I want this algorithm to only run handle_data every 5 minutes, or at least only make trades every five minutes. How can I do this?

I know of the schedule_function but I don't think that will work like this with minute data as it won't stop the algo from running just reset it periodically. Any suggestions?

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Total Returns
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Beta
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Sharpe
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Sortino
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Max Drawdown
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Benchmark Returns
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 Returns 1 Month 3 Month 6 Month 12 Month
 Alpha 1 Month 3 Month 6 Month 12 Month
 Beta 1 Month 3 Month 6 Month 12 Month
 Sharpe 1 Month 3 Month 6 Month 12 Month
 Sortino 1 Month 3 Month 6 Month 12 Month
 Volatility 1 Month 3 Month 6 Month 12 Month
 Max Drawdown 1 Month 3 Month 6 Month 12 Month
def initialize(context):
context.stocks = sid(19920)

# Will be called on every trade event for the securities you specify.
def handle_data(context, data):
stock = context.stocks
prices = history(bar_count = 13, frequency = '1m', field = 'price')
momentum = prices.ix[-12] - prices.ix[-1]
x = momentum[stock]
print int(x)
order_target_percent(stock,x)
''' if momentum[stock] < -1.5:
order_target_percent(stock,-1)
elif momentum[stock] < -1:
order_target_percent(stock, -.75)
elif momentum[stock] < -.5:
order_target_percent(stock, -.5)
elif momentum[stock] < -.25:
order_target_percent(stock, -.25)'''


There was a runtime error.
8 responses

This is a really easy way of doing it. It counts each minute and when handle_data() has been called 5 times, it executes the logic.

Happy coding

EDIT: I forgot you should also have if not get_open_orders(stock): before the order_target_percent line.

63
Loading...
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
 Returns 1 Month 3 Month 6 Month 12 Month
 Alpha 1 Month 3 Month 6 Month 12 Month
 Beta 1 Month 3 Month 6 Month 12 Month
 Sharpe 1 Month 3 Month 6 Month 12 Month
 Sortino 1 Month 3 Month 6 Month 12 Month
 Volatility 1 Month 3 Month 6 Month 12 Month
 Max Drawdown 1 Month 3 Month 6 Month 12 Month
def initialize(context):
context.stocks = [sid(19920)] # list of stocks
context.minuteCounter = 0 # this tracks number of handle_data calls

def handle_data(context, data):
# increment the minute counter
context.minuteCounter += 1

# test the counter
if context.minuteCounter >= 5:
context.minuteCounter = 0

# trade logic:

stock = context.stocks[0] # get the first stock in the list
prices = history(bar_count = 13, frequency = '1m', field = 'price')
momentum = prices.ix[-12] - prices.ix[-1]
x = momentum[stock]
print x
order_target_percent(stock,x)
''' if momentum[stock] < -1.5:
order_target_percent(stock,-1)
elif momentum[stock] < -1:
order_target_percent(stock, -.75)
elif momentum[stock] < -.5:
order_target_percent(stock, -.5)
elif momentum[stock] < -.25:
order_target_percent(stock, -.25)'''


There was a runtime error.

What does 'if not get_open_orders(stock):' do?
And how does 'context.minuteCounter' work?

minuteCounter is a variable which gets incremented every update (every minute) and whenever it exceeds 5 minutes, your logic runs and the variable is reset. The not get_open_orders(stock) makes sure that you don't send orders for a stock for which you already have orders sent for. That's a good precaution if you don't want to bother properly accounting for outstanding orders, because otherwise, if your existing orders and the new ones you send both get filled, you'll exceed your desired position.

Note that instead of the minute counter variable as suggested by James Jack, I'd just do

    if get_datetime().minute % 5 != 0: # Only run when the minute is divisible by 5 .
return    # And don't execute the rest of this function

stock = context.stocks
prices = history(bar_count = 13, frequency = '1m', field = 'price')
....



It gets incremented automatically by quantopian? Also is there a way to log what time the orders took place just so I can double check that it's working?

Hi Ari,

If you're talking about my example code, the algo increments context.minuteCounter without help from Quantopian.

If you're talking about Alex's suggestion of if get_datetime().minute % 5 != 0 then there is no counter. Instead the current time is used, which is updated by Quantopian.

To log the current time you can use print "the time is {}".format(get_datetime('US/Eastern').time())

Since we know that on a typical trading day there are 390 minutes, we can create schedule_functions in a loop that fire very 5 minutes, if the day is cut short e.g. a holiday, or half day, then the extra schedule_functions will never execute.Try this...

def initialize(context):
for minute in range(0, 390, 5):
schedule_function(trade, date_rules.every_day(), time_rules.market_open(minute)

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The approaches presented here are actually only workarounds for a missing generalization of the API to arbitrary scheduling intervals between 1m and 1w. Are there any plans to improve the API in this regard?

total_minutes_pipeline = 6*60 + 5

for i in range(1, total_minutes_five_minute):
if i % 5 == 0:
schedule_function(
five_minute,
date_rules.every_day(),
time_rules.market_open(minutes=i),
True
)


This is from the help docs. This sets the function, 'five_minute()' to run every 5 minutes.

I would not try to change how often 'handle_data' runs, rather define a new function scheduled every 5 minutes.