RD-Agent 教程 - 第三章：进阶使用技巧
3.1 量化金融代理深度解析
量化金融场景架构
RD-Agent 的量化金融代理 RD-Agent(Q) 是业界首个数据驱动的多代理量化策略开发框架。它通过协调因子-模型共同优化，实现了全栈式量化研究开发自动化。
核心架构设计
graph TB
A[市场数据] --> B[数据预处理]
B --> C[因子挖掘代理]
B --> D[模型演进代理]
C --> E[因子评估]
D --> F[模型评估]
E --> G[联合优化代理]
F --> G
G --> H[策略回测]
H --> I[性能分析]
I --> J{满足要求?}
J -->|否| K[知识反馈]
K --> C
K --> D
J -->|是| L[策略部署]
style C fill:#e1f5fe
style D fill:#f3e5f5
style G fill:#e8f5e8
style H fill:#fff3e0
Qlib 框架集成
RD-Agent(Q) 深度集成了微软的 Qlib 量化投资库，提供：
数据管理：
- 🗃️ 标准化金融数据格式
- 📈 实时和历史数据接口
- 🔄 数据自动更新机制
- 🧹 数据清洗和预处理
回测引擎：
- 📊 高性能向量化回测
- 💰 交易成本和滑点模拟
- 📋 多种组合构建策略
- 📈 风险指标计算
模型库：
- 🤖 机器学习模型集成
- 📊 传统量化因子
- 🧠 深度学习模型
- 🔄 模型集成策略
因子挖掘和优化
rdagent fin_factor 详解
因子挖掘代理专注于发现和优化有效的量化因子。
启动命令：
# 基本因子挖掘
rdagent fin_factor
# 指定配置文件
rdagent fin_factor --config factor_config.yaml
# 指定迭代次数
export FACTOR_MAX_LOOP=15
rdagent fin_factor
工作流程：
- 假设生成阶段
# 基于市场理论和历史经验生成因子假设
factor_hypotheses = [
"技术指标动量因子",
"财务质量因子",
"市场情绪因子",
"宏观经济因子"
]
- 因子实现阶段
# 自动生成因子计算代码
class MomentumFactor:
def calculate(self, data):
# 自动生成的动量因子计算逻辑
return (data['close'] / data['close'].shift(20) - 1)
- 因子评估阶段
# 多维度因子评估
evaluation_metrics = {
'IC': 0.045, # 信息系数
'IC_IR': 1.2, # 信息比率
'rank_IC': 0.038, # 排序IC
'turnover': 0.8, # 换手率
'max_drawdown': 0.15 # 最大回撤
}
因子生成策略
技术分析因子：
technical_factors = {
"momentum": [
"price_momentum_5d", "price_momentum_20d",
"volume_momentum", "volatility_momentum"
],
"mean_reversion": [
"rsi_divergence", "bollinger_position",
"price_deviation"
],
"trend": [
"ma_trend", "macd_signal", "trend_strength"
]
}
基本面因子：
fundamental_factors = {
"profitability": [
"roe_trend", "roa_improvement",
"gross_margin_stability"
],
"growth": [
"revenue_growth_consistency", "eps_growth_quality",
"cash_flow_growth"
],
"valuation": [
"pe_relative", "pb_sector_adjusted",
"ev_ebitda_normalized"
]
}
另类因子：
alternative_factors = {
"sentiment": [
"news_sentiment_score", "social_media_buzz",
"analyst_revision_momentum"
],
"network": [
"supply_chain_strength", "industry_correlation",
"peer_performance_influence"
]
}
因子有效性评估
评估框架：
class FactorEvaluator:
def __init__(self, benchmark_data, start_date, end_date):
self.data = benchmark_data
self.start_date = start_date
self.end_date = end_date
def evaluate_factor(self, factor_values):
"""综合评估因子有效性"""
metrics = {}
# 1. 信息系数分析
metrics['IC'] = self.calculate_IC(factor_values)
metrics['IC_std'] = self.calculate_IC_stability(factor_values)
metrics['IC_IR'] = metrics['IC'] / metrics['IC_std']
# 2. 单调性检验
metrics['monotonicity'] = self.test_monotonicity(factor_values)
# 3. 换手率分析
metrics['turnover'] = self.calculate_turnover(factor_values)
# 4. 回测表现
backtest_result = self.backtest_factor(factor_values)
metrics.update(backtest_result)
return metrics
模型演进和优化
rdagent fin_model 详解
模型演进代理专注于预测模型的自动化开发和优化。
支持的模型类型：
| 模型类别 | 具体模型 | 适用场景 | 特点 |
|---|---|---|---|
| 线性模型 | LinearRegression, Ridge, Lasso | 简单快速预测 | 可解释性强 |
| 树模型 | XGBoost, LightGBM, CatBoost | 表格数据建模 | 特征重要性 |
| 神经网络 | MLP, TabNet, DeepFM | 复杂关系建模 | 非线性拟合 |
| 时序模型 | LSTM, GRU, Transformer | 序列数据预测 | 时间依赖 |
| 集成模型 | Stacking, Voting, Blending | 提升预测精度 | 模型融合 |
模型架构搜索
自动化架构搜索：
class ModelArchitectureSearch:
def __init__(self):
self.search_space = {
"n_layers": [2, 3, 4, 5],
"hidden_dims": [64, 128, 256, 512],
"dropout_rate": [0.1, 0.2, 0.3, 0.4],
"activation": ["relu", "tanh", "gelu"],
"optimizer": ["adam", "adamw", "sgd"],
"learning_rate": [0.001, 0.01, 0.1]
}
