Hi guys,
As you may have seen in the forum, we have just conduct a backtest on our news and blog data set. The backtest we did represented holding periods from a few days to months. However, there have been excessive demand for an intraday trading strategy on our data set. We would like some help on fixing the code so it will exit all positions 5 minutes before the market close.
Seong Lee gave us a head start on fixing some of the code for us. However, it does not exit all the positions at the end of the trading day (5 mins before market close).
I would really appreciate the help from the community to get this code going so we can start a backtest utilizing intraday strategies.
Instructions: Please make the code exit all positions 5 minutes before market close.
#Code Structure Provided by: Derek Tishler ([email protected] )
import numpy as np
import pandas as pd
from pytz import timezone
from datetime import datetime, timedelta
from zipline.utils.tradingcalendar import get_early_closes
def initialize(context):
#Use file from dropbox to assign sid's and sentiment values to trade on.
set_symbol_lookup_date('2015-04-01')
# Universe is set daily by inputs from the cvs fetch. But we will set a benchmark for comparison.
context.stocks = symbols('FOXA',
'ATVI',
'ADBE',
'AKAM',
'ALXN',
'ALTR',
'AMZN',
'AMGN',
'ADI',
'AAPL',
'AMAT',
'ADSK',
'ADP',
'AVGO',
'BIDU',
'BBBY',
'BIIB',
'BRCM',
'CHRW',
'CA',
'CTRX',
'CELG',
'CERN',
'CHTR',
'CHKP',
'CSCO',
'CTXS',
'CTSH',
'COST',
'DTV',
'DISH',
'DLTR',
'EBAY',
'EQIX',
'EXPE',
'EXPD',
'ESRX',
'FFIV',
'FB',
'FAST',
'FISV',
'GRMN',
'GILD',
'GOOG',
'HSIC',
'ILMN',
'INTC',
'INTU',
'ISRG',
'KLAC',
'GMCR',
'KRFT',
'LMCK',
'LMCA',
'LLTC',
'MAR',
'MAT',
'MXIM',
'MU',
'MSFT',
'MDLZ',
'MNST',
'MYL',
'NTAP',
'NFLX',
'NVDA',
'NXPI',
'ORLY',
'PCAR',
'PAYX',
'QCOM',
'REGN',
'ROST',
'SNDK',
'SBAC',
'STX',
'SIAL',
'SIRI',
'SPLS',
'SBUX',
'SRCL',
'SYMC',
'TSLA',
'TXN',
'PCLN',
'TSCO',
'TRIP',
'VRSK',
'VRTX',
'VIAB',
'VIP',
'VOD',
'WDC',
'WFM',
'WYNN',
'XLNX',
'YHOO',
)
context.minutes_early = 5
set_benchmark(symbol('QQQ'))
# set a more realistic commission for IB, remove both this and slippage when live trading in IB
set_commission(commission.PerShare(cost=0.014, min_trade_cost=1.4))
# Default slippage values, but here to mess with for fun.
set_slippage(slippage.VolumeShareSlippage(volume_limit=0.25, price_impact=0.1))
#Only needed in testing/debugging to ensure orders are closed like in IB
# schedule_function(end_of_day, date_rules.every_day(), time_rules.market_close(minutes=5))
schedule_function(exit_all_positions,
date_rules.every_day(),
time_rules.market_close(minutes=5))
fetch_csv("https://dl.dropboxusercontent.com/u/70792051/Accern%20Backtest/noUTC10.csv",
date_column ='start_date',
date_format = '%m-%d-%Y %H:%M')
#Article Sentiment
context.upper_bound = 0.35
context.lower_bound = -0.35
#Impact Score
context.upper_bound_a = 35
context.lower_bound_a = 35
#Source Rank
context.upper_bound_c = 4
context.lower_bound_c = 4
"""
From quantopian help
"""
def exit_all_positions(context, data):
for stock in context.portfolio.positions:
if context.portfolio.positions[stock].amount != 0 and stock in data and type(stock) == type(sid(24)):
order_target_percent(stock, 0)
# Will be called on every trade event for the securities you specify.
def handle_data(context, data):
#Get EST Time
context.exchange_time = pd.Timestamp(get_datetime()).tz_convert('US/Eastern')
#Check that our portfolio does not contain any invalid/external positions/securities
check_invalid_positions(context, data)
for stock in data:
if ('article_sentiment' and 'event_impact_score_entity_1' and 'overall_source_rank') in data[stock]:
record(Accern_Article_Sentiment = data[stock]['article_sentiment'], upperBound = context.upper_bound, lowerBound = context.lower_bound)
record(Event_Impact_Entity = data[stock]['event_impact_score_entity_1'], upperBound = context.upper_bound_a, lowerBound = context.lower_bound_a)
record(Overall_Source_Rank = data[stock]['overall_source_rank'], upperBound = context.upper_bound_c, lowerBound = context.lower_bound_c)
# We will not place orders if a stock is already in the process of handeling an order(fill time)
if check_if_no_conflicting_orders(stock):
try:
# Go Long(buy), or exit and then buy(since minute mode so this condition will be valid all day
if (data[stock]['article_sentiment'] > context.upper_bound) and (data[stock]['event_impact_score_entity_1'] > context.upper_bound_a) and (data[stock]['overall_source_rank'] > context.upper_bound_c):
# If we hav no positions, then we are good to buy
if context.portfolio.positions[stock.sid].amount == 0:
buy_position(context, data, stock)
