def minutes_till_trading():
''' Time till NZX is open for trading '''
now = util.get_nz_time()
open_dt = datetime.strptime(config.open_time, '%I:%M%p')
close_dt = datetime.strptime(config.close_time, '%I:%M%p')
# market is open today
if now.strftime('%A').lower() not in config.days_closed:
# open now
if now.time() >= open_dt.time() and now.time() <= close_dt.time():
return 0
# hasn't open yet
if now.time() < open_dt.time():
open_time = datetime.combine(now, open_dt.time()) - now
return open_time.total_seconds() / 60
# market has closed
for i in range(7):
future = now + timedelta(days=(i + 1))
if future.strftime('%A').lower() not in config.days_closed:
next_open = datetime.combine(future, open_dt.time()) - now
return next_open.total_seconds() / 60
util.log("market is never open according to config", error=True)
def should_buy(market_price, history, margin_percent):
''' Decides if the bot should buy or not '''
# ignore zero divide errors
np.seterr(divide='ignore', invalid='ignore')
try:
# calculate vwap
history = history.groupby(history.index.date, group_keys=False)
history = history.apply(Market.vwap).fillna(
0) ## Fill NaN's in df with 0's
# calculate direction
moves = np.gradient(history['vwap'])
median = np.median(moves)
average = np.average(moves)
# calculate margin price
margin_price = history['vwap'][-1]
margin_price -= (margin_price * (margin_percent / 100))
print(f'median:\t {median:.4g}')
print(f'averag:\t {average:.4g}')
print(f'market:\t {market_price:.4g}')
print(f'margin:\t {margin_price:.4g}')
# agree if going up and below margin
if median > 0 and average > 0 and market_price <= margin_price: # might like to consider changing the 0's?
return True
except Exception as e:
util.log(f'Warning: {e}')
return False
def should_buy(market_price, history, margin_percent):
''' Decides if the bot should buy or not '''
# ignore zero divide errors
np.seterr(divide='ignore', invalid='ignore')
try:
# calculate vwap
history = history.groupby(history.index.date, group_keys=False)
history = history.apply(Market.vwap)
# calculate direction
moves = np.gradient(history['vwap'])
median = np.median(moves)
average = np.average(moves)
# calculate margin price
margin_price = history['vwap'][-1]
margin_price -= (margin_price * (margin_percent/100))
# agree if going up and below margin
if median > 0 and average > 0 and market_price <= margin_price:
return True
except Exception as e:
util.log(f'Warning: {e}')
return False
def perform_buying(client, investments, companies, balance):
''' Find new stocks to buy from the companies '''
for company in companies:
# don't double invest
if company['id'] in investments:
continue
# ignore penny stocks
price = float(company['market_price'])
if price < config.minimum_stock_price:
continue
c = company['code']
sleep(2)
print()
print(f'Considering buying {c}')
symbol = company['code'] + '.NZ'
stock = yfinance.Ticker(symbol)
try:
history = stock.history(period='1mo', interval='15m')
except RuntimeError:
print('Probably not enough data at this resolution. Skipping')
continue
# buy if it is a bargain
should_buy = Market.should_buy(price, history, 0.4) # 0.4 is margin %, i.e. 0.4%
print(should_buy)
if should_buy:
buy_amount = config.buy_amount
# value shares more as dividends are upcoming
if company['dividends']:
dividends_soon = util.dividends_soon(company['dividends'])
if dividends_soon and config.dividends_bonus > 1:
buy_amount *= config.dividends_bonus
# check we have balance
if balance < buy_amount:
util.log(f'Want to buy {symbol} but not enough $$$')
break
# submit the buy order
util.log(f'Buying ${buy_amount} of {symbol}')
client.buy(company, buy_amount)
balance -= buy_amount
def perform_selling(client, portfolio, companies, dividends):
''' Look to sell stocks from the portfolio '''
for company in portfolio:
fund_id = company['fund_id']
contribution = float(company['contribution'])
current_value = float(company['value'])
# does company give dividends?
if fund_id in dividends:
util.log(f'Not selling {fund_id}:{code} due to dividends')
continue
# sell if we're making a profit
if Market.should_sell(contribution, current_value):
code = util.get_code_from_id(fund_id, companies)
util.log(f'Selling ${current_value} of {code}')
client.sell(company, float(company['shares']))
def perform_buying(client, investments, companies, balance):
''' Find new stocks to buy from the companies '''
for company in companies:
# don't double invest
if company['id'] in investments:
continue
# ignore penny stocks
price = float(company['market_price'])
if price < config.minimum_stock_price:
continue
symbol = company['code'] + '.NZ'
stock = yfinance.Ticker(symbol)
history = stock.history(period='1mo', interval='15m')
# buy if it is a bargain
if Market.should_buy(price, history, 0.4):
buy_amount = config.buy_amount
# value shares more as dividends are upcoming
if company['dividends']:
dividends_soon = util.dividends_soon(company['dividends'])
if dividends_soon and config.dividends_bonus > 1:
buy_amount *= config.dividends_bonus
# check we have balance
if balance < buy_amount:
util.log(f'Want to buy {symbol} but not enough $$$')
break
# submit the buy order
util.log(f'Buying ${buy_amount} of {symbol}')
client.buy(company, buy_amount)
balance -= buy_amount
balance = float(profile['user']['wallet_balance'])
portfolio = profile['portfolio']
dividends = profile['upcoming_dividends']
investments = util.get_fund_ids(portfolio)
companies = client.get_companies()
# it's show time
perform_selling(client, portfolio, companies, dividends)
perform_buying(client, investments, companies, balance)
if __name__ == '__main__':
# config
util.log('Loaded config')
# init client
client = sharesies.Client()
if client.login(config.username, config.password):
util.log('Connected to Sharesies')
else:
util.log('Failed to login', error=True)
# trade loop
while True:
minutes_till_open = Market.minutes_till_trading()
if minutes_till_open == 0:
util.log('Market is currently open!')
scan_market(client)