Capitalizing on Margin Available

This week, we shipped a new chart on all backtests, optimizations, and new deployments of live trading algorithms -- the Portfolio Margin plot. This chart plots the margin used for significant holdings in your portfolio over time to help our community fully utilize capital and understand the impact of hedging on their strategies.

The adage "It’s not about timing the market, but about time in the market" tells us that simply having your capital deployed increases the probability of participating in significant moves. This adage is true for many quantitative investors as well. Smart beta, factor portfolios, and momentum strategies can all improve their returns by increasing capital utilization.

The new chart taps into the QuantConnect platform's superpower -- our high-fidelity portfolio margin modeling in backtesting and live trading. Our margin modeling system provides historical, realistic margin usage, mimicking the constraints of the real world. This system models multiple asset classes, even within the same portfolio. If the market dips, we model automatic brokerage margin calls liquidating some of the portfolio. This margin engine solves for derivative products, such as future option contracts, in strategies like straddles, condors, or spreads.

In the example below, we long and short two Option contracts to combine and reduce margin usage. The blue area represents a naked short position, and the gray area represents a group formed by a hedging long position. The naked short is purchased first, resulting in large swings in margin-used, but it is then hedged by the long to form a bear call spread, capping its margin used (gray).

We have deployed this new feature to all users on QuantConnect, and we hope it helps improve your understanding of the capital at work in your strategy. For more information, see our documentation on the Portfolio Margin chart.

Happy Coding,

QuantConnect Team

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.

var scheduledTime = _algorithm.Time.AddMinutes(1);
_algorithm.Schedule.On(
_algorithm.DateRules.On(scheduledTime.Date),
_algorithm.TimeRules.At(scheduledTime.TimeOfDay),
() =>
{
StopLossTicket = _algorithm.StopMarketOrder(_security.Symbol, -Quantity, StopLossPrice, tag: "ATR Stop");
_algorithm.CheckToCancelRemainingEntries();
});

Upvote

1 person upvoted this

Yuri Lopukhov

Yuri Lopukhov | January 2025

I'm not a C# coder myself, I suppose your solution should work, thank you for sharing. Yes, I've also noticed significant increase of returns when reducing entry to first minute (and also increasing actual leverage used). But it also increases chances of this issue affecting backtests, so not really sure if we can trust those results yet.

Upvote

3 people upvoted this

Yuri Lopukhov

Yuri Lopukhov | January 2025

Actually, your solution will delay stop loss order by exactly one minute always, while on minute resolution stop orders would be delayed until the next minute start, i.e. if entry was filled at 9:36:20, your solution will place order at 9:37:20, while backtest would do that at 9:37:00. Maybe you could replace seconds with zeros for scheduledTime variable like this: scheduledTime = new DateTime(scheduledTime.Year, scheduledTime.Month, scheduledTime.Day, scheduledTime.Hour, scheduledTime.Minute, 0);

Upvote

2 people upvoted this

Yuri Lopukhov

Yuri Lopukhov | January 2025

I don't know about you, but for me this change didn't improve things at all )

Upvote

1 person upvoted this

Lars Klawitter

Lars Klawitter | January 2025

I didn't actually expect the change to improve performance in backtesting, but it is there to avoid the stop losses to be placed too early in live trading, a phenomenon that we would not see in backtesting. So the artificial delay not changing the performance in backtesting would rather give me confidence in the viability. Wasn't that the concern you raised originally, when you pointed out the discrepancy between backtesting and live trading order placement?

Upvote

1 person upvoted this

Lars Klawitter

Lars Klawitter | January 2025

and where the very short opening range is concerned: I guess that's the biggest concern. I've been paper trading on an IBKR paper trading account for three weeks and so far it behaves very similar to a corresponding backtest, but the real acid test will be actual live trading (which only makes sense for cash books >$100k on brokerages like IBKR with trading fees)

Upvote

1 person upvoted this

CaseyS INVESTOR

Hedgian | March 2024

This is awesome. Will be super helpful for my risk parity use-case to fully understand the impact on my Interactive Brokers portfolio margin account.

