def dump_to_csv(dirName):
if not os.path.exists(dirName):
os.makedirs(dirName)
filters = ['fa_eps5years_pos','fa_epsqoq_o20','fa_epsyoy_o25','fa_epsyoy1_o15','fa_estltgrowth_pos','fa_roe_o10','sh_instown_o10','ta_highlow52w_a50h','ta_rsi_nos50'] # Shows companies in NASDAQ which are in the S&P500
stock_list = Screener(filters=filters, order='ticker')
print((stock_list))
stock_list.to_csv(dirName,get_screener_name())
#dump_to_csv('screenerOutput')
def test_get_ticker_details_sequential_requests(self):
stocks = Screener(filters=[
'sh_curvol_o300', 'ta_highlow52w_b0to10h', 'ind_stocksonly',
'sh_outstanding_o1000'
])
ticker_details = stocks.get_ticker_details()
count = 0
for _ in ticker_details:
count += 1
assert len(stocks) == count == len(ticker_details)
def dump_to_csv(dirName):
if not os.path.exists(dirName):
os.makedirs(dirName)
filters = [
'fa_debteq_u0.5', 'fa_epsqoq_o25', 'fa_roe_o15', 'fa_salesqoq_o20',
'sh_avgvol_o200', 'sh_instown_o60', 'sh_price_o5', 'sh_short_u5'
] # Shows companies in NASDAQ which are in the S&P500
stock_list = Screener(filters=filters, order='ticker')
print((stock_list))
stock_list.to_csv(dirName, get_screener_name())
def dump_to_csv(dirName):
if not os.path.exists(dirName):
os.makedirs(dirName)
filters = ['geo_usa','ta_change_u','ta_highlow20d_nh','ta_highlow50d_nh','ta_highlow52w_nh','ta_perf_dup'] # Shows companies in NASDAQ which are in the S&P500
stock_list = Screener(filters=filters, order='ticker')
print((stock_list))
stock_list.to_csv(dirName,get_screener_name())
#dump_to_csv('screenerOutput')
def dump_to_csv(dirName):
if not os.path.exists(dirName):
os.makedirs(dirName)
filters = ['sh_avgvol_o1000', 'ta_pattern_channelup', 'ta_perf_1wdown'
] # Shows companies in NASDAQ which are in the S&P500
stock_list = Screener(filters=filters, order='ticker')
print((stock_list))
stock_list.to_csv(dirName, get_screener_name())
#dump_to_csv('screenerOutput')
def top_N_biggest(self, N, table=None, stock_list=None):
filters = ['geo_usa', 'sh_curvol_o2000']
if stock_list is None:
stock_list = Screener(filters=filters,
order='-marketcap',
rows=N,
table=table)
else:
stock_list = Screener(tickers=stock_list,
order='-marketcap',
rows=N,
table=table)
self.log(stock_list)
return stock_list
def top_N_volatility(self, N, table=None, stock_list=None):
filters = ['geo_usa', 'cap_largeover', 'sh_avgvol_o1000']
if stock_list is None:
stock_list = Screener(filters=filters,
order='-volatility1w',
rows=N,
table=table)
else:
stock_list = Screener(tickers=stock_list,
order='-volatility1w',
rows=N,
table=table)
self.log(stock_list)
return stock_list
def __init__(self, update=True):
filters = [
'exch_Any', 'idx_Any', 'geo_usa', 'sh_price_u50', 'avgvol_o50'
]
print("Filtering stocks..")
