def Get1000m(self, api):
"""Get1000m:
api - alpaca rest api
"""
#
# Collect the latest 1000 minute bars
#
nyc = timezone('America/New_York')
today = datetime.now(tz=nyc)
d1 = today.strftime("%Y-%m-%dT%H:%M:%S-05:00")
s1_day = (today + timedelta(days=-15)).strftime("%Y-%m-%dT%H:%M:%S-05:00")
minute_prices = runTicker(api, self.ticker, timeframe=TimeFrame.Day, start=s1_day, end=d1, limit=100000)
minute_prices = minute_prices[-1000:]
return minute_prices
def get_tickers(_out_tickers = []):
print('Getting current ticker data...')
in_instruct_name=STOCK_DB_PATH+'/Instructions/out_momentum_instructions.csv'
instruct = pd.read_csv(in_instruct_name,delimiter=' ')
tickers = []
if 'ticker' in instruct.columns:
tickers = instruct['ticker'].unique()
print(tickers)
print('Success.')
# Load assets
assets = []
for tick in tickers:
assets +=[api.get_asset(tick)]
symbols = [asset.symbol for asset in assets if asset.tradable]
nyc = timezone('America/New_York')
today = datetime.now(tz=nyc)
todayFilter = (today + timedelta(days=-2))
d1 = today.strftime("%Y-%m-%dT%H:%M:%S-05:00")
s1_day = (today + timedelta(days=-10)).strftime("%Y-%m-%dT%H:%M:%S-05:00")
s1_min = (today).strftime("%Y-%m-%dT09:30:00-05:00")
for s in symbols:
trade = api.get_latest_trade(s)
day_prices = runTicker(api, s, timeframe=TimeFrame.Day, start=s1_day, end=d1)
#minute_prices = runTicker(api, s, timeframe=TimeFrame.Day, start=s1_min, end=d1)
if len(day_prices)<2 or 'volume' not in day_prices.columns:
continue
prevday_volume = day_prices['volume'][-2]
open_price = day_prices['open'][-1]
if open_price<=0.0:
continue
if True or trade.p >= min_share_price and \
trade.p <= max_share_price and \
prevday_volume*trade.p > min_last_dv and \
(open_price - trade.p)/open_price >= 0.035:
my_stock = Stock(s, api.get_asset(tick), day_prices, prevday_volume, trade)
found = False
for ts in _out_tickers:
if s==ts.ticker:
found=True
if not found:
_out_tickers+=[my_stock]
return _out_tickers
ticker='WRN'
#ticker='NTNX' #TDUP, SAVA, DOMO
#ticker='SAVA' #TDUP, SAVA, DOMO
#ticker='TSLA' #TDUP, SAVA, DOMO
#ticker='DOMO'
#ticker='KFY'
#ticker='X'
#ticker='HP'
#ticker='CVS'
ticker='WBA'
ticker='BSET'
today = datetime.datetime.now(tz=est) + datetime.timedelta(minutes=-40)
d1 = today.strftime("%Y-%m-%dT%H:%M:%S-04:00")
thirty_days = (today + datetime.timedelta(days=-30)).strftime("%Y-%m-%dT%H:%M:%S-04:00")
if True:
minute_prices_thirty = runTicker(api, ticker, timeframe=TimeFrame.Minute, start=thirty_days, end=d1)
minute_prices_spy = runTicker(api, 'SPY', timeframe=TimeFrame.Minute, start=thirty_days, end=d1)
# add the extra data
AddData(minute_prices_thirty)
AddData(minute_prices_spy)
minute_prices_spy['signif_volume_spy'] = minute_prices_spy.signif_volume
minute_prices_thirty = minute_prices_thirty.join(minute_prices_spy.signif_volume_spy,how='left')
minute_prices_thirty['signif_volume_over_spy'] = minute_prices_thirty.signif_volume / minute_prices_thirty.signif_volume_spy
# get the last 5 days
minute_prices = GetTimeSlot(minute_prices_thirty,days=10)
