def backtest(begin_date, end_date):
"""
策略回测。结束后打印出收益曲线(沪深300基准)、年化收益、最大回撤、
:param begin_date: 回测开始日期
:param end_date: 回测结束日期
"""
# 初始现金1000万
cash = 1E7
# 单只股票的仓位是20万
single_position = 2E5
# 时间为key的净值、收益和同期沪深基准
df_profit = pd.DataFrame(columns=['net_value', 'profit', 'hs300'])
# 获取回测开始日期和结束之间的所有交易日，并且是按照正序排列
all_dates = get_trading_dates(begin_date, end_date)
# 获取沪深300的在回测开始的第一个交易日的值
hs300_begin_value = DB_CONN['daily'].find_one(
{'code': '000300', 'index': True, 'date': all_dates[0]},
projection={'close': True})['close']
# 获取回测周期内的股票池数据，
# adjust_dates：正序排列的调整日列表；
# date_codes_dict： 调整日和当期的股票列表组成的dict，key是调整日，value是股票代码列表
adjust_dates, date_codes_dict = stock_pool(begin_date, end_date)
# 股票池上期股票代码列表
last_phase_codes = None
# 股票池当期股票代码列表
this_phase_codes = None
# 待卖的股票代码集合
to_be_sold_codes = set()
# 待买的股票代码集合
to_be_bought_codes = set()
# 持仓股票dict，key是股票代码，value是一个dict，
# 三个字段分别为：cost - 持仓成本，volume - 持仓数量，last_value：前一天的市值
holding_code_dict = dict()
# 前一个交易日
last_date = None
# 在交易日的顺序，一天天完成信号检测
for _date in all_dates:
print('Backtest at %s.' % _date)
# 当期持仓股票的代码列表
before_sell_holding_codes = list(holding_code_dict.keys())
"""
持仓股的除权除息处理
如果当前不是第一个交易日，并且有持仓股票，则处理除权除息对持仓股的影响
这里的处理只考虑复权因子的变化，而实际的复权因子变化有可能是因为除权、除息以及配股，
那么具体的持仓股变化要根据它们的不同逻辑来处理
"""
if last_date is not None and len(before_sell_holding_codes) > 0:
# 从daily数据集中查询出所有持仓股的前一个交易日的复权因子
last_daily_cursor = DB_CONN['daily'].find(
{'code': {'$in': before_sell_holding_codes}, 'date': last_date, 'index': False},
projection={'code': True, 'au_factor': True})
# 构造一个dict，key是股票代码，value是上一个交易日的复权因子
code_last_aufactor_dict = dict([(daily['code'], daily['au_factor']) for daily in last_daily_cursor])
# 从daily数据集中查询出所有持仓股的当前交易日的复权因子
current_daily_cursor = DB_CONN['daily'].find(
{'code': {'$in': before_sell_holding_codes}, 'date': _date, 'index': False},
projection={'code': True, 'au_factor': True})
# 一只股票一只股票进行处理
for current_daily in current_daily_cursor:
# 当前交易日的复权因子
current_aufactor = current_daily['au_factor']
# 股票代码
code = current_daily['code']
# 从持仓股中找到该股票的持仓数量
last_volume = holding_code_dict[code]['volume']
# 如果该股票存在前一个交易日的复权因子，则对持仓股数量进行处理
if code in code_last_aufactor_dict:
# 上一个交易日的复权因子
last_aufactor = code_last_aufactor_dict[code]
# 计算复权因子变化后的持仓股票数量，如果复权因子不发生变化，那么持仓数量是不发生变化的
# 相关公式是：
# 市值不变：last_close * last_volume = pre_close * current_volume
# 价格的关系：last_close * last_aufactor = pre_close * current_aufactor
# 转换之后得到下面的公式：
current_volume = int(last_volume * (current_aufactor / last_aufactor))
# 改变持仓数量
holding_code_dict[code]['volume'] = current_volume
print('持仓量调整：%s, %6d, %10.6f, %6d, %10.6f' %
(code, last_volume, last_aufactor, current_volume, current_aufactor))
"""
卖出的逻辑处理：
卖出价格是当日的开盘价，卖出的数量就是持仓股的数量，卖出后获得的资金累加到账户的可用现金上
"""
print('待卖股票池：', to_be_sold_codes, flush=True)
# 如果有待卖股票，则继续处理
if len(to_be_sold_codes) > 0:
# 从daily数据集中查询所有待卖股票的开盘价，这里用的不复权的价格，以模拟出真实的交易情况
sell_daily_cursor = DB_CONN['daily'].find(
{'code': {'$in': list(to_be_sold_codes)}, 'date': _date, 'index': False, 'is_trading': True},
projection={'open': True, 'code': True}
)
# 一只股票一只股票处理
for sell_daily in sell_daily_cursor:
# 待卖股票的代码
code = sell_daily['code']
# 如果股票在持仓股里
if code in before_sell_holding_codes:
# 获取持仓股
holding_stock = holding_code_dict[code]
# 获取持仓数量
holding_volume = holding_stock['volume']
# 卖出价格为当日开盘价
sell_price = sell_daily['open']
# 卖出获得金额为持仓量乘以卖出价格
sell_amount = holding_volume * sell_price
# 卖出得到的资金加到账户的可用现金上
cash += sell_amount
# 获取该只股票的持仓成本
cost = holding_stock['cost']
# 计算持仓的收益
single_profit = (sell_amount - cost) * 100 / cost
print('卖出 %s, %6d, %6.2f, %8.2f, %4.2f' %
(code, holding_volume, sell_price, sell_amount, single_profit))
# 删除该股票的持仓信息
del holding_code_dict[code]
to_be_sold_codes.remove(code)
print('卖出后，现金: %10.2f' % cash)
"""
买入的逻辑处理：
买入的价格是当日的开盘价，每只股票可买入的金额为20万，如果可用现金少于20万，就不再买入了
"""
print('待买股票池：', to_be_bought_codes, flush=True)
# 如果待买股票集合不为空，则执行买入操作
if len(to_be_bought_codes) > 0:
# 获取所有待买入股票的开盘价
buy_daily_cursor = DB_CONN['daily'].find(
{'code': {'$in': list(to_be_bought_codes)}, 'date': _date, 'is_trading': True, 'index': False},
projection={'code': True, 'open': True}
)
# 处理所有待买入股票
for buy_daily in buy_daily_cursor:
# 判断可用资金是否够用
if cash > single_position:
# 获取买入价格
buy_price = buy_daily['open']
# 获取股票代码
code = buy_daily['code']
# 获取可买的数量，数量必须为正手数
volume = int(int(single_position / buy_price) / 100) * 100
# 买入花费的成本为买入价格乘以实际的可买入数量
buy_amount = buy_price * volume
# 从现金中减去本次花费的成本
cash -= buy_amount
# 增加持仓股中
holding_code_dict[code] = {
