Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ntaffzii/Skill-Agents --skill backtest-expertgit clone --depth 1 https://github.com/ntaffzii/Skill-AgentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ntaffzii/skill-agents/backtest-expert)<a href="https://agentmods.dev/skills/ntaffzii/skill-agents/backtest-expert"><img src="https://agentmods.dev/badge/skills/ntaffzii/skill-agents/backtest-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ntaffzii/skill-agents/backtest-expert"><img src="https://agentmods.dev/badge/skills/ntaffzii/skill-agents/backtest-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00143 | $0.01462 |
| Opus 5.5 | $0.00057 | $0.00585 |
| Sonnet 5.5 | $0.00029 | $0.00292 |
| Haiku 4.5 | $0.00014 | $0.00146 |
Grade A, and why
backtest-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backtest Expert
Overview
position-sizer and drawdown-circuit-breaker manage risk once you've already decided to trade a rule — neither tells you whether the rule itself actually works. This skill fills that gap: given historical price data and a set of buy/sell signals (either supplied directly or generated from a simple moving-average crossover), it simulates the trades a long-only, one-position-at-a-time strategy would have taken and reports the standard backtest metrics.
Not financial advice. A backtest describes how a rule performed on one specific historical sample. It does not predict future performance, and a rule tuned by trial-and-error against the same data it's being "backtested" on will look better than it is (overfitting/curve-fitting) — see Known limitations.
When to use
- ทดสอบกฎเทรดกับข้อมูลราคาย้อนหลัง ก่อนใช้เงินจริง
- Evaluating a simple moving-average crossover (or any buy/sell signal series you already have) against historical prices
- Comparing win rate, average return, max drawdown, and profit factor across a few rule variants on the same data
When NOT to use
- Sizing or risk-gating a specific upcoming trade — that's
position-sizer/drawdown-circuit-breaker, not this skill - The user wants live/real-time strategy execution — this is historical simulation only, it does not place or track live trades
- Short selling, multi-position, or portfolio-level backtesting — this engine is long-only and one-position-at-a-time; a strategy needing more than that needs a different tool
Core knowledge
Trade simulation: long-only, one position at a time. Enters on a "buy" signal only while flat; exits on a "sell" signal only while in a position. A "buy" while already in a position, or a "sell" while flat, is a no-op — it does not pyramid into a second position or close a position that doesn't exist.
Metrics reported:
| Metric | Meaning |
|---|---|
| Win rate | % of completed trades with a positive return |
| Average return | Mean return % per completed trade |
| Total return | Compounded return across all trades, starting from a normalized equity of 1.0 |
| Max drawdown | Largest peak-to-trough decline in the equity curve across the whole run — same peak/current formula as drawdown-circuit-breaker |
| Profit factor | Gross profit ÷ gross loss (sum of winning trades' returns over the absolute sum of losing trades' returns) — undefined (None) when there are no losing trades to divide by |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 67 lines · 143 tokens per session scan A 8852d1fad1db
backtest-expert is a skill published in the GitHub repository ntaffzii/Skill-Agents (4 stars, last pushed 15d ago), licensed MIT. It adds 143 tokens to every session and 1,462 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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