Just started using ib_insync. I am trying to get the tick data into a dataframe.
Here is the relevant code:
def onPendingTickers(tickers, conn=conn):
for t in tickers:
# 'CREATE TABLE IF NOT EXISTS {} (timestamp timestamp, bid_qty INT, bid REAL, ask REAL, ' \
# 'ask_qty INT, high REAL, low REAL, close REAL, open REAL, contractID INT)'
# print(t)
c.execute('INSERT INTO {} (timestamp, bid_qty, bid, ask, ask_qty, high, low, close, open, contractID)'
' VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?);'.format(t.contract.pair()),
(t.time, t.bidSize, t.bid, t.ask, t.askSize, t.high, t.low, t.close, t.open, t.contract.conId))
# print(t.time, t.bidSize, t.bid, t.ask, t.askSize, t.high, t.low, t.close, t.open, t.contract.conId)
conn.commit()
ib.pendingTickersEvent += onPendingTickers
ib.sleep(60*60)
ib.pendingTickersEvent -= onPendingTickers
When I run this code in a terminal, it prints the ticker, I am not sure what exactly needs to be changed here.
If you just want to get ticks without displaying the information, here's some sample code that you should be able to run:
from ib_insync import *
import pandas as pd
import numpy as np
# Connect to IB; args are (IP address, device number, client ID)
def ibConnect(port,clientID):
connection = ib.connect('127.0.0.1', port, clientID)
ib.sleep(0)
return ()
# Disconnect from IB
def ibDisconnect():
ib.disconnect()
ib.sleep(0)
return
# Set up a futures contract
def ibFuturesContract(symbol, expirationDate, exchange):
futuresContract = Future(symbol, expirationDate, exchange)
return futuresContract
# Realtime Ticks Subscription
def ibGetTicker (contract):
ticker = ib.ticker(contract)
return [ticker]
ib = IB()
ibConnect(7496,300)
contract = ibFuturesContract('YM',20210618,'ECBOT')
# Start the real-time tick subscription
ib.reqMktData(contract, '', False, False)
# Real Time Ticks
global ticker
ticker = ibGetTicker(contract)
# Get just the last tick each second and put it into a data table
x = 0
while x < 10:
ib.sleep(1)
if ticker is not None:
df = util.df(ticker)
if (x == 0):
dt = df
else:
dt = dt.append(df)
x = x + 1
print (dt)
ib.cancelMktData(contract)
ibDisconnect()
Related
I have a csv file / pandas dataframe which looks like this. It contains various portfolio compositions for a portfolio which is re-balanced everyday according to my own calculations.
date asset percentage
4-Jan-21 AAPL 12.00%
4-Jan-21 TSM 1.00%
4-Jan-21 IBM 31.00%
4-Jan-21 KO 15.00%
4-Jan-21 AMD 41.00%
5-Jan-21 DELL 23.00%
5-Jan-21 TSM 12.20%
5-Jan-21 IBM 15.24%
5-Jan-21 KO 1.50%
5-Jan-21 NKE 7.50%
5-Jan-21 TSLA 9.50%
5-Jan-21 CSCO 3.30%
5-Jan-21 JPM 27.76%
6-Jan-21 AMD 45%
6-Jan-21 BA 0.50%
6-Jan-21 ORCL 54.50%
7-Jan-21 AAPL 50.00%
7-Jan-21 KO 50.00%
...
I want to test a strategy with a 12 asset portfolio.
AAPL,TSM,IBM,KO,AMD,DELL,NKE,TSLA,CSCO,JPM,BA,ORCL
So let's say on 4Jan2021, the portfolio's composition would be 12% in apple, 1% in TSM.. etc. I want to be able to check the prices and know how many I should be holding.
The next day, 5Jan2021, the composition will change to 23% in Dell.. etc, if the stock isn't in this list means its 0% for that day.
I have been looking at backtrader as a backtesting platform, however, the code I have seen in the repo mostly shows how to do stuff with indicators, like SMA cross over, RSI...
