113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
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# flake8: noqa
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"""
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下载通达信可转债1分钟bar => vnpy项目目录/bar_data/
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上海股票 => SSE子目录
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深圳股票 => SZSE子目录
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"""
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import os
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import sys
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import csv
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import json
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from collections import OrderedDict
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import pandas as pd
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vnpy_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
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if vnpy_root not in sys.path:
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sys.path.append(vnpy_root)
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os.environ["VNPY_TESTING"] = "1"
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from vnpy.data.tdx.tdx_stock_data import *
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from vnpy.trader.utility import load_json
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from vnpy.trader.utility import get_csv_last_dt
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# 保存的1分钟指数 bar目录
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bar_data_folder = os.path.abspath(os.path.join(vnpy_root, 'bar_data'))
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# 开始日期（每年大概需要几分钟）
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start_date = '20160101'
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# 创建API对象
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api_01 = TdxStockData()
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symbol_dict = api_01.symbol_dict
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# 逐一指数合约下载并更新
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for k, symbol_info in symbol_dict.items():
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stock_code, market_id = k.split('_')
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if market_id == '0':
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exchange_name = '深交所'
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exchange = Exchange.SZSE
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else:
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exchange_name = '上交所'
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exchange = Exchange.SSE
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if symbol_info.get('stock_type') != 'cb_cn':
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continue
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stock_name = symbol_info.get('name')
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print(f'开始更新:{exchange_name}/{stock_name}, 代码:{stock_code}')
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bar_file_folder = os.path.abspath(os.path.join(bar_data_folder, f'{exchange.value}'))
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if not os.path.exists(bar_file_folder):
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os.makedirs(bar_file_folder)
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# csv数据文件名
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bar_file_path = os.path.abspath(os.path.join(bar_file_folder, f'{stock_code}_{start_date}_1m.csv'))
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# 如果文件存在，
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if os.path.exists(bar_file_path):
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# 取最后一条时间
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last_dt = get_csv_last_dt(bar_file_path)
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else:
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last_dt = None
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if last_dt:
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start_dt = last_dt - timedelta(days=1)
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print(f'文件{bar_file_path}存在，最后时间:{start_date}')
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else:
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start_dt = datetime.strptime(start_date, '%Y%m%d')
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print(f'文件{bar_file_path}不存在，或读取最后记录错误,开始时间:{start_date}')
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result, bars = api_01.get_bars(symbol=stock_code,
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period='1min',
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callback=None,
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start_dt=start_dt,
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return_bar=False)
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# [dict] => dataframe
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if not result or len(bars) == 0:
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continue
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if last_dt is None:
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data_df = pd.DataFrame(bars)
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data_df.set_index('datetime', inplace=True)
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data_df = data_df.sort_index()
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# print(data_df.head())
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print(data_df.tail())
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data_df.to_csv(bar_file_path, index=True)
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print(f'首次更新{stock_code} {stock_name}数据 => 文件{bar_file_path}')
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continue
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# 获取标题
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headers = []
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with open(bar_file_path, "r", encoding='utf8') as f:
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reader = csv.reader(f)
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for header in reader:
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headers = header
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break
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bar_count = 0
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# 写入所有大于最后bar时间的数据
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with open(bar_file_path, 'a', encoding='utf8', newline='\n') as csvWriteFile:
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writer = csv.DictWriter(f=csvWriteFile, fieldnames=headers, dialect='excel',
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extrasaction='ignore')
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for bar in bars:
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if bar['datetime'] <= last_dt:
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continue
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bar_count += 1
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writer.writerow(bar)
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print(f'更新{stock_code} {stock_name} 数据 => 文件{bar_file_path}, 最后记录:{bars[-1]}')
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print('更新完毕')
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os._exit(0)
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