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… |
