def await_market_open(num):
setup_folders()
num += 1
if num > 4:
print("market not opening today")
return
print("checking time...")
client = build_client()
today = datetime.date.today()
try:
clock = client.get_hours_for_single_market(
market=client.Markets.EQUITY, date=today).json()["equity"]["EQ"]
except:
clock = client.get_hours_for_single_market(
market=client.Markets.EQUITY,
date=today).json()["equity"]["equity"]
# app starts right at 9:30 est from scheduler
# if it's a trading day, start the app
if clock["isOpen"] == True:
print("Beginning process.")
scraper() #
assess('skip') #
time.sleep(
1
) # This whole process (from scrape to starting watchdog) takes about 2-5 minutes
daily_trader(
'initial'
) # so there's also inherently a delay between market open and when the app
time.sleep(1) # starts trading
run_watchdog(0) #
else:
print("market not open today.")
sys.exit()
def kill_trade(symbol, qty, price, order_type):
client = build_client()
try:
if order_type == "long":
client.place_order(account_id=ACCOUNT_ID,
order_spec=equity_sell_market(symbol, qty))
logging.info("Killed trade")
record_trade(symbol, price)
else:
client.place_order(account_id=ACCOUNT_ID,
order_spec=equity_buy_market(symbol, qty))
logging.info("Killed trade")
record_trade(symbol, price)
except:
logging.info(f"Unexpected error killing trade: {sys.exc_info()}")
pass
def run_watchdog(count):
# poor man's web socket
while count < 6500:
count += 1
try:
# for the first hour, rescan every 15 min
# if count % 5 != 0 and count < 10:
if count <= 900 and count % 225 == 0:
rescan_stocks()
# for the rest of the day, rescan every hour
elif count > 900 and count % 900 == 0:
rescan_stocks()
# otherwise, run watchdog
else:
client = build_client()
tCLT = threading.Thread(target=check_long_trades(client))
tCLT.start()
tCST = threading.Thread(target=check_short_trades(client))
tCST.start()
tCLT.join()
tCST.join()
tRL = threading.Thread(target=rate_limiter(
count)) # this function has exit conditions
tRL.start()
tRL.join()
except SystemExit:
cancel_all('all')
sys.exit()
except KeyboardInterrupt:
cancel_all('all')
sys.exit()
except:
logging.info(
"something went wrong running process, probably connection timeout."
)
logging.info(f"Unexpected error: {sys.exc_info()}")
pass
def start_test(count):
cancel_all('all')
client = build_client()
if count < 1:
count += 1
symbols = ['CRBP', 'QD', 'WPG', 'ANY']
for symbol in symbols:
ph = client.get_price_history(
symbol=symbol,
period_type=client.PriceHistory.PeriodType.DAY,
period=client.PriceHistory.Period.ONE_DAY,
frequency_type=client.PriceHistory.FrequencyType.MINUTE,
frequency=client.PriceHistory.Frequency.EVERY_FIFTEEN_MINUTES,
start_datetime=None,
end_datetime=None,
need_extended_hours_data=None).json()
print(ph['symbol'])
df = pd.json_normalize(ph, 'candles')
# pprint(len(df))
# pprint(df)
# pprint(df['close'])
# pprint(df['high'])
# pprint(df['low'])
# pprint(df['volume'])
raw_vwap = VolumeWeightedAveragePrice(high=df['high'],
low=df['low'],
close=df['close'],
volume=df['volume'],
window=14)
raw_ema = EMAIndicator(close=df['close'], window=9)
print("vwap:")
pprint(raw_vwap.vwap[len(raw_vwap.vwap) - 1])
print("ema:")
pprint(raw_ema._close[len(raw_ema._close) - 1])
print("len ema:")
print(len(raw_ema._close))
def daily_trader(str_):
list_of_files = glob.glob("./csv's/trades/*.csv")
sorted_files = sorted(list_of_files, key=os.path.getctime)
most_recent_file = sorted_files[-1] # last file should be most recent one
client = build_client()
# cancel all open orders
cancel_all('orders')
print("Getting account balance...")