def search_best_architecture(self, train_data, valid_data):
"""搜索最优模型架构"""
best_config = None
best_score = float('-inf')
for config in self.generate_configurations():
model = self.build_model(config)
score = self.evaluate_model(model, train_data, valid_data)
if score > best_score:
best_score = score
best_config = config
return best_config, best_score
超参数优化
多种优化算法：
from optuna import create_study
from sklearn.model_selection import cross_val_score
class HyperparameterOptimizer:
def __init__(self, model_class, param_space):
self.model_class = model_class
self.param_space = param_space
def optimize_with_optuna(self, X_train, y_train, n_trials=100):
"""使用 Optuna 进行超参数优化"""
def objective(trial):
params = {}
for param_name, param_range in self.param_space.items():
if isinstance(param_range, list):
params[param_name] = trial.suggest_categorical(
param_name, param_range
)
elif isinstance(param_range, tuple):
if isinstance(param_range[0], int):
params[param_name] = trial.suggest_int(
param_name, param_range[0], param_range[1]
)
else:
params[param_name] = trial.suggest_float(
param_name, param_range[0], param_range[1]
)
model = self.model_class(**params)
scores = cross_val_score(model, X_train, y_train, cv=5)
return scores.mean()
study = create_study(direction='maximize')
study.optimize(objective, n_trials=n_trials)
return study.best_params, study.best_value
因子模型联合优化
rdagent fin_quant 详解
联合优化代理是 RD-Agent(Q) 的核心创新，实现因子和模型的协同演进。
联合优化策略：
- 交替优化策略
class AlternatingOptimization:
def __init__(self, factor_agent, model_agent):
self.factor_agent = factor_agent
self.model_agent = model_agent
def optimize(self, max_iterations=10):
for i in range(max_iterations):
# 固定模型，优化因子
new_factors = self.factor_agent.evolve_factors(
current_model=self.current_model
)
# 固定因子，优化模型
new_model = self.model_agent.evolve_model(
current_factors=new_factors
)
# 评估联合性能
performance = self.evaluate_joint_performance(
new_factors, new_model
)
if performance > self.best_performance:
self.update_best_solution(new_factors, new_model)
- 多目标优化策略
class MultiObjectiveOptimization:
def __init__(self):
self.objectives = [
"maximize_sharpe_ratio",
"minimize_max_drawdown",
"maximize_information_ratio",
"minimize_turnover"
]
def pareto_optimization(self, population):
"""帕累托前沿优化"""
pareto_front = []
for individual in population:
is_dominated = False
for other in population:
if self.dominates(other, individual):
is_dominated = True
break
if not is_dominated:
pareto_front.append(individual)
return pareto_front
多目标优化算法
NSGA-II 实现：
class NSGA2Optimizer:
def __init__(self, population_size=100, generations=50):
self.population_size = population_size
self.generations = generations
def optimize(self, factor_space, model_space):
# 初始化种群
population = self.initialize_population()
for generation in range(self.generations):
# 评估适应度
fitness_values = self.evaluate_population(population)
# 非支配排序
fronts = self.fast_non_dominated_sort(population, fitness_values)
# 选择下一代
next_population = self.select_next_generation(
fronts, population, fitness_values
)
# 交叉和变异
offspring = self.crossover_and_mutation(next_population)
population = offspring
return self.get_pareto_front(population)
协作演进机制
知识共享机制：
class KnowledgeSharing:
def __init__(self):
self.factor_knowledge_base = {}
self.model_knowledge_base = {}
self.interaction_patterns = {}
def share_factor_insights(self, factor_id, insights):
"""分享因子洞察"""
