def run_daily_best_param(Tele2, trade_day_list, criteria_df):
base_time_start = datetime.time(8,30,0)
base_time_end_list = [datetime.time(8,40,0)]
test_length_list = [300]
sma_list = [0.5]
over_limit_time_list = [100]
bounce_stop_list = [80]
column_list = [['Date','Max Profit', 'Above Ratio', 'Below Ratio', 'Max min Spread','Average Theo', 'Std of Theo', 'Base Start', 'Base End', 'Test Start', 'Test End', 'SMA Side Ratio Threshold', 'Bounce Stop Threshold', 'Over Limit Time', 'Trading Cost']]
for test_trade_day in trade_day_list:
test_trade_day_list = [test_trade_day]
max_profit = -10000
for base_time_end in base_time_end_list:
test_time_start = base_time_end
for test_length in test_length_list:
test_time_end = (ts.cal_datetime(test_time_start) + datetime.timedelta(minutes = test_length)).time()
for sma in sma_list:
for bounce_stop in bounce_stop_list:
for over_limit_time in tqdm(over_limit_time_list):
[daily_profit_result, trade_count, limit_stop_count, bounce_stop_count, time_stop_count, loss_record, above_ratio, below_ratio, theo_change, absolute_theo_change, max_min_spread, average, std_dev] = dbfrt.daily_best_feature_record_test(Tele2, test_trade_day_list, base_time_start, base_time_end, test_time_start, test_time_end, sma_threshold = sma, stop_threshold = bounce_stop, bounce_stop_threshold = bounce_stop, over_limit_length = over_limit_time, trade_cost = 30)
if daily_profit_result[0] > max_profit:
max_profit = daily_profit_result[0]
single_column = [test_trade_day, daily_profit_result[0], above_ratio, below_ratio, max_min_spread, average, std_dev, base_time_start, base_time_end, test_time_start, test_time_end, sma, bounce_stop, over_limit_time, 30]
column_list.append(single_column)
loss_df = pd.DataFrame(loss_record[1:], columns=loss_record[0])
result_df = pd.DataFrame(column_list[1:], columns=column_list[0])
result_df.index = result_df['Date']
del result_df['Date']
final_result_df = result_df.join(criteria_df, how = 'left')
del final_result_df['date']
del final_result_df['time']
def run_bounce_match_strategy(Tele2, trade_day_list):
base_time_start = datetime.time(8,30,0)
base_time_end_list = [datetime.time(8,40,0)]
test_length_list = [300,310,320,330,340,350,360,370,380,390]
sma_list = [0.5]
over_limit_time_list = [100]
bounce_stop_list = [80]
theo_diff_list = [50]
column_list = [['Profit', 'Trade Number', ' Time Stop Count', 'Limite Stop Count', 'Bounce Stop Count', 'Base Start', 'Base End', 'Test Start', 'Test End', 'SMA Side Ratio Threshold', 'Bounce Stop Threshold', 'Over Limit Time', 'Theo Diff Threshold', 'Trading Cost']]
for base_time_end in base_time_end_list:
test_time_start = base_time_end
for test_length in test_length_list:
test_time_end = (ts.cal_datetime(test_time_start) + datetime.timedelta(minutes = test_length)).time()
for sma in sma_list:
for bounce_stop in bounce_stop_list:
for theo_diff in theo_diff_list:
for over_limit_time in tqdm(over_limit_time_list):
[daily_profit_result, trade_count, limit_stop_count, bounce_stop_count, time_stop_count, daily_record, bounce_stop_threshold] = bms.bounce_match_strategy(Tele2, trade_day_list, base_time_start, base_time_end, test_time_start, test_time_end, sma_threshold = sma, stop_threshold = bounce_stop, bounce_stop_threshold = bounce_stop, over_limit_length = over_limit_time, theo_diff = theo_diff, trade_cost = 30)
