def mrTest(filename):
stocks = ['C', 'FDX', 'KO', 'MSFT', 'SBUX', 'NFLX', 'LUV']
for s in stocks:
csvFile = open(s+filename, 'a')
f = csv.writer(csvFile)
table = {}
f.writerow([s])
f.writerow(['t', 'R^2', 'AIC'])
path = createPath(s + '/' + s + 'mrValidation.csv')
models = loadModels(path, "mr")
remainingStocks = copy.deepcopy(stocks)
remainingStocks.remove(s)
for line in models:
for rs in remainingStocks:
i = line['timewindow']
featurePath = createPath(rs + "/")
features = loadFeatures(featurePath, i)
prices = []
ema = []
rsi = []
macd = []
for row in features:
prices.append(row['price'])
ema.append(row['ema'])
rsi.append(row['rsi'])
macd.append(row['macd'])
y = np.array(prices)
x = np.vstack([ema, rsi, macd]).T
validationStats = anova.ols(y, x, [line['constant'], line['ema'], line['rsi'], line['macd']])
r2 = validationStats.R2
aic = validationStats.ll()[1]
# average out the results over k folds
stats = [r2, aic]
stats = [val/float(len(remainingStocks)) for val in stats]
if i in table:
table[i] = [sum(item) for item in izip(table[i], stats)]
else:
table[i] = stats
print i
print stats
# write the averaged results
for i in range(2, 202, 2):
table[i].insert(0, i)
print table[i]
f.writerow(table[i])
csvFile.flush()
def emaTest(filename):
stocks = ['C', 'FDX', 'KO', 'MSFT', 'SBUX', 'NFLX', 'LUV']
for s in stocks:
csvFile = open(s + filename, 'a')
f = csv.writer(csvFile)
table = {}
f.writerow([s])
f.writerow(['t', 'R^2', 'AIC'])
path = createPath(s + '/' + s + 'emaValidation.csv')
models = loadModels(path, "emaOnly")
remainingStocks = copy.deepcopy(stocks)
remainingStocks.remove(s)
for line in models:
for rs in remainingStocks:
i = line['timewindow']
featurePath = createPath(rs + "/")
features = loadFeatures(featurePath, i)
prices = []
ema = []
for row in features:
prices.append(row['price'])
ema.append(row['ema'])
y = np.array(prices)
x = np.vstack([ema]).T
validationStats = anova.ols(y, x,
[line['constant'], line['ema']])
r2 = validationStats.R2
aic = validationStats.ll()[1]
# average out the results over k folds
stats = [r2, aic]
stats = [val / float(len(remainingStocks)) for val in stats]
if i in table:
table[i] = [sum(item) for item in izip(table[i], stats)]
else:
table[i] = stats
print i
print stats
# write the averaged results
for i in range(2, 202, 2):
table[i].insert(0, i)
print table[i]
f.writerow(table[i])
csvFile.flush()
'''
Created on Apr 11, 2011
@author: bash125
'''
from tools.loadData import loadStock, createPath
import os
from tools import technicalFeatures as tf
stocks = ['C', 'FDX', 'MSFT', 'SBUX', 'NFLX', 'LUV']
def ensure_dir(f):
d = os.path.dirname(f)
if not os.path.exists(d):
os.makedirs(d)
for s in stocks:
timeseries = loadStock(s + '/' + s)
path = createPath(s + '/' + 'Timewindow Features')
ensure_dir(path)
path += '/CSV/'
ensure_dir(path)
twf = tf.TechnicalFeatures(timeseries)
for i in range(2, 202, 2):
twf.setTimewindow(i)
csvFile = path + 'timewindowFeatures' + str(i) + '.csv'
twf.writeCsv(csvFile, twf.prices, twf.ema, twf.rsi, twf.macd)
'''
Created on Apr 11, 2011
@author: bash125
'''
from tools.loadData import loadStock, createPath
import os
from tools import technicalFeatures as tf
stocks = ['C', 'FDX', 'MSFT', 'SBUX', 'NFLX', 'LUV']
def ensure_dir(f):
d = os.path.dirname(f)
if not os.path.exists(d):
os.makedirs(d)
for s in stocks:
timeseries = loadStock(s + '/' + s)
path = createPath(s + '/' + 'Timewindow Features')
ensure_dir(path)
path += '/CSV/'
ensure_dir(path)
twf = tf.TechnicalFeatures(timeseries)
for i in range(2, 202, 2):
twf.setTimewindow(i)
csvFile = path + 'timewindowFeatures' + str(i) + '.csv'
twf.writeCsv(csvFile, twf.prices, twf.ema, twf.rsi, twf.macd)
