def news_stock_analyzer(stock_ticker):
stock_score = 0
stock_score += nc.sentiment_analyzer(news.get_news(stock_ticker))
print(stock_ticker, "news score:", stock_score)
if stock_score >= 0.35 and stock_ticker not in all_active_positions.keys():
alpaca.create_order(stock_ticker, 1) #todo: calculate order amount
active_positions_to_check[stock_ticker] = sdg.get_current_stock_data(
stock_ticker)['Close']
all_active_positions[stock_ticker] = sdg.get_current_stock_data(
stock_ticker)['Close']
print("Based on News analysis, buying", stock_ticker)
def daytrading_stock_analyzer(stocks):
for stock_ticker in stocks: #purchases stocks based on daytrading patterns
try:
stock_score = 0
stock_score += sa.moving_average_checker(stock_ticker)
stock_score += sa.volume_checker(stock_ticker)
if stock_score >= 0.2 and stock_ticker not in all_active_positions.keys():
alpaca.create_order(stock_ticker, 1) #todo: calculate order amount
active_positions_to_check[stock_ticker] = sdg.get_current_stock_data(stock_ticker)['Close']
all_active_positions[stock_ticker] = sdg.get_current_stock_data(stock_ticker)['Close']
print("Based on daytrading pattern analysis, buying", stock_ticker, "Stock Score: ", stock_score)
except Exception as e:
pass
def moving_average_checker(ticker_symbol) -> int:
stock_status = sdg.get_current_stock_data(ticker_symbol)
if stock_status['Close'] > stock_status['EMA'] and stock_status['PREVPRICE'] < stock_status['PREVSMA']:
return 0.3
elif stock_status['Close'] < stock_status['SMA'] and stock_status['PREVPRICE'] > stock_status['PREVSMA']:
return -0.3
return 0
def volume_checker(ticker_symbol) -> int:
stock_status = sdg.get_current_stock_data(ticker_symbol)
print(ticker_symbol, ": Price: ", sdg.get_price_slope(ticker_symbol)/(0.2*stock_status['Close']), " Volume: ", sdg.get_volume_slope(ticker_symbol)/(0.2*stock_status['Volume']))
if sdg.get_price_slope(ticker_symbol)/(0.2*stock_status['Close']) > 0.04 and sdg.get_volume_slope(ticker_symbol)/(0.2*stock_status['Volume']) > 1:
return 0.2
elif sdg.get_price_slope(ticker_symbol)/(0.2*stock_status['Close']) < -0.03 and sdg.get_volume_slope(ticker_symbol)/(0.2*stock_status['Volume']) > 0.8:
return -0.2
return 0
def check_perform_sell(stock_ticker, purchase_price):
while True:
current_stock_price = sdg.get_current_stock_data(stock_ticker)['Close']
price_change_percent = util.calculate_price_change(current_stock_price, all_active_positions[stock_ticker])
print("Checking", stock_ticker, "Gains/Losses", price_change_percent, "Price: $", current_stock_price)
if sa.moving_average_checker(stock_ticker) < 0 or price_change_percent <= -const.MAX_STOP_LOSS_PERCENT or sa.volume_checker(stock_ticker) < 0:
alpaca.sell_position(stock_ticker)
del all_active_positions[stock_ticker]
break
def volume_checker(ticker_symbol) -> int:
stock_status = sdg.get_current_stock_data(ticker_symbol)
try:
volume = sdg.get_volume_slope(ticker_symbol) / (0.2 *
stock_status['Volume'])
except ZeroDivisionError:
volume = 0
try:
price = sdg.get_price_slope(ticker_symbol) / (0.2 *
stock_status['Close'])
except ZeroDivisionError:
price = 0
print(ticker_symbol, ": Price: ", price, " Volume: ", volume)
if price > 0.04 and volume > 1:
return 0.2
elif price < -0.03 and volume > 0.8:
return -0.2
return 0
import matplotlib.pyplot as plt
import stock_data_gatherer as sdg
import stock_analysis
stock_prices = []
n = 180
ticker = "AAPL"
for i in range(-n,0,1):
stock_prices.append(sdg.get_historical_data(ticker, "1d", "1m").iloc[i].to_dict()['Volume'])
plt.plot(list(range(n)), stock_prices, 'xb-')
# plt.ylim([130, 134])
print("Price Slope: ", sdg.get_volume_slope(ticker)/(0.2*sdg.get_current_stock_data(ticker)['Volume']))
plt.show()