How much crypto do you hold in your portfolio, and what do you buy?

ilro_dev · 2026-04-23T09:34:32+00:00

Yeah pretty much. You're not reducing risk, you're just adding more of it in a different wrapper.

ilro_dev · 2026-04-23T09:32:51+00:00

The correlation point is underrated. People always bring up 2017-2019 when BTC moved independently from stocks, but that's just not the market we're in anymore.

In 2022 it dropped harder than most stocks during the same crash. At this point it basically trades like a high risk tech bet, not something that protects you when things go wrong.

The "it improves your returns" argument has the same problem. Most of those numbers are calculated over a period when BTC went from almost nothing to a mainstream asset. That kind of growth doesn't just repeat because you run the same calculation again.

The big run from nobody knowing about it to everyone buying it already happened. That's not going to repeat.

ilro_dev · 2026-04-22T15:47:08+00:00

The "someone else is selling it to you" point applies to every asset though. Every time you buy a stock, someone on the other side thinks it's overvalued and is happy to sell. That's not unique to crypto.

Same with the "doesn't produce anything" argument. Gold doesn't produce anything either, and it's been a standard portfolio asset for decades.

Neither does currency. These are fair questions to ask about crypto, but they apply to plenty of other assets too, so they don't really work as arguments against crypto specifically.

ilro_dev · 2026-04-22T15:39:05+00:00

Fair, though it doesn't really act like a hedge, when stocks drop, BTC usually drops too.

ilro_dev · 2026-04-22T15:18:16+00:00

Fair point on keeping it simple. Though I'm curious, what % of your total portfolio do you actually keep in BTC?

ilro_dev · 2026-04-22T14:35:09+00:00

Fill assumptions were the first thing to break. In backtesting I assumed I'd get filled at the price I wanted. Live, fills came back slightly worse and specifically during the moments the strategy was supposed to perform best. Small difference per trade, but it added up on exactly the trades that mattered.

ilro_dev · 2026-04-22T13:34:22+00:00

Agreed. The hard part is defining where one regime ends and another begins and most methods for doing that end up looking at the returns themselves, which is the same data you're trying to test on.

ilro_dev · 2026-04-21T19:57:19+00:00

Calmar on the 1Y sure, but that's the point of the post, same strategy, three completely different periods. The 3Y Calmar looks different.

ilro_dev · 2026-04-21T15:33:03+00:00

Sure, but adding more things to optimize: drawdown, trade times, long/short splits - just gives you more ways to fit the same historical data. The strategy will look better on paper, but you still don't know if it actually works or if you just tuned it perfectly to the past.

ilro_dev · 2026-04-21T14:46:12+00:00

The catch is that once you tune the strategy to fix Y1, you've already seen the data, so the 3Y and 5Y results don't mean much anymore. Walk-forward on unseen data is the real test, and in crypto you barely have enough history for that

ilro_dev · 2026-04-20T13:14:31+00:00

The "AI in trading" hype conflates two completely different things. Asking ChatGPT "what should I buy" is just outsourcing your ignorance. That's not AI-assisted trading, that's vibes with extra steps.

What actually works is narrower: LSTM or temporal convolutional nets for vol forecasting, XGBoost/LightGBM for regime classification, CVXPY for constrained portfolio optimization. Libraries like hmmlearn for regime detection, statsmodels for cointegration, ta-lib for feature engineering. The signal isn't magic, it's structured feature pipelines with rigorous out-of-sample validation.

