def Get_Earnings_Surprise(stock):
'''Go to Yahoo Finance and scrape the Analyst Estimates. Determine if the Earning Suprises were positive.
Return the results in a JSON object.'''
analyst_estimates = 'http://ca.finance.yahoo.com/q/ae?s=' + stock + '+Analyst+Estimates'
#Get Last 4 EPS Surprises
analyst_estimates_web = requests.get(analyst_estimates)
analyst_estimates_text = analyst_estimates_web.content
analyst_estimates_soup = bs(analyst_estimates_text, "lxml")
analyst_estimates_rows = analyst_estimates_soup.findAll('tr')
#i = 0
try:
for n in analyst_estimates_rows:
n = str(n)
if mods.Match('<tr><td class="yfnc_tablehead1"(.*?)EPS Est', n):
n = n.split('<td class="yfnc_tabledata1">')
est_list = [n[1], n[2], n[3], n[4]]
for lst, item in enumerate(est_list):
est_list[lst] = float(item.replace('</tr>', '').replace('</td>', ''))
elif mods.Match('<tr><td class="yfnc_tablehead1"(.*?)EPS Actual', n):
n = n.split('<td class="yfnc_tabledata1">')
actual_list = [n[1], n[2], n[3], n[4]]
for lst, item in enumerate(actual_list):
actual_list[lst] = float(item.replace('</tr>', '').replace('</td>', ''))
elif mods.Match('<tr><th class="yfnc_tablehead1"(.*?)Revenue Est', n):
n = n.split('<th class="yfnc_tablehead1" scope="col" style="font-size:11px;text-align:right;" width="18%">')
dates = [n[1], n[2], n[3], n[4]]
for lst, item in enumerate(dates):
dates[lst] = item.replace('</tr>', '').replace('</td>', '').replace('</th>', '').replace('<br/>', ' ')
surprise1, surprise2, surprise3, surprise4 = 1 - est_list[0]/actual_list[0], 1 - est_list[1]/actual_list[1], 1 - est_list[2]/actual_list[2], 1 - est_list[3]/actual_list[3]
#Test if Earnings Surprise was Positive
is_postive_pes_surprise = False
if surprise1 > 0 and surprise2 > 0 and surprise3 > 0 and surprise4 > 0:
is_postive_pes_surprise = True
#Create Ditionary of Results
results = collections.OrderedDict()
results['Stock'] = stock
results['DateRun'] = mods.Today()
results['TestRun'] = 'Earnings_Surprise'
results[dates[0].replace('Qtr.', '').replace('Current', '',).replace('Next', '').replace('Year', '').strip()] = surprise1
results[dates[1].replace('Qtr.', '').replace('Current', '',).replace('Next', '').replace('Year', '').strip()] = surprise2
results[dates[2].replace('Qtr.', '').replace('Current', '',).replace('Next', '').replace('Year', '').strip()] = surprise3
results[dates[3].replace('Qtr.', '').replace('Current', '',).replace('Next', '').replace('Year', '').strip()] = surprise4
results['Actuals'] = actual_list
results['EST'] = est_list
results['Result'] = is_postive_pes_surprise
return results
except:
results = collections.OrderedDict()
results['stock'] = stock
results['TestRun'] = 'Earnings_Surprise'
results['DateRun'] = mods.Today()
results['Result'] = 'Test Failed'
return results
def Get_Revenue(stock):
'''Go to Yahoo Finance and Pull the Last Three Years of Revenue Results and Determine if Revenue Passes or Fails Test.
Return the results as a JSON object.'''
