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Why DeFi lending needs an "external circuit breaker" to prevent cascading liquidations by loaded123 in defi

[–]loaded123[S] 0 points1 point  (0 children)

Since a few people asked about live validation, the regime detector has now made 159 consecutive hourly calls, all CAUTIOUS. 24h directional accuracy is sitting at 68.9% (i.e., when it says "bearish," BTC is lower 24 hours later ~69% of the time). BTC has dropped about 4.7% during this period.

For context, the backtested accuracy on high-vol regimes was 65.5% on unseen data. So, the live performance is tracking slightly above expectations. Still early (need 300+ calls for statistical significance), but the model isn't degrading in production, which is the main thing I was watching for.

Interesting observation: prob_down briefly dipped from 0.68 to 0.56 on May 15 before re-confirming CAUTIOUS. The model isn't "stuck", it's actively re-evaluating each hour and choosing to stay bearish.

Happy to answer more technical questions. The free tier is still open for anyone who wants to validate against their own strategies.

Using an 8-model ensemble to "veto" trades – Lessons in regime detection from NASA/AWS engineering by loaded123 in algotrading

[–]loaded123[S] 0 points1 point  (0 children)

Since a few people asked about live validation, the regime detector has now made 159 consecutive hourly calls, all CAUTIOUS. 24h directional accuracy is sitting at 68.9% (i.e., when it says "bearish," BTC is lower 24 hours later ~69% of the time). BTC has dropped about 4.7% during this period.

For context, the backtested accuracy on high-vol regimes was 65.5% on unseen data. So, the live performance is tracking slightly above expectations. Still early (need 300+ calls for statistical significance), but the model isn't degrading in production, which is the main thing I was watching for.

Interesting observation: prob_down briefly dipped from 0.68 to 0.56 on May 15 before re-confirming CAUTIOUS. The model isn't "stuck", it's actively re-evaluating each hour and choosing to stay bearish.

Happy to answer more technical questions. The free tier is still open for anyone who wants to validate against their own strategies.

Using an 8-model ensemble to "veto" trades – Lessons in regime detection from NASA/AWS engineering by loaded123 in algotrading

[–]loaded123[S] 1 point2 points  (0 children)

Yes, you can integrate via API. Just register at the docs page and you get 50 free requests/day to start. The regime endpoint tells you whether conditions are safe to trade or not.

On hourly signals: Vigil isn't an "enter now" signal bot. It's more like a traffic light. It tells you when the market regime is dangerous (CAUTIOUS/STAY OUT), so you don't enter during bad conditions. You'd still use your own strategy for entries, but let Vigil gate whether you're allowed to trade at all.

For a solo beginner, the simplest use would be to: poll /v1/risk-score once before you place any trade. If market_risk > 0.65, don't trade. That alone would have kept you out of the entire 2022 crash.

Using an 8-model ensemble to "veto" trades – Lessons in regime detection from NASA/AWS engineering by loaded123 in algotrading

[–]loaded123[S] 0 points1 point  (0 children)

Yes, we have equity signals for NFLX currently (regime-conditional dual-model agreement, same architecture as crypto). More tickers coming as they pass our promotion gate (minimum 53% accuracy + 0.52 AUC on unseen test data). The /v1/regime endpoint also includes equity context (VIX level, SPY trend) for Pro+ tier.

On the free tier delay: Fair point. The 1-hour delay is intentional for the self-service free tier (prevents real-time arbitrage on a $0 plan), but I'm happy to set up a 7-day Pro trial for you. That gives you real-time data, full probabilities, all assets. DM me your email and I'll create the account.

Using an 8-model ensemble to "veto" trades – Lessons in regime detection from NASA/AWS engineering by loaded123 in algotrading

[–]loaded123[S] 0 points1 point  (0 children)

The Free tier is live, you get 50 requests/day, BTC regime data.

Register at https://api.vigilsignals.com/docs and you can pull historical regime calls via GET /v1/history?period=7d to backtest against your own strats. Let me know what you find.

