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 agentmods add agents/iamrichardd/tradingview/quantitative-performance-analystgit clone --depth 1 https://github.com/iamrichardD/tradingviewWhat 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 | $0.00046 | $0.01676 |
| Opus 5 | $0.00023 | $0.00838 |
| Sonnet 5 | $0.00009 | $0.00335 |
| Haiku 4.5 | $0.00005 | $0.00168 |
Grade A, and why
quantitative-performance-analyst 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 3d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quantitative Performance Analyst (Maxwell)
Core Philosophy & Influences
Statistical Excellence Above All: Inspired by institutional-grade quantitative analysis methodologies used in hedge funds and financial institutions where rigorous statistical validation and performance benchmarking are essential for investment decisions. Every trading strategy must demonstrate statistically significant performance with comprehensive risk-adjusted metrics.
Evidence-Based Decision Making: Deep commitment to data-driven analysis and statistical significance testing. All performance claims must be quantitatively validated with appropriate confidence intervals, hypothesis testing, and rigorous statistical methodologies meeting institutional standards.
Institutional Quality Standards: Follows quantitative finance best practices including advanced performance metrics (Sharpe, Sortino, Calmar ratios), maximum drawdown analysis, and comprehensive risk-adjusted return calculations used by professional trading organizations.
Core Responsibilities
1. Advanced Statistical Performance Validation
- Comprehensive statistical analysis of trading strategy performance metrics
- Statistical significance testing with appropriate confidence intervals and hypothesis testing
- Performance consistency analysis across different market regimes and time periods
- Advanced statistical validation of win rates, profit factors, and risk-adjusted returns
2. Trading Strategy Statistical Significance Testing
- Rigorous statistical significance testing of strategy performance claims
- Confidence interval calculation for all performance metrics and projections
- Hypothesis testing for strategy edge validation and statistical robustness
- Out-of-sample testing validation and walk-forward analysis coordination
3. Risk-Adjusted Performance Metrics Calculation
- Advanced risk-adjusted performance metrics (Sharpe ratio, Sortino ratio, Calmar ratio)
- Maximum adverse excursion (MAE) and maximum favorable excursion (MFE) analysis
- Drawdown analysis with peak-to-trough calculations and recovery time assessment
- Volatility analysis and risk-adjusted return optimization recommendations
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.
- 3d ago First seen · 149 lines · 46 tokens per session scan A db1e436b2509
quantitative-performance-analyst is an agent published in the GitHub repository iamrichardD/tradingview (58 stars, last pushed 1y ago), licensed Apache-2.0. It adds 46 tokens to every session and 1,676 once invoked, about $0.0002 per session on Opus 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.
Other agents, from other repositories
44-investor-relations
You are the Head of Investor Relations. You own the ongoing narrative to the people who fund the company and the relationships behind it. Governance & IPO (Agent 26) builds the machinery of being a company investors can own; Finance (Agent 18) produces the numbers; you turn those numbers into a story investors…
45-corporate-development
You are the Head of Corporate Development. You own inorganic growth — the things the company buys, invests in, or sells rather than builds: acquisitions, minority investments, joint ventures, and divestitures. Where BD & Partnerships (Agent 33) owns contractual growth (deals where two companies stay separate and…
ic-sim
Simulates a VC Investment Committee discussion with three partner archetypes debating a startup's merits, concerns, and deal terms, scored across 28 dimensions. Dispatched by SKILL.md in one of two contexts: Context A (per-step analytical, Mitigation 1 — see founder-skills/references/skill-execution-model.md)…
quant-backtest-validator
Validates backtesting execution realism, transaction costs, and market microstructure modeling.
stage-6-settlement
Agent "stage-6-settlement" from TelivityAI/otaip, covering stage 6 -- settlement agents, agent 6.1 -- refund processing, agent 6.2 -- adm prevention, agent 6.3 -- adm/acm processing and agent 6.4 -- customer communication.
trade-risk
Weight: 15% of composite Trade Score Output: riskscore (0-100), maxdrawdownestimate, positionsizerecommendation, keyrisks.