index   StartDelivery   Production(intervall)   Quantity    VWAP
17  2017-01-03 01:00:00 2017-01-02 19:00:00       45       36,56666667
18  2017-01-03 01:00:00 2017-01-02 19:15:00       1,4      36,01428571
...
69  2017-01-03 02:00:00 2017-01-02 22:15:00       8,7      34
70  2017-01-03 02:00:00 2017-01-02 23:45:00       5,3      33,63773585

import mysql.connector
import numpy as np
import pandas as pd
import datetime




conn=mysql.connector.connect(user='AriHeck',password='none',host='local',database='DataEvaluation', port=3308)
df = pd.read_sql("select StartOfDelivery,EndOfDelivery,ProdDateTime,PriceEURpMW,QuantityMW, PriceEURpMW*QuantityMW as 'p*Q'  from `Production`
where timestampdiff(hour,StartOfDelivery,EndOfDelivery)=1 AND StartOfDelivery >= '2017-01-03 01:00:00'  AND StartOfDelivery < '2017-01-03 03:00:00'", con=conn)

#Delivery Time Loop
dt=datetime.datetime.strptime('2017-01-03 01:00:00', "%Y-%m-%d %H:%M:%S")
end_date=datetime.datetime.strptime('2017-01-05 00:00:00', "%Y-%m-%d %H:%M:%S")

#Dummies loops
incr_delivery_loop=datetime.timedelta(hours=1)
incr_production_loop=datetime.timedelta(minutes=15)
delta_start=datetime.timedelta(days=1)
delta_end=datetime.timedelta(minutes=30)

#Dummies Data
a=1
delivery_array=[0]*a
production_array=[0]*a
time_remaining_array=[0]*a
VWAP_array=np.zeros(a)
quantity_array=np.zeros(a)

#Start Delivery time loop
while (dt <= end_date):

    #Production Time Loop:
        #Start Production: 1 Day before delivery 15:00 (3:00 PM), End Production 30 mins before delivery
    prod_time=(dt-delta_start).replace(hour=15)
    end_prod=dt-delta_end

    while (prod_time<=end_prod):

        quantity=df[(df['StartOfDelivery']==dt)& (df['ProdDateTime']>=prod_time) & (df['ProdDateTime']<(prod_time+incr_production_loop))].QuantityMW.sum()
        if (quantity==0):
            VWAP=0
        else:
            #Calculate  Volume Weighted Average Price
            pq_total=df[(df['StartOfDelivery']==dt)& (df['ProdDateTime']>=prod_time) & (df['ProdDateTime']<(prod_time+incr_production_loop))]['p*Q'].sum()
            VWAP=pq_total/quantity

        #Save values to  arrays
        VWAP_array=np.append(VWAP_array,VWAP)
        quantity_array=np.append(quantity_array,quantity)
        delivery_array.append(dt)
        production_array.append(prod_time)

       #Increments
        prod_time=prod_time+incr_production_loop

    dt=dt+incr_delivery_loop
#END LOOPS

#Save to Dataframe
WAP_dict={'TimeOfDelivery':delivery_array,
          'ProductionDateTime':production_array,
          'VWAP':VWAP_array,
          'Quantity':quantity_array,}
df_WAP=pd.DataFrame(WAP_dict)
df_WAP=df_WAP[['TimeOfDelivery','ProductionDateTime','Quantity','VWAP']]


#Output
print(df_WAP.head(50))