'volume': volume, # 持仓量
'cost': buy_amount, # 持仓成本
'last_value': buy_amount # 初始前一日的市值为持仓成本
}
print('买入 %s, %6d, %6.2f, %8.2f' % (code, volume, buy_price, buy_amount))
print('买入后，现金: %10.2f' % cash)
# 持仓股代码列表
holding_codes = list(holding_code_dict.keys())
"""
股票池调整日的处理逻辑：
如果当前日期是股票池调整日，那么需要获取当期的备选股票列表，同时找到
本期被调出的股票，如果这些被调出的股票是持仓股，则需要卖出
"""
# 判断当前交易日是否为股票池的调整日
if _date in adjust_dates:
print('股票池调整日：%s，备选股票列表：' % _date, flush=True)
# 如果上期股票列表存在，也就是当前不是第一期股票，则将
# 当前股票列表设为上期股票列表
if this_phase_codes is not None:
last_phase_codes = this_phase_codes
# 获取当期的股票列表
this_phase_codes = date_codes_dict[_date]
print(this_phase_codes, flush=True)
# 如果存在上期的股票列表，则需要找出被调出的股票列表
if last_phase_codes is not None:
# 找到被调出股票池的股票列表
out_codes = find_out_stocks(last_phase_codes, this_phase_codes)
# 将所有被调出的且是在持仓中的股票添加到待卖股票集合中
for out_code in out_codes:
if out_code in holding_code_dict:
to_be_sold_codes.add(out_code)
# 检查是否有需要第二天卖出的股票
for holding_code in holding_codes:
#if is_rsi_over_sold(holding_code, _date):
#if is_boll_break_up(holding_code, _date):
#if is_macd_gold(holding_code, _date):
#if is_fractal_up(holding_code, _date):
if is_k_down_break_ma10(holding_code, _date):
to_be_sold_codes.add(holding_code)
# 检查是否有需要第二天买入的股票
to_be_bought_codes.clear()
if this_phase_codes is not None:
for _code in this_phase_codes:
#if _code not in holding_codes and is_rsi_over_bought(_code, _date):
#if _code not in holding_codes and is_boll_break_down(_code, _date):
#if _code not in holding_codes and is_macd_gold(_code, _date):
#if _code not in holding_codes and is_fractal_down(_code, _date):
if _code not in holding_codes and is_k_up_break_ma10(_code, _date):
to_be_bought_codes.add(_code)
# 计算总资产
total_value = 0
# 获取所有持仓股的当日收盘价
holding_daily_cursor = DB_CONN['daily'].find(
{'code': {'$in': holding_codes}, 'date': _date, 'index':False},
projection={'close': True, 'code': True}
)
# 计算所有持仓股的总市值
for holding_daily in holding_daily_cursor:
code = holding_daily['code']
holding_stock = holding_code_dict[code]
# 单只持仓的市值等于收盘价乘以持仓量
value = holding_daily['close'] * holding_stock['volume']
# 总市值等于所有持仓股市值的累加之和
total_value += value
# 计算单只股票的持仓收益
profit = (value - holding_stock['cost']) * 100 / holding_stock['cost']
# 计算单只股票的单日收益
one_day_profit = (value - holding_stock['last_value']) * 100 / holding_stock['last_value']
# 更新前一日市值
holding_stock['last_value'] = value
print('持仓: %s, %10.2f, %4.2f, %4.2f' %
(code, value, profit, one_day_profit))
# 总资产等于总市值加上总现金
total_capital = total_value + cash
# 获取沪深300的当日收盘值
hs300_current_value = DB_CONN['daily'].find_one(
{'code': '000300', 'index': True, 'date': _date},
projection={'close': True})['close']
print('收盘后，现金: %10.2f, 总资产: %10.2f' % (cash, total_capital))
last_date = _date
# 将当日的净值、收益和沪深300的涨跌幅放入DataFrame
df_profit.loc[_date] = {
'net_value': round(total_capital / 1e7, 2),
'profit': round(100 * (total_capital - 1e7) / 1e7, 2),
'hs300': round(100 * (hs300_current_value - hs300_begin_value) / hs300_begin_value, 2)
}
print(df_profit)
# 计算最大回撤
drawdown = compute_drawdown(df_profit['net_value'])
# 计算年化收益和夏普比率
annual_profit, sharpe_ratio = compute_sharpe_ratio(df_profit['net_value'])
print('回测结果 %s - %s，年化收益： %7.3f, 最大回撤：%7.3f, 夏普比率：%4.2f' %
(begin_date, end_date, annual_profit, drawdown, sharpe_ratio))
# 绘制收益曲线
df_profit.plot(title='Backtest Result', y=['profit', 'hs300'], kind='line')
plt.show()
def backtest(begin_date, end_date):
"""
策略回测。结束后打印出收益曲线(沪深300基准)、年化收益、最大回撤、
:param begin_date: 回测开始日期
:param end_date: 回测结束日期
"""
cash = 1E7
single_position = 2E5
# 时间为key的净值、收益和同期沪深基准
df_profit = pd.DataFrame(columns=['net_value', 'profit', 'hs300'])
all_dates = get_trading_dates(begin_date, end_date)
hs300_begin_value = DB_CONN['daily'].find_one(
{
'code': '000300',
'index': True,
'date': all_dates[0]
},
projection={'close': True})['close']
adjust_dates, date_codes_dict = stock_pool(begin_date, end_date)
last_phase_codes = None
this_phase_codes = None
to_be_sold_codes = set()
to_be_bought_codes = set()
holding_code_dict = dict()
last_date = None
# 按照日期一步步回测
for _date in all_dates:
print('Backtest at %s.' % _date)
# 当期持仓股票列表
before_sell_holding_codes = list(holding_code_dict.keys())
# 处理复权
if last_date is not None and len(before_sell_holding_codes) > 0:
last_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': before_sell_holding_codes
},
'date': last_date,
'index': False
},
projection={
'code': True,
'au_factor': True,
'_id': False
})
code_last_aufactor_dict = dict()
