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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,"1651.24","1670.24","1630.24","46757","7953438044.00","2.42","-0.30","-4.99","0.37"],["2024-02-07","1626.27","1656.23","1659.83","1606.85","52601","8899606867.00","3.27","2.16","34.99","0.42"],["2024-02-06","1575.25","1621.24","1630.72","1571.34","52472","8740646588.00","3.77","2.92","45.99","0.42"],["2024-02-05","1541.24","1575.25","1582.68","1540.49","53130","8590683071.00","2.74","2.21","34.01","0.42"],["2024-02-02","1560.24","1541.24","1573.02","1526.24","39382","6336691194.00","3.01","-0.90","-14.03","0.31"],["2024-02-01","1550.74","1555.27","1569.13","1546.24","26485","4272298576.00","1.48","0.33","5.12","0.21"],["2024-01-31","1558.24","1550.15","1565.21","1543.37","29649","4770104891.00","1.40","-0.52","-8.09","0.24"],["2024-01-30","1595.22","1558.24","1595.22","1555.24","30866","5010784454.00","2.51","-2.32","-37.00","0.25"],["2024-01-29","1585.54","1595.24","1605.82","1585.53","31421","5189014946.00","1.28","0.80","12.68","0.25"],["2024-01-26","1575.60","1582.56","1589.34","1568.24","26955","4402111882.00","1.33","-0.04","-0.68","0.21"],["2024-01-25","1572.06","1583.24","1586.24","1567.24","31206","5094508165.00","1.21","0.76","12.00","0.25"],["2024-01-24","1574.24","1571.24","1579.08","1548.80","33216","5373603703.00","1.94","0.45","7.00","0.26"]]}}'''
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 = 0
def 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 + 1
next_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 = 1.0
for buy_date, sell_date in zip(buy_dates, sell_dates):
if sell_date in data.index:
calc_cnt = calc_cnt + 1
buy_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_return
best_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_value
best_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
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