revenue = 'http://ca.finance.yahoo.com/q/is?s=' + stock + '&annual'
#Get Revenue Data from Last Income Statement
revenue_web = requests.get(revenue)
revenue_text = revenue_web.content
revenue_soup = bs(revenue_text, "lxml")
try:
# Get Last Three Years of Revenue
revenue = []
for n in revenue_soup.find_all('tr'):
n = str(n)
if mods.Match('<tr><td colspan="2">\n<strong>\n\s+Total Revenue', n):
n = n.replace('\n', '').replace('</strong>', '').replace('</td>', '').replace('<td align="right">', '').replace('</tr', '').strip()
n = n.split('<strong>')
for item in n:
item = item.strip()
if mods.Match('\d+', item):
item = item.replace(' ', '').replace('>', '').replace(',', '').replace('\xc2\xa0\xc2\xa0', '').strip()
revenue.append(item)
last_reported_rev, second_reported_rev, third_reported_rev = float(revenue[0]), float(revenue[1]), float(revenue[2])
#
# #Get Dates for Last Three Reported Years
dates = mods.get_reported_dates(stock)
last_reported_dt, year_before_reported_dt, two_yr_before_reported_dt = dates[0], dates[1], dates[2]
# #Test if Revenue Results are Positive
is_positive_rev = False
if last_reported_rev > second_reported_rev and second_reported_rev > third_reported_rev:
is_positive_rev = True
#
# #Create Ditionary of Results
results = collections.OrderedDict()
results['stock'] = stock
results['TestRun'] = 'Revenue_Test'
results['DateRun'] = mods.Today()
results['Result'] = is_positive_rev
results[last_reported_dt] = last_reported_rev
results[year_before_reported_dt] = second_reported_rev
results[two_yr_before_reported_dt] = third_reported_rev
return results
except:
results = collections.OrderedDict()
results['stock'] = stock
results['TestRun'] = 'Revenue_Test'
results['DateRun'] = mods.Today()
results['Result'] = 'Test Failed'
return results
def Get_Earnings_V_Industry(stock):
'''Go to Yahoo finance and pull information from Analyst Estimates. Determine if the earnings
are favorable compared to the industry. Return the results as a JSON object.'''
analyst_estimates = 'http://ca.finance.yahoo.com/q/ae?s=' + stock + '+Analyst+Estimates'
#Get Last 4 EPS Surprises
analyst_estimates_web = requests.get(analyst_estimates)
analyst_estimates_text = analyst_estimates_web.content
analyst_estimates_soup = bs(analyst_estimates_text)
analyst_estimates_rows = analyst_estimates_soup.findAll('tr')
#i = 0
try:
for n in analyst_estimates_rows:
n = str(n)
if mods.Match('<tr><td class="yfnc_tablehead1"(.*?)Price/Earnings',
n):
n = n.split('<td class="yfnc_tabledata1">')
pe_list = [n[1], n[2], n[3], n[4]]
for lst, item in enumerate(pe_list):
pe_list[lst] = float(
item.replace('</tr>', '').replace('</td>',
'').replace('%', ''))
#Test if Earnings Surprise was Positive
is_postive_pe_v_industry = False
if pe_list[0] > pe_list[1]:
is_postive_pe_v_industry = True
#Create Ditionary of Results
results = collections.OrderedDict()
results['Stock'] = stock
results['DateRun'] = mods.Today()
results['TestRun'] = 'Earnings Versus Industry'
results['PE Estimate: ' + stock] = pe_list[0]
results['Industry Estimate:'] = pe_list[1]
results['Result'] = is_postive_pe_v_industry
return results
except:
results = collections.OrderedDict()
results['stock'] = stock
results['TestRun'] = 'Earnings Versus Industry'
results['DateRun'] = mods.Today()
results['Result'] = 'Test Failed'
return results
def Get_Options(stock, strike_price):
# # Use Pandas Data Reader to pull data from Yahoo Finance
all_data = {}
#for ticker in symbol:
all_data = web.get_data_yahoo(stock, '1/1/2015', mods.Today())
lst = []
last = 0
for current in all_data['Adj Close']:
if len(lst) == 0:
lst.append(np.nan)
last = current
else:
lst.append(current / last)
last = current
all_data['Percent Changed'] = lst
# # Run Monte Carlo simulations. 1000 iterations for 60 weeks to determine number of times the value of the stock is greater than the strike price.
simulation_end = []
num_iters = 100
num_weeks = 10
changed_avg = all_data['Percent Changed'].mean()
changed_stdev = all_data['Percent Changed'].std()
for i in range(0, num_iters):
for i in range(0, num_weeks):
if i == 0:
wk_end = all_data['Adj Close'][0]
else:
wk_end = norminv(np.random.random_sample(), changed_avg,
changed_stdev) * wk_end
simulation_end.append(max(0, wk_end - strike_price))
return all_data
def Get_Growth_Forecast(stock):
'''Go to Yahoo Finance and get the analyst estimates. Determine if the last 4 EPS surprise results
were positive. Return the results as a JSON object.'''
