ETH Reddit Mentions | ChartExchange

Putting an LLM in charge of portfolio allocation has three hard problems — non-determinism, backtest cost, and overfitting — and this open-source harness is my attempt to attack all three. The framing: a strong-reasoning LLM *proposes* allocations, a deterministic layer *disposes*. Posting for methodology critique, not as a strategy. Paper/sim only — no live or forward track record.
The default universe is deliberately small and interpretable: **Bitcoin as the primary risk asset, gold (PAXG live / XAUT fallback) as the designated hedge/anchor**, plus ETH and TRX. BTC / gold / cash each carry a hard weight-floor (>0), encoded both in the deterministic strategy and as an LLM hard-anchor constraint — the allocator can't fully abandon the hedge or go all-in on risk.
**Attacking all three:**
**1. LLM non-determinism** — same inputs don't give the same output, and there's no single "correct" allocation. Mitigations: temperature 0; every response parsed as structured JSON and Pydantic-validated with bounded fields (size 1–25%, leverage 1–5x, next-check 5–120 min) with a bounded retry that re-injects the validation error; and an always-on deterministic risk layer (risk manager + hard decision validator + circuit breakers) gates every action, so a variable or occasionally-wrong proposal can never become an unsafe order. To be clear: the backtest ensemble aggregators use *deterministic stub voters*, not real LLM votes — there is no live voting / self-consistency system here.
**2. Backtest cost** — re-running an LLM across thousands of historical windows is expensive. LLM decisions are cached content-addressed by SHA-256 snapshot hash (immutable), so a CPCV run replays across all combinatorial paths and re-parametrized sweeps *without* re-invoking the model. A cache-coverage report prints LLM hit/miss/% and flags when a subset fell back to equal-weight. Honest caveats: coverage is only a hash hit-rate, not a measure of decision quality; 2 of 4 portfolio subsets had 0% real LLM coverage and are equal-weight fallback; cached decisions are gitignored (reproducing the LLM column needs live spend). The deterministic-baseline path backtests with zero LLM calls.
**3. Overfitting** — past performance doesn't imply future profit, so the validation stack matters more than any single number. Implemented in code: CPCV (purge + embargo), CSCV→PBO (0.089 state-reset / 0.333 raw on a 28-config sweep), Deflated + Probabilistic Sharpe, walk-forward, and an untouched OOS holdout. Costs modeled: 4bps taker + 5bps slippage + 0.01%/day funding.
**Honest limitations (please read before the numbers):**
* The strong headline figure (\~31.5% contiguous CAGR, Sharpe \~0.9) is the **deterministic baseline**, not the LLM. Where the LLM actually ran, it **underperformed** equal-weight / min-variance on both Sharpe and drawdown.
* Bear-market drawdown hit \~24–27%, breaching the 15% target.
* The gold hedge was configured but **never fired** in the champion run (the drawdown threshold wasn't hit), so I make no claim it drove returns.
* Backtest/sim only; paper P&L is an optimistic upper bound.
Stack: Python 3.10+, CCXT, Pydantic v2, the `ta` library, SQLite WAL. \~620 deterministic tests (mocked CCXT + pre-recorded LLM responses). Providers: Claude / OpenAI / Gemini / local (Ollama, vLLM, etc.). Paper mode is the default and needs no exchange keys.
**Critique I'd genuinely value:**
* Is content-addressed caching of LLM decisions a defensible way to make CPCV tractable, or does it smuggle in leakage I'm not seeing?
* How would you fairly evaluate an LLM allocator when real coverage is partial and re-running is costly?
* Does the PBO gap (0.089 state-reset vs 0.333 raw) say what I think it says about that sweep?
MIT-licensed, repo link in a comment below. It's a research harness — read the code, adapt it to your own risk tolerance. Not advice, and not something I'd claim is profitable.

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