self.factor_knowledge_base[factor_id] = {
'effectiveness': insights['ic_score'],
'stability': insights['ic_std'],
'best_models': insights['compatible_models'],
'market_regimes': insights['effective_periods']
}
def share_model_insights(self, model_id, insights):
"""分享模型洞察"""
self.model_knowledge_base[model_id] = {
'architecture': insights['model_config'],
'performance': insights['validation_score'],
'best_factors': insights['important_features'],
'hyperparameters': insights['optimal_params']
}
def get_recommendations(self, current_factors, current_model):
"""获取优化建议"""
recommendations = {
'factor_suggestions': [],
'model_suggestions': [],
'joint_strategies': []
}
# 基于历史知识生成建议
for factor in current_factors:
if factor in self.factor_knowledge_base:
knowledge = self.factor_knowledge_base[factor]
recommendations['model_suggestions'].extend(
knowledge['best_models']
)
return recommendations
金融报告分析
rdagent fin_factor_report 使用
从金融报告中自动提取量化因子是 RD-Agent 的独特能力。
支持的报告类型：
- 📋 年度报告 (10-K, Annual Report)
- 📊 季度报告 (10-Q, Quarterly Report)
- 📰 新闻公告 (8-K, Press Release)
- 📈 分析师报告 (Research Report)
- 🏛️ 监管文件 (SEC Filings)
使用示例：
# 基本报告分析
rdagent fin_factor_report --report-folder ./financial_reports
# 指定报告类型
rdagent fin_factor_report \
--report-folder ./reports \
--report-type annual \
--language zh
# 批量处理
rdagent fin_factor_report \
--report-folder ./reports \
--batch-size 50 \
--parallel-workers 4
报告解析和特征提取
文本预处理流水线：
class ReportPreprocessor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm")
self.financial_terms = self.load_financial_dictionary()
def preprocess_report(self, report_text):
"""预处理财务报告"""
# 1. 文档清理
cleaned_text = self.clean_document(report_text)
# 2. 段落分割
sections = self.extract_sections(cleaned_text)
# 3. 实体识别
entities = self.extract_financial_entities(sections)
# 4. 情感分析
sentiment_scores = self.analyze_sentiment(sections)
return {
'sections': sections,
'entities': entities,
'sentiment': sentiment_scores,
'metadata': self.extract_metadata(report_text)
}
因子提取策略：
class FinancialFactorExtractor:
def __init__(self):
self.factor_extractors = {
'growth_factors': GrowthFactorExtractor(),
'quality_factors': QualityFactorExtractor(),
'sentiment_factors': SentimentFactorExtractor(),
'risk_factors': RiskFactorExtractor()
}
def extract_factors(self, processed_report):
"""从处理后的报告中提取因子"""
extracted_factors = {}
for factor_type, extractor in self.factor_extractors.items():
factors = extractor.extract(processed_report)
extracted_factors[factor_type] = factors
return extracted_factors
class GrowthFactorExtractor:
def extract(self, report_data):
"""提取成长性因子"""
growth_factors = {}
# 收入增长质量
revenue_mentions = self.find_revenue_discussions(report_data)
growth_factors['revenue_growth_quality'] = self.analyze_growth_quality(
revenue_mentions
)
# 市场扩张策略
expansion_mentions = self.find_expansion_discussions(report_data)
growth_factors['expansion_strategy_score'] = self.score_expansion_strategy(
expansion_mentions
)
# 研发投入承诺
rd_mentions = self.find_rd_discussions(report_data)
growth_factors['rd_commitment_score'] = self.analyze_rd_commitment(
rd_mentions
)
return growth_factors
情感分析和信号挖掘
多维度情感分析：
class FinancialSentimentAnalyzer:
def __init__(self):
self.sentiment_model = pipeline(
"sentiment-analysis",
model="ProsusAI/finbert"
)
self.uncertainty_detector = UncertaintyDetector()
self.forward_looking_detector = ForwardLookingDetector()
