def readData(fileAddress):
with open(fileAddress, newline='') as csvfile:
data = csv.reader(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_NONNUMERIC)
dataList = list(data)
ages = dataList[0]
heights = dataList[1]
if (len(dataList) >= 3):
sampleSizes = dataList[2]
stdDeviations = dataList[3]
return CurveFit.Data(ages, heights, stdDeviations/np.sqrt(sampleSizes), system_error=0.1/np.sqrt(3))
else:
return CurveFit.Data(ages, heights, np.array([1.] * len(ages)))
def createBasinPointsPlane(self,p,v):
import matplotlib.pyplot as plt
self.planes =CurveFit.Planes()
pointCnt = 200
self.xp = np.empty(pointCnt)
self.yp = np.empty(pointCnt)
self.zp = np.empty(pointCnt)
for i in range(pointCnt):
self.xp[i] = np.random.random_integers(0,self.xlen)
self.yp[i] = np.random.random_integers(0,self.ylen)
self.zp[i] = self.planes.depth(p,v,self.xp[i],self.yp[i])
self.cf = CurveFit.curveFit(self.xp, self.yp, self.zp, self.res)
self.tck = self.cf.fitSurface()
self.cf.createRegGrid()
def addRandomError(data, sigma, sampleSize, minAge, maxAge):
new_x_arr = []
new_y_arr = []
new_variance_arr = []
for index in range(len(data.x_array)):
age = data.x_array[index]
if (age >= minAge and age <= maxAge and not age.is_integer()):
new_x_arr.append(age)
new_y_arr.append(
random.gauss(data.y_array[index], sigma / np.sqrt(sampleSize)))
new_variance_arr.append(sigma * generateChiSquare(sampleSize - 1) /
sampleSize)
return CurveFit.Data(new_x_arr, new_y_arr,
new_variance_arr / np.sqrt(sampleSize))
def createBasinManual(self):
self.createBasinPoints()
# x = [294.0, 141.0, 252.0, 426.0, 178.0, 19.0, 499.0, 63.0, 321.0, 162.0]
# y = [422.0, 408.0, 146.0, 264.0, 430.0, 19.0, 431.0, 284.0, 63.0, 56.0]
# z = [3.81, 6.43, 6.00, 6.97, 4.4, 2.49, 0.1, 2.31, 4.98, 0]
x = [29.40, 14.10, 25.20, 42.60, 17.80, 1.90, 49.90, 6.30, 32.10, 16.20]
y = [42.20, 40.80, 14.60, 26.40, 43.00, 1.90, 43.10, 28.40, 6.30, 5.60]
z = [3.81, 6.43, 6.00, 6.97, 4.4, 2.49, 0.1, 2.31, 4.98, 0]
#fig3 = plt.figure()
#ax = fig3.add_subplot(111, projection='3d')
#ax.scatter(x[:],y[:],z[:], c='r', marker='.')
for i in range(self.xtrapoints):
# print i, self.xp[i+self.xlen*2], self.yp[i+self.xlen*2], self.zp[i+self.xlen*2]
self.xp[i+self.xlen*2] = x[i]
self.yp[i+self.xlen*2] = y[i]
self.zp[i+self.xlen*2] = z[i]
# print i, self.xp[i+self.xlen*2], self.yp[i+self.xlen*2], self.zp[i+self.xlen*2]
self.cf = CurveFit.curveFit(self.xp, self.yp, self.zp, self.res)
self.tck = self.cf.fitSurface()
self.cf.createRegGrid()
self.cf.clamp(self.minz,self.maxz)
import CurveFit as cf import matplotlib.pyplot as plt # QUESTAO 1 # a) plota a dispersao dos dados------------------------------------------------- xi = np.array([1872, 1890, 1900, 1920, 1940, 1950, 1960, 1970, 1980, 1991, 1996]) yi = np.array([9.9, 14.3, 17.4, 30.6, 41.2, 51.9, 70.2, 93.1, 119.0, 146.2, 157.1]) plt.plot(xi, yi, 'ro') # --------------------------------------------------------------------------------- # b) calcula a funcao quadrática e os resultados------------------------------------ resultsp, polynomial = cf.fitpolynomial(xi, yi, 2) # Cria a linha da funcao e plota xx = np.linspace(xi[0], xi[-1]) plt.plot(xx, np.polyval(polynomial,xx),'b-') # ----------------------------------------------------------------------------------- # c) calcula a funcao exponencial(A e b) e os resultados ---------- ----------------- resultse, A, b = cf.fitexponencial(xi, yi) # Cria a linha da funcao e plota usando a Ae^(b*x) xx = np.linspace(xi[0], xi[-1]) plt.plot(xx, A * np.exp(b*xx),'g-') plt.grid();plt.show()
def createBasin(self, flat = False, manPoints = [], edgePoints = 10):
self.createBasinPoints(flat, manPoints, edgePoints)
self.cf = CurveFit.curveFit(self.xp, self.yp, self.zp, self.res)
self.tck = self.cf.fitSurface()
self.cf.createRegGrid()
self.cf.clamp(self.minz,self.maxz)
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
