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 QuantJourneyOrg/quantjourney-bt --skill qj-strategy-ideasgit clone --depth 1 https://github.com/QuantJourneyOrg/quantjourney-btWrote 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/quantjourneyorg/quantjourney-bt/qj-strategy-ideas)<a href="https://agentmods.dev/skills/quantjourneyorg/quantjourney-bt/qj-strategy-ideas"><img src="https://agentmods.dev/badge/skills/quantjourneyorg/quantjourney-bt/qj-strategy-ideas/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/quantjourneyorg/quantjourney-bt/qj-strategy-ideas"><img src="https://agentmods.dev/badge/skills/quantjourneyorg/quantjourney-bt/qj-strategy-ideas.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 | $0.00000 | $0.00653 |
| Opus 5 | $0.00000 | $0.00327 |
| Sonnet 5 | $0.00000 | $0.00131 |
| Haiku 4.5 | $0.00000 | $0.00065 |
Grade A, and why
qj-strategy-ideas 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 12d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QuantJourney Strategy Ideas
Use this skill to turn a strategy idea into a runnable QuantJourney backtest.
First decision: weights or orders
- Weights (
execution_mode="weights") — portfolio thinking: factor portfolios, rotation, long/cash, long/short, risk overlays, scheduled rebalancing. Implement_compute_signalsand_compute_weights. - Orders (
execution_mode="orders") — execution thinking: stop-losses, limits, brackets, trailing stops, gaps. Implement_compute_orders.
Prototype in weight mode; switch to orders only when the fill is the point.
Map the idea to the nearest example
| Idea shape | Start from |
|---|---|
| Trend on a basket | W01 (SMA), W22 (MACD) |
| Mean reversion | W03 / W21 (Bollinger), O02 |
| Momentum rotation | W04, W18 |
| Long/short factor | W15 (momentum), W16 (reversal) |
| Pairs / market-neutral | W13 (ratio), W14 (hedge ratio) |
| Risk-scaled exposure | W17/W18 (vol target), W19/W20 (risk parity) |
| Realistic stops/brackets | O06, O09, O12, O14 |
| Intraday | W07–W09, O15–O16 |
| Validate / tune | WF01–WF05 |
Copy the closest file in strategies/, change the rule, keep the structure.
The weight-mode pattern
class MyStrategy(Backtester):
def _compute_signals(self) -> pd.DataFrame: # dates x instruments panel
feat = self.instruments_data.get_feature("SMA_50_close")
return (feat > self.instruments_data.get_feature("SMA_200_close")).astype(float)
def _compute_weights(self) -> pd.DataFrame:
active = self.signals == 1.0
return active.div(active.sum(axis=1), axis=0).fillna(0.0).clip(upper=0.25)
Rules
- Data arrives as a panel (dates × instruments) — ranking across the universe
on each date is one line of pandas (
.nlargest,.rank(axis=1)). - Signal on day t trades on day t+1 — the engine applies
shift(1); never hand-build look-ahead. - Features come from
get_feature(...): prices (adj_close,high), computed metrics (returns), orindicators_confignames (SMA_50_close,RSI_14_close). Multi-output indicators (MACD, Bollinger) are computed inline fromadj_close. - Long/short weights are allowed (sum ≈ 0 for market-neutral); short borrow/financing is not modeled — say so in the docstring.
- Keep the universe small enough to read the report; use widely available symbols.
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.
- 12d ago First seen · 58 lines · 0 tokens per session scan A 043a042fb489
qj-strategy-ideas is a skill published in the GitHub repository QuantJourneyOrg/quantjourney-bt (53 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 653 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 2026-08-30.
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