def cal_signal_(df0, df1, df2, strat_time, end_time, cs0):
'''
# df0 == 原始time，ohlcc,:np.array ：['candle_begin_time', 'open', 'high', 'low', 'close', 'volume', 'days', 'huors', 'minutes']
# df1 == 信号统计数据列:np.array ：['candle_begin_time','signal', 'pos', 'opne_price', 'per_lr', 'sl']
# df2 == 指标列:np.array
'''
# 配置参数
da, zhong, xiao, stop_n, max_stop_win = cs0
# 配置临时变量
open_pos_size = 1
max_log_high = np.nan
min_log_low = np.nan
stop_loss_price = np.nan
for i in range(2, df0.shape[0]): #类似于vnpy:on_bar
# 交易时间判断：日：1-28，and （时，分：9：15-16：29）
# trading_time_con = ((df0[i, 7] == 9) and (df0[i, 8] >= 15)) or \
# ((df0[i, 7] == 16) and (df0[i, 8] <= 29)) or \
# ((df0[i, 7] > 9) and (df0[i, 7] < 16))
trading_time_con = True
# 交易所日盘，开放
if trading_time_con:
# 快捷变量
open_bar = df0[i][1]
high_bar = df0[i][2]
low_bar = df0[i][3]
last_close = df0[i - 1][4]
clsoe_bar = df0[i][4]
# === 仓位统计。
df1, now_open_prcie, now_pos = cal_per_pos(i,
df1,
open_pos_size,
open_bar,
clsoe_bar,
last_close,
sxf=1,
slip=1)
# === 当日变量加载
max_log_high = max(high_bar, df2[i - 1, 7])
min_log_low = min(low_bar, df2[i - 1, 6])
ma_da = df2[i][1]
ma_da2 = df2[i - 1][1]
ma_zhong = df2[i][2]
ma_zhong2 = df2[i - 1][2]
ma_xiao = df2[i][3]
ma_xiao2 = df2[i - 1][3]
if now_pos == 0:
long_condition = (ma_xiao > ma_zhong) and (ma_xiao2 <=
ma_zhong2)
long_condition &= (ma_xiao > ma_da)
# long_condition &= (clsoe_bar > ma_zhong)and(last_close <= ma_zhong2) #突破中线
short_condition = False #(ma_xiao < ma_zhong) and (ma_zhong < ma_da)
if long_condition:
df1[i][1] = 1 #buy signal
max_log_high = high_bar
# 空：突破最低线。
elif short_condition:
min_log_low = low_bar
df1[i][1] = -1
elif now_pos > 0:
stop_loss_price = now_open_prcie - stop_n if (
max_log_high -
now_open_prcie < stop_n) else now_open_prcie + 50
stop_loss_con = (clsoe_bar < stop_loss_price) and (
last_close >= stop_loss_price)
stop_win_con = (
(max_log_high - now_open_prcie > max_stop_win)) and (
clsoe_bar < ma_xiao) and (last_close >= ma_xiao2)
close_pos = (ma_xiao < ma_zhong)
if close_pos:
df1[i][1] = 0
elif stop_loss_con:
df1[i][1] = 0
elif stop_win_con:
df1[i][1] = 0
elif now_pos < 0:
stop_loss_price = min_log_low + stop_n
stop_loss_con = (clsoe_bar > stop_loss_price) and (
last_close <= stop_loss_price)
stop_win_con = False #((clsoe_bar < stop_win_price)) and (last_close >= stop_win_price)
close_pos = False #(clsoe_bar >me ) and (last_close <= me)
if close_pos:
df1[i][1] = 0
elif stop_loss_con:
df1[i][1] = 0
elif stop_win_con:
df1[i][1] = 0
# 记录指标 绘图等等
df2[i, 4] = stop_loss_price
df2[i, 6] = min_log_low
df2[i, 7] = max_log_high
res0 = cal_tongji(df_input=df1)
res0 = np.concatenate((res0, cs0))
return df0, df1, df2, res0
def cal_signal_(df0, df1, df2, strat_time, end_time, cs0, sxf=1, slip=1):
'''
# df0 == 原始time，ohlcc,:np.array ：['candle_begin_time', 'open', 'high', 'low', 'close', 'volume', 'days', 'huors', 'minutes']
# df1 == 信号统计数据列:np.array ：['candle_begin_time','signal', 'pos', 'opne_price', 'per_lr', 'sl']
# df2 == 指标列:np.array
'''
# 配置参数
da, zhong, xiao, stop_n, max_stop_win = cs0
