cross-market-strategy
Cross-Market Strategy generates trading signals for portfolios spanning different asset classes and exchanges. It automatically detects market type, applies tailored indicator parameters, and adjusts position sizes by volatility so crypto or forex positions don't dominate your risk budget. The underlying engine handles calendar shifts, market rules, and capital allocation per symbol.
Cross-Market Strategy lets you backtest portfolios mixing stocks, crypto, and forex with market-specific parameters and volatility-adjusted position sizing.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-27
Cross-Market Strategy lets you backtest portfolios mixing stocks, crypto, and forex with market-specific parameters and volatility-adjusted position sizing. Cross-Market Strategy generates trading signals for portfolios spanning different asset classes and exchanges. It automatically detects market type, applies tailored indicator parameters, and adjusts position sizes by volatility so crypto or forex positions don't dominate your risk budget. The underlying engine handles calendar shifts, market rules, and capital allocation per symbol.
Use it when
- Cross-Market Strategy adjusts portfolio weights to account for volatility differences across asset classes.
- Cross-Market Strategy detects market type and applies market-specific indicator parameters automatically.
Verify before relying
Read SKILL.md below before installing (2 files). Open directory: indexed for reading, not audited.
Install
HKUDS/Vibe-Trading/cross-market-strategy · repository language: Python
Open directory. Skills are indexed for reading, not audited. Review a skill's body before installing it.
Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
Can cross-market-strategy backtest multiple markets at once?
Yes. Cross-Market Strategy generates trading signals for portfolios spanning different asset classes and exchanges simultaneously. It handles calendar shifts, market rules, and capital allocation per symbol, allowing you to backtest stocks, crypto, forex, and bonds in a single unified framework without manual synchronization.
How does cross-market-strategy adjust for volatility differences?
Cross-Market Strategy adjusts portfolio weights to account for volatility differences across asset classes, so crypto or forex positions don't dominate your risk budget. It automatically scales position sizes based on each market's volatility profile, keeping your overall portfolio risk balanced even when combining low-volatility bonds with high-volatility crypto.
What market-specific features does cross-market-strategy support?
Cross-Market Strategy detects market type and applies market-specific indicator parameters automatically. Rather than using one-size-fits-all settings, it tailors technical indicators and signal thresholds to stocks, crypto, forex, or bonds individually, then combines those signals into a coherent portfolio strategy.
Can cross-market-strategy detect risk-on/risk-off regimes?
Yes. Cross-Market Strategy implements cross-market signal patterns including risk-on/off detection and momentum spillover across asset classes. This lets you model how market sentiment shifts propagate—for example, when equity weakness triggers crypto selloffs—and adjust your portfolio positioning accordingly.
What license does cross-market-strategy use?
Cross-Market Strategy is released under the MIT license, allowing free use, modification, and distribution in both open-source and commercial projects with minimal restrictions.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
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.
|
(truncated - see the full file via the links below)
File tree — 2 files
agent/src/skills/cross-market-strategy/SKILL.md
agent/src/skills/cross-market-strategy/example_signal_engine.py
Let your AI agent find skills like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 56,283 agent skills by what they can do, searchable in plain language.
wish › “Build a backtest strategy combining assets from different markets (stocks, crypto, forex)”
Give your agent the search over MCP, or paste the wish link into any chat. No install? Search from any chat →
Related skills
strategy-generate lets you design trading strategies by defining entry/exit logic, then automatically backtests them across stocks, crypto, and other instruments. Write your signal engine in Python, configure your parameters, and the skill handles data loading and performance analysis—no boilerplate required.
stock-data provides 47 CLI tools for analyzing individual stocks and cryptocurrencies across multiple markets. Query real-time prices, technical indicators, chip distribution, and fund flow for A-shares, Hong Kong stocks, US equities, and digital assets with automatic market detection.
Correlation Analysis helps traders and portfolio managers understand how crypto assets move in tandem across different market conditions. It covers Pearson, Spearman, and Kendall correlation methods, rolling correlation windows for regime detection, hierarchical clustering to group similar assets, and tail dependence analysis to reveal crash co-movement that standard metrics miss. Use it to build diversified portfolios, generate pairs trading signals, and protect against concentration risk.
Correlation-Analysis discovers asset pairs with strong co-movement for pairs trading and hedging through multiple analytical modes: co-movement scanning, deep return correlation studies, sector clustering, and cointegration testing. It computes Pearson, Spearman, and Kendall correlations, rolling dynamics, and spread Z-scores to identify statistically significant relationships.
Monitor real-time capital flows between mainland China and Hong Kong markets through Stock Connect channels. Northbound flows reveal foreign institutional appetite for A-shares, while Southbound flows show mainland investor positioning in HK assets. Includes sector allocation tracking, AH premium analysis, and composite scoring to identify cross-border risk sentiment.
Multi-factor ranks stocks by computing and standardizing multiple factors—momentum, reversal, volatility, and volume—then combines them into a composite score to select top performers for equal-weight portfolios. Built-in support for value metrics like PE and ROE on supported markets. The newer ZooSignalEngine integrates 450+ pre-built alphas from the registry for flexible long-only, short-only, or long-short strategies.
More skills Stock 持仓组合监控 (unlicensed) · options-strategy (MIT)