analyst_estimates = 'http://ca.finance.yahoo.com/q/ae?s=' + stock + '+Analyst+Estimates'
#Get Last 4 EPS Surprises
analyst_estimates_web = requests.get(analyst_estimates)
analyst_estimates_text = analyst_estimates_web.content
analyst_estimates_soup = bs(analyst_estimates_text, 'lxml')
analyst_estimates_rows = analyst_estimates_soup.findAll('tr')
#i = 0
try:
for n in analyst_estimates_rows:
n = str(n)
if mods.Match('<tr><td class="yfnc_tablehead1"(.*?)Next 5 Years',
n):
n = n.split('<td class="yfnc_tabledata1">')
est_list = [n[1], n[2], n[3], n[4]]
for lst, item in enumerate(est_list):
est_list[lst] = float(
item.replace('</tr>', '').replace('</td>',
'').replace('%', ''))
#Test if Earnings Surprise was Positive
is_postive_growth_forecast = False
if est_list[0] > 8:
is_postive_growth_forecast = True
#Create Ditionary of Results
results = collections.OrderedDict()
results['Stock'] = stock
results['DateRun'] = mods.Today()
results['TestRun'] = 'Growth_Forecast'
results['Growth Forecast: ' + stock] = est_list[0]
results['Result'] = is_postive_growth_forecast
return results
except:
results = collections.OrderedDict()
results['stock'] = stock
results['TestRun'] = 'Growth_Forecast'
results['DateRun'] = mods.Today()
results['Result'] = 'Test Failed'
return results
def Get_Shares_Short(stock):
'''Got to Yahoo Finance and determine the Insider Tracking of the stock. Return the results as a
JSON object.'''
insider_transactions = 'http://ca.finance.yahoo.com/q/it?s=' + stock + '+Insider+Transactions'
#Get Insider Transaction Data (Last 6 Months and Last 3 Months)
insider_transactions_web = requests.get(insider_transactions)
insider_transactions_text = insider_transactions_web.content
insider_transactions_soup = bs(insider_transactions_text)
insider_transactions_rows = insider_transactions_soup.findAll('tr')
i = 0
insider_list = []
for n in insider_transactions_rows:
if n.text[0:10] == 'Net Shares':
n = str(n)
n = n.split('<td align="right" class="yfnc_tabledata1"')
insider_list.append(n[1].replace('</td>', '').replace(
'</tr>', '').replace(' width="20%">',
'').replace('>', '').replace(',', ''))
try:
#Test if Positive Insider Tracking (Positive Purchase of Stock in Last 3 Months)
is_postive_insider = False
if mods.Match('\(\d+\)', insider_list[1]):
is_postive_insider = True
#Create Ditionary of Results
results = collections.OrderedDict()
results['Stock'] = stock
results['DateRun'] = mods.Today()
results['TestRun'] = 'Insider_Trading'
results['Insider Purchasing 6 Month: ' + stock] = insider_list[0]
results['Insider Purchasing 3 Month: ' + stock] = insider_list[1]
results['Result'] = is_postive_insider
return results
except:
results = collections.OrderedDict()
results['stock'] = stock
results['TestRun'] = 'Insider_Trading'
results['DateRun'] = mods.Today()
results['Result'] = 'Test Failed'
return results
def main():
for stock in stocks:
data = collections.OrderedDict()
data['DateRun'] = mods.Today()
data['Stock'] = stock
stock = stock.lower()
if mods.get_balance_sheet(stock, 'Common Stocks') != None:
#Revenue Test
revenue_result = revenue.Get_Revenue(stock)
Dict_Aggregator(data, revenue_result)
#4 Key Result Tests
key_result = key.Get_Key_Indicators(stock)
for item in key_result:
Dict_Aggregator(data, item)
#Positive Return on Equity
roe_result = roe.Get_ROE(stock)
Dict_Aggregator(data, roe_result)
#Analyst Opinion
opinion_result = opinion.Get_Analyst_Opinion(stock)
Dict_Aggregator(data, opinion_result)
#Earnings Forecast
forecast_result = forecast.Get_Earnings_Forecast(stock)
Dict_Aggregator(data, forecast_result)
#Earnings Per Share
eps_result = eps.Get_EPS(stock)
Dict_Aggregator(data, eps_result)
#Earnings Surprise
eps_surprise = surprise.Get_Earnings_Surprise(stock)
Dict_Aggregator(data, eps_surprise)
#Growth Forecast
growth = growth_forecast.Get_Growth_Forecast(stock)
Dict_Aggregator(data, growth)
#PEG Ratio
peg_ratio = peg.Get_PEG_Ratio(stock)
Dict_Aggregator(data, peg_ratio)
#Earnings Versus Industry
pevi = industry.Get_Earnings_V_Industry(stock)
Dict_Aggregator(data, pevi)
#Insider Trading
short = shares_short.Get_Shares_Short(stock)
Dict_Aggregator(data, short)
mods.Insert_Data(export, 'Results', Evaluator(data))
def Get_Key_Indicators(stock):
'''Use the Common Modules to scrape data from different websites for the stock. Determine if the results are positive.
