cfdaily
116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
#!/usr/bin/env python3
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"""
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导入赵云提供的 510300.SSE 日线数据到 vnpy sqlite 数据库
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按照赵云提供的步骤：
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1. 读取 parquet 文件
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2. 转换为 vnpy BarData
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3. 写入数据库
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"""
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import sys
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import types
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# 兼容性模块
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print("🔧 [IMPORT] 加载vnpy.app兼容性模块...")
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vnpy_app_module = types.ModuleType('vnpy.app')
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sys.modules['vnpy.app'] = vnpy_app_module
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submodules = ['cta_strategy', 'cta_backtester', 'data_manager']
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for name in submodules:
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full_name = f'vnpy.app.{name}'
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submodule = types.ModuleType(full_name)
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sys.modules[full_name] = submodule
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setattr(vnpy_app_module, name, submodule)
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from vnpy_ctastrategy import (
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CtaTemplate,
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StopOrder,
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TickData,
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BarData,
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TradeData,
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OrderData,
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BarGenerator,
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ArrayManager,
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)
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from vnpy.trader.constant import Direction, Offset, Interval, Exchange
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sys.modules['vnpy.app.cta_strategy'].CtaTemplate = CtaTemplate
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vnpy_app_module.CtaTemplate = CtaTemplate
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from vnpy_ctabacktester import BacktesterEngine
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sys.modules['vnpy.app.cta_backtester'].BacktesterEngine = BacktesterEngine
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vnpy_app_module.BacktesterEngine = BacktesterEngine
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print("✅ [IMPORT] vnpy.app兼容性模块加载完成！")
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import pandas as pd
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from vnpy.trader.object import BarData
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from vnpy.trader.database import get_database
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def main():
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print("\n🚀 [IMPORT] 开始导入 510300.SSE 日线数据")
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# 1. 读取parquet文件
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parquet_path = "/Users/chufeng/nas/stock-data/sanguo_quant_live/zhaoyun-data/data/raw/daily/sh510300_daily.parquet"
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print(f"\n📖 [IMPORT] 读取数据: {parquet_path}")
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df = pd.read_parquet(parquet_path)
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print(f"✅ [IMPORT] 读取完成，共 {len(df)} 行")
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print(f" 时间范围: {df['trade_date'].min()} ~ {df['trade_date'].max()}")
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print(f" 列: {list(df.columns)}")
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# 2. 转换为 vnpy BarData
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print(f"\n🔧 [IMPORT] 转换为 BarData...")
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bars = []
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for idx, row in df.iterrows():
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bar = BarData(
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symbol="510300",
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exchange=Exchange.SSE,
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interval=Interval.DAILY,
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datetime=row["trade_date"],
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open_price=row["open"],
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high_price=row["high"],
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low_price=row["low"],
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close_price=row["close"],
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volume=row["volume"],
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turnover=row["amount"],
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gateway_name="DATA"
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)
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bars.append(bar)
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print(f"✅ [IMPORT] 转换完成，共 {len(bars)} 个BarData")
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# 3. 写入数据库
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print(f"\n💾 [IMPORT] 写入数据库...")
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db = get_database()
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# 先统计原有数据
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from datetime import datetime
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start_dt = datetime(2012, 5, 28)
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end_dt = datetime(2026, 3, 27)
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existing = db.load_bar_data("510300", Exchange.SSE, Interval.DAILY, start_dt, end_dt)
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print(f"⚠️ [IMPORT] 原有数据: {len(existing)} 条在这个范围内")
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# 保存数据
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db.save_bar_data(bars)
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print(f"✅ [IMPORT] 保存完成，共写入 {len(bars)} 条")
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# 验证写入
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existing_after = db.load_bar_data("510300", Exchange.SSE, Interval.DAILY, start_dt, end_dt)
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print(f"✅ [IMPORT] 写入后验证: {len(existing_after)} 条在这个范围内")
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# 验证目标时间范围
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print(f"\n✅ [IMPORT] 验证目标时间范围 2021-01-01 ~ 2026-03-01:")
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start_target = datetime(2021, 1, 1)
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end_target = datetime(2026, 3, 1)
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target_bars = db.load_bar_data("510300", Exchange.SSE, Interval.DAILY, start_target, end_target)
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print(f" 找到 {len(target_bars)} 条数据")
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if len(target_bars) > 0:
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print(f" 第一条: {target_bars[0].datetime}")
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print(f" 最后一条: {target_bars[-1].datetime}")
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print(f" ✅ 满足需求！")
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print("\n🎉 [IMPORT] 导入完成！")
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if __name__ == '__main__':
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main()
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