def get_fg_index_df(self, cdate):
df = BlockReader().get_df(ct.TONG_DA_XIN_FG_INDEX_PATH)
df = df.loc[df.blockname == self.name]
df = df[['code']]
df['date'] = cdate
df = df.reset_index(drop = True)
return df
def __init__(self):
self.day_reader = TdxDailyBarReader()
self.minline_reader = TdxLCMinBarReader()
self.block_reader = BlockReader()
# pandas数据显示设置
pd.set_option('display.max_columns', None) # 显示所有列
def block(self, group=False, custom=False):
'''
获取板块数据
:param group:
:param custom:
:return: pd.dataFrame or None
'''
reader = BlockReader()
symbol = os.path.join(self.tdxdir, 'block_zs.dat')
if symbol is not None:
return reader.get_df(symbol, group)
return None
def index(self, symbol='incon.dat', group=False):
'''
获取指数数据
:param symbol:
:param group:
:return: pd.dataFrame or None
'''
reader = BlockReader()
symbol = os.path.join(self.tdxdir, symbol)
if symbol is not None:
return reader.get_df(symbol, group)
return None
def main(input, output, datatype):
"""
通达信数据文件读取
"""
if datatype == 'daily':
reader = TdxDailyBarReader()
elif datatype == 'ex_daily':
reader = TdxExHqDailyBarReader()
elif datatype == 'lc':
reader = TdxLCMinBarReader()
elif datatype == 'gbbq':
reader = GbbqReader()
elif datatype == 'block':
reader = BlockReader()
else:
reader = TdxMinBarReader()
try:
df = reader.get_df(input)
if (output):
click.echo("写入到文件 : " + output)
df.to_csv(output)
else:
print(df)
except Exception as e:
print(str(e))
def block(self, symbol='block', custom=False, group=False):
'''
获取板块数据
参考: http://blog.sina.com.cn/s/blog_623d2d280102vt8y.html
:param custom:
:param symbol:
:param group:
:return: pd.dataFrame or None
'''
if custom:
reader = CustomerBlockReader()
vipdoc = os.path.join(self.tdxdir, 'T0002', 'blocknew',
'{}'.format(symbol))
else:
reader = BlockReader()
vipdoc = os.path.join(self.tdxdir, 'T0002', 'hq_cache',
'{}.dat'.format(symbol))
fmt = TYPE_GROUP if group else None
if os.path.exists(vipdoc):
return reader.get_df(vipdoc, format)
else:
logger.error('未找到所需的文件: {}'.format(vipdoc))
return None
def main(input, output, datatype):
"""
通达信数据文件读取
"""
if datatype == 'daily':
reader = TdxDailyBarReader()
elif datatype == 'ex_daily':
reader = TdxExHqDailyBarReader()
elif datatype == 'lc':
reader = TdxLCMinBarReader()
elif datatype == 'gbbq':
reader = GbbqReader()
elif datatype == 'block':
reader = BlockReader()
elif datatype == 'customblock':
reader = CustomerBlockReader()
elif datatype == 'history_financial' or datatype == 'hf':
reader = HistoryFinancialReader()
else:
reader = TdxMinBarReader()
try:
df = reader.get_df(input)
if output:
click.echo("写入到文件 : " + output)
df.to_csv(output)
else:
print(df)
except Exception as e:
print(str(e))
def get_style_block():
"""
返回股票对应的指数
block_zs.dat 对应通达信指数板块
block_gn.dat 对应通达信概念板块
block_fg.dat 对应通达信风格板块 融资融券 已高送转 近期弱势
index 为 code
columns 为指数，如果为指数成份股 则为2
:return:
"""
filename = '{}{}{}'.format(TDX_DIR, os.sep,
'T0002\\hq_cache\\block_fg.dat')
return BlockReader().get_df(filename).pivot(index='code',
columns='blockname',
values='block_type')
def _load_instruments(self, market,
freq) -> Dict[str, List[Tuple[str, str]]]:
"""
返回股票对应的指数
block_zs.dat 对应通达信指数板块
block_gn.dat 对应通达信概念板块
block_fg.dat 对应通达信风格板块 融资融券 已高送转 近期弱势
blockname block_type code_index code
板块名 2表示属于该板块 从0开始的计数 股票代码 6和9开头的是sh
:param market: block name
:param freq: no need
:return -> dict: {instrument: [(begin, end), (begin, end), (begin, end)]}
"""
freq = str(Freq(freq))
logger.info(f"get instruments {market} begin......")
