Here is a simple python script that can be built further for algorithmic trading. You will need to get an API key from Alpha Advantage or similar service to get the quotes.
To make the trades, you will need to link the script to a trading API from TDAmeritrade or your favorite broker who has an API. This part of the script is not included here.
The algorithm shown is a very simple one. Basically, buy the stock and then wait till the price is [1]% more than the purchase price. and then sell. Wait of [nn] minutes. Repeat.
You can program more complex strategies by building upon this mechanism.
def stock_price(stk='QQQ',end_hr=16, end_min=00, after_sell_minutes=0):
import urllib
import urllib2
import time
import re
import json
import os
import sys
import datetime
import random
# Get API key from Alpha Vantage
st_sym = stk
st_ind = 'nyse'
run_time_min = 30
run_time_int = 1 # default wait time in seconds for next check
cash_bal = 10000.0
wig = 5.0
url_prefix = "https://www.alphavantage.co/query?function=BATCH_STOCK_QUOTES&symbols="
url_symbols = st_sym.upper()
url_postfix = "&apikey=__YOUR API KEY__"
url_to_query = url_prefix + url_symbols + url_postfix
print '' + 'Query made: ' + url_to_query + ''
response = urllib2.urlopen(url_to_query)
headers = response.info()
data = response.read()
# do a loop for nnn minutes
run_loops = int(run_time_min * 60 / run_time_int)
t_h = datetime.datetime.now().hour
t_m = datetime.datetime.now().minute
min_price = 0.
max_price = 0.
loopcount = 0
print "Run till %d:%d"%(end_hr,end_min)
print 'Run | SYM | Time | Price | Act | Shares | Cash | Portfolio'
print '--------------------------------------------------------------'
while (t_h <= end_hr) & (t_m <= end_min):
urlreq = urllib2.Request(url_to_query)
try:
response = urllib2.urlopen(urlreq) #open the link to get the data
data = response.read()
response.close() #this closes the open URL link
# convert the data to dict.
decoded_data = json.loads(data)
# for k,v in decoded_data.items(): print k,v
sq1 = decoded_data['Stock Quotes']
sq2 = sq1[0]
sq2s = sq2['1. symbol']
sq2t = sq2['4. timestamp'][11:]
sq2v = int(sq2['3. volume'])
sq2p = float(sq2['2. price'])
all_well = True
except:
all_well = False
if all_well:
# see if this is a buy opport or sell.
act_now = "HOLD"
random_wait = 0
show_res = False
if cash_bal > 5000:
buy_shr = int((cash_bal - wig) / sq2p) - 1
cash_bal = cash_bal - (buy_shr * sq2p + wig)
buy_price = sq2p
shr_owned = buy_shr
act_now = "BUY"
# FUNCTION BUY_STOCK() --- not included in this script
show_res = True
else:
# see if this is sell situation
if sq2p > buy_price * 1.01:
sell_shr = shr_owned
sell_price = sq2p
cash_bal = cash_bal + (sell_price * sell_shr - wig)
shr_owned = shr_owned - sell_shr
act_now = "SELL"
# FUNCTION SELL_STOCK() --- not included in this script
show_res = True
# estimate the random wait time for next trade.
r1 = int(after_sell_minutes * 60 * 0.9)
r2 = int(after_sell_minutes * 60 * 1.1)
random_wait = random.randrange(r1,r2)
else:
pass
port_val = cash_bal + shr_owned * sq2p
# update the min and max price
if min_price == 0.: min_price = sq2p
if max_price == 0.: max_price = sq2p
if sq2p < min_price: min_price = sq2p
if sq2p > max_price: max_price = sq2p
t_s = datetime.datetime.now().second
if t_s % 10 == 0: show_res = True
# only show results every 10 secs or when there is buy or sell
if show_res: print "%4d | %4s | %s | %7.3f | %4s | %.1f | %.1f | %.1f" %(loopcount, sq2s, sq2t, sq2p, act_now, shr_owned, cash_bal, port_val)
sys.stdout.flush() #make sure that the data is written out before sleep
else:
pass
# ----- now to sleep ------
t_h = datetime.datetime.now().hour
t_m = datetime.datetime.now().minute
loopcount = loopcount + 1
#now go to sleep - use a random element
# that is prespefied based on whether there was a sale or not.
time.sleep(run_time_int + random_wait)
# write summary.
print 'Process end: %s'%time.ctime()
print 'Min price: %.2f'%min_price
print 'Max price: %.2f'%max_price
print 'Min Max Range: %.1f%%'%((max_price/min_price-1)*100)
print 'End Portfolio Value: %.1f'%port_val