import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdate # custom tick marks for time series
import seaborn as sns
import yfinance as yf
import plotly.graph_objects as go
import plotly.express as px

def clean_stock_data(df):
    df.reset_index(inplace=True)
    df.columns = df.columns.to_flat_index()
    df.columns = ["_".join(col) for col in df.columns]
    df.rename(columns={"Date_": "Date"}, inplace=True)
    df["Date"] = pd.to_datetime(df["Date"])
    return df

# pull stock data for Crowdstrike, Fortinet, Palo alto network, zscaler and from S&P500 and Nasdaq indices
stock_data = yf.download("CRWD FTNT PANW ZS ^GSPC ^IXIC", start="2022-01-01", end="2026-02-20")
stock_data.head()

[*********************100%***********************]  6 of 6 completed

# download stock data
stock_data = clean_stock_data(stock_data)

stock_data.to_csv('stocks.csv', index=False)
stock_data

# Reads stock csv and parses Date column type from string to datetime64 since excel removes type
stock_data = pd.read_csv('./stocks.csv', parse_dates=['Date']) 

stock_data.head()

stock_data.info()

<class 'pandas.DataFrame'>
RangeIndex: 1036 entries, 0 to 1035
Data columns (total 31 columns):
 #   Column        Non-Null Count  Dtype         
---  ------        --------------  -----         
 0   Date          1036 non-null   datetime64[us]
 1   Close_CRWD    1036 non-null   float64       
 2   Close_FTNT    1036 non-null   float64       
 3   Close_PANW    1036 non-null   float64       
 4   Close_ZS      1036 non-null   float64       
 5   Close_^GSPC   1036 non-null   float64       
 6   Close_^IXIC   1036 non-null   float64       
 7   High_CRWD     1036 non-null   float64       
 8   High_FTNT     1036 non-null   float64       
 9   High_PANW     1036 non-null   float64       
 10  High_ZS       1036 non-null   float64       
 11  High_^GSPC    1036 non-null   float64       
 12  High_^IXIC    1036 non-null   float64       
 13  Low_CRWD      1036 non-null   float64       
 14  Low_FTNT      1036 non-null   float64       
 15  Low_PANW      1036 non-null   float64       
 16  Low_ZS        1036 non-null   float64       
 17  Low_^GSPC     1036 non-null   float64       
 18  Low_^IXIC     1036 non-null   float64       
 19  Open_CRWD     1036 non-null   float64       
 20  Open_FTNT     1036 non-null   float64       
 21  Open_PANW     1036 non-null   float64       
 22  Open_ZS       1036 non-null   float64       
 23  Open_^GSPC    1036 non-null   float64       
 24  Open_^IXIC    1036 non-null   float64       
 25  Volume_CRWD   1036 non-null   int64         
 26  Volume_FTNT   1036 non-null   int64         
 27  Volume_PANW   1036 non-null   int64         
 28  Volume_ZS     1036 non-null   int64         
 29  Volume_^GSPC  1036 non-null   int64         
 30  Volume_^IXIC  1036 non-null   int64         
dtypes: datetime64[us](1), float64(24), int64(6)
memory usage: 251.0 KB

crowdstrike_ticker = yf.Ticker("CRWD")
fortinet_ticker = yf.Ticker("FTNT")
palo_ticker = yf.Ticker("PANW")
zscaler_ticker = yf.Ticker("ZS")

crowdstrike_ticker.quarterly_financials

closing_prices = stock_data[['Date', 'Close_CRWD', 'Close_FTNT', 'Close_PANW', 'Close_ZS', 'Close_^GSPC', 'Close_^IXIC']]
closing_prices.columns = ['Date', 'CRWD', 'FTNT', 'PANW', 'ZS', 'S&P500', 'NASDAQ']

closing_prices.tail(20)

sns.set_theme(style='ticks') # set theme to allow minor ticks to show

plt.figure(figsize=(15, 8)) # increases width and height of plot to create more space and reduce crowding of lines

