def get_head(context, bar_dict):
prices = history_bars(bar_dict, context.period + 1, '1d', 'close')
b_prices = history_bars('000002.XSHE', 6, '1d', 'close')
volumes = history_bars('000002.XSHE', context.period + 1, '1d', 'volume')
score = {}
for stock in prices.columns:
p = prices[stock]
v = volumes[stock]
temp = 0
for i in list(range(1, context.period)):
if p[i] > p[i - 1]:
if v[i] > v[i - 1]:
temp = temp + 2
else:
temp = temp + 1
else:
if v[i] < v[i - 1]:
temp = temp - 1
else:
temp = temp - 2
score[stock] = temp
s = pd.Series(score, name='scores')
s.sort(ascending=False)
context.to_buy = s.index[0:context.number]
if b_prices[0] / b_prices[4] <= 0.92:
context.to_buy = []
def handle_bar(context, bar_dict):
'''
交易函数
'''
s1 = history_bars(context.stocks[0], 21, '1d', 'close')
s2 = history_bars(context.stocks[1], 21, '1d', 'close')
logger.debug('s1:' + str(s1))
logger.debug('s2:' + str(s2))
import pdb
pdb.set_trace()
s1delta = (s1[-1] - s1[0]) / s1[0]
s2delta = (s2[-1] - s2[0]) / s2[0]
log_str = '元和 ' + str(s1[0]) + '->' + str(s1[-1]) + ' 涨幅: ' + str(
round(s1delta, 3)) + ' 债券 ' + str(s2[0]) + '->' + str(
s2[-1]) + ' 涨幅: ' + str(round(s2delta, 3))
trading = None
if s1delta is not None and s2delta is not None:
if s1delta < 0 and s2delta < 0:
trading = 'cash'
elif s1delta > s2delta:
trading = context.stocks[0]
else:
trading = context.stocks[1]
if trading is None or trading == context.hold:
return
if context.hold is not None and context.hold != 'cash':
order_target_percent(context.hold, 0)
log_str += ' sell ' + context.hold
if trading != 'cash':
order_target_percent(trading, 1)
log_str += ' buy ' + trading
context.hold = trading
logger.info(log_str)
def calculate_if_A_stock_premium_rate(context,today):
exchange_rate_row = context.exchange_rate_df[(context.exchange_rate_df.日期 <= today)].tail(1)
exchange_rate = exchange_rate_row.loc[:,'收盘价(元)'].values[0]
# inquiry H stock prices
H_price = pd.DataFrame([])
for H_code in context.A_H_stock_df.H_code:
H_price_row = context.H_stock_price_all[(context.H_stock_price_all.Date <= today) &
(context.H_stock_price_all.H_code == H_code)].tail(1)
H_price = H_price.append(H_price_row)
H_price.drop(['Open', 'High', 'Low', 'Volume', 'Adj Close'], inplace=True, axis=1, errors='ignore')
H_price.rename(columns={'Close': 'H_price_HKD'}, inplace=True)
H_price['H_price_RMB'] = H_price['H_price_HKD'].map(
lambda x: x * exchange_rate)
# inquiry A stock prices
A_price = pd.DataFrame([])
for A_code in context.A_H_stock_df.A_code:
A_price_hist = history_bars(order_book_id=A_code,bar_count=1,frequency='1d',fields='close')
A_price_row = pd.DataFrame(data={'A_code': A_code, 'A_price': A_price_hist[0]},index=np.arange(1))
A_price = A_price.append(A_price_row)
# join the A+H prices
A_H_price_compare_result = pd.merge(context.A_H_stock_df,H_price,how='left',on=['H_code'])
A_H_price_compare_result = pd.merge(A_H_price_compare_result,A_price,how='left',on=['A_code'])
A_H_price_compare_result['A股溢价率'] = 100 * (A_H_price_compare_result['A_price'] -
A_H_price_compare_result['H_price_RMB']) \
/ A_H_price_compare_result['H_price_RMB']
