I built an AI crypto trading bot, then proved it had no edge

A developer built an AI crypto trading bot and rigorously tested it for an edge, finding that the directional LLM-based strategy had no edge after accounting for costs, while a delta-neutral funding carry strategy on altcoin perpetuals showed a small but real structural edge of 10–20% annually with a $100k capacity ceiling. The project is published as open-source research with tools to disprove false edges.

A crypto trading research project — and an honest record of what happened when I tested it for a real edge. It started as an AI-driven directional bot: deterministic market features → LLM trade suggestions → a multi-gate risk engine → paper/live execution. Then I did the thing most trading projects skip: I tried, rigorously, to disprove that it made money. It didn't. So I kept testing — pairs, stat-arb, cross-exchange — and disproved those too. One strategy survived: a delta-neutral funding carry on high-consistency altcoin perpetuals. This repo contains both halves: the full directional bot which has no edge , and I can show you why and the carry strategy which has a real, structural, but small edge . The whole point is the methodology — see docs/METHODOLOGY.md . ReadEducational/research only. Not financial advice. Paper by default. Trading risks total loss. As published, the carry edge is backtested and paper-tested, DISCLAIMER.md first.notproven over a long live track record. Most "edges" are illusions that vanish once you account for real costs or test out-of-sample. The directional bot's mechanical signal was +0.035R gross but −0.243R after realistic slippage and fees . The LLM layer Cerebras and Gemini, tested on a gated replay did not beat break-even. Pairs/stat-arb was overfit — every pair that looked good in-sample reversed out-of-sample. Cross-exchange funding differences were already arbitraged to ~0.4%/yr. The only thing that held up under honest cost accounting was harvesting funding on perpetuals that are structurally hard to short, where longs persistently pay shorts. Held delta-neutral long spot + short perp , that pays roughly 10–20%/yr, market-neutral , with a hard capacity ceiling around $100k . | Module | Role | |---|---| sentinel.carry | The strategy that survived. Scanner, simulator, delta-neutral position, risk-parity book, manager, persistence, 24/7 runner | sentinel.core | Scheduler, pipeline orchestration, positions, daily report directional bot | sentinel.data | CCXT market data, deterministic features, historical cache | sentinel.ai | LLM client, prompts, reflection, offline dataset tooling | sentinel.risk | Multi-gate risk engine, sizing, kill switch | sentinel.exec | Broker interface, paper/MEXC execution | sentinel.backtest | Backtest engine, LLM gated replay, cost sweep — the disproof tools | sentinel.store | SQLite WAL persistence | sentinel.admin | Telegram admin bot | The carry strategy is deliberately split into a pure, unit-tested core scoring, sizing, accounting, scheduling and a thin network/time layer price fetch, the loop . That's why it's testable and restart-safe. Requires Python 3.12+ . git clone https://github.com/blitzcrieg1/sentinel-trader-research.git cd sentinel-trader-research python -m venv .venv Windows: .\.venv\Scripts\Activate.ps1 Linux/macOS: source .venv/bin/activate pip install -e . cp .env.example .env defaults are paper-safe Run the carry strategy paper, no exchange keys needed — public data only : python -m sentinel.carry.run --capital 10000 --state data/carry book.json -v It scans MEXC perpetuals, curates a basket by funding consistency , opens simulated delta-neutral hedges, accrues funding every 8h 00/08/16 UTC , and persists the book so it survives restarts. Add TELEGRAM BOT TOKEN + TELEGRAM ADMIN CHAT ID to .env for 8-hourly reports optional . Run the directional bot paper : python -m sentinel.main It needs an LLM key GEMINI API KEY and uses dummy MEXC keys for paper. It works — it just doesn't have an edge. That's the point. pytest carry core: scoring, sizing, delta-neutral accounting, persistence ruff check . mypy sentinel/ The sharpest objection to a consistency-screened basket is survivorship/look-ahead bias. sentinel/carry/walkforward.py confronts it: at each rebalance the basket is picked on past-only data, scored on the next, unseen window, then bootstrapped into a 95% CI and compared to a random-selection baseline. python -m sentinel.carry.walkforward --scan --train 600 --test 120 --top-n 6 -v If the edge only existed in hindsight, the out-of-sample yield collapses to the baseline and the CI straddles zero. The no-look-ahead property is unit-tested. See docs/METHODOLOGY.md /blitzcrieg1/sentinel-trader-research/blob/main/docs/METHODOLOGY.md §5.5. And because capacity is the whole story for a thin-market edge, capacity.py turns the "~$100k ceiling" into a net-yield-vs-notional curve square-root market impact + participation cap + a sensitivity band : python -m sentinel.carry.capacity --symbol XMR USDT -v Linux + systemd . Units are in deploy/ — sentinel-carry.service the strategy and sentinel-trader.service the directional bot . Both run fine on a small always-on box a Raspberry Pi or a cheap mini PC . The carry book uses atomic writes and SQLite uses WAL, so a power loss won't corrupt state. It is an honest, end-to-end study of whether a retail crypto edge exists, with the code to reproduce every finding. It is a working, market-neutral carry harvester with a structural reason to work and modest, capacity-limited returns. It is not a way to get rich from a small stake. A ~15% market-neutral return is excellent — but 15% of a small number is a small number. At small capital, your savings rate matters far more than any strategy's return. It is not financial advice or a turnkey money machine. See DISCLAIMER.md /blitzcrieg1/sentinel-trader-research/blob/main/DISCLAIMER.md . Funding carry is not new , and this repo doesn't claim to have discovered it. It's one of the most replicated ideas in crypto, and the structural reason it works is well documented — a BitMEX study https://markets.financialcontent.com/chroniclejournal/article/gnwcq-2025-10-14-bitmex-study-finds-cryptocurrency-funding-rates-positive-92-of-the-time found funding is positive ~92% of the time. There are many implementations: Execution / detection bots — e.g. aoki-h-jp/funding-rate-arbitrage https://github.com/aoki-h-jp/funding-rate-arbitrage , ARBOT https://github.com/IrakliXYZ/ARBOT , HL-Delta https://github.com/cgaspart/HL-Delta . These detect funding opportunities and run the delta-neutral trade. They're useful — but they're execution engines : none ship walk-forward validation, an out-of-sample permutation/selection-bias guard, or a capacity curve. Academic — e.g. a leveraged BTC carry study https://papers.ssrn.com/sol3/papers.cfm?abstract id=5292305 ~16%/yr, Sharpe 6.1 . Rigorous, but single-asset and theoretical. Honest "AI bot" writeups — e.g. Jiri Dolejs' LLM trading bot https://medium.com/@kojott/i-built-an-ai-trading-bot-and-let-it-trade-for-9-days-heres-what-happened-6ceb69ad4d08 , which independently reaches the same conclusion about LLM signals that §2 does. What this repo adds is not the strategy — it's the discipline around it : - A documented disproof of the strategies that don't survive directional, LLM, pairs, cross-exchange , so the carry isn't presented in a vacuum. - A validation layer the execution bots skip — walk-forward + bootstrap, a permutation test for selection bias, and a capacity curve, all runnable. Honest limitations METHODOLOGY §6 /blitzcrieg1/sentinel-trader-research/blob/main/docs/METHODOLOGY.md , including the parts that are unproven or only operationally mitigable. The edge is the crowded part; the rigor and the candor are the point. Built and tested in paper. The hardest and most valuable result here was learning to disprove my own ideas with data.