# 配置临时变量
open_pos_size = 1
max_log_high = np.nan
min_log_low = np.nan
stop_loss_price = np.nan
stop_price = np.nan
for i in range(2, df0.shape[0]): #类似于vnpy:on_bar
# 交易时间判断：日：1-28，and （时，分：9：15-16：29）
trading_time_con = ((df0[i, 7] == 9) and (df0[i, 8] >= 15)) or \
((df0[i, 7] == 16) and (df0[i, 8] <= 29)) or \
((df0[i, 7] > 9) and (df0[i, 7] < 16))
# 交易所日盘，开放
if trading_time_con:
# 快捷变量
open_bar = df0[i][1]
high_bar = df0[i][2]
low_bar = df0[i][3]
last_close = df0[i - 1][4]
clsoe_bar = df0[i][4]
stop_price = stop_price
# === 仓位统计。
df1, now_open_prcie, now_pos = cal_per_pos_with_stop_order(
i,
df1,
open_pos_size,
open_bar,
clsoe_bar,
last_close,
stop_price,
sxf=1,
slip=1)
# === 当日变量加载
if ((df0[i, 7] == 9) and (df0[i, 8] == 15)):
# inner_trade_nums = 1
max_log_high = high_bar
min_log_low = low_bar
else:
max_log_high = max(high_bar, max_log_high)
min_log_low = min(low_bar, min_log_low)
ma_da = df2[i][1]
ma_da2 = df2[i - 1][1]
ma_zhong = df2[i][2]
ma_zhong2 = df2[i - 1][2]
ma_xiao = df2[i][3]
ma_xiao2 = df2[i - 1][3]
# 我们交易时间
we_trade_con = ((df0[i, 6] >= strat_time[0]) &
(df0[i, 6] <= end_time[0]))
we_trade_con &=(((df0[i, 7] == strat_time[1]) & (df0[i, 8] > strat_time[2])) | \
((df0[i, 7] == end_time[1]) & (df0[i, 8] < end_time[2])) |\
((df0[i, 7] > strat_time[1]) & (df0[i, 7] < end_time[1])))
# 信号生成
if we_trade_con:
if now_pos == 0:
long_condition = (ma_xiao > ma_zhong) and (ma_zhong > ma_da
) #三金叉
long_condition &= (ma_zhong > ma_zhong2) and (
ma_da > ma_da2) #向上方向
long_condition &= (clsoe_bar > ma_zhong) and (
last_close <= ma_zhong2) #突破中线
short_condition = False #(ma_xiao < ma_zhong) and (ma_zhong < ma_da)
if long_condition:
# df1[i][1] = 1 #buy signal
res_sig = to_deal_stop_price(1, ma_zhong2 + 1000,
ma_zhong2, high_bar,
low_bar, now_pos)
# print(res_sig, type(res_sig),list(res_sig))
df1[i][1], stop_price = res_sig
# print()
max_log_high = high_bar
# 空：突破最低线。
elif short_condition:
min_log_low = low_bar
df1[i][1] = -1
elif now_pos > 0:
res_sig = to_deal_stop_price(0, ma_zhong2 + 1000, ma_da2,
high_bar, low_bar, now_pos)
# print(res_sig, type(res_sig), list(res_sig))
df1[i][1], stop_price = res_sig
# stop_loss_price = now_open_prcie-stop_n if (max_log_high-now_open_prcie < 80) else now_open_prcie + 20
# stop_loss_con = (clsoe_bar < stop_loss_price) and (last_close >= stop_loss_price)
# stop_win_con = ((max_log_high-min_log_low > max_stop_win)) and (clsoe_bar <ma_xiao) and (last_close>=ma_xiao2)
# close_pos = ((clsoe_bar < ma_da)) and (last_close >= ma_da2)
# if close_pos :
# df1[i][1] = 0
# elif stop_loss_con:
# df1[i][1] = 0
# elif stop_win_con:
# df1[i][1] = 0
elif now_pos < 0:
stop_loss_price = min_log_low + stop_n
stop_loss_con = (clsoe_bar > stop_loss_price) and (
last_close <= stop_loss_price)
stop_win_con = False #((clsoe_bar < stop_win_price)) and (last_close >= stop_win_price)
close_pos = False #(clsoe_bar >me ) and (last_close <= me)
if close_pos:
df1[i][1] = 0
elif stop_loss_con:
df1[i][1] = 0
elif stop_win_con:
df1[i][1] = 0
else: #非交易时间段
if now_pos != 0:
df1[i][1] = 0
