s=Sentiment() debug=False # create sentiment analyzer from nltk.sentiment.vader import SentimentIntensityAnalyzer sid = SentimentIntensityAnalyzer() import statsmodels.api as sm1 from statsmodels.sandbox.regression.predstd import wls_prediction_std # univariate stacked lstm example from numpy import array from keras.models import Sequential from keras.layers import LSTM from keras.layers import Dense from keras.utils import plot_model api = ALPACA_REST() inputTxt='Honest Company reports Q1 EPS (13c) vs. 1c last year' inputTxt='Lennar reports Q2 adjusted EPS $2.95, consensus $2.36' inputTxt='Cognyte reports Q1 EPS (20c), consensus (15c)' inputTxt='Brookdale Senior Living resumed with a Buy at Stifel' inputTxt='Anglo American price target raised to 3,670 GBp from 3,500 GBp at Morgan Stanley' #inputTxt='GMS Inc. reports Q4 adjusted EPS $1.07, consensus 82c' #inputTxt='CalAmp reports Q1 adjusted EPS 8c, consensus 7c' #inputTxt='Adagene initiated with a Buy at China Renaissance' #inputTxt='Molecular Partners indicated to open at $20, IPO priced at $21.25' #inputTxt='WalkMe indicated to open at $33.20, IPO priced at $31' inputTxt='Bassett Furniture reports Q2 EPS 60c, two est. 35c' s.Parse(inputTxt,'Honest Company', 'HON', sid=sid, nlp=nlp, is_earnings=True) #s.Sentiment(sid=sid,nlp=nlp,is_earnings=is_earnings) print(s)
import alpaca_trade_api as tradeapi
import os
from ReadData import ALPACA_REST
#from alpaca_trade_api.rest import REST
#api = tradeapi.REST()
#export APCA_API_BASE_URL='https://paper-api.alpaca.markets'
#export APCA_API_BASE_URL='https://api.alpaca.markets' # for live markets
#ALPACA_ID = os.getenv('ALPACA_ID')
#ALPACA_PAPER_KEY = os.getenv('ALPACA_PAPER_KEY')
#ALPHA_ID = os.getenv('ALPHA_ID')
api = ALPACA_REST()
#paper-api.alpaca.markets
ticker = 'X'
ticker = 'TSLA'
# Get our position in AAPL.
#aapl_position = api.get_position(ticker)
# Get a list of all of our positions.
portfolio = api.list_positions()
#print(portfolio)
# Print the quantity of shares for each position.
for position in portfolio:
print("{} shares of {} market: {} cost_basis: {}".format(
position.qty, position.symbol, position.market_value,
position.cost_basis))
# Get a list of all of our history.
hist = api.get_portfolio_history(date_start='2021-07-01',
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)
#!/usr/bin/python
import requests
import time,os,sys
from ta.trend import macd
import numpy as np
from datetime import datetime, timedelta
from pytz import timezone
import pandas as pd
from ReadData import ALPACA_REST,ALPACA_STREAMCONN,runTicker
from alpaca_trade_api.rest import TimeFrame
from watchdog.observers import Observer
from file_change_monitor import MyFileHandler
# Load API
api = ALPACA_REST()
STOCK_DB_PATH = os.getenv('STOCK_DB_PATH')
global _out_tickers
session = requests.session()
# We only consider stocks with per-share prices inside this range
min_share_price = 2.0
max_share_price = 70.0
# Minimum previous-day dollar volume for a stock we might consider
min_last_dv = 500000
# Stop limit to default to
default_stop = .95
# How much of our portfolio to allocate to any one position
risk = 0.001
'fit_expectations', 'stddev',
'fit_diff_significance',
'current_price'
]),
ignore_index=True)
else:
# decide what information to update! certainly the current price
pass
return out_df
if __name__ == "__main__":
# execute only if run as a script
# Collect APIs
api = ALPACA_REST()
ts = ALPHA_TIMESERIES()
sqlcursor = SQL_CURSOR()
# collect date and time
filter_shift_days = 0
today = datetime.datetime.now(tz=est) #+ datetime.timedelta(minutes=5)
outFileName = 'News/signif_%s_%s_%s.csv' % (today.day, today.month,
today.year)
inFileName = 'News/table_%s_%s_%s.csv' % (
today.day, today.month, today.year) # HACK!!!
