backtest_lottery
Evaluate prediction algorithm accuracy for lottery games by testing historical data with configurable training windows and step sizes.
Instructions
回测预测算法，评估预测准确性
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| lottery_type | Yes | 彩票类型 | |
| window_size | No | 窗口大小（训练数据期数） | |
| step | No | 步长（每次移动的期数） |
Implementation Reference
- src/swlc_mcp/server.py:1538-1578 (handler)MCP tool handler for 'backtest_lottery': fetches historical lottery data, converts to dict format, calls BacktestEngine.run_backtest, and formats the summary results for output.
elif name == "backtest_lottery": lottery_type = arguments.get("lottery_type") window_size = arguments.get("window_size", 100) step = arguments.get("step", 50) try: # 获取历史数据用于回测 historical_data = await lottery_service.get_historical_data(lottery_type, window_size * 2) if len(historical_data) < window_size: return [types.TextContent(type="text", text=f"历史数据不足，需要至少{window_size}期数据")] # 转换为字典格式 history_dict = [{ 'period': r.period, 'numbers': r.numbers, 'special_numbers': r.special_numbers, 'draw_date': r.draw_date } for r in historical_data] # 执行回测 backtest_result = await lottery_service.backtest_engine.run_backtest( lottery_type, history_dict, window_size=window_size, step=step ) text_lines = [ f"{lottery_type}回测结果：\n", f"总回测期数：{backtest_result.total_periods}期", f"平均准确率：{backtest_result.average_accuracy:.2%}", f"最佳策略：{backtest_result.best_strategy}", "\n各策略表现：" ] for strategy, performance in backtest_result.strategy_performance.items(): text_lines.append(f"- {strategy}: 准确率 {performance:.2%}") return [types.TextContent(type="text", text="\n".join(text_lines))] except Exception as e: logger.error(f"回测失败: {e}") return [types.TextContent(type="text", text=f"回测失败：{str(e)}")] - src/swlc_mcp/server.py:1251-1278 (schema)Input schema for the backtest_lottery tool, defining parameters: lottery_type (required, enum), window_size (default 100), step (default 50).
name="backtest_lottery", description="回测预测算法，评估预测准确性", inputSchema={ "type": "object", "properties": { "lottery_type": { "type": "string", "enum": ["双色球", "福彩3D", "七乐彩", "快乐8"], "description": "彩票类型" }, "window_size": { "type": "integer", "minimum": 50, "maximum": 500, "default": 100, "description": "窗口大小（训练数据期数）" }, "step": { "type": "integer", "minimum": 10, "maximum": 100, "default": 50, "description": "步长（每次移动的期数）" } }, "required": ["lottery_type"] } ) - src/swlc_mcp/backtest.py:42-118 (handler)Core implementation of backtesting in BacktestEngine.run_backtest: uses sliding window on historical data, generates predictions via PredictionManager for each window, calculates accuracy, aggregates results into BacktestSummary.
async def run_backtest(self, lottery_type: str, historical_data: List[Dict], window_size: int = 100, step: int = 50) -> BacktestSummary: """运行回测""" try: if len(historical_data) < window_size: raise ValueError(f"历史数据不足，需要至少{window_size}期数据") results = [] chart_data = { 'periods': [], 'accuracy': [], 'precision': [] } # 滑动窗口回测 for start_idx in range(0, len(historical_data) - window_size, step): end_idx = start_idx + window_size test_period = start_idx + window_size if test_period >= len(historical_data): break # 训练数据（历史数据） train_data = historical_data[start_idx:end_idx] # 测试数据（目标期） test_data = historical_data[test_period] # 为每个方法生成预测 method_results = {} for method in self.methods: try: predictions = await self.prediction_manager.predict( lottery_type, train_data, method=method, count=5 ) # 计算预测准确性 accuracy = self._calculate_prediction_accuracy( predictions, test_data, lottery_type ) method_results[method] = { 'predictions': predictions, 'accuracy': accuracy } except Exception as e: logger.error(f"方法{method}预测失败: {e}") method_results[method] = { 'predictions': [], 'accuracy': 0.0 } # 记录结果 period_result = BacktestResult( period=test_data.get('period', f'Period_{test_period}'), actual_numbers=test_data.get('numbers', []), actual_special_numbers=test_data.get('special_numbers'), predictions=method_results, accuracy=max([r['accuracy'] for r in method_results.values()]), method=max(method_results.items(), key=lambda x: x[1]['accuracy'])[0] ) results.append(period_result) # 更新图表数据 chart_data['periods'].append(period_result.period) chart_data['accuracy'].append(period_result.accuracy) chart_data['precision'].append(self._calculate_precision(method_results)) # 生成摘要 summary = self._generate_summary(results, chart_data) return summary except Exception as e: logger.error(f"回测失败: {e}") raise - src/swlc_mcp/server.py:1028-1280 (registration)Registration of the backtest_lottery tool in the MCP server's list_tools() method, which returns the list of available tools including this one.