stock_list = Screener(filters=filters, order='ticker')
stock_list.to_csv('data/stocks.csv')
#print(stock_list)
with open('data/stocks.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
for row in csv_reader:
global exportList
exportList.append(row[1])
def test_get_ticker_details_sequential_requests(self):
""" Tests that `get_ticker_details` method returns correct number of ticker details. """
stocks = Screener(filters=[
"sh_curvol_o300",
"ta_highlow52w_b0to10h",
"ind_stocksonly",
"sh_outstanding_o1000",
])
ticker_details = stocks.get_ticker_details()
count = 0
for _ in ticker_details:
count += 1
assert len(stocks) == count == len(ticker_details)
def GrabStockList():
filters = [
"an_recom_holdbetter,cap_midunder,geo_usa,sh_avgvol_o300,sh_curvol_o300,sh_price_5to50,ta_highlow52w_b5h,ta_perf_13wup,ta_perf2_ytdup,ta_rsi_nob60,ta_sma20_pca,ta_sma200_pa,ta_sma50_pa"
]
stock_list = Screener(filters=filters)
for stock in stock_list:
print(stock["Ticker"])
def DailySectorsScreen():
sectorsTktList = [
'IWM', 'XLF', 'EEM', 'XLE', 'XLK', 'XLV', 'IYT', 'XLU', 'XLI', 'XLY',
'IYR', 'XLP', 'XLB', 'TLT', 'GLD', 'UUP', 'RTH', 'IYZ', 'SMH', 'DBC',
'USO'
]
return Screener(tickers=sectorsTktList, table='Overview', order='-change')
def etfDailyData(filePrefix, outputDir):
src = 'screener_results.csv'
filtro = ['ind_exchangetradedfund']
tables_list = ['Overview', 'Performance', 'Technical']
desc_change = '-change'
os.chdir(outputDir)
dirpath = os.getcwd()
print("current directory is : " + dirpath)
for tabla in tables_list:
stk_list = Screener(filters=filtro, order=desc_change, table=tabla)
stk_list.to_csv(src)
dst = filePrefix + '_etf_' + tabla.lower() + '.csv'
os.rename(src, dst)
return 0
def dump_to_csv(dirName):
if not os.path.exists(dirName):
os.makedirs(dirName)
#http://freetraderchat.com/blog/top-5-finviz-scans/
#https://finviz.com/screener.ashx?v=111&f=cap_midunder,fa_epsqoq_o5,fa_salesqoq_o5,sh_instown_o10,sh_price_u5,ta_averagetruerange_o0.25,ta_sma20_pa,ta_sma200_pa,ta_sma50_pa&ft=4
filters = ['cap_midunder','fa_epsqoq_o5','fa_salesqoq_o5','sh_instown_o10','sh_price_u5','ta_averagetruerange_o0.25','ta_sma20_pa','ta_sma200_pa','ta_sma50_pa'] # Shows companies in NASDAQ which are in the S&P500
stock_list = Screener(filters=filters, order='ticker')
print((stock_list))
stock_list.to_csv(dirName,get_screener_name())
#dump_to_csv('screenerOutput')
def dump_to_csv(dirName):
if not os.path.exists(dirName):
os.makedirs(dirName)
filters = [
'sh_curvol_o200', 'sh_price_u7', 'sh_relvol_o1.5', 'sh_short_low',
'ta_rsi_nos50', 'ta_sma20_pa', 'ta_sma200_pa', 'ta_sma50_pa'
] # Shows companies in NASDAQ which are in the S&P500
stock_list = Screener(filters=filters, order='ticker')
print((stock_list))
stock_list.to_csv(dirName, get_screener_name())
#dump_to_csv('screenerOutput')
def top_N_bouce_back(self, N, table=None, stock_list=None):
filters = [
'geo_usa', 'cap_largeover', 'sh_avgvol_o1000', 'ta_highlow52w_b30h'
]
if stock_list is None:
stock_list = Screener(filters=filters,
order='change',
rows=N,
table=table)
else:
stock_list = Screener(tickers=stock_list,
order='change',
rows=N,
table=table)
self.log(stock_list)
return stock_list
def dump_to_csv(dirName):
if not os.path.exists(dirName):
os.makedirs(dirName)
# https://www.intelligenttrendfollower.com/how-to-find-trend-following-stock-ideas-on-finviz/
#https://finviz.com/screener.ashx?v=111&f=ta_pattern_channelup2&ft=3?a=128563647
filters = ['ta_pattern_channelup2'] # Shows companies in NASDAQ which are in the S&P500
stock_list = Screener(filters=filters, order='ticker')
print((stock_list))
stock_list.to_csv(dirName,get_screener_name())
#dump_to_csv('screenerOutput')
def Top_N_SMA20_crossing_SMA50(self, N, table=None, stock_list=None):
filters = [
'geo_usa', 'cap_midover', 'sh_avgvol_o2000', 'ta_sma20_cross50'
]
if stock_list is None:
stock_list = Screener(filters=filters,
order='sma20',
rows=N,
table=table)
else:
stock_list = Screener(tickers=stock_list,
order='sma20',
rows=N,
table=table)
self.log(stock_list)
return stock_list
def stkDailyDataT(filePrefix, outputDir):
src = 'screener_results.csv'
filtro = ['ind_stocksonly']
tables_list = ['Technical']
desc_change = '-change'
os.chdir(outputDir)
dirpath = os.getcwd()
print("current directory is : " + dirpath)
for tabla in tables_list:
print("tabla stkDailyData " + tabla)
stk_list = Screener(filters=filtro, order=desc_change, table=tabla)
stk_list.to_csv(src)
dst = filePrefix + '_stk_t_' + tabla.lower() + '.csv'
os.rename(src, dst)
return 0
def top_N_oversold(self, N, table=None, stock_list=None):
filters = ['geo_usa', 'cap_largeover', 'sh_curvol_o2000']
if stock_list is None:
stock_list = Screener(filters=filters,
order='rsi',
rows=N,
table=table)
else:
print("from ticker pool")
#stock_list = Screener(tickers=stock_list, filters=['sh_curvol_o2000'], order='rsi', rows=N, table=table)
stock_list = Screener(tickers=stock_list,
filters=['sh_curvol_o2000'],
order='-rsi',
rows=N,
table=table)
self.log(stock_list)
return stock_list
def FindTopStocks():
print("Searching Stocks...")