# 记录指标 绘图等等
df2[i, 4] = stop_loss_price
df2[i, 6] = min_log_low
df2[i, 7] = max_log_high
res0 = cal_tongji(df_input=df1)
res0 = np.concatenate((res0, cs0))
return df0, df1, df2, res0
def cal_signal_(df0, df1, df2, strat_time, end_time, cs0, all_day=0):
'''
# df0 == 原始time，ohlcc,:np.array ：['candle_begin_time', 'open', 'high', 'low', 'close', 'volume', 'days', 'huors', 'minutes']
# df1 == 信号统计数据列:np.array ：['candle_begin_time','signal', 'pos', 'opne_price', 'per_lr', 'sl']
# df2 == 指标列:np.array
'''
# 配置参数
tp_n, zuhe_limit, max_stop_win = cs0[-3:]
# 配置临时变量
open_pos_size = 1
max_log_high = np.nan
min_log_low = np.nan
stop_loss_price = np.nan
for i in range(2, df0.shape[0]): #类似于vnpy:on_bar
# 交易时间判断：日：1-28，and （时，分：9：15-16：29）
trading_time_con = ((df0[i, 7] == 9) and (df0[i, 8] >= 15)) or \
((df0[i, 7] == 16) and (df0[i, 8] <= 29)) or \
((df0[i, 7] > 9) and (df0[i, 7] < 16))
if all_day > 0:
trading_time_con |= True
# 交易所日盘，开放
if trading_time_con:
# 快捷变量
open_bar = df0[i][1]
high_bar = df0[i][2]
low_bar = df0[i][3]
last_close = df0[i - 1][4]
close_bar = df0[i][4]
# === 仓位统计。
df1, now_open_prcie, now_pos = cal_per_pos(i,
df1,
open_pos_size,
open_bar,
close_bar,
last_close,
sxf=1,
slip=1)
# === 当日变量加载
if ((df0[i, 7] == 9) and (df0[i, 8] == 15)):
# inner_trade_nums = 1
max_log_high = high_bar
min_log_low = low_bar
else:
max_log_high = max(high_bar, max_log_high)
min_log_low = min(low_bar, min_log_low)
tp = df2[i][1]
tp2 = df2[i - 1][1]
zhyinzi = df2[i][2]
ma_xiao = df2[i][3]
ma_xiao2 = df2[i - 1][3]
stop_line = df2[i][4]
# 我们交易时间
we_trade_con = ((df0[i, 6] >= strat_time[0]) &
(df0[i, 6] <= end_time[0]))
we_trade_con &=(((df0[i, 7] == strat_time[1]) & (df0[i, 8] > strat_time[2])) | \
((df0[i, 7] == end_time[1]) & (df0[i, 8] < end_time[2])) |\
((df0[i, 7] > strat_time[1]) & (df0[i, 7] < end_time[1])))
if all_day > 0:
we_trade_con |= True
# 信号生成
if we_trade_con:
if now_pos == 0:
long_condition = (tp > tp2) and (tp >= tp_n) and (
zhyinzi > zuhe_limit)
# long_condition &= (close_bar>ma_xiao)and(last_close<ma_xiao2) #向上方向
short_condition = False #(ma_xiao < ma_zhong) and (ma_zhong < ma_da)
if long_condition:
df1[i][1] = 1 #buy signal
max_log_high = high_bar
# 空：突破最低线。
elif short_condition:
min_log_low = low_bar
df1[i][1] = -1
elif now_pos > 0:
stop_loss_price = stop_line if (
max_log_high - now_open_prcie < 80) else max(
now_open_prcie + 20, stop_line)
stop_loss_con = (close_bar < stop_loss_price) and (
last_close >= stop_loss_price)
stop_win_con = (
(max_log_high - now_open_prcie > max_stop_win)) and (
close_bar < ma_xiao) and (last_close >= ma_xiao2)
close_pos = False
if close_pos:
df1[i][1] = 0
elif stop_loss_con:
df1[i][1] = 0
elif stop_win_con:
df1[i][1] = 0
elif now_pos < 0:
stop_loss_price = min_log_low
stop_loss_con = (close_bar > stop_loss_price) and (
last_close <= stop_loss_price)
stop_win_con = False #((close_bar < stop_win_price)) and (last_close >= stop_win_price)
close_pos = False #(close_bar >me ) and (last_close <= me)
if close_pos:
df1[i][1] = 0
elif stop_loss_con:
df1[i][1] = 0