def analyze_comprehensive_sentiment(self, text_sections):
"""综合情感分析"""
sentiment_scores = {}
for section_name, text in text_sections.items():
# 基础情感分析
basic_sentiment = self.sentiment_model(text)
# 不确定性检测
uncertainty_score = self.uncertainty_detector.detect(text)
# 前瞻性语句检测
forward_looking = self.forward_looking_detector.detect(text)
sentiment_scores[section_name] = {
'polarity': basic_sentiment[0]['score'],
'label': basic_sentiment[0]['label'],
'uncertainty': uncertainty_score,
'forward_looking': forward_looking,
'confidence': self.calculate_confidence(
basic_sentiment, uncertainty_score
)
}
return sentiment_scores
class SignalGenerator:
def __init__(self):
self.signal_weights = {
'management_tone': 0.3,
'financial_health': 0.4,
'market_position': 0.2,
'future_outlook': 0.1
}
def generate_trading_signals(self, factor_scores, sentiment_scores):
"""生成交易信号"""
composite_score = 0.0
# 加权合成得分
for factor_type, weight in self.signal_weights.items():
if factor_type in factor_scores:
composite_score += factor_scores[factor_type] * weight
# 情感调整
sentiment_adjustment = self.calculate_sentiment_adjustment(
sentiment_scores
)
final_score = composite_score * sentiment_adjustment
# 生成信号
if final_score > 0.6:
signal = "STRONG_BUY"
elif final_score > 0.3:
signal = "BUY"
elif final_score > -0.3:
signal = "HOLD"
elif final_score > -0.6:
signal = "SELL"
else:
signal = "STRONG_SELL"
return {
'signal': signal,
'confidence': abs(final_score),
'score': final_score,
'components': {
'factor_score': composite_score,
'sentiment_adjustment': sentiment_adjustment
}
}
3.2 CoSTEER 演进框架深度解析
演进框架核心原理
CoSTEER (Collaborative evolving STrategy for automatic data-cEntric dEvelopment fRamework) 是 RD-Agent 的核心演进引擎，实现了智能体的自主学习和持续改进。
协作演进策略设计
CoSTEER 框架基于以下核心原理：
- 多代理协作 - 不同专业代理协同工作
- 知识积累 - 从历史经验中学习和改进
- 自适应演进 - 根据反馈动态调整策略
- RAG增强 - 检索增强生成提升决策质量
class CoSTEER(Developer[Experiment]):
"""CoSTEER 演进框架核心类"""
def __init__(
self,
settings: CoSTEERSettings,
eva: RAGEvaluator,
es: EvolvingStrategy,
evolving_version: int = 2,
with_knowledge: bool = True,
knowledge_self_gen: bool = True,
max_loop: int | None = None,
):
self.settings = settings
self.max_loop = settings.max_loop if max_loop is None else max_loop
self.knowledge_base_path = Path(settings.knowledge_base_path)
self.with_knowledge = with_knowledge
self.knowledge_self_gen = knowledge_self_gen
self.evolving_strategy = es
self.evaluator = eva
# 初始化 RAG 系统
self.rag_strategy = self._init_rag_strategy(evolving_version)
知识管理机制
多层次知识表示：
class KnowledgeManagementSystem:
def __init__(self):
self.knowledge_layers = {
'factual_knowledge': FactualKnowledgeBase(),
'procedural_knowledge': ProceduralKnowledgeBase(),
'experiential_knowledge': ExperientialKnowledgeBase(),
'meta_knowledge': MetaKnowledgeBase()
}
def store_knowledge(self, knowledge_item):
"""分层存储知识"""
knowledge_type = self.classify_knowledge(knowledge_item)
self.knowledge_layers[knowledge_type].store(knowledge_item)
def retrieve_relevant_knowledge(self, query_context):
"""检索相关知识"""
relevant_knowledge = {}
for layer_name, knowledge_base in self.knowledge_layers.items():
relevant_items = knowledge_base.query(
query_context,
similarity_threshold=0.7
)
relevant_knowledge[layer_name] = relevant_items
return self.synthesize_knowledge(relevant_knowledge)
知识表示格式：
@dataclass
class KnowledgeItem:
"""知识项数据结构"""
id: str
content: str
knowledge_type: KnowledgeType
domain: str