# Normalize
normalized = closing_prices.copy()

# For each col (except Date), normalize closing price to 100% of 1st closing price. Each closing price starts at 100
for col in ['CRWD', 'FTNT', 'PANW', 'S&P500', 'NASDAQ']:
    
    # Divide each closing price by 1st closing price in each col and then multiplying by 100. Result is % of starting value (100 = 100% of start)
    normalized[col] = (closing_prices[col] / closing_prices[col].iloc[0]) * 100
    
# Melt
normalized_melt = normalized.melt(
    id_vars='Date',
    value_vars=['CRWD', 'FTNT', 'PANW', 'NASDAQ', 'S&P500'],
    var_name='Stock',
    value_name='Normalized_Price'
)


# Draw lineplot of closing price vs date with 3rd variable (stock) expressed through color
g = sns.lineplot(data=normalized_melt, 
                 x='Date', 
                 y='Normalized_Price', 
                 hue='Stock', 
                 hue_order=['CRWD', 'FTNT', 'PANW', ],
                 palette=["#ff0000", "#f1730b", "#00D5FF"],
                 alpha=0.85,
                 linewidth=2,
                 
)

dense_dot_style = (0, (1, 1))

# plots benchmarks (NASDAQ and S&P500)
sns.lineplot(data=normalized_melt, 
             x='Date', 
             y='Normalized_Price', 
             hue='Stock', 
             hue_order=['NASDAQ', 'S&P500'],
             palette=["#562d59", "#005800"], 
             #linestyle=dense_dot_style,
             linewidth=1.75,
             alpha=0.65
             
)


# Draw line of incidence at Crowdstrike outage in 2024
g.axvline(x = pd.to_datetime('2024-07-19'), color='black', alpha=0.5, label='Crowdstrike Outage', linestyle='--')

# 
g.axhline(y=100, color='brown', alpha=0.5, label='Start Value (Jan 2022)', linestyle='--')

# Set labels and title
g.set(xlabel='Date', ylabel='Normalized Price (% of Jan 2022 start value)', title='Stock Performances vs Market Benchmarks (Normalized) Over Time')

# Customize tickers to be per year/month
g.xaxis.set_major_locator(mdate.YearLocator())
g.xaxis.set_minor_locator(mdate.MonthLocator(bymonth=[4,7,10]))

# add grid lines back for major and minor axis (had to use style w/o to get minor tick marks)
g.grid(which='both', linestyle='--', linewidth=0.75, alpha=0.7)

plt.legend()
plt.show()

# transposes dataframes so dates are rows and columns are financial stats
crowdstrike_ticker = crowdstrike_ticker.financials.T
palo_ticker = palo_ticker.financials.T 
fortinet_ticker = fortinet_ticker.financials.T 
zscaler_ticker = zscaler_ticker.financials.T 

crowdstrike_ticker

ticker_df = {
    'Date': [],
    'Stock': [],
    'Total Revenue': [],
}

tickers = {'CRWD': crowdstrike_ticker, 'PANW': palo_ticker, 'FTNT': fortinet_ticker, 'ZS': zscaler_ticker}

# Populate dictionary used to build dataframe. Includes dates stock ticker ,and total revenues of CRWD ticker and competitors
for ticker_name, ticker in tickers.items():
    annual_rev_df = ticker['Total Revenue']
    annual_rev_df = annual_rev_df.reset_index()
    annual_rev_df = annual_rev_df.rename(columns={'index':'Date'})
    
    ticker_df['Date'].extend(annual_rev_df['Date'])
    ticker_df['Stock'].extend([ticker_name] * len(annual_rev_df))
    ticker_df['Total Revenue'].extend(annual_rev_df['Total Revenue'])
    
# Create Dataframe from dictionary and drop rows containing NA Total Revenue
ticker_df = pd.DataFrame(ticker_df)
ticker_df = ticker_df.dropna(subset='Total Revenue')

# adds columns for year periods
ticker_df['year'] = ticker_df['Date'].dt.to_period(freq='Y').astype(str) # year periods

year_order = sorted(ticker_df['year'].unique())

