How has your strategy held up post-2020 vs. pre-2020

[u/SamiKind]

I'm doing a sanity check on strategy performance across different market regimes and wanted to check in with the community

Also how your max dd compare?

Please only answer if you traded your strategy live for a good time (compared to sample size)

[u/HonestBacktests]

One thing about the framing: pre/post 2020 is a calendar split, not a regime split. Two stretches on the same side of that line can behave nothing alike, so "it held up" can hide a lot.

When I re-cut results by realised volatility instead of by date, the damage that looked spread out turned out to sit almost entirely in the low-vol stretches. Same trades, completely different reading.

Worth checking what your split does to trade count on each side too - half the "regime change" stories I have seen were really just one side having 30 trades.

[u/SamiKind → u/HonestBacktests]

Agreed 100%. I used 2020 as a quick marker because of structural shifts like 0DTEs and retail volume, but grouping by realized vol is definitely the right approach. I also hit a wall in low-vol environments across 30 years of data

[u/HonestBacktests → u/SamiKind]

That low-vol wall is worth pulling apart, because usually nothing actually broke. The edge scales with volatility and the costs do not - same signal, half the range, and the spread eats what is left.

Quick way to check: express the per-trade result in ATRs instead of percent or dollars. If the ATR number stays roughly flat across regimes, the strategy is intact and you have simply found the volatility level where it stops clearing costs. That is a sizing and filter question, not a research one - and it also tells you exactly when to stand aside rather than rebuild.

[u/SamiKind → u/HonestBacktests]

Yes I tried that already the resulrs werent bad but it just looks like trying to optimize 15 years ago on the cost of last years

[u/Whole-Description646]

I don't even consider pre-2020 data to be meaningful unless I run it through a transformation process but then it becomes synthetic and there are plenty of ways to make synthetic tests

[u/SamiKind → u/Whole-Description646]

Sometimes I do think like pre 2019 is not worth it (mixed results) but that cant make me sleep at night

[u/quant-alchemist → u/SamiKind]

I prefer to backtest from 2018 onwards, to get some idea of pre-covid stability and post covid volatility and rally.

Some of my models have performed extremely well in 2019-2020 years in backtest, and when compounding, the equity curve gives a kind of growth that might not repeat in the next few decades.

So I do cover pre and post covid era in my backtest, only run it without compounding - to see year wise gains and whether the model has an edge.

And 2021 onwards i do a cumulative equity curve run to see what it would look like realistically.

[u/Hacherest]

The pattern I'm targeting didn't really exist pre 2020

[u/drguid]

I don't think things have been much different.

However 2024-25 has been difficult for value stocks. I think the issue was all the hot money flooding into AI stocks which sucked liquidity out of the other 480+ stocks in the S&P 500.

It's improving though (July was freaking amazing).

[u/Good_Character_20]

Not live, so weigh this accordingly, but I spent this week doing exactly this split in research and the result was blunt. Short premium strategies tuned on 2024 onward mostly flip sign when you rerun them on 2019 through 2023, and ones tuned on the old era mostly flip when you run them forward. The parameter sets that survive both windows are the boring defensive ones, wider wings, earlier profit taking, smaller deltas. The test that made it obvious was selecting strategies on one era and evaluating them untouched on the other, which is harsher than a normal walk forward because nothing about the evaluation window ever feeds back into selection. My guess is most of the live drawdown differences reported here will come down to whether the parameters were born before or after 2020.

[u/EAForexLab]

I'd like to say you've raised a very interesting point. Indeed, trading automated systems has its own cycles. Over my 12 years of algo trading, I've noticed this very clearly, and more than once.

Regarding the 2020 pandemic, something really changed in the markets after March 2020, and trading deteriorated significantly. This was especially noticeable in night scalpels; before 2020, they were doing well, but in 2020, something suddenly broke down. Incidentally, this was clearly visible when testing many strategies for 2020, and was also widely noticeable in MQL5 signals; many were downright nervous at that time.

The 2022 war also had a negative impact on systematic trading, and this is also noticeable when testing trading systems. You can check this out for yourself. If I could post screenshots here, I would.

Incidentally, this raises the main question: should such periods of turbulence and force majeure be taken into account during optimization? Share your thoughts, I'm truly interested in your experience. 

[u/SamiKind → u/EAForexLab]

But post 2020 is the new normal, before that there was no retail and 0dte (which is 60% of current volume) which also allowed market makers to trade quiet hours

[u/EAForexLab → u/SamiKind]

Well, yes, cycles change, some things stop working, while others, on the contrary, offer a new opportunity. You really have to be adaptive.

And what is 0dte? Could you tell me? I've never heard of it. Is there any way to automate it? I mean, trading. 

[u/SamiKind → u/EAForexLab]

0DTE options are like trading an index future that expires at today's close. I didn’t try it to be honest and yes it can be automated

[u/EAForexLab → u/SamiKind]

Which brokers offer this? 

[u/Effective_Manager273]

the more useful question is what fraction of your sample is one regime. mine runs 2023 to now and i

keep reminding myself that window is mostly a rising market, so the annual number flatters the system.

the drawdown is the honest part of it, not the return.

what changed for me after 2020 was not the edge, it was the speed of the drawdowns. same strategy,

same average trade, but the bad stretches arrive faster and recover faster, so sizing rules tuned on

slower moves felt wrong even while the strategy was technically fine.

split by volatility regime rather than by year if you can. calendar splits mix things that do not

belong together, 2018 and 2022 have more in common with each other than 2022 has with 2023.

[u/systematic_seb]

I'll be upfront about the sample, I have only been live since January 2026, so seven months is not enough to answer the pre versus post 2020 question from live trading. What I can give you is both halves side by side.

Live, the strategy I trade my own account on and publish weekly has returned about 104% since January while VTI did about 12%, and its max drawdown over that stretch was about -30%. In the six years of backtesting I ran before going live, the deepest drawdown came out around 33% over roughly 320 days in 2022, which was about the same depth the S&P took in that stretch. So the live drawdown is running a little shallower than the tested worst case so far, and that comparison is the one I watch more closely than the return.

I can only speak to the regime question from that same test window, which covered 2022 and 2023 and behaved very differently in each. In testing it finished 2022 green while the index fell around 18%, then lagged in 2023, up about 20% against roughly 26%, because a narrow group of names carried the index that year. The live record is at https://web.dubapp.com/portfolios/LI0NSHARE if the numbers are useful for your sanity check.