symbols = [line.strip() for line in open("../data/goodstocks_vol.txt").readlines()]
# symbols = ["F", "AAPL"]
nsymbols = len(symbols)
tickerIDs = {} # key is stock name, val is a unique number (I will use iticker for it)
fname = "forBDT_092915.txt"
if len(sys.argv) > 1: fname = sys.argv[-1]
fh = open(fname,"w")
fhTickers = open(fname.replace(".txt","_tickers.txt"),"w")
valNamesDict, mainDict = {}, {}
for iticker,ticker in enumerate(symbols):
# stock = gs.getStock(ticker, (2013, 1, 1), (2014, 12, 31))["days"] # for calculating
# stock = gs.getStock(ticker, (2014, 1, 1), (2014, 12, 20))["days"] # for calculating
stock = gs.getStock(ticker, (2014, 12, 20), (2015, 10, 1))["days"] # for calculating
drawProgressBar(1.0*iticker/nsymbols)
if(len(stock) < 50): continue
quotes = []
timesclose = []
for day in sorted(stock.keys()):
vals = stock[day]
o, h, l, c, v = vals["o"], vals["h"], vals["l"], vals["c"], vals["v"]
quotes.append( [day,o,h,l,c,v] )
quotes = np.array(quotes)
if(quotes[-1][4] > 600.0): continue # ignore expensive stocks
def doBenchmark(self, params={}, progressBar=True, userOnly=False, debug=False):
self.cleanVars()
if self.performChecks():
print "[BT] Can't benchmark"
return
for isymbol, symbol in enumerate(self.symbols):
self.isymbol = isymbol
if progressBar:
self.drawProgressBar()
stock = gs.getStock(symbol, self.d0, self.d2)
quotes = u.dictToList(stock) # [day,o,h,l,c]
if len(quotes) < 15:
continue
try:
dBuy, dSell = self.strategy(quotes, self.d1, self.d2, params)
except Exception as e:
print "[BT] Problem running user strategy"
print e
continue
if len(dBuy.keys()) < 1:
continue # pointless if we don't buy
if len(dSell.keys()) < 1:
continue # pointless if we don't sell -- then it's just BAH
try:
self.report[symbol] = {}
self.report[symbol]["ndays"] = len(quotes)
# USER STRATEGY
price = 0
ledger = tr.Ledger(self.money)
for quote in quotes:
day, price, h, l, c = quote
if day in dSell:
ledger.sellStock(symbol, day, price, fraction=dSell[day])
elif day in dBuy:
ledger.buyStock(symbol, day, price)
ledger.sellStock(symbol, day, price) # sell outstanding shares to finish up
self.report[symbol]["user"] = [
ledger.getProfit(),
0.0,
ledger.getNumTrades(),
ledger.getWinPercent(),
ledger.getAvgWinProfitPercent(),
ledger.getAvgLossProfitPercent(),
]
if not userOnly:
# RANDOM STRATEGY
profits = []
for i in range(100):
price = 0
ledgerRand = tr.Ledger(self.money)
days = quotes[:, 0]
np.random.shuffle(days)
# want to do a random trade on avg every 3-10 days
# so we take the first #days/rand(3,10) random days, then sort them
days = sorted(days[: len(days) // random.randint(3, 10)])
days = days[len(days) % 2 :] # even number of entries, so we always sell what we buy
buy = True # buy initially
for day in days:
if buy:
ledgerRand.buyStock(symbol, day, price=stock["days"][day]["c"])
else:
ledgerRand.sellStock(symbol, day, price=stock["days"][day]["c"])
buy = not buy # alternate between buy and sell
profits.append(ledgerRand.getProfit())
profits = np.array(profits)
# BUY AND HOLD
ledgerBAH = tr.Ledger(self.money) # buy and hold
ledgerBAH.buyStock(symbol, quotes[0][0], quotes[0][4])
ledgerBAH.sellStock(symbol, quotes[-1][0], quotes[-1][4])
self.report[symbol]["rand"] = [
round(np.mean(profits), 2),
round(np.std(profits)),
ledgerRand.getNumTrades(),
ledgerRand.getWinPercent(),
ledgerRand.getAvgWinProfitPercent(),
ledgerRand.getAvgLossProfitPercent(),
]
self.report[symbol]["bah"] = [
ledgerBAH.getProfit(),
0.0,
ledgerBAH.getNumTrades(),
ledgerBAH.getWinPercent(),
ledgerBAH.getAvgWinProfitPercent(),
ledgerBAH.getAvgLossProfitPercent(),
]
if debug:
ledger.printLedger()
except Exception as e:
print "[BT] Some other error"
print e
continue
self.postBenchmark()
import os, sys
import numpy as np
import sklearn.cluster as skc
import getStocks as gs
import utils as u
import indicators as ind
plotdir = "../plots/"
# stock = gs.getStock("AAPL", 2005, 2010)
stock = gs.getStock("DIS", (2008, 6, 20), (2010, 10, 7))["days"]
quotes = []
normquotes = []
fractions = []
timesclose = []
for day in sorted(stock.keys()):
vals = stock[day]
o, h, l, c = vals["o"], vals["h"], vals["l"], vals["c"]
# "normalize" values (subtract out open price)
no = round(o - o, 2)
nh = round(h - o, 2)
nl = round(l - o, 2)
nc = round(c - o, 2)
quotes.append([day, o, h, l, c])
normquotes.append([day, no, nh, nl, nc])