# converts revenue from scientific notation to billions of dollars 
ticker_df['Total Revenue'] = ticker_df['Total Revenue'] / 1e9

# Create facet grid, faceted on Stock ticker. Each plot is a barplot of Total Revenue (Billions) vs. Year
g = sns.FacetGrid(ticker_df, col='Stock', col_wrap=2, col_order=['CRWD', 'PANW', 'FTNT', 'ZS'])
g.map_dataframe(sns.barplot, x='year', y='Total Revenue', formatter=lambda s: f"{s[0:4]}", order=year_order)

# Set axis labels and titles for each subplot
g.set_axis_labels(x_var='Year', y_var='Total Revenue (Billions)')
g.set_titles(col_template='{col_name}')

# set limit for yaxis on each subplot
g.set(ylim=(0,10))

# Adds label for each subplot noting the Total Revenue (Billions) each year available
for ax in g.axes.flat:
    ax.bar_label(ax.containers[0], fmt='%.2f',fontsize=10)

plt.show()

fig = go.Figure(data=[go.Candlestick(x=stock_data['Date'],
                open=stock_data['Open_CRWD'],
                high=stock_data['High_CRWD'],
                low=stock_data['Low_CRWD'],
                close=stock_data['Close_CRWD'], 
                name='CRWD',
                )]
)

# Vertical line of incidence at CrowdStrike outage date
fig.add_vline(
    x='2024-07-19',
    line_width=2,
    line_dash='dash',            
    line_color='teal',
    opacity=0.5                   # transparency (0=invisible, 1=fully opaque)
)

fig.update_layout(
    height=600,
    title="Crowdstrike Stock since 2022",
    xaxis_title='Date',
    title_x=0.5,
    yaxis_title='Price (USD)',
    plot_bgcolor='#0d0d0d',
    xaxis_gridcolor='#333333',
    yaxis_gridcolor='#333333',    
    annotations=[dict(
        x='2024-07-19', y=0.08, xref='x', yref='paper',
        showarrow=False, xanchor='left', text='Crowdstrike Outage', font=dict(color='white'))]
)

fig.update_xaxes(
    rangeselector=dict(
        buttons=[
            dict(count=7,  label='1 Week',  step='day',   stepmode='backward'),
            dict(count=1,  label='1 Month',  step='month', stepmode='backward'),
            # dict(count=3,  label='3 Months',  step='month', stepmode='backward'),
            dict(count=1, label='YTD', step='year', stepmode='todate'),
            dict(count=1,  label='1 Year',  step='year',  stepmode='backward'),
            dict(count=2,  label='2 Years',  step='year',  stepmode='backward'),
            dict(step='all', label='All')
        ]
    ),
    rangeslider=dict(visible=True),
    hoverformat='%b. %d %Y', 
    dtick='M3',          # tick every quarter (3 months)
    tickformat='%b. %Y'   # format as '{Month name} {Full year}'
)

fig.show(renderer='notebook')

stock_data[['Date', 'Close_CRWD', 'Close_FTNT', 'Close_PANW', 'Close_ZS', 'Volume_CRWD', 'Volume_FTNT', 'Volume_PANW', 'Volume_ZS']]

# subset of stock data
close_df = stock_data[['Date', 'Close_CRWD', 'Close_FTNT', 'Close_PANW', 'Close_ZS']] 

vol_df = stock_data[['Date', 'Volume_CRWD', 'Volume_FTNT', 'Volume_PANW', 'Volume_ZS']] 


close_df.columns = ['Date', 'CRWD', 'FTNT', 'PANW', 'ZS']
vol_df.columns = ['Date', 'CRWD', 'FTNT', 'PANW', 'ZS']


close_df = close_df.melt(id_vars='Date', value_vars=['CRWD', 'FTNT', 'PANW', 'ZS'], var_name='Ticker', value_name='Closing Price')

vol_df = vol_df.melt(id_vars='Date', value_vars=['CRWD', 'FTNT', 'PANW', 'ZS'], var_name='Ticker', value_name='Volume')

anim_df = close_df.merge(vol_df, on=['Date','Ticker'])