elif stop_win_con:
df1[i][1] = 0
else: #非交易时间段
if now_pos != 0:
df1[i][1] = 0
# 记录指标 绘图等等
df2[i, 4] = stop_loss_price
df2[i, 6] = min_log_low
df2[i, 7] = max_log_high
res0 = cal_tongji(df_input=df1)
res0 = np.concatenate((res0, cs0))
return df0, df1, df2, res0
def cal_signal_(df0, df1, df2, strat_time, end_time, cs0):
'''
# df0 == 原始time，ohlcc,:np.array ：['candle_begin_time', 'open', 'high', 'low', 'close', 'volume', 'days', 'huors', 'minutes']
# df1 == 信号统计数据列:np.array ：['candle_begin_time','signal', 'pos', 'opne_price', 'per_lr', 'sl']
# df2 == 指标列:np.array
'''
# 'atr_day', '移动止赢', 'n_atr', '小均线', '中均线', '大均线', '缠绕指标']
# 6 7 8 9 10 11 12
# 配置参数:止盈，ma_len（atr），atr_倍数，交易次数
stop_win_n, ma_xiao_n,ma_zhong_n,ma_da_n,ma_day_da_n,cr_num, atr_sw_n = cs0
# 配置临时变量
inner_trade_nums = 1000
open_pos_size = 1
max_log_high = np.nan
min_log_low = np.nan
log_stop_win = 0
for i in range(10, df0.shape[0]): # 类似于vnpy:on_bar
# 交易时间判断：日：1-28，and （时，分：2：30-16：20）
trading_time_con = ((df0[i, 6] >= strat_time[0]) and (df0[i, 6] <= end_time[0])) and \
((df0[i, 7] == 9) and (df0[i, 8] >= 15)) or \
((df0[i, 7] == 16) and (df0[i, 8] <= 29)) or \
((df0[i, 7] > 9) and (df0[i, 7] < 16))
# 我们交易时间
we_trade_con = ((df0[i, 6] >= strat_time[0]) and (df0[i, 6] <= end_time[0])) and \
((df0[i, 7] == strat_time[1]) and (df0[i, 8] > strat_time[2])) or \
((df0[i, 7] == end_time[1]) and (df0[i, 8] < end_time[2])) or \
((df0[i, 7] > strat_time[1]) and (df0[i, 7] < end_time[1]))
# 交易所日盘，开放
if trading_time_con:
# 快捷变量
open_bar = df0[i][1]
high_bar = df0[i][2]
low_bar = df0[i][3]
last_close = df0[i - 1][4]
close_bar = df0[i][4]
atr_n = df2[i][8]*atr_sw_n
xiao_ma = df2[i][9]
zhong_ma = df2[i][10]
da_ma = df2[i][11]
chanraoindex = df2[i][12]
day_ma = df2[i][13]
last_day_ma = df2[i-1][13]
last_da = df2[i-1][11]
last_zhong = df2[i-1][10]
last_xiao = df2[i-1][9]
# === 仓位统计。
df1, now_open_prcie, now_pos = cal_per_pos(i, df1, open_pos_size, open_bar, close_bar, last_close, sxf=1, slip=1)
# === 当日变量加载
if ((df0[i, 7] == 9) and (df0[i, 8] == 15)):
inner_trade_nums = 100
max_log_high = high_bar
min_log_low = low_bar
log_stop_win = 0
else:
max_log_high = max(max(open_bar,close_bar), max_log_high)
min_log_low = min(min(open_bar,close_bar), min_log_low)
if max_log_high > (now_open_prcie + stop_win_n):
stop_loss_price = xiao_ma
elif max_log_high > (now_open_prcie + stop_win_n):
stop_loss_price = 0.5*(xiao_ma+zhong_ma)
else:
stop_loss_price = max(da_ma,(now_open_prcie - atr_n))
log_stop_win = max(log_stop_win,stop_loss_price)
if we_trade_con:
if now_pos == 0:
log_stop_win = (da_ma - atr_n)
long_condition = (xiao_ma > zhong_ma)and(last_xiao <= last_zhong)and( close_bar > da_ma) #小中金叉
long_condition&= (last_zhong<zhong_ma) #大周期均是向上
long_condition &= (chanraoindex < cr_num) #缠绕条件
long_condition &= (last_day_ma < day_ma) #大周期条件
if long_condition and inner_trade_nums > 0:
inner_trade_nums -= 1
max_log_high = high_bar
df1[i][1] = 1
elif now_pos > 0:
stop_win_con = (close_bar < log_stop_win)
stop_loss_con = False
close_pos = False
if close_pos:
df1[i][1] = 0
elif stop_loss_con:
df1[i][1] = 0
elif stop_win_con:
df1[i][1] = 0
else: # 非交易时间段
if now_pos != 0:
df1[i][1] = 0
# 记录指标 绘图等等
# ['开仓线','atr_day','小均线']
df2[i, 2] = log_stop_win
df2[i, 3] = min_log_low
df2[i, 4] = max_log_high
# df2[i, 7] = log_stop_win
res0 = cal_tongji(df_input=df1)
res0 = np.concatenate((res0, cs0))
return df0, df1, df2, res0
def cal_signal_(df0, df1, df2, strat_time, end_time, cs0, sxf=1, slip=1):
'''
# df0 == 原始time，ohlcc,:np.array ：['candle_begin_time', 'open', 'high', 'low', 'close', 'volume', 'days', 'huors', 'minutes']
# df1 == 信号统计数据列:np.array ：['candle_begin_time','signal', 'pos', 'opne_price', 'per_lr', 'sl']
# df2 == 指标列:np.array
'''
# 配置参数:止盈，ma_len（atr），atr_倍数，交易次数
stop_win_n, ma_len, da_ma_n,dazhouqi,sw_atrn,shift_n = cs0
trade_inner_nums =100
# 配置临时变量
inner_trade_nums = trade_inner_nums
open_pos_size = 1
max_log_high = np.nan
min_log_low = np.nan
max_sp = np.nan
for i in range(10, df0.shape[0]): # 类似于vnpy:on_bar
# 交易时间判断：日：1-28，and （时，分：9：30-16：00）
trading_time_con = ((df0[i, 6] >= strat_time[0]) and (df0[i, 6] <= end_time[0])) and \
((df0[i, 7] == 9) and (df0[i, 8] >= 15)) or \
((df0[i, 7] == 16) and (df0[i, 8] <= 29)) or \
((df0[i, 7] > 9) and (df0[i, 7] < 16))
# 我们交易时间
we_trade_con = ((df0[i, 6] >= strat_time[0]) and (df0[i, 6] <= end_time[0])) and \
((df0[i, 7] == strat_time[1]) and (df0[i, 8] > strat_time[2])) or \
((df0[i, 7] == end_time[1]) and (df0[i, 8] < end_time[2])) or \
((df0[i, 7] > strat_time[1]) and (df0[i, 7] < end_time[1]))
# 交易所日盘，开放
if trading_time_con:
# 快捷变量 '6=小均线', 'n_atr','8移动止赢','9大均线','10max_n'
open_bar = df0[i][1]
high_bar = df0[i][2]
low_bar = df0[i][3]
last_close = df0[i - 1][4]
clsoe_bar = df0[i][4]
swin_atr_n = df2[i-1][7] * sw_atrn
ma_xiao = df2[i][6]
da_ma = df2[i][9]
max_n_p = df2[i-1][10]
up_1 = df2[i-1][11]
dn_1 = df2[i - int(shift_n)][12]
big_zq = df2[i-1][12]
# === 仓位统计。
df1, now_open_prcie, now_pos = cal_per_pos(i, df1, open_pos_size, open_bar, clsoe_bar, last_close, sxf=1, slip=1)
# === 当日变量加载
if ((df0[i, 7] == 9) and (df0[i, 8] == 15)):
inner_trade_nums = trade_inner_nums
max_log_high = high_bar
min_log_low = low_bar
else:
max_log_high = max(max(open_bar,clsoe_bar), max_log_high)
min_log_low = min(min(open_bar,clsoe_bar), min_log_low)
stop_win_price = now_open_prcie - stop_win_n
moving_swin_price = np.nan
# 信号生成
if we_trade_con:
if now_pos == 0:
long_condition = (clsoe_bar < dn_1) and (last_close >= dn_1) and (ma_xiao<da_ma) and (ma_xiao<big_zq)
if long_condition and inner_trade_nums > 0:
inner_trade_nums -= 1
df1[i][1] = -1
max_sp = ma_xiao+swin_atr_n
elif now_pos < 0:
stop_loss_con = False
if min_log_low < stop_win_price:
moving_swin_price = ma_xiao
else:
moving_swin_price = ma_xiao+swin_atr_n
max_sp = min(moving_swin_price,max_sp)
stop_win_con = (clsoe_bar > max_sp)
close_pos = False
if close_pos:
df1[i][1] = 0
elif stop_loss_con:
df1[i][1] = 0
elif stop_win_con:
df1[i][1] = 0
else: # 非交易时间段
if now_pos != 0:
df1[i][1] = 0
# 记录指标 绘图等等
# ['开仓线','atr_day','小均线']
# df2[i, 1] = #stop_loss_price
df2[i, 2] = stop_win_price