account = client.get_account(
account_id=ACCOUNT_ID).json()["securitiesAccount"]
# print(f"account: {account}")
# choose between account["currentBalances"][totalCash] for cash accounts,
# or account["currentBalances"]["buyingPower"] for margin accounts
if account["type"] == "CASH":
# plan to use a cash account to avoid PDT rule, so need to spread the
# trades over 3 days to allow cash to settle. Also, using this
# number to automatically calculate qty of shares for each trade
buying_power = account["currentBalances"]["totalCash"]
if buying_power == 0.0:
buying_power = account["currentBalances"][
"cashAvailableForTrading"]
else:
# later, when using a margin account, this becomes
# a part of the risk management strategy
buying_power = account["currentBalances"]["buyingPower"]
daily_investment = round(float(buying_power) / 3, 2)
print(daily_investment)
# count rows
with open(most_recent_file, newline='') as csvfile:
reader = csv.DictReader(csvfile)
print("Counting trades...")
num_of_trades = len(list(reader))
csvfile.close()
# create orders from most recent gap up analysis
with open(most_recent_file, newline='') as csvfile:
reader = csv.DictReader(csvfile)
print("Creating orders...")
try:
investment_per_trade = round(daily_investment / num_of_trades, 2)
# if number of trades == 0
except:
investment_per_trade = 0.0
order_num = 0
error_num = 0
entries = 0
print(f"Num of trades is {num_of_trades}")
print(f"inv per trade is {investment_per_trade}")
for row in reader:
entries += 1
symbol = row['Symbol']
last = float(row['Last'])
quote = client.get_quote(symbol)
bid = quote.json()[symbol]["bidPrice"]
ask = quote.json()[symbol]["askPrice"]
avg_price = round((bid + ask) / 2, 2)
trade_check = check_for_trade(symbol)
if trade_check:
continue
else:
pass
# if the price has moved down more than 15 cents, avg + 0.15 will be lower than last, and try to short the stock
if avg_price + 0.15 < last:
order_type = "short"
entry_price = round(last - 0.10, 2)
sl_price = str(round(entry_price * 1.07, 2))
# otherwise, price is stable or moving up, so make a long order
else:
order_type = "long"
entry_price = round(last + 0.10, 2)
sl_price = str(round(entry_price * 0.93, 2))
qty = int(round(investment_per_trade / entry_price, 0))
print(f"qty is {qty}")
if entry_price == 0 or qty == 0:
continue
volume = row['Volume'] # to be used later
gap_up_percent = row['Gap Up%'] # to be used later
print(
f"Symbol: {symbol} side: {order_type} target entry: {entry_price} Stop Loss price: {sl_price}"
)
print(f"Symbol: {symbol} Vol: {volume} Gap Up% {gap_up_percent}")
try:
create_order(client, symbol, entry_price, qty, order_type)
order_num += 1
except Exception as err:
print(f"Error with {symbol}: {err}")
error_num += 1
pass
print(f"{entries} Entries found.")
print(f"{order_num} Orders created.")
print(f"{error_num} Errors encountered.")
csvfile.close()
def cancel_all(str_):
""" Pass 'all', 'trades', or 'orders' depending on what needs to be canceled. """
errors = []
client = build_client()
# cancel all open orders
if str_ == "all" or str_ == "orders":
try:
orders = client.get_orders_by_path(account_id=ACCOUNT_ID, max_results=None, from_entered_datetime=None, to_entered_datetime=None, status=client.Order.Status.QUEUED, statuses=None).json()
print("Canceling orders...")
# orders = orders.json()
for order in orders:
order_id = order["orderId"]
print(f"Order ID: {order_id}")
client.cancel_order(order_id=order_id, account_id=ACCOUNT_ID)
print("Orders canceled.")
except:
print("Unexpected error canceling orders:", sys.exc_info())
errors.append(sys.exc_info())
pass
# close all trades
if str_ == "all" or str_ == "trades":
try:
account_with_positions = client.get_account(account_id=ACCOUNT_ID, fields=client.Account.Fields.POSITIONS).json()["securitiesAccount"]
try:
positions = account_with_positions["positions"]
except:
positions = {}
print("Closing positions...")
if positions == {}:
print("No positions to close.")
else:
for trade in positions:
print(positions)
# submit sell order for the position
symbol = trade["instrument"]["symbol"]
short_quantity = trade["shortQuantity"]
long_quantity = trade["longQuantity"]
try:
if long_quantity > 0:
client.place_order(
account_id=ACCOUNT_ID,
order_spec=equities.equity_sell_market(symbol, long_quantity)
)
# it might be a short position, so try a buy order
elif short_quantity > 0:
client.place_order(
account_id=ACCOUNT_ID,
order_spec=equities.equity_buy_market(symbol, short_quantity)
)
else:
print("No trades to close.")