confidence: float
source: str
timestamp: datetime
usage_count: int = 0
success_rate: float = 0.0
def to_embedding(self) -> np.ndarray:
"""转换为向量表示"""
return self.embedding_model.encode(self.content)
def update_effectiveness(self, success: bool):
"""更新知识有效性"""
self.usage_count += 1
if success:
self.success_rate = (
(self.success_rate * (self.usage_count - 1) + 1.0)
/ self.usage_count
)
else:
self.success_rate = (
self.success_rate * (self.usage_count - 1)
/ self.usage_count
)
自适应优化算法
多策略演进算法：
class AdaptiveEvolutionStrategy:
def __init__(self):
self.evolution_strategies = {
'genetic_algorithm': GeneticAlgorithmStrategy(),
'differential_evolution': DifferentialEvolutionStrategy(),
'particle_swarm': ParticleSwarmStrategy(),
'simulated_annealing': SimulatedAnnealingStrategy()
}
self.strategy_performance = {}
self.adaptive_weights = {}
def evolve_population(self, population, generation):
"""自适应演进种群"""
# 选择演进策略
selected_strategies = self.select_strategies(generation)
new_population = []
for strategy_name, weight in selected_strategies.items():
strategy = self.evolution_strategies[strategy_name]
# 按权重分配种群
sub_population_size = int(len(population) * weight)
sub_population = population[:sub_population_size]
# 演进子种群
evolved_sub_pop = strategy.evolve(sub_population)
new_population.extend(evolved_sub_pop)
# 更新策略性能
self.update_strategy_performance(new_population, generation)
return new_population
def select_strategies(self, generation):
"""基于历史性能选择演进策略"""
if generation < 5:
# 初期均匀分配
return {name: 1.0/len(self.evolution_strategies)
for name in self.evolution_strategies.keys()}
# 基于性能调整权重
total_performance = sum(self.strategy_performance.values())
return {
name: performance / total_performance
for name, performance in self.strategy_performance.items()
}
自定义演进策略
策略配置和调整
配置文件示例：
# costeer_config.yaml
costeer_settings:
max_loop: 10
knowledge_base_path: "./knowledge_base"
new_knowledge_base_path: "./new_knowledge"
evolving_version: 2
evolution_strategy:
population_size: 50
mutation_rate: 0.1
crossover_rate: 0.8
selection_method: "tournament"
tournament_size: 5
rag_settings:
embedding_model: "text-embedding-3-small"
similarity_threshold: 0.75
max_retrieved_docs: 10
rerank_top_k: 5
evaluation_settings:
metrics: ["accuracy", "f1_score", "auc_roc"]
cross_validation_folds: 5
test_ratio: 0.2
knowledge_management:
auto_cleanup: true
cleanup_threshold: 0.3
knowledge_update_frequency: 100
max_knowledge_items: 10000
自定义演进策略实现：
class CustomEvolutionStrategy(EvolvingStrategy):
"""自定义演进策略"""
def __init__(self, config: dict):
super().__init__()
self.config = config
self.population_size = config.get('population_size', 50)
self.mutation_rate = config.get('mutation_rate', 0.1)
self.crossover_rate = config.get('crossover_rate', 0.8)
def initialize_population(self, size: int = None):
"""初始化种群"""
size = size or self.population_size
population = []
for _ in range(size):
individual = self.create_random_individual()
population.append(individual)
return population
def evolve_generation(self, population, fitness_scores):
"""演进一代"""
new_population = []
# 精英选择 - 保留最优个体
elite_count = int(len(population) * 0.1)
elite_indices = np.argsort(fitness_scores)[-elite_count:]
for idx in elite_indices:
new_population.append(population[idx])
# 交叉和变异生成新个体
while len(new_population) < len(population):
# 选择父代
parent1 = self.tournament_selection(population, fitness_scores)
