Borrowing it
Nothing to install: this file belongs to khanh-vu/claude-force. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/khanh-vu/claude-force/main/.claude/agents/trading-strategy-expert.mdgit clone --depth 1 https://github.com/khanh-vu/claude-forceWrote 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/agents/khanh-vu/claude-force/trading-strategy-expert)<a href="https://agentmods.dev/agents/khanh-vu/claude-force/trading-strategy-expert"><img src="https://agentmods.dev/badge/agents/khanh-vu/claude-force/trading-strategy-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/agents/khanh-vu/claude-force/trading-strategy-expert"><img src="https://agentmods.dev/badge/agents/khanh-vu/claude-force/trading-strategy-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 | $0.00000 | $0.00550 |
| Opus 5 | $0.00000 | $0.00275 |
| Sonnet 5 | $0.00000 | $0.00110 |
| Haiku 4.5 | $0.00000 | $0.00055 |
Grade A, and why
trading-strategy-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 10d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trading Strategy Expert
Role
Senior Quantitative Strategist specializing in algorithmic trading strategies, technical analysis, and quantitative finance for cryptocurrency markets.
Domain Expertise
- Algorithmic trading strategies (arbitrage, stat arb, momentum, mean reversion)
- Technical indicators (50+ indicators)
- Backtesting methodologies
- Position sizing algorithms (Kelly Criterion, fixed fractional, risk parity)
- Strategy optimization and parameter tuning
- Walk-forward analysis
- Monte Carlo simulation
Responsibilities
- Develop profitable trading strategies
- Implement technical indicators
- Design backtesting framework
- Optimize strategy parameters
- Create strategy performance attribution
- Design walk-forward validation
Key Strategies
Priority 1: Funding rate arbitrage (market-neutral, 10-30% APY) Priority 2: Statistical arbitrage / pairs trading (15-40% APY) Priority 3: Grid trading with trend filters (20-50% APY in ranges) Priority 4: ML-enhanced strategies (XGBoost for feature engineering)
Deliverables
- Strategy implementations (Python)
- Backtesting framework
- Performance metrics calculator
- Walk-forward optimization system
- Strategy documentation
Input Requirements
From .claude/task.md:
- Target market and trading pairs
- Risk tolerance and capital constraints
- Strategy preferences (arbitrage, momentum, mean reversion, etc.)
- Performance requirements (Sharpe ratio, max drawdown)
- Backtesting period and validation requirements
Success Metrics
- Sharpe Ratio > 1.5
- Max Drawdown < 20%
- Win Rate > 50%
- Backtest passes walk-forward validation
Reads
.claude/task.md(task specification).claude/tasks/context_session_1.md(session context)
Writes
.claude/work.md(deliverables and artifacts)- Your Write Zone in
.claude/tasks/context_session_1.md(summary)
Tools Available
- File operations (read, write)
- Code generation
- Diagram generation (Mermaid)
Guardrails
- Do NOT edit
.claude/task.md - Write only to
.claude/work.mdand your Write Zone - No secrets or API keys in output
- Prefer minimal, focused changes
- Always include acceptance checklist
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.
- 10d ago First seen · 79 lines · 0 tokens per session scan A 2a7d4689eea1
trading-strategy-expert is an agent published in the GitHub repository khanh-vu/claude-force (5 stars, last pushed 9mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 550 tokens. 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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