Simple Stock Analysis
Produced in R Markdown
Paul Jozefek
2020-12-07
Retrieving Data
> import numpy as np
+ import pandas as pd
+ import matplotlib.pyplot as plt
+ import pandas_datareader
+ import datetime
+ import pandas_datareader.data as webIMPORT HISTORICAL DATA - CAR COMPANIES
> start = datetime.datetime(2012, 1, 1)
+ end = datetime.datetime(2017, 1, 1)
+ tesla = web.DataReader("TSLA", 'yahoo', start, end)
+ ford = web.DataReader("F", 'yahoo', start, end)
+ gm = web.DataReader("GM",'yahoo',start,end) High Low Open Close Volume Adj Close
Date
2012-01-03 5.900 5.530 5.788 5.616 4640500 5.616
2012-01-04 5.734 5.500 5.642 5.542 3150500 5.542
2012-01-05 5.586 5.370 5.552 5.424 5027500 5.424
2012-01-06 5.558 5.282 5.440 5.382 4931500 5.382
2012-01-09 5.498 5.224 5.400 5.450 4485000 5.450 High Low Open Close Volume Adj Close
Date
2012-01-03 11.25 10.99 11.00 11.13 45709900.0 7.511252
2012-01-04 11.53 11.07 11.15 11.30 79725200.0 7.625980
2012-01-05 11.63 11.24 11.33 11.59 67877500.0 7.821692
2012-01-06 11.80 11.52 11.74 11.71 59840700.0 7.902675
2012-01-09 11.95 11.70 11.83 11.80 53981500.0 7.963414 High Low Open Close Volume Adj Close
Date
2012-01-03 21.180000 20.750000 20.830000 21.049999 9321300.0 16.103352
2012-01-04 21.370001 20.750000 21.049999 21.150000 7856700.0 16.179853
2012-01-05 22.290001 20.959999 21.100000 22.170000 17880600.0 16.960161
2012-01-06 23.030001 22.240000 22.260000 22.920000 18234500.0 17.533915
2012-01-09 23.430000 22.700001 23.200001 22.840000 12084500.0 17.472712Data Visualization
Price, Volume, Value
ADJUSTED CLOSE
> tesla['Adj Close'].plot(label='Tesla',
+ figsize=(8,4),title='Adj Close');
+ gm['Adj Close'].plot(label='GM');
+ ford['Adj Close'].plot(label='Ford');
+ plt.legend();
+ plt.show()
VOLUME
> tesla['Volume'].plot(label='Tesla',
+ figsize=(8,4),title='Volume Traded');
+ gm['Volume'].plot(label='gm');
+ ford['Volume'].plot(label='ford');
+ plt.legend();
+ plt.show()
Ford had a big spike in 2013
220363000.0Timestamp('2013-12-18 00:00:00')> # What happened:
+ # http://money.cnn.com/2013/12/18/news/companies/ford-profit/
+ # https://www.usatoday.com/story/money/cars/2013/12/18/ford-2014-profit-warning/4110015/
+ # https://media.ford.com/content/dam/fordmedia/North%20America/US/2014/01/28/4QFinancials.pdfVALUE TRADED
The product of the average price and total volume would yield the value that was traded. For simplicity I have used the adjusted closing price.
> tesla['Total Traded'] = tesla['Adj Close']*tesla['Volume']
+ ford['Total Traded'] = ford['Adj Close']*ford['Volume']
+ gm['Total Traded'] = gm['Adj Close']*gm['Volume']> tesla['Total Traded'].plot(label='Tesla',figsize=(8,4));
+ gm['Total Traded'].plot(label='GM');
+ ford['Total Traded'].plot(label='Ford');
+ plt.legend();
+ plt.ylabel('Total Traded');
+ plt.show()
Tesla had a big spike in 2014.
Timestamp('2014-02-25 00:00:00')> # What happened?