for last_daily in last_daily_cursor:
code_last_aufactor_dict[
last_daily['code']] = last_daily['au_factor']
current_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': before_sell_holding_codes
},
'date': _date,
'index': False
},
projection={
'code': True,
'au_factor': True,
'_id': False
})
for current_daily in current_daily_cursor:
print(current_daily['code'], _date)
current_aufactor = current_daily['au_factor']
code = current_daily['code']
before_volume = holding_code_dict[code]['volume']
if code in code_last_aufactor_dict:
last_aufactor = code_last_aufactor_dict[code]
after_volume = int(before_volume *
(current_aufactor / last_aufactor))
holding_code_dict[code]['volume'] = after_volume
print('持仓量调整：%s, %6d, %10.6f, %6d, %10.6f' %
(code, before_volume, last_aufactor, after_volume,
current_aufactor))
# 卖出
print('待卖股票池：', to_be_sold_codes, flush=True)
if len(to_be_sold_codes) > 0:
sell_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': list(to_be_sold_codes)
},
'date': _date,
'index': False,
'is_trading': True
},
projection={
'open': True,
'code': True
})
for sell_daily in sell_daily_cursor:
code = sell_daily['code']
if code in before_sell_holding_codes:
holding_stock = holding_code_dict[code]
holding_volume = holding_stock['volume']
sell_price = sell_daily['open']
sell_amount = holding_volume * sell_price
cash += sell_amount
cost = holding_stock['cost']
single_profit = (sell_amount - cost) * 100 / cost
print('卖出 %s, %6d, %6.2f, %8.2f, %4.2f' %
(code, holding_volume, sell_price, sell_amount,
single_profit))
del holding_code_dict[code]
to_be_sold_codes.remove(code)
print('卖出后，现金: %10.2f' % cash)
# 买入
print('待买股票池：', to_be_bought_codes, flush=True)
if len(to_be_bought_codes) > 0:
buy_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': list(to_be_bought_codes)
},
'date': _date,
'is_trading': True,
'index': False
},
projection={
'code': True,
'open': True
})
for buy_daily in buy_daily_cursor:
if cash > single_position:
buy_price = buy_daily['open']
code = buy_daily['code']
volume = int(int(single_position / buy_price) / 100) * 100
buy_amount = buy_price * volume
cash -= buy_amount
holding_code_dict[code] = {
'volume': volume,
'cost': buy_amount,
'last_value': buy_amount
}
print('买入 %s, %6d, %6.2f, %8.2f' %
(code, volume, buy_price, buy_amount))
print('买入后，现金: %10.2f' % cash)
# 持仓股代码列表
holding_codes = list(holding_code_dict.keys())
# 如果调整日，则获取新一期的股票列表
if _date in adjust_dates:
print('股票池调整日：%s，备选股票列表：' % _date, flush=True)
# 暂存为上期的日期
if this_phase_codes is not None:
last_phase_codes = this_phase_codes
this_phase_codes = date_codes_dict[_date]
print(this_phase_codes, flush=True)
# 找到所有调出股票代码，在第二日开盘时卖出
if last_phase_codes is not None:
out_codes = find_out_stocks(last_phase_codes, this_phase_codes)
for out_code in out_codes:
if out_code in holding_code_dict:
to_be_sold_codes.add(out_code)
# 检查是否有需要第二天卖出的股票
for holding_code in holding_codes:
if is_k_down_break_ma10(holding_code, _date):
to_be_sold_codes.add(holding_code)
# 检查是否有需要第二天买入的股票
to_be_bought_codes.clear()
if this_phase_codes is not None:
for _code in this_phase_codes:
if _code not in holding_codes and is_k_up_break_ma10(
_code, _date):
to_be_bought_codes.add(_code)
# 计算总资产
total_value = 0
holding_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': holding_codes
},
'date': _date
},
projection={
'close': True,
'code': True
})
for holding_daily in holding_daily_cursor:
code = holding_daily['code']
holding_stock = holding_code_dict[code]
value = holding_daily['close'] * holding_stock['volume']
total_value += value
profit = (value -
holding_stock['cost']) * 100 / holding_stock['cost']
one_day_profit = (value - holding_stock['last_value']
) * 100 / holding_stock['last_value']
holding_stock['last_value'] = value
print('持仓: %s, %10.2f, %4.2f, %4.2f' %
(code, value, profit, one_day_profit))
total_capital = total_value + cash
hs300_current_value = DB_CONN['daily'].find_one(
{
'code': '000300',
'index': True,
'date': _date
},
projection={'close': True})['close']
print('收盘后，现金: %10.2f, 总资产: %10.2f' % (cash, total_capital))
last_date = _date
df_profit.loc[_date] = {
'net_value':
round(total_capital / 1e7, 2),
'profit':
round(100 * (total_capital - 1e7) / 1e7, 2),
'hs300':
round(
100 * (hs300_current_value - hs300_begin_value) /
hs300_begin_value, 2)
}
df_profit.plot(title='Backtest Result', y=['profit', 'hs300'], kind='line')
plt.show()
def backtest(begin_date, end_date, fun_sell, fun_buy):
'''
回测系统
parameter:
fun_sell: 卖出信号
fun_buy: 买入信号函数
'''
# 设置初始值
cash = 1E7
single_positon = 2E5
df_profit = pd.DataFrame(columns=['net_value', 'profit', 'hs300'])
# 得到回测日期
all_dates = get_trading_dates(begin_date, end_date)
adjust_dates, date_codes_dict = stock_pool(begin_date, end_date)
hs300_begin_value = daily_collection.find_one(
{
'code': '000300',
'index': True,
'date': adjust_dates[0]
},
projection={
'close': True,
'_id': False
})['close']
holding_code_dict = dict()
last_date = None
this_phase_codes = None
last_phase_codes = None
to_be_bought_codes = set()
to_be_sold_codes = set()
for _date in all_dates:
print('Back test begin at: %s' % _date)
before_sell_holding_codes = list(holding_code_dict.keys())
# 对于每一个回测日期处理复权
if last_date is not None and len(before_sell_holding_codes) > 0:
# produce_au(before_sell_holding_codes)
last_daily_cursor = daily_collection.find(
{
'code': {
'$in': before_sell_holding_codes
},
'date': last_date
},
projection={
'code': True,
'_id': False,
'au_factor': True
}).hint([('code', 1), ('date', 1)])
code_last_aufactor_dict = dict()
for last_daily in last_daily_cursor:
code_last_aufactor_dict[
last_daily['code']] = last_daily['au_factor']
current_daily_cursor = daily_collection.find(
{
'code': {
'$in': before_sell_holding_codes
},
'date': _date
},
projection={
'code': True,
'_id': False,
'au_factor': True
}).hint([('code', 1), ('date', 1)])
for current_daily in current_daily_cursor:
current_aufactor = current_daily['au_factor']
code = current_daily['code']
before_volume = holding_code_dict[code]['volume']
if code in code_last_aufactor_dict:
last_aufactor = code_last_aufactor_dict[code]
after_volume = int(before_volume *
(current_aufactor / last_aufactor))
holding_code_dict[code]['volume'] = after_volume
print(
'hold volume adjust: code: %s, %6d, %10.6f, %6d, %10.6f'
% (code, before_volume, last_aufactor, after_volume,
current_aufactor))
# 卖出上一期持仓股
# print('to sell stocks: %s' % to_be_sold_codes, flush=True)
if len(to_be_sold_codes) > 0:
sell_daily_cursor = daily_collection.find(
{
'code': {
'$in': list(to_be_sold_codes)
},
'date': _date,
'index': True,
'is_trading': True
},
projection={
'open': True,
'code': True,
'_id': False
}).hint([('code', 1), ('date', -1)])
for sell_daily in sell_daily_cursor:
sell_code = sell_daily['code']
if sell_code in before_sell_holding_codes:
holding_stock = before_sell_holding_codes[code]
sell_price = sell_daily['open']
holding_volume = holding_stock['volume']
sell_amount = holding_volume * sell_price
cash += sell_amount
cost = holding_stock['cost']
single_profit = (sell_amount - cost) * 100 / cost
print('sell: %s, %6d, %6.2f, %8.2f, %4.2f' %
(code, holding_volume, sell_price, sell_amount,
single_profit))
del holding_code_dict[code]
to_be_sold_codes.remove(code)
print('cash after sell: %10.2f' % cash)
# 买入这一期股票
# print('to buy stocks: ', to_be_bought_codes, flush=True)
if len(to_be_bought_codes) > 0:
buy_daily_cursor = daily_collection.find(
{
'code': {
'$in': list(to_be_bought_codes)
},
'date': _date,
'index': False,
'is_trading': True
},
projection={
'code': True,
'_id': False,
'open': True
}).hint([('code', 1), ('date', -1)])
for buy_daily in buy_daily_cursor:
if cash > single_positon:
code = buy_daily['code']
buy_price = buy_daily['open']
buy_volume = int(
int(single_positon / buy_price) / 100) * 100
buy_amount = buy_price * buy_volume
cash -= buy_amount
holding_code_dict[code] = {
'volume': buy_volume,
'last_value': buy_amount,
'cost': buy_amount
}
print('buy %s, %6d, %6.2f, %8.2f' %
(code, buy_volume, buy_price, buy_amount))
print('cash after buy: %10.2f' % cash)
# 计算收益率
holding_codes = list(holding_code_dict.keys())
if _date in adjust_dates:
print('stock pool adjust date: %s' % _date)
if this_phase_codes is not None:
last_phase_codes = this_phase_codes
this_phase_codes = date_codes_dict[_date]
# print(this_phase_codes, flush=True)
if last_phase_codes is not None:
out_codes = find_out_stocks(last_phase_codes, this_phase_codes)
for out_code in out_codes:
if out_code in holding_code_dict:
to_be_sold_codes.add(out_code)
for holding_code in holding_codes:
if fun_sell(holding_code, _date):
to_be_sold_codes.add(holding_code)
to_be_bought_codes.clear()
if this_phase_codes is not None:
for _code in this_phase_codes:
if _code not in holding_codes and fun_buy(_code, _date):
to_be_bought_codes.add(_code)
# 计算持仓股票市值
total_value = 0
holding_daily_cursor = daily_collection.find(
{
'code': {
'$in': holding_codes
},
'date': _date,
