{"id":"c48980cd-beb2-4c17-a84f-d6bc4dcb3494","slug":"clawhub-gechengling-ai-trading-backtester","name":"AI Trading Strategy Backtester","description":"AI-powered quantitative trading strategy backtesting assistant. Designs, codes, and evaluates trading strategies across historical market data. Supports A-share (China), Hong Kong, US equity markets. Covers mean reversion, momentum, breakout, pairs trading, and machine learning-based strategies. Built for quantitative analysts and retail traders. 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Designs, codes, and evaluates trading strategies across historical market data. Supports A-share (China), Hong Kong, US equity markets. Covers mean reversion, momentum, breakout, pairs trading, and machine learning-based strategies. Built for quantitative analysts and retail traders. Keywords: trading backtest, quantitative strategy, algorithmic trading, Python backtesting, backtrader, vectorbt, trading strategy, momentum, mean reversion, pairs trading, A-share strategy, financial data, technical indicators.","source":"CLAWHUB","sourceId":"clawhub:s17ewqc4f2s6gpcbm88hy7fgvn85kg1g:ai-trading-backtester","homepage":"https://clawhub.ai/gechengling/skills/ai-trading-backtester","repository":"https://clawhub.ai/gechengling/ai-trading-backtester","documentation":"https://www.xpersona.co/agent/clawhub-gechengling-ai-trading-backtester","protocols":["OPENCLEW"],"examples":[{"kind":"example","language":"python","snippet":"import pandas as pd\nimport numpy as np\nimport backtrader as bt\n\nclass MomentumStrategy(bt.Strategy):\n    params = (\n        ('lookback', 20),       # 回望期\n        ('hold_period', 5),    # 持有期\n        ('rank_percentile', 0.2),  # 选股分位数\n    )\n\n    def __init__(self):\n        self.inds = {}\n        for d in self.datas:\n            self.inds[d] = {}\n            self.inds[d]['momentum'] = bt.indicators.RateOfChange(\n                d.close, period=self.params.lookback\n            )\n\n    def next(self):\n        # 按动量排序，取前20%\n        rankings = sorted(\n            self.datas,\n            key=lambda d: self.inds[d]['momentum'][0],\n            reverse=True\n        )[:int(len(self.datas) * self.params.rank_percentile)]\n\n        # 平仓不在榜单的持仓\n        for d in self.datas:\n            if d not in rankings and self.getposition(d).size > 0:\n                self.close(d)\n\n        # 买入榜单中的标的\n        # 注意：rankings 为空时 1.0/len(rankings) 会除零，必须先返回\n        if not rankings:\n            return\n        for d in rankings:\n            if self.getposition(d).size == 0:\n                self.order_target_percent(d, 1.0 / len(rankings))"},{"kind":"example","language":"python","snippet":"cerebro = bt.Cerebro()\ncerebro.addstrategy(MomentumStrategy, lookback=20, hold_period=5, rank_percentile=0.2)\n# 关键：传入的是\"回望期内的动量\"，必须剔除上市不足 lookback 天的新股，\n# 否则新股会因为没有足够历史而产生极端 ROC 值，长期占据榜单前排。"}]}}