# transform Date column to a monthly period and make this a new column as string type
anim_df['month'] = anim_df['Date'].dt.to_period('M').astype(str)

# inserts the popped month series as first column
anim_df.insert(0, 'Month', anim_df.pop('month'))

anim_df = anim_df.drop(columns='Date')

# compute daily return before grouping for volatility
anim_df = anim_df.sort_values(['Ticker', 'Month'])
anim_df['Daily Return'] = anim_df.groupby('Ticker')['Closing Price'].pct_change()

monthly_df = anim_df.groupby(['Month', 'Ticker']).agg(
    Close=('Closing Price', 'last'),
    Volume=('Volume', 'mean'),
    Volatility=('Daily Return', lambda x: x.std() * (21 ** 0.5))
).reset_index()


# normalize each ticker's price to 100 at start
base = monthly_df.groupby('Ticker')['Close'].transform('first')
monthly_df['Indexed Price'] = (monthly_df['Close'] / base) * 100

fig = px.scatter(
    monthly_df,
    x='Indexed Price',
    y='Volume',
    animation_frame='Month',
    animation_group='Ticker',
    size='Volatility',
    color='Ticker',
    hover_name='Ticker',
    size_max=60,
    range_x=[0, monthly_df['Indexed Price'].max() * 1.1],
    range_y=[-monthly_df['Volume'].max() * 0.15, monthly_df['Volume'].max() * 1.5],
    title='CRWD vs Competitors — Indexed Price vs Volume',
    labels={
        'Indexed Price': 'Growth Since Jan 2022 (Base = 100)',
        'Volume': 'Avg Daily Volume',
        'Volatility': 'Monthly Volatility'
    }
)

fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 700  # ms per frame

# for nbconvert HTML export
from IPython.display import display, HTML

animation_html = fig.to_html(full_html=False, include_plotlyjs='cdn', auto_play=False)
display(HTML(animation_html))

import calendar

# pull just what we need
df = stock_data[['Date', 'Volume_CRWD']].copy()
df['Year'] = df['Date'].dt.year.astype(int)
df['Month'] = df['Date'].dt.month.astype(int)

# pivot to Year x Month grid with mean daily volume
pivot = df.pivot_table(index='Year', columns='Month', values='Volume_CRWD', aggfunc='mean', fill_value=0)

# OR USE: pivot = df.groupby(['Year', 'Month'])['Volume_CRWD'].mean().unstack()


# rename columns from 1-12 to Jan-Dec
pivot.columns = [calendar.month_abbr[m] for m in pivot.columns]

# convert to millions for readable annotations
pivot_millions = pivot / 1_000_000

# plot
fig, ax = plt.subplots(figsize=(14, 6))

sns.heatmap(
    pivot_millions,
    ax=ax,
    cmap='YlOrRd',
    fmt='.1f',
    linewidths=0.5,
    linecolor='white',
    cbar_kws={'label': 'Avg Daily Volume (Millions)'},
    annot=True,
    annot_kws={'size': 9}
)

ax.set_title("CrowdStrike (CRWD) — Seasonal Trading Volume Heatmap", fontsize=14, pad=15)
ax.set_xlabel("Month", fontsize=12, labelpad=15)
ax.set_ylabel("Year", fontsize=12, labelpad=15)

plt.show()