'index': False
},
projection={
'code': True,
'_id': False,
'close': True
}).hint([('code', 1), ('date', -1)])
for holding_daily in holding_daily_cursor:
code = holding_daily['code']
holding_stock = holding_code_dict[code]
value = holding_daily['close'] * holding_stock['volume']
total_value += value
profit = (value -
holding_stock['cost']) * 100 / holding_stock['cost']
one_day_profit = (value - holding_stock['last_value']
) * 100 / holding_stock['last_value']
holding_stock['last_value'] = value
# print('holding stocks: %s, %10.2f, %4.2f, %4.2f' %
# (code, value, profit, one_day_profit))
# 计算总资产
total_capital = total_value + cash
# 计算基准收益
hs300_current_value = daily_collection.find_one(
{
'code': '000300',
'date': _date,
'index': True
},
projection={
'code': True,
'close': True,
'_id': False
})['close']
print('after close, cash: %10.2f, total_capital: %10.2f' %
(cash, total_capital))
dt_date = datetime.strptime(_date, '%Y-%m-%d')
df_profit.loc[dt_date] = {
'net_value':
round(total_capital / 1e7, 2),
'profit':
round(100 * (total_capital - 1e7) / 1e7, 2),
'hs300':
round(
100 * (hs300_current_value - hs300_begin_value) /
hs300_begin_value, 2)
}
drawdown = compute_drawdown(df_profit['net_value'])
annual_profit, sharpe_ratio = compute_sharpe_ratio(df_profit['net_value'])
print(
'Backtest result: %s - %s, annual_profit: %7.3f, maxdrawdown：%7.3f, sharpe ratio: %4.2f'
% (begin_date, end_date, annual_profit, drawdown, sharpe_ratio))
df_profit.plot(title='Backtest Result', y=['profit', 'hs300'], kind='line')
plt.show()
def backtest(begin_date,
end_date,
stop_method=None,
pos_method='equal',
is_saving=False,
save_name=None):
"""
Arguments:
begin_date: 回测开始日期
end_date: 回测结束日期
stop_method : 止损方式
None : 无止损
fixed : 固定比例止损
float : 浮动止损
ATR_float_dynamic : 动态ATR浮动止损
ATR_float_static : 静态ATR浮动止损
pos_method : 头寸分配方式
equal : 均仓分配头寸
atr : 按照ATR分配头寸
Returns:
Account: 数据类型,dict
init_assets : 初始资产, 默认1E7
history_table : 交割单
net_value : 每日净值
final_net_value : 最终日净值
profit : 收益
day_profit : 每日收益
positions : 每日仓位
stop_loss : 止损的方式和止损参数
position_manage : 头寸管理方式和相关参数
"""
# 记录止损时间点
# stop_lose_position_date_current = []
# stop_lose_position_date = []
# 记录回测账户信息
Account = {}
# 仓位相关的初始化
position_manage = {}
if pos_method == 'equal':
single_position = 2E5
position_manage['头寸分配方式'] = '均仓'
Account['position_manage'] = position_manage
elif pos_method == 'atr':
single_position = 0
position_manage['头寸分配方式'] = 'ATR分配头寸'
position_manage['ATR_WIN'] = ATR_WIN
position_manage['RISK_RATIO'] = RISK_RATIO
Account['position_manage'] = position_manage
positions = pd.Series() # 记录每日仓位信息
stop_loss = {}
cash = INIT_ASSETS
init_assets = cash
Account['init_assets'] = init_assets
Account['start'] = begin_date
Account['end'] = end_date
if stop_method is None:
Account['stop_loss'] = '无止损'
elif stop_method == 'fixed':
stop_loss['单日跌幅比例'] = SINGLE_DAY_MAX_DROP_RATE
stop_loss['累计跌幅比例'] = MAX_DROP_RATE
stop_loss['止损方式'] = '固定比例止损'
Account['stop_loss'] = stop_loss
elif stop_method == 'float':
stop_loss['跌幅比例'] = MAX_DROP_RATE
stop_loss['止损方式'] = '浮动止损'
Account['stop_loss'] = stop_loss
elif (stop_method == 'ATR_float_dynamic') or (stop_method
== 'ATR_float_static'):
stop_loss['ATR_WIN'] = ATR_WIN
stop_loss['ATR_RATIO'] = ATR_RATIO
if stop_method == 'ATR_float_dynamic':
stop_loss['止损方式'] = '动态ATR浮动止损'
elif stop_method == 'ATR_float_static':
stop_loss['止损方式'] = '静态ATR浮动止损'
Account['stop_loss'] = stop_loss
# 时间为key的净值、收益和同期沪深基准
df_profit = pd.DataFrame(columns=['net_value', 'profit', 'hs300'])
# 时间为key的单日收益和同期沪深基准
df_day_profit = pd.DataFrame(columns=['profit', 'hs300'])
all_dates = get_trading_dates(begin_date, end_date)
hs300_begin_value = DB_CONN['daily'].find_one(
{
'code': '000300',
'date': all_dates[0],
'index': True
},
projection={'close': True})['close']
adjust_dates, date_codes_dict = stock_pool(begin_date, end_date)
last_phase_codes = None
this_phase_codes = None
to_be_sold_codes = set()
to_be_bought_codes = set()
holding_code_dict = dict()
last_date = None
last_entry_dates = {} # 用于记录入场时间
history_table = pd.DataFrame() # 记录 交割单
last_total_capital = 1e7 # 前一天的总资产值，初始值为初始总资产
last_hs300_close = hs300_begin_value # 前一天的HS300值，初始值为第一天的值
net_value = 1 # 净值
count = 0
# 按照日期一步步回测
for _date in all_dates:
print('Backtest at %s.' % _date)
# 当期持仓股票列表
before_sell_holding_codes = list(holding_code_dict.keys())
# 处理复权
if last_date is not None and len(before_sell_holding_codes) > 0:
last_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': before_sell_holding_codes
