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R.I.P Harry Brown - where's my t-shirt?

A variation on Harry Brown's excellent permanent portfolio concept as described in "Fail Safe Investing"
checkout the book or the website crawling road for more info

7 responses

Thanks @Fred, this is a particularly interesting algo to share. Your t-shirt is on its way!

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Jean and I were exploring this algo again, and patched up the code to match the latest API. Below is the algo running over a daily bar test through yesterday. Interestingly, the benchmark just recently started out performing.

The "outperformance" seems relative to start date (attached starting at 2005 and the permanent portfolio is much better; starting in 2008 the same) -- is there a more rigourous way to say "the benchmark started outperforming"?

Interesting, the benchmark seems to almost catch up just before a massive market drop :).

Just a minor fix to be able to run minute backtest.

Minute backtest was raising a KeyError

That was because of

2005-01-03handle_data:1698ERRORNo data for TLT at 2005-01-03 14:31:00+00:00, skipping processing of bar and continuing.  

so rebalance function was failing and so context.rebalance_date wasn't defined as so if data[context.gold].datetime > context.rebalance_date + datetime.timedelta(days=90): was raising an error : KeyError: 'rebalance_date' There was a runtime error on line 53.

I just changed

context.first_time = False  
rebalance(context, data)  

to

rebalance(context, data)  
context.first_time = False # after rebalancing!  

to fix this

Hello,
I'm new here. Thanks to share your experience abou PP Portfolio.
I had executed your code and there are lot of warning message
Line 25: data[sid(N)] is deprecated. Use data.current. Learn more here.

Is this code always correct ?

Patrick,
Try this simplified version.

# Harry Brown's permanent portfolio  
# ---------------------------------------------------------------------------------  
assets, proportion = symbols('VTI', 'GLD', 'TLT', 'SHY'), [0.25, 0.25, 0.25, 0.25]  
# ---------------------------------------------------------------------------------  
def initialize(context):  
     schedule_function(trade, date_rules.month_start(), time_rules.market_open(minutes=65))  
def trade(context,data):  
    if get_open_orders(): return  
    for i in range(len(assets)):  
        if data.can_trade(assets[i]):  
            order_target_percent(assets[i], proportion[i])  
def before_trading_start(context,data):  
    record(leverage = context.account.leverage)  

You may look at Combining Strategic and Tactical Asset Allocation.

Thanks Vladimir,

I appreciate your code review.
Concise and accurate.

I like