Lars Klawitter INVESTOR

January 2025

To Yuri's earlier point of there being a difference between backtests and live trading as to when the stop orders are placed:

I've been running the strategy (using a larger universe and shorter opening range as per my previous post) on IBKR paper trading (i.e. on an actual IBKR account, not via QC paper trading) and as Yuri suggested, the stop orders are at times placed within the same minute. Mostly 20-40 seconds after the entry:

in one case the stop order was placed the same instance as the entry:

I tried second resolution, but that seems impractical given the large universe size.
So this is my attempt at an artificially delayed stop order placement:


                var scheduledTime = _algorithm.Time.AddMinutes(1);
                _algorithm.Schedule.On(
                    _algorithm.DateRules.On(scheduledTime.Date),
                    _algorithm.TimeRules.At(scheduledTime.TimeOfDay),
                    () =>
                    {
                        StopLossTicket = _algorithm.StopMarketOrder(_security.Symbol, -Quantity, StopLossPrice, tag: "ATR Stop");
                        _algorithm.CheckToCancelRemainingEntries();
                    });

I'm not a C# coder, so I definitely don't know what I'm doing. Backtests with this code produce by and large comparable results with the original code, so I'll try paper trading next.
Would the above code change make sense to you?

Yuri Lopukhov INVESTOR

So far I can see two issues:

VolumeSMA is not warmed up when new symbols are added to universe, which means these symbols will not be selected for the first 14 trading days in universe
On Second resolution it chooses different symbols to scan and trade. Perhaps this is related to the first issue.

I can't fix the first issue in C#, so I guess I will switch to Python version unless somebody else fixes the C# version and share it. Not sure if fixing it will improve results as well.

Derek Melchin

QuantConnect | January 2025

Yes, we opted to not warm up the VolumeSMA to minimize the number of code lines for readers.

... and to increase the execution speed.

Jake Chen

Jake Chen | January 2025

I've mentally accepted issue 1... My core assumption is that the top 1000 symbols sorted by volume will not change drastically from month to month (when symbols are added/removed) and thus the number of these lagged-14 symbols shouldn't be too much. I suppose a way to get around this would be to simply increase from 1000 to like 1250 or something, and mentally write-off the bottom 250 as constantly rotating and never in-play.

Although I don't have data to back up that core assumption haha, so I'm be happy to be challenged there.

Jack Pizza INVESTOR

Sum Sum | January 2025

define: “does really well with 1 minute” ….. 2000-2002 still utterly collapses fail to see well really well fits in….

or does really well when overfitting super hard?

hmm, I hadn't tested timeframes as early as 2000 before and you're right, the strategy doesn't perform as well as in later timeframes (2010+) and the whole period of July 2001- end of 2002 is basically flat, but I'm still getting a Sharpe ratio >1 over the whole period and only 10% drawdown. But yeah, I still distrust the results, I guess I've got quite a bit of overfitting going on and I'm uncertain as to how the short opening range will do in live trading.

Quant Stratege INVESTOR

These backtests are not representative of live performance. When adding slippage, it can be significant at the open due to volatility, small-cap stocks, and using stop orders, making the results much less appealing.

Just add this line when adding securities:

security.set_slippage_model(MarketImpactSlippageModel(self))

You're right. I had previously only simulated very small constant slippages, but MarketImpactSlippage has quite a savage effect…
If it looks too good to be true…

This slippage model is for strategies with large market impact. If you are planning to trade hundreds of millions, sure, this strategy might not work for you. For smaller accounts, market impact on top US stocks will be much smaller, especially if universe_size is smaller than 2000. Also, that requires proper parameters, defined here: https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/slippage/supported-models#05-Market-Impact-Model If you just use default parameters, you probably just assume much worse default scenario.

looking at the capacity, it is somewhere between $100k and $500k. If you add to that the fact that with IBKR's fee structure, anything below $100k is not viable either, you end up with a fairly narrow band of viable cash book sizes - which might not be a bad thing!

the capacity can be slightly increased by filtering for minimum dollar values either in the universe selection and/or when selecting the "stocks in play". That only goes so far before eroding all alpha. I've been trying a dynamic filter using a multiple of the portfolio value.

AleNoc INVESTOR

Alessandro Nocentini | January 2025

I noticed a difference between the backtest and the selection with live data (data provider QuantConnect). What data provider do you use for live trading?