My question is: Is it possible to create and test a portfolio based on these compositions I have so I can check the return of this strategy? It would check this frame, and know how many stocks in a ticker to buy or sell on that particular day.
So the universe of stocks I am buying or sell is AAPL,TSM,IBM,KO,AMD,DELL,NKE,TSLA,CSCO,JPM,BA,ORCL
So on 4-Jan-21 it might look like,
dictionary['4Jan2021'] = {'AAPL':0.12,
'TSM':0.01,
'IBM':0.31,
'KO':0.15,
'AMD':0.41,}
On 5-Jan-21 it will look like,
dictionary['5Jan2021'] = {'DELL':0.23,
'TSM':0.122,
'IBM':0.1524,
'KO':0.015,
'NKE':0.075,
'TSLA':0.095,
'CSCO':0.033,
'JPM':0.2776,}
If the ticker isnt there means its 0%.
The portfolio composition needs to change everyday.
The first thing you will want to do it load your targets with your datas. I like
personally to attach the target to the dataline as I add it to backtrader.
tickers = {"FB": 0.25, "MSFT": 0.4, "TSLA": 0.35}
for ticker, target in tickers.items():
data = bt.feeds.YahooFinanceData(
dataname=ticker,
timeframe=bt.TimeFrame.Days,
fromdate=datetime.datetime(2019, 1, 1),
todate=datetime.datetime(2020, 12, 31),
reverse=False,
)
data.target = target
cerebro.adddata(data, name=ticker)
In next you will want to go through each data, and determine the current allocation. If the current allocation is too far from the desired allocation (threshold) you trade all datas.
Notice there is a buffer variable. This will reduce the overall value of the account for calculating units to trade. This helps avoid margin.
You will use a dictionary to track this information.
def next(self):
track_trades = dict()
total_value = self.broker.get_value() * (1 - self.p.buffer)
for d in self.datas:
track_trades[d] = dict()
value = self.broker.get_value(datas=[d])
allocation = value / total_value
units_to_trade = (d.target - allocation) * total_value / d.close[0]
track_trades[d]["units"] = units_to_trade
# Can check to make sure there is enough distance away from ideal to trade.
track_trades[d]["threshold"] = abs(d.target - allocation) > self.p.threshold
Check all the thresholds to determine if trading. If any of datas need trading, then all need trading.
rebalance = False
for values in track_trades.values():
if values['threshold']:
rebalance = True
if not rebalance:
return
Finally, execute your trades. Always sell first to generate cash in the account and avoid margins.
# Sell shares first
for d, value in track_trades.items():
if value["units"] < 0:
self.sell(d, size=value["units"])
# Buy shares second
for d, value in track_trades.items():
if value["units"] > 0:
self.buy(d, size=value["units"])
Here is the all of the code for your reference.
import datetime
import backtrader as bt
class Strategy(bt.Strategy):
params = (
("buffer", 0.05),
("threshold", 0.025),
)
def log(self, txt, dt=None):
""" Logging function fot this strategy"""
dt = dt or self.data.datetime[0]
if isinstance(dt, float):
dt = bt.num2date(dt)
print("%s, %s" % (dt.date(), txt))
def print_signal(self):
self.log(
f"o {self.datas[0].open[0]:7.2f} "
f"h {self.datas[0].high[0]:7.2f} "
f"l {self.datas[0].low[0]:7.2f} "
f"c {self.datas[0].close[0]:7.2f} "
f"v {self.datas[0].volume[0]:7.0f} "
)
def notify_order(self, order):
""" Triggered upon changes to orders. """
# Suppress notification if it is just a submitted order.
if order.status == order.Submitted:
return
# Print out the date, security name, order number and status.
type = "Buy" if order.isbuy() else "Sell"
self.log(
f"{order.data._name:<6} Order: {order.ref:3d} "
f"Type: {type:<5}\tStatus"
f" {order.getstatusname():<8} \t"
f"Size: {order.created.size:9.4f} Price: {order.created.price:9.4f} "
f"Position: {self.getposition(order.data).size:5.2f}"
)
if order.status == order.Margin:
return
# Check if an order has been completed
if order.status in [order.Completed]:
self.log(
f"{order.data._name:<6} {('BUY' if order.isbuy() else 'SELL'):<5} "
# f"EXECUTED for: {dn} "
f"Price: {order.executed.price:6.2f} "
f"Cost: {order.executed.value:6.2f} "
f"Comm: {order.executed.comm:4.2f} "
f"Size: {order.created.size:9.4f} "
)
def notify_trade(self, trade):
"""Provides notification of closed trades."""
