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