Return the results in JSON objects.'''
try:
# Get Last Three Years of Net Income
net_income = mods.get_net_income(stock, 'Net Income')
net_income = mods.Is_Negative(net_income)
last_reported_ni, second_reported_ni, third_reported_ni = float(
net_income[0]), float(net_income[1]), float(net_income[2])
#Get Last Three Years of Common Stock
common_stock = mods.get_balance_sheet(stock, 'Common Stocks')
common_stock = mods.Is_Negative(common_stock)
last_reported_cs, second_reported_cs, third_reported_cs = float(
common_stock[0]), float(common_stock[1]), float(common_stock[2])
treasury_stock = mods.get_balance_sheet(stock, 'Treasury Stock')
treasury_stock = mods.Is_Negative(treasury_stock)
last_reported_ts, second_reported_ts, third_reported_ts = float(
treasury_stock[0]), float(treasury_stock[1]), float(
treasury_stock[2])
#Get Dates for Last Three Reported Years
dates = mods.get_reported_dates(stock)
last_reported_dt, year_before_reported_dt, two_yr_before_reported_dt = dates[
0], dates[1], dates[2]
#Test if EPS is postivie for last three years
last_reported_eps, second_reported_eps, third_reported_eps = last_reported_ni / (
last_reported_cs - last_reported_ts), second_reported_ni / (
second_reported_cs - second_reported_ts), third_reported_ni / (
third_reported_cs - third_reported_ts)
is_positive_eps = False
if last_reported_eps > second_reported_eps and second_reported_eps > third_reported_eps:
is_positive_eps = True
#Get Cash On Hand Last Three Years
cash = mods.get_balance_sheet(stock, 'Cash')
cash = mods.Is_Negative(cash)
last_reported_cash, second_reported_cash, third_reported_cash = float(
cash[0]), float(cash[1]), float(cash[2])
#Get Long Term Debt
debt = mods.get_balance_sheet(stock, '>Long-Term Debt')
debt = mods.Is_Negative(debt)
last_reported_debt, second_reported_debt, third_reported_debt = float(
debt[0]), float(debt[1]), float(debt[2])
#Determine Long Term Investments
investments = mods.get_balance_sheet(stock, 'Long-Term Investments')
investments = mods.Is_Negative(investments)
last_reported_investments, second_reported_investments, third_reported_investments = float(
investments[0]), float(investments[1]), float(investments[2])
#Test if Net Income has been positive the last three years
is_positive_ni = False
if last_reported_ni > second_reported_ni and second_reported_ni > third_reported_ni:
is_positive_ni = True
#Test if Cash on Hand is positive
is_positive_cash = False
if last_reported_cash > 5000000:
is_positive_cash = True
#Create Ditionarys of Results
results_cash = collections.OrderedDict()
results_cash['Stock'] = stock
results_cash['TestRun'] = 'Cash'
results_cash['DateRun'] = mods.Today()
results_cash['Result'] = is_positive_cash
results_cash[last_reported_dt] = last_reported_cash
results_cash[year_before_reported_dt] = second_reported_cash
