2:
+    steps = st.number_input("Time steps (days)", value=365)
+    
+with col3:
+    price = st.number_input("Base Price", value = 100)
+
+with col4:
+    ratio = st.number_input("Fluctuation factor [0 - 1]", value=0.9)
+ 
+ 
+if 'data' not in st.session_state:
+    st.session_state['data'] = create_datafram(date, steps, price, ratio)
+
+
+if st.button('Create Time-series data'):
+    st.session_state.data = create_datafram(date, steps, price, ratio)
+
+
+
+st.header('Show Price Data', divider=True)
+
+
+col5, col6 = st.columns((1,2))
+
+with col5:
+        
+    st.dataframe(st.session_state.data, use_container_width=True)
+            
+with col6:
+    
+    fig = px.line(st.session_state.data, x="timestamp", y="price", title='Time Series Price Data')
+    
+    st.plotly_chart(fig, use_container_width=True)
+    #st.line_chart(st.session_state.data, x="timestamp", y="price", use_container_width=True)
+    
+
+
+
+def cal_ipa(df_in: pd.DataFrame, window_size: int = 120, std_adjust : float = 2.0, ipa_col: str='IPA', adj_price: bool= False):
+
+    if adj_price:
+        
+        df_in['price'] = df_in['price'] - (1 - df_in['price'].rolling(window=window_size).std())
+        
+    df_in[ipa_col] = abs((df_in['price'] - df_in['price'].rolling(window=window_size).mean()) / (df_in['price'].rolling(window=window_size).std()*std_adjust))
+    
+    return df_in
+
+def cal_anomaly(df_in):
+    
+    return ...
+
+    
+st.header('Anomaly IPA Analysis', divider=True)
+
+a_col1, a_col2, a_col3, a_col4 = st.columns(4)
+
+with a_col1:
+    window_quater = st.number_input("Quater window (days)", value=120)
+
+with a_col2:
+    window_month = st.number_input("Monthly window (days)", value=30)
+    
+with a_col3:
+    std_adj = st.number_input("Standard Deviation Adjustment", value=2.5)
+    
+with a_col4:
+    adj_price = st.toggle("Adjust Price")
+
+
+b_col1, b_col2 = st.columns(2)
+
+with b_col1:
+    gamma = st.number_input("Gamma adjustment", value=0.8)   
+    
+
+
+if 'ipa_df' not in st.session_state:
+    st.session_state['ipa_df'] = st.session_state.data.copy()
+else:
+    st.session_state.ipa_df = st.session_state.data.copy()
+    
+if not adj_price:
+    st.session_state.ipa_df = st.session_state.data.copy()
+
+st.session_state.ipa_df = cal_ipa(st.session_state.ipa_df, window_size=window_month, std_adjust=std_adj, ipa_col='IPA_m', adj_price=adj_price)
+st.session_state.ipa_df = cal_ipa(st.session_state.ipa_df, window_size=window_quater, std_adjust=std_adj, ipa_col='IPA_q', adj_price=adj_price)
+
+w_adj = np.random.uniform(low=0.9, high=1.0, size=steps)
+
+st.session_state.ipa_df['IPA_q'] = st.session_state.ipa_df['IPA_q'] * w_adj
+st.session_state.ipa_df['IPA_m'] = st.session_state.ipa_df['IPA_m'] * w_adj
+
+st.session_state.ipa_df['ipa_adj'] = gamma* st.session_state.ipa_df['IPA_m'] + (1-gamma)*st.session_state.ipa_df['IPA_q']
+
+st.session_state.ipa_df['Warning'] = 0
+#df.loc[df['IPA'] < 0.5, 'Warning'] = 'Normal'
+st.session_state.ipa_df.loc[st.session_state.ipa_df['ipa_adj'] >= 0.5, 'Warning'] = 1
+st.session_state.ipa_df.loc[st.session_state.ipa_df['ipa_adj'] >= 1.0, 'Warning'] = 2
+
+
+c_col1, c_col2 = st.columns((1,2))
+
+with c_col1:
+    st.dataframe(st.session_state.ipa_df)
+
+with c_col2:
+    # fig, ax1 = plt.subplots(figsize=(14, 5), dpi=160)
+    # ax2 = ax1.twinx()
+
+    # ax1.plot(st.session_state.ipa_df['price'])
+    # #ax1.set_ylim(90, 150)
+    # ax1.set_xlim(120, 365)
+
+    # #ax2.plot(df['IPA'], c='red')
+    # ax2.fill_between(st.session_state.ipa_df.index, st.session_state.ipa_df['ipa_adj'], color='blue', alpha=0.1)
+    # ax2.fill_between(st.session_state.ipa_df.index, st.session_state.ipa_df['Warning'], color='orange', alpha=0.3)
+    # ax2.set_xlim(120, 365)
+    # ax2.set_ylim(0, 2)
+
+    # st.pyplot(fig)
+    
+# =================================================================
+
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatter(
+        x=st.session_state.ipa_df['timestamp'], 
+        y=st.session_state.ipa_df['price'],
+        hoverinfo='x+y',
+        mode='lines',
+        line=dict(width=1.0, color='rgb(100, 100, 100)'),
+        name='Price'
+    ))
+
+    fig.add_trace(
+        go.Scatter(
+            x=st.session_state.ipa_df['timestamp'], 
+            y=st.session_state.ipa_df['ipa_adj'],
+            hoverinfo='x+y',
+            mode='lines',
+            line=dict(width=1.0, color='rgb(0, 230, 230)'),
+            stackgroup='one',
+            name='IPA Value',
+            yaxis='y2'
+            )
+        ,)
+
+    fig.add_trace(
+        go.Scatter(
+            x=st.session_state.ipa_df['timestamp'], 
+            y=st.session_state.ipa_df['Warning'],
+            hoverinfo='x+y',
+            mode='lines',
+            line=dict(width=1.0, color='rgb(230, 172, 0)'),
+            stackgroup='one',
+            name='Anomaly Index',
+            yaxis='y3'
+            )
+        ,)
+
+
+    fig.update_layout(
+        
+        legend=dict(orientation="h"),
+        
+        yaxis=dict(
+            title=dict(text="Price"),
+            side="left",
+            range=(min(st.session_state.ipa_df['price']*0.99), max(st.session_state.ipa_df['price']*1.01)),
+        ),
+        yaxis2=dict(
+            title='',
+            side="right",
+            range=(0, 2),
+            overlaying="y",
+            showgrid=False,
+        ),
+        yaxis3=dict(
+            title='Anomaly Index',
+            side="right",
+            range=(0, 2),
+            overlaying="y",
+            showgrid=False,
+        ),
+    )
+
+    #fig.update_yaxes(title_text="<b>primary</b> yaxis title")
+    #fig.update_yaxes(title_text="<b>secondary</b> yaxis title", secondary_y=True)
 
-"""
-# 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])),
-    ))
+    fig.update_layout(
+        title_text="IPA Anomaly detection chart",
+    )
+    st.plotly_chart(fig)