prettify

app.py

@@ -0,0 +1,270 @@
+import streamlit as st
+import pandas as pd
+from sqlalchemy import create_engine
+from dotenv import load_dotenv
+import yfinance as yf
+import plotly.graph_objs as go
+import datetime
+from datetime import timedelta
+import os
+from pandas.tseries.offsets import BDay
+from getDailyData import get_daily
+from streamlit_lightweight_charts import renderLightweightCharts
+load_dotenv()
+
+st.title('🎮 GamedaySPX Data Monitor')
+
+# Get the data for daily first
+data_daily, df_final_daily, final_row_daily = get_daily()
+
+# Get historical data
+spx = yf.Ticker('^GSPC')
+prices = spx.history(interval='30m') 
+
+date_select = st.date_input(
+    'Select data for chart',
+    value=datetime.datetime.today() - BDay(5),
+    min_value=prices.index[0],
+)
+
+engine = create_engine(
+        f"mysql+mysqldb://{os.getenv('DATABASE_USERNAME')}:" \
+        f"{os.getenv('DATABASE_PASSWORD')}@{os.getenv('DATABASE_HOST')}/" \
+        f"{os.getenv('DATABASE')}?ssl_ca=ca-certificates.crt&ssl_mode=VERIFY_IDENTITY"
+    )
+
+q = f'''SELECT AsOf, Predicted, CalibPredicted, Pvalue, ModelNum FROM results where AsOf >= '{date_select}'
+'''
+
+df_all_results = pd.read_sql_query(q, con=engine.connect())
+df_all_results['AsOf'] = df_all_results['AsOf'].dt.tz_localize('America/New_York')
+
+
+df_all_results2 = df_all_results.merge(prices.reset_index()[['Datetime','Open','High','Low','Close']], left_on = 'AsOf', right_on = 'Datetime')
+df_all_results2['Color'] = df_all_results2['Predicted'].apply(lambda x: 'green' if x >=0.6 else 'red' if x < 0.4 else 'yellow')
+df_all_results2['PredDir'] = df_all_results2['Predicted'].apply(lambda x: 'Up' if x >=0.6 else 'Down' if x < 0.4 else 'Neutral')
+
+
+# Load your data
+df1 = df_all_results2.set_index('AsOf')
+df1 = df1.loc[df1.index > str(date_select)]
+
+dts = df1.groupby(df1.index.date).head(1).reset_index()['AsOf']
+daily_closes = data_daily.loc[df1.index.date, 'PrevClose'].drop_duplicates().reset_index()
+daily_closes['FirstBar'] = dts
+levels = data_daily.loc[df1.index.date, ['H1','H2','L1','L2','Open']].drop_duplicates().reset_index()
+levels['FirstBar'] = dts
+
+# Create a candlestick trace with custom colors based on the CandleColor column
+candlestick_trace = go.Candlestick(
+    x=df1.index,
+    open=df1['Open'],
+    high=df1['High'],
+    low=df1['Low'],
+    close=df1['Close'],
+    increasing_fillcolor='#3399ff',
+    decreasing_fillcolor='#ff5f5f',
+    increasing_line_color='#3399ff',  # Color for decreasing candles
+    decreasing_line_color='#ff5f5f',  # Color for decreasing candles
+    name='30m'
+)
+
+df_up = df1.loc[df1['PredDir']=='Up']
+df_down = df1.loc[df1['PredDir']=='Down']
+df_neutral = df1.loc[df1['PredDir']=='Neutral']
+
+scatter_up = go.Scatter(
+    x=df_up.index,
+    y=df_up['High'] * 1.001,
+    mode='markers',
+    marker=dict(size=8),
+    marker_color=df_up['Color'],
+    marker_symbol='triangle-up',
+    name='Up'
+)
+
+scatter_down = go.Scatter(
+    x=df_down.index,
+    y=df_down['Low'] * 0.999,
+    mode='markers',
+    marker=dict(size=8),
+    marker_color=df_down['Color'],
+    marker_symbol='triangle-down',
+    name='Down'
+)
+
+scatter_neut = go.Scatter(
+    x=df_neutral.index,
+    y=df_neutral[['Open','High','Low','Close']].mean(axis=1),
+    mode='markers',
+    marker=dict(size=7),
+    marker_color=df_neutral['Color'],
+    marker_symbol='diamond-open',
+    name='Neutral'
+)
+
+# Create a layout
+layout = go.Layout(
+    title=dict(text='OHLC Chart with Predictions', xanchor='center', yanchor='top', y=0.9,x=0.5),
+
+    xaxis=dict(title='Date'),
+    yaxis=dict(title='Price'),
+    template='plotly_dark',
+    xaxis_rangeslider_visible=False,
+    width=750,
+    height=500
+)
+
+# Create a figure
+fig = go.Figure(data=[candlestick_trace, scatter_up, scatter_neut, scatter_down], layout=layout)
+
+fig.update_xaxes(
+        rangebreaks=[
+            # NOTE: Below values are bound (not single values), ie. hide x to y
