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order_target_percent ordering too much?


I'm struggling to figure out what I'm missing with the order_target_percent function. When I run the following code, it seems to work correctly, oscillating between investing 100% in the ETF and 100% in cash (allowing for small amounts of extra cash because of fractional share issues etc.).

However, when I run the code with the two securities below, it sometimes "blows up," ordering stocks well in excess of 100% of the portfolio value and generating losses of almost 2000%. I had thought that the function would basically allocate a certain % of the total portfolio value between a list of securities, so I could (in theory) say order_target_percent(sid1, 0.5) on one sid and order_target_percent(sid2, 0.5) on the other and it would split the total value 50/50 between the two stocks, or alternatively say order_target_percent(sid1, 1) to put all money into that stock, or order_target_percent(sid1, 0) to sell off all interest in sid1.

Anyone know what I'm missing?



import talib

# Put any initialization logic here.  The context object will be passed to  
# the other methods in your algorithm.  
def initialize(context):  
    context.stocks = [ sid(8554), sid(27102) ]  
    context.longs = { }

# Will be called on every trade event for the securities you specify.  
def handle_data(context, data):  
    prices = history(50, '1d', 'price') # 50-day lookback window  
    for stock in context.stocks:  
        if context.longs.get(stock) is None:  
            context.longs[stock] = 0.0  
        price = data[stock].price  
        upper, middle, lower = talib.BBANDS(prices[stock], timeperiod=50, nbdevup=2, nbdevdn=2, matype=0)  
        upper, middle, lower = upper[-1], middle[-1], lower[-1] # latest b-band value  
        mavg200 = data[stock].mavg(200)  
        if price <= lower and price <= mavg200:  
            context.longs[stock] = 1.0  
        if price >= upper and price >= mavg200:  
            context.longs[stock] = 0.0

    total_longs = sum(context.longs.values())  
    if total_longs > 0:  
        log.debug("total longs is {tl}".format(tl=total_longs))  
    for stock in context.stocks:  
        if total_longs <= 0:  
            order_target_percent(stock, 0.0)  
            order_target_percent(stock, context.longs[stock] / total_longs)  
            log.debug("adjusting {s} to {p} percent".format(s=stock, p=100.0*context.longs[stock] / total_longs))  
3 responses

Hi Andy,

You ran into one of the current rough edges - the ordering method doesn't check for open orders before calculating the number of shares to buy.

A large order may take several bars to fill, according to the slippage model. While this order is being filled, handle_data is getting called every minute and your order_target_percent is adjusting its values, creating a queue of open orders. This may lead to large swings of order amounts.

In your code on line 15, I added a check for open orders. Hope this helps!


Clone Algorithm
Backtest from to with initial capital
Total Returns
Max Drawdown
Benchmark Returns
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 53fe565ccb53bb076b8b1ce2
There was a runtime error.

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Thanks Alisa!


Occasionally I find this post. I would like to say, could I use the following code at the very beginning to avoid overbuying?
... set_slippage(slippage.FixedSlippage(spread=0.00))