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 ajeeshworkspace/indian-trading-skills --skill backtest-expertgit clone --depth 1 https://github.com/ajeeshworkspace/indian-trading-skillsWrote 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/ajeeshworkspace/indian-trading-skills/backtest-expert)<a href="https://agentmods.dev/skills/ajeeshworkspace/indian-trading-skills/backtest-expert"><img src="https://agentmods.dev/badge/skills/ajeeshworkspace/indian-trading-skills/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/ajeeshworkspace/indian-trading-skills/backtest-expert"><img src="https://agentmods.dev/badge/skills/ajeeshworkspace/indian-trading-skills/backtest-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00041 | $0.03031 |
| Opus 5.5 | $0.00016 | $0.01212 |
| Sonnet 5.5 | $0.00008 | $0.00606 |
| Haiku 4.5 | $0.00004 | $0.00303 |
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 — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backtest Expert — Indian Market Strategy Validation
Core Philosophy
"Find strategies that break the least, not profit the most."
A strategy that survives stress testing across multiple market regimes, transaction cost assumptions, and parameter perturbations is far more valuable than one that shows spectacular returns on a single optimized parameter set. Overfitting is the silent killer of trading accounts.
6-Step Backtesting Workflow
Step 1: State the Hypothesis (1 Sentence Edge)
Before writing a single line of code, articulate why the strategy should work in one clear sentence.
Good hypotheses:
- "Stocks that gap up >3% on above-average volume after consolidation tend to continue higher for 2-5 days on NSE."
- "Nifty 50 stocks that revert to their 20-day mean after RSI drops below 30 produce positive expectancy within 5 trading sessions."
- "Selling strangles on Bank Nifty on Wednesday expiry with delta <0.15 captures time decay faster than gamma risk materializes."
Bad hypotheses:
- "This indicator combination looks good on the chart." (no edge articulated)
- "I saw someone on Twitter making money with this." (no reasoning)
Ask yourself:
- What behavioral or structural edge am I exploiting?
- Why would this edge persist? (Structural > Behavioral > Statistical)
- Who is on the other side of this trade, and why are they losing?
Step 2: Codify Rules (No Ambiguity)
Every rule must be binary — a computer must be able to execute it without interpretation.
Rule Categories
| Category | What to Define | Example |
|---|---|---|
| Universe | Which stocks/instruments | Nifty 200 constituents, F&O stocks only, market cap >5000 Cr |
| Entry | Exact trigger conditions | Close > 20 EMA AND RSI(14) crosses above 40 AND volume > 1.5x 20-day avg |
| Exit — Target | Profit-taking rule | Close 3% above entry OR trailing stop of 1.5 ATR |
| Exit — Stop | Loss-cutting rule | Close below entry-day low OR 2% fixed stop |
| Exit — Time | Maximum holding period | Exit after 10 trading sessions if neither target nor stop hit |
| Position Sizing | How much capital per trade | 5% of equity per position, max 10 concurrent positions |
| Filters | When NOT to trade | Skip if stock is in F&O ban period, skip 2 days around results |
What ships with it
3 files 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 · 266 lines · 41 tokens per session scan A 4127b525d0e6
backtest-expert is a skill published in the GitHub repository ajeeshworkspace/indian-trading-skills (75 stars, last pushed 2mo ago), licensed MIT. It adds 41 tokens to every session and 3,031 once invoked, about $0.0002 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 2026-08-30.
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