+ # http://money.cnn.com/2014/02/25/investing/tesla-record-high/
+ # https://blogs.wsj.com/moneybeat/2014/02/25/tesla-shares-surge-on-morgan-stanley-report/
+ # https://www.washingtonpost.com/news/wonk/wp/2014/02/25/teslas-stock-is-up-644-why-it-may-not-last/
+ # http://www.cnbc.com/2014/02/25/tesla-soars-ford-falls-in-consumer-reports-study.htmlPLOT WITH MOVING AVERAGE - GM
> gm['MA50'] = gm['Adj Close'].rolling(50).mean()
+ gm['MA200'] = gm['Adj Close'].rolling(200).mean()
+ gm[['Adj Close','MA50','MA200']].plot(
+ label='gm',figsize=(8,4));
+ plt.show()
Scatter Matrix
> car_comp = pd.concat([tesla['Adj Close'],
+ gm['Adj Close'],
+ ford['Adj Close']],axis=1)
+ car_comp.columns = ['Tesla Adj Close',
+ 'GM Adj Close',
+ 'Ford Adj Close']
+ car_comp Tesla Adj Close GM Adj Close Ford Adj Close
Date
2012-01-03 5.616000 16.103352 7.511252
2012-01-04 5.542000 16.179853 7.625980
2012-01-05 5.424000 16.960161 7.821692
2012-01-06 5.382000 17.533915 7.902675
2012-01-09 5.450000 17.472712 7.963414
... ... ... ...
2016-12-23 42.667999 30.899082 10.105044
2016-12-27 43.905998 30.769215 10.048276
2016-12-28 43.948002 30.431576 9.934735
2016-12-29 42.936001 30.422913 9.918513
2016-12-30 42.737999 30.163183 9.837415
[1258 rows x 3 columns]
Candlestick Chart
FORD JAN 2012
> from mplfinance.original_flavor import candlestick_ohlc
+ from matplotlib.dates import DateFormatter, date2num, WeekdayLocator, DayLocator, MONDAY
+
+ # Rest the index to get a column of January Dates
+ ford_reset = ford.loc['2012-01':'2012-01'].reset_index()
+
+ # Create a new column of numerical "date" values for matplotlib to use
+ ford_reset['date_ax'] = ford_reset['Date']. \
+ apply(lambda date: date2num(date))
+ ford_values = [tuple(vals) for vals in ford_reset \
+ [['date_ax', 'Open', 'High', 'Low', 'Close']].values]
+
+ mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
+ alldays = DayLocator() # minor ticks on the days
+ weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
+ dayFormatter = DateFormatter('%d') # e.g., 12
+
+ #Plot it
+ fig, ax = plt.subplots()
+ fig.subplots_adjust(bottom=0.2)
+ ax.xaxis.set_major_locator(mondays)
+ ax.xaxis.set_minor_locator(alldays)
+ ax.xaxis.set_major_formatter(weekFormatter)
+
+ candlestick_ohlc(ax, ford_values, width=0.6,
+ colorup='g',colordown='r');
+ plt.show()
Basic Financial Analysis
Daily Percentage Change
\[ r_t = \frac{p_t}{p_{t-1}} -1\]
> # Method 1: Using shift
+ tesla['returns'] = (tesla['Adj Close'] /
+ tesla['Adj Close'].shift(1) ) - 1 High Low Open ... Adj Close Total Traded returns
Date ...
2012-01-03 5.900 5.530 5.788 ... 5.616 2.606105e+07 NaN
2012-01-04 5.734 5.500 5.642 ... 5.542 1.746007e+07 -0.013177
2012-01-05 5.586 5.370 5.552 ... 5.424 2.726916e+07 -0.021292
2012-01-06 5.558 5.282 5.440 ... 5.382 2.654133e+07 -0.007743
2012-01-09 5.498 5.224 5.400 ... 5.450 2.444325e+07 0.012635
[5 rows x 8 columns]> # Method 2: Using pct_change()
+ tesla['returns'] = tesla['Adj Close'].pct_change(1)
+ ford['returns'] = ford['Adj Close'].pct_change(1)
+ gm['returns'] = gm['Adj Close'].pct_change(1) High Low Open ... Adj Close Total Traded returns
Date ...