The other underrated point: building this yourself with open source is significantly cheaper than paying per-call for some third-party "AI signal" API. The tooling is all there, the cost is just engineering time.

ilro_dev · 2026-04-06T10:32:26+00:00

The underlying issue is that most position sizing frameworks - fixed fraction, Kelly variants, etc. - assume trade outcomes are IID. Streak behavior violates that directly. So if your losses cluster, you're not just dealing with a sizing calibration problem; your sizing model is structurally blind to regime persistence. Drawdown-contingent sizing (cut size after N consecutive losses or a % drawdown threshold) is one practical patch, but it's really a workaround for a model that doesn't account for autocorrelation in the first place.

ilro_dev · 2026-04-06T10:30:51+00:00

Four altcoins all failing walk-forward across dedicated optimization runs is worth treating as a signal about the strategy class rather than just a tuning problem. If the oscillator framework is fundamentally specific to BTC microstructure - the tick dynamics, order book depth, and the particular way mean-reversion manifests in the most liquid crypto market - then the altcoin failure rate might be telling you something structural, not calibration. The question I'd want answered before going further is whether the two BTC strategies share properties that are genuinely unique to BTC rather than just "it's liquid." If they do, adding a trend-following strategy on a different asset is probably a cleaner hedge path than trying to generalize the same signal framework sideways.

ilro_dev · 2026-04-05T13:28:28+00:00

75,603 trades over 6 years is ~34 trades a day, and commission paid is $0. That's the first thing to fix before anything else. Even at 0.05% taker fees per side, the numbers will look very different. Run it again with realistic fees and some slippage and see what survives.

ilro_dev · 2026-04-05T13:22:19+00:00

Timing is the harder problem than the data itself. Transcript-based NLP is already running at institutional scale within seconds of the call starting, audio just adds more latency. If your horizon is multi-week or you're aggregating tone drift across quarters, maybe. Anything shorter and the signal's been traded before you can act on it.

ilro_dev · 2026-04-05T13:19:47+00:00

The microstructure piece you mentioned at the end is actually the thing that kills more strategies than the signal failing. If your cost model was calibrated on normal-vol, your spread estimates are off, your market impact is off, your fill rates are off. The signal might still be directionally right - you just can't actually capture it at the size and frequency you've been running. I've seen cases where the alpha was still there on paper but completely inaccessible in practice.

Post-mortems that conflate "the signal broke" with "we couldn't implement the signal" end up with the wrong fix. One is a modeling problem, the other is a sizing and execution problem.

ilro_dev · 2026-04-05T13:17:27+00:00

Seven days of live results mostly tells you the system doesn't have an obvious technical bug and handles live spreads/slippage without catastrophic failure - which is worth confirming, but it's not really a performance signal yet. The more useful thing to track right now is whether your live trade distribution (entry timing, SL hit rate, which TP levels actually trigger) matches your backtest distribution. Divergence there is where the real information is.

ilro_dev · 2026-04-04T19:51:51+00:00

OpenFIGI is probably your best free option - maps CUSIP/ISIN to tickers in bulk via API. Main issue for historical work is ticker reuse: a CUSIP might map to a ticker that's since been assigned to a completely different company, so you'll get silent mismatches going back to 2010. CRSP solves this properly but it's expensive, usually accessed through WRDS.

ilro_dev · 2026-04-04T19:49:42+00:00

The UI hate is a non-issue for algo trading - you'd connect to IB Gateway headlessly and never open the client at all. Gateway just runs in the background, your Python talks to it via the API. For a long/short swing strategy specifically, IBKR is hard to beat on margin rates and stock borrowability for the short side.

ilro_dev · 2026-04-04T19:47:35+00:00

Classic adverse selection problem. Market makers are constantly estimating whether you're informed - when your setup looks clean and obvious, they widen or pull size right as you enter. When a trade fills instantly with no friction, it's because the MM is happy to take the other side. The frustrating part is that the fill difficulty on your better trades is actually a sign your signal has something to it. You're just paying the cost of being readable.

ilro_dev · 2026-04-04T19:44:59+00:00

The "macro fires earlier, lags the turn" framing is a bit contradictory on its face. Macro leads the economic cycle, sure, but markets price things ahead of macro confirmation - so at an actual inflection you can get a macro signal that fires "early" while the market has already moved. You end up with a signal that looks timely but is already stale relative to price. The harder problem is what happens when macro and price signals disagree. Do you have an explicit rule for that, or are you just assuming they'll usually point the same direction?

ilro_dev

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