df = []
try:
df = pd.read_csv(inFileName)
except (FileNotFoundError) as e:
print("Testing multiple exceptions. {}".format(e.args[-1]))
def main(args):
stream = ALPACA_STREAM(data_feed='sip')
api = ALPACA_REST()
fleet = {}
# Move old signals so that we do not consider them
MoveOldSignals(api)
# checking for trades to execute!
event_handler = MyHandler(fleet,api,stream)
observer = Observer(timeout=1)
observer.schedule(event_handler, path='/Users/schae/testarea/finances/FinanceMonitor/Instructions/', recursive=True)
observer.start()
symbols = args.symbols
for symbol in symbols:
if args.lot>0:
algo = BullishAlgo(api, symbol, lot=args.lot, limit=args.limit, target=args.target, df=[])
fleet[symbol] = algo
# Trigger the loading of the trades
event_handler.on_modified(True)
async def on_bars(data):
if data.symbol in fleet:
fleet[data.symbol].on_bar(data)
for symbol in symbols:
print(symbol)
sys.stdout.flush()
#stream.subscribe_trades(on_bars, symbol)
stream.subscribe_bars(on_bars, symbol)
async def on_trade_updates(data):
logger.info(f'trade_updates {data}')
symbol = data.order['symbol']
if symbol in fleet:
fleet[symbol].on_order_update(data.event, data.order)
stream.subscribe_trade_updates(on_trade_updates)
async def periodic():
while True:
if not api.get_clock().is_open:
logger.info('exit as market is not open')
sys.exit(0)
await asyncio.sleep(30)
positions = api.list_positions()
for symbol, algo in fleet.items():
pos = [p for p in positions if p.symbol == symbol]
algo.checkup(pos[0] if len(pos) > 0 else None)
loop = asyncio.get_event_loop()
while 1:
try:
loop.run_until_complete(asyncio.gather(stream._run_forever(),periodic()))
except (ConnectionResetError,urllib3.exceptions.ProtocolError,requests.exceptions.ConnectionError,APIError,ValueError,AttributeError,RuntimeError,TimeoutError):
print('Connection error. will try to restart')
pass
loop.close()
observer.stop()
observer.join()
plt.axhline(y=h[0], color=h[1], linestyle=h[2]) #xmin=h[1], xmax=h[2],
if doSupport:
techindicators.supportLevels(my_stock_info)
if draw: plt.show()
if doPDFs: plt.savefig(outdir + '%s.pdf' % (saveName))
plt.savefig(outdir + '%s.png' % (saveName))
if not draw: plt.close()
plt.close()
ticker = 'X'
j = 0
readType = 'full'
sqlcursor = SQL_CURSOR()
ts = ALPHA_TIMESERIES()
api = ALPACA_REST()
stock_info, j = ConfigTable(ticker, sqlcursor, ts, readType, j)
trade_days = api.get_bars(ticker, TimeFrame.Minute, "2021-05-03", "2021-05-03",
'raw').df
#trade_days = api.get_bars(ticker, TimeFrame.Minute, "2021-04-30", "2021-05-03T12:17:00-04:00", 'raw').df
trade_days = trade_days.tz_convert(tz='US/Eastern')
spy, j = ConfigTable('SPY', sqlcursor, ts, readType, hoursdelay=2)
AddInfo(spy, spy, debug=debug)
AddInfo(stock_info, spy, debug=debug)
stock_info['sma20d'] = stock_info['adj_close'] - stock_info['sma20']
stock_infoc = stock_info #GetTimeSlot(stock_info,days=70)
stock_infoc['daily_return'] = stock_infoc['adj_close'].pct_change()
import alpaca_trade_api
import statsmodels.api as sm1
from statsmodels.sandbox.regression.predstd import wls_prediction_std
from Earnings import GetIncomeStatement, GetPastEarnings, GetStockOverview, GetBalanceSheetQuarterly, GetBalanceSheetAnnual
import base
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
import matplotlib.dates as mdates
import pytz
import datetime
est = pytz.timezone('US/Eastern')
api = ALPACA_REST()
ts = ALPHA_TIMESERIES()
STOCK_DB_PATH = os.getenv('STOCK_DB_PATH')
sqlcursor = SQL_CURSOR('%s/stocksAV.db' % STOCK_DB_PATH)
matplotlib.use("agg")
sns.set_style('darkgrid')
_lock = RendererAgg.lock
def clear_form():
st.session_state["tickerKey"] = "Select"
def FitWithBand(my_index,
