Category Archives: Present Projects

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Financial modelling

Stock price climbs in anticipation of good earnings. If earnings per share (EPS) beat Wall Street expectations, then depending on Surprise %, the price changes.

Stock price climbs in anticipation of good earnings. If earnings per share (EPS) beat Wall Street expectations, then depending on Surprise %, the price changes. The black dot represents the earnings report release. Image generated using Python.

In this project, my goal is to model and improve my investment strategy. I test the flaws, strengths and limits of the strategy.

Flow chart of the steps I took in developing an earnings-based algorithm.

Flow chart of the steps I took in developing an earnings-based algorithm.

My research led me to Quantopian. It is an online platform that enables one to program trading strategies in Python and then back-test, live-test, live-trade and possibly license the same strategies. My draw to Quantopian was the availability of free-of-cost minute-by-minute stock price data and other data.

Through experience, I learned that programming the trading strategy outside the Quantopian platform can be very time-consuming. With your own code, you can be bogged down in tedious tasks such as fixing dates (line #56), plotting results (line #66), etc instead of focusing on the actual trading algorithm. I share a portion of my home-brewed program to show how tedious it can be. One can use zipline package to ease the pain.

 

Data acquisition

Using Python and iMacros scripts, I downloaded earnings calendars of 2,143 US companies starting from 1998 to 2020.

Screenshot showing one of many Python data-mining scripts I used to download earnings calendars. YES, I read and parsed over 20,000 html pages !!

Screenshot showing one of many Python data-mining scripts I used to download earnings calendars. YES, I read and parsed over 20,000 html pages !! Thanks to Beautiful Soup.

Summary of my earnings database. I still have missing information on 54,076 earnings releases (e.g. Wall St. expectations and actual EPS); a fraction of those have future release dates, i.e., Q3 and Q4 of 2017, 2018, and 2019. Of the 64,588 earnings releases with complete info, 70% beat market expectations.

Summary of my earnings database. I still have missing information on 54,076 earnings releases (e.g. Wall St. expectations and actual EPS); a fraction of those have future release dates, i.e., Q3 and Q4 of 2017, 2018, and 2019. Of the 64,588 earnings releases with complete info, 70% beat market expectations.

Trend analysis

For a select companies, I analyzed historical stock prices around the earnings calendar. I find a few observable trends before and after earnings releases as I demonstrate in the figures below:

Stock price climbs in anticipation of good earnings. If earnings per share (EPS) beat Wall Street expectations, then depending on Surprise %, the price changes.

Stock price climbs in anticipation of good earnings. If earnings per share (EPS) beat Wall Street expectations, then, depending on Surprise %, the price changes.

Stock price climbs in anticipation of good earnings. If earnings per share (EPS) beat Wall Street expectations, then depending on Surprise %, the price changes.

Stock price climbs in anticipation of good earnings. If earnings per share (EPS) beat Wall Street expectations, then, depending on Surprise %, the price changes.

Regression

Here, I mathematically derived 3 signals to identify optimum buy and sell times before earnings release day. Some traders widely use the Moving Average Convergence Divergence (MACD) signal.

Sample trade using my earnings-based algorithm. The buy/sell trades are executed days before a the release of quarterly earnings report. I use the signals (dotted lines) to decide.

Simulated trade using my earnings-based algorithm. The buy/sell trades are executed days before a the release of quarterly earnings report. I use the signals (dotted lines) to decide.

Sample trade using my earnings-based algorithm. The buy/sell trades are executed days before a the release of quarterly earnings report.

Sample trade using my earnings-based algorithm. The buy/sell trades are executed days before a the release of quarterly earnings report.

Back-testing

I am currently back-testing my earnings-based algorithm on Quantopian’s Python-based platform. A sample result is shown in the plot below. The platform executes the algorithm over a specified period, starting capital (US$ 10,000.00), type of securities, a set of stocks, commission and other brokerage fees and of course, the user-generated algorithm written in Python. Quantopian then outputs the total returns of the algorithmic trades among other variables.

Sample back-test results I did with a different algorithm. Here, I used minute stock price data.

Sample back-test results I did with a test algorithm from Jan-2015 to Jul-2017. Here, I used minute stock price data available on Quantopian. I use this platform to test trading strategies.

Sample back-test transactions with minute data on Quantopian.

Sample back-test transactions with minute data. I did the simulation on Quantopian.


 


Example code of programming for finance in Python
1    # Author: Jonah Kadoko 
2    # Date: 07-19-17 
3    # Description: 
4    # The goal of this project is to be able to test specific trading algorithms using Python 
5     
6    import matplotlib.pyplot as plt 
7    import matplotlib.dates as mdates 
8    import datetime 
9    import numpy as np 
10   import urllib 
11    
12   def month2num(month): 
13       # this function changes the month from letters to numbers 
14       month_list ={ 
15           'Jan' : 1, 
16           'Feb' : 2, 
17           'Mar' : 3, 
18           'Apr' : 4, 
19           'May' : 5, 
20           'Jun' : 6, 
21           'Jul' : 7, 
22           'Aug' : 8, 
23           'Sep' : 9, 
24           'Oct' : 10, 
25           'Nov' : 11, 
26           'Dec' : 12 
27           } 
28       return month_list[month] 
29    
30   def stock_data(ticker): 
31       fig = plt.figure() 
32       ax1 = plt.subplot2grid((1,1), (0,0)) 
33       # ticker = 'GOOG' 
34       stock_price_data = [] 
35       open_p = []; vol = []; close_p = []; trade_date = [] 
36       stock_price_url='https://www.google.com/finance/historical?output=csv&startdate=Jul+20+2008&enddate=Jul+19+2017&q='+\ 
37           ticker 
38       raw_data = urllib.request.urlopen(stock_price_url).read().decode() 
39       split_raw_data = raw_data.split('\n') 
40       print(split_raw_data[0]+'\n') 
41    
42       for line in split_raw_data: 
43           split_line = line.split(',') 
44           if '\ufeff' in line or len(split_line) != 6: 
45               print('debug_1') 
46           else: 
47               # print('debug_2') 
48               # print(split_line[1]) 
49               # stock_price_data.append(line) 
50               open_p.append(float(split_line[1])) 
51               close_p.append(float(split_line[4])) 
52               if '-' in split_line[5]: 
53                   vol.append(0) 
54               else: 
55                   vol.append(float(split_line[5])) 
56               # Fix date and timing 
57               split_date=split_line[0].split('-') 
58               week_day = int(split_date[0]); 
59               mon = month2num(split_date[1]); 
60               yr = 2000 + int(split_date[2]) 
61               date_temp = datetime.datetime(year=yr, month=mon, day=week_day) 
62               trade_date.append(mdates.date2num(date_temp)) 
63               # print(date[0].isoformat()) 
64               # print(split_line[0]) 
65       # print(stock_price_data[0:1]) 
66       ax1.plot_date(trade_date, open_p, 'red') 
67       plt.xlabel('Date') 
68       plt.ylabel('Stock price, $') 
69       plt.title('Stock price of '+ticker) 
70       plt.show() 
71    
72    
73   stock_data("NOK") 
74   

 

Stock price of Nokia Corporation from July 20, 2008 to July 20, 2017.

Stock price of Nokia Corporation from July 20, 2008 to July 20, 2017. I generated the plot using the Python code shown above.

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