parent2 = self.tournament_selection(population, fitness_scores)
# 交叉
if random.random() < self.crossover_rate:
child1, child2 = self.crossover(parent1, parent2)
else:
child1, child2 = parent1, parent2
# 变异
if random.random() < self.mutation_rate:
child1 = self.mutate(child1)
if random.random() < self.mutation_rate:
child2 = self.mutate(child2)
new_population.extend([child1, child2])
return new_population[:len(population)]
def crossover(self, parent1, parent2):
"""交叉操作"""
# 实现具体的交叉逻辑
# 这里以参数交叉为例
child1_params = {}
child2_params = {}
for key in parent1.parameters.keys():
if random.random() < 0.5:
child1_params[key] = parent1.parameters[key]
child2_params[key] = parent2.parameters[key]
else:
child1_params[key] = parent2.parameters[key]
child2_params[key] = parent1.parameters[key]
child1 = parent1.copy()
child2 = parent2.copy()
child1.parameters = child1_params
child2.parameters = child2_params
return child1, child2
def mutate(self, individual):
"""变异操作"""
mutated = individual.copy()
for key, value in mutated.parameters.items():
if random.random() < self.mutation_rate:
if isinstance(value, (int, float)):
# 高斯变异
noise = np.random.normal(0, 0.1 * abs(value))
mutated.parameters[key] = value + noise
elif isinstance(value, str):
# 字符串变异（如模型名称）
mutated.parameters[key] = self.mutate_string(value)
return mutated
评估器自定义
多指标评估器：
class MultiMetricEvaluator(RAGEvaluator):
"""多指标评估器"""
def __init__(self, metrics_config: dict):
super().__init__()
self.metrics_config = metrics_config
self.metric_weights = metrics_config.get('weights', {})
self.metric_calculators = self._init_metric_calculators()
def evaluate(self, experiment: Experiment) -> Dict[str, float]:
"""多指标评估"""
metrics = {}
for metric_name, calculator in self.metric_calculators.items():
try:
score = calculator.calculate(experiment)
metrics[metric_name] = score
except Exception as e:
logger.warning(f"计算指标 {metric_name} 失败: {e}")
metrics[metric_name] = 0.0
# 计算加权综合得分
weighted_score = self._calculate_weighted_score(metrics)
metrics['weighted_score'] = weighted_score
return metrics
def _calculate_weighted_score(self, metrics: Dict[str, float]) -> float:
"""计算加权综合得分"""
total_score = 0.0
total_weight = 0.0
for metric_name, score in metrics.items():
if metric_name in self.metric_weights:
weight = self.metric_weights[metric_name]
total_score += score * weight
total_weight += weight
return total_score / total_weight if total_weight > 0 else 0.0
class CustomMetricCalculator:
"""自定义指标计算器"""
def __init__(self, metric_name: str, calculation_func: callable):
self.metric_name = metric_name
self.calculation_func = calculation_func
def calculate(self, experiment: Experiment) -> float:
"""计算指标值"""
return self.calculation_func(experiment)
# 使用示例
def sharpe_ratio_calculator(experiment):
"""计算夏普比率"""
returns = experiment.get_returns()
if len(returns) == 0 or returns.std() == 0:
return 0.0
return returns.mean() / returns.std() * np.sqrt(252)
def max_drawdown_calculator(experiment):
"""计算最大回撤"""
cumulative_returns = (1 + experiment.get_returns()).cumprod()
running_max = cumulative_returns.expanding().max()
drawdown = (cumulative_returns - running_max) / running_max
return -drawdown.min() # 返回正值
# 创建自定义评估器
metrics_config = {
'weights': {
'sharpe_ratio': 0.4,
'max_drawdown': 0.3,
'information_ratio': 0.3
}
}
evaluator = MultiMetricEvaluator(metrics_config)
evaluator.add_metric_calculator('sharpe_ratio', sharpe_ratio_calculator)
