feat: Initialize PolyWeather project structure including modules for data collection, analysis, strategy, trading, utilities, configuration, and a Streamlit dashboard.

2026-02-05 19:52:02 +08:00
commit 9617425c6e
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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from datetime import datetime, timedelta
+import sys
+sys.path.insert(0, '..')
+
+st.set_page_config(
+    page_title="Polymarket Trading Dashboard",
+    page_icon="📊",
+    layout="wide"
+)
+
+# Custom CSS
+st.markdown("""
+<style>
+    .stMetric {
+        background-color: #1e1e1e;
+        padding: 15px;
+        border-radius: 10px;
+    }
+    .stMetric label {
+        color: #888;
+    }
+    .stMetric [data-testid="stMetricValue"] {
+        color: #00ff88;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Header
+st.title("📊 Polymarket Trading Dashboard")
+st.markdown("---")
+
+# Sidebar
+with st.sidebar:
+    st.header("⚙️ Settings")
+    market_id = st.text_input("Market ID", "weather-ankara-temperature")
+    refresh_rate = st.slider("Refresh Rate (seconds)", 10, 300, 60)
+    st.markdown("---")
+    st.header("📈 Quick Stats")
+    st.metric("Total PnL", "$0.00", "+0%")
+    st.metric("Open Positions", "0")
+    st.metric("Win Rate", "N/A")
+
+# Main content
+col1, col2, col3, col4 = st.columns(4)
+
+with col1:
+    st.metric(
+        label="Current Price",
+        value="$0.92",
+        delta="+2.3%"
+    )
+
+with col2:
+    st.metric(
+        label="Model Prediction",
+        value="7.2°C",
+        delta="+0.5°C"
+    )
+
+with col3:
+    st.metric(
+        label="Confidence Score",
+        value="0.78",
+        delta="+0.05"
+    )
+
+with col4:
+    st.metric(
+        label="Signal",
+        value="BUY",
+        delta="Strong"
+    )
+
+st.markdown("---")
+
+# Charts
+col_left, col_right = st.columns(2)
+
+with col_left:
+    st.subheader("📉 Price History")
+    
+    # Demo price data
+    dates = pd.date_range(start=datetime.now() - timedelta(days=7), periods=168, freq='H')
+    prices = [0.85 + i * 0.0005 + (i % 24) * 0.001 for i in range(168)]
+    
+    df_prices = pd.DataFrame({
+        'Date': dates,
+        'Price': prices
+    })
+    
+    fig_price = px.line(df_prices, x='Date', y='Price', 
+                        template='plotly_dark',
+                        color_discrete_sequence=['#00ff88'])
+    fig_price.update_layout(
+        height=300,
+        margin=dict(l=0, r=0, t=0, b=0)
+    )
+    st.plotly_chart(fig_price, use_container_width=True)
+
+with col_right:
+    st.subheader("🌡️ Temperature Forecast")
+    
+    # Demo temperature data
+    forecast_dates = pd.date_range(start=datetime.now(), periods=72, freq='H')
+    temps = [5 + (i % 24) * 0.3 + (i // 24) * 0.5 for i in range(72)]
+    
+    df_temp = pd.DataFrame({
+        'Date': forecast_dates,
+        'Temperature': temps
+    })
+    
+    fig_temp = px.line(df_temp, x='Date', y='Temperature',
+                       template='plotly_dark',
+                       color_discrete_sequence=['#ff6b6b'])
+    fig_temp.update_layout(
+        height=300,
+        margin=dict(l=0, r=0, t=0, b=0)
+    )
+    st.plotly_chart(fig_temp, use_container_width=True)
+
+st.markdown("---")
+
+# Decision Factors
+st.subheader("🎯 Decision Factors")
+
+factors_col1, factors_col2 = st.columns(2)
+
+with factors_col1:
+    # Factor scores
+    factors = {
+        'Statistical Prediction': 0.85,
+        'Data Consensus': 0.90,
+        'Volume Signal': 0.65,
+        'Orderbook Analysis': 0.72,
+        'Technical Indicators': 0.58,
+        'Whale Signal': 0.45
+    }
+    
+    fig_factors = go.Figure(go.Bar(
+        x=list(factors.values()),
+        y=list(factors.keys()),
+        orientation='h',
+        marker_color=['#00ff88' if v > 0.65 else '#ffaa00' if v > 0.4 else '#ff6b6b' 
+                     for v in factors.values()]
+    ))
+    fig_factors.update_layout(
+        template='plotly_dark',
+        height=250,
+        margin=dict(l=0, r=0, t=0, b=0),
+        xaxis_title="Score",
+        xaxis_range=[0, 1]
+    )
+    st.plotly_chart(fig_factors, use_container_width=True)
+
+with factors_col2:
+    # Order book visualization
+    st.markdown("**📚 Order Book**")
+    
+    bids = [
+        {"price": 0.91, "size": 500},
+        {"price": 0.90, "size": 800},
+        {"price": 0.89, "size": 1200},
+    ]
+    asks = [
+        {"price": 0.93, "size": 600},
+        {"price": 0.94, "size": 400},
+        {"price": 0.95, "size": 900},
+    ]
+    
+    orderbook_df = pd.DataFrame({
+        'Bid Price': [b['price'] for b in bids],
+        'Bid Size': [b['size'] for b in bids],
+        'Ask Price': [a['price'] for a in asks],
+        'Ask Size': [a['size'] for a in asks]
+    })
+    
+    st.dataframe(orderbook_df, use_container_width=True, hide_index=True)
+
+st.markdown("---")
+
+# Recent Trades
+st.subheader("📝 Recent Trades")
+
+trades_df = pd.DataFrame({
+    'Time': ['10:30:15', '10:28:42', '10:25:11'],
+    'Side': ['BUY', 'BUY', 'SELL'],
+    'Price': ['$0.92', '$0.91', '$0.88'],
+    'Amount': ['$100', '$150', '$75'],
+    'Status': ['✅ Filled', '✅ Filled', '✅ Filled']
+})
+
+st.dataframe(trades_df, use_container_width=True, hide_index=True)
+
+# Footer
+st.markdown("---")
+st.markdown("*Last updated: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "*")