← Experiment 001: the live desk
The Lab · Research Proposal · Paper in progress
Agentic Trading Under Pre-Registration: Momentum, Sentiment and Volatility Targeting at Retail Scale
Abstract. Most retail algorithmic traders lose money, and most published trading results are survivorship-biased marketing. This study asks whether an autonomous AI agent, governed by pre-registered promotion criteria, can manage a deliberately small real crypto account without falling into the failure modes that destroy retail accounts: overtrading, tight-stop bleed, euphoria entries and unsized volatility. We describe an agentic audition protocol in which candidate strategies are generated and evaluated by fleets of AI research agents, must survive fee-inclusive backtests across nine years and ten assets, walk-forward windows spanning five market regimes, and bootstrap Monte Carlo analysis — before touching real capital, which itself remains in a withdrawal-disabled exchange sub-account. Five audition rounds (100+ backtests, 21 strategy variants) are complete — including a forensic audit of a real high-frequency retail bot whose live month paid 14.6× more in fees than it earned — and no variant has yet earned promotion, which we argue is the protocol succeeding, not failing. Preliminary findings include a large positive effect of volatility-targeted position sizing (identical signals: −62% → +229%), a sentiment gate that neutralised the 2021 mania cold-start, the persistent failure of short-selling and tight-stop styles, and the result that trade frequency, not signal quality, is the dominant predictor of retail account ruin.
1 · Background and motivation
Three forces motivate this study. First, the documented failure rate of retail algorithmic and leveraged trading — regulators worldwide report the large majority of retail derivative accounts lose money. Second, the arrival of capable AI agents makes it cheap to generate plausible-looking strategies at scale, which makes disciplined rejection machinery more valuable, not less: the bottleneck is no longer ideas but honest evaluation. Third, almost all public evidence in this space is promotional; experiments that publish their losses are rare. This project runs the entire lifecycle — research, simulation, live trading — in public, from a township-founded lab, on an account small enough that its loss is survivable and its lessons are general.
2 · Research questions
- RQ1. Can any rules-based strategy, net of real fees, beat buy-and-hold across full crypto market cycles at retail scale?
- RQ2. How much of strategy performance is attributable to position sizing rather than entry/exit signals?
- RQ3. Does aggregated crowd sentiment (Crypto Fear & Greed Index) carry usable signal beyond price-derived features?
- RQ4. Do pre-registered promotion gates materially change which strategies reach live capital, compared with common practice (deploying the best backtest)?
- RQ5. Can a high-frequency technical-ensemble bot — intraday charts, multi-indicator scoring, Bayesian trade gating — achieve positive expectancy net of fees at retail scale, and does its live behaviour match a faithful backtest of its rules?
3 · Hypotheses
- H1. Time-series momentum with a long-term regime filter outperforms the market on risk-adjusted terms across regimes, but not on absolute terms in strong bull windows.
- H2. Volatility-targeted sizing improves compound growth and drawdown for identical signals (supported in Round 4: −62% → +229%, max drawdown halved).
- H3. An extreme-greed entry gate improves cold-start performance in mania regimes and never harms full-path performance (partially supported in Round 4).
- H4. Intraday ensemble signals contain gross edge that survives fees once gated selectively enough (rejected in Round 5: at every gate level tested, gross expectancy was ≈ 0 and net expectancy was below the fee, in bull and bear regimes alike).
4 · Methodology
- Agentic audition protocol. Each round, a fleet of AI research agents backtests candidate strategies in parallel (freqtrade engine, fee-inclusive), extracts structured metrics, and a deterministic script — not a model — applies the gate.
- Pre-registration. Promotion criteria are committed to code before results exist: profitability in and out of sample, beating the market in a supermajority of walk-forward windows, minimum trade counts, statistical significance (p < 0.10), and Monte Carlo loss-probability bounds.
- Walk-forward evaluation. Yearly and half-yearly windows spanning the 2018 collapse, 2019 recovery, COVID 2020, 2021 mania, 2022 cascade (Luna/FTX), the 2024 ETF era and the 2025–26 bear.
- Bootstrap Monte Carlo. 10,000 resamples of each finalist's realised trade sequence, yielding outcome distributions, loss probabilities and drawdown percentiles rather than point estimates.
- Phased promotion. Backtest → live simulation (weeks, on live prices) → live capital, with the account held in a withdrawal-disabled AI sub-account (OAuth-scoped) as a structural safety bound.
- Radical transparency. Every daily decision and its reasoning is published; losing results are reported with the same prominence as winning ones.
- Live-system forensics. Where a real trading system exists, its complete live trade ledger (every fill, fee and stop) is audited alongside a faithful re-implementation of its rules, so live behaviour and backtest behaviour can be compared directly — Round 5 audited a production bot's 708 live trades this way.
5 · Data
- Daily and intraday OHLCV for ten major crypto assets, Aug 2017 – present (Binance spot; Bybit derivatives for two-sided tests) — the full liquid history of the asset class. Crypto is 16 years old; no longer series exists.
- Crypto Fear & Greed Index, Feb 2018 – present (3,100+ daily readings) as the crowd-sentiment proxy.
- All macro/geopolitical shocks enter through prices; event-study extensions are future work.
6 · Preliminary findings (Rounds 1–4)
| # | Finding | Evidence |
|---|---|---|
| F1 | Fees and overtrading, not signal quality, destroy small accounts first. | 194-trade strategy burned ~27% of account in fees; 1,800+ trade tight-stop style lost 96%. |
| F2 | Short-selling failed in every configuration tested. | Three rounds; e.g. two-sided regime flip −89% vs long-only −7% on identical filters. |
| F3 | Position sizing dominates signal choice. | Identical entries/exits: −62% naive vs +229% vol-targeted, drawdown 97% → 51%. |
| F4 | Crowd sentiment helps exactly where theory predicts. | Extreme-greed gate turned the 2021 cold-start window from −83% to +69%; no full-path effect. |
| F5 | Nothing tested beats holding across full cycles; edges are not yet statistically significant. | Market +1,168% over 9y; best variant +229%, p = 0.47; promotion denied by pre-registered gate. |
| F6 | A live retail bot's fee bill can exceed its profit by an order of magnitude while looking "profitable" day to day. | Audited production bot: 708 real trades in one month, net +$0.80, fees $11.65 (14.6× net); long book +$5.00, short book −$4.20. |
| F7 | Trade frequency, not signal quality, is the dominant predictor of ruin; selectivity only slows the bleed. | Faithful port of the bot's rules: −96% to −100% in and out of sample (6,500–20,000 trades); at maximum gating still −57% on 12 trades/day. Gross edge ≈ 0 at every gate level, both regimes. |
7 · Planned work
- Round 6: machine-learning candidates (FreqAI) under strictly walk-forward training, same gates; and a daily-timeframe descendant of the Round 5 ensemble (the signal stack re-tested at the frequency where fees stop dominating).
- Live phase study: tracking error between simulation and live execution at minimum order sizes.
- Write-up: full paper covering protocol, results and the sociology of "profit in every trade" expectations; preprint first, then peer review.
8 · Ethics and disclosure
The study trades only the author's own, deliberately small capital in a withdrawal-disabled sub-account. It is not investment advice, offers no managed product, and solicits no funds. Published results include losses. Nothing in this study constitutes financial services under South African law.
DRAFT v0.2 · 2026-08-17 · Live experiment and daily decision journal: mngomezulu.africa/lab/agent-trading