"""
使用示例：时间序列因子挖掘流程
"""
from pipeline import FactorPipeline
from factors import FactorMiner, create_default_factors

# 方式1：使用默认流程（最简单）
def example_simple():
    """简单示例"""
    pipeline = FactorPipeline(
        ret_horizon=1,      # 未来1期收益率
        ic_window=30,       # IC计算窗口
        commission=0.001,   # 手续费0.1%
        slippage=0.0005     # 滑点0.05%
    )
    
    # 运行完整流程
    results = pipeline.run_full_pipeline(
        file_path="ETH_USDT-1h.feather",
        min_ic=0.01,        # 最小IC阈值
        min_tstat=1.5,      # 最小t统计量
        weight_method='risk_parity',  # 权重方法：risk_parity, regression, equal
        buy_threshold=0.8,  # 买入阈值（标准差倍数）
        sell_threshold=-0.8 # 卖出阈值（标准差倍数）
    )
    
    return results


# 方式2：分步骤执行（更灵活）
def example_step_by_step():
    """分步骤示例"""
    pipeline = FactorPipeline(ret_horizon=1, ic_window=30)
    
    # 步骤1：加载和预处理数据
    pipeline.load_and_preprocess("ETH_USDT-1h.feather")
    
    # 步骤2：因子挖掘（可以使用自定义因子）
    custom_miner = create_default_factors()
    # 可以在这里添加自定义因子
    # custom_miner.register_rule_factor('CUSTOM', your_custom_function)
    pipeline.mine_factors(custom_miner)
    
    # 步骤3：因子检验
    pipeline.validate_factors(min_ic=0.01, min_tstat=1.5)
    
    # 步骤4：因子组合
    pipeline.combine_factors(weight_method='risk_parity')
    
    # 步骤5：生成信号
    signals = pipeline.generate_signals(buy_threshold=0.8, sell_threshold=-0.8)
    
    # 步骤6：回测
    backtest_results = pipeline.backtest(signals)
    
    return {
        'factors': pipeline.factors,
        'score': pipeline.score,
        'signals': signals,
        'backtest': backtest_results
    }


# 方式3：自定义因子
def example_custom_factors():
    """自定义因子示例"""
    from factors import RuleFactor
    import pandas as pd
    import numpy as np
    
    # 定义自定义因子函数
    def my_custom_factor(data: pd.DataFrame) -> pd.Series:
        """自定义因子：价格与均线的距离"""
        return (data['close'] - data['ema8']) / data['ema8']
    
    # 创建因子挖掘器
    miner = create_default_factors()
    
    # 注册自定义因子
    miner.register_rule_factor('CUSTOM_DISTANCE', my_custom_factor)
    
    # 使用自定义因子挖掘器
    pipeline = FactorPipeline()
    pipeline.load_and_preprocess("ETH_USDT-1h.feather")
    pipeline.mine_factors(custom_miner=miner)
    pipeline.validate_factors()
    pipeline.combine_factors()
    pipeline.backtest()
    
    return pipeline


if __name__ == "__main__":
    # 运行简单示例
    print("运行简单示例...")
    results = example_simple()
    
    # 保存结果
    if results['factors'] is not None:
        results['factors'].to_csv("factors_output.csv")
        print("\n因子数据已保存到 factors_output.csv")
    
    if results['score'] is not None:
        results['score'].to_csv("score_output.csv")
        print("综合得分已保存到 score_output.csv")
    
    if results['backtest'] is not None and 'equity' in results['backtest']:
        results['backtest']['equity'].to_csv("equity_curve.csv")
        print("权益曲线已保存到 equity_curve.csv")