# We have some positions, if they are short, then exit that position so we can go long.
else:
if context.portfolio.positions[stock.sid].amount < 0:
exit_position(context, data, stock)
# Go short(sell), or exit and then short(since minute mode so this condition will be valid all day
elif (data[stock]['article_sentiment'] < context.lower_bound) and (data[stock]['event_impact_score_entity_1'] > context.lower_bound_a) and (data[stock]['overall_source_rank'] > context.lower_bound_c):
# If we have no positions, then we are good to buy
if context.portfolio.positions[stock.sid].amount == 0:
short_position(context, data, stock)
# We have some positions, if they are long, then exit that position so we can go short.
else:
if context.portfolio.positions[stock.sid].amount > 0:
exit_position(context, data, stock)
except:
pass
def buy_position(context, data, stock):
# Place an order, and store the ID to fetch order info
orderId = order_target_percent(stock, 0.05)
# How many shares did we just order, since we used target percent of availible cash to place order not share count.
shareCount = get_order(orderId).amount
# We need to calculate our own inter cycle portfolio snapshot as its not updated till next cycle.
value_of_open_orders(context, data)
availibleCash = context.portfolio.cash-context.cashCommitedToBuy-context.cashCommitedToSell
log.info("+ BUY {0:,d} of {1:s} at ${2:,.2f} for ${3:,.2f} / ${4:,.2f} @ {5:d}:{6:d}"\
.format(shareCount,
stock.symbol,data[stock]['price'],
data[stock]['price']*shareCount,
availibleCash,
context.exchange_time.hour,
context.exchange_time.minute))
def short_position(context, data, stock):
#orderId = order_target_percent(stock, -1.0/len(data))
orderId = order_target_percent(stock, -0.05)
# How many shares did we just order, since we used target percent of availible cash to place order not share count.
shareCount = get_order(orderId).amount
# We need to calculate our own inter cycle portfolio snapshot as its not updated till next cycle.
value_of_open_orders(context, data)
availibleCash = context.portfolio.cash-context.cashCommitedToBuy+context.cashCommitedToSell
log.info("- SHORT {0:,d} of {1:s} at ${2:,.2f} for ${3:,.2f} / ${4:,.2f} @ {5:d}:{6:d}"\
.format(shareCount,
stock.symbol,data[stock]['price'],
data[stock]['price']*shareCount,
availibleCash,
context.exchange_time.hour,
context.exchange_time.minute))
def exit_position(context, data, stock):
order_target(stock, -1)
value_of_open_orders(context, data)
availibleCash = context.portfolio.cash-context.cashCommitedToBuy-context.cashCommitedToSell
log.info("- Exit {0:,d} of {1:s} at ${2:,.2f} for ${3:,.2f} / ${4:,.2f} @ {5:d}:{6:d}"\
.format(int(context.portfolio.positions[stock.sid].amount),
stock.symbol,
data[stock]['price'],
data[stock]['price']*context.portfolio.positions[stock.sid].amount,
availibleCash,
context.exchange_time.hour,
context.exchange_time.minute))
################################################################################
def check_if_no_conflicting_orders(stock):
# Check that we are not already trying to move this stock
open_orders = get_open_orders()
safeToMove = True
if open_orders:
for security, orders in open_orders.iteritems():
for oo in orders:
if oo.sid == stock.sid:
if oo.amount != 0:
safeToMove = False
return safeToMove
#
def check_invalid_positions(context, securities):
# Check that the portfolio does not contain any broken positions
# or external securities
for sid, position in context.portfolio.positions.iteritems():
if sid not in securities and position.amount != 0:
errmsg = \
"Invalid position found: {sid} amount = {amt} on {date}"\
.format(sid=position.sid,
amt=position.amount,
date=get_datetime())
raise Exception(errmsg)
def end_of_day(context, data):
# cancle any order at the end of day. Do it ourselves so we can see slow moving stocks.
open_orders = get_open_orders()
if open_orders:# or context.portfolio.positions_value > 0.:
#log.info("")
log.info("*** EOD: Stoping Orders & Printing Held ***")
# Print what positions we are holding overnight
for stock in data:
if context.portfolio.positions[stock.sid].amount != 0:
log.info("{0:s} has remaining {1:,d} Positions worth ${2:,.2f}"\
.format(stock.symbol,
context.portfolio.positions[stock.sid].amount,
context.portfolio.positions[stock.sid].cost_basis\
*context.portfolio.positions[stock.sid].amount))
# Cancle any open orders ourselves(In live trading this would be done for us, soon in backtest too)
if open_orders:
# Cancle any open orders ourselves(In live trading this would be done for us, soon in backtest too)
for security, orders in open_orders.iteritems():
for oo in orders:
log.info("X CANCLED {0:s} with {1:,d} / {2:,d} filled"\
.format(security.symbol,
oo.filled,
oo.amount))
cancel_order(oo)
#
log.info('')
def value_of_open_orders(context, data):
# Current cash commited to open orders, bit of an estimation for logging only
context.currentCash = context.portfolio.cash
open_orders = get_open_orders()
context.cashCommitedToBuy = 0.0
context.cashCommitedToSell = 0.0
if open_orders:
for security, orders in open_orders.iteritems():
for oo in orders:
# Estimate value of existing order with current price, best to use order conditons?
if(oo.amount>0):
context.cashCommitedToBuy += oo.amount * data[oo.sid]['price']
elif(oo.amount<0):
context.cashCommitedToSell += oo.amount * data[oo.sid]['price']
#
