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Multiple fills on LMT, MKT orders?

In my backtest, I am seeing multiple fills for orders placed as limit and market.

Specifically a $10k trade in SPEX on 8/14/13 was opened as a limit at 8:52, and 8:56am, and then closed MKT at 12:59 pm and the following day's open.

I'm guessing that my order size is higher than the volume in the minute bar and that you limit fills to some percent of volume.

Is that correct?

Is there a way to modify that behavior, I think for back-testing, I'm more concerned about getting the right price and time than estimating liquidity (market orders especially!).

karl

Clone Algorithm
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Backtest from to with initial capital
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
import pandas as pd
#import sys

def initialize(context):
    # import the custom CSV data file
    fetch_csv("https://dl.dropboxusercontent.com/u/12888310/DY_13_08_12_to_16.csv",
              pre_func=preview,
              post_func=preview,
              date_column='start date',
              universe_func=my_universe)
    context.tradesize = 10000

# my_universe returns a set of securities that define your universe.
def my_universe(context, fetcher_data):
    # fetcher_data is the data resulting from the CSV file from fetcher.

    # set my_stocks to be every security in the fetcher_data
    my_stocks = set(fetcher_data['sid'])

    # log the size of the universe for debugging
    context.count = len(my_stocks)
    print 'total universe size: {c}'.format(c=context.count)

    # return the securities we identified earlier
    return my_stocks

# see a snapshot of your CSV for debugging
def preview(df):
    log.info(' %s ' % df.head())
    return df

def handle_data(context,data):
    # Convert to EST timezone
    exchange_time = pd.Timestamp(get_datetime()).tz_convert('US/Eastern')
  
    # loop over the stocks in universe
    for stock in data:
      # Send limit orders in at first bar
      if data[stock]['dt'].date() == exchange_time.date() and exchange_time.hour == 9 and exchange_time.minute == 50:
         try:
             log.info('Limit Entry: ' + str(stock.symbol) + ' limit ' + str(data[stock]['dLimit']) + ' side ' + str(data[stock]['bLong']))
             #if((data[stock]['bLong'])==True):
             #    log.info(data[stock]['bLong'])
             #order_value(stock, context.tradesize, data[stock]['dLimit'])
             if (data[stock]['bLong'] == True):
                 order_value(stock, context.tradesize, data[stock]['dLimit'])
             else:
                 order_value(stock, -context.tradesize, data[stock]['dLimit'])
                
             #log.info('Entry date for : ' + str(stock.symbol))
         except:    #catch all
             #e = sys.exc_info()[0]
             #log.info('Error:  ' & e )
             log.info('Excetption for: ' + str(stock.symbol))       
      if data[stock]['dt'].date() == exchange_time.date() and exchange_time.hour == 15 and exchange_time.minute == 58:
         order(stock, -context.portfolio.positions[stock].amount)
         log.info('Exit date for : ' + str(stock.symbol))
There was a runtime error.
2 responses

Slippage Models - Got it. How do I delete a post?

Karl, we generally don't delete posts do that others can learn from the information. And yes, slippage is used to simulate market liquidity for order transactions. Glad it's working for you now!

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