return
except:
print("Unexpected error closing trades:", sys.exc_info())
errors.append(sys.exc_info())
pass
except:
print("Unexpected error closing trades:", sys.exc_info())
errors.append(sys.exc_info())
pass
if len(errors) > 0:
print("errors with closing orders and positions:")
print(errors)
else:
print("All orders canceled and positions closed.")
def assess(str):
if str == "prompt":
scrape_first = input("Scrape first, y/n? ")
else:
scrape_first = "n"
if scrape_first == "y" or scrape_first == "Y" or scrape_first == "yes" or scrape_first == "Yes":
scraper()
time.sleep(10)
else:
pass
# get 2 latest csv's from csv's directory
list_of_files = glob.glob(
"./csv's/raw/*.csv") # * means all if need specific format then *.csv
sorted_files = sorted(list_of_files, key=os.path.getctime)
recent_gap_down = sorted_files[
-1] # last file is gap downs, due to order in which they are scraped
recent_gap_up = sorted_files[-2] # second to last will be gap ups
gap_ups = []
gap_downs = []
client = build_client()
account = client.get_account(
account_id=ACCOUNT_ID).json()["securitiesAccount"]
# choose between account["currentBalances"][totalCash] for cash accounts,
# or account["currentBalances"]["buyingPower"]
if account["type"] == "CASH":
# plan to use a cash account to avoid PDT rule, so need to spread the
# trades over 3 days to allow cash to settle. Also, using this
# number to automatically calculate qty of shares for each trade
buying_power = account["currentBalances"]["totalCash"]
else:
# later, when using a margin account, this becomes
# a part of the risk management strategy
buying_power = account["currentBalances"]["buyingPower"]
if buying_power < 250:
bottom_limit = 0.01
upper_limit = 10.0
else:
bottom_limit = 1.0
upper_limit = 11.0
# analyze gap ups first
# trade these, as they have something working favorably for them
# which others will also bank on
# looking for gap up over 4%, volume over 300k, and last daily closing price under $20
with open(recent_gap_up, newline='') as csvfile:
stockreader = csv.DictReader(csvfile, delimiter=',', quotechar='"')
print("===== Gap Ups: =====")
for row in stockreader:
try:
# grab variables
symbol = row['Symbol']
last = float(row['Last'])
volume = int(float(row['Volume'].replace(",", "")))
gap_up_percent = float(row['Gap Up%'].replace("%", ""))
# check for criteria above
if volume >= 300000 and last >= bottom_limit and last <= upper_limit and gap_up_percent >= 4.0:
print(
f"{symbol}: Last - {last}, Gap Up% - {gap_up_percent}, Volume - {volume}"
)
gap_ups.append([symbol, last, volume, gap_up_percent])
else:
# don't fit criteria
print(f"Skipped {symbol}: Outside parameters.")
except:
# quick fix for random rows in csv
print("Error")
continue
csvfile.close()
location = get_date_and_time("trades")
with open(location, 'w', newline='') as csvfile:
fieldnames = ['Symbol', 'Last', 'Volume', 'Gap Up%']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for stock in gap_ups:
writer.writerow({
"Symbol": stock[0],
"Last": stock[1],
"Volume": stock[2],
"Gap Up%": stock[3],
})
csvfile.close()
#anazlyze gap downs
# looking for something that had a high price and has fallen due to recent news
# add to a watchlist to profit off of recovery, or short the stock and profit off price drop
with open(recent_gap_down, newline='') as csvfile:
stockreader = csv.DictReader(csvfile, delimiter=',', quotechar='"')
print("===== Gap Downs: =====")
for row in stockreader:
try:
symbol = row['Symbol']
last = float(row['Last'])
volume = int(float(row['Volume'].replace(",", "")))
gap_down_percent = float(row['Gap Down%'].replace("%", ""))
if volume >= 300000 and last >= bottom_limit and last <= upper_limit and gap_down_percent < -4.5:
print(
f"{symbol}: Last - {last}, Gap Down% - {gap_down_percent}, Volume - {volume}"
)
gap_downs.append([symbol, last, volume, gap_down_percent])
else:
print(f"Skipped {symbol}: Outside parameters.")
except:
print("Error")
continue
csvfile.close()
location = get_date_and_time("watches")
with open(location, 'w', newline='') as csvfile:
fieldnames = ['Symbol', 'Last', 'Volume', 'Gap Down%']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for stock in gap_downs:
writer.writerow({
"Symbol": stock[0],
"Last": stock[1],
"Volume": stock[2],
"Gap Down%": stock[3],
})
csvfile.close()