@yi-fang

@yi-fang said in Tracking an Index with equal weight positions:

@nicola-prada
I think pre-processing is requied if additional information are required for stock selection.

Yes. The coolest part would be being able to also rank, day by day, only the stocks that are members of the index according to one or more indicators. And to use the rank position as a system parameter to generate buy and sell signals.

@backtrader said in Tracking an Index with equal weight positions:

You preprocess the data externally and feed and create a data set without survivorship bias

Yes. Do you think preprocessing all the data with pandas and, at the same time do some kind of ranking of the ones that for any date are member of the index is a preferable solution for speed?

Or would you opt for the "membership" column into the csv data and let BT ranking day by day all the stocks that have this membership flag set to True?

Ideally the main goal would be to backtest systems like the one in the great article you linked: https://cookbook.backtrader.com/recipes/articles/2019-05-20-momentum-strategy/momentum-strategy/
while being sure to keep track of the index composition.

@yi-fang

Thanks for sharing your code! It might be a simple and effective shortcut for the alignment problem. Still, I have to know if this particular stock is a member of the index or not. Pre-processing the data would also add the benefit to speed up some ranking of the stocks that are part of the index for portfolio selection. What do you think?

Thank you for your answers!

@backtrader

To be sure we are on the same page, I'd like to specify how I would write the process if I had to code it from scratch not using BackTrader and dealing with realtime.

Let’s add a simple moving average system to complicate the things a little bit.

For every day in test and for real time:

1. Ask the data provider (I use Bloomberg professional but I can also download data into CSV/Excel from it) the composition of index "X" at the date. For Eurostoxx 50 it would give me a list 50 tickers. Obviously this list will change in time, usually it is reviewed every three months. I could optimize the request to be made only at the end of the quarter but to be sure let’s ask Bloomberg the membership data every single day.
2. Ask the data provider the needed n historical data (PX_LAST) of all the tickers in the members list and for every Series compute a moving average on it. Let’s say a 50 day moving average.

System rules:

1. If a ticker is in this list but not in my portfolio, BUY it at next open if the last close is above it's moving average.
2. If a ticker is in my portfolio but not in the list (so it's not anymore a member of the index), SELL it at next open. I don’t won’t it to be part of my portfolio.
3. If a ticker is in my portfolio and it's also in the index list but the last close is below the moving average sell it at next open.

Size:

1. Compute the size of each new position according to some formula (equal weight to be simple but could be some more complex algorithm like minimum variance, risk parity…)

Now, If I only want to backtest and not to deal with realtime trading I have a big advantage.

I could ask Bloomberg (or any other data provider that can give me this information) the historical composition of the index I'm tracking for each day in the test. In the end, at the very last day, with a simple loop I can get all the tickers that have been members of the index in the period of time between test_start_date and test_end_date. This is why I thought I could simply add a column “membership” with a Y/N flag.

You’re right about the fact not all the historical series would start the same day. We have two options here:

1. Find a way to have BackTrader asking the CSV data provider, at each heartbeat, the composition of the index, the same as I’d do with Bloomberg in real time if I had to write the whole process from scratch. Is it possible with BackTrader?
2. Cheat a little bit with the benefit of insight. Align, before importing the data, all the series to start at the same day with pandas. This could mean having to add anther column to be sure I won’t buy/sell a ticker on fake data. Sure, I would have to pre-process all the data with pandas before feeding BackTrader with it but it wouldn't be a big deal. And if done with care it won't damage the validity of the test.

I'd like to know if there are better ways to handle this.

@Yi-Fang

Thanks, I am well aware of all the problems to compute the size using the last close, the only data we know for sure, and the possibile slippage I would get at next open (positive or negative). I guess the best option is, as you pointed out, to keep some free cash (5%?) and to prioritise the sells orders to be sure to get all the buy orders filled. I didn't mention it in my posto only to keep the things the simplest as I could

Hello,

I'd like to build a simple model that simply tracks an index, the Eurostoxx 50 (SX5E), with equal weight positions at the moment of the first order).

My idea is:

1. Get the historical composition of the index, daily
2. Add a column "MEMBERSHIP" to the CSV price file with a 0 or 1 if the security is member of the index.

Rules should be something like that:

If ticker['MEMBER'] == 1 and ticker not in portfolio Buy next open
If ticker['MEMBER'] == 0 and ticker in portfolio Exit Long next open
Size the positions (Portfolio Mark to market + cash) / len(index_members)

Is there a better (simpler) approach with BackTrader to accomplish something like this?

Obviously a better model should resize the positions to mantain equal weights but let's keep the things simple.

Thanks

Nicola

Hello,

My name's Nicola, from Italy, and I am going to start my journey with BackTrader. Before I start I have a couple of questions, I'll post them in two separate topics.

This is the first one.

When we short a security we must:

1. pay commissions.
2. pay an interest rate to the lender.
3. pay the dividend (ex date) to the lender if the security pays a dividend while we are short.

I am pretty sure BackTrader can take care of the first point but what about dividend and interest?

Does it needs extra series for dividends or can we simply add them to the price CSV file as a column?

If we are in a long position and the securities pays a dividend, is there a way to increment the account by the dividend amount?

Thanks!

Nicola