A_H_price_compare_result = A_H_price_compare_result.sort_values(by='A股溢价率',ascending=1)
context.A_H_price_compare_result = A_H_price_compare_result
#logger.info(context.A_H_price_compare_result)
return context
def handle_bar(context, bar_dict):
context.order_count += 1
if context.order_count == 1:
update_universe(context.s2)
his = history_bars(context.s2, 5, '1d', 'close')
print(sorted(his.tolist()))
print(sorted([24.1, 23.71, 23.82, 23.93, 23.66]))
assert sorted(his.tolist()) == sorted([26.06, 26.13, 26.54, 26.6, 26.86])
def handle_bar(context, bar_dict):
context.order_count += 1
if context.order_count == 1:
update_universe(context.s2)
his = history_bars(context.s2, 5, '1d', 'close')
print(sorted(his.tolist()))
print(sorted([24.1, 23.71, 23.82, 23.93, 23.66]))
assert sorted(his.tolist()) == sorted([26.06, 26.13, 26.54, 26.6, 26.86])
def _history_bars(self, order_book_id, bar_count, freq, dt):
if self.fetch_data_by_api:
bars = history_bars(
order_book_id, bar_count, freq, fields=None)
else:
bars = self.rqalpha_env.data_proxy.history_bars(
order_book_id, bar_count, freq, field=None,
dt=dt)
return bars
def handle_bar(context, bar_dict):
"""
Description : 选择的证券的数据更新将会触发此段逻辑
Arg :
Returns :
Raises :
"""
# 需要买的股票列表
_lst_buy_stock = []
# 遍历所有的股票
for _stock in context.stocks:
# 首先取得前面多少日的收盘价
_data = history_bars(_stock, context.N, '1d', "close")
# 判断是否是前面几天啦
if len(_data) < context.N:
continue
# 取得最小值和最大值
_min = _data.min()
_max = _data.max()
_close = _data[-1]
# 先判断是否达到卖出的条件，
# 条件是，达到止损
if _close < _max * (100 - context.loss)/100:
# 然后判断是否有买入
if _stock in context.portfolio.positions.keys():
order_target_percent(_stock, 0)
logger.info("清空股票：{}".format(_stock))
# 判断是否满足买的条件
if ((_close > _min * (100 + context.M)/100)
and _close ==_max):
logger.info("买入股票{}".format(_stock))
_lst_buy_stock.append(_stock)
# 输出有多少个股票买入
# 因为有时候有很多股票需要买入，这里要判断最值得的。
# 我只买10个股票。
# 这里要判断要买那个股票
_lst_buy_stock_2 = [] # 这个是最终要选择的股票
if (len(_lst_buy_stock) > 10):
# 按照ATX来购买。趋势高的就买喽。
pass
else:
_lst_buy_stock_2 = _lst_buy_stock
# 如果现在持有的股票不在这个最终要选择的股票里边，就卖了吧
if _stock in context.portfolio.positions.keys():
if _stock not in _lst_buy_stock_2:
order_target_percent(_stock, 0)
logger.info("清空股票：{}".format(_stock))
# 然后购买最终持有的股票。
for _stock in _lst_buy_stock_2:
order_target_percent(_stock, 0.1)
logger.info("购买股票：{}".format(_stock))
logger.info("总共买入股票数量:{}".format(len(_lst_buy_stock)))
def handle_bar(context, bar_dict):
# pass
#
#def handle_bar_weekly(context, bar_dict):
'''
交易函数
'''
hist_s1 = history_bars(context.stocks[0], context.window_size + 1, '1d',
'close')
hist_s2 = history_bars(context.stocks[1], context.window_size + 1, '1d',
'close')
curr_s1 = bar_dict[context.stocks[0]].close
curr_s2 = bar_dict[context.stocks[1]].close
logger.debug('s1:' + str(hist_s1))
logger.debug('s2:' + str(hist_s2))
s1delta = (curr_s1 - hist_s1[0]) / hist_s1[0]
s2delta = (curr_s2 - hist_s2[0]) / hist_s2[0]
log_str = '[' + context.now.isoformat() + ']'
log_str += '沪深300 ' + str(hist_s1[0]) + '->' + str(curr_s1)
log_str += ' 涨幅: ' + str(round(s1delta, 3))