if trade.isclosed:
self.log(
"{} Closed: PnL Gross {}, Net {},".format(
trade.data._name,
round(trade.pnl, 2),
round(trade.pnlcomm, 1),
)
)
def next(self):
track_trades = dict()
total_value = self.broker.get_value() * (1 - self.p.buffer)
for d in self.datas:
track_trades[d] = dict()
value = self.broker.get_value(datas=[d])
allocation = value / total_value
units_to_trade = (d.target - allocation) * total_value / d.close[0]
track_trades[d]["units"] = units_to_trade
# Can check to make sure there is enough distance away from ideal to trade.
track_trades[d]["threshold"] = abs(d.target - allocation) > self.p.threshold
rebalance = False
for values in track_trades.values():
if values['threshold']:
rebalance = True
if not rebalance:
return
# Sell shares first
for d, value in track_trades.items():
if value["units"] < 0:
self.sell(d, size=value["units"])
# Buy shares second
for d, value in track_trades.items():
if value["units"] > 0:
self.buy(d, size=value["units"])
if __name__ == "__main__":
cerebro = bt.Cerebro()
tickers = {"FB": 0.25, "MSFT": 0.4, "TSLA": 0.35}
for ticker, target in tickers.items():
data = bt.feeds.YahooFinanceData(
dataname=ticker,
timeframe=bt.TimeFrame.Days,
fromdate=datetime.datetime(2019, 1, 1),
todate=datetime.datetime(2020, 12, 31),
reverse=False,
)
data.target = target
cerebro.adddata(data, name=ticker)
cerebro.addstrategy(Strategy)
# Execute
cerebro.run()
####################################
############# EDIT ###############
####################################
There was an additional requiest for adding in variable allocations per day per security. The following code accomplishes that.
import datetime
import backtrader as bt
class Strategy(bt.Strategy):
params = (
("buffer", 0.05),
("threshold", 0.025),
)
def log(self, txt, dt=None):
""" Logging function fot this strategy"""
dt = dt or self.data.datetime[0]
if isinstance(dt, float):
dt = bt.num2date(dt)
print("%s, %s" % (dt.date(), txt))
def print_signal(self):
self.log(
f"o {self.datas[0].open[0]:7.2f} "
f"h {self.datas[0].high[0]:7.2f} "
f"l {self.datas[0].low[0]:7.2f} "
f"c {self.datas[0].close[0]:7.2f} "
f"v {self.datas[0].volume[0]:7.0f} "
)
def notify_order(self, order):
""" Triggered upon changes to orders. """
# Suppress notification if it is just a submitted order.
if order.status == order.Submitted:
return
# Print out the date, security name, order number and status.
type = "Buy" if order.isbuy() else "Sell"
self.log(
f"{order.data._name:<6} Order: {order.ref:3d} "
f"Type: {type:<5}\tStatus"
f" {order.getstatusname():<8} \t"
f"Size: {order.created.size:9.4f} Price: {order.created.price:9.4f} "
f"Position: {self.getposition(order.data).size:5.2f}"
)
if order.status == order.Margin:
return
# Check if an order has been completed
if order.status in [order.Completed]:
self.log(
f"{order.data._name:<6} {('BUY' if order.isbuy() else 'SELL'):<5} "
# f"EXECUTED for: {dn} "
f"Price: {order.executed.price:6.2f} "
f"Cost: {order.executed.value:6.2f} "
f"Comm: {order.executed.comm:4.2f} "
f"Size: {order.created.size:9.4f} "
)
def notify_trade(self, trade):
"""Provides notification of closed trades."""
