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 agiprolabs/claude-trading-skills --skill backtradergit clone --depth 1 https://github.com/agiprolabs/claude-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/agiprolabs/claude-trading-skills/backtrader)<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/backtrader"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/backtrader/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/agiprolabs/claude-trading-skills/backtrader"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/backtrader.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00025 | $0.02535 |
| Opus 5 | $0.00013 | $0.01267 |
| Sonnet 5 | $0.00005 | $0.00507 |
| Haiku 4.5 | $0.00003 | $0.00253 |
Grade A, and why
backtrader 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 11d 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 — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backtrader
Backtrader is a Python event-driven backtesting framework that processes data bar-by-bar, simulating realistic execution with a built-in broker, order management, and position tracking. Unlike vectorized frameworks (vectorbt, pandas), backtrader walks through history one bar at a time, firing callbacks that let you implement complex order logic that depends on previous fills, partial executions, and conditional brackets.
Event-Driven vs Vectorized
| Aspect | Backtrader (event-driven) | vectorbt (vectorized) |
|---|---|---|
| Execution model | Bar-by-bar callbacks | Whole-array operations |
| Speed | Slower (Python loop) | Fast (NumPy/Numba) |
| Order types | Market, limit, stop, stop-limit, bracket, OCO | Market only (native) |
| Realism | Built-in broker with commission, slippage, margin | Manual slippage modeling |
| Multi-timeframe | Native resampledata | Manual alignment |
| Best for | Complex strategies, bracket orders, portfolio | Fast parameter sweeps, simple signals |
Use backtrader when you need:
- Bracket orders (entry + stop loss + take profit as a unit)
- Stop-limit or trailing stop orders
- Order-dependent logic (scale in after first fill, cancel if not filled in N bars)
- Multi-timeframe strategies (daily signals, hourly execution)
- Realistic commission and slippage modeling
Use vectorbt when you need:
- Fast parameter optimization over thousands of combinations
- Simple long/short signals without complex order management
- Quick prototyping and statistical analysis of results
Core Concepts
Backtrader has five core objects that interact through an event loop:
1. Cerebro (the engine)
The central orchestrator. You add strategies, data feeds, analyzers, and sizers to Cerebro, then call run().
import backtrader as bt
cerebro = bt.Cerebro()
cerebro.addstrategy(MyStrategy, fast_period=10, slow_period=30)
cerebro.adddata(data_feed)
cerebro.broker.setcash(100_000)
cerebro.broker.setcommission(commission=0.003) # 0.3%
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="sharpe")
cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
cerebro.run()
What ships with it
4 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.
- 11d ago First seen · 349 lines · 25 tokens per session scan A e29655d92897
backtrader is a skill published in the GitHub repository agiprolabs/claude-trading-skills (354 stars, last pushed 8d ago), licensed MIT. It adds 25 tokens to every session and 2,535 once invoked, about $0.0001 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.
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