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 aAAaqwq/AGI-Super-Team --skill backtesting-trading-strategiesgit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/backtesting-trading-strategies)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/backtesting-trading-strategies"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/backtesting-trading-strategies/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/aaaaqwq/agi-super-team/backtesting-trading-strategies"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/backtesting-trading-strategies.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 | $0.00094 | $0.01539 |
| Opus 5 | $0.00047 | $0.00770 |
| Sonnet 5 | $0.00019 | $0.00308 |
| Haiku 4.5 | $0.00009 | $0.00154 |
Grade A, and why
backtesting-trading-strategies 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 6d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backtesting Trading Strategies
Overview
Validate trading strategies against historical data before risking real capital. This skill provides a complete backtesting framework with 8 built-in strategies, comprehensive performance metrics, and parameter optimization.
Key Features:
- 8 pre-built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)
- Full performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown)
- Parameter grid search optimization
- Equity curve visualization
- Trade-by-trade analysis
Prerequisites
Install required dependencies:
pip install pandas numpy yfinance matplotlib
Optional for advanced features:
pip install ta-lib scipy scikit-learn
Instructions
Step 1: Fetch Historical Data
python {baseDir}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d
Data is cached to {baseDir}/data/{symbol}_{interval}.csv for reuse.
Step 2: Run Backtest
Basic backtest with default parameters:
python {baseDir}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y
Advanced backtest with custom parameters:
# Example: backtest with specific date range
python {baseDir}/scripts/backtest.py \
--strategy rsi_reversal \
--symbol ETH-USD \
--period 1y \
--capital 10000 \
--params '{"period": 14, "overbought": 70, "oversold": 30}'
Step 3: Analyze Results
Results are saved to {baseDir}/reports/ including:
*_summary.txt- Performance metrics*_trades.csv- Trade log*_equity.csv- Equity curve data*_chart.png- Visual equity curve
Step 4: Optimize Parameters
Find optimal parameters via grid search:
python {baseDir}/scripts/optimize.py \
--strategy sma_crossover \
--symbol BTC-USD \
--period 1y \
--param-grid '{"fast_period": [10, 20, 30], "slow_period": [50, 100, 200]}'
Output
Performance Metrics
| Metric | Description |
|---|---|
| Total Return | Overall percentage gain/loss |
| CAGR | Compound annual growth rate |
| Sharpe Ratio | Risk-adjusted return (target: >1.5) |
| Sortino Ratio | Downside risk-adjusted return |
| Calmar Ratio | Return divided by max drawdown |
What ships with it
11 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.
- config/settings.yaml 1.4 KB
- data/BTC_USD_1d.csv 30 KB
- data/ETH_USD_1d.csv 35 KB
- references/errors.md 3.7 KB
- references/examples.md 4.8 KB
- references/implementation.md 4.5 KB
- scripts/backtest.py 11 KB runs code
- scripts/fetch_data.py 4.7 KB runs code
- scripts/metrics.py 12 KB runs code
- scripts/optimize.py 6.2 KB runs code
- scripts/strategies.py 11 KB runs code
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.
- 6d ago First seen · 201 lines · 94 tokens per session scan A 310cc7635df0
backtesting-trading-strategies is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed yesterday), licensed MIT. It adds 94 tokens to every session and 1,539 once invoked, about $0.0005 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 2026-09-05.
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