if trade.isclosed:
self.log(
"{} Closed: PnL Gross {}, Net {},".format(
trade.data._name,
round(trade.pnl, 2),
round(trade.pnlcomm, 1),
)
)
def __init__(self):
for d in self.datas:
d.target = {
datetime.datetime.strptime(date, "%d-%b-%y").date(): allocation
for date, allocation in d.target.items()
}
def next(self):
date = self.data.datetime.date()
track_trades = dict()
total_value = self.broker.get_value() * (1 - self.p.buffer)
for d in self.datas:
if date not in d.target:
if self.getposition(d):
self.close(d)
continue
target_allocation = d.target[date]
track_trades[d] = dict()
value = self.broker.get_value(datas=[d])
current_allocation = value / total_value
net_allocation = target_allocation - current_allocation
units_to_trade = (
(net_allocation) * total_value / d.close[0]
)
track_trades[d]["units"] = units_to_trade
# Can check to make sure there is enough distance away from ideal to trade.
track_trades[d]["threshold"] = abs(net_allocation) > self.p.threshold
rebalance = False
for values in track_trades.values():
if values["threshold"]:
rebalance = True
if not rebalance:
return
# Sell shares first
for d, value in track_trades.items():
if value["units"] < 0:
self.sell(d, size=value["units"])
# Buy shares second
for d, value in track_trades.items():
if value["units"] > 0:
self.buy(d, size=value["units"])
if __name__ == "__main__":
cerebro = bt.Cerebro()
allocations = [
("AAPL", "4-Jan-21", 0.300),
("TSM", "4-Jan-21", 0.200),
("IBM", "4-Jan-21", 0.300),
("KO", "4-Jan-21", 0.2000),
("AMD", "4-Jan-21", 0.1000),
("DELL", "5-Jan-21", 0.200),
("TSM", "5-Jan-21", 0.20),
("IBM", "5-Jan-21", 0.1),
("KO", "5-Jan-21", 0.1),
("NKE", "5-Jan-21", 0.15),
("TSLA", "5-Jan-21", 0.10),
("CSCO", "5-Jan-21", 0.050),
("JPM", "5-Jan-21", 0.1),
("AMD", "6-Jan-21", 0.25),
("BA", "6-Jan-21", 0.25),
("ORCL", "6-Jan-21", 0.50),
("AAPL", "7-Jan-21", 0.5000),
("KO", "7-Jan-21", 0.5000),
]
ticker_names = list(set([alls[0] for alls in allocations]))
targets = {ticker: {} for ticker in ticker_names}
for all in allocations:
targets[all[0]].update({all[1]: all[2]})
for ticker, target in targets.items():
data = bt.feeds.YahooFinanceData(
dataname=ticker,
timeframe=bt.TimeFrame.Days,
fromdate=datetime.datetime(2020, 12, 21),
todate=datetime.datetime(2021, 1, 8),
reverse=False,
)
data.target = target
cerebro.adddata(data, name=ticker)
cerebro.addstrategy(Strategy)
cerebro.broker.setcash(1000000)
# Execute
cerebro.run()
I've got this netcdf of weather data (one of thousands that require postgresql ingestion). I'm currently capable of inserting each band into a postgis-enabled table at a rate of about 20-23 seconds per band. (for monthly data, there is also daily data that i have yet to test.)
I've heard of different ways of speeding this up using COPY FROM, removing the gid, using ssds, etc... but I'm new to python and have no idea how to store the netcdf data to something I could use COPY FROM or what the best route might be.
If anyone has any other ideas on how to speed this up, please share!
