def dashboard():
if request.method == 'GET':
global symbol
connector = db.dbConnector()
connection = connector.createConnection()
scrapedStocks = connector.getMentions(connection)
symbols = []
for x in scrapedStocks:
symbols.append(x['symbol'])
this = datetime.datetime.today() - timedelta(days=5)
time = str(this.year) + "-" + str(this.month) + "-" + str(
this.day) + " " + str(datetime.time(9, 35))
day = str(this.year) + "-" + str(this.month) + "-" + str(this.day)
print(day)
trades = connector.getTradeRecents(connection, time)
trades = trades[0:25]
stonks = connector.getBarsByTime(connection, time)
stonks = stonks[0:25]
results = connector.getResultsByDate(connection, day)
for x in results:
diff = x['endCash'] - x['startCash']
percentChange = diff / floaty(x['startCash']) + 1
x['percentChange'] = percentChange
strategy = ad.Strategy()
hammerChanges = strategy.calculateProfits(connector, connection,
'hammer')
morningStarChanges = strategy.calculateProfits(connector, connection,
'morningStar')
senkouBChanges = strategy.calculateProfits(connector, connection,
'senkouB_')
if request.method == 'POST':
symbol = request.form.get('symbol')
df = api.polygon.historic_agg_v2(symbol,
1,
'minute',
_from='2020-07-01',
to='2020-07-01').df
df['timestamp'] = df.index
df = df.reset_index(drop=True)
return render_template('graph.html', df=df.to_json(), symbol=symbol)
return render_template('dashboard_copy.html',
symbols=symbols,
trades=trades,
stonks=stonks,
results=results,
hammerChanges=hammerChanges,
morningStarChanges=morningStarChanges,
senkouBChanges=senkouBChanges)
ALPACA_SECRET_KEY = r'ysaezMtOX8sUghmR534ZGzEXzCTVpkDt26BEIY8e'
APCA_RETRY_MAX=0
api = tradeapi.REST(
key_id=ALPACA_KEY_ID,
secret_key=ALPACA_SECRET_KEY,
base_url='https://paper-api.alpaca.markets'
)
# Connect to the database
connector = db.dbConnector()
connection = connector.createConnection()
#Create time window to make api call for
window = datetime.datetime.now() - datetime.timedelta(minutes=1)
unscreened_stocks = connector.getMentions(connection)
strategy = ad.Strategy()
aggregateData = []
# iterate through symbols getting bar info for each symbol of last minute
for x in unscreened_stocks[0:len(unscreened_stocks)-1]:
df = pd.DataFrame()
tryCounter = 0
data = []
# check if df has a value to account for API response time
while df.empty and tryCounter < 5:
df = api.get_barset(x["symbol"], '1Min', limit=1, after=window).df
tryCounter +=1
if not df.empty:
data.append(x['symbol'])
data.append(floaty(df.iloc[0][0]))
data.append(floaty(df.iloc[0][1]))
data.append(floaty(df.iloc[0][2]))
def hammer_algo(api, stock, from_time, to_time, cash):
alltrades = []
cash = floaty(cash)
marketOpen = datetime.time(hour=9, minute=40, second=0, microsecond=0)
buyClose = datetime.time(hour=12, minute=0, second=0, microsecond=0)
currentPosition = None
barNumber = 1
buyPrice = 0
shares = 0
takeProfitPercent = 1.06
lossProfitPercent = .96
maxPosition = cash * .1
takeProfit = floaty()
lossProfit = floaty()
df = api.polygon.historic_agg_v2(stock,
1,
'minute',
_from=from_time,
to=to_time).df
strategy = ad.Strategy()
try:
while barNumber <= len(df) - 1:
if (df.index[barNumber].time() > marketOpen and
df.index[barNumber].time() < buyClose) or currentPosition:
if barNumber > 5:
closePrice = df.iloc[barNumber]['close']
start = barNumber - 5
sma = strategy.backtestSMA(df, start, barNumber)
if strategy.isHammerBar(df.iloc[barNumber]) and sma < 0:
alltrades.append(
str((df.index[barNumber])) + ' Buy at: ' +
str(closePrice))
buyPrice = df.iloc[barNumber]['close']
shares = int(maxPosition / closePrice)
cash = cash - shares * closePrice
takeProfit = takeProfitPercent * buyPrice
lossProfit = lossProfitPercent * buyPrice
currentPosition = 1
if currentPosition == 1:
if closePrice >= takeProfit:
if (buyPrice < closePrice):
alltrades.append(
str((df.index[barNumber])) + ' Sell at: ' +
str(closePrice))
cash += (shares * closePrice)
currentPosition = 0
elif closePrice <= lossProfit:
alltrades.append(
str((df.index[barNumber])) + ' Sell at: ' +
str(closePrice))
cash += (shares * closePrice)
currentPosition = 0
elif buyClose <= df.index[barNumber].time():
alltrades.append(
str((df.index[barNumber])) + ' Sell at: ' +
str(closePrice))
cash += (shares * closePrice)
currentPosition = 0
barNumber += 1
except Exception as e:
print(str(e.args))
return alltrades, floaty(cash)