minute_prices_spy_10d = GetTimeSlot(minute_prices_spy,days=10)
#minute_prices=minute_prices_thirty
print(minute_prices)
AddDataShort(minute_prices)
else: plt.savefig(outdir + '%s.png' % (saveName))
if not draw: plt.close()
plt.close()
api = ALPACA_REST()
ts = ALPHA_TIMESERIES()
ticker = 'SPY'
#ticker='TLT'
#ticker='QQQ'
#ticker='GLD'
#ticker='HAL'
#ticker='GUSH'
#ticker='AVCT'
#ticker='RZV'
spy = runTicker(api, ticker)
stock_info = None
spy = None
sqlcursor = SQL_CURSOR()
readType = 'full'
spy, j = ConfigTable(ticker, sqlcursor, ts, readType)
print('spy')
print(spy)
# add info
if len(spy) == 0:
print('ERROR - empy info %s' % ticker)
spy['daily_return'] = spy['adj_close'].pct_change(periods=1) + 1
spy['openclosepct'] = (spy.close - spy.open) / spy.open + 1
def GenerateSignal(ticker,
out_file_name='out_bull_instructions.csv',
price_targets=[]):
connectionCal = SQL_CURSOR('earningsCalendarv2.db')
fd = ALPHA_FundamentalData()
sqlcursor = SQL_CURSOR()
ts = ALPHA_TIMESERIES()
api = ALPACA_REST()
stockInfoQuarter, stockInfoAnnual, company_overview = CollectEarnings(
ticker, connectionCal)
# annual balance sheet
balance_sheet_annual = fd.get_balance_sheet_annual(ticker)[0]
balance_sheet_annual['fiscalDateEnding'] = pd.to_datetime(
balance_sheet_annual['fiscalDateEnding'], errors='coerce')
for d in balance_sheet_annual.columns:
if d not in ['fiscalDateEnding', 'totalAssets']:
balance_sheet_annual[d] = pd.to_numeric(balance_sheet_annual[d],
errors='coerce')
# quarterly income statement
income_statement_quarterly = fd.get_income_statement_quarterly(ticker)[0]
for d in income_statement_quarterly.columns:
if d not in ['fiscalDateEnding', 'reportedCurrency']:
income_statement_quarterly[d] = pd.to_numeric(
income_statement_quarterly[d], errors='coerce')
for d in ['fiscalDateEnding']:
income_statement_quarterly[d] = pd.to_datetime(
income_statement_quarterly[d], errors='coerce')
if debug:
print(income_statement_quarterly)
print(income_statement_quarterly.dtypes)
tstock_info, j = ConfigTable(ticker, sqlcursor, ts, 'full')
spy, j = ConfigTable('SPY', sqlcursor, ts, 'compact')
#print(spy.columns)
est = pytz.timezone('US/Eastern')
today = datetime.now(tz=est) + maindatetime.timedelta(minutes=-40)
#today = datetime.utcnow() + maindatetime.timedelta(minutes=-30)
d1 = today.strftime("%Y-%m-%dT%H:%M:%S-04:00")
five_days = (
today +
maindatetime.timedelta(days=-7)).strftime("%Y-%m-%dT%H:%M:%S-04:00")
minute_prices = []
ntry = 0
while ntry < 3:
try:
minute_prices = runTicker(api,
ticker,
timeframe=TimeFrame.Minute,
start=five_days,
end=d1)
break
except (requests.exceptions.ConnectionError):
ntry += 1
# may want to restrict to NYSE open times
try:
spy = AddInfo(spy, spy)
tstock_info = AddInfo(tstock_info, spy, AddSupport=True)
except (ValueError, KeyError):
print('Error processing adding info %s' % ticker)
recent_quotes = getQuotes(api, ticker)
if debug:
print(tstock_info[[
'adj_close', 'sma20', 'sma20cen', 'vwap10cen', 'vwap10'
]][50:-10])
earn = Earnings(ticker, income_statement_quarterly, company_overview,
