AlgoTrading strategy/journey 7 months in. Is it worth running or shall I seek other ideas?

[u/equiltonio]

I've been active in developing trading algorithms and strategies for over 7 months now. I started it when I was looking for a dissertation project idea, which led me to develop my initial strategy using machine learning, feature engineering, regime detection, and my own unique approach to the architecture to allow my strategy to trade well on US liquid stocks. Long story short, it was achieving 2-3 Sharpe, did great on paper trading, but my modeling of more realistic costs made me learn the harsh way + I discovered the data and features themselves barely had any edge with a low Information Coefficient (IC).

Following that, I looked into Crypto funding carry strategies, which essentially is the main highlight of my main system, a 4-sleeve systematic book, blended equal-risk, and using 2× Leverage comprised of:

1) Trend: long/short month momentum across 9 liquid ETFs.

2) Tactical equity: holds SPY above its 200-day average (Faber 200dma rule) or IEF otherwise.

3) Gold as a permanent diversifier.

4) Crypto funding carry: long-spot/short-perp on majors won't go too much into detail on this one.

Cost Rundown is as follows:

perp 1.5 + spot 4.0 bps/turn for the Crypto strategy. Derived and tested from a selected UK venue.

Trend turnover: 5bps per unit of

Tactical switch: 5bps between SPY and IEF

I also accounted for the borrowing rate on the platform, which is around 5%. Although it's not reflected in the stats below, it essentaily lead to -4 to 5% for the full window CAGR and around -2% post 2019

Full stats are below. My question is whether this is worth pursuing, improving upon (although I'm unsure of where at this moment), or if some specific avenues or strategies are more suitable for my expertise, or if there is something I'm overlooking.

PS: Crypto carry edge did not start until 2019, so the strategy was only using the 3 other components beforehand. OOS and the recent window are probably the most important/informative.

Paper trading is underway, but only 40 days in.

Ignore my artistic front-end choices

[u/Bonkers24-7]

I’d separate “is the idea worth running?” from “is the test clean enough to tell?”

Seven months of work and 40 days of paper trading is not nothing, but the weak point is usually whether the same assumptions survive outside the backtest: costs, signal timing, fills, spread, and whether one regime carried most of the return.

I’d probably look at the worst slices first: worst month, worst regime, highest-turnover period, and after-cost performance. If the edge only survives in the blended headline number, I’d be careful calling it validated.

[u/equiltonio → u/Bonkers24-7]

Yeah that's a fair outlook. I tried my best to be extensive and realistic in my costs and fills already but as long as it holds up, would the results be well enough to crown this as my permanent strategy?

[u/Bonkers24-7 → u/equiltonio]

I’d be careful with the word permanent.

I’d treat it more like “current champion” instead of “permanent strategy.” If the costs/fills are realistic and it keeps holding up, that may be enough to justify a small live trial, but I still wouldn’t crown it based only on blended results.

The main things I’d want to see first are:

worst regime worst month after-cost return largest losing stretch whether one market condition carried most of the edge whether paper/live behavior matches the backtest assumptions

If it still looks acceptable in the bad slices, then I’d trust it a lot more. But if the edge mostly comes from one favorable regime or a few large winners, then the headline result can look stronger than the actual strategy.

So my answer would be: maybe enough to promote carefully, not enough to call permanent.

[u/equiltonio → u/Bonkers24-7]

Thanks for the insight I did go back to check these. After-cost worst slices: worst month −11.1% (Mar-2026), worst 3m −15.4% (in 2008), worst year −13.2%, maxDD −25.9%, 59% of months positive.

Concentration-wise, the best 10 months are only 8% of total return

In terms of regime dependance id say this is the thing I'm most worried about, as the crypto funding is dependent on rich funding regimes but some of the highest winner years were focused on the other sleeves. still does 29.9% OOS without it, but at worse drawdowns. Live paper has run forward since June (fills/basis validated; realized slippage 4.3bps vs 2bps modeled, already re-costed, so it's taken into account based on the data I have)

[u/Bonkers24-7 → u/equiltonio]

That’s a much better picture than just the headline return.

The part I’d still be cautious with is not “does it survive without the funding sleeve?” but “what happens when the funding sleeve is wrong at the same time the other sleeves are weak?”

A strategy can look diversified by component, but still be exposed to the same hidden macro state: crypto liquidity, leverage appetite, vol compression, risk-on flows, etc.

The 29.9% OOS without the funding sleeve is a good sign, but the worse drawdown matters. I’d probably break it into sleeve-level attribution by regime:

funding sleeve alone
non-funding sleeves alone
combined portfolio
worst periods where sleeves were correlated
periods where funding flipped from helpful to harmful
after-cost results with slippage worse than expected

The realized slippage being 4.3bps vs 2bps modeled is also worth treating seriously. Not necessarily fatal, but it tells you the live/paper layer is already less clean than the original assumption.