df2[i, 3] = min_log_low
df2[i, 4] = max_log_high
df2[i, 8] = max_sp
res0 = cal_tongji(df_input=df1)
res0 = np.concatenate((res0, cs0))
return df0, df1, df2, res0
def cal_signal_(df0, df1, df2, strat_time, end_time, cs0, sxf=1, slip=1):
'''
# df0 == 原始time，ohlcc,:np.array ：['candle_begin_time', 'open', 'high', 'low', 'close', 'volume', 'days', 'huors', 'minutes']
# df1 == 信号统计数据列:np.array ：['candle_begin_time','signal', 'pos', 'opne_price', 'per_lr', 'sl']
# df2 == 指标列:np.array
'''
# 配置参数:止盈，ma_len（atr），atr_倍数，交易次数
stop_win_n, ma_len, da_ma_n,sw_atrn,trade_inner_nums = cs0
# 配置临时变量
inner_trade_nums = trade_inner_nums
min_n_p0 = 0
open_pos_size = 1
max_log_high = np.nan
min_log_low = np.nan
for i in range(10, df0.shape[0]): # 类似于vnpy:on_bar
# 交易时间判断：日：1-28，and （时，分：9：30-16：00）
trading_time_con = ((df0[i, 6] >= strat_time[0]) and (df0[i, 6] <= end_time[0])) and \
((df0[i, 7] == 9) and (df0[i, 8] >= 15)) or \
((df0[i, 7] == 16) and (df0[i, 8] <= 29)) or \
((df0[i, 7] > 9) and (df0[i, 7] < 16))
# 我们交易时间
we_trade_con = ((df0[i, 6] >= strat_time[0]) and (df0[i, 6] <= end_time[0])) and \
((df0[i, 7] == strat_time[1]) and (df0[i, 8] > strat_time[2])) or \
((df0[i, 7] == end_time[1]) and (df0[i, 8] < end_time[2])) or \
((df0[i, 7] > strat_time[1]) and (df0[i, 7] < end_time[1]))
# 交易所日盘，开放
if trading_time_con:
# 快捷变量
open_bar = df0[i][1]
high_bar = df0[i][2]
low_bar = df0[i][3]
last_close = df0[i - 1][4]
clsoe_bar = df0[i][4]
swin_atr_n = df2[i-1][8] * sw_atrn
ma_xiao = df2[i][7]
da_ma = df2[i][10]
da_ma_1 = df2[i-1][10]
# === 仓位统计。
df1, now_open_prcie, now_pos = cal_per_pos(i, df1, open_pos_size, open_bar, clsoe_bar, last_close, sxf=1, slip=1)
# === 当日变量加载
if ((df0[i, 7] == 9) and (df0[i, 8] == 15)):
inner_trade_nums = trade_inner_nums
max_log_high = high_bar
min_log_low = low_bar
else:
max_log_high = max(max(open_bar,clsoe_bar), max_log_high)
min_log_low = min(min(open_bar,clsoe_bar), min_log_low)
inner_low = df2[i - 1][3]
if min_log_low < (now_open_prcie - stop_win_n):
stop_loss_price = ma_xiao+swin_atr_n
stop_win_con = (clsoe_bar > ma_xiao+swin_atr_n)
elif min_log_low < now_open_prcie - stop_win_n * 0.5:
stop_loss_price = ma_xiao +swin_atr_n*0.5
stop_win_con = (clsoe_bar > ma_xiao+swin_atr_n*0.5)
else:
stop_loss_price = (ma_xiao )
stop_win_con = (clsoe_bar > ma_xiao )
moving_swin_price = np.nan
min_n_p = df2[i - 1][11]
if we_trade_con:
if now_pos == 0:
short_condition = (clsoe_bar < min_n_p) and (last_close >= min_n_p) and (min_n_p < ma_xiao)
short_condition |=( (clsoe_bar<da_ma) or (da_ma <= da_ma_1 ) )
# 空：突破最低线。
if short_condition and inner_trade_nums > 0:
inner_trade_nums -= 1
df1[i][1] = -1
elif now_pos < 0:
stop_loss_con = False
close_pos = False
if close_pos:
df1[i][1] = 0
elif stop_loss_con:
df1[i][1] = 0
elif stop_win_con:
df1[i][1] = 0
else: # 非交易时间段
if now_pos != 0:
df1[i][1] = 0
# 记录指标 绘图等等
# ['开仓线','atr_day','小均线']
df2[i, 2] = stop_loss_price
df2[i, 3] = min_log_low
df2[i, 4] = max_log_high
df2[i, 9] = moving_swin_price
df2[i - 1][11] = min_n_p
