Dead end approach to stock predition on hackerrank.com

stocks.py

#Dead end approach to https://www.hackerrank.com/challenges/stockprediction
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats

fi = open("sample.txt", "rt")
lis = fi.read().split("\n")
stocks = {}

for li in lis:
	li = li.strip()
	if len(li) == 0: continue
	if li[0] == "*": continue
	vals = li.split(" ")
	stockName = vals.pop(0)
	stockPrices = map(float, vals)
	#av = sum(stockPrices) / len(stockPrices)
	stocks[stockName] = np.array(stockPrices)

fi = open("models.txt", "wt")
print stocks.keys()
for sn in stocks:
	st = stocks[sn]

	fivedayhistory = st[5:] + 0.5 * st[4:-1] + 0.25 * st[3:-2] - 0.25 * st[2:-3] - 0.5 * st[1:-4] - st[:-5]
	fourdayhistory = st[5:] + 0.5 * st[4:-1] - 0.5 * st[3:-2] - st[2:-3]
	threedayhistory = st[5:] - st[3:-2]
	twodayhistory = st[5:] - st[4:-1]
	histories = [fivedayhistory, fourdayhistory, threedayhistory, twodayhistory]
	histNames = ["5h", "4h", "3h", "2h"]
	twodayfuture = st[6:] - st[5:-1]
	threedayfuture = st[7:] - st[5:-2]
	fourdayfuture = st[7:] + 0.5 * st[6:-1] - 0.5 * st[5:-2] - st[4:-3]
	futures = [twodayfuture, threedayfuture, fourdayfuture]
	futNames = ["2f", "3f", "4f"]

	bestModel = None
	bestPVal = 0.0
	for hi, hin in zip(histories, histNames):
		for fu, fun in zip(futures, futNames):
			hi2 = hi[:len(fu)]
			reg = stats.linregress(hi2, fu)
			#print sn, hin, fun, reg
			if abs(reg.rvalue) > bestPVal:
				bestPVal = abs(reg.rvalue)
				bestModel = (hin, fun, reg)
	fi.write("{},{},{},{},{},{}\n".format(sn, bestModel[2].slope, bestModel[2].rvalue, bestModel[2].intercept, bestModel[0], bestModel[1]))

	#plt.plot(fivedayhistory[:-3], fourdayfuture)
	

#plt.show()

#################################################################


import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import math

fi = open("sample.txt", "rt")
lis = fi.read().split("\n")
stocks = {}

for li in lis:
	li = li.strip()
	if len(li) == 0: continue
	if li[0] == "*": continue
	vals = li.split(" ")
	stockName = vals.pop(0)
	stockPrices = map(float, vals)
	#av = sum(stockPrices) / len(stockPrices)
	stocks[stockName] = np.array(stockPrices)

fi = open("models.txt", "rt")
models = {}
for li in fi.read().split("\n"):
	vals = li.strip().split(",")
	if len(vals) < 5: continue
	models[vals[0]] = vals[1:]

money = 100
holding = {}
days = len(stocks[list(stocks.keys())[0]])

for d in range(5,days):
	predUp = []
	for sn in stocks:
		st = stocks[sn]
		mo = models[sn]

		history = {}
		history["5h"] = st[d] + 0.5 * st[d-1] + 0.25 * st[d-2] - 0.25 * st[d-3] - 0.5 * st[d-4] - st[d-5]
		history["4h"] = st[d] + 0.5 * st[d-1] - 0.5 * st[d-2] - st[d-3]
		history["3h"] = st[d] - st[d-2]
		history["2h"] = st[d] - st[d-1]

		hist = history[mo[3]]
		pred = float(mo[0]) * hist + float(mo[2])
		predUp.append((pred, sn))
		#print d, sn, hist, pred

	#Buying
	predUp2 = []
	for row in predUp:
		if stocks[row[1]][d] < money and row[0] > 0.0:
			predUp2.append(row)

	if len(predUp2) > 0:
		predUp2.sort()
		bestStock = predUp2[-1][1]
		toBuy = int(math.floor(money / stocks[bestStock][d]))
		assert toBuy > 0
		if bestStock not in holding:
			holding[bestStock] = 0
		print "BUY", toBuy, bestStock, "at", stocks[bestStock][d]
		holding[bestStock] += toBuy
		money -= stocks[bestStock][d] * toBuy

	#Selling
	predDown = []
	for row in predUp:
		if row[0] < 0.0:
			predDown.append(row)
	predDown.sort()

	for row in predDown:
		if row[1] in holding and holding[row[1]] > 0:
			toSell = holding[row[1]]
			assert toSell > 0

			print "SELL", toSell, row[1], "at", stocks[row[1]][d]
			holding[row[1]] -= toSell
			money += stocks[row[1]][d] * toSell
			
	print d, money, holding

for sn in holding:
	toSell = holding[sn]
	if toSell == 0: continue

	holding[sn] -= toSell
	money += stocks[sn][d] * toSell

print d, money, holding