# "czce":28, "dce":29, "shfe":30, "cffex":47
if market in ["future", "commodity", "czce", "dce", "shfe", "cffex"]:
if market == "future":
exchanges = ["czce", "dce", "shfe", "cffex"]
elif market == "commodity":
exchanges = ["czce", "dce", "shfe"]
else:
exchanges = market
instruments_list, files_list = self.get_instrument_by_exchange(
exchanges, freq)
else:
file_path = self.block_dir.joinpath("block_zs.dat")
df = BlockReader().get_df(file_path)
if market in df["blockname"].unique():
instruments_series = df[df["blockname"] == market]["code"]
else:
instruments_series = df["code"]
instruments_list = instruments_series \
.apply(lambda x: "SH" + x if x.startswith("9") or x.startswith("6") else "SZ" + x) \
.to_list()
files_list = \
[get_file_path_from_instrument(self.tdx_path, instrument, freq=freq)[1] for instrument in
instruments_list]
workers = max(min(C.get_kernels(freq), len(instruments_list)), 1)
with tqdm(total=len(instruments_list)) as p_bar:
with ProcessPoolExecutor(max_workers=workers) as executor:
futures = {}
for instrument, file_path in zip(instruments_list, files_list):
futures[executor.submit(get_begin_and_end, file_path,
False)] = instrument
p_bar.update()
error_code = {}
_instruments = {}
with tqdm(total=len(futures)) as p_bar:
for _future in as_completed(futures):
try:
_begin_time, _end_time = _future.result()
_instruments.setdefault(futures[_future], []).append(
(_begin_time, _end_time))
except Exception:
error_code[futures[_future]] = traceback.format_exc()
p_bar.update()
if error_code:
logger.info(f"instruments errors： {error_code}")
logger.info(f"get instruments {market} end......")
return _instruments
class TdxLocalHelper:
BlockReader_TYPE_FLAT = 0
BlockReader_TYPE_GROUP = 1
def __init__(self):
self.day_reader = TdxDailyBarReader()
self.minline_reader = TdxLCMinBarReader()
self.block_reader = BlockReader()
# pandas数据显示设置
pd.set_option('display.max_columns', None) # 显示所有列
#pd.set_option('display.max_rows', None) # 显示所有行
# 解析日线文件数据
#返回值：date:open,high,low,close,amount,volume
def read_tdx_local_day(self):
#df = reader.get_df(config.tdx_local_sz_day + "sz000001.day") sh000001.day
df = self.day_reader.get_df(config.tdx_local_sh_day + "sh000001.day")
print(df)
# 批量格式化日期字段，去掉分隔符
def format_date(self, date):
return ((str(date))[0:10]).replace('-', '')
# 解析1分钟和5分钟数据
#返回值：date: open,high,low,close,amount,volume
#csv格式：code,ts_code,trade_date(缩写）,trade_time,time_index,open,high,low,close,amount,volume
def read_tdx_local_minline_all(self, full_path,filename=""):
#df = reader.get_df(config.tdx_local_sz_minline1 + "sz399001.lc1")
#df = reader.get_df(config.tdx_local_sz_minline5 + "sz399001.lc5")
#df = self.minline_reader.get_df(config.tdx_local_sh_minline1 + "sh600300.lc1")
code = filename[2:8]
ts_code = code + "." + filename[0:2].upper()
df = self.minline_reader.get_df(full_path)
df.insert(0, 'trade_time', df.index)
df.insert(0, 'trade_date', df.index.floor('D'))
df.insert(0, 'ts_code', ts_code)
df.insert(0, 'code', code)
df.reset_index(drop=True,inplace=True) #参考：https://zhuanlan.zhihu.com/p/110819220?from_voters_page=true
df.insert(4, 'time_index', df.index)
#df['trade_time'] = pd.to_datetime(df['trade_time'], infer_datetime_format=True).dt.normalize() # strftime('%m/%d/%Y') # format='%m/%d/%Y').dt.date
#df['trade_time'] = df['trade_time'].apply(lambda x: x.strftime('%H:%M:%S'))
df['trade_time'] = pd.to_datetime(df['trade_time'], format='%H:%M:%S').dt.strftime('%H:%M:%S')
df['time_index'] = df['trade_time'].apply(lambda x: datatime_util.stockTradeTime2Index(x))
df['trade_date'] = df['trade_date'].apply(lambda x: self.format_date(x))
df['open'] = df['open'].apply(lambda x: round(x,2))
df['high'] = df['high'].apply(lambda x: round(x, 2))
