Forward curve using get_data

Corey.Stewart

If I want a forward curve, for, say, NYMEX WTI I can enter:

wti, err = ek.get_data('0#CL:', fields=['CF_Last','EXPIR_Date'])

and get the forward curve as it is right now. But is there a way to use get_data to get it for another date? For example, how the curve was 3 weeks ago (I'm ignoring rolls, etc.)?

I can use get_timeseries to pull CLc1, CLc2, etc. and build a dataframe to get the snapshots in time that I need, but that approach is not very efficient.

Example:

wti_rics = ['CLc1','CLc2','CLc3','CLc4','CLc5','CLc6','CLc7',

'CLc8','CLc9','CLc10','CLc11','CLc12']

wti = ek.get_timeseries(wti_rics,

fields=['TIMESTAMP','Close'],

start_date = (date.today()-timedelta(days=31)).strftime("%Y-%m-%d"),

end_date = (date.today()).strftime("%Y-%m-%d"),

interval = "daily"

)

c1 = wti.iloc[20][0]

c2 = wti.iloc[20][1]

c3 = wti.iloc[20][2]

c4 = wti.iloc[20][3]

c5 = wti.iloc[20][4]

c6 = wti.iloc[20][5]

c7 = wti.iloc[20][6]

c8 = wti.iloc[20][7]

c9 = wti.iloc[20][8]

c10 = wti.iloc[20][9]

c11 = wti.iloc[20][10]

c12 = wti.iloc[20][11]

w1 = wti.iloc[15][0]

w2 = wti.iloc[15][1]

w3 = wti.iloc[15][2]

w4 = wti.iloc[15][3]

w5 = wti.iloc[15][4]

w6 = wti.iloc[15][5]

w7 = wti.iloc[15][6]

w8 = wti.iloc[15][7]

w9 = wti.iloc[15][8]

w10 = wti.iloc[15][9]

w11 = wti.iloc[15][10]

w12 = wti.iloc[15][11]

t1 = wti.iloc[5][0]

t2 = wti.iloc[5][1]

t3 = wti.iloc[5][2]

t4 = wti.iloc[5][3]

t5 = wti.iloc[5][4]

t6 = wti.iloc[5][5]

t7 = wti.iloc[5][6]

t8 = wti.iloc[5][7]

t9 = wti.iloc[5][8]

t10 = wti.iloc[5][9]

t11 = wti.iloc[5][10]

t12 = wti.iloc[5][11]

wti_data = [['Mo1',c1,w1,t1],['Mo2',c2,w2,t2],['Mo3',c3,w3,t3],['Mo4',c4,w4,t4],['Mo5',c5,w5,t5],['Mo6',c6,w6,t6],['Mo7',c7,w7,t7],

['Mo8',c8,w8,t8],['Mo9',c9,w9,t9],['Mo10',c10,w10,t10],['Mo11',c11,w11,t11],['Mo12',c12,w12,t12]]

wti_evolution = pd.DataFrame(wti_data, columns = ['Contract','Current','1 Week Ago','3 Weeks Ago'])

chavalit-jintamalit

Hi @Corey.Stewart

I am not sure if this is what you are looking for?

wti_rics = ['CLc1','CLc2','CLc3','CLc4','CLc5','CLc6','CLc7','CLc8','CLc9','CLc10','CLc11','CLc12']
fields = ['TR.ClosePrice.date','TR.ClosePrice',
          'TR.ClosePrice(SDate=2021-06-23).date','TR.ClosePrice(SDate=2021-06-23)',
          'TR.ClosePrice(SDate=2021-06-09).date','TR.ClosePrice(SDate=2021-06-09)']

df,e = ek.get_data(wti_rics, fields)
df

Corey.Stewart

Any ideas here? Is there a more intuitive way to do what I'm trying to accomplish?

Corey.Stewart

Yes, much more efficient - thank you. I've never worked with TR.ClosePrice, so I'll need to modify what you have to have dynamic, rather than static, dates, but otherwise this is perfect.