开源的ETF数据源python包value300
参数扫描结果lookback5, top_n1 最优收益 94.01%核心逻辑每周一买入过去 5 天涨幅最大的 1 只 ETF全仓持有次周一轮动。代码部分# -*- coding: utf-8 -*- 动量策略每周一买入过去 5 天涨幅最大的 1 只 ETF次周轮动 from value300 import Value300 client Value300() def backtest(etfs, lookback5, top_n1, capital100000): all_data {e: client.get_history(e, days365, fieldsminimal) for e in etfs} dates [d[trade_date] for d in list(all_data.values())[0]] import datetime mondays [d for d in dates if datetime.datetime.strptime(d, %Y-%m-%d).weekday() 0] positions {} trades [] for date in mondays: # 动量排名 ranks [] for code, data in all_data.items(): try: idx next(i for i, d in enumerate(data) if d[trade_date] date) except StopIteration: continue if idx lookback: continue mom (data[idx][close] / data[idx - lookback][close] - 1) * 100 ranks.append({code: code, mom: mom, price: data[idx][close]}) ranks.sort(keylambda x: x[mom], reverseTrue) selected ranks[:top_n] # 卖出 for code, shares in list(positions.items()): data all_data[code] try: idx next(i for i, d in enumerate(data) if d[trade_date] date) capital shares * data[idx][close] except (StopIteration, IndexError): pass positions {} # 买入 if selected and capital 0: per capital / len(selected) for s in selected: shares int(per / s[price]) if shares: positions[s[code]] shares capital - shares * s[price] value capital sum( shares * next(d[close] for d in all_data[code] if d[trade_date] date) for code, shares in positions.items() ) trades.append({date: date, value: value}) ret (trades[-1][value] / 100000 - 1) * 100 print(f初始: 100,000 → 终值: {trades[-1][value]:,.0f} 收益: {ret:.2f}%) return trades if __name__ __main__: trades backtest([510300, 510500, 159915, 588000, 510880]) for t in trades: print(f{t[date]} {t[value]:10,.0f})
