def autoDayLoop(broker, connection):
"""
autoDayLoop: automatically buys and sells for the day
broker - broker name to run as
connection - database connection object
return: none
"""
# fetch broker info [name, bank, date] and owned stocks
brokerInfo = fetchSqlData.fetchBrokerInfo(broker, connection)
ownedStocks = fetchSqlData.queryAllOwned(broker, connection)
today = brokerInfo[2]
quarterDate = brokerInfo[2] - timedelta(days=90)
yearDate = brokerInfo[2] - timedelta(days=365)
# check to sell
if ownedStocks:
# get 90 days ago
for stocks in ownedStocks:
# get 60 day pointset
points = fetchSqlData.getPointsDateRange(quarterDate, today,
stocks[0],
["Date", "AdjClose"],
connection)
kmeans = CurveFit.getKmean(points)
# if its above high kmean sellall
if points[-1][1] > kmeans[0]:
sellStock(brokerInfo, stocks[0], stocks[1], connection)
print(stocks, "SOLD")
# get stock list
stockList = fetchSqlData.getStockList(connection)
highScores = []
totalNumStocks = len(stockList)
onStockNum = 0
oldfraction = 0
loadingBar(0)
# score all stocks
for stocks in stockList:
onStockNum += 1
#print(stocks[0])
# if stock exists on dates
if fetchSqlData.stockDateExists(
today, stocks[0], connection) and fetchSqlData.stockDateExists(
yearDate, stocks[0], connection):
# load pointests
pointSet = fetchSqlData.getPointsDateRange(yearDate, today,
stocks[0],
["Date", "AdjClose"],
connection)
secondSet = fetchSqlData.getPointsDateRange(
quarterDate, today, stocks[0], ["Date", "AdjClose"],
connection)
# score and append if higher
score = CurveFit.compositeScore(pointSet, secondSet)
CurveFit.appendHigher(
[stocks, score[0], score[1], score[2], pointSet[-1][1]],
highScores, 10)
fraction = int((onStockNum / totalNumStocks) * 50)
if oldfraction != fraction:
loadingBar(fraction)
oldfraction = fraction
stdout.write("\r")
stdout.flush()
# check for which stock to buy
for goodStocks in highScores:
#newSet = fetchSqlData.getPointsDateRange(quarterDate, today, goodStocks[0][0], ["Date","AdjClose"], connection)
#newPoly = CurveFit.polyFit(newSet,9)
#CurveFit.drawPoints(newSet,newPoly)
#newKmean = CurveFit.getKmean(newSet)
# buy if below kmean
if goodStocks[2] > goodStocks[4] and goodStocks[3] > goodStocks[4]:
# check bank for how much to buy
brokerInfo = fetchSqlData.fetchBrokerInfo(broker, connection)
totalMoney = (valueStocks(broker, brokerInfo[2], connection) +
brokerInfo[1]) / 10
toBuy = int(totalMoney / goodStocks[4])
if toBuy > 1000:
toBuy = 1000
# Buy stock
if toBuy > 0:
if buyStock(brokerInfo, goodStocks[0][0], toBuy, connection):
print(goodStocks, toBuy)
# Done if stock was bought
break
