def handle_bar(context, bar_dict):
df = all_instruments('FenjiA')
df_to_assert = df.loc[df['order_book_id'] == '150247.XSHE']
assert df_to_assert.iloc[0, 0] == 'CMAJ'
assert df_to_assert.iloc[0, 7] == '工银中证传媒A'
assert all_instruments().shape >= (8000, 4)
assert all_instruments('CS').shape >= (3000, 16)
assert all_instruments('ETF').shape >= (120, 9)
assert all_instruments('LOF').shape >= (130, 9)
assert all_instruments('FenjiMu').shape >= (10, 9)
assert all_instruments('FenjiA').shape >= (120, 9)
assert all_instruments('FenjiB').shape >= (140, 9)
assert all_instruments('INDX').shape >= (500, 8)
assert all_instruments('Future').shape >= (3500, 16)
def init(context):
"""
Description : 程序初始化
Arg :
Returns :
Raises :
"""
# 这里设置参数
context.LST_MA = [5, 10, 20, 60] # 均线。
context.LST_DUOTOU = [] # 保存的是均线持续多头的列表。
context.DUOTOU_DAYS = 7 # 均线排列持续天数判断为多头趋势
context.BACK_MA = 10 # 回测均线
context.NUM_STOCKS = 10 # 最多持有股票数量
context.STOP_PERCENT = 10 # 止损比例
# 获得所有股票，
_all_cn_stock = list(all_instruments('CS')['order_book_id'])
# 删除ST股票, 不对ST股票做回测。
# 上市90天的股票不做回测，因为前几天都是疯涨。然后暴跌。
_all_cn_stock = [
_stock for _stock in _all_cn_stock
if not is_st_stock(_stock, 1)
and is_predate_listed_date(_stock, 90)]
for _book_id in _all_cn_stock:
# 在这里添加指标的相关数据
# 如下先取得K线数据
_close = get_bars_all(_book_id, '1d', "close")
_open = get_bars_all(_book_id, '1d', "open")
_high = get_bars_all(_book_id, '1d', "high")
_low = get_bars_all(_book_id, '1d', "low")
_limit_down = get_bars_all(_book_id, '1d', "limit_down")
_limit_up = get_bars_all(_book_id, '1d', "limit_up")
_total_turnover = get_bars_all(_book_id, '1d', "total_turnover")
_volume = get_bars_all(_book_id, '1d', "volume")
# 计算指标
# _ma_5 = talib.SMA(_close, context.TIME_PERIOD)
# 将所有的均线保存在这个数组中。
_lst_ma = []
for _ma in context.LST_MA:
_lst_ma.append(talib.SMA(_close, _ma))
# 然后取得一个逻辑数组，是判断是否是均线多头的。
_bool_up = []
for _i in range(len(_lst_ma) - 1):
# 每个都跟后边的比较一下。
_bool_up = _bool_up and (_lst_ma[_i + 1] > _lst_ma[_i])
# 将指标数据保存
add_indicator(_book_id, '1d', "duotou", _bool_up)
# 然后有个变量看看是否回撤的
_ma_huiche = talib.SMA(_close, context.BACK_MA)
_huiche = _close < _ma_huiche
# 保存这个回撤的
add_indicator(_book_id, '1d', "huiche", _huiche)
# 设置进入股票池
update_universe(_all_cn_stock)
pass
def handle_bar(context, bar_dict):
date = context.now.replace(hour=0, minute=0, second=0)
df = all_instruments('CS')
assert (df['listed_date'] <= date).all()
assert (df['de_listed_date'] >= date).all()
assert all(not is_suspended(o) for o in df['order_book_id'])
assert (df['type'] == 'CS').all()
df1 = all_instruments('Stock')
assert sorted(df['order_book_id']) == sorted(df1['order_book_id'])
df2 = all_instruments('Future')
assert (df2['type'] == 'Future').all()
assert (df2['listed_date'] <= date).all()
assert (df2['de_listed_date'] >= date).all()
df3 = all_instruments(['Future', 'Stock'])
assert sorted(list(df['order_book_id']) + list(df2['order_book_id'])) == sorted(df3['order_book_id'])
def handle_bar(context, bar_dict):
date = context.now.replace(hour=0, minute=0, second=0)
df = all_instruments('CS')
assert (df['listed_date'] <= date).all()
assert (df['de_listed_date'] >= date).all()
assert all(not is_suspended(o) for o in df['order_book_id'])
assert (df['type'] == 'CS').all()
df1 = all_instruments('Stock')
assert sorted(df['order_book_id']) == sorted(df1['order_book_id'])
df2 = all_instruments('Future')
assert (df2['type'] == 'Future').all()
assert (df2['listed_date'] <= date).all()
assert (df2['de_listed_date'] >= date).all()
df3 = all_instruments(['Future', 'Stock'])
assert sorted(list(df['order_book_id']) + list(df2['order_book_id'])) == sorted(df3['order_book_id'])
def init(context):
"""
Description : 程序初始化
Arg :
Returns :
Raises :
"""
# 这里设置参数
context.TIME_PERIOD = 5
context.N = 20 # 计算多少日内的
context.M = 10 # %M
context.loss = 10 # 止损百分比
context.min_up = 3 # 最低上涨
# 获得所有股票，
_all_cn_stock = list(all_instruments('CS')['order_book_id'])
# 删除ST股票, 不对ST股票做回测。
# 上市90天的股票不做回测，因为前几天都是疯涨。然后暴跌。
_all_cn_stock = [
_stock for _stock in _all_cn_stock
if not is_st_stock(_stock, 1) and is_predate_listed_date(_stock, 90)
]
# 保存股票列表
context.stocks = _all_cn_stock
for _book_id in _all_cn_stock:
pass
# 在这里添加指标的相关数据
# 如下先取得K线数据
# _close = get_bars_all(_book_id, '1d', "close")
# _open = get_bars_all(_book_id, '1d', "open")
# _high = get_bars_all(_book_id, '1d', "high")
# _low = get_bars_all(_book_id, '1d', "low")
# _limit_down = get_bars_all(_book_id, '1d', "limit_down")
# _limit_up = get_bars_all(_book_id, '1d', "limit_up")
# _total_turnover = get_bars_all(_book_id, '1d', "total_turnover")
# _volume = get_bars_all(_book_id, '1d', "volume")
# # 计算指标
# _ma_5 = talib.SMA(_close, context.TIME_PERIOD)
# # 将指标数据保存
# add_indicator(_book_id, '1d', "ma_5", _ma_5)
#
# 设置进入股票池
update_universe(_all_cn_stock)
pass