Jake Chen | February 2025

I'm seeing the same difference. I use TradeStation. Were you able to resolve this issue?

Evan lightner INVESTOR

I don't know where to start, I'll have to write a whole post and how I'm planning to implement this strategy. But first of all thank you @derek for sharing this (and of course for everything you've done with QC -its been a game changer for me).

But first of all, I just want to comment with one simple non-coding question….

Why are a good amount of people HATING on this strategy? I understand the backtest cherry pick , but for a bare bones boiler plate ( i messed around with some variables like holdings and percent risk and still got good results) , this is the best algo I've seen in a while, especially for being shared so freely - not to mention brand new piece of research in the community.

Is there just some sort of deep skepticism in the quant community at large I guess, inherently? I suppose that fits!

Kevin Patterson INVESTOR

February 2025

Does anyone else end up with very different symbols when running the exact same algo in backtesting vs live (IBKR paper trading)? For example when it is set to choose 20 symbols, I'll see maybe only half overlap –- and of course the ones going positive for the day in the backtest are the ones not picked up live 😅. Seems the relative volume calculations aren't exactly the same and it doesn't take much to move the symbols you get. This is my first foray in to large universe algo's, is this type discrepancy common with large universe backtests or is there settings to help make it line up better with live? S

Some folks were asking about sized nodes: after trying the python version on IBKR (with a few mods) an L1-2 node will make it through one day (it crashes after close though, so likely you need the next level up if you dont want to restart daily)

Thanks for all the python related posts, even if I don't end up trading it, the algo has been super helpful for learning more about the QC code and had some good recipes in it that I think would be helpful for any algorithm.

Yuri Lopukhov | a month ago

Note that the universe selection is happening on the first day of the month in backtesting. While on live it will run upon deployment. Also, indicators with recursive formulas, like ATR may have different values on different periods even when these periods are larger than their "period" (because it is not actually a period, but a factor). That are few things that come to my mind, I am still running it on paper and didn't dig into this yet.

Evan lightner

evan lightner | 2 days ago

I came back to this thread to ask this literal exact same question! I'm trying to understand, *why* are the symbols different in the IBKR paper trading algo versus the QC paper brokerage (back/forward testing)? Are you saying if I launch both the QC paper brokerage live node (or backtest) *and* the IBKR (paper or cash) live node at the exact same time at the beginning of the month, they will *then* both trade the same symbols? Additionally, I've been thinking (before seeing this comment) that the VolumeSMA not being warmed up when new symbols are added (as you confirmed above) was the reason for this discrepancy. But is that not relevant?. What is the reason VolSMA this isn't being warmed up anyway (and therefore needs 14 days)? Is there not a way to do this, or was that just a choice for readability/speed? Thanks Yuri (and Kevin)..

Yuri Lopukhov | 2 days ago

The reason VolumeSMA is not warmed up is because it requires 14 days of minute data for all securities in the universe for initial warm up, and then for each security added to universe (but that is a much smaller issue). This greatly increases start up time and ram requirements, which may cause time out delay on live instances and perhaps even in backtesting. Not regarding to this issue specifically, I should mention that backtesting on minute resolution provides very different results from second resolution even after all technical issues are resolved, because this strategy trades on very volatile market opening and order fills are very different on minute and second resolutions.

Platform

Radically Open-Source Algorithmic Trading Engine

Join Our Discord Channel

Quarterly Open-Source Trading Competition

Draft Discussions

Bookmarked Discussions

SEARCH DISCUSSIONS

359,900 Quants.

VOTE FOR UPCOMING FEATURES

JOIN OUR Announcements MAILING LIST

IN THIS RESEARCH

PARTICIPANTS

Actions

Join QuantConnect for Free

Platform

SIGN IN

Radically Open-Source Algorithmic Trading Engine

Join Our Discord Channel

Quarterly Open-Source Trading Competition

Draft Discussions

Bookmarked Discussions

SEARCH DISCUSSIONS

359,900 Quants.

VOTE FOR UPCOMING FEATURES

JOIN OUR Announcements MAILING LIST

IN THIS RESEARCH

PARTICIPANTS

SHARE RESEARCH

SHARE DISCUSSION

SHARE ARTICLE

SHARE

Actions

Join QuantConnect for Free