Cross Market Strategy

When to Use

When the user requests a backtest with codes from different markets, e.g. ["000001.SZ", "BTC-USDT"], ["TD.TO", "PNG.V"], 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(fill_method=None).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())