Determining the best return on investiment (60 day periods)

Using historical closing prices of MSFT when was a good time to buy and sell based on the return on investment metric.

Return on investment:

(sold_price – purchase_price) / purchase_price

For example, if you buy 20 shares of Joe's Pizza for $10 a share, your investment cost is $200. 
If you sell those shares for $250, then your ROI is ($250-200)/$200 for a total of 0.25 or 25%.



In [3]:

    
import pandas as pd
import datetime

%matplotlib inline

Q. Using all the historical closing prices of MSFT shares calculate which 60 day periods yielded a return on investment greater than 25%.



In [4]:

    
ls *.csv









    



 Volume in drive C is Windows
 Volume Serial Number is 6AD0-5968

 Directory of C:\Users\nafagg\Documents\GitHub\notebooks\Introductions

30/04/2015  07:26 PM           430,328 msft.csv
               1 File(s)        430,328 bytes
               0 Dir(s)  411,151,962,112 bytes free



In [5]:

    
stock = pd.read_csv('msft.csv', parse_dates=['Date']).set_index('Date').sort()
stock.tail()



In [6]:

    
stock["Open"].plot(figsize=(15, 5));



In [7]:

    
def ROI(series):
    """Return on investiment"""
    return (series[-1] - series[0]) / series[0]



In [8]:

    
days = 60 
stock["ROI"] = pd.rolling_apply(stock["Close"], days, ROI)
stock.tail()



In [9]:

    
good_times = stock[stock.ROI > 0.25]
good_times.tail()



In [10]:

    
ax = stock["Close"].plot(figsize=(15, 5));
ax.plot(good_times.index, good_times["Close"], 'or', alpha=0.3);

Q. Can you form a cost function to maximize ROI? Using scipy.optimize.



In [ ]:

	Open	High	Low	Close	Volume	Adj Close
Date
2015-04-20	41.73	43.17	41.68	42.91	45738800	42.91
2015-04-21	43.00	43.15	42.53	42.64	25932200	42.64
2015-04-22	42.67	43.13	42.55	42.99	24856600	42.99
2015-04-23	42.89	43.61	42.80	43.34	45029600	43.34
2015-04-24	45.66	48.14	45.65	47.87	127995200	47.87

	Open	High	Low	Close	Volume	Adj Close	ROI
Date
2015-04-20	41.73	43.17	41.68	42.91	45738800	42.91	-0.090504
2015-04-21	43.00	43.15	42.53	42.64	25932200	42.64	-0.092959
2015-04-22	42.67	43.13	42.55	42.99	24856600	42.99	0.007736
2015-04-23	42.89	43.61	42.80	43.34	45029600	43.34	0.052197
2015-04-24	45.66	48.14	45.65	47.87	127995200	47.87	0.139491

	Open	High	Low	Close	Volume	Adj Close	ROI
Date
2013-06-03	34.92	35.63	34.83	35.59	51252600	33.89887	0.271071
2013-06-04	35.62	35.74	34.77	34.99	65529500	33.32738	0.255472
2013-06-06	34.84	35.11	34.49	34.96	37618500	33.29881	0.252149
2013-06-07	35.25	35.78	35.06	35.67	40757300	33.97507	0.267591
2013-06-10	35.51	35.65	35.14	35.47	35994500	33.78457	0.264979