Here is the ingestion script
import netCDF4, psycopg2, time
# Establish connection
db1 = psycopg2.connect("host=localhost dbname=postgis_test user=********** password=********")
cur = db1.cursor()
# Create Table in postgis
print(str(time.ctime()) + " CREATING TABLE")
try:
cur.execute("DROP TABLE IF EXISTS table_name;")
db1.commit()
cur.execute(
"CREATE TABLE table_name (gid serial PRIMARY KEY not null, thedate DATE, thepoint geometry, lon decimal, lat decimal, thevalue decimal);")
db1.commit()
print("TABLE CREATED")
except:
print(psycopg2.DatabaseError)
print("TABLE CREATION FAILED")
rawvalue_nc_file = 'netcdf_file.nc'
nc = netCDF4.Dataset(rawvalue_nc_file, mode='r')
nc.variables.keys()
lat = nc.variables['lat'][:]
lon = nc.variables['lon'][:]
time_var = nc.variables['time']
dtime = netCDF4.num2date(time_var[:], time_var.units)
newtime = [fdate.strftime('%Y-%m-%d') for fdate in dtime]
rawvalue = nc.variables['tx_max'][:]
lathash = {}
lonhash = {}
entry1 = 0
entry2 = 0
lattemp = nc.variables['lat'][:].tolist()
for entry1 in range(lat.size):
lathash[entry1] = lattemp[entry1]
lontemp = nc.variables['lon'][:].tolist()
for entry2 in range(lon.size):
lonhash[entry2] = lontemp[entry2]
for timestep in range(dtime.size):
print(str(time.ctime()) + " " + str(timestep + 1) + "/180")
for _lon in range(lon.size):
for _lat in range(lat.size):
latitude = round(lathash[_lat], 6)
longitude = round(lonhash[_lon], 6)
thedate = newtime[timestep]
thevalue = round(float(rawvalue.data[timestep, _lat, _lon] - 273.15), 3)
if (thevalue > -100):
cur.execute("INSERT INTO table_name (thedate, thepoint, thevalue) VALUES (%s, ST_MakePoint(%s,%s,0), %s)",(thedate, longitude, latitude, thevalue))
db1.commit()
cur.close()
db1.close()
print(" Done!")
If you're certain most of the time is spent in PostgreSQL, and not in any other code of your own, you may want to look at the fast execution helpers, namely cur.execute_values() in your case.
Also, you may want to make sure you're in a transaction, so the database doesn't fall back to an autocommit mode. ("If you do not issue a BEGIN command, then each individual statement has an implicit BEGIN and (if successful) COMMIT wrapped around it.")
Something like this could do the trick -- not tested though.
for timestep in range(dtime.size):
print(str(time.ctime()) + " " + str(timestep + 1) + "/180")
values = []
cur.execute("BEGIN")
for _lon in range(lon.size):
for _lat in range(lat.size):
latitude = round(lathash[_lat], 6)
longitude = round(lonhash[_lon], 6)
thedate = newtime[timestep]
thevalue = round(
float(rawvalue.data[timestep, _lat, _lon] - 273.15), 3
)
if thevalue > -100:
values.append((thedate, longitude, latitude, thevalue))
psycopg2.extras.execute_values(
cur,
"INSERT INTO table_name (thedate, thepoint, thevalue) VALUES %s",
values,
template="(%s, ST_MakePoint(%s,%s,0), %s)"
)
db1.commit()
I'm having some strange things happen when converting strings to floats in a loop.
So when I use the following code exactly it writes:
[1468436874000, 0.00254071495719],
[1468528803000, 0.00341349353996],
[1468688596000, 0.000853373384991],
[1468871365000, 0.00256012015497],
It stops short, there should be about 30 lines more than that and those are the wrong calculations.
The function is:
def main():
minprice = pricelist('MIN')
maxprice = pricelist('MAX')
avgprice = pricelist('AVG')
avgsqft = sqftlist()
datelist = getdates()
index = fileparser()
with open('/home/andrew/Desktop/index3.htm', 'w') as file:
file.writelines(data[:index[0]])
for date, minprice, maxprice in zip(datelist, minprice, maxprice):
file.writelines('[%s, %s, %s],\n' % (date, minprice, maxprice))
file.writelines(data[index[1]:index[2]])
for date, avgprice in zip(datelist, avgprice):
file.writelines('[%s, %s],\n' % (date, avgprice))
file.writelines(data[index[3]:index[4]])
for date, avgprice, avgsqft in zip(datelist, avgprice, avgsqft):
file.writelines('[%s, %s],\n' % (date, ((float(avgprice))/(float(avgsqft)))))
file.writelines(data[index[5]:])
file.close()
The error is:
file.writelines('[%s, %s],\n' % (date, ((float(avgprice))/(float(avgsqft)))))
ValueError: could not convert string to float: .
