[update] 下载股票1m和日线数据，同时更新5/15/30分钟
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msincenselee
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@ -1,6 +1,6 @@
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# flake8: noqa
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"""
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下载通达信股票合约1分钟bar => vnpy项目目录/bar_data/
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下载通达信股票合约1分钟&日线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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@ -18,8 +18,10 @@ if vnpy_root not in sys.path:
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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.data.common import resample_bars_file
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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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from vnpy.trader.util_wechat import send_wx_msg
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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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@ -30,83 +32,100 @@ start_date = '20160101'
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# 创建API对象
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api_01 = TdxStockData()
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# 更新本地合约缓存信息
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# 额外需要数据下载的基金列表
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stock_list = load_json('stock_list.json')
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symbol_dict = api_01.symbol_dict
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# 逐一合约下载并更新
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for stock_code in stock_list:
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market_id = get_tdx_market_code(stock_code)
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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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# 下载所有的股票数据
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num_stocks = 0
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for period in ['1min', '1day']:
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for symbol in symbol_dict.keys():
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symbol_info = symbol_dict[symbol]
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stock_code = symbol_info['code']
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if ('stock_type' in symbol_info.keys() and symbol_info['stock_type'] in ['stock_cn', 'cb_cn']) or stock_code in stock_list:
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# if stock_code in stock_list:
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# print(symbol_info['code'])
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if symbol_info['exchange'] == 'SZSE':
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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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else:
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continue
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num_stocks += 1
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symbol_info = symbol_dict.get(f'{stock_code}_{market_id}')
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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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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}_{period[0:2]}.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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# 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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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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result, bars = api_01.get_bars(symbol=stock_code,
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period=period,
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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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# 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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# 全新数据
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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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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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# 增量更新
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else:
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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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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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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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with open(bar_file_path, 'a', encoding='utf8') as csvWriteFile:
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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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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('更新完毕')
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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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# 输出 5、15、30分钟的数据
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if period == '1min':
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out_files, err_msg = resample_bars_file(vnpy_root=vnpy_root, symbol=stock_code, exchange=exchange, x_mins=[5,15,30])
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msg = 'tdx股票数据补充完毕: num_stocks={}'.format(num_stocks)
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send_wx_msg(content=msg)
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os._exit(0)
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0
vnpy/data/__init__.py
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0
vnpy/data/__init__.py
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78
vnpy/data/common.py
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78
vnpy/data/common.py
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import os
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import pandas as pd
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def resample_bars_file(vnpy_root, symbol, exchange, x_mins=[], include_day=False):
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"""
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重建x分钟K线（和日线）csv文件
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:param symbol:
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:param x_mins: [5, 15, 30, 60]
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:param include_day: 是否也重建日线
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:return: out_files,err_msg
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"""
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err_msg = ""
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out_files = []
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# 1分钟 csv文件路径
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csv_file = os.path.abspath(os.path.join(vnpy_root, 'bar_data', exchange.value, f'{symbol}_1m.csv'))
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if not os.path.exists(csv_file):
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err_msg = f'{csv_file} 文件不存在，不能转换'
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return out_files, err_msg
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# 载入1分钟csv => dataframe
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df_1m = pd.read_csv(csv_file)
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datetime_format = "%Y-%m-%d %H:%M:%S"
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# 转换时间，str =》 datetime
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df_1m["datetime"] = pd.to_datetime(df_1m["datetime"], format=datetime_format)
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# 使用'datetime'字段作为索引
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df_1m.set_index("datetime", inplace=True)
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# 设置df数据中每列的规则
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ohlc_rule = {
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'open': 'first', # open列：序列中第一个的值
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'high': 'max', # high列：序列中最大的值
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'low': 'min', # low列：序列中最小的值
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'close': 'last', # close列：序列中最后一个的值
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'volume': 'sum', # volume列：将所有序列里的volume值作和
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'amount': 'sum', # amount列：将所有序列里的amount值作和
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"symbol": 'first',
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"trading_date": 'first',
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"date": 'first',
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"time": 'first',
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# "pre_close": 'first',
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# "turnover_rate": 'last',
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# "change_rate": 'last'
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}
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for x_min in x_mins:
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# 目标文件
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target_file = os.path.abspath(
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os.path.join(vnpy_root, 'bar_data', exchange.value, f'{symbol}_{x_min}m.csv'))
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# 合成x分钟K线并删除为空的行 参数 closed：left类似向上取值既 09：30的k线数据是包含09：30-09：35之间的数据
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#df_target = df_1m.resample(f'{x_min}min', how=ohlc_rule, closed='left', label='left').dropna(axis=0, how='any')
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df_target = df_1m.resample(f'{x_min}min', closed='left', label='left').agg(ohlc_rule).dropna(axis=0,
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how='any')
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# dropna(axis=0, how='any') axis参数0：针对行进行操作 1：针对列进行操作 how参数any：只要包含就删除 all：全是为NaN才删除
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if len(df_target) > 0:
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df_target.to_csv(target_file)
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print(f'生成[{x_min}分钟] => {target_file}')
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out_files.append(target_file)
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if include_day:
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# 目标文件
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target_file = os.path.abspath(
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os.path.join(vnpy_root, 'bar_data', exchange.value, f'{symbol}_1d.csv'))
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# 合成x分钟K线并删除为空的行 参数 closed：left类似向上取值既 09：30的k线数据是包含09：30-09：35之间的数据
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# df_target = df_1m.resample(f'D', how=ohlc_rule, closed='left', label='left').dropna(axis=0, how='any')
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df_target = df_1m.resample(f'D', closed='left', label='left').agg(ohlc_rule).dropna(axis=0, how='any')
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# dropna(axis=0, how='any') axis参数0：针对行进行操作 1：针对列进行操作 how参数any：只要包含就删除 all：全是为NaN才删除
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if len(df_target) > 0:
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df_target.to_csv(target_file)
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print(f'生成[日线] => {target_file}')
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out_files.append(target_file)
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return out_files,err_msg
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