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: ```python 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. ```python 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()) 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 ``` ### 4. Cross-Market Signal Patterns 1. **Momentum spillover**: BTC 7-day momentum as overlay for A-share tech sectors 2. **Risk-on/Risk-off**: USD/CNH rate + VIX proxy to reduce equity exposure 3. **Hedging**: Long A-shares + short crypto delta as tail hedge 4. **Correlation regime**: When rolling correlation > 0.6, reduce to single-market exposure; when < 0.2, maximize diversification ### 5. What the Engine Handles (Don't Worry About) - **Trading calendar alignment**: signals are shifted on each symbol's own calendar, then ffill'd to unified dates - **Market rules**: T+1 for A-shares, funding fees for crypto, swap for forex — all per-symbol - **Capital allocation**: shared pool, strategy just sets target weights via signals - **Commission/slippage**: dispatched to correct sub-engine per symbol ## config.json for Cross-Market ```json { "source": "auto", "codes": ["000001.SZ", "BTC-USDT"], "start_date": "2024-01-01", "end_date": "2025-03-31", "interval": "1D", "initial_cash": 1000000, "engine": "daily" } ``` - `source` **must** be `"auto"` for cross-market (routes each symbol to its loader) - `extra_fields` should be `null` (not all markets support fundamentals) - `leverage` defaults to 1.0 (CompositeEngine inherits from config) ## Market Detection Heuristics | Pattern | Market | |---------|--------| | `000001.SZ`, `600519.SH` | A-share | | `AAPL.US` | US equity | | `700.HK` | HK equity | | `TD.TO`, `PNG.V` | Canada equity (TSX / TSXV) | | `BTC-USDT` | Crypto | | `IF2406.CFFEX` | China futures | | `ESZ4` | Global futures | | `EUR/USD` | Forex | ## Supporting Files - [example_signal_engine.py](example_signal_engine.py) — complete cross-market strategy example