Oddly, when I comment out the other for loops before it, the result is:
[1468436874000, 2.82644376127],
[1468528803000, 2.86702735915],
[1468688596000, 2.8546107764],
[1468871365000, 2.8546107764],
[1468871996000, 2.8546107764],
[1468919656000, 2.85383420662],
[1469004050000, 2.85189704903],
[1469116491000, 2.87361540168],
[1469189815000, 2.86059636119],
[1469276601000, 2.83694745621],
[1469367041000, 2.83903252711],
[1469547497000, 2.83848688853],
[1469649630000, 2.83803033196],
[1469736031000, 2.82327110329],
[1469790030000, 2.82650020338],
[1469876430000, 2.96552660866],
[1470022624000, 2.93407180385],
Moreover, when I use enumerate instead of zip (and make the appropriate changes), it works. I've examined both lists at the fifth item for anything unusual because that's where it's getting hung up, but there is nothing odd there in either list. Since it does work fine with enumerate I'll just do that for now. But I'm new to Python/programming in general and want to understand what exactly is causing this.
UPDATE Should have included this the first time.
# file.writelines(data[:index[0]+1])
# for date, minprice, maxprice in zip(datelist, minprice, maxprice):
# file.writelines('[%s, %s, %s],\n' % (date, minprice, maxprice))
# file.writelines(data[index[1]:index[2]+1])
# for date, avgprice in zip(datelist, avgprice):
# file.writelines('[%s, %s],\n' % (date, avgprice))
# file.writelines(data[index[3]:index[4]+1])
# time.sleep(1)
for date, avgprice, avgsqft in zip(datelist, avgprice, avgsqft):
# file.writelines(
print'[%s, %s],\n' % (date, ((float(avgprice))/(float(avgsqft))))
# file.writelines(data[index[5]:])
# file.close()
Prints... (correctly)
[1468436874000, 2.82644376127],
[1468528803000, 2.86702735915],
[1468688596000, 2.8546107764],
[1468871365000, 2.8546107764],
[1468871996000, 2.8546107764],
[1468919656000, 2.85383420662],
etc...
Debug by printing the values of avgprice and avgsqft in your code. You are getting some string as it's value which can not be converted to float
I have the following code:
def create_table():
c.execute('CREATE TABLE IF NOT EXISTS TEST(SITE TEXT, SPORT TEXT, TOURNAMENT TEXT, TEAM_1 TEXT, TEAM_2 TEXT, DOUBLE_CHANCE_1X TEXT, DOUBLE_CHANCE_X2 TEXT, DOUBLE_CHANCE_12 TEXT, DRAW_1 TEXT, DRAW_2 TEXT DATE_ODDS TEXT, TIME_ODDS TEXT)')
create_table()
def data_entry():
c.execute("INSERT INTO TEST(SITE, SPORT, TOURNAMENT, TEAM_1, TEAM_2, DOUBLE_CHANCE_1X, DOUBLE_CHANCE_X2, DOUBLE_CHANCE_12, DATE_ODDS, TIME_ODDS) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
(Site, sport.strip(), tournament.strip(), team_1.strip(), team_2.strip(), x_odd.strip(), y_odd.strip(), z_odd.strip(), Date_odds, Time_odds))
conn.commit()
def double_chance():
c.execute("UPDATE TEST SET DOUBLE_CHANCE_1X = x_odd, DOUBLE_CHANCE_X2 = y_odd, DOUBLE_CHANCE_12 = z_odd WHERE TOURNAMENT = tournament and TEAM_1 = team_1 and TEAM_2 = team_2 and DATE_ODDS = Date_odds and TIME_ODDS = Time_odds")
conn.commit()
driver.get(link)
Date_odds = time.strftime('%Y-%m-%d')
Time_odds = time.strftime('%H:%M')
sport = (driver.find_element_by_xpath(".//*[#id='breadcrumb']/li[2]/a")).text #example Footbal
tournament = (driver.find_element_by_xpath(".//*[#id='breadcrumb']/li[4]/a")).text #example Premier League
try:
div = (driver.find_element_by_xpath(".//*[#id='breadcrumb']/li[5]/a")).text #to find any division if exists
except NoSuchElementException:
