def save_data1(df,time_id):
    database_name = 'is_data_test'
    df.open.rename(time_id).to_frame().T.to_hdf(database_name,mode='a',append=True,key='open')
    df.close.rename(time_id).to_frame().T.to_hdf(database_name,mode='a',append=True,key='close')
    df.high.rename(time_id).to_frame().T.to_hdf(database_name,mode='a',append=True,key='high')
    df.low.rename(time_id).to_frame().T.to_hdf(database_name,mode='a',append=True,key='low')
    df.vol.rename(time_id).to_frame().T.to_hdf(database_name,mode='a',append=True,key='vol')
    
def save_data(df,time_id):
    database_name = 'is_data_test'
    for i in ['open','high','low','close','vol']:
        df[i].rename(time_id).to_frame().T.to_hdf(database_name,mode='a',append=True,key= i )

begin = 1
data_old = pd.DataFrame()
while begin:
    sleep(1)
    try:
        login_info = Solution.login()
        session_key = login_info.session_key
        login_info.success
        begin = 0
    except:
        continue
all_data = []
pnl_data = []
old_seq = -1
print('begin save data')
while 1:
    try:
        get_data = Solution.question()
    except:
        sleep(0.5)
        continue
    current_sequence = get_data.sequence
    if current_sequence == old_seq:
        sleep(0.5)
        continue
    data = pd.DataFrame([x.values for x in get_data.dailystk],columns=['sequence_id','code','open','high','low','close','vol'])
    save_data(data,current_sequence)
    if data_old.shape[0] ==0:
        data_old = data
    all_data.append(data)
    pnl = get_data.capital
    print('pnl:',pnl,'time:',current_sequence)
    pnl_data.append((current_sequence,pnl))
#     pd.DataFrame(pnl_data).to_pickle('pnl_int_1.pkl')
    ## alpha
    if current_sequence > 0:
#         alpha1 = ((data['vol']-data_old['vol'])/data_old['vol']).rank()
        alpha1 = -data['vol']
        alpha2 = (data['close']-data_old['close'])/data_old['close']
        positions = factor_scale(alpha1) + factor_scale(alpha2)
#         positions = alpha1.pct_change().iloc[current_sequence+1]
        positions = (positions - positions.mean())
        positions = positions/abs(positions).sum()
        positions = np.array([ max(-0.08,min(x,0.08)) for x in positions])
        pos_vol = pnl*positions/(data['close'])*0.9
        pos_vol = [int(x) for x in pos_vol]
        msg = Solution.submit_answer(session_key,current_sequence,pos_vol)
        print(msg)
    data_old = data
    ##
    old_seq = current_sequence
    if current_sequence == 599:
        break
    sleep(0.5)