stocks = []
for pattern in patterns:
stocks_ = Screener(signal=pattern)
for stock in stocks_:
stocks.append(stock['Ticker'])
return stocks
def YTDSectorsScreen():
sectorsTktList = [
'IWM', 'XLF', 'EEM', 'XLE', 'XLK', 'XLV', 'IYT', 'XLU', 'XLI', 'XLY',
'IYR', 'XLP', 'XLB', 'TLT', 'GLD', 'UUP', 'RTH', 'IYZ', 'SMH', 'DBC',
'USO'
]
return Screener(tickers=sectorsTktList,
table='Performance',
order='-perfytd')
def stockScreener(shortable, min_s_float, max_s_float, minOSS, maxOSS,
PriceLimit):
filters = ['exch_nasd']
stock_list = Screener(filters=filters, order='price')
AcceptedStocks = []
print(len(stock_list))
for stock in stock_list:
#print(stock['Ticker'], stock['Price'])
symbol = stock['Ticker']
sData = GetStockData(symbol)
price = float(sData['Price'])
if (price > PriceLimit):
break
if ('-' not in sData['Insider Own']):
if (float(sData['Insider Own'].replace('%', '')) < 10):
continue
stock_shortable = sData['Shortable'] == 'Yes'
OutStandingShares = sData['Shs Outstand']
Week52Range = sData['52W Range']
L52 = float(Week52Range[0:Week52Range.index('-') - 1])
H52 = float(Week52Range[Week52Range.index('-') + 1::])
if (H52 > price and 100 * (price / H52) >= 15):
continue
if (price < L52):
continue
if OutStandingShares[-1] != 'M':
continue
else:
OutStandingShares = float(
OutStandingShares[0:len(OutStandingShares) - 1])
floatVal = sData['Shs Float']
if (floatVal == '-'):
continue
else:
try:
floatVal = float(floatVal[0:len(floatVal) - 1])
if (floatVal > 5.0):
continue
print("Good Float!")
except ValueError:
print("Float Val Error" + floatVal)
shortFloat = 0.0
try:
shortFloat = float(sData['Short Float'].replace('%', ''))
except ValueError:
continue
if (stock_shortable == shortable and shortFloat >= min_s_float
and shortFloat <= max_s_float and minOSS <= OutStandingShares
and maxOSS >= OutStandingShares):
AcceptedStocks.append(
stockData(symbol, shortFloat, OutStandingShares, floatVal))
print(symbol + " Meets the requirements")
else:
print(symbol + " Does not meet requirements!")
