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 kayzaa/k.i.t.-bot --skill quant-enginegit clone --depth 1 https://github.com/kayzaa/k.i.t.-botWrote 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/kayzaa/k.i.t.-bot/quant-engine)<a href="https://agentmods.dev/skills/kayzaa/k.i.t.-bot/quant-engine"><img src="https://agentmods.dev/badge/skills/kayzaa/k.i.t.-bot/quant-engine/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/kayzaa/k.i.t.-bot/quant-engine"><img src="https://agentmods.dev/badge/skills/kayzaa/k.i.t.-bot/quant-engine.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 | $0.00905 |
| Opus 5 | $0.00000 | $0.00452 |
| Sonnet 5 | $0.00000 | $0.00181 |
| Haiku 4.5 | $0.00000 | $0.00090 |
Grade A, and why
quant-engine 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 8d 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.
This is a copy
100% identical to quant-engine — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
📈 Quant Engine
K.I.T.'s Quantitative Trading Brain - Wall Street algorithms for everyone!
Features
📊 Statistical Arbitrage
- Pairs trading with cointegration
- Mean reversion on spreads
- Dynamic hedge ratios
- Z-score based entry/exit
🚀 Momentum Strategies
- Cross-sectional momentum
- Time-series momentum
- Momentum factor portfolios
- Breakout detection
📉 Mean Reversion
- Bollinger Band strategies
- RSI extreme detection
- VWAP reversion
- Overnight gap strategies
🎯 Factor Models
- Multi-factor alpha models
- Risk factor decomposition
- Factor rotation strategies
- Custom factor construction
🔬 Backtesting
- Walk-forward analysis
- Monte Carlo simulation
- Transaction cost modeling
- Slippage estimation
Usage
from quant_engine import QuantEngine
engine = QuantEngine()
# Statistical arbitrage
pairs = await engine.find_cointegrated_pairs(
symbols=["BTC", "ETH", "SOL", "AVAX"],
lookback=90 # days
)
for pair in pairs:
print(f"{pair.asset1}/{pair.asset2}")
print(f" Cointegration: {pair.coint_pvalue:.4f}")
print(f" Hedge ratio: {pair.hedge_ratio:.4f}")
print(f" Current Z-score: {pair.zscore:.2f}")
# Get trading signal
signal = await engine.get_stat_arb_signal(
pair=pairs[0],
entry_zscore=2.0,
exit_zscore=0.5
)
# Momentum strategy
momentum = await engine.momentum_scan(
symbols=["BTC", "ETH", "SOL", "AVAX", "DOT"],
lookback=20 # days
)
print(f"Top momentum: {momentum[0].symbol} ({momentum[0].return_pct:.1%})")
# Backtest strategy
results = await engine.backtest(
strategy="mean_reversion",
symbol="BTC/USDT",
start_date="2023-01-01",
end_date="2024-01-01"
)
print(f"Sharpe Ratio: {results.sharpe_ratio:.2f}")
print(f"Max Drawdown: {results.max_drawdown:.1%}")
print(f"Win Rate: {results.win_rate:.1%}")
Strategies
| Strategy | Type | Avg Return | Sharpe | Win Rate |
|---|---|---|---|---|
| Stat Arb | Market Neutral | 15-25% | 1.5-2.5 | 55-60% |
| Momentum | Trend | 20-40% | 1.0-2.0 | 45-55% |
| Mean Reversion | Counter-trend | 10-20% | 1.2-1.8 | 60-70% |
| Factor | Multi-factor | 15-30% | 1.5-2.5 | 50-60% |
What ships with it
2 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.
- 8d ago First seen · 135 lines · 0 tokens per session scan A d498826aca49
quant-engine is a skill published in the GitHub repository kayzaa/k.i.t.-bot (5 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 905 tokens. A static security scan graded it A with 0 findings. It is 100% identical to quant-engine, differing in 0 lines, and is treated as a copy.
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