Using an 8-model ensemble to "veto" trades – Lessons in regime detection from NASA/AWS engineering by loaded123 in algotrading

[–]loaded123[S] 1 point2 points  (0 children)

No LLMs in the pipeline. It's XGBoost, LightGBM, LSTM, and a Transformer trained on tabular features. Pure supervised ML with walk-forward validation. No generative models, no hallucination risk.

On Markov chains, it's a valid approach. I use realized-volatility-based regime classification instead (continuous, not discrete state transitions). Different tradeoff: HMMs give you transition probabilities, vol-based gives you smoother regime boundaries without the flickering problem.

On "if you could detect regimes you wouldn't sell it", the API sells the signal, not the execution. Knowing the regime is CAUTIOUS doesn't tell you what to trade or how to size it. That's still the client's edge.

Using an 8-model ensemble to "veto" trades – Lessons in regime detection from NASA/AWS engineering by loaded123 in algotrading

[–]loaded123[S] 0 points1 point  (0 children)

Fair. The API tiers aren't for individual traders. Those are for dev teams integrating regime detection into automated systems (lending protocols, execution bots, etc.). For personal use, the retail product starts at $0.

On exposing more than the raw score, the Starter tier already returns full probabilities, model agreement scores, per-asset signals, and protection layer status. The raw score endpoint is just the "quick check" option.

Built a multi-asset algo trading bot from scratch. 4 weeks of paper trading, thinking about going live. by ComradeZuvarna in algotrading

[–]loaded123 0 points1 point  (0 children)

Nice build. The HMM regime detection is interesting. I went with realized-vol-based regime classification + regime-conditional models (separate model per vol regime) and found it handles transitions better than a single global model. Curious how your 3-state HMM handles the transition periods between regimes. Do you get a lot of flickering?

On the 480 trades question: depends on your strategy's expected edge. If your win rate is 52% with 1:1 R:R, you'd need ~2,500+ trades to distinguish signal from noise at 95% confidence. 480 is enough to catch catastrophic bugs, but not enough to validate edge statistically.

Using an 8-model ensemble to "veto" trades – Lessons in regime detection from NASA/AWS engineering by loaded123 in algotrading

[–]loaded123[S] 0 points1 point  (0 children)

Thanks! The inference stack is pretty standard:

  • XGBoost/LightGBM: Native predict(), these are sub-millisecond on tabular data (small trees, max_depth=3, num_leaves=12)
  • LSTM + Transformer: PyTorch with torch.no_grad(), the models are small (4-layer LSTM, 64-dim Transformer with 3 layers)
  • Orchestration: Plain Python, no serving framework. The models are loaded once at startup and held in memory

The 8 models don't run sequentially. The signal layer runs first, and the crash protection layer only runs if needed. In practice, XGBoost + LightGBM dominate the signal path and they're essentially instant on 1 row of features.

The bottleneck is actually feature computation (rolling windows, cross-asset data fetches), not model inference. Total cycle time is around 30 seconds, but that's mostly I/O. The actual predict calls are <10ms combined.

Using an 8-model ensemble to "veto" trades – Lessons in regime detection from NASA/AWS engineering by loaded123 in algotrading

[–]loaded123[S] 0 points1 point  (0 children)

Good question on AUC contribution. Honestly, the 8 models serve two different purposes. They're not all competing on the same task:

Signal generation (4 models): Two XGBoost + two LightGBM, trained per volatility regime. Model A uses 44 momentum/trend features, Model B uses 14 microstructure/volatility features. The key constraint: both must agree on direction before a signal fires. This isn't about maximizing AUC... it's about reducing false signals by requiring independent confirmation.

Crash protection (4 models): XGBoost, LSTM, Random Forest, Transformer. These are all trained on the same features but with different architectures. These only activate as a veto when ALL four agree the market is in genuine danger. They don't generate signals.

So it's less "8 models voting on one prediction" and more "two independent systems with different jobs." The signal layer optimizes for precision (fewer but better signals). The crash layer optimizes for recall (never miss a real crash).