return AcceptedStocks
def dump_to_csv(dirName):
if not os.path.exists(dirName):
os.makedirs(dirName)
#http://freetraderchat.com/blog/top-5-finviz-scans/
#https://finviz.com/screener.ashx?v=111&f=sh_curvol_o200,sh_price_u10,ta_change_u,ta_changeopen_u,ta_perf_dup,ta_perf2_d15o&ft=4
filters = [
'sh_curvol_o200', 'sh_price_u10', 'ta_change_u', 'ta_changeopen_u',
'ta_perf_dup', 'ta_perf2_d15o'
] # Shows companies in NASDAQ which are in the S&P500
stock_list = Screener(filters=filters, order='ticker')
print((stock_list))
stock_list.to_csv(dirName, get_screener_name())
#dump_to_csv('screenerOutput')
def get_current_dataframe():
filters = [
'fa_debteq_u0.5', 'fa_epsqoq_o25', 'fa_roe_o15', 'fa_salesqoq_o20',
'sh_avgvol_o200', 'sh_instown_o60', 'sh_price_o5', 'sh_short_u5'
] # Shows companies in NASDAQ which are in the S&P500
stock_list = Screener(filters=filters, order='ticker')
print(type(stock_list))
#dump_to_csv('screenerOutput')
def compilepimary():
dictionary_ticker_mc = {}
filters = ['cap_midover','geo_usa']
stock_list = Screener(filters=filters, table='Valuation', order='market cap') # Get the performance table and sort it by price ascending
for stock in stock_list:
dictionary_ticker_mc[stock['Ticker']] = stock['Market Cap']
remove = [k for k,v in dictionary_ticker_mc.items() if v == '-']
for k in remove:
del dictionary_ticker_mc[k]
return dictionary_ticker_mc
def test_get_screener_data_sequential_requests(self):
stocks = Screener(filters=[
'sh_curvol_o300', 'ta_highlow52w_b0to10h', 'ind_stocksonly'
])
count = 0
for _ in stocks:
count += 1
assert len(stocks) == count
def dump_to_csv(dirName):
if not os.path.exists(dirName):
os.makedirs(dirName)
#http://freetraderchat.com/blog/top-5-finviz-scans/
#https://finviz.com/screener.ashx?v=111&f=sh_avgvol_o200,sh_price_u10,ta_averagetruerange_o0.25,ta_change_d,ta_pattern_horizontal,ta_perf_ddown,ta_perf2_d5u&ft=4
filters = [
'sh_avgvol_o200', 'sh_price_u10', 'ta_averagetruerange_o0.25',
'ta_change_d', 'ta_pattern_horizontal', 'ta_perf_ddown', 'ta_perf2_d5u'
] # Shows companies in NASDAQ which are in the S&P500
stock_list = Screener(filters=filters, order='ticker')
print((stock_list))
stock_list.to_csv(dirName, get_screener_name())
#dump_to_csv('screenerOutput')
def dump_to_csv(dirName):
if not os.path.exists(dirName):
os.makedirs(dirName)
#https://www.reddit.com/r/CANSLIM/comments/6rfupr/finviz_screeners_settings/
#https://finviz.com/screener.ashx?v=111&f=cap_small,fa_eps5years_o20,fa_epsqoq_o20,fa_epsyoy_o20,fa_sales5years_o20,fa_salesqoq_o20,sh_curvol_o200&ft=4
filters = [
'cap_small', 'fa_eps5years_o20', 'fa_epsqoq_o20', 'fa_epsyoy_o20',
'fa_sales5years_o20', 'fa_salesqoq_o20', 'sh_curvol_o200'
] # Shows companies in NASDAQ which are in the S&P500
stock_list = Screener(filters=filters, order='ticker')
print((stock_list))
stock_list.to_csv(dirName, get_screener_name())
#dump_to_csv('screenerOutput')
def dump_to_csv(dirName):
if not os.path.exists(dirName):
os.makedirs(dirName)
#http://www.winninginvesting.com/small_cap_screen.htm
#https://finviz.com/screener.ashx?v=111&f=cap_small,fa_epsyoy_o15,fa_epsyoy1_o15,fa_estltgrowth_o15,fa_peg_u1,fa_roe_pos,geo_usa,sh_avgvol_o500,sh_instown_o40&ft=4
filters = [
'cap_small', 'fa_epsyoy_o15', 'fa_epsyoy1_o15', 'fa_estltgrowth_o15',
'fa_peg_u1', 'fa_roe_pos', 'geo_usa', 'sh_avgvol_o500',
'sh_instown_o40'
] # Shows companies in NASDAQ which are in the S&P500
stock_list = Screener(filters=filters, order='ticker')
print((stock_list))
stock_list.to_csv(dirName, get_screener_name())
#dump_to_csv('screenerOutput')
def test_get_screener_data_sequential_requests(self):
""" Tests that basic Screener example returns correct number of stocks. """
stock_list = Screener(filters=[
"sh_curvol_o300", "ta_highlow52w_b0to10h", "ind_stocksonly"
])
count = 0
for _ in stock_list:
count += 1
assert len(stock_list) == count