如何进行股票回测？
简单回测用平台。复杂回测租用抢占式服务器分布式计算。平台更方便的是图形化展示，回测算力十分鸡肋。
先弄明白什么是回测？
- 股票回测是指，利用过去一段时间内的市场数据，分析不同买入/卖出策略下的收益表现。
回测有什么作用？
- 无论是提出猜想去验证，还是漫无目的的因子组合找寻最优策略，我们都可以通过回测过程加深对交易和数据的认识。
回测的方法有哪些?
- 机器学习或者蒙特卡洛模拟，都是基于规则的思想。无论你用什么方法实现，核心目标都是计算出不同交易策略的收益从而进行对比分析。
哪些数据适合做回测？
- 相较于基本面或消息面数据，行情数据在市场上有完整的交易时间序列。行情数据是最合适作为回测的数据类型。
回测的粒度分为哪些？
- 按照行情数据的归集级别，可分为Tick级，秒级，分钟级，日级等粒度。后者可以通过归集前者数据产生。
如何选择回测的粒度？
- 应根据自身数据资源和计算资源以及策略类型来判断。
- 在T+1交易制度下，普通交易者回测Tick级的高频数据难度非常大，在普通交易频率下，日线或者分钟级回测更有实际意义。
- 而在T+0领域例如可转债、ETF，则需要更细粒度的数据，比如秒级或者Tick级。
以下是单股日线回测结果，根据过去一段时间内换手和收盘账户以及今日开盘涨幅为策略因子，产生参数组合，分别计算不同参数组合下的收益率，排序后得到组合收益列表。
完整代码:
import json
import time
import pandas as pd
import numpy as np# 假设某一支票的K线格式如下
'''klines = [{"日期": "2023-01-01", "开盘": 100, "收盘": 105, "最高": 110, "最低": 98, "成交量": 1000000, "成交额": 105000000, "振幅": 12.24,"涨跌幅": 5.0, "涨跌额": 5, "换手率": 5.2},...{"日期": "2023-01-02", "开盘": 106, "收盘": 102, "最高": 108, "最低": 99, "成交量": 800000, "成交额": 81600000, "振幅": 8.08,"涨跌幅": -3.81, "涨跌额": -3, "换手率": 4.1},
]'''# 线上实际数据格式
k = 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k = json.loads(k)klines = []
# 数据清洗+格式化
for l in k['data']['list']:dic = {}for f in range(len(k['data']['fields'])):if k['data']['fields'][f] == '日期':dic[k['data']['fields'][f]] = l[f]else:dic[k['data']['fields'][f]] = float(l[f])klines.append(dic)data = pd.DataFrame(klines)
data['日期'] = pd.to_datetime(data['日期'])# 以日期为索引，排序
data['日期'] = data['日期'].dt.date
data.set_index('日期', inplace=True)
data = data.sort_index(ascending=True)# 由基本数据计算衍生数据，如:开盘涨幅=今开/昨收；收盘涨幅=今收/昨收；量比=今日成交量/(前5日平均成交量)
data['开盘涨幅'] = (data['开盘'] / data['收盘'].shift(1) - 1) * 100
data['收盘涨幅'] = (data['收盘'] / data['收盘'].shift(1) - 1) * 100
data['量比'] = data['成交量'] / data['成交量'].shift(1).rolling(window=5).mean()# 数据格式验证
print('pands读入数据格式：', data.to_string(max_rows=None, max_cols=None))calc_cnt = 0def backtest(n, turn_over_threshold, open_return_threshold, close_return_threshold):# 计算过去n天的换手率、开盘涨幅、收盘涨幅和量比的平均值或其它统计量data['平均换手率'] = data['换手率'].shift(1).rolling(window=n).mean()data['平均开盘涨幅'] = data['开盘涨幅'].shift(1).rolling(window=n).mean().round(2)data['平均收盘涨幅'] = data['收盘涨幅'].shift(1).rolling(window=n).mean().round(2)# 初始化交易信号,每次覆写data['买入信号'] = (data['平均换手率'] > turn_over_threshold) & \(data['平均开盘涨幅'] > open_return_threshold) & \(data['平均收盘涨幅'] > close_return_threshold)# 条件符合则买入，记录买入日期buy_dates = data[data['买入信号']].index# 由买入日期推算卖日期sell_dates = []for index in buy_dates:try:loc = data.index.get_loc(index)next_loc = loc + 1next_index = data.index[next_loc]sell_dates.append(next_index)except IndexError:sell_dates.append(None) # 不存在下一个交易日global calc_cnt# 计算收益率total_return = 1.0for buy_date, sell_date in zip(buy_dates, sell_dates):if sell_date in data.index:calc_cnt = calc_cnt + 1buy_price = data.loc[buy_date, '开盘']sell_price = data.loc[sell_date, '开盘']turn_over = data.loc[buy_date, '平均换手率']open_return = data.loc[buy_date, '平均开盘涨幅']close_return = data.loc[buy_date, '平均收盘涨幅']daily_return = round(sell_price / buy_price, 4)total_return = round(total_return * daily_return, 4) # 计算复利# 输出参数组合，方便校验print(calc_cnt, '{}_{}_{}_{}'.format(n, turn_over_threshold, open_return_threshold, close_return_threshold),buy_date, sell_date, buy_price, sell_price, turn_over, open_return, close_return, daily_return,total_return)return total_returnbest_params = {}
max_return = 0# 定义要测试的n值范围
n_values = range(1, 2) # 例如n从1到29天# 枚举定义各个阈值的可能取值
turn_over_thresholds = [1, 2, 3, 4, 5] # 平均换手率
open_return_thresholds = [0.5, 1, 1.5, 2] # 平均开盘涨幅
close_return_thresholds = [0.5, 1, 1.5, 2] # 平均收盘涨幅# 参数生产
turn_over_thresholds = np.round(np.arange(0, 2, 0.1), 2)
open_return_thresholds = np.round(np.arange(-3, 3, 0.5), 2)
close_return_thresholds = np.round(np.arange(-3, 3, 0.5), 2)k_len = len(data)
param_size = len(n_values) * len(turn_over_thresholds) * len(open_return_thresholds) * len(close_return_thresholds)# 计算预估
print("K线长度:", k_len)
print("参数组合量:", param_size)
print("预估最大计算次数:", param_size * k_len)print("3s后开始计算\n\n\n\n")
time.sleep(3)# 打印表头
print('触发计数', '参数组合', '买入日期', '卖出日期', '买入价格', '卖出价格', '平均换手率', '平均开盘涨幅', '平均收盘涨幅', '单笔收益', '累计收益')start_time = time.time()
# 遍历所有组合
for n in n_values:for turn_over in turn_over_thresholds:for open_return in open_return_thresholds:for close_return in close_return_thresholds:return_value = backtest(n, turn_over, open_return, close_return)# 记录最大累计收益的参数值if return_value > max_return:max_return = return_valuebest_params = {'倒推天数': n,'平均换手率大于': turn_over,'平均开盘涨幅大于': open_return,'平均收盘涨幅大于': close_return,}end_time = time.time()
elapsed_time = end_time - start_time
print("最佳参数组合:", best_params)
print("最高收益率:", max_return)
print("总计算次数:", calc_cnt)
print("耗时(s):", elapsed_time)课后思考: 将单股日线回测改为全市场并行回测
参考链接:离线回测-历史数据下载 · 开发文档
原文地址:https://zhuanlan.zhihu.com/p/1913635911742259398