},
'date': last_date,
'index': False
},
projection={
'code': True,
'au_factor': True,
'_id': False
})
code_last_aufactor_dict = dict()
for last_daily in last_daily_cursor:
code_last_aufactor_dict[
last_daily['code']] = last_daily['au_factor']
current_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': before_sell_holding_codes
},
'date': _date,
'index': False
},
projection={
'code': True,
'au_factor': True,
'_id': False
})
for current_daily in current_daily_cursor:
print(current_daily['code'], _date)
current_aufactor = current_daily['au_factor']
code = current_daily['code']
before_volume = holding_code_dict[code]['volume']
if code in code_last_aufactor_dict:
last_aufactor = code_last_aufactor_dict[code]
after_volume = int(before_volume *
(current_aufactor / last_aufactor))
holding_code_dict[code]['volume'] = after_volume
print('持仓量调整：%s, %6d, %10.6f, %6d, %10.6f' %
(code, before_volume, last_aufactor, after_volume,
current_aufactor))
# 卖出
print('待卖股票池：', to_be_sold_codes, flush=True)
if len(to_be_sold_codes) > 0:
sell_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': list(to_be_sold_codes)
},
'date': _date,
'index': False,
'is_trading': True
},
projection={
'open': True,
'code': True,
'low_limit': True
})
for sell_daily in sell_daily_cursor:
code = sell_daily['code']
# 若开盘价是跌停价不准卖出
open_price = sell_daily['open']
low_limit = sell_daily['low_limit']
if (code in before_sell_holding_codes) & (open_price >
low_limit):
holding_stock = holding_code_dict[code]
holding_volume = holding_stock['volume']
sell_price = sell_daily['open']
sell_amount = holding_volume * sell_price
cash += sell_amount
cost = holding_stock['cost']
single_profit = (sell_amount - cost) * 100 / cost
last_entry_dates[code] = None
print('卖出 %s, %6d, %6.2f, %8.2f, %4.2f' %
(code, holding_volume, sell_price, sell_amount,
single_profit))
# 记录 交易记录
count += 1
_order = {
'datetime': _date,
'code': code,
'price': sell_price,
'amount': -1 * holding_volume,
'cash': cash
}
temp = pd.DataFrame(data=_order, index=[count])
history_table = pd.concat([history_table, temp])
del holding_code_dict[code]
to_be_sold_codes.remove(code)
print('卖出后，现金: %10.2f' % cash)
# 买入
print('待买股票池：', to_be_bought_codes, flush=True)
if len(to_be_bought_codes) > 0:
buy_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': list(to_be_bought_codes)
},
'date': _date,
'index': False,
'is_trading': True
},
projection={
'code': True,
'open': True,
'high_limit': True
})
for buy_daily in buy_daily_cursor:
# 若开盘价是涨停价不准买入
open_price = buy_daily['open']
high_limit = buy_daily['high_limit']
code = buy_daily['code']
# ===========================ATR分配头寸 code start=========================
if pos_method == 'atr':
ATR = calc_ATR(code, _date)
if ATR is not None:
single_position = init_assets * RISK_RATIO / (
ATR_RATIO * ATR) // 100 * 100
else:
single_position = 0
if (cash > single_position) & (open_price < high_limit) & (
single_position != 0):
buy_price = buy_daily['open']
volume = int(int(single_position / buy_price) / 100) * 100
if volume > 100:
buy_amount = buy_price * volume
cash -= buy_amount
holding_code_dict[code] = {
'volume': volume,
'cost': buy_amount,
'last_value': buy_amount
}
last_entry_dates[code] = _date
print('买入 %s, %6d, %6.2f, %8.2f' %
(code, volume, buy_price, buy_amount))
# 记录 交易记录
count += 1
_order = {
'datetime': _date,
'code': code,
'price': buy_price,
'amount': volume,
'cash': cash
}
temp = pd.DataFrame(data=_order, index=[count])
history_table = pd.concat([history_table, temp])
print('买入后，现金: %10.2f' % cash)
# 持仓股代码列表
holding_codes = list(holding_code_dict.keys())
# 如果调整日，则获取新一期的股票列表
if _date in adjust_dates:
print('股票池调整日：%s，备选股票列表：' % _date, flush=True)
# 暂存为上期的日期
if this_phase_codes is not None:
last_phase_codes = this_phase_codes
this_phase_codes = date_codes_dict[_date]
print(this_phase_codes, flush=True)
# 找到所有调出股票代码，在第二日开盘时卖出
if last_phase_codes is not None:
out_codes = find_out_stocks(last_phase_codes, this_phase_codes)
for out_code in out_codes:
if out_code in holding_code_dict:
to_be_sold_codes.add(out_code)
# 检查是否有需要第二天卖出的股票
for holding_code in holding_codes:
if is_k_down_break_ma10(holding_code, _date):
to_be_sold_codes.add(holding_code)
# ===========================止损判断 code start=========================
if stop_method is not None:
stop_loss_positions(holding_code, _date, last_entry_dates,
to_be_sold_codes, stop_method)
# 检查是否有需要第二天买入的股票
to_be_bought_codes.clear()
