def parseUSDaily(code):
path = "C:/project/stockdata/UShistory/%s.csv" % code
daily = pd.read_csv(path)
# only retrieve the needed columns
daily = daily[[
'timestamp', 'volume', 'open', 'high', 'low', 'close', 'chg',
'percent', 'turnoverrate', 'amount', 'pe', 'pb', 'ps', 'pcf',
'market_capital'
]]
daily['ts_code'] = code
daily['trade_date'] = daily['timestamp'].map(
lambda x: time.strftime('%Y/%m/%d', time.localtime(x / 1000)))
daily = daily.drop('timestamp', axis=1)
# change datatype to float64
daily = daily.replace(to_replace='None', value='0')
datatype = {
"volume": "int64",
"open": "float64",
"high": "float64",
"low": "float64",
"close": "float64",
"chg": "float64",
"percent": "float64",
"turnoverrate": "float64",
"amount": "float64",
"pe": "float64",
"pb": "float64",
"ps": "float64",
"pcf": "float64",
"market_capital": "float64",
"ts_code": "object",
"trade_date": "object"
}
daily = daily.astype(datatype)
daily = daily.drop_duplicates(['ts_code', 'trade_date'])
daily = daily.set_index(['ts_code', 'trade_date'])
tableName = "daily" + str(getDBIndex(code))
print(tableName)
daily.to_sql(name=tableName, con=usEngine, if_exists="append")
FileLogger.info("write data to Database successfully on code: %s" % code)
print(tableName)
daily.to_sql(name=tableName, con=usEngine, if_exists="append")
FileLogger.info("write data to Database successfully on code: %s" % code)
def getDBIndex(code):
sum = 0
for i in range(0, len(code)):
sum = sum + ord(code[i])
index = sum % 30
return index
if __name__ == "__main__":
# 查询语句：select ts_code from usstock.stocklist;
stockdf = pd.read_csv("C:/project/Tushare/usstock/code.csv")
errordf = pd.read_csv("C:/project/Tushare/usstock/get_error_ts_code.csv")
errorList = errordf['ts_code'].to_numpy()
stockList = stockdf[~stockdf['ts_code'].isin(errorList)]
stockList = stockList['ts_code'].to_numpy()
for code in stockList:
FileLogger.info("running on code: %s" % code)
try:
parseUSDaily(code)
except Exception as ex:
FileLogger.error(ex)
FileLogger.error("write data to Database error on code: %s" % code)
time.sleep(1)
running_loss = [0 for num in range(len(mlps))]
train_loss = []
print(f"Fold {k + 1}")
for epoch in range(epochs):
for i, (x_batch, y_batch) in enumerate(train_loader):
x_batch, y_batch = x_batch.to(device), y_batch.to(device)
optimizer.zero_grad() # N个Model清除梯度
for j, model in enumerate(mlps):
y_pred = model.forward(x_batch)
loss = criterion(y_pred, y_batch.squeeze().long())
loss.backward()
running_loss[j] += loss.item()
optimizer.step() #
print(f"Epoch {epoch + 1} / {epochs}..")
Log.info(f"Epoch {epoch + 1} / {epochs}..")
for i in range(len(mlps)):
train_loss.append(running_loss[i] / float(len(train)))
print(f"{i} - Train loss: {train_loss[i]:.7f}..")
Log.info(f"{i} - Train loss: {train_loss[i]:.7f}..")
model.eval()
pre = []
vote_correct = 0
mlps_correct = [0 for i in range(len(mlps))]
test_loss = [0 for i in range(len(mlps))]
with torch.no_grad():
for i, (x_batch, y_batch) in enumerate(valid_loader):