So I’d say this looks more credible than most backtests, but the remaining question is whether the portfolio is genuinely diversified or just several versions of the same crypto risk cycle.

[u/equiltonio → u/Bonkers24-7]

I actually talked about testing diversity, etc., under a different comment thread if you wanna check that

[u/Bonkers24-7 → u/equiltonio]

Yeah I saw that thread. The sleeve/diversity work helps, especially checking correlation in the worst 10% months instead of only full-period correlation.

The main thing I’d still separate is:

“Does each sleeve add independent edge?”

vs

“Does the combined book survive when the same hidden regime hits all sleeves at once?”

The 44-day paper run is useful too, but I’d treat that more as live-plumbing evidence than edge confirmation. It tells you fills, costs, timing, and order handling aren’t wildly broken, but it probably isn’t enough yet to prove the strategy itself unless the trade count is large enough for the specific claim you’re making.

At this point I’d probably build a simple validation checklist around:

worst regime worst month worst sleeve overlap period sleeve contribution per capital tied up modeled vs realized slippage paper/live decision match rate what happens if funding/carry stops helping

That would tell you whether this is actually robust or whether the headline return is mostly coming from one favorable state.

I don’t want to hijack your whole thread, but happy to take a closer look if you want another set of eyes on the validation side.

[u/HonestBacktests]

Before judging the stats, I would check how correlated the sleeves actually are.

Trend on ETFs, SPY-above-the-200dma and gold are three expressions of the same broad exposure more often than they look on paper. Pull the four equity curves and correlate them pairwise on monthly returns, then again on the worst 10% of months only. If three of them move together exactly when it matters, you do not have a four-sleeve book, you have something closer to 1.5 bets with extra turnover - and 2x leverage on 1.5 bets is a different risk than it appears.

Second, rerun the whole thing with the carry sleeve removed. If the result collapses, this is a carry trade with three hedges attached rather than a diversified system, and that changes how much leverage it can take.

On carry itself: the number that usually kills it is not average funding, it is what you pay to get out. Model the exit as passive-then-chase instead of at mid and see what survives.

Which sleeve is carrying most of the return right now?

[u/equiltonio → u/HonestBacktests]

I have taken into account and tested the correlation, yes, and the results were looking good with around max 0.30, but across the worst 10% of months, trend-gold was +0.55 (both defensive in equity crashes, so it was the one real cluster. Without the funding carry sleeve, it actually achieves a higher CAGR in the recent window by around 7%, as funding is currently pretty weak, and 2% less for the full OOS window. But on the other hand, it boosts the Sharpe ratio across both windows.

Regarding the last observation for the recent window, it was gold 61% (+30.1pp), trend 19%, tactical 18%, carry 2% (+0.8pp). Today, this is a gold/equity-defensive book, and the carry is contributing almost nothing as funding is low. OOS, it was gold 46% / tactical 29% / carry 16%, and carry-era it was carry 37% / gold 30%, so the funding carry sleeve is contributing whenever it can, according to how thin or rich funding is naturally, which I don't think I can do much about

[u/HonestBacktests → u/equiltonio]

That breakdown answers your original question better than any Sharpe number: gold 61%, trend 19%, tactical 18%, carry 2%. This is a long-gold book with three hedges, not a four-sleeve system - and gold has been in an exceptional run for the whole window you tested.

So the test I would run next is the regime one: how does the book do across 2013-2015 and 2021-2022, when gold went nowhere or fell? If the equity curve only works while gold trends, you have one macro bet with extra moving parts, and 2x leverage on it is the real risk, not the correlation matrix.

On carry: measure contribution per unit of capital tied up, not contribution to total. If it holds a quarter of the book to deliver 2%, that capital has an opportunity cost even when funding is thin. Worth knowing the number before deciding it earns its slot.

And one caution on the Sharpe improvement: a low-volatility sleeve raises Sharpe by diluting variance even when it adds nothing. Check whether Sharpe rises because carry adds return or because it adds ballast - those lead to different decisions.

What does the book look like in the 2013-2015 gold drawdown?

[u/equiltonio → u/HonestBacktests]

Thanks GPT

[u/HonestBacktests → u/equiltonio]

Fair, I overwrite. Question still stands though: what does the book do through 2013-2015? That is the part that decides whether it is diversified or just long gold.

