The pyfolio code has a simplistic breakdown of transactions into round trip trades. I think it only works if a trade closes to 0 holdings; I've thought about making a better trade mapping using proper avg cost basis lots etc, but haven't felt the urgent need yet. But perhaps for a simple system their breakdown would help you?

@Eric, The concept I believe you're going for here is what's called MFE/MAE, and has to do with price excursions for specified durations after a trade. MFE/MAE analytics would be a cool addition I would gather to add to the tearsheet. I recall building edge calculations into various strategies some time ago, and it's not hard. Just maintain a collection of trades (signals actually) and specified durations at which to measure price. One would want to do this inside the strategy so that you can take advantage of the edge measurement as it unfolds and changes.

And when you get done with a back test you can easily print out a custom log like this:

Periods Wins  Win%   Ave chg% Edge ratio MFE avg MAE avg Signals  
------- ----  ----  --------- ---------- ------- ------- -------  
     10   40  67.80     0.033      2.742    1.94    0.71      59  
     20   48  81.36     0.126      2.844    4.01    1.41      59  
     30   45  76.27     0.144      2.791    4.76    1.71      59  
     40   32  54.24     0.134      1.984    5.11    2.58      59  

Translated we have:
• Number of periods after a signal (trade)
• How many times, at X periods, was current price above entry price (long trades only here)
• The percent of the wins
• What was the avg price change % after X periods
• The MFE divided by the MAE
• MFE, the maximum excursion of price, in the favorable direction, as a measure of % price
• MAE, the maximum excursion of price, in the adverse direction, as a measure of % price
• The number of signals (trades)

This is just a sample but you can see it's just a matter of record keeping and reporting.

What is then possible is to keep a queue of signals rather than a total collection, and expire the oldest after some number is reached. This would then allow you to keep a running, changing edge measurement which you can then use as a factor in your trades. If edge is > 2.0 keep trading, BUT, keep recording the signals regardless of whether you traded them or not. That's the important part. Signals are NOT trades.

Hi Market Tech,

Thanks for explaining. I agree that this would be a great addition to pyfolio / the tear sheet. Any interest in doing a pull request that adds the MFE/MAE analysis?

Thomas, only if I could write the code in C#.

Market Tech,
You hit the nail, that is exactly what I was thinking.

Thomas,
Is there any plan to add this to pyfolio / the tear sheet? or do I need to develop my own?

Thanks
EG

Erick, and others,

I think we can add this to pyfolio. Certain seems feasible. It would definitely be easier if we can make the simplifying assumption that a trade in a ticker is either an opening or closing transaction, e.g. I buy X shares, then sell all X shares down to 0 for a complete round turn in all the shares (and that shares transacted would never follow some multi-"rebalancing" from say, X number of shares, then buying more up to Y number of shares, then selling down to some other number of shares, and then buying again, before closing the whole position to 0 shares, N number of transactions after the very first transaction.) Something like that which addresses various rebalance frequencies could be done (and we've experimented with some ways in-house as a way to approximate risk-reward in these not simple cases), but is certainly more convoluted to capture all cases, and I'm sure everyone would want it done differently (LIFO, FIFO, vs. most tax-efficient, etc).

Thoughts? Is the simplifying assumption reasonable -- that a single buy can be matched with single sell down to 0 shares, as well as a single short matched with a single cover of the short to 0 shares? If this is the case, then it would be easy to supply a flag to the tearsheet function in pyfolio to 'treat_all_trades_as_roundturns' (or something along those lines) such that those tables of figures would be computed. I only suggest the flag, to minimize/cutout calculation time, because I don't think the calculations make as much sense for an algo that simply rebalances a portfolio to different weights every month.

Thanks for the idea!
-Justin

Justin,

Thanks for the quick response. In my opinion the simplifying assumption for the round trip sounds very good. I believe that this will help us to build stronger algos that have higher Risk:Reward. I don't think that in this community we have been exploring that concept enough. We are usually more focus on lower Draw Down or higher Alpha and low beta etc. At the end of the day for individual retail traders we want to be able to get higher rewards and knowing that we might have to get some draw downs here and there. I will let others to comment. However you have one vote here.

EG

Justin

I took a look to pyfolio and it looks like the tearsheet for round trip has been Implemeted.Do you know if it is already working on quantopian research? If so do you have an example? Thanks

EG

Hi Erick,
We're still working on pushing the latest pyfolio update to the Research environment. I will get a handle on the release date, and respond to this thread.

Great. Thanks Justin