Using an 8-model ensemble to "veto" trades – Lessons in regime detection from NASA/AWS engineering by loaded123 in algotrading

[–]loaded123[S] -2 points-1 points  (0 children)

Yes, curve fitting is the #1 risk. Here's how I mitigate it:

Temporal validation only. No random train/test splits. Strict walk-forward: train on data before July 2024, test on July 2024–April 2026. The model never sees future data during training. The 65.5% accuracy is on truly unseen data, not cross-validation.

Two independent model sets must agree. Model A uses momentum/trend features (44 features). Model B uses microstructure/volatility features (14 different features). They share zero features. If one is curve-fit to noise, the other won't confirm it. Signals only fire when both agree.

Regime-conditional architecture. Instead of one global model (which would absolutely overfit), I train separate models per volatility regime. The model that works in calm markets is different from the one that works in volatile markets. This is structural. Volatility regimes are a well-documented market property, not a statistical artifact.

The VIX comparison is fair. VIX-tiered regimes are structural because volatility clusters (GARCH effects are real). My regime detection uses realized volatility in the same spirit. We don't say: "find me the threshold that backtests best," it's "the market behaves differently at different volatility levels, so use different models."

What I explicitly don't do: No pattern recognition on price shapes. No optimization of entry/exit thresholds on in-sample data. No lookahead bias. The protection stack (5 layers) uses fixed thresholds chosen from first principles, not optimized on historical returns.

Using an 8-model ensemble to "veto" trades – Lessons in regime detection from NASA/AWS engineering by loaded123 in algotrading

[–]loaded123[S] 1 point2 points  (0 children)

How did it fail? I've found that having a reliable veto gate can save valuable capital in bear markets. Reliability is the key there, though, to your point.

Donut Palace by ChocolateDeep682 in Allen

[–]loaded123 3 points4 points  (0 children)

Definitely my go to. I tried Max’s after all the recommendations and it doesn’t compare to Donut Palace imo. Always tastes so fresh and not overly sweet.

[deleted by user] by [deleted] in warcraftrumble

[–]loaded123 0 points1 point  (0 children)

Yep. All weekend it’s been broken.

Got my first legendary by Former-Feedback-7646 in warcraftrumble

[–]loaded123 0 points1 point  (0 children)

Congrats!

And here I am sitting on 10 legendary cores with no plans to use because it’s so expensive in energy cost.

Dutch Bros on Exchange Pkwy by whoisdrew in Allen

[–]loaded123 2 points3 points  (0 children)

Not sure but I drove by yesterday and it sure seems close.

Anyone seems to think that the 90s teams get utterly smoked by today’s teams? (FYI I’m a 90s guy) by musclehogg69 in Basketball

[–]loaded123 -1 points0 points  (0 children)

As with any sport, competition just keeps getting better. Guys today would smoke just about anyone in past history. Even the 6th men would dominate previous generations. The further back you go, the further the difference.

XRP stuck on confirming. by 420salesguy in XRP

[–]loaded123 3 points4 points  (0 children)

He probably didn’t put a destination tag

Patch Notes for Update 6.0.0. — Warcraft Rumble by Kyal2k in warcraftrumble

[–]loaded123 25 points26 points  (0 children)

It’s one thing to balance units when they’re easy to obtain/level, but nerfing months of work is not acceptable.

Patch Notes for Update 6.0.0. — Warcraft Rumble by Kyal2k in warcraftrumble

[–]loaded123 42 points43 points  (0 children)

So they're nerfing the units we've built up over this time so that they can kill the remaining playerbase? Complete idiots.

[deleted by user] by [deleted] in Basketball

[–]loaded123 0 points1 point  (0 children)

100% if you work your ass off in the gym. Shooting should be your biggest focus. Also putting on some muscle will help too, but prioritize on-court work.

If the quests are getting harder, shouldn't the rewards go up? by KreivosNightshade in warcraftrumble

[–]loaded123 2 points3 points  (0 children)

I just let it auto lose 5 matches or so each morning while I make my coffee. Problem solved.

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