log_str += ' 中证500 ' + str(hist_s2[0]) + '->' + str(curr_s2)
log_str += ' 涨幅: ' + str(round(s2delta, 3))
trading = None
if s1delta is not None and s2delta is not None:
if s1delta < 0 and s2delta < 0:
trading = 'cash'
elif s1delta > s2delta:
trading = context.stocks[0]
else:
trading = context.stocks[1]
if trading is None or trading == context.hold:
return
if context.hold != 'cash':
order_target_percent(context.hold, 0)
log_str += ' sell ' + context.hold
if trading != 'cash':
order_target_percent(trading, 0.99)
log_str += ' buy ' + trading
context.hold = trading
logger.info(log_str)
def _history_bars(self, order_book_id, bar_count, freq, dt):
if self.fetch_data_by_api and ExecutionContext.phase() in (
EXECUTION_PHASE.BEFORE_TRADING,
EXECUTION_PHASE.ON_BAR,
EXECUTION_PHASE.ON_TICK,
EXECUTION_PHASE.AFTER_TRADING,
EXECUTION_PHASE.SCHEDULED):
bars = history_bars(
order_book_id, bar_count, freq, fields=None)
else:
bars = self.rqalpha_env.data_proxy.history_bars(
order_book_id, bar_count, freq,
field=["datetime", "open", "high", "low", "close", "volume"],
dt=dt)
return bars
def _history_bars(self, order_book_id, bar_count, freq, dt):
if self.fetch_data_by_api and ExecutionContext.phase() in (
EXECUTION_PHASE.BEFORE_TRADING,
EXECUTION_PHASE.ON_BAR,
EXECUTION_PHASE.ON_TICK,
EXECUTION_PHASE.AFTER_TRADING,
EXECUTION_PHASE.SCHEDULED):
bars = history_bars(
order_book_id, bar_count, freq, fields=None)
else:
bars = self.rqalpha_env.data_proxy.history_bars(
order_book_id, bar_count, freq,
field=["datetime", "open", "high", "low", "close", "volume"],
dt=dt)
return bars
def handle_bar(context, bar_dict):
"""
Description : 选择的证券的数据更新将会触发此段逻辑
Arg :
Returns :
Raises :
"""
# 需要买的股票列表
_lst_buy_stock = []
# 遍历所有的股票
for _stock in context.stocks:
# 首先取得前面多少日的收盘价
_data = history_bars(_stock, context.N + 1, '1d', "close")
# 判断是否是前面几天啦
if len(_data) < context.N + 1:
continue
# 取得最小值和最大值
_data_2 = _data[0:context.N]
_min = _data_2.min()
_max = _data_2.max()
_close_1 = _data_2[-1]
_close_2 = _data[-1]
# 先判断是否达到卖出的条件，
# 条件是，达到止损
if _close_2 < _close_1 * (100 + context.min_up):
# 然后判断是否有买入
if _stock in context.portfolio.positions.keys():
order_target_percent(_stock, 0)
# logger.info("清空股票：{}".format(_stock))
# 判断是否是有仓位
if _stock in context.portfolio.positions.keys():
# 如果是有降低，就清仓啦
if _close_2 < _close_1:
order_target_percent(_stock, 0)
# logger.info("清空股票：{}".format(_stock))
# 判断是否满足买的条件
if ((_close_1 > _min * (100 + context.M) / 100) and _close_1 == _max
and (_close_2 - _close_1) / _close_1 * 100 > context.min_up):
# logger.info("买入股票{}".format(_stock))
# order_target_percent(_stock, 0.1)
_lst_buy_stock.append(_stock)
logger.info("{}".format(len(_lst_buy_stock)))
def handle_bar(context, bar_dict):
return_list = history_bars(context.s1, 5, '1d', 'close')
if str(context.now.date()) == '2016-12-29':
assert return_list.tolist() == [9.08, 9.1199, 9.08, 9.06, 9.08]
def handle_bar(context, bar_dict):
return_list = history_bars(context.s1, 5, '1d', 'close')
if str(context.now.date()) == '2016-12-29':
assert return_list.tolist() == [9.08, 9.12, 9.08, 9.06, 9.08]
