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Hey all,
I'm just seeking some guidance on how to do this better. I was just doing some basic research to compare Monday's opening and low. The code code returns two lists, one with the returns (Monday's close - open/Monday's open) and a list that's just 1's and 0's to reflect if the return was positive or negate.
Please take a look as I'm sure there's a better way to do it in pandas but I just don't know how.
#Monday only
m_list = [] #results list
h_list = [] #hit list (close-low > 0)
n=0 #counter variable
for t in history.index:
if datetime.datetime.weekday(t[1]) == 1: #t[1] is the timestamp in multi index (if timestemp is a Monday)
x = history.ix[n]['open']-history.ix[n]['low']
m_list.append((history.ix[n]['open']-history.ix[n]['low'])/history.ix[n]['open'])
if x > 0:
h_list.append(1)
else:
h_list.append(0)
n += 1 #add to index counter
else:
n += 1 #add to index counter
print("Mean: ", mean(m_list), "Max: ", max(m_list),"Min: ",
min(m_list), "Hit Rate: ", sum(h_list)/len(h_list))
Derek Melchin
Hi Mike,
The code snippet that was posted correctly calculates the mean, max, min and hit rate from the history DataFrame. However, it can be made more efficient by utilizing vectorized operations. For instance,
mondays = history.index.map(lambda x: datetime.weekday(x.date()) == 1) history = history.loc[mondays] history.open_to_low_abs = (history.open - history.low) / history.open print( f"Mean: {history.open_to_low_abs.mean()}\n" f"Max: {history.open_to_low_abs.max()}\n" f"Min: {history.open_to_low_absmin()}\n" f"Hit Rate: {sum(history.open_to_low_abs > 0)/len(history)}" )Produces the same results, but requires less time to run as it has no for loops. For a relatively small DataFrame, like in this example, the speed difference may not be noticeable. But as the DataFrame increases in size, it will become more noticeable.
See the attached research notebook to view all the code.
Best,
Derek
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Mike R
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
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