2012-01-03 5.900 5.530 5.788 ... 5.616 2.606105e+07 NaN
2012-01-04 5.734 5.500 5.642 ... 5.542 1.746007e+07 -0.013177
2012-01-05 5.586 5.370 5.552 ... 5.424 2.726916e+07 -0.021292
2012-01-06 5.558 5.282 5.440 ... 5.382 2.654133e+07 -0.007743
2012-01-09 5.498 5.224 5.400 ... 5.450 2.444325e+07 0.012635
[5 rows x 8 columns] High Low Open ... Adj Close Total Traded returns
Date ...
2012-01-03 11.25 10.99 11.00 ... 7.511252 3.433386e+08 NaN
2012-01-04 11.53 11.07 11.15 ... 7.625980 6.079828e+08 0.015274
2012-01-05 11.63 11.24 11.33 ... 7.821692 5.309169e+08 0.025664
2012-01-06 11.80 11.52 11.74 ... 7.902675 4.729016e+08 0.010354
2012-01-09 11.95 11.70 11.83 ... 7.963414 4.298770e+08 0.007686
[5 rows x 8 columns] High Low Open ... MA50 MA200 returns
Date ...
2012-01-03 21.180000 20.750000 20.830000 ... NaN NaN NaN
2012-01-04 21.370001 20.750000 21.049999 ... NaN NaN 0.004751
2012-01-05 22.290001 20.959999 21.100000 ... NaN NaN 0.048227
2012-01-06 23.030001 22.240000 22.260000 ... NaN NaN 0.033829
2012-01-09 23.430000 22.700001 23.200001 ... NaN NaN -0.003491
[5 rows x 10 columns]HISTOGRAMS
> tesla['returns'].hist(bins=100,label='Tesla',
+ figsize=(10,8),alpha=0.5);
+ gm['returns'].hist(bins=100,label='GM',alpha=0.5);
+ ford['returns'].hist(bins=100,label='Ford',alpha=0.5);
+ plt.legend();
+ plt.show()
KDE PLOT
> tesla['returns'].plot(kind='kde',label='Tesla',
+ figsize=(12,6));
+ gm['returns'].plot(kind='kde',label='GM');
+ ford['returns'].plot(kind='kde',label='Ford');
+ plt.legend();
+ plt.show()
BOX PLOT
> box_df = pd.concat([tesla['returns'],
+ gm['returns'],ford['returns']],axis=1)
+ box_df.columns = ['Tesla Returns',' GM Returns','Ford Returns']
+ box_df.plot(kind='box',figsize=(8,11),colormap='jet');
+ plt.show()
Comparing Daily Returns between Stocks
SCATTER MATRIX - RETURNS
SCATTER - GM & FORD RETURNS
> box_df.plot(kind='scatter',x=' GM Returns',
+ y='Ford Returns',alpha=0.4,figsize=(10,8));
+ plt.show()
Cumulative Daily Returns
\[\text{Cumulative value at time } t = \prod_{i=1}^t(1+r_i)\]
df[daily_cumulative_return] = ( 1 + df[pct_daily_return] ).cumprod()
This formula will yield the ending value for each dollar invested.
High Low ... returns Cumulative Return
Date ...
2016-12-23 42.689999 41.542000 ... 0.023459 7.597578
2016-12-27 44.450001 42.883999 ... 0.029015 7.818019
2016-12-28 44.759998 43.439999 ... 0.000957 7.825499
2016-12-29 43.840000 42.824001 ... -0.023027 7.645299
2016-12-30 43.500000 42.335999 ... -0.004612 7.610042
[5 rows x 9 columns]Over this time frame, $1 invested in Tesla would have resulted in an ending value of $7.61
> ford['Cumulative Return'] = (1 + ford['returns']).cumprod()
+ gm['Cumulative Return'] = (1 + gm['returns']).cumprod()CUMULATIVE RETURN PLOT
> tesla['Cumulative Return'].plot(label='Tesla',
+ figsize=(8,4),title='Cumulative Return');
+ ford['Cumulative Return'].plot(label='Ford');
+ gm['Cumulative Return'].plot(label='GM');
+ plt.legend();
+ plt.show()
CUMULATIVE RETURN PLOT - GM & FORD
> ford['Cumulative Return'].plot(label='Ford',
+ figsize=(8,4),title='Cumulative Return');
+ gm['Cumulative Return'].plot(label='GM');
+ plt.legend();
+ plt.show()