Price	Close						High				...	Open				Volume
Ticker	CRWD	FTNT	PANW	ZS	^GSPC	^IXIC	CRWD	FTNT	PANW	ZS	...	PANW	ZS	^GSPC	^IXIC	CRWD	FTNT	PANW	ZS	^GSPC	^IXIC
Date
2022-01-03	198.330002	66.624001	90.701668	301.829987	4796.560059	15832.799805	205.574997	72.424004	93.400002	323.549988	...	93.073334	322.600006	4778.140137	15732.500000	5311600	9515000	7802400	2576000	3831020000	4429960000
2022-01-04	189.190002	64.428001	87.333336	281.579987	4793.540039	15622.719727	197.445007	66.683998	90.721664	298.190002	...	89.574997	297.964996	4804.509766	15852.139648	6503400	9829000	9586200	3827700	4683170000	5131110000
2022-01-05	179.289993	60.698002	84.143333	262.329987	4700.580078	15100.169922	191.360001	63.939999	86.748337	280.369995	...	85.541664	275.010010	4787.990234	15547.160156	5003400	12033000	7494000	3437700	4887960000	5031850000
2022-01-06	187.490005	62.799999	88.818336	260.209991	4696.049805	15080.860352	192.169998	63.933998	90.166664	266.320007	...	83.500000	261.000000	4693.390137	15024.150391	6368600	12004000	16553400	2439600	4295280000	4790820000
2022-01-07	188.990005	62.931999	87.581665	261.529999	4677.029785	14935.900391	193.270004	63.966000	91.366669	268.359985	...	87.809998	258.380005	4697.660156	15095.719727	3531900	8805000	13586400	1696000	4181510000	4238070000

	Date	Close_CRWD	Close_FTNT	Close_PANW	Close_ZS	Close_^GSPC	Close_^IXIC	High_CRWD	High_FTNT	High_PANW	...	Open_PANW	Open_ZS	Open_^GSPC	Open_^IXIC	Volume_CRWD	Volume_FTNT	Volume_PANW	Volume_ZS	Volume_^GSPC	Volume_^IXIC
0	2022-01-03	198.330002	66.624001	90.701668	301.829987	4796.560059	15832.799805	205.574997	72.424004	93.400002	...	93.073334	322.600006	4778.140137	15732.500000	5311600	9515000	7802400	2576000	3831020000	4429960000
1	2022-01-04	189.190002	64.428001	87.333336	281.579987	4793.540039	15622.719727	197.445007	66.683998	90.721664	...	89.574997	297.964996	4804.509766	15852.139648	6503400	9829000	9586200	3827700	4683170000	5131110000
2	2022-01-05	179.289993	60.698002	84.143333	262.329987	4700.580078	15100.169922	191.360001	63.939999	86.748337	...	85.541664	275.010010	4787.990234	15547.160156	5003400	12033000	7494000	3437700	4887960000	5031850000
3	2022-01-06	187.490005	62.799999	88.818336	260.209991	4696.049805	15080.860352	192.169998	63.933998	90.166664	...	83.500000	261.000000	4693.390137	15024.150391	6368600	12004000	16553400	2439600	4295280000	4790820000
4	2022-01-07	188.990005	62.931999	87.581665	261.529999	4677.029785	14935.900391	193.270004	63.966000	91.366669	...	87.809998	258.380005	4697.660156	15095.719727	3531900	8805000	13586400	1696000	4181510000	4238070000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1031	2026-02-12	411.540009	84.260002	162.809998	170.899994	6832.759766	22597.150391	420.670013	88.599998	167.949997	...	166.289993	171.990005	6957.540039	23142.869141	3502500	7805800	19646500	2499300	7118700000	8855290000
1032	2026-02-13	429.640015	85.559998	166.949997	177.720001	6836.169922	22546.669922	432.850006	86.529999	170.490005	...	165.029999	174.339996	6834.270020	22561.460938	3536700	8091300	12577000	2434100	5718360000	7964830000
1033	2026-02-17	414.285004	81.690002	163.500000	172.589996	6843.220215	22578.380859	425.881989	85.419998	166.009995	...	165.679993	175.824997	6819.859863	22394.759766	4428100	6061100	16586500	2038100	5418480000	7654300000
1034	2026-02-18	415.760010	80.690002	152.350006	172.130005	6881.310059	22753.630859	421.760010	81.260002	155.279999	...	149.550003	167.345001	6855.479980	22629.849609	2752700	5853300	26756800	2542100	5098160000	7705590000
1035	2026-02-19	422.140015	81.669998	150.990005	168.990005	6861.890137	22682.730469	424.000000	81.769997	152.679993	...	152.479996	169.639999	6861.339844	22639.880859	2000400	4584100	13545800	3337100	5151690000	6984780000