if this_phase_codes is not None:
for _code in this_phase_codes:
if _code not in holding_codes and is_k_up_break_ma10(
_code, _date):
to_be_bought_codes.add(_code)
# 计算总资产
total_value = 0
holding_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': holding_codes
},
'date': _date
},
projection={
'close': True,
'code': True
})
for holding_daily in holding_daily_cursor:
code = holding_daily['code']
holding_stock = holding_code_dict[code]
value = holding_daily['close'] * holding_stock['volume']
total_value += value
profit = (value -
holding_stock['cost']) * 100 / holding_stock['cost']
one_day_profit = (value - holding_stock['last_value']
) * 100 / holding_stock['last_value']
holding_stock['last_value'] = value
print('持仓: %s, %10.2f, %4.2f, %4.2f' %
(code, value, profit, one_day_profit))
total_capital = total_value + cash
positions.loc[_date] = total_value / total_capital
hs300_current_value = DB_CONN['daily'].find_one(
{
'code': '000300',
'date': _date,
'index': True
},
projection={'close': True})['close']
print('收盘后，现金: %10.2f, 总资产: %10.2f' % (cash, total_capital))
last_date = _date
net_value = np.round(total_capital / 1e7, 4)
df_profit.loc[_date] = {
'net_value':
np.round(total_capital / 1e7, 4),
'profit':
np.round(100 * (total_capital - 1e7) / 1e7, 4),
'hs300':
np.round(
100 * (hs300_current_value - hs300_begin_value) /
hs300_begin_value, 4)
}
# 计算单日收益
df_day_profit.loc[_date] = {
'profit':
np.round(
100 * (total_capital - last_total_capital) /
last_total_capital, 4),
'hs300':
np.round(
100 * (hs300_current_value - last_hs300_close) /
last_hs300_close, 4)
}
# 暂存当日的总资产和HS300，作为下一个交易日计算单日收益的基础
last_total_capital = total_capital
last_hs300_close = hs300_current_value
Account['history_table'] = history_table
Account['net_value'] = df_profit['net_value']
Account['final_net_value'] = net_value
Account['profit'] = df_profit
Account['day_profit'] = df_day_profit
Account['positions'] = positions
try:
if is_saving:
if save_name is None:
print('保存失败,没有输入save_name, 请使用save_file函数重新保存')
else:
save_file(Account, save_name)
except:
print('保存失败,请使用save_file函数重新保存')
return Account
def backtest(begin_date, end_date):
"""
策略回测。结束后打印出收益曲线(沪深300基准)、年化收益、最大回撤、
:param begin_date: 回测开始日期
:param end_date: 回测结束日期
"""
stop_lose_position_date_current = []
stop_lose_position_date = []
cash = 1E7
single_position = 2E5
# 时间为key的净值、收益和同期沪深基准
df_profit = pd.DataFrame(columns=['net_value', 'profit', 'hs300'])
all_dates = get_trading_dates(begin_date, end_date)
hs300_begin_value = DB_CONN['daily'].find_one(
{
'code': '000300',
'date': all_dates[0],
'index': True
},
projection={'close': True})['close']
adjust_dates, date_codes_dict = stock_pool(begin_date, end_date)
last_phase_codes = None
this_phase_codes = None
to_be_sold_codes = set()
to_be_bought_codes = set()
holding_code_dict = dict()
last_date = None
last_entry_dates = {}
# 按照日期一步步回测
for _date in all_dates:
print('Backtest at %s.' % _date)
# 当期持仓股票列表
before_sell_holding_codes = list(holding_code_dict.keys())
# 处理复权
if last_date is not None and len(before_sell_holding_codes) > 0:
last_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': before_sell_holding_codes
},
'date': last_date,
'index': False
},
projection={
'code': True,
'au_factor': True,
'_id': False
})
code_last_aufactor_dict = dict()
for last_daily in last_daily_cursor:
code_last_aufactor_dict[
last_daily['code']] = last_daily['au_factor']
current_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': before_sell_holding_codes
},
'date': _date,
'index': False
},
projection={
'code': True,
'au_factor': True,
'_id': False
})
for current_daily in current_daily_cursor:
print(current_daily['code'], _date)
current_aufactor = current_daily['au_factor']
code = current_daily['code']
before_volume = holding_code_dict[code]['volume']
if code in code_last_aufactor_dict:
last_aufactor = code_last_aufactor_dict[code]
after_volume = int(before_volume *
(current_aufactor / last_aufactor))
holding_code_dict[code]['volume'] = after_volume
print('持仓量调整：%s, %6d, %10.6f, %6d, %10.6f' %
(code, before_volume, last_aufactor, after_volume,
current_aufactor))
# 卖出
print('待卖股票池：', to_be_sold_codes, flush=True)
if len(to_be_sold_codes) > 0:
sell_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': list(to_be_sold_codes)
},
'date': _date,
'index': False,
'is_trading': True
},
projection={
'open': True,
'code': True,
'low_limit': True
})
for sell_daily in sell_daily_cursor:
code = sell_daily['code']