balance_sheet_annual, stockInfoQuarter, stockInfoAnnual,
tstock_info, minute_prices, recent_quotes)
#earn.BuildPDF()
earn.WriteCSV(out_file_name, price_targets)
print(income_statement_quarterly.dtypes)
tstock_info, j = ConfigTable(ticker, sqlcursor, ts, 'compact')
spy, j = ConfigTable('SPY', sqlcursor, ts, 'compact')
est = pytz.timezone('US/Eastern')
today = datetime.now(tz=est) + maindatetime.timedelta(minutes=-40)
#today = datetime.utcnow() + maindatetime.timedelta(minutes=-30)
d1 = today.strftime("%Y-%m-%dT%H:%M:%S-04:00")
five_days = (
today +
maindatetime.timedelta(days=-7)).strftime("%Y-%m-%dT%H:%M:%S-04:00")
minute_prices = runTicker(api,
ticker,
timeframe=TimeFrame.Minute,
start=five_days,
end=d1)
# may want to restrict to NYSE open times
#minute_prices = minute_prices[(minute_prices.index.hour>13) & (minute_prices.index.hour<20)
#print(minute_prices)
try:
spy = AddInfo(spy, spy)
tstock_info = AddInfo(tstock_info, spy, AddSupport=True)
except (ValueError, KeyError):
print('Error processing adding info to SPY %s' % ticker)
recent_quotes = getQuotes(api, ticker)
if debug:
print(tstock_info[[
'adj_close', 'sma20', 'sma20cen', 'vwap10cen', 'vwap10'
def generateFigure(apiA, tickerA):
"""generateFigure:
apiA - alpaca api
tickerA - str - ticker
"""
figs = []
doSPYComparison = False
today = datetime.datetime.now(tz=est) #+ datetime.timedelta(minutes=5)
d1 = today.strftime("%Y-%m-%dT%H:%M:%S-05:00")
thirty_days = (
today +
datetime.timedelta(days=-13)).strftime("%Y-%m-%dT%H:%M:%S-05:00")
minute_prices_thirty = []
try:
minute_prices_thirty = runTicker(apiA,
tickerA,
timeframe=TimeFrame.Minute,
start=thirty_days,
end=d1)
AddData(minute_prices_thirty)
minute_prices_spy = []
if doSPYComparison:
minute_prices_spy = runTicker(apiA,
'SPY',
timeframe=TimeFrame.Minute,
start=thirty_days,
end=d1)
AddData(minute_prices_spy)
minute_prices_spy[
'signif_volume_spy'] = minute_prices_spy.signif_volume
minute_prices_spy['change_spy'] = minute_prices_spy.change
minute_prices_spy[
'minute_diffHL_spy'] = minute_prices_spy.minute_diffHL
minute_prices_spy[
'minute_diff_spy'] = minute_prices_spy.minute_diff
minute_prices_thirty = minute_prices_thirty.join(
minute_prices_spy.signif_volume_spy, how='left')
minute_prices_thirty = minute_prices_thirty.join(
minute_prices_spy.change_spy, how='left')
minute_prices_thirty = minute_prices_thirty.join(
minute_prices_spy.minute_diff_spy, how='left')
minute_prices_thirty = minute_prices_thirty.join(
minute_prices_spy.minute_diffHL_spy, how='left')
minute_prices_thirty[
'signif_volume_over_spy'] = minute_prices_thirty.signif_volume / minute_prices_thirty.signif_volume_spy
minute_prices_thirty[
'change_over_spy'] = minute_prices_thirty.change - minute_prices_thirty.change_spy
minute_prices_thirty['signif_volume_over_spy'].where(
minute_prices_thirty['signif_volume_over_spy'] < 150.0,
150.0,
inplace=True)
minute_prices_thirty['signif_volume_over_spy'].where(
minute_prices_thirty['signif_volume_over_spy'] > -150.0,
-150.0,
inplace=True)
# draw figures
fig = go.Figure(data=go.Scatter(