	Date	Close_CRWD	Close_FTNT	Close_PANW	Close_ZS	Close_^GSPC	Close_^IXIC	High_CRWD	High_FTNT	High_PANW	...	Open_PANW	Open_ZS	Open_^GSPC	Open_^IXIC	Volume_CRWD	Volume_FTNT	Volume_PANW	Volume_ZS	Volume_^GSPC	Volume_^IXIC
0	2022-01-03	198.330002	66.624001	90.701668	301.829987	4796.560059	15832.799805	205.574997	72.424004	93.400002	...	93.073334	322.600006	4778.140137	15732.500000	5311600	9515000	7802400	2576000	3831020000	4429960000
1	2022-01-04	189.190002	64.428001	87.333336	281.579987	4793.540039	15622.719727	197.445007	66.683998	90.721664	...	89.574997	297.964996	4804.509766	15852.139648	6503400	9829000	9586200	3827700	4683170000	5131110000
2	2022-01-05	179.289993	60.698002	84.143333	262.329987	4700.580078	15100.169922	191.360001	63.939999	86.748337	...	85.541664	275.010010	4787.990234	15547.160156	5003400	12033000	7494000	3437700	4887960000	5031850000
3	2022-01-06	187.490005	62.799999	88.818336	260.209991	4696.049805	15080.860352	192.169998	63.933998	90.166664	...	83.500000	261.000000	4693.390137	15024.150391	6368600	12004000	16553400	2439600	4295280000	4790820000
4	2022-01-07	188.990005	62.931999	87.581665	261.529999	4677.029785	14935.900391	193.270004	63.966000	91.366669	...	87.809998	258.380005	4697.660156	15095.719727	3531900	8805000	13586400	1696000	4181510000	4238070000

