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poly/main.py

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+import requests
+import json
+
+# API endpoint
+url = "https://gamma-api.polymarket.com/markets"
+
+# Parameters
+params = {
+    "clob_token_ids": "867120030737778362406620246392661182987264708958188075536191457143522877699"
+}
+
+# Make the GET request
+try:
+    response = requests.get(url, params=params)
+    response.raise_for_status()  # Raise an error for bad status codes
+
+    # Parse JSON response
+    market_data = response.json()
+
+    # Pretty-print the JSON data
+    print("Market Data:")
+    print(json.dumps(market_data, indent=4, sort_keys=True))
+
+except requests.exceptions.RequestException as e:
+    print(f"Error fetching market data: {e}")

poly/mainfr.py

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+import pandas as pd
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from dash import Dash, dcc, html, Input, Output
+from sqlalchemy import create_engine
+from datetime import datetime
+
+# 数据库连接配置
+DB_CONFIG = {
+    'host': '8.155.23.172',
+    'port': 3306,
+    'user': 'root2',
+    'password': 'tG0f6PVYh18le41BCb',
+    'database': 'elonX'
+}
+TABLE_NAME = 'poly_after2024'
+db_uri = f"mysql+pymysql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DB_CONFIG['host']}:{DB_CONFIG['port']}/{DB_CONFIG['database']}"
+engine = create_engine(db_uri)
+
+# 第一步：提取用户的交易记录（从2024-01-01开始）
+user_query = """
+select IF(maker_asset_id = 0, 'buy', 'sell') as sellorbuy,
+       maker_amount_filled/1000000 as maker_amount,
+       taker_amount_filled/1000000 as taker_amount,
+       maker_asset_id, taker_asset_id,
+       timestamp, IF(maker_asset_id = 0, taker_asset_id, maker_asset_id) as assetid 
+from poly_after2024
+where maker = '0x7c3db723f1d4d8cb9c550095203b686cb11e5c6b' and timestamp >= 1704085200
+union all
+select IF(taker_asset_id = 0, 'buy', 'sell') as sellorbuy,
+       taker_amount_filled/1000000 as maker_amount,
+       maker_amount_filled/1000000 as taker_amount,
+       maker_asset_id, taker_asset_id,
+       timestamp, IF(maker_asset_id = 0, taker_asset_id, maker_asset_id) as assetid 
+from poly_after2024
+where taker = '0x7c3db723f1d4d8cb9c550095203b686cb11e5c6b' and timestamp >= 1704085200
+"""
+user_df = pd.read_sql_query(user_query, engine)
+
+# 第二步：提取涉及的assetid的交易记录，计算价格
+asset_ids = user_df['assetid'].unique()
+asset_query = f"""
+select timestamp, maker_amount_filled/1000000 as maker_amount, taker_amount_filled/1000000 as taker_amount,
+       maker_asset_id, taker_asset_id,
+       IF(maker_asset_id = 0, taker_asset_id, maker_asset_id) as assetid
+from poly_after2024
+where (IF(maker_asset_id = 0, taker_asset_id, maker_asset_id) in ({','.join([f"'{x}'" for x in asset_ids])}))
+and timestamp >= 1704085200
+"""
+asset_df = pd.read_sql_query(asset_query, engine)
+
+# 计算价格：金额/数量
+asset_df['price'] = 0.0
+for idx, row in asset_df.iterrows():
+    if row['maker_asset_id'] == '0':
+        asset_df.at[idx, 'price'] = row['maker_amount'] / row['taker_amount']
+    else:
+        asset_df.at[idx, 'price'] = row['taker_amount'] / row['maker_amount']
+asset_df['date'] = pd.to_datetime(asset_df['timestamp'], unit='s').dt.date
+daily_prices = asset_df.groupby(['date', 'assetid'])['price'].mean().reset_index()
+
+# 第三步：计算用户持仓
+user_df['date'] = pd.to_datetime(user_df['timestamp'], unit='s').dt.date
+holdings = {}  # 记录持仓：{assetid: 数量}
+daily_holdings = []  # 按天记录持仓和市值
+dates = pd.date_range(start='2024-01-01', end='2025-05-22', freq='D')
+
+for date in dates:
+    date = date.date()
+    day_trades = user_df[user_df['date'] <= date]
+    # 更新持仓
+    for idx, trade in day_trades.iterrows():
+        assetid = trade['assetid']
+        amount = trade['maker_amount'] if trade['sellorbuy'] == 'buy' else -trade['maker_amount']
+        holdings[assetid] = holdings.get(assetid, 0) + amount
+        if holdings[assetid] <= 0:  # 持仓为0，移除
+            del holdings[assetid]
+    # 计算市值
+    total_value = 0
+    asset_values = {}
+    for assetid, amount in holdings.items():
+        price_data = daily_prices[(daily_prices['assetid'] == assetid) & (daily_prices['date'] <= date)]
+        if not price_data.empty:
+            latest_price = price_data.sort_values('date', ascending=False).iloc[0]['price']
+            value = amount * latest_price
+            total_value += value
+            asset_values[assetid] = value
+    daily_holdings.append({'date': date, 'total_value': total_value, 'asset_values': asset_values})