df['low'] = df['low'].apply(lambda x: round(x, 2))
df['close'] = df['close'].apply(lambda x: round(x, 2))
csv_filename = config.tdx_csv_minline1_all + ts_code + ".csv"
if os.path.isfile(csv_filename):
os.remove(csv_filename)
df.to_csv(csv_filename, index=False, mode='w', header=True, sep=',', encoding="utf_8_sig")
else:
df.to_csv(csv_filename, index=False, mode='w', header=True, sep=',', encoding="utf_8_sig")
# 解析1分钟和5分钟数据，输出简单形式，只返回price和vol
# 返回值：date: open,high,low,close,amount,volume
# csv格式：code,ts_code,trade_date(缩写）,trade_time,time_index,price,volume
def read_tdx_local_minline_simple(self, full_path, filename=""):
# df = reader.get_df(config.tdx_local_sz_minline1 + "sz399001.lc1")
# df = reader.get_df(config.tdx_local_sz_minline5 + "sz399001.lc5")
# df = self.minline_reader.get_df(config.tdx_local_sh_minline1 + "sh600300.lc1")
code = filename[2:8]
ts_code = code + "." + filename[0:2].upper()
df = self.minline_reader.get_df(full_path)
df.insert(0, 'trade_time', df.index)
df.insert(0, 'trade_date', df.index.floor('D'))
df.insert(0, 'ts_code', ts_code)
df.insert(0, 'code', code)
df.reset_index(drop=True, inplace=True) # 参考：https://zhuanlan.zhihu.com/p/110819220?from_voters_page=true
df.insert(4, 'time_index', df.index)
# df['trade_time'] = pd.to_datetime(df['trade_time'], infer_datetime_format=True).dt.normalize() # strftime('%m/%d/%Y') # format='%m/%d/%Y').dt.date
# df['trade_time'] = df['trade_time'].apply(lambda x: x.strftime('%H:%M:%S'))
df['trade_time'] = pd.to_datetime(df['trade_time'], format='%H:%M:%S').dt.strftime('%H:%M:%S')
df['time_index'] = df['trade_time'].apply(lambda x: datatime_util.stockTradeTime2Index(x))
df['trade_date'] = df['trade_date'].apply(lambda x: self.format_date(x))
#df['open'] = df['open'].apply(lambda x: round(x, 2))
#df['high'] = df['high'].apply(lambda x: round(x, 2))
#df['low'] = df['low'].apply(lambda x: round(x, 2))
df['close'] = df['close'].apply(lambda x: round(x, 2))
df.rename(columns={'close': 'price'}, inplace=True)
df.drop(['open', 'high', 'low', 'amount'], axis=1, inplace=True)
csv_filename = config.tdx_csv_minline1_simple + ts_code + ".csv"
if os.path.isfile(csv_filename):
os.remove(csv_filename)
df.to_csv(csv_filename, index=False, mode='w', header=True, sep=',', encoding="utf_8_sig")
else:
df.to_csv(csv_filename, index=False, mode='w', header=True, sep=',', encoding="utf_8_sig")
# 解析板块数据
#扁平格式返回值：blockname,block_type,code_index,code
def read_tdx_local_block(self):
##指数板块 风格板块 概念板块 一般板块
block_filename = ["block_zs.dat", "block_fg.dat", "block_gn.dat", "block.dat"]
for block in block_filename:
df = self.block_reader.get_df(config.tdx_local_block + block) # 默认扁平格式
df_group = self.block_reader.get_df(config.tdx_local_block + block, self.BlockReader_TYPE_GROUP) #分组格式
filename = config.tdx_csv_block + block[0:-4] + ".csv"
filename_group = config.tdx_csv_block + block[0:-4] + "_group" + ".csv"
if os.path.isfile(filename):
df.to_csv(filename, index=False, mode='a', header=False, sep=',', encoding="utf_8_sig")
df_group.to_csv(filename_group, index=False, mode='a', header=False, sep=',', encoding="utf_8_sig")
else:
df.to_csv(filename, index=False, mode='w', header=True, sep=',', encoding="utf_8_sig")
df_group.to_csv(filename_group, index=False, mode='w', header=True, sep=',', encoding="utf_8_sig")
# 解析分时图数据
def read_tdx_local_fst(self):
reader = TdxMinBarReader() #这个reader不能解析分时图文件
# df = reader.get_df(config.tdx_local_sz_minline + "sz399001.lc1")
df = reader.get_df(config.tdx_local_fst + "sh20200417.tfz")
print(df)
# 解析分时图文件,没有解析出来
"""
通达信的zst的数据记录是每6508个字节为一天的数据,
每26个字节为一个分钟的记录,这26个字节是这样分配的,
时间占两个字节化为十进制为570的话表示9:30分(570/60=9.5)
下一个是占四个字节的叫现价,