evaluator.add_metric_calculator('max_drawdown', max_drawdown_calculator)
知识库管理
知识库优化策略：
class KnowledgeBaseManager:
"""知识库管理器"""
def __init__(self, config: dict):
self.config = config
self.knowledge_store = {}
self.embedding_cache = {}
self.access_frequency = defaultdict(int)
self.last_access_time = {}
def add_knowledge(self, knowledge_item: KnowledgeItem):
"""添加知识项"""
# 检查知识库大小限制
if len(self.knowledge_store) >= self.config.get('max_items', 10000):
self._cleanup_knowledge()
self.knowledge_store[knowledge_item.id] = knowledge_item
self.embedding_cache[knowledge_item.id] = knowledge_item.to_embedding()
self.last_access_time[knowledge_item.id] = datetime.now()
def query_knowledge(
self,
query: str,
top_k: int = 10,
similarity_threshold: float = 0.7
) -> List[KnowledgeItem]:
"""查询相关知识"""
query_embedding = self._get_query_embedding(query)
similarities = {}
for item_id, item_embedding in self.embedding_cache.items():
similarity = cosine_similarity(
query_embedding.reshape(1, -1),
item_embedding.reshape(1, -1)
)[0][0]
if similarity >= similarity_threshold:
similarities[item_id] = similarity
self.access_frequency[item_id] += 1
self.last_access_time[item_id] = datetime.now()
# 按相似度排序并返回top-k
sorted_items = sorted(
similarities.items(),
key=lambda x: x[1],
reverse=True
)[:top_k]
return [self.knowledge_store[item_id] for item_id, _ in sorted_items]
def _cleanup_knowledge(self):
"""清理知识库"""
cleanup_count = int(len(self.knowledge_store) * 0.1) # 清理10%
# 基于访问频率和时间的综合评分
scores = {}
current_time = datetime.now()
for item_id, knowledge_item in self.knowledge_store.items():
frequency_score = self.access_frequency[item_id]
time_score = (
current_time - self.last_access_time[item_id]
).days
effectiveness_score = knowledge_item.success_rate
# 综合评分（频率高、时间近、效果好的得分高）
composite_score = (
frequency_score * 0.4 +
effectiveness_score * 0.4 -
time_score * 0.2
)
scores[item_id] = composite_score
# 删除评分最低的项目
items_to_remove = sorted(
scores.items(),
key=lambda x: x[1]
)[:cleanup_count]
for item_id, _ in items_to_remove:
del self.knowledge_store[item_id]
del self.embedding_cache[item_id]
del self.access_frequency[item_id]
del self.last_access_time[item_id]
logger.info(f"清理了 {cleanup_count} 个知识项")
多代理协作机制
研究代理职责
class ResearchAgent:
"""研究代理 - 负责假设生成和策略规划"""
def __init__(self, knowledge_base: KnowledgeBase):
self.knowledge_base = knowledge_base
self.hypothesis_generator = HypothesisGenerator()
self.strategy_planner = StrategyPlanner()
def generate_hypotheses(self, problem_context: dict) -> List[Hypothesis]:
"""生成研究假设"""
# 从知识库检索相关理论
relevant_knowledge = self.knowledge_base.query(
problem_context['description']
)
# 基于领域知识生成假设
hypotheses = []
for knowledge_item in relevant_knowledge:
hypothesis = self.hypothesis_generator.generate_from_knowledge(
knowledge_item, problem_context
)
hypotheses.append(hypothesis)
# 生成创新假设
novel_hypotheses = self.hypothesis_generator.generate_novel(
problem_context, existing_hypotheses=hypotheses
)
hypotheses.extend(novel_hypotheses)
return self.rank_hypotheses(hypotheses)
def plan_research_strategy(self, hypothesis: Hypothesis) -> ResearchPlan:
"""规划研究策略"""
return self.strategy_planner.create_plan(
hypothesis=hypothesis,
available_resources=self.get_available_resources(),
time_constraints=self.get_time_constraints()
)
开发代理功能
class DevelopmentAgent:
"""开发代理 - 负责代码实现和测试验证"""
