238 lines
8.3 KiB
Python
238 lines
8.3 KiB
Python
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) |