Issue with timestamps

EDIT: So I figured out this problem. Essentially I was getting really confused by the date issue within the transaction log. The transaction log suffers from the same problem that the debug logs used to suffer from. Essentially the trade log thinks its yesterday when its today. If the trade is listed as filled April 4 it actually filled on April 5. However, if you print a statement saying "I bought SPY on this date." the statement would print on the 4th because the order is placed on the 4th but filled on the 5th (I'm using daily data).

7 responses
4
Total Returns
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Alpha
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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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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 datetime
import math
import numpy
import pandas

def clean_col(df):
df = df.fillna(0)
df = df[['Nshares', 'sid']]
return df

def initialize(context):
#
context.SPY=sid(8554)
context.Sectors=[sid(19662), sid(19659), sid(19656), sid(19661), sid(19655), sid(19658), sid(19660), sid(19654), sid(19657)]

date_column='Date',
post_func=clean_col)

def handle_data(context, data):

for i in context.Sectors:
if 'Nshares' in data[i] and data[i]['Nshares']!=0:
log.debug("Ordering "+str(data[i]['Nshares'])+" of ticker "+str(i))
order(i,data[i]['Nshares'])
#else:


This backtest was created using an older version of the backtester. Please re-run this backtest to see results using the latest backtester. Learn more about the recent changes.
There was a runtime error.
4
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 datetime
import math
import numpy
import pandas

def clean_col(df):
df = df.fillna(0)
df = df[['Nshares', 'sid']]
return df

def initialize(context):
#
context.SPY=sid(8554)
context.Sectors=[sid(19662), sid(19659), sid(19656), sid(19661), sid(19655), sid(19658), sid(19660), sid(19654), sid(19657)]

date_column='Date',
post_func=clean_col)

def handle_data(context, data):

for i in context.Sectors:
if 'Nshares' in data[i] and data[i]['Nshares']!=0:
log.debug("Ordering "+str(data[i]['Nshares'])+" of ticker "+str(i))
order(i,data[i]['Nshares'])
#else:


This backtest was created using an older version of the backtester. Please re-run this backtest to see results using the latest backtester. Learn more about the recent changes.
There was a runtime error.

We've had a series of discussions about how to manage timestamps better. Not surprisingly, it's a tough problem. UTC, DST, "market time" and each Quantopian member's local timezone make for some twisted time relationship. The first goal is to make it easy for the algo writer to understand what's going on without having to think about it. The second goal is to make it easy for people to share ideas even when they are in different timezones. It's difficult to satisfy all of the goals and remain consistent. I'm hopeful that next week we'll settle on an overall strategy.

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Understood. Plus consider astronauts in orbit who are trading their Scottrade accounts - you need to take into account relativistic effects, etc. (Just kidding, obviously.... nerdy joke)

Daniel,

It could be true sooner than you think.....

http://www.antonkreil.com/space/

P.

That is awesome Peter. I often think about how I could exploit arbitrage opportunities if we colonized another planet in the solar system and had exchanges on both planets. The interplanetary transmission could take 5-10 minutes.... there'd definitely be some way to manipulate that lag in information.

Really interesting subject to think about. Great article about it from Wilmott.com:

Space-Time Finance: The Relativity Theory’s Implications for Mathematical Finance