再下四个字节叫均价,
另外还有两个字节为该分钟成交量(现在有可能已经改为四个字节),
剩下的14个字节是预留的,
"""
def parse_fst_file(self):
full_path = config.tdx_local_fst + "sh20200417.tfz"
filesize = os.path.getsize(full_path) # 文件字节数
print("filesize为: %s" % (filesize))
if filesize == 0: return
#print(chardet.detect(open(full_path, mode='rb').read())) #查看文件编码格式
file = open(full_path, "rb")
try:
i = 0
while True:
print("游标位置：", file.tell())
stock_date = file.read(2)
cur_price = file.read(4)
arr_price = file.read(4)
vol = file.read(4)
stock_reservation = file.read(12)
stock_date = unpack("h", stock_date)
cur_price = unpack("l", cur_price)
arr_price = unpack("l", arr_price)
vol = unpack("l", vol)
#stock_reservation = unpack("s", stock_reservation)
print(stock_date)
print(cur_price)
print(arr_price)
print(vol)
print(stock_reservation)
i = i + 1
if i == 2:break
for line in file:
result = chardet.detect(line)
print("code: ", result)
buf_size = len(line)
rec_count = buf_size // 32
begin = 0
end = 32
print("行内容：", line)
print("buf_size：", buf_size)
print("rec_count：", rec_count)
a = unpack('IIIIIfII', line[begin:end])
print("解码后的数据0: %s" % (str(a[0])))
print("解码后的数据1: %s" % (str(a[1])))
print("解码后的数据2: %s" % (str(a[2])))
break
finally:
file.close()
def get_block_info2():
# 默认扁平格式
df = BlockReader().get_df(".../T0002/hq_cache/block_zs.dat")
print(df)
def tdx_block_reader():
# stock_data = ts.get_stock_basics()
# stock_data.to_csv('stock.csv',columns=['name'])
stock_edges = pd.DataFrame(columns=['fromID', 'toID', 'type'])
block_nodes = {}
begin_code = int(900001)
#指数版块
zs_df = BlockReader().get_df("C:/new_tdx/T0002/hq_cache/block_zs.dat")
for row in zs_df.iterrows():
if row[1]['blockname'] not in block_nodes:
block_nodes[row[1]['blockname']] = begin_code
begin_code += 1
stock_edges = stock_edges.append(
{
'fromID': row[1]['code'],
'toID': str(block_nodes[row[1]['blockname']]),
'type': '属于'
},
ignore_index=True)
#风格版块
fg_df = BlockReader().get_df("C:/new_tdx/T0002/hq_cache/block_fg.dat")
for row in fg_df.iterrows():
if row[1]['blockname'] not in block_nodes:
block_nodes[row[1]['blockname']] = begin_code
begin_code += 1
stock_edges = stock_edges.append(
{
'fromID': row[1]['code'],
'toID': str(block_nodes[row[1]['blockname']]),
'type': '属于'
},
ignore_index=True)
#概念版块
gn_df = BlockReader().get_df("C:/new_tdx/T0002/hq_cache/block_gn.dat")
for row in gn_df.iterrows():
if row[1]['blockname'] not in block_nodes:
block_nodes[row[1]['blockname']] = begin_code
begin_code += 1
stock_edges = stock_edges.append(
{
'fromID': row[1]['code'],
'toID': str(block_nodes[row[1]['blockname']]),
'type': '属于'
},
ignore_index=True)
# 地区
block_area = ts.get_area_classified()
for row in block_area.iterrows():
if row[1]['area'] not in block_nodes:
block_nodes[row[1]['area']] = begin_code
begin_code += 1
stock_edges = stock_edges.append(
{
'fromID': row[1]['code'],
'toID': str(block_nodes[row[1]['area']]),
'type': '属于'
},
ignore_index=True)
# 行业
block_industry = ts.get_industry_classified()
for row in block_industry.iterrows():
if row[1]['c_name'] not in block_nodes:
block_nodes[row[1]['c_name']] = begin_code
begin_code += 1
stock_edges = stock_edges.append(
{
'fromID': row[1]['code'],
'toID': str(block_nodes[row[1]['c_name']]),
'type': '属于'
},
ignore_index=True)
#edges to csv
stock_edges.to_csv('stock_edges.csv', index=None, encoding="utf-8")
# nodes to csv
stock_data = ts.get_stock_basics()
nodes_df = pd.DataFrame(columns=['code', 'name'])
for k, v in block_nodes.items():
# print(k)
nodes_df = nodes_df.append({
'code': str(v),
'name': k
},
ignore_index=True)
nodes_df = nodes_df.set_index('code')
nodes_df = nodes_df.append(stock_data)
nodes_df.to_csv('stock_nodes.csv', columns=['name'], encoding="utf-8")