x=minute_prices_thirty['i'],
y=minute_prices_thirty['close'],
error_y=dict(type='data',
symmetric=False,
array=minute_prices_thirty.high -
minute_prices_thirty.close,
arrayminus=minute_prices_thirty.close -
minute_prices_thirty.low)))
fig.update_layout(xaxis_title="Minute Bars",
yaxis_title="%s Price" % tickerA)
figs += [fig]
fig = go.Figure(data=[
go.Candlestick(x=minute_prices_thirty['i'],
open=minute_prices_thirty['open'],
high=minute_prices_thirty['high'],
low=minute_prices_thirty['low'],
close=minute_prices_thirty['close'])
])
figs += [fig]
fig = make_subplots(specs=[[{"secondary_y": True}]])
# Add traces
fig.add_trace(go.Scatter(x=minute_prices_thirty['i'],
y=minute_prices_thirty['close'],
name='price'),
secondary_y=False)
if doSPYComparison:
fig.add_trace(go.Scatter(
x=minute_prices_thirty['i'],
y=minute_prices_thirty['signif_volume_over_spy'],
name='volume sigificance/SPY'),
secondary_y=True)
fig.add_trace(go.Scatter(x=minute_prices_thirty['i'],
y=minute_prices_thirty['slope'],
name='slope'),
secondary_y=True)
fig.add_trace(go.Scatter(x=minute_prices_thirty['i'],
y=minute_prices_thirty['signif_volume'],
name='volume sigificance'),
secondary_y=True)
# Set x-axis title
fig.update_xaxes(title_text="Minute Bars")
# Set y-axes titles
fig.update_yaxes(title_text="Price", secondary_y=False)
fig.update_yaxes(title_text="Slope", secondary_y=True)
figs += [fig]
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(go.Scatter(
x=minute_prices_thirty['time'],
y=minute_prices_thirty['close'],
name='price',
error_y=dict(type='data',
symmetric=False,
array=minute_prices_thirty.high -
minute_prices_thirty.close,
arrayminus=minute_prices_thirty.close -
minute_prices_thirty.low)),
secondary_y=False)
fig.add_trace(go.Scatter(x=minute_prices_thirty['time'],
y=minute_prices_thirty['slope'],
name='slope'),
secondary_y=True)
fig.add_trace(go.Scatter(x=minute_prices_thirty['time'],
y=minute_prices_thirty['signif_volume'],
name='volume sigificance'),
secondary_y=True)
# Set x-axis title
fig.update_layout(xaxis_title="Date", yaxis_title="%s Price" % tickerA)
# Set y-axes titles
fig.update_yaxes(title_text="Price", secondary_y=False)
fig.update_yaxes(title_text="Slope", secondary_y=True)
figs += [fig]
except:
pass
return minute_prices_thirty, figs
def generateMeanRevFigure(apiA, sqlA, tsA, tickerA, doRelativeToSpy=False):
"""generateMeanRevFigure:
apiA - alpaca api
sqlA - mysql cursor
tsA - time series api
tickerA - str - ticker
doRelativeToSpy - bool - compute the ratio to SPY for performance
"""
figs = []
today = datetime.datetime.now(tz=est)
d1 = today.strftime("%Y-%m-%dT%H:%M:%S-05:00")
d1_set = today.strftime("%Y-%m-%d")
#d1_set = "2022-01-19"
twelve_hours = (
today +
datetime.timedelta(hours=-12)).strftime("%Y-%m-%dT%H:%M:%S-05:00")
minute_prices = runTicker(apiA,
tickerA,
timeframe=TimeFrame.Minute,
start=twelve_hours,
end=d1)
daily_prices, j = ConfigTable(tickerA, sqlA, tsA, 'full', hoursdelay=18)
# add todays numbers if available
if len(minute_prices) > 0:
df_today = pd.DataFrame([[
