‘market’: ‘us_market’, ‘marketCap’: 22667776000, ‘maxAge’: 1, ‘maxSupply’: None, ‘messageBoardId’: ‘finmb_1342560’, ‘morningStarOverallRating’: None, ‘morningStarRiskRating’: None, ‘mostRecentQuarter’: 1604102400, ‘navPrice’: None, ‘netIncomeToCommon’: -270000000, ‘nextFiscalYearEnd’: 1643673600, ‘open’: 379.71, ‘openInterest’: None, ‘payoutRatio’: 0, ‘pegRatio’: 0.18, ‘phone’: ‘817 424 2000’, ‘previousClose’: 193.6, ‘priceHint’: 2, ‘priceToBook’: 63.78803, ‘priceToSalesTrailing12Months’: 4.3914475, ‘profitMargins’: -0.053239997, ‘quoteType’: ‘EQUITY’, ‘regularMarketDayHigh’: 413.98, ‘regularMarketDayLow’: 250, ‘regularMarketOpen’: 379.71, ‘regularMarketPreviousClose’: 193.6, ‘regularMarketPrice’: 379.71, ‘regularMarketVolume’: 50397132, ‘revenueQuarterlyGrowth’: None, ‘sector’: ‘Consumer Cyclical’, ‘sharesOutstanding’: 69747000, ‘sharesPercentSharesOut’: 0.8858, ‘sharesShort’: 61782730, ‘sharesShortPreviousMonthDate’: 1607990400, ‘sharesShortPriorMonth’: 68127116, ‘shortName’: ‘GameStop Corporation’, ‘shortPercentOfFloat’: 2.2642, ‘shortRatio’: 2.81, ‘startDate’: None, ‘state’: ‘TX’, ‘strikePrice’: None, ‘symbol’: ‘GME’, ‘threeYearAverageReturn’: None, ‘toCurrency’: None, ‘totalAssets’: None, ‘tradeable’: False, ‘trailingAnnualDividendRate’: 0, ‘trailingAnnualDividendYield’: 0, ‘trailingEps’: -4.224, ‘twoHundredDayAverage’: 19.636906, ‘volume’: 50397132, ‘volume24Hr’: None, ‘volumeAllCurrencies’: None, ‘website’: ‘
http://www.gamestop.com‘, ‘yield’: None, ‘ytdReturn’: None, ‘zip’: ‘76051’}
GME.recommendations
Analyst Recommendations for GME
Step 10: You can get the earnings calendar with
Step 11: Chart the stock price using Open, High, Low and Close. First, we create a pandas dataframe:
df = GME.history(period=”max”)
df
Step 12: Some data manipulation is necessary before we plot. Note the field names Open, High, Low, Close which are referenced in the loop below.
#Reseting the index
df = df.reset_index()
#Converting the datatype to float
for i in [‘Open’, ‘High’, ‘Close’, ‘Low’]:
df[i] = df[i].astype(‘float64’)
Step 13: Plot the results using plotly’s graphics objects library go. This will provide a slider so you can home in on a time period of interest. Here I show the full time series, then the January 2021 performance.
fig = go.Figure([go.Scatter(x=df[‘Date’], y=df[‘High’])])
fig.update_xaxes(
rangeslider_visible=True,
rangeselector=dict(
buttons=list([
dict(count=1, label=”1m”, step=”month”,
stepmode=”backward”),
dict(count=6, label=”6m”, step=”month”,
stepmode=”backward”),
dict(count=1, label=”YTD”, step=”year”,
stepmode=”todate”),
dict(count=1, label=”1y”, step=”year”,
stepmode=”backward”),
dict(step=”all”)
])
)
)
fig.show()