div = ""
market = driver.find_element_by_xpath(".//*[contains(#id,'ip_market_name_')]")
market_name = market.text
market_num = market.get_attribute('id')[-9:]
print market_num
team_1 = (driver.find_element_by_xpath(".//*[#id='ip_marketBody" + market_num + "']/tr/td[1]//*[contains(#id,'name')]")).text
team_2 = (driver.find_element_by_xpath(".//*[#id='ip_marketBody" + market_num + "']/tr/td[3]//*[contains(#id,'name')]")).text
print sport, tournament, market_name, team_1, team_2
data_entry() #first SQL call
for ip in driver.find_elements_by_xpath(".//*[contains(#id,'ip_market3')]"):
num = ip.get_attribute('id')[-9:]
type = (driver.find_element_by_xpath(".//*[contains(#id,'ip_market_name_" + num + "')]")).text
if type == 'Double Chance':
print type
print num
x_odd = (driver.find_element_by_xpath(".//*[#id='ip_market" + num + "']/table/tbody/tr/td[1]//*[contains(#id,'price')]")).text
y_odd = (driver.find_element_by_xpath(".//*[#id='ip_market" + num + "']/table/tbody/tr/td[2]//*[contains(#id,'price')]")).text
z_odd = (driver.find_element_by_xpath(".//*[#id='ip_market" + num + "']/table/tbody/tr/td[3]//*[contains(#id,'price')]")).text
print x_odd, y_odd, z_odd
double_chance() #second SQL call
c.close()
conn.close()
Update:
Based on the answer below I updated the code, but I can't make it work.
When I run it, I get the following error:
sqlite3.OperationalError: no such column: x_odd
What should I do?
Update 2:
I found the solution:
I created an unique ID in order to be able to select exactly the row I want when I run the second SQL query. In this case it doesn't modify any other rows:
def double_chance():
c.execute("UPDATE TEST SET DOUBLE_CHANCE_1X = (?), DOUBLE_CHANCE_X2 = (?), DOUBLE_CHANCE_12 = (?) WHERE ID = (?)",(x_odd, y_odd, z_odd, ID_unique))
conn.commit()
Now it works perfectly.
Use the UPDATE statement to update columns in an existing row.
UPDATE TEST SET DRAW_1=value1,DRAW_2=value2 WHERE column3=value3;
If data_entry(1) is always called first, then change the statement in data_entry_2() to UPDATE. If not you will need to check if the row exists in both cases and INSERT or UPDATE accordingly.
I'm having trouble inserting data into my table. I have a list of stocks that I pass to the function getStockData.
I use a for loop to iterate through the list and get the data for each ticker symbol. At the end I put all the information into a dictionary. My final step is to insert the data into a table. I've been unsuccessful at inserting the data in the dictionary into my table.
def getStockData(x):
nowdate = raw_input("What Is Todays Date?: ")
print "Todays list has %d stocks on it\n" % len(x)
for stock in x:
stockPrice = ystockquote.get_price(stock)
stockPriceChange = ystockquote.get_change(stock)
originalPrice = float(stockPrice) + (float(stockPriceChange) * -1)
changePercentage = (float(stockPriceChange) / originalPrice) * 100
stockDict = {'Date': nowdate, 'Ticker Symbol': stock, 'Closing Price': stockPrice,
'Price Change': stockPriceChange, 'Percentage Changed': changePercentage}
conn = db.connect('stocks.db')
cursor = conn.cursor()
cursor.execute('insert into losers values (?, ?, ?, ?, ?)', (stockDict['Date'], stockDict['Ticker Symbol'], stockDict['Price Change'],
stockDict['Percentage Changed'], stockDict['Closing Price']) )
conn.close()
I think you forget to commit your data to your DB before close.
Try
conn.commit()