total_profit = np.sum(daily_profit_result[~np.isnan(daily_profit_result)])
single_column = [total_profit, trade_count, time_stop_count, limit_stop_count, bounce_stop_count, base_time_start, base_time_end, test_time_start, test_time_end, sma, bounce_stop, over_limit_time, theo_diff, 30]
column_list.append(single_column)
#
daily_df = pd.DataFrame(daily_record[1:], columns=daily_record[0])
result_df = pd.DataFrame(column_list[1:], columns=column_list[0])
# result_df.to_excel("C:\\Users\\achen\\Desktop\\Monocular\\Automatic Backtest\\RTY Morning Trend PL\\RTY_20190101_20190725_BOUNCE_MATCH_VARIOUS_PARAM_NO_FILTER.xlsx")
return daily_df, result_df
bounce_stop_list = [80]
theo_diff_list = [50]
loss_stop_list = [500]
column_list = [[
'Profit', 'Trade Number', ' Time Stop Count', 'Limite Stop Count',
'Bounce Stop Count', 'Base Start', 'Base End', 'Test Start', 'Test End',
'SMA Side Ratio Threshold', 'Bounce Stop Threshold', 'Over Limit Time',
'Theo Diff Threshold', 'Loss Stop', 'Trading Cost'
]]
for base_time_end in base_time_end_list:
test_time_start = base_time_end
for test_length in test_length_list:
test_time_end = (ts.cal_datetime(test_time_start) +
datetime.timedelta(minutes=test_length)).time()
for sma in sma_list:
for bounce_stop in bounce_stop_list:
for theo_diff in theo_diff_list:
for over_limit_time in tqdm(over_limit_time_list):
for loss_stop in tqdm(loss_stop_list):
[
daily_profit_result, trade_count,
limit_stop_count, bounce_stop_count,
time_stop_count, daily_record,
bounce_stop_threshold
] = bms.bounce_match_strategy(
Tele2,
trade_day_list,
def rolling_backtest(Tele2, end_datetime, trade_day_list):
import telescope as ts
# import telescope_metrics as tm
import strategy_backtest2_loss as sbtest2l
#dmb.dir_match_backtest(r"Z:\KaiData\Theo\2019\RTY.ESTheo.mpk", datetime.datetime(2019, 7, 16, 0, 0), datetime.datetime(2019, 7, 17, 23, 59))
START = end_datetime - datetime.timedelta(days=6)
END = end_datetime
print(START)
print(END)
# Tele2 = ts.Telescope()
# Tele2.load_dfs([r"Z:\KaiData\Theo\2019\RTY.ESTheo.mpk"])
# Tele2.df = Tele2.df.resample('5S', fill_method = 'bfill')
# tm.apply_all_metrics(Tele2.df, sma_period = 600)
# Tele2.clip_time(datetime.time(8,30,0), datetime.time(12,0,0))
chosen_Tele = ts.Telescope(df=Tele2.choose_date(START, END))
# date_list = chosen_Tele.df['date'].unique()
#
# trade_day_list = []
# not_trade_day_list = []
# for date in date_list:
# if ts.select_date_time(chosen_Tele.df, date, datetime.time(8,30,0))['TheoPrice'].empty:
# not_trade_day_list.append(date)
# else:
# trade_day_list.append(date)
test_day = trade_day_list[-1]
trade_day_list = trade_day_list[:-1]
#
# Tele2.df = Tele2.df.resample('5S', fill_method = 'bfill')
#
# Tele2.parse_datetime()
# tm.apply_all_metrics(Tele2.df, sma_period = 600)
#
# Tele2.clip_time(datetime.time(8,30,0), datetime.time(12,0,0))
base_time_start = datetime.time(8, 30, 0)
base_time_end_list = [
datetime.time(8, 31, 0),
datetime.time(8, 33, 0),
datetime.time(8, 35, 0),
datetime.time(8, 40, 0),
datetime.time(8, 45, 0)
]
test_length_list = [360]
sma_list = [0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95]
stop_list = [0]
bounce_stop_list = [80, 120, 160, 200, 240, 280, 320]
column_list = [[
'Profit', 'Trade Number', ' Time Stop Count', 'Limite Stop Count',
'Bounce Stop Count', 'Base Start', 'Base End', 'Test Start',
'Test End', 'SMA Side Ratio Threshold', 'Stop Threshold',
'Bounce Stop Threshold', 'Trading Cost'
]]
for base_time_end in base_time_end_list:
test_time_start = base_time_end
for test_length in test_length_list:
test_time_end = (ts.cal_datetime(test_time_start) +
datetime.timedelta(minutes=test_length)).time()
for sma in sma_list:
for stop in stop_list:
for bounce_stop in bounce_stop_list:
absolute_stop = bounce_stop - stop
[
daily_profit_result, trade_count, limit_stop_count,
bounce_stop_count, time_stop_count, loss_record
] = sbtest2l.strategy_backtest2_loss(
chosen_Tele,
trade_day_list,
base_time_start,
base_time_end,
test_time_start,
test_time_end,
sma_threshold=sma,
stop_threshold=absolute_stop,
bounce_stop_threshold=bounce_stop,
trade_cost=30)
total_profit = np.sum(
daily_profit_result[~np.isnan(daily_profit_result)]
)
single_column = [
total_profit, trade_count, time_stop_count,
limit_stop_count, bounce_stop_count,
base_time_start, base_time_end, test_time_start,
test_time_end, sma, stop, bounce_stop, 30
]
column_list.append(single_column)
result_df = pd.DataFrame(column_list[1:], columns=column_list[0])
parameter_series = result_df.loc[result_df['Profit'].idxmax(), :]
print(result_df['Profit'].max())
base_time_start = parameter_series['Base Start']
base_time_end = parameter_series['Base End']
test_time_start = parameter_series['Test Start']
test_time_end = parameter_series['Test End']
sma = parameter_series['SMA Side Ratio Threshold']
stop = parameter_series['Stop Threshold']
bounce_stop = parameter_series['Bounce Stop Threshold']
trading_cost = parameter_series['Trading Cost']
[
test_profit_result, trade_count, limit_stop_count, bounce_stop_count,
time_stop_count, loss_record
] = sbtest2l.strategy_backtest2_loss(chosen_Tele, [test_day],
base_time_start,
base_time_end,
test_time_start,
test_time_end,
sma_threshold=sma,
stop_threshold=absolute_stop,
bounce_stop_threshold=bounce_stop,
trade_cost=trading_cost)
return base_time_start, base_time_end, sma, bounce_stop, result_df[
'Profit'].max(), test_profit_result[0]
base_time_end_list = [datetime.time(8,40,0)]
test_length_list = [100]
sma_list = [0.7]
stop_list = [0]
bounce_stop_list = [120]
column_list = [['Profit', 'Trade Number', ' Time Stop Count', 'Limite Stop Count', 'Bounce Stop Count', 'Base Start', 'Base End', 'Test Start', 'Test End', 'SMA Side Ratio Threshold', 'Stop Threshold', 'Bounce Stop Threshold','Trading Cost']]
for base_time_end in base_time_end_list:
test_time_start = base_time_end
for test_length in test_length_list:
test_time_end = (ts.cal_datetime(test_time_start) + datetime.timedelta(minutes = test_length)).time()
for sma in sma_list:
for stop in stop_list:
for bounce_stop in tqdm(bounce_stop_list):
absolute_stop = bounce_stop - stop
[daily_profit_result, trade_count, limit_stop_count, bounce_stop_count, time_stop_count, loss_record] = sbtest2l.strategy_backtest2_loss(Tele2, trade_day_list, base_time_start, base_time_end, test_time_start, test_time_end, sma_threshold = sma, stop_threshold = absolute_stop, bounce_stop_threshold = bounce_stop, trade_cost = 30)
total_profit = np.sum(daily_profit_result[~np.isnan(daily_profit_result)])
single_column = [total_profit, trade_count, time_stop_count, limit_stop_count, bounce_stop_count, base_time_start, base_time_end, test_time_start, test_time_end, sma, stop, bounce_stop, 30]
column_list.append(single_column)
loss_df = pd.DataFrame(loss_record[1:], columns=loss_record[0])
result_df = pd.DataFrame(column_list[1:], columns=column_list[0])
#result_df.to_excel("C:\\Users\\achen\\Desktop\\Monocular\\Automatic Backtest\\RTY Morning Trend PL\\RTY_20190401_20190717_PARAMETER_TEST3_PL.xlsx")
def rolling_pairwise_theo_diff_sum_profit(corr_df, data_mpk_file_path, start_date, end_date, start_time = datetime.time(8, 0, 0), end_time = datetime.time(15, 15, 0), time_frame_length = 5, rolling_step = '1T', corr_threshold = 3, up_threshold = 100, down_threshold = 100, trade_cost = 30, output_excel_path= r"C:\Users\achen\Desktop\Monocular\rolling_pairwise_theo_diff_corr.xlsx"):
import numpy as np
import pandas as pd
import telescope as ts
import telescope_metrics as tm
import datetime
from scipy.stats.stats import pearsonr
from tqdm import tqdm
#--------------Input Parameters---------------
CORR_DF = corr_df
df_2019 = data_mpk_file_path
START = start_date
END = end_date
STIME = start_time
ETIME = end_time
TIME_FRAME_LENGTH = time_frame_length
ROLLING_STEP = rolling_step #(has to be a factor of TIME_FRAME_LENGTH)
UP_THRESHOLD = up_threshold
DOWN_THRESHOLD = down_threshold
TRADE_COST = trade_cost
CORR_THRESHOLD = corr_threshold
#---------------------------------------------
#--------------Input Parameters---------------
#df_2019 = r"Z:\KaiData\Theo\2019\YM.ESTheo.mpk"
#START = datetime.datetime(2019, 7, 8, 0, 0)
#END = datetime.datetime(2019, 7, 12, 23, 59)
#STIME = datetime.time(8, 0, 0)
#ETIME = datetime.time(15, 15, 0)
#TIME_FRAME_LENGTH = 5
#ROLLING_STEP = '1T' #(has to be a factor of TIME_FRAME_LENGTH)
#---------------------------------------------
df_lst = [df_2019]
Tele2 = ts.Telescope()
Tele2.load_dfs(df_lst)
Tele2.choose_range(START, END)
Tele2.resample(ROLLING_STEP)
tm.apply_all_metrics(Tele2.df, sma_period = 10)
Tele2.parse_datetime()
Tele2.specific_timeframe(STIME, ETIME)
#print(Tele2.df)
#print(Tele2.grouped)
#print(Tele2.num_of_groups)
#print(Tele2.group_names)
date_list = Tele2.df['date'].unique()
time_list = Tele2.df['time'].unique()
ignore_col_num = int(TIME_FRAME_LENGTH/int(ROLLING_STEP[0])+1)
# change_direction_match_ratio_list = []
# correlatation_list = []
print(time_list[:-ignore_col_num])
#print(Tele2.df['TheoPrice'])
column_list = [time_list[:-ignore_col_num]]
for first_time in tqdm(time_list[:-ignore_col_num]):
if first_time < (ts.cal_datetime(ETIME) - datetime.timedelta(minutes = TIME_FRAME_LENGTH)).time():
first_start = first_time;
first_end = (ts.cal_datetime(first_time) + datetime.timedelta(minutes = TIME_FRAME_LENGTH)).time();
print(first_start)
first_temp_df = ts.select_timeframe(Tele2.df, first_start, first_end)
single_column = []
for second_time in time_list[:-ignore_col_num]:
if (second_time >= (ts.cal_datetime(first_time) + datetime.timedelta(minutes = TIME_FRAME_LENGTH)).time()) & (second_time < (ts.cal_datetime(ETIME) - datetime.timedelta(minutes = TIME_FRAME_LENGTH)).time()):
second_start = second_time
second_end = (ts.cal_datetime(second_start) + datetime.timedelta(minutes = TIME_FRAME_LENGTH)).time()
second_temp_df = ts.select_timeframe(Tele2.df, second_start, second_end)
first_time_move_list = []
second_time_move_list = []
for date in date_list:
# print(first_temp_df[(first_temp_df['date'] == date) & (first_temp_df['time'] == first_end)]['TheoPrice'])
# print(ts.select_date_time(first_temp_df, date, first_end)['TheoPrice'][0])
first_move_theo = ts.select_date_time(first_temp_df, date, first_end)['TheoPrice'][0] - ts.select_date_time(first_temp_df, date, first_start)['TheoPrice'][0]
second_move_theo = ts.select_date_time(second_temp_df, date, second_end)['TheoPrice'][0] - ts.select_date_time(second_temp_df, date, second_start)['TheoPrice'][0]
if ((not np.isnan(first_move_theo)) & (not np.isnan(second_move_theo))):
first_time_move_list.append(first_move_theo)
second_time_move_list.append(second_move_theo)
# print(first_time_move_list)
# print(second_time_move_list)
# count = 0
# for i in range(len(first_time_move_list)):
# if ((first_time_move_list[i] > 0) & (second_time_move_list[i] > 0)) or ((first_time_move_list[i] < 0) & (second_time_move_list[i] < 0)):
# count += 1
# match_ratio = count/len(first_time_move_list)
# change_direction_match_ratio_list.append(match_ratio)
score = 0
correlation = corr_df.loc[first_start,second_start]
for i in range(len(first_time_move_list)):
if first_time_move_list[i] >= UP_THRESHOLD:
if correlation >= CORR_THRESHOLD:
print(correlation)
score += second_time_move_list[i]
score = score - TRADE_COST
if correlation <= -CORR_THRESHOLD:
score += -second_time_move_list[i]
score = score - TRADE_COST
if first_time_move_list[i] <= -DOWN_THRESHOLD:
if correlation >= CORR_THRESHOLD:
score += -second_time_move_list[i]
score = score - TRADE_COST
if correlation <= -CORR_THRESHOLD:
score += second_time_move_list[i]
score = score - TRADE_COST
# corr = pearsonr(first_time_move_list,second_time_move_list)[0]
single_column.append(score)
else:
single_column.append(np.nan)
print(single_column)
column_list.append(single_column)
result = pd.DataFrame(column_list[1:], columns=column_list[0])
result['time'] = time_list[:-ignore_col_num]
result.set_index('time', inplace=True)
#output_df = pd.DataFrame(list(zip(time_list, correlatation_list, change_direction_match_ratio_list)), columns =['start time', 'correlation with next 5 min', 'change direction match ratio'])
#output_df.to_csv(r"C:\Users\achen\Desktop\Monocular\5_min_rolling_correlation_direction_match.csv")
result.to_excel(output_excel_path)
return result
#print(Tele2.num_of_groups)
#print(Tele2.group_names)
date_list = Tele2.df['date'].unique()
time_list = Tele2.df['time'].unique()
change_direction_match_ratio_list = []
correlatation_list = []
first_move_abs_sum = []
second_move_abs_sum = []
#print(Tele2.df['TheoPrice'])
for time in time_list:
if time < (ts.cal_datetime(datetime.time(15, 15, 0)) - datetime.timedelta(minutes = 10)).time():
print(time)
first_start = time;
first_end = (ts.cal_datetime(time) + datetime.timedelta(minutes = 5)).time();
second_start = (ts.cal_datetime(time) + datetime.timedelta(minutes = 5)).time()
second_end = (ts.cal_datetime(time) + datetime.timedelta