results_cash[two_yr_before_reported_dt] = third_reported_cash
results_ni = collections.OrderedDict()
results_ni['Stock'] = stock
results_ni['TestRun'] = 'Net_Income'
results_ni['DateRun'] = mods.Today()
results_ni['Result'] = is_positive_ni
results_ni[last_reported_dt] = last_reported_ni
results_ni[year_before_reported_dt] = second_reported_ni
results_ni[two_yr_before_reported_dt] = third_reported_ni
debt_data = collections.OrderedDict()
debt_data['Stock'] = stock
debt_data['TestRun'] = 'Long_Term_Debt'
debt_data['DateRun'] = mods.Today()
debt_data['Result'] = 'Data_Only'
debt_data[last_reported_dt] = last_reported_debt
debt_data[year_before_reported_dt] = second_reported_debt
debt_data[two_yr_before_reported_dt] = third_reported_debt
invest_data = collections.OrderedDict()
invest_data['Stock'] = stock
invest_data['TestRun'] = 'Long_Term_Investment'
invest_data['DateRun'] = mods.Today()
invest_data['Result'] = 'Data_Only'
invest_data[last_reported_dt] = last_reported_investments
invest_data[year_before_reported_dt] = second_reported_investments
invest_data[two_yr_before_reported_dt] = third_reported_investments
return [results_cash, results_ni, invest_data, debt_data]
except:
results_cash = collections.OrderedDict()
results_cash['stock'] = stock
results_cash['TestRun'] = 'Cash'
results_cash['DateRun'] = mods.Today()
results_cash['Result'] = 'Test Failed'
results_ni = collections.OrderedDict()
results_ni['stock'] = stock
results_ni['TestRun'] = 'Net_Income'
results_ni['DateRun'] = mods.Today()
results_ni['Result'] = 'Test Failed'
return [results_cash, results_ni]
#Testing:
#stock = 'aapl'
#print Get_Key_Indicators(stock)
#print mods.get_balance_sheet(stock, 'Treasury Stock')
#print get_balance_sh(stock, 'Common Stocks')
last = current
else:
lst.append(current / last)
last = current
all_data['Percent Changed'] = lst
# # Run Monte Carlo simulations. 1000 iterations for 60 weeks to determine number of times the value of the stock is greater than the strike price.
simulation_end = []
num_iters = 100
num_weeks = 10
changed_avg = all_data['Percent Changed'].mean()
changed_stdev = all_data['Percent Changed'].std()
for i in range(0, num_iters):
for i in range(0, num_weeks):
if i == 0:
wk_end = all_data['Adj Close'][0]
else:
wk_end = norminv(np.random.random_sample(), changed_avg,
changed_stdev) * wk_end
simulation_end.append(max(0, wk_end - strike_price))
return all_data
end = mods.Today()
start = '1/1/2015'
#print web.DataReader('AAPL', 'yahoo-actions', start, end)
import datetime
datetime.datetime.strptime('1/1/15')
print len(web.get_data_yahoo(stock, start, end))
def Get_Earnings_Forecast(stock):
'''Go to Yahoo Finance and scrape the Analyst Estimates. Determine if the Earnings Forecast is postive.
Return the results in a JSON object.'''