# 若开盘价是跌停价不准卖出
open_price = sell_daily['open']
low_limit = sell_daily['low_limit']
if (code in before_sell_holding_codes) & (open_price >
low_limit):
holding_stock = holding_code_dict[code]
holding_volume = holding_stock['volume']
sell_price = sell_daily['open']
sell_amount = holding_volume * sell_price
cash += sell_amount
cost = holding_stock['cost']
single_profit = (sell_amount - cost) * 100 / cost
last_entry_dates[code] = None
print('卖出 %s, %6d, %6.2f, %8.2f, %4.2f' %
(code, holding_volume, sell_price, sell_amount,
single_profit))
del holding_code_dict[code]
to_be_sold_codes.remove(code)
print('卖出后，现金: %10.2f' % cash)
# 买入
print('待买股票池：', to_be_bought_codes, flush=True)
if len(to_be_bought_codes) > 0:
buy_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': list(to_be_bought_codes)
},
'date': _date,
'index': False,
'is_trading': True
},
projection={
'code': True,
'open': True,
'high_limit': True
})
for buy_daily in buy_daily_cursor:
# 若开盘价是涨停价不准买入
open_price = buy_daily['open']
high_limit = buy_daily['high_limit']
if (cash > single_position) & (open_price < high_limit):
buy_price = buy_daily['open']
code = buy_daily['code']
volume = int(int(single_position / buy_price) / 100) * 100
buy_amount = buy_price * volume
cash -= buy_amount
holding_code_dict[code] = {
'volume': volume,
'cost': buy_amount,
'last_value': buy_amount
}
last_entry_dates[code] = _date
print('买入 %s, %6d, %6.2f, %8.2f' %
(code, volume, buy_price, buy_amount))
print('买入后，现金: %10.2f' % cash)
# 持仓股代码列表
holding_codes = list(holding_code_dict.keys())
# 如果调整日，则获取新一期的股票列表
if _date in adjust_dates:
print('股票池调整日：%s，备选股票列表：' % _date, flush=True)
# 暂存为上期的日期
if this_phase_codes is not None:
last_phase_codes = this_phase_codes
this_phase_codes = date_codes_dict[_date]
print(this_phase_codes, flush=True)
# 找到所有调出股票代码，在第二日开盘时卖出
if last_phase_codes is not None:
out_codes = find_out_stocks(last_phase_codes, this_phase_codes)
for out_code in out_codes:
if out_code in holding_code_dict:
to_be_sold_codes.add(out_code)
# 检查是否有需要第二天卖出的股票
for holding_code in holding_codes:
if is_k_down_break_ma10(holding_code, _date):
to_be_sold_codes.add(holding_code)
"""
止损条件:
1.当日亏损超过3%
2.累计亏损超过10%
满足其一就卖出
注意，这里的回测逻辑是无法应用到模拟盘的，因为用了当天的收盘价去计算亏损；
当然在回测里没问题，毕竟是第二天要卖出的股票，所以姑且当做收盘后的判断吧；
"""
# 当天收盘价
current_close = DB_CONN['daily'].find_one({
'code': holding_code,
'date': _date,
'index': False
})['close']
# 买入时的价格和日期
entry_date = last_entry_dates[holding_code]
entry_daily_cursor = DB_CONN['daily'].find_one({
'code': holding_code,
'date': entry_date,
'index': False
})
entry_price = entry_daily_cursor['open']
entry_date_close = entry_daily_cursor['close']
if (entry_date == _date) & (((entry_price - entry_date_close) /
entry_price) > CURRENT_MAX_DROP_RATE):
to_be_sold_codes.add(holding_code)
stop_lose_position_date_current.append(_date)
elif ((entry_price - current_close) / entry_price) > MAX_DROP_RATE:
to_be_sold_codes.add(holding_code)
stop_lose_position_date.append(_date)
# 检查是否有需要第二天买入的股票
to_be_bought_codes.clear()
if this_phase_codes is not None:
for _code in this_phase_codes:
if _code not in holding_codes and is_k_up_break_ma10(
_code, _date):
to_be_bought_codes.add(_code)
# 计算总资产
total_value = 0
holding_daily_cursor = DB_CONN['daily'].find(
{
'code': {
'$in': holding_codes
},
'date': _date
},
projection={
'close': True,
'code': True
})
for holding_daily in holding_daily_cursor:
code = holding_daily['code']
holding_stock = holding_code_dict[code]
value = holding_daily['close'] * holding_stock['volume']
total_value += value
profit = (value -
holding_stock['cost']) * 100 / holding_stock['cost']
one_day_profit = (value - holding_stock['last_value']
) * 100 / holding_stock['last_value']
holding_stock['last_value'] = value
print('持仓: %s, %10.2f, %4.2f, %4.2f' %
(code, value, profit, one_day_profit))
total_capital = total_value + cash
hs300_current_value = DB_CONN['daily'].find_one(
{
'code': '000300',
'date': _date,
'index': True
},
projection={'close': True})['close']
print('收盘后，现金: %10.2f, 总资产: %10.2f' % (cash, total_capital))
last_date = _date
df_profit.loc[_date] = {
'net_value':
round(total_capital / 1e7, 2),
'profit':
round(100 * (total_capital - 1e7) / 1e7, 2),
'hs300':
round(
100 * (hs300_current_value - hs300_begin_value) /
hs300_begin_value, 2)
}
print(np.sort(list(set(stop_lose_position_date))))
print(np.sort(list(set(stop_lose_position_date_current))))
df_profit.plot(title='Backtest Result', y=['profit', 'hs300'], kind='line')
plt.show()