@server.list_tools() async def list_tools() -> List[types.Tool]: """列出所有可用工具""" return [ types.Tool( name="get_latest_ssq", description="获取双色球最新开奖结果", inputSchema={ "type": "object", "properties": {}, "required": [] } ), types.Tool( name="get_latest_3d", description="获取福彩3D最新开奖结果", inputSchema={ "type": "object", "properties": {}, "required": [] } ), types.Tool( name="get_latest_qlc", description="获取七乐彩最新开奖结果", inputSchema={ "type": "object", "properties": {}, "required": [] } ), types.Tool( name="get_latest_kl8", description="获取快乐8最新开奖结果", inputSchema={ "type": "object", "properties": {}, "required": [] } ), types.Tool( name="get_historical_data", description="获取指定彩票类型的历史开奖数据", inputSchema={ "type": "object", "properties": { "lottery_type": { "type": "string", "enum": ["双色球", "福彩3D", "七乐彩", "快乐8"], "description": "彩票类型" }, "periods": { "type": "integer", "minimum": 1, "maximum": 1000, "default": 10, "description": "获取期数" } }, "required": ["lottery_type"] } ), types.Tool( name="analyze_numbers", description="分析彩票号码统计信息，包括热号、冷号等", inputSchema={ "type": "object", "properties": { "lottery_type": { "type": "string", "enum": ["双色球", "福彩3D", "七乐彩", "快乐8"], "description": "彩票类型" }, "periods": { "type": "integer", "minimum": 5, "maximum": 1000, "default": 30, "description": "分析期数" } }, "required": ["lottery_type"] } ), types.Tool( name="analyze_seq_numbers", description="分析号码连续出现概率（滑窗），返回理论值与实测值", inputSchema={ "type": "object", "properties": { "lottery_type": { "type": "string", "enum": ["双色球", "福彩3D", "七乐彩", "快乐8"], "description": "彩票类型" }, "periods": { "type": "integer", "minimum": 5, "maximum": 1000, "default": 100, "description": "分析期数" }, "sequence_length": { "type": "integer", "minimum": 1, "maximum": 10, "default": 2, "description": "连续期数" } }, "required": ["lottery_type"] } ), types.Tool( name="generate_random_numbers", description="生成随机彩票号码推荐", inputSchema={ "type": "object", "properties": { "lottery_type": { "type": "string", "enum": ["双色球", "福彩3D", "七乐彩", "快乐8"], "description": "彩票类型" }, "count": { "type": "integer", "minimum": 1, "maximum": 10, "default": 1, "description": "生成组数" } }, "required": ["lottery_type"] } ), types.Tool( name="sync_lottery_data", description="同步指定彩票类型的最新数据到本地数据库", inputSchema={ "type": "object", "properties": { "lottery_type": { "type": "string", "enum": ["双色球", "福彩3D", "七乐彩", "快乐8"], "description": "彩票类型" }, "periods": { "type": "integer", "minimum": 1, "maximum": 50, "default": 10, "description": "同步期数" } }, "required": ["lottery_type"] } ), types.Tool( name="force_sync_data", description="强制同步指定彩票类型的最新数据到本地数据库", inputSchema={ "type": "object", "properties": { "lottery_type": { "type": "string", "enum": ["双色球", "福彩3D", "七乐彩", "快乐8"], "description": "彩票类型" }, "periods": { "type": "integer", "minimum": 1, "maximum": 1000, "default": 20, "description": "同步期数" } }, "required": ["lottery_type"] } ), types.Tool( name="get_database_info", description="获取本地数据库统计信息", inputSchema={ "type": "object", "properties": {}, "required": [] } ), types.Tool( name="predict_lottery", description="预测彩票号码，基于历史数据生成预测结果", inputSchema={ "type": "object", "properties": { "lottery_type": { "type": "string", "enum": ["双色球", "福彩3D", "七乐彩", "快乐8"], "description": "彩票类型" }, "method": { "type": "string", "enum": ["rule"], "default": "rule", "description": "预测方法" }, "count": { "type": "integer", "minimum": 1, "maximum": 20, "default": 5, "description": "预测组数" }, "strategy": { "type": "string", "enum": ["all", "balanced", "cold_recovery", "hot_focus", "interval_balance", "contrarian"], "default": "all", "description": "预测策略" } }, "required": ["lottery_type"] } ), types.Tool( name="backtest_lottery", description="回测预测算法，评估预测准确性", inputSchema={ "type": "object", "properties": { "lottery_type": { "type": "string", "enum": ["双色球", "福彩3D", "七乐彩", "快乐8"], "description": "彩票类型" }, "window_size": { "type": "integer", "minimum": 50, "maximum": 500, "default": 100, "description": "窗口大小（训练数据期数）" }, "step": { "type": "integer", "minimum": 10, "maximum": 100, "default": 50, "description": "步长（每次移动的期数）" } }, "required": ["lottery_type"] } ) ] - src/swlc_mcp/predictor.py:214-223 (helper)PredictionManager.predict method used by backtest to generate predictions for accuracy calculation; delegates to RuleBasedPredictor.
async def predict(self, lottery_type: str, historical_data: List[Dict], method: str = 'rule', count: int = 5, strategy: Optional[str] = None) -> List[PredictionResult]: """执行预测""" try: # 目前仅实现规则+策略 return self.rule_predictor.predict(lottery_type, historical_data, count=count, strategy=strategy) except Exception as e: logger.error(f"预测失败: {e}") return self.rule_predictor.predict(lottery_type, historical_data, count=count, strategy=strategy)