forecast = 'http://ca.finance.yahoo.com/q/ae?s=' + stock + '+Analyst+Estimates'
#Get Last 4 Quarters of Earnings Forecasts
forecast_web = requests.get(forecast)
forecast_text = forecast_web.content
forecast_soup = bs(forecast_text)
forecast_rows = forecast_soup.findAll('tr')
#i = 0
try:
lists = []
for n in forecast_rows:
n = str(n)
if mods.Match('<tr><th class="yfnc_tablehead1"(.*?)Earnings Est',
n):
n = n.split(
'<th class="yfnc_tablehead1" scope="col" style="font-size:11px; font-weight:normal;text-align:right;" width="18%">'
)
dates_list = [n[1], n[2], n[3], n[4]]
for lst, item in enumerate(dates_list):
dates_list[lst] = item.replace('</tr>', '').replace(
'</td>', '').replace('</th>',
'').replace('<br/>', ' ')
elif mods.Match(
'<tr><td class="yfnc_tablehead1"(.*?)Avg. Estimate', n):
n = n.split('<td class="yfnc_tabledata1">')
forecast_list = [n[1], n[2], n[3], n[4]]
try:
for lst, item in enumerate(forecast_list):
forecast_list[lst] = float(
item.replace('</tr>', '').replace('</td>', ''))
lists.append(forecast_list)
except:
dummy = 0
#Test for Positive Forecasts
is_positive_earnings_forecast = False
if lists[0][0] < lists[0][1] and lists[0][1] < lists[0][2] and lists[
0][2] < lists[0][3]:
is_positive_earnings_forecast = True
#Create Ditionary of Results
results = collections.OrderedDict()
results['Stock'] = stock
results['TestRun'] = 'Earnings_Forecast'
results['DateRun'] = mods.Today()
results['Result'] = is_positive_earnings_forecast
results['Current Qtr'] = lists[0][0]
results['Next Qtr'] = lists[0][1]
results['Current Year'] = lists[0][2]
results['Next Year'] = lists[0][3]
return results
except:
results = collections.OrderedDict()
results['stock'] = stock
results['TestRun'] = 'Earnings_Forecast'
results['DateRun'] = mods.Today()
results['Result'] = 'Test Failed'
return results
##Testing:
#stock = 'AAPL'
#print Get_Earnings_Forecast(stock)
def Get_Analyst_Opinion(stock):
'''Go to Yahoo Finance and get the last three monts of Analyst Opinions, determine if the analyst optinion is postive,
and return the results as a JSON object.'''
analyst_opinion = 'http://ca.finance.yahoo.com/q/ao?s=' + stock + '+Analyst+Opinion'
analyst_opinion_web = requests.get(analyst_opinion)
analyst_opinion_text = analyst_opinion_web.content
analyst_opinion_soup = bs(analyst_opinion_text)
#i = 0
try:
for n in analyst_opinion_soup.find_all('tr'):
if n.text[0:6] == 'Strong':
strong_buy = str(n)
strong_buy = strong_buy.split(
'<td align="center" class="yfnc_tabledata1">')
for lst, item in enumerate(strong_buy):
strong_buy[lst] = item.replace('</td>', '')
strong_buy[-1] = strong_buy[-1].replace('</tr>', '')
strong_buy = [
float(strong_buy[1]),
float(strong_buy[2]),
float(strong_buy[3]),
float(strong_buy[4])
]
elif n.text[0:3] == 'Buy':
buy = str(n)
buy = buy.split('<td align="center" class="yfnc_tabledata1">')
for lst, item in enumerate(buy):
buy[lst] = item.replace('</td>', '')
buy[-1] = buy[-1].replace('</tr>', '')
buy = [
float(buy[1]),
float(buy[2]),
float(buy[3]),
float(buy[4])
]
elif n.text[0:4] == 'Hold':
hold = str(n)
hold = hold.split(
'<td align="center" class="yfnc_tabledata1">')
for lst, item in enumerate(hold):
hold[lst] = item.replace('</td>', '')
hold[-1] = hold[-1].replace('</tr>', '')
hold = [
float(hold[1]),
float(hold[2]),
float(hold[3]),
float(hold[4])
]
elif n.text[0:12] == 'Underperform':
underperform = str(n)
underperform = underperform.split(
'<td align="center" class="yfnc_tabledata1">')
for lst, item in enumerate(underperform):
underperform[lst] = item.replace('</td>', '')
underperform[-1] = underperform[-1].replace('</tr>', '')
underperform = [
float(underperform[1]),
float(underperform[2]),
float(underperform[3]),
float(underperform[4])
]
elif n.text[0:4] == 'Sell':
sell = str(n)
sell = sell.split(
'<td align="center" class="yfnc_tabledata1">')