res0 = cal_tongji(df_input=df1)
res0 = np.concatenate((res0, cs0))
return df0, df1, df2, res0
def cal_signal_(df0, df1, df2, strat_time, end_time, cs0, sxf=1, slip=1):
'''
# df0 == 原始time，ohlcc,:np.array ：['candle_begin_time', 'open', 'high', 'low', 'close', 'volume', 'days', 'huors', 'minutes']
# df1 == 信号统计数据列:np.array ：['candle_begin_time','signal', 'pos', 'opne_price', 'per_lr', 'sl']
# df2 == 指标列:np.array
'''
# 配置参数:止盈，ma_len（atr），atr_倍数，交易次数
stop_win_n, ma_len, da_ma_n, da_zq, sw_atrn, shift_n = cs0
trade_inner_nums = 1000
# 配置临时变量
inner_trade_nums = trade_inner_nums
open_pos_size = 1
max_log_high = np.nan
min_log_low = np.nan
max_sp = np.nan
for i in range(10, df0.shape[0]): # 类似于vnpy:on_bar
# 交易时间判断：日：1-28，and （时，分：9：30-16：00）
trading_time_con = ((df0[i, 6] >= strat_time[0]) and (df0[i, 6] <= end_time[0])) and \
((df0[i, 7] == 9) and (df0[i, 8] >= 15)) or \
((df0[i, 7] == 16) and (df0[i, 8] <= 29)) or \
((df0[i, 7] > 9) and (df0[i, 7] < 16))
# 我们交易时间
we_trade_con = ((df0[i, 6] >= strat_time[0]) and (df0[i, 6] <= end_time[0])) and \
((df0[i, 7] == strat_time[1]) and (df0[i, 8] > strat_time[2])) or \
((df0[i, 7] == end_time[1]) and (df0[i, 8] < end_time[2])) or \
((df0[i, 7] > strat_time[1]) and (df0[i, 7] < end_time[1]))
# 交易所日盘，开放
if trading_time_con:
# '小均线', 'n_atr','移动止赢','大均线','max_n','up','dn','大周期','大均线','zsc_up','zsc_dn','zsc_ma','zsc0']
# 6'小均线', 'n_atr', '移动止赢', '大均线', 'max_n',
# 11'up', 'dn', '大周期', '大均线2', 'zsc_up',
# 16'zsc_dn', 'zsc_ma', 'zsc0'
open_bar = df0[i][1]
high_bar = df0[i][2]
low_bar = df0[i][3]
last_close = df0[i - 1][4]
clsoe_bar = df0[i][4]
swin_atr_n = df2[i - 1][7] * sw_atrn
ma_xiao = df2[i][6]
da_ma = df2[i][9]
dazhouqi = df2[i][13]
da_ma2 = df2[i][14]
z_up = df2[i][15]
z_up_1 = df2[i - 1][15]
z_dn = df2[i][16]
z_me = df2[i][17]
z_zsc0 = df2[i][18]
z_zsc0_1 = df2[i - 1][18]
# print(z_zsc0,z_up)
# === 仓位统计。
df1, now_open_prcie, now_pos = cal_per_pos(i,
df1,
open_pos_size,
open_bar,
clsoe_bar,
last_close,
sxf=1,
slip=1)
# === 当日变量加载
if ((df0[i, 7] == 9) and (df0[i, 8] == 15)):
inner_trade_nums = trade_inner_nums
max_log_high = high_bar
min_log_low = low_bar
else:
max_log_high = max(max(open_bar, clsoe_bar), max_log_high)
min_log_low = min(min(open_bar, clsoe_bar), min_log_low)
inner_low = df2[i - 1][3]
stop_win_price = now_open_prcie + stop_win_n
moving_swin_price = np.nan
# 信号生成
if we_trade_con:
if now_pos == 0:
long_condition = (z_zsc0 > z_up_1) and (
z_zsc0_1 <= z_up_1) and (clsoe_bar >
dazhouqi) and (da_ma2 < da_ma)
if long_condition and inner_trade_nums > 0:
inner_trade_nums -= 1
df1[i][1] = 1
max_sp = max_sp - swin_atr_n
elif now_pos > 0:
stop_loss_con = False
close_pos = False
if max_log_high > stop_win_price:
moving_swin_price = ma_xiao
else:
moving_swin_price = ma_xiao - swin_atr_n
stop_win_con = (clsoe_bar < moving_swin_price)
if close_pos:
df1[i][1] = 0
elif stop_loss_con:
df1[i][1] = 0
elif stop_win_con:
df1[i][1] = 0
else: # 非交易时间段
if now_pos != 0:
df1[i][1] = 0
# 记录指标 绘图等等
# ['开仓线','atr_day','小均线']
# df2[i, 1] = #stop_loss_price
df2[i, 2] = stop_win_price