	2025-10-31	2025-07-31	2025-04-30	2025-01-31	2024-10-31
Tax Effect Of Unusual Items	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
Tax Rate For Calcs	2.100000e-01	2.100000e-01	2.100000e-01	2.100000e-01	2.100000e-01
Normalized EBITDA	5.551200e+07	3.187000e+06	-1.911500e+07	1.764100e+07	5.129700e+07
Net Income From Continuing Operation Net Minority Interest	-3.399700e+07	-7.767500e+07	-1.102070e+08	-9.228200e+07	-1.682200e+07
Reconciled Depreciation	7.184900e+07	6.803800e+07	6.405700e+07	5.744000e+07	5.524800e+07
Reconciled Cost Of Revenue	3.078050e+08	3.102830e+08	2.891430e+08	2.739900e+08	2.550870e+08
EBITDA	5.551200e+07	3.187000e+06	-1.911500e+07	1.764100e+07	5.129700e+07
EBIT	-1.633700e+07	-6.485100e+07	-8.317200e+07	-3.979900e+07	-3.951000e+06
Net Interest Income	4.395200e+07	4.402700e+07	3.866500e+07	3.993300e+07	4.561400e+07
Interest Expense	6.931000e+06	6.823000e+06	6.715000e+06	6.664000e+06	6.587000e+06
Interest Income	5.088300e+07	5.085000e+07	4.538000e+07	4.659700e+07	5.220100e+07
Normalized Income	-3.399700e+07	-7.767500e+07	-1.102070e+08	-9.228200e+07	-1.682200e+07
Net Income From Continuing And Discontinued Operation	-3.399700e+07	-7.767500e+07	-1.102070e+08	-9.228200e+07	-1.682200e+07
Total Expenses	1.303687e+09	1.281931e+09	1.228090e+09	1.143839e+09	1.065901e+09
Total Operating Income As Reported	-6.944300e+07	-1.129790e+08	-1.246560e+08	-8.530100e+07	-5.572300e+07
Diluted Average Shares	2.513260e+08	2.499090e+08	2.484320e+08	2.469330e+08	2.455360e+08
Basic Average Shares	2.513260e+08	2.499090e+08	2.484320e+08	2.469330e+08	2.455360e+08
Diluted EPS	-1.400000e-01	-3.100000e-01	-4.400000e-01	-3.700000e-01	-7.000000e-02
Basic EPS	-1.400000e-01	-3.100000e-01	-4.400000e-01	-3.700000e-01	-7.000000e-02
Diluted NI Availto Com Stockholders	-3.399700e+07	-7.767500e+07	-1.102070e+08	-9.228200e+07	-1.682200e+07
Net Income Common Stockholders	-3.399700e+07	-7.767500e+07	-1.102070e+08	-9.228200e+07	-1.682200e+07
Net Income	-3.399700e+07	-7.767500e+07	-1.102070e+08	-9.228200e+07	-1.682200e+07
Minority Interests	-9.000000e+03	-3.000000e+04	7.860000e+05	4.490000e+05	-3.000000e+03
Net Income Including Noncontrolling Interests	-3.398800e+07	-7.764500e+07	-1.109930e+08	-9.273100e+07	-1.681900e+07
Net Income Continuous Operations	-3.398800e+07	-7.764500e+07	-1.109930e+08	-9.273100e+07	-1.681900e+07
Tax Provision	1.072000e+07	5.971000e+06	2.110600e+07	4.626800e+07	6.281000e+06
Pretax Income	-2.326800e+07	-7.167400e+07	-8.988700e+07	-4.646300e+07	-1.053800e+07
Other Income Expense	2.223000e+06	-2.722000e+06	-3.896000e+06	-1.095000e+06	-4.290000e+05
Other Non Operating Income Expenses	2.223000e+06	-2.722000e+06	-3.896000e+06	-1.095000e+06	-4.290000e+05
Net Non Operating Interest Income Expense	4.395200e+07	4.402700e+07	3.866500e+07	3.993300e+07	4.561400e+07
Interest Expense Non Operating	6.931000e+06	6.823000e+06	6.715000e+06	6.664000e+06	6.587000e+06
Interest Income Non Operating	5.088300e+07	5.085000e+07	4.538000e+07	4.659700e+07	5.220100e+07
Operating Income	-6.944300e+07	-1.129790e+08	-1.246560e+08	-8.530100e+07	-5.572300e+07
Operating Expense	9.958820e+08	9.716480e+08	9.389470e+08	8.698490e+08	8.108140e+08
Research And Development	3.475640e+08	3.466680e+08	3.341290e+08	3.151420e+08	2.756020e+08
Selling General And Administration	6.483180e+08	6.249800e+08	6.048180e+08	5.547070e+08	5.352120e+08
Selling And Marketing Expense	4.810320e+08	4.470240e+08	4.396170e+08	4.095040e+08	4.082670e+08
General And Administrative Expense	1.672860e+08	1.779560e+08	1.652010e+08	1.452030e+08	1.269450e+08
Other Gand A	1.672860e+08	1.779560e+08	1.652010e+08	1.452030e+08	1.269450e+08
Gross Profit	9.264390e+08	8.586690e+08	8.142910e+08	7.845480e+08	7.550910e+08
Cost Of Revenue	3.078050e+08	3.102830e+08	2.891430e+08	2.739900e+08	2.550870e+08
Total Revenue	1.234244e+09	1.168952e+09	1.103434e+09	1.058538e+09	1.010178e+09
Operating Revenue	1.234244e+09	1.168952e+09	1.103434e+09	1.058538e+09	1.010178e+09

