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 skloxo/TideTrading --skill cross-market-strategygit clone --depth 1 https://github.com/skloxo/TideTradingWrote 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/skloxo/tidetrading/cross-market-strategy)<a href="https://agentmods.dev/skills/skloxo/tidetrading/cross-market-strategy"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/cross-market-strategy/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/skloxo/tidetrading/cross-market-strategy"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/cross-market-strategy.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 | $0.00027 | $0.01064 |
| Opus 5 | $0.00014 | $0.00532 |
| Sonnet 5 | $0.00005 | $0.00213 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
cross-market-strategy 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.
This is a copy
97% identical to cross-market-strategy — 5 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
When the user requests a backtest with codes from different markets — e.g. ["000001.SZ", "BTC-USDT"] or ["AAPL.US", "EUR/USD", "600519.SH"].
The CompositeEngine handles calendar alignment, shared capital, and market rules automatically. The strategy only needs to output per-symbol signals.
Key Concepts
1. Market Classification in generate()
Group symbols by market type and apply market-specific indicator parameters:
def generate(self, data_map):
groups = {}
for code, df in data_map.items():
market = self._detect_market(code)
groups.setdefault(market, {})[code] = df
signals = {}
for market, market_data in groups.items():
params = MARKET_PARAMS[market]
for code, df in market_data.items():
signals[code] = self._market_signal(df, params)
return signals
2. Per-Market Parameter Tables
Different markets have very different dynamics. Using the same parameters everywhere produces poor results.
| Parameter | A-Share | Crypto | US Equity | Forex |
|---|---|---|---|---|
| MA fast | 5 | 7 | 10 | 10 |
| MA slow | 20 | 25 | 50 | 30 |
| RSI period | 14 | 10 | 14 | 14 |
| Vol lookback | 20 | 14 | 20 | 20 |
| Typical daily vol | 1-2% | 3-8% | 1-2% | 0.3-0.8% |
3. Volatility-Adjusted Weights (Critical)
BTC daily vol ~ 5%, A-share daily vol ~ 1.5%. Without vol-adjustment, crypto eats the entire risk budget.
def _vol_adjust(self, signals, data_map):
vols = {}
for code, df in data_map.items():
ret = df["close"].pct_change().dropna()
vols[code] = ret.rolling(20).std().iloc[-1] if len(ret) > 20 else ret.std()
inv_vols = {c: 1.0 / (v + 1e-10) for c, v in vols.items()}
total_inv = sum(inv_vols.values())
adjusted = {}
for code, sig in signals.items():
weight = inv_vols[code] / total_inv * len(signals)
adjusted[code] = (sig * weight).clip(-1.0, 1.0)
return adjusted
What ships with it
1 file 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 · 114 lines · 27 tokens per session scan A 128e06a72e86
cross-market-strategy is a skill published in the GitHub repository skloxo/TideTrading (10 stars, last pushed 4d ago), licensed MIT. It adds 27 tokens to every session and 1,064 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to cross-market-strategy, differing in 5 lines, and is treated as a copy.
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