df2[i, 3] = min_log_low
df2[i, 4] = max_log_high
df2[i, 8] = moving_swin_price
res0 = cal_tongji(df_input=df1)
res0 = np.concatenate((res0, cs0))
return df0, df1, df2, res0
def cal_signal_(df0, df1, df2, strat_time, end_time, cs0):
'''
# df0 == 原始time，ohlcc,:np.array ：['candle_begin_time', 'open', 'high', 'low', 'close', 'volume', 'days', 'huors', 'minutes']
# df1 == 信号统计数据列:np.array ：['candle_begin_time','signal', 'pos', 'opne_price', 'per_lr', 'sl']
# df2 == 指标列:np.array
'''
# 配置参数:止盈，ma_len（atr），atr_倍数，交易次数
stop_win_n, ma_len,big_ma, base_line,extend_n = cs0
# 配置临时变量
inner_trade_nums = 1000
open_pos_size = 1
max_log_high = np.nan
min_log_low = np.nan
for i in range(10, df0.shape[0]): # 类似于vnpy:on_bar
# 交易时间判断：日：1-28，and （时，分：2：30-16：20）
trading_time_con = ((df0[i, 6] >= strat_time[0]) and (df0[i, 6] <= end_time[0])) and \
((df0[i, 7] == 9) and (df0[i, 8] >= 15)) or \
((df0[i, 7] == 16) and (df0[i, 8] <= 29)) or \
((df0[i, 7] > 9) and (df0[i, 7] < 16))
# 我们交易时间
we_trade_con = ((df0[i, 6] >= strat_time[0]) and (df0[i, 6] <= end_time[0])) and \
((df0[i, 7] == strat_time[1]) and (df0[i, 8] > strat_time[2])) or \
((df0[i, 7] == end_time[1]) and (df0[i, 8] < end_time[2])) or \
((df0[i, 7] > strat_time[1]) and (df0[i, 7] < end_time[1]))
# 交易所日盘，开放
if trading_time_con:
# 快捷变量
open_bar = df0[i][1]
high_bar = df0[i][2]
low_bar = df0[i][3]
last_close = df0[i - 1][4]
clsoe_bar = df0[i][4]
ma_xiao = df2[i][7]
da_ma = df2[i][10]
# === 仓位统计。
df1, now_open_prcie, now_pos = cal_per_pos(i, df1, open_pos_size, open_bar, clsoe_bar, last_close, sxf=1, slip=1)
# === 当日变量加载
if ((df0[i, 7] == 9) and (df0[i, 8] == 15)):
inner_trade_nums = 100
max_log_high = high_bar
min_log_low = low_bar
else:
max_log_high = max(max(open_bar,clsoe_bar), max_log_high)
min_log_low = min(min(open_bar,clsoe_bar), min_log_low)
inner_low = df2[i - 1][3]
if max_log_high > (now_open_prcie + stop_win_n):
stop_loss_price = ma_xiao
stop_win_con = (clsoe_bar < ma_xiao)
# elif max_log_high > (now_open_prcie + stop_win_n2):
# stop_loss_price = ma_xiao
# stop_win_con = (clsoe_bar < ma_xiao)
else:
stop_loss_price = (max_log_high-clsoe_bar )
stop_win_con = (max_log_high-clsoe_bar > 50)
moving_swin_price = np.nan
if we_trade_con:
if now_pos == 0:
short_condition = (open_bar < clsoe_bar) and (open_bar - low_bar > abs(open_bar - clsoe_bar)*extend_n)
short_condition &= clsoe_bar - open_bar >base_line
short_condition &= clsoe_bar > da_ma
# 空：突破最低线。
if short_condition and inner_trade_nums > 0:
inner_trade_nums -= 1
max_log_high = high_bar
df1[i][1] = 1
elif now_pos > 0:
stop_loss_con = False
close_pos = False
if close_pos:
df1[i][1] = 0
elif stop_loss_con:
df1[i][1] = 0
elif stop_win_con:
df1[i][1] = 0
else: # 非交易时间段
if now_pos != 0:
df1[i][1] = 0
# 记录指标 绘图等等
# ['开仓线','atr_day','小均线']
df2[i, 2] = stop_loss_price
df2[i, 3] = min_log_low
df2[i, 4] = max_log_high
df2[i, 9] = moving_swin_price
res0 = cal_tongji(df_input=df1)
# if not res0:return df0, df1, df2, cs0
res0 = np.concatenate((res0, cs0))
return df0, df1, df2, res0