	Date	CRWD	FTNT	PANW	ZS	S&P500	NASDAQ
1016	2026-01-22	453.769989	77.620003	182.270004	208.309998	6913.350098	23436.019531
1017	2026-01-23	452.489990	81.639999	180.179993	209.619995	6915.609863	23501.240234
1018	2026-01-26	468.329987	83.070000	184.220001	214.550003	6950.229980	23601.359375
1019	2026-01-27	476.660004	81.709999	183.500000	219.669998	6978.600098	23817.099609
1020	2026-01-28	469.190002	82.080002	183.740005	213.949997	6978.029785	23857.449219
1021	2026-01-29	444.619995	81.510002	176.199997	200.630005	6969.009766	23685.119141
1022	2026-01-30	441.404999	81.260002	176.970001	200.009995	6939.029785	23461.820312
1023	2026-02-02	438.850006	81.290001	175.419998	200.610001	6976.439941	23592.109375
1024	2026-02-03	421.730011	79.300003	166.240005	188.050003	6917.810059	23255.189453
1025	2026-02-04	415.359985	81.120003	166.720001	185.789993	6882.720215	22904.580078
1026	2026-02-05	377.160004	78.930000	154.770004	169.389999	6798.399902	22540.589844
1027	2026-02-06	395.500000	82.760002	159.320007	167.330002	6932.299805	23031.210938
1028	2026-02-09	408.040009	85.930000	166.000000	170.179993	6964.819824	23238.669922
1029	2026-02-10	413.390015	85.529999	165.509995	174.339996	6941.810059	23102.470703
1030	2026-02-11	415.809998	87.820000	165.300003	171.000000	6941.470215	23066.470703
1031	2026-02-12	411.540009	84.260002	162.809998	170.899994	6832.759766	22597.150391
1032	2026-02-13	429.640015	85.559998	166.949997	177.720001	6836.169922	22546.669922
1033	2026-02-17	414.285004	81.690002	163.500000	172.589996	6843.220215	22578.380859
1034	2026-02-18	415.760010	80.690002	152.350006	172.130005	6881.310059	22753.630859
1035	2026-02-19	422.140015	81.669998	150.990005	168.990005	6861.890137	22682.730469

Defining the function to clean the stock data¶

Pull stock data¶

Clean and Download data¶

Read Stock Data from CSV¶

Download Ticker data for Crowdstrike, Fortinet, Palo Alto networks, and zscaler¶

1. Time Series Chart¶

Key Takeaways¶

2. Facet chart¶

Key Takeaways¶

3. Choose your own chart¶

Key Takeaways¶

Animated plot¶

Key Takeaways¶

Heatmap¶

Key Takeaways¶

Should you invest in CrowdStrike?¶

Collab Link¶

	Tax Rate For Calcs	Normalized EBITDA	Net Income From Continuing Operation Net Minority Interest	Reconciled Depreciation	Reconciled Cost Of Revenue	EBITDA	EBIT	Net Interest Income	Interest Expense	...	Operating Expense	Research And Development	Selling General And Administration	Selling And Marketing Expense	General And Administrative Expense	Other Gand A	Gross Profit	Cost Of Revenue	Total Revenue	Operating Revenue
2025-01-31	0.210000	294801000.0	-19271000.0	213956000.0	991481000.0	294801000.0	80845000.0	169863000.0	26311000.0	...	3.082573e+09	1.076901e+09	2.005672e+09	1.523356e+09	482316000.0	482316000.0	2.962143e+09	991481000.0	3.953624e+09	3.953624e+09
2024-01-31	0.262439	293827000.0	89327000.0	145254000.0	755723000.0	293827000.0	148573000.0	123174000.0	25756000.0	...	2.301827e+09	7.684970e+08	1.533330e+09	1.140566e+09	392764000.0	392764000.0	2.299832e+09	755723000.0	3.055555e+09	3.055555e+09
2023-01-31	0.210000	-40754000.0	-183245000.0	93810000.0	601231000.0	-40754000.0	-134564000.0	27176000.0	25319000.0	...	1.830117e+09	6.083640e+08	1.221753e+09	9.044090e+08	317344000.0	317344000.0	1.640005e+09	601231000.0	2.241236e+09	2.241236e+09
2022-01-31	0.210000	-65982000.0	-234802000.0	68810000.0	383221000.0	-65982000.0	-134792000.0	-21443000.0	25231000.0	...	1.210921e+09	3.712830e+08	8.396380e+08	6.165460e+08	223092000.0	223092000.0	1.068373e+09	383221000.0	1.451594e+09	1.451594e+09