Dec

16

 A shocking day so far. [2013/12/16]. Stocks and bonds up. Dax up 1 percentage point more than the S&P. 21 point range on S&P so far. S&P down 15 points in 1 second at 10:23 pm. Like the end of a fireworks show, or the sumo battle in edspec that had 25 turns of fate–elapsed time 30 seconds. How fortunate are those that have enough capital so that they can just buy and hold and live off profits without concerning themselves with these ephemeral movements designed to relieve the weak of their chips. 

Richard Owen writes: 

At my maths professors' retirement dinner, attended by many now asset allocating actuaries, the institutional pension money is obsessed with short term trading of 7pc return and no vol. Which is due to implied liability rates and an absence of gilt yields. Prince Charles has felt it incumbent upon him to disclaim the short term focus and I am not sure how to take being in synch with HRH.

Similarly, there was further consensus interest in bitcoin and missing the boat, 'cos a guy two offices down has made ten times', now making four out of four social occasions at which it has featured. The other commonality has been people in London doing their basement or extension as residences are trading at 4 or 5 times the construction costs. With the idea of tiding over a dry bonus year by borrowing more for a no brainer by becoming a property development nubile. Similarly a friend of a friend has pyramided London property into a ~£40m personally guaranteed, thinly equtized, bank financed block of flats with the belief that one ignores yield and makes the relative value. Mr Cameron and Carney have promised them a stop loss and they have listened.


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  1. Andre Wallin on December 17, 2013 1:08 pm

    python code for random walk divergences from 100MA

    download anaconda from Continuum analytics and use Spyder as the code editor and console.

    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    ts = pd.Series(np.random.randn(1000), index=pd.date_range(’1/1/2000′,
    periods=1000))
    ts = ts.cumsum()
    ma=pd.rolling_mean(ts, 100)

    plt.plot(ts)
    plt.plot(ma)
    plt.show()

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