minute_prices['open'][0], minute_prices['high'].max(),
minute_prices['low'].min(), minute_prices['close'][-1],
minute_prices['close'][-1], minute_prices['volume'].sum(), 0.0, 1.0
]],
columns=[
'open', 'high', 'low', 'close',
'adj_close', 'volume', 'dividendamt',
'splitcoef'
],
index=[d1_set
]) #index=[minute_prices.index[-1]])
df_today.index = pd.to_datetime(df_today.index)
if len(daily_prices
) == 0 or daily_prices.index[-1] < df_today.index[0]:
daily_prices = pd.concat([daily_prices, df_today])
daily_prices = daily_prices.sort_index()
#st.table(daily_prices.tail())
filter_shift_days = 0
if filter_shift_days > 0:
daily_prices = GetTimeSlot(daily_prices,
days=6 * 365,
startDate=todayFilter)
daily_prices_60d = GetTimeSlot(daily_prices, days=60 + filter_shift_days)
daily_prices_180d = GetTimeSlot(daily_prices, days=180 + filter_shift_days)
daily_prices_365d = GetTimeSlot(daily_prices, days=365 + filter_shift_days)
daily_prices_3y = GetTimeSlot(daily_prices,
days=3 * 365 + filter_shift_days)
daily_prices_5y = GetTimeSlot(daily_prices,
days=5 * 365 + filter_shift_days)
daily_prices_180d['daily_return'] = daily_prices_180d[
'adj_close'].pct_change(periods=1)
figs += [
FitWithBand(
daily_prices_60d.index,
daily_prices_60d[['adj_close', 'high', 'low', 'open', 'close']],
ticker=tickerA,
outname='60d')
]
figs += [
FitWithBand(
daily_prices_180d.index,
daily_prices_180d[['adj_close', 'high', 'low', 'open', 'close']],
ticker=tickerA,
outname='180d')
]
figs += [
FitWithBand(
daily_prices_365d.index,
daily_prices_365d[['adj_close', 'high', 'low', 'open', 'close']],
ticker=tickerA,
outname='365d')
]
figs += [
FitWithBand(
daily_prices_3y.index,
daily_prices_3y[['adj_close', 'high', 'low', 'open', 'close']],
ticker=tickerA,
outname='3y')
]
figs += [
FitWithBand(
daily_prices_5y.index,
daily_prices_5y[['adj_close', 'high', 'low', 'open', 'close']],
ticker=tickerA,
outname='5y')
]
# Compute relative to spy
if doRelativeToSpy:
spy, j = ConfigTable('SPY', sqlA, tsA, 'full', hoursdelay=18)
if filter_shift_days > 0:
spy = GetTimeSlot(spy, days=6 * 365, startDate=todayFilter)
spy_daily_prices_60d = GetTimeSlot(spy, days=60 + filter_shift_days)
spy_daily_prices_365d = GetTimeSlot(spy, days=365 + filter_shift_days)
spy_daily_prices_5y = GetTimeSlot(spy,
days=5 * 365 + filter_shift_days)
figs += [
FitWithBand(daily_prices_365d.index,
daily_prices_365d[[
'adj_close', 'high', 'low', 'open', 'close'
]],
ticker=tickerA,
outname='365dspycomparison',
spy_comparison=spy_daily_prices_365d[[
'adj_close', 'high', 'low', 'open', 'close'
]])
]
figs += [
FitWithBand(
daily_prices_60d.index,
daily_prices_60d[['adj_close', 'high', 'low', 'open',
'close']],
ticker=tickerA,
outname='60dspycomparison',
spy_comparison=spy_daily_prices_60d[[
'adj_close', 'high', 'low', 'open', 'close'
]])
]
figs += [
FitWithBand(
daily_prices_5y.index,
daily_prices_5y[['adj_close', 'high', 'low', 'open', 'close']],
ticker=tickerA,
outname='5yspycomparison',
spy_comparison=spy_daily_prices_5y[[
'adj_close', 'high', 'low', 'open', 'close'
]])
]
return figs