+            dict(bounds=["sat", "mon"]),  # hide weekends, eg. hide sat to before mon
+            dict(bounds=[16, 9.5], pattern="hour"),  # hide hours outside of 9.30am-4pm
+            # dict(values=["2019-12-25", "2020-12-24"])  # hide holidays (Christmas and New Year's, etc)
+        ]
+    )
+
+fig.update_layout(
+    shapes = [dict(
+        x0=d-timedelta(minutes=15), x1=d-timedelta(minutes=15), y0=0, y1=1, xref='x', yref='paper',
+        line_width=0.5, opacity=0.5, line_dash='dot') for d in df1.loc[df1['ModelNum']==0].index],
+    legend=dict(yanchor="top", y=1.05, xanchor="center", x=0.5, orientation='h'),
+    margin=dict(l=20, r=20, t=80, b=20)
+)
+
+
+# Define the y-positions for your horizontal lines
+pairs = [(start, level) for start, level in zip(daily_closes['FirstBar'], daily_closes['PrevClose'])]
+
+# Add horizontal lines to the figure
+for pair in pairs:
+    start = pair[0] 
+    end = start + BDay(1) - timedelta(minutes=15)
+    level = pair[1]
+    fig.add_shape(
+        type="line",
+        x0=start,
+        x1=end,
+        y0=level,
+        y1=level,
+        xref='x',
+        yref='y',
+        line=dict(
+            width=0.5,
+            dash="dot",
+        ),
+    )
+
+for start in levels['FirstBar']:
+    end = start + BDay(1)-timedelta(minutes=15)
+    vals = levels.loc[levels['FirstBar']==start, ['H1','H2','L1','L2','Open']].values[0]
+    H1 = vals[0]
+    H2 = vals[1]
+    L1 = vals[2]
+    L2 = vals[3]
+    Open = vals[4]
+    # Plot H1
+    fig.add_shape(
+        type="line",
+        x0=start,
+        x1=end,
+        y0=H1,
+        y1=H1,
+        xref='x',
+        yref='y',
+        line=dict(
+            width=0.5,
+            dash="solid",
+            color="#ff5f5f"
+            ),
+        )
+    # Plot H2
+    fig.add_shape(
+        type="line",
+        x0=start,
+        x1=end,
+        y0=H2,
+        y1=H2,
+        xref='x',
+        yref='y',
+        line=dict(
+            width=1,
+            dash="solid",
+            color="#ff5f5f"
+            ),
+        )
+    # Plot L1
+    fig.add_shape(
+        type="line",
+        x0=start,
+        x1=end,
+        y0=L1,
+        y1=L1,
+        xref='x',
+        yref='y',
+        line=dict(
+            width=0.5,
+            dash="solid",
+            color="#3399ff"
+            ),
+        )
+    # Plot L2
+    fig.add_shape(
+        type="line",
+        x0=start,
+        x1=end,
+        y0=L2,
+        y1=L2,
+        xref='x',
+        yref='y',
+        line=dict(
+            width=1,
+            dash="solid",
+            color="#3399ff"
+            ),
+        )
+    # Plot Open
+    fig.add_shape(
+        type="line",
+        x0=start,
+        x1=end,
+        y0=Open,
+        y1=Open,
+        xref='x',
+        yref='y',
+        line=dict(
+            width=1,
+            dash="solid",
+            color="#cccccc"
+            ),
+        )
+
+fig.for_each_xaxis(lambda x: x.update(showgrid=False))
+fig.for_each_yaxis(lambda x: x.update(showgrid=False))
+
+# Show the figure
+st.plotly_chart(fig, use_container_width=True)
+
+# Important levels
+df_levels = pd.DataFrame(levels[['H2','H1','Open','L1','L2']].iloc[-1]).round(2)
+df_levels.columns = ['Levels']
+df_levels.astype(float).round(2)
+
+# For historical reference
+df_all_results['Symbol'] = df_all_results['Predicted'].apply(lambda x: '🟩' if x >=0.6 else '🟥' if x < 0.4 else '🟨')
+today_df = df_all_results[['AsOf','Symbol','Predicted','CalibPredicted','Pvalue']].tail(13)[::-1]
+today_df = today_df.set_index('AsOf', drop=True)
+df_show = (today_df.style
+           .format(formatter={
+               'Predicted':'{:.1%}',
+               'CalibPredicted':'{:.1%}',
+               'Pvalue':'{:.2f}',
+               })
+)
+
+
+st.dataframe(df_levels.T,use_container_width=True)
+st.dataframe(df_show,use_container_width=True)

requirements.txt

@@ -15,4 +15,5 @@ sqlalchemy<2.0
 python-dotenv
 scikit-learn
 plotly
-streamlit
+streamlit
+streamlit-lightweight-charts