# fractions.append( abs(o-c)/(h-l) ) # what fraction of (high-low) is |open-close|?
import os, sys, random
import numpy as np
import getStocks as gs
import utils as u
import indicators as ind
import tradeReport as tr
#symbol = "WMT"
symbol = "NFLX"
# symbol = "F"
stock = gs.getStock(symbol, (2011, 6, 1), (2015, 6, 1)) # for calculating
quotes = u.dictToList(stock) # [day,o,h,l,c]
timesclose = quotes[:,[0,4]]
emas = []
for i in [1,2]: emas.append( ind.ematimes(timesclose,10*i) )
crossovertimes = ind.crossovertimes(emas)
dCrossovers = {} # turn into dict for fast lookup
for time,rising in crossovertimes: dCrossovers[time] = rising
# CROSSOVER STRATEGY
price = 0
ledger = tr.Ledger(1000)
for quote in quotes:
day,price,h,l,c = quote
if(day in dCrossovers):
rising = dCrossovers[day]
doCuts = False
writeBDTtext = True
makePlots = True
fh = None
dInds = {}
bkgPercentinc = np.array([])
sigPercentinc = np.array([])
if (writeBDTtext):
fh = open("forBDT.txt", "w")
for iticker, ticker in enumerate(symbols):
# stock = gs.getStock(ticker, (2013, 1, 1), (2014, 12, 31))["days"] # for calculating
# stock = gs.getStock(ticker, (2014, 1, 1), (2014, 12, 20))["days"] # for calculating
stock = gs.getStock(ticker, (2014, 12, 20),
(2015, 10, 1))["days"] # for calculating
drawProgressBar(1.0 * iticker / nsymbols)
if (len(stock) < 50): continue
quotes = []
timesclose = []
for day in sorted(stock.keys()):
vals = stock[day]
o, h, l, c, v = vals["o"], vals["h"], vals["l"], vals["c"], vals["v"]
quotes.append([day, o, h, l, c, v])
quotes = np.array(quotes)
if (quotes[-1][4] > 300.0): continue # ignore expensive stocks
import os, sys
import numpy as np
import sklearn.cluster as skc
import getStocks as gs
import utils as u
import indicators as ind
plotdir = "../plots/"
# stock = gs.getStock("AAPL", 2005, 2010)
stock = gs.getStock("DIS", (2008, 6, 20), (2010, 10, 7))["days"]
quotes = []
normquotes = []
fractions = []
timesclose = []
for day in sorted(stock.keys()):
vals = stock[day]
o, h, l, c = vals["o"], vals["h"], vals["l"], vals["c"]
# "normalize" values (subtract out open price)
no = round(o - o, 2)
nh = round(h - o, 2)
nl = round(l - o, 2)
nc = round(c - o, 2)
quotes.append([day, o, h, l, c])
normquotes.append([day, no, nh, nl, nc])
# fractions.append( abs(o-c)/(h-l) ) # what fraction of (high-low) is |open-close|?
def doBenchmark(self,
params={},
progressBar=True,
userOnly=False,
debug=False):
self.cleanVars()
if self.performChecks():
print "[BT] Can't benchmark"
return
for isymbol, symbol in enumerate(self.symbols):
self.isymbol = isymbol
if (progressBar): self.drawProgressBar()
stock = gs.getStock(symbol, self.d0, self.d2)
quotes = u.dictToList(stock) # [day,o,h,l,c]
if (len(quotes) < 15): continue
try:
dBuy, dSell = self.strategy(quotes, self.d1, self.d2, params)
except Exception as e:
print "[BT] Problem running user strategy"
print e
continue
if (len(dBuy.keys()) < 1): continue # pointless if we don't buy
if (len(dSell.keys()) < 1):
continue # pointless if we don't sell -- then it's just BAH
try:
self.report[symbol] = {}
self.report[symbol]["ndays"] = len(quotes)
# USER STRATEGY
price = 0
ledger = tr.Ledger(self.money)
for quote in quotes:
day, price, h, l, c = quote
if (day in dSell):
ledger.sellStock(symbol,
day,
price,
fraction=dSell[day])
elif (day in dBuy):
ledger.buyStock(symbol, day, price)
ledger.sellStock(symbol, day,
price) # sell outstanding shares to finish up
self.report[symbol]["user"] = [
ledger.getProfit(), 0.0,
ledger.getNumTrades(),
ledger.getWinPercent(),
ledger.getAvgWinProfitPercent(),
ledger.getAvgLossProfitPercent()
]
if (not userOnly):
# RANDOM STRATEGY
profits = []
for i in range(100):
price = 0
ledgerRand = tr.Ledger(self.money)
days = quotes[:, 0]
np.random.shuffle(days)
# want to do a random trade on avg every 3-10 days
# so we take the first #days/rand(3,10) random days, then sort them
days = sorted(days[:len(days) //