for lst, item in enumerate(sell):
sell[lst] = item.replace('</td>', '')
sell[-1] = sell[-1].replace('</tr>', '')
sell = [
float(sell[1]),
float(sell[2]),
float(sell[3]),
float(sell[4])
]
#Create Pandas Dataframe
table = [strong_buy, buy, hold, underperform, sell]
columns = ['Strong Buy', 'Buy', 'Hold', 'Underperform', 'Sell']
index = [
'This Month', 'Last Month', 'Two Months Ago', 'Three Months Ago'
]
df = pd.DataFrame(table, columns, index)
#Determine if the analyst opinion suggests to buy the stock
is_positive_analyst_opinion = False
if df['This Month']['Strong Buy'] + df['This Month']['Buy'] > df[
'This Month']['Hold'] + df['This Month']['Underperform'] + df[
'This Month']['Sell']:
is_positive_analyst_opinion = True
#Create Ditionary of Results
results = collections.OrderedDict()
results['Stock'] = stock
results['TestRun'] = 'Analyst_Opinion'
results['DateRun'] = mods.Today()
results['DataFrame'] = df.reset_index().to_json(orient='index')
results['Result'] = is_positive_analyst_opinion
return results
except:
results = collections.OrderedDict()
results['stock'] = stock
results['TestRun'] = 'Analyst_Opinion'
results['DateRun'] = mods.Today()
results['Result'] = 'Test Failed'
return results
##Testing:
#stock = 'AAPL'
#print Get_Analyst_Opinion(stock)
def Get_ROE(stock):
'''Go to Yahoo Finance. Get the results from the Key Statistics page, Income Statement, and Balance Sheet.
Determine if the results are positive for a good return on equity. Return the results as a JSON object.'''
key_stats = 'http://ca.finance.yahoo.com/q/ks?s=' + stock + '+Key+Statistics'
income_statement = 'http://ca.finance.yahoo.com/q/is?s=' + stock + '+Income+Statement&annual'
balance_sheet = 'http://ca.finance.yahoo.com/q/bs?s=' + stock + '+Balance+Sheet&annual'
#Get Last 12 Months ROE
key_stats_web = requests.get(key_stats)
key_stats_text = key_stats_web.content
key_stats_soup = bs(key_stats_text, "lxml")
for n in key_stats_soup.find_all('tr'):
if n.text[0:16] == 'Return on Equity':
amount = n.text.split(':')
amount = str(amount[1])
roe_12month = float(amount[0:-1])
# Get Last Three Years of Return on Equity (Net Income/Stock Holder's Equity)
# First get the Last Three Years of Net Income from Income Statement
income_statement_web = requests.get(income_statement)
income_statement_text = income_statement_web.content
income_statement_soup = bs(income_statement_text)
income_statement_rows = income_statement_soup.findAll('tr')
#i = 0
try:
for row in income_statement_rows:
row = str(row)
if mods.Match('<tr><td colspan="2">\n<strong>\n\s+Net Income', row):
row = row.split('\n')
last_reported_ni, second_reported_ni, third_reported_ni = row[6].strip(), row[10].strip(), row[14].strip()
last_reported_ni, second_reported_ni, third_reported_ni = float(last_reported_ni[0:-4].replace(',', '')),float(second_reported_ni[0:-4].replace(',', '')), float(third_reported_ni[0:-4].replace(',', ''))
# Second get the Last Three Years of StockHolder's Equity from Balance Sheet
balance_sheet_web = requests.get(balance_sheet)
balance_sheet_text = balance_sheet_web.content
balance_sheet_soup = bs(balance_sheet_text, 'lxml')
balance_sheet_rows = balance_sheet_soup.findAll('tr')
for row in balance_sheet_rows:
row = str(row)
if mods.Match('<tr><td colspan="2">\n<strong>\n\s+Total Stockholder Equity', row):
row = row.split('\n')
last_reported_se, second_reported_se, third_reported_se = row[6].strip(), row[10].strip(), row[14].strip()
last_reported_se, second_reported_se, third_reported_se = float(last_reported_se[0:-4].replace(',', '')),float(second_reported_se[0:-4].replace(',', '')), float(third_reported_se[0:-4].replace(',', ''))
#Third Calculate ROE (Net Income/Stock Holders Equity)
last_reported_roe, second_reported_roe, third_reported_roe = last_reported_ni/last_reported_se, second_reported_ni/second_reported_se, third_reported_ni/third_reported_se
