from userlib import no

wholeinstruments = D.history_data(D.instruments(start_date='2017-07-03', end_date='2017-07-03') , start_date='2017-07-03', end_date='2017-07-03', fields=['list_date'])
oldinstruments = wholeinstruments[wholeinstruments.list_date < '2008-01-01']
numpy.random.seed(111)
instruments = np.random.choice(oldinstruments.instrument, size=80, replace=False)

split_date = datetime.datetime.strptime('2016-06-01', '%Y-%m-%d')
x_train = []
train_y = []
x_test = []
test_y = []
train_input = []
train_output = []
test_input = []
test_output = []
###############################
data_ = D.features(instruments, start_date='2008-01-01', end_date='2016-10-01', 
           fields=['fs_operating_revenue_yoy_0','amount_0',
                   'close_0','fs_deducted_profit_0','fs_net_profit_0','market_cap_float_0',
                   'fs_common_equity_0','fs_cash_equivalents_0','fs_account_receivable_0','fs_fixed_assets_0','fs_proj_matl_0',
                   
                   'fs_construction_in_process_0','fs_fixed_assets_disp_0','fs_account_payable_0', 'fs_total_liability_0',
                   'fs_paicl_up_capital_0','fs_capital_reserves_0','fs_surplus_reserves_0', 'fs_undistributed_profit_0',
                   'fs_eqy_belongto_parcomsh_0',
                   
                   'fs_total_equity_0','fs_gross_revenues_0', 'fs_total_operating_costs_0','fs_selling_expenses_0',
                   'fs_financial_expenses_0',
                   'fs_general_expenses_0', 'fs_operating_profit_0','fs_total_profit_0','fs_income_tax_0',
                   
                   'fs_net_income_0', 'fs_cash_ratio_0','fs_non_current_liabilities_0','fs_current_liabilities_0',
                   'fs_non_current_assets_0',
                   'fs_current_assets_0','fs_net_cash_flow_0','fs_free_cash_flow_0','fs_operating_revenue_0'])

p_data = D.history_data(['000001.SHA'], '2013-09-01', '2016-10-01', ['close', 'amount','turn'])
p_data=p_data.sort_index(ascending=False)
p_data.dropna(inplace=True)
p_data.reset_index(drop=True, inplace=True)
data   = D.history_data(instruments, '2013-09-01', '2016-10-01', ['close', 'amount','turn'])
#######################################################################################################################33
#################################3333
for i in range(len(instruments)):
    instrument = instruments[i]
    print(instrument )
    mb=D.history_data(instrument, '2016-09-30', '2016-09-30',['industry_sw_level1'])
    mdd=str(mb.industry_sw_level1[0])
    Str='SW'+mdd+'.SHA'
    h_data = D.history_data([Str], '2013-09-01', '2016-10-01', ['close', 'amount','turn'])
    h_data=h_data.sort_index(ascending=False)
    h_data.dropna(inplace=True)
    h_data.reset_index(drop=True, inplace=True)
    #print(h_data)
    data1 = data[data.instrument==instrument]
    data1=data1.sort_index(ascending=False)
    data1.dropna(inplace=True)
    data1.reset_index(drop=True, inplace=True)
    data1['CLOSE']=p_data['close']
    data1['AMOUNT']=p_data['amount']
    data1['TURN']=p_data['turn']
    data1['h_close']=h_data['close']
    data1['h_amount']=h_data['amount']
    data1['h_turn']=h_data['turn']  
    ############################################################################################################3333
    data0 = data_[data_.instrument==instrument]
    data0=data0.fillna(value=0.0001)
    data0=data0.sort_index(ascending=False)
    data0.dropna(inplace=True)
    data0.reset_index(drop=True, inplace=True)
    #################################################################
    x, y, z = no.train_data(data0,data1, future_len=60)
    x_train =x_train + list(x[z<split_date])
    train_y =train_y+ list(y[z<split_date])
    #######################################################################
    #print(np.array(x_train).shape)
    #############################################################
    x_test =x_test+  list(x[z>=split_date])
    test_y = test_y+ list(y[z>=split_date])
    x=[]
    if (i+1) % 2== 0:
        print(i)

X_train=np.array(x_train)

x_train = []

X_test = np.array(x_test) 

x_test = []

y_train =np.array(train_y)

y_test =np.array(test_y)

def shuffle_data(data_x, data_y):
    rand = np.random.choice(len(data_x), size=len(data_x), replace=False)
    x = data_x[rand]
    y = data_y[rand]
    return x, y
X_train, y_train = shuffle_data(X_train, y_train )

from userlib import note

model=note.train_model()

model.fit(X_train, y_train, batch_size=64, epochs=24, verbose=2, validation_split=0.1, shuffle=True)

model.evaluate(X_test, y_test, batch_size=32, verbose=0)

predictions = model.predict(X_test)
print(predictions.shape)

import matplotlib.pyplot as plt
m=0
n=820
plt.plot(predictions[m:n])
plt.plot(test_y[m:n])