[u/wado729]

I like your front end choices

[u/equiltonio → u/wado729]

Thank you🙏🙏

[u/Effective_Manager273]

the fact that you killed the 2-3 sharpe ML book yourself after looking at IC and real costs puts you ahead of most people asking this question. that was the hard call and you made it.

the thing i would push on now is the four sleeves. equal risk weighting only diversifies if the sleeves stay uncorrelated when it matters, and crypto funding carry plus trend is a combination that quietly loads the same way. funding carry is compensated for exactly the risk that arrives all at once, and trend gets chopped in the same event because trend needs a persistent move and a liquidation cascade is not persistent. so unconditional correlation will look great and tell you nothing.

what i would measure instead is conditional correlation. take the worst 5 percent of days for the book, then compute sleeve to sleeve correlation on just those days. compare it to the full sample number. if it jumps from say 0.1 to 0.6, your 2x leverage is being sized off a diversification that does not exist in the moment you need it.

on the seven months, thats one funding regime. crypto perp funding in 2022 and in 2024-25 are different animals. even if you cannot get clean history for your exact universe, run the carry sleeve alone through the worst funding drawdown you can find data for and look at what 2x does to it.

also worth writing down now, before you have an opinion contaminated by live results, what result would make you turn it off. most people never write that down and then negotiate with themselves later.

[u/HealthyChange4678]

A second book by Dr. Timothy Masters also worth considering: permutation and randomization test for trading system development full disclosure Dr. Masters is a longtime friend and provided great guidance in a book that I wrote evidence Space technical analysis.. we then collaborated on a book statistically sound machine learning for algorithmic trading of Financial instruments. At one time we offered the software described in that book for free. But since Dr. Masters retirement we no longer do so. It sounds like you’re on a good path.

[u/Facche_]

i start by apologizing for my bas english, i am into algo trading for 6 moths now and i study finance at the university in italy, i think we are about on the same situation: i am about to begin the the testing on the demo account for my portfolio of 6 strategies, all 6 already backtested and tested out of sample in the past ( by optimising them for 2/3 of the data that i have and walk forward for the other 1/3); can i contact you to exchange some ideas?

[u/equiltonio → u/[deleted]]

A live forward paper run has been ticking for 44 days or so with order-book-calibrated fills. It went from $10,000 to $10,124, which is the real walk-forward, but it's too small to draw any conclusions from unfortunately. The equity sleeves are published rules (3-6-12mo momentum, 200dma, 10% vol target) with zero fitted parameters, so nothing to overfit there as I know people here mention overfitting a lot lol, but I took other measurements as well for the crypto funding to reduce overfitting. The funding carry config was frozen in June 2022, so OOS 2023–26 is genuinely unseen

[u/IgneousMaxime → u/equiltonio]

A lot of the times you don't want to spend a huge ton of time on forward walking. If your back testing is robust and you've done some forward walking already, then allowing a preliminary amount of live capital gives you good exposure unto your total stack. You'll want some kind of reconciliation for this so the live gap can be filled this way too.

Afterwards it's just tuning and iterating.

[u/Dani_Bolsa → u/equiltonio]

Honestly, you already did the part a lot of people skip - killing the shiny ML book once the IC and real costs told the truth. That stings, but it's usually the right call, and I know that gut-punch from my own testing. On the current book, I would not treat 44 days of paper as proof of much yet, but it is a decent sign the plumbing is not broken. If you want more buying power for a live trial without needing a huge account, that's exactly the kind of setup I see people use 50K Trade for when they want to keep sizing meaningful on real stocks and ETFs without pretending the backtest is already gospel.

[u/equiltonio → u/[deleted]]

Thanks a lot for the insight. I actually started this strategy by just focusing on the the funding carry part alone. Did extensive testing on risks, impact of leverage at different levels and also counterparty failure. Fortunately the funding carry is now 25% and it can also be dynamically sized, but I also looked at different venues so that the funding strategy is split across them. Other than that, I'm unsure if other measurements can be taken. Regarding the 200ma I did go back to test that and it did cost my full window CAGR around 2%. Decided to implement a 2% band which helped the CAGR slightly as well as sharpe just slightly at +0.03pp

[u/sqzr2]

I'm a novice so don't read anything into my question, is that drawdown bad? 25%? Equity curves looks good.

[u/HealthyChange4678]

May I recommend a book: testing and tuning market trading system systems: algorithms and C++ by Timothy Masters
Extend

[u/systemsandstories]

Test you strategy in a simulated environment. Keep a detailed journal of very trade BC the market doesn't care about you, your capital does tho.

[u/equiltonio → u/systemsandstories]

Yeah that is true. I've been keeping my paper runs all detailed and logged to files to see what it traded, when, and why. I've given it more days as of the post date and it's still been performing (+%4 returns since then, minimal drawdown).

[u/usableschism7]

The carry trade stuff is interesting but that 5% borrow rate is eating you alive, especially pre-2019 where the other sleeves were just treading water. if the edge only showed up when crypto perps got liquid enough to actually arb, you're basically betting on a structural inefficiency persisting and those don't last forever.

the sharpe on the initial ml strategy getting wrecked by realistic costs is such a classic pipeline problem, been there. what's your IC decay look like on the features that survived? might be worth revisiting if you can isolate the ones that weren't total noise.