#Get Dates for Last Three Reported Years
last_reported_dt, year_before_reported_dt, two_yr_before_reported_dt = mods.get_reported_dates(stock)
is_positive_rev = False
now = datetime.datetime.now()
if int(last_reported_dt[0:2]) - now.month < 6:
if roe_12month > second_reported_roe and second_reported_roe > third_reported_roe:
is_positive_rev = True
# Time since last annual reported revenue was less than 6 months
else:
if last_reported_roe > second_reported_roe and second_reported_roe > third_reported_roe:
is_positive_rev = True
#Create Ditionary of Results
results = collections.OrderedDict()
results['Stock'] = stock
results['DateRun'] = mods.Today()
results['TestRun'] = 'Positive_Return_on_Equity'
results['Result'] = is_positive_rev
results[last_reported_dt] = last_reported_roe
results[year_before_reported_dt] = second_reported_roe
results[two_yr_before_reported_dt] = third_reported_roe
results['12 Month Return On Equity'] = roe_12month
return results
except:
results = collections.OrderedDict()
results['stock'] = stock
results['TestRun'] = 'Positive_Return_on_Equity'
results['DateRun'] = mods.Today()
results['Result'] = 'Test Failed'
return results
def Get_EPS(stock):
#Go to Yahoo Finance and Pull the Last Three Years of Revenue Results and Determine if Revenue Passes or Fails Test
#Current Year: http://finance.yahoo.com/q/ks?s=AAPL+Key+Statistics
#Last Years Income Statement: http://finance.yahoo.com/q/is?s=AAPL+Income+Statement&annual
key_stats = 'http://ca.finance.yahoo.com/q/ks?s=' + stock + '+Key+Statistics'
market_watch = 'http://www.marketwatch.com/investing/stock/' + stock + '/financials'
#Get Last 12 Months Earnings Per Share
key_stats_web = requests.get(key_stats)
key_stats_text = key_stats_web.content
key_stats_soup = bs(key_stats_text)
for n in key_stats_soup.find_all('tr'):
if n.text[0:11] == 'Diluted EPS':
amount = n.text.split(':')
eps_12month = float(amount[1])
# Get Last Three Years of Earnings Per Share
market_watch_web = requests.get(market_watch)
market_watch_text = market_watch_web.content
market_watch_soup = bs(market_watch_text)
market_watch_rows = market_watch_soup.findAll('tr')
#tag = '<tr class="mainRow"> \
#<td class="rowTitle"><a data-ref="ratio_Eps1YrAnnualGrowth" href="#"><span class="expand"></span></a> EPS (Basic)</td>'
#i = 0
try:
for row in market_watch_rows:
row = str(row)
if mods.Match('<tr class="mainRow">\n(.*?)EPS \(Diluted\)', row):
row = row.split('<td class="valueCell">')
last_reported_eps, second_reported_eps, third_reported_eps = float(
row[5][0:4].strip()), float(row[4][0:4].strip()), float(
row[3][0:4].strip())
#Get Dates for Last Three Reported Years
last_reported_dt, year_before_reported_dt, two_yr_before_reported_dt = mods.get_reported_dates(
stock)
#print mods.get_reported_dates(stock)
is_positive_eps = False
now = datetime.datetime.now()
if int(last_reported_dt[0:2]) - now.month < 6:
if eps_12month > second_reported_eps and second_reported_eps > third_reported_eps:
is_positive_eps = True
# Time since last annual reported revenue was less than 6 months
else:
if last_reported_eps > second_reported_eps and second_reported_eps > third_reported_eps:
is_positive_eps = True
'''
'''
#Create Ditionary of Results
results = collections.OrderedDict()
results['stock'] = stock
results['TestRun'] = 'EPS'
results['DateRun'] = mods.Today()
results['Result'] = is_positive_eps
results[last_reported_dt] = last_reported_eps
results[year_before_reported_dt] = second_reported_eps
results[two_yr_before_reported_dt] = third_reported_eps
results['12 Month Reported EPS'] = eps_12month
return results
except:
results = collections.OrderedDict()
results['stock'] = stock
results['TestRun'] = 'EPS'
results['DateRun'] = mods.Today()
results['Result'] = 'Test Failed'
return results
#Testing:
#stock = 'AAPL'
#print Get_EPS(stock)