Replicate Buy-on-Gap strategy in the book Algorithmic Trading

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I replicate the rule of the strategy in Ernest Chan's book. I want to implement it on Quantopian, but there seems to be many problems, like no "open" in USEquityPricing, some error in constructing standard deviation of close price in CustomFactor. So depressed...

The rules for the strategy are:
1. Select all stocks near the market open whose returns from their
previous day’s lows to today’s opens are lower than one standard
deviation. The standard deviation is computed using the daily closeto-
close returns of the last 90 days. These are the stocks that “gapped
down.”
2. Narrow down this list of stocks by requiring their open prices to be
higher than the 20-day moving average of the closing prices.
3. Buy the 10 stocks within this list that have the lowest returns from their
previous day’s lows. If the list has fewer than 10 stocks, then buy the
entire list.
4. Liquidate all positions at the market close.

6 responses

MD K

from quantopian.algorithm import attach_pipeline, pipeline_output  
from quantopian.pipeline import Pipeline  
from quantopian.pipeline import CustomFactor  
from quantopian.pipeline.data.builtin import USEquityPricing  
from quantopian.pipeline.factors import SimpleMovingAverage

# define standard deviation of past close price  
class StandardDev(CustomFactor):  
    # pre-declared inputs and window length  
    inputs = [USEquityPricing.close]  
    window_length = 90  
    # compute standard deviation  
    def compute(self, today, assets, out, close):  
        out[:] = close[-window_length:].diff().std()


# initialization  
def initialize(context):  
    # set commission and slippage  
    set_commission(commission.PerShare(cost=0.0075, min_trade_cost=1))  
    set_slippage(slippage.VolumeShareSlippage(volume_limit=0.1, price_impact=0.1))  
    # Create and attach an empty Pipeline.  
    pipe = Pipeline()  
    pipe = attach_pipeline(pipe, name='my_pipeline')

    # Construct Factors  
    std_90 = StandardDev()  
    sma_20 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=20)  
    open_low = USEquityPricing.open - USEquityPricing.low

    # Construct Filter.  
    open_lt_std = (open_low < std_90)  
    open_gt_sma = (USEquityPricing.open > sma_20)  
    # Register outputs.  
    pipe.add(open_low, 'open-low')

    # Remove rows for which the Filter returns False.  
    pipe.set_screen(open_lt_std and open_gt_sma)


def before_trading_start(context, data):  
    # output result  
    results = pipeline_output('my_pipeline')  
    print results.head(5)

    # Define a universe with the results of a Pipeline.  
    # Take the first ten assets by open minus low.  
    update_universe(results.sort('open_low').index[:10])

Jamie McCorriston

Hi Mingda,

It is a known issue that access to USEquityPricing.open is broken on Quantopian due to conflicts with our security sandbox. This will be fixed in an upcoming release. Regarding the error message raised in the code you shared, the problem was in the comparison between USEquityPricing.low data and your custom factor. Check out the code I shared for how to make it work. The logic isn't the same but I wanted to share it just so you could see what the code should look like.

I hope this helps.

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MD K

Thank you so much! The main problem is still USEquityPricing.open. Maybe I would try some other methods.

E.G. Benke

Really...
USEquityPricing.open is broken?
Any idea when that might be fixed?

Simon Thornington

Note that this will not solve your problem at all, since USEquityPricing.open is, or will be, yesterday's open. There is no way to screen the universe on today's open, since the Pipeline runs at 8:45am or something like that.

Grant Kiehne

Hello Jamie,

Once USEquityPricing.open is fixed, would the Q research platform allow for overnight gap screening? It seems that one could establish the securities list of gainers/losers before running a backtest. One could not go live with the strategy, but at least the feasibility could be studied.

Grant

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