RBOB vs CL

Discussion in 'Commodity Futures' started by Ditch, Feb 13, 2015.

  1. Ditch

    Ditch

    The ratio between RBOB and CL has gone through the roof lately. From what I understand due to strikes at refineries. So a spread seems like a winner as soon as those strikes end. Any thoughts on this?
     
    Last edited: Feb 13, 2015
  2. You mean the spread has grown large or what? It is a simple crack spread. I know they had come down significantly but have spiked quite a bit lately. Not sure if they are high or not though, still new to them.
     
  3. Ditch

    Ditch

    Not the crack spread in dollars, but the ratio between RBOB and CL is near a multi-year high.
     
  4. tagoma

    tagoma

    As FCXo suggests, it is all about gasoline crack spread, that is the difference between gasoline (product out of the refinery process) and crude oil (the main feedstock). In the industry, they use for USGC unleaded 87 conv. - WTI at Cushing, for instance.

    Well, it seems unplanned repairs at some major US refineries, as colleral damage of US nationwide strike of United Steelworkers, took its toll on gasoline prices this week. I am not sure actual runcuts are that big, but there is a 'risk' and market is paying for it.

    With this week's bounce, gasoline crack is now hovering over last year's level ... which is just in the middle of the 5-year range.

    Some dirty work (using futures, almost ignoring seasonality, ...) seems to confirm this.

    # I'm not sure why I cannot download any longer several series at a time
    # Quandl doesn't seem to support square brackets, now
    # And I'm unable to download only selected fields eg the 4th column (that is 'settle')
    # Hum ... That worked before, if I remember well
    import Quandl
    tok = 'blablabla'
    start= '2005-01-01'
    rbob_qdl_code = 'OFDP/FUTURE_RB1' # $/gal
    wti_qdl_code = 'CHRIS/ICE_T2' # $/bbl
    rbob = Quandl.get(dataset=rbob_qdl_code, trim_start=start, authtoken=tok)
    wti = Quandl.get(dataset=wti_qdl_code, trim_start=start)
    df = pd.concat([rbob['Settle'], wti['Settle']], axis=1)
    crack = 42 * df.ix[:,0] - df.ix[:,1] # crack into $/bbl
    plot(crack) # quick chart
    crack.describe()

    '''
    count 2263.000000
    mean 15.325448
    std 10.079828
    min -7.945400
    25% 7.215600
    50% 12.717800
    75% 23.770700
    max 47.804000
    Name: Settle, dtype: float64
    '''
     
  5. Guile

    Guile

    trade the spread on it
     
  6. Ditch

    Ditch

    thanks for the comments guys.
     
  7. Guile

    Guile

    why not look at rbob front vs back
     
  8. Ditch

    Ditch

    i'm looking for opportunities where i don't have to stay in more than a few days