random.randint(3, 10)])
days = days[
len(days) %
2:] # even number of entries, so we always sell what we buy
buy = True # buy initially
for day in days:
if (buy):
ledgerRand.buyStock(
symbol, day, price=stock["days"][day]['c'])
else:
ledgerRand.sellStock(
symbol, day, price=stock["days"][day]['c'])
buy = not buy # alternate between buy and sell
profits.append(ledgerRand.getProfit())
profits = np.array(profits)
# BUY AND HOLD
ledgerBAH = tr.Ledger(self.money) # buy and hold
ledgerBAH.buyStock(symbol, quotes[0][0], quotes[0][4])
ledgerBAH.sellStock(symbol, quotes[-1][0], quotes[-1][4])
self.report[symbol]["rand"] = [
round(np.mean(profits), 2),
round(np.std(profits)),
ledgerRand.getNumTrades(),
ledgerRand.getWinPercent(),
ledgerRand.getAvgWinProfitPercent(),
ledgerRand.getAvgLossProfitPercent()
]
self.report[symbol]["bah"] = [
ledgerBAH.getProfit(), 0.0,
ledgerBAH.getNumTrades(),
ledgerBAH.getWinPercent(),
ledgerBAH.getAvgWinProfitPercent(),
ledgerBAH.getAvgLossProfitPercent()
]
if debug:
ledger.printLedger()
except Exception as e:
print "[BT] Some other error"
print e
continue
self.postBenchmark()
import numpy as np
import sklearn.cluster as skc
import getStocks as gs
import utils as u
import indicators as ind
import tradeReport as tr
import random
plotdir = "../plots/"
#symbol = "WMT"
#symbol = "NFLX"
symbol = "F"
stock = gs.getStock(symbol, (2014, 6, 1), (2015, 6, 1))["days"] # for calculating
d1,d2 = (2014,3,10),(2014,6,15) # for plotting
quotes = []
normquotes = []
timesclose = []
for day in sorted(stock.keys()):
vals = stock[day]
o, h, l, c = vals["o"], vals["h"], vals["l"], vals["c"]
# "normalize" values (subtract out open price)
no = round(o-o,2)
nh = round(h-o,2)
nl = round(l-o,2)
nc = round(c-o,2)
import numpy as np
import sklearn.cluster as skc
import getStocks as gs
import utils as u
import indicators as ind
import tradeReport as tr
import random
plotdir = "../plots/"
#symbol = "WMT"
#symbol = "NFLX"
symbol = "F"
stock = gs.getStock(symbol, (2014, 6, 1),
(2015, 6, 1))["days"] # for calculating
d1, d2 = (2014, 3, 10), (2014, 6, 15) # for plotting
quotes = []
normquotes = []
timesclose = []
for day in sorted(stock.keys()):
vals = stock[day]
o, h, l, c = vals["o"], vals["h"], vals["l"], vals["c"]
# "normalize" values (subtract out open price)
no = round(o - o, 2)
nh = round(h - o, 2)
nl = round(l - o, 2)
nc = round(c - o, 2)
rising = dCrossovers[day]
if(rising):
dBuy[day] = 1
hasBought = True
boughtPrice = quotes[i][4]
return dBuy
symbols = [line.strip() for line in open("../data/nasdaqlisted.txt").readlines()][:1000]
today = datetime.datetime.today()
dtuple = (today.year,today.month,today.day-3)
goodsymbols = []
for symbol in symbols:
stock = gs.getStock(symbol, (2015, 8, 1), dtuple)
quotes = u.dictToList(stock) # [day,o,h,l,c]
array_quotes = np.array(quotes)
print symbol
if(len(quotes) < 25): continue
if np.mean(quotes[:,4]) > 20: continue
if np.mean(quotes[:,4]) < 0.25: continue
buys = crossover(quotes)
if u.tuple2inum(dtuple) in buys:
print symbol, "#"*40
goodsymbols.append(symbol)
print "-"*40
print " ".join(goodsymbols)
print "-"*40
import os, sys
import numpy as np
import sklearn.cluster as skc
import getStocks as gs
import utils as u
import indicators as ind
import cluster as cl
plotdir = "../plots/"
stock = gs.getStock("F", (2010, 1, 1), (2011, 10, 5))["days"] # for calculating
d1,d2 = (2010,8,1),(2011,3,5) # for plotting
quotes = []
timesclose = []
for day in sorted(stock.keys()):
vals = stock[day]
o, h, l, c = vals["o"], vals["h"], vals["l"], vals["c"]
quotes.append( [day,o,h,l,c] )
timesclose.append([day,c])
timesclose = np.array(timesclose)
bbands = ind.bbtimes(timesclose, 20)
emas = [ ind.ematimes(timesclose,i) for i in [5,10] ]
quotes = np.array(quotes)
rsis = ind.rsitimes(quotes[:,[0,1]],14)