+
+# 转换为DataFrame
+daily_holdings_df = pd.DataFrame(daily_holdings)
+
+# 第四步：饼图数据（默认显示整个时间段的市场占比）
+market_share = user_df.groupby('assetid').apply(
+    lambda x: x[x['sellorbuy'] == 'buy']['taker_amount'].sum()
+).reset_index(name='amount')
+total_amount = market_share['amount'].sum()
+
+# 第五步：初始化Dash应用
+app = Dash(__name__)
+
+# 初始图表
+fig = make_subplots(rows=1, cols=2, specs=[[{"type": "pie"}, {"type": "scatter"}]], subplot_titles=("市场占比", "资产价值随时间变化"))
+
+# 饼图（初始）
+fig.add_trace(
+    go.Pie(
+        labels=market_share['assetid'],
+        values=market_share['amount'],
+        hoverinfo='label+value',
+        textinfo='percent',
+        texttemplate='%{percent:.2%}',
+        marker=dict(colors=['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c']),
+        showlegend=False
+    ),
+    row=1, col=1
+)
+
+# 折线图
+fig.add_trace(
+    go.Scatter(
+        x=daily_holdings_df['date'],
+        y=daily_holdings_df['total_value'],
+        mode='lines+markers',
+        name='资产价值',
+        customdata=daily_holdings_df['asset_values'],
+        hovertemplate='<b>日期</b>: %{x}<br><b>总资产</b>: %{y:.2f}'
+    ),
+    row=1, col=2
+)
+
+# 布局
+fig.update_layout(
+    title_text="交易员 0x7c3db723f1d4d8cb9c550095203b686cb11e5c6b 资产分析",
+    showlegend=False,
+    height=500,
+    width=1000
+)
+
+# 饼图中心显示总金额
+fig.add_annotation(
+    text=f"${total_amount:,.2f}<br>总计",
+    x=0.18, y=0.5,
+    showarrow=False,
+    font=dict(size=16)
+)
+
+# Dash布局
+app.layout = html.Div([
+    dcc.Graph(id='main-graph', figure=fig),
+    dcc.Store(id='holdings-data', data=daily_holdings_df.to_dict('records'))  # 存储持仓数据
+])
+
+# 回调：当鼠标悬停在折线图上时，更新饼图
+@app.callback(
+    Output('main-graph', 'figure'),
+    Input('main-graph', 'hoverData'),
+    Input('holdings-data', 'data')
+)
+def update_pie_chart(hoverData, holdings_data):
+    # 恢复初始图表
+    fig = make_subplots(rows=1, cols=2, specs=[[{"type": "pie"}, {"type": "scatter"}]], subplot_titles=("市场占比", "资产价值随时间变化"))
+    holdings_df = pd.DataFrame(holdings_data)
+
+    # 折线图保持不变
+    fig.add_trace(
+        go.Scatter(
+            x=holdings_df['date'],
+            y=holdings_df['total_value'],
+            mode='lines+markers',
+            name='资产价值',
+            customdata=holdings_df['asset_values'],
+            hovertemplate='<b>日期</b>: %{x}<br><b>总资产</b>: %{y:.2f}'
+        ),
+        row=1, col=2
+    )
+
+    # 如果没有hover数据，显示默认饼图
+    if hoverData is None:
+        fig.add_trace(
+            go.Pie(
+                labels=market_share['assetid'],
+                values=market_share['amount'],
+                hoverinfo='label+value',
+                textinfo='percent',
+                texttemplate='%{percent:.2%}',
+                marker=dict(colors=['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c']),
+                showlegend=False
+            ),
+            row=1, col=1
+        )
+        fig.add_annotation(
+            text=f"${total_amount:,.2f}<br>总计",
+            x=0.18, y=0.5,
+            showarrow=False,
+            font=dict(size=16)
+        )
+    else:
+        # 获取悬停的日期
+        hover_date = hoverData['points'][0]['x']
+        hover_date = pd.to_datetime(hover_date).date()
+        # 找到该日期的持仓数据
+        holdings_row = holdings_df[holdings_df['date'] == hover_date]
+        if not holdings_row.empty:
+            asset_values = holdings_row.iloc[0]['asset_values']
+            labels = list(asset_values.keys())
+            values = list(asset_values.values())
+            total_value = sum(values)
+            fig.add_trace(
+                go.Pie(
+                    labels=labels,
+                    values=values,
+                    hoverinfo='label+value',
+                    textinfo='percent',
+                    texttemplate='%{percent:.2%}',
+                    marker=dict(colors=['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c']),
+                    showlegend=False
+                ),
+                row=1, col=1
+            )
+            fig.add_annotation(
+                text=f"${total_value:,.2f}<br>总计",
+                x=0.18, y=0.5,
+                showarrow=False,
+                font=dict(size=16)
+            )
+
+    fig.update_layout(
+        title_text="交易员 0x7c3db723f1d4d8cb9c550095203b686cb11e5c6b 资产分析",
+        showlegend=False,
+        height=500,
+        width=1000
+    )
+    return fig
+
+# 运行Dash应用
+if __name__ == '__main__':
+    app.run_server(debug=True)