def __init__(self, code_generator: CodeGenerator, validator: CodeValidator):
self.code_generator = code_generator
self.validator = validator
self.implementation_cache = {}
def implement_hypothesis(self, hypothesis: Hypothesis) -> Implementation:
"""实现研究假设"""
# 生成实现代码
implementation_code = self.code_generator.generate_code(
specification=hypothesis.specification,
constraints=hypothesis.constraints
)
# 验证代码正确性
validation_result = self.validator.validate(implementation_code)
if not validation_result.is_valid:
# 修复代码问题
fixed_code = self.code_generator.fix_issues(
implementation_code,
validation_result.issues
)
implementation_code = fixed_code
# 创建实现对象
implementation = Implementation(
code=implementation_code,
hypothesis=hypothesis,
validation_result=validation_result
)
# 缓存实现
self.implementation_cache[hypothesis.id] = implementation
return implementation
def optimize_implementation(
self,
implementation: Implementation,
feedback: Feedback
) -> Implementation:
"""基于反馈优化实现"""
optimization_suggestions = self.analyze_feedback(feedback)
optimized_code = self.code_generator.optimize_code(
implementation.code,
optimization_suggestions
)
return Implementation(
code=optimized_code,
hypothesis=implementation.hypothesis,
parent_implementation=implementation
)
代理间通信协议
class AgentCommunicationProtocol:
"""代理间通信协议"""
def __init__(self):
self.message_queue = {}
self.subscriptions = defaultdict(list)
self.message_handlers = {}
def register_agent(self, agent_id: str, agent: Agent):
"""注册代理"""
self.message_queue[agent_id] = []
agent.set_communication_protocol(self)
def subscribe(self, agent_id: str, message_type: str):
"""订阅消息类型"""
self.subscriptions[message_type].append(agent_id)
def publish(self, sender_id: str, message_type: str, message: dict):
"""发布消息"""
# 添加消息元数据
full_message = {
'sender': sender_id,
'type': message_type,
'timestamp': datetime.now(),
'content': message
}
# 发送给所有订阅者
for subscriber_id in self.subscriptions[message_type]:
if subscriber_id != sender_id: # 不发送给自己
self.message_queue[subscriber_id].append(full_message)
def get_messages(self, agent_id: str) -> List[dict]:
"""获取代理的消息"""
messages = self.message_queue.get(agent_id, [])
self.message_queue[agent_id] = [] # 清空队列
return messages
class CollaborativeWorkflow:
"""协作工作流"""
def __init__(self, agents: List[Agent], protocol: AgentCommunicationProtocol):
self.agents = {agent.id: agent for agent in agents}
self.protocol = protocol
self.workflow_state = WorkflowState.INITIALIZED
def execute_collaborative_task(self, task: CollaborativeTask):
"""执行协作任务"""
# 分解任务
subtasks = self.decompose_task(task)
# 分配子任务给合适的代理
task_assignments = self.assign_tasks(subtasks)
# 执行子任务
results = {}
for agent_id, assigned_tasks in task_assignments.items():
agent = self.agents[agent_id]
agent_results = agent.execute_tasks(assigned_tasks)
results[agent_id] = agent_results
# 协调和整合结果
integrated_result = self.integrate_results(results)
return integrated_result
def coordinate_agents(self):
"""协调代理活动"""
# 处理代理间消息
for agent_id, agent in self.agents.items():
messages = self.protocol.get_messages(agent_id)
if messages:
agent.process_messages(messages)
# 检查是否需要协调
coordination_needed = self.check_coordination_needs()
if coordination_needed:
self.perform_coordination()
本章内容展示了 RD-Agent 的高级功能和深度定制能力，包括量化金融代理的专业功能、CoSTEER 演进框架的核心机制，以及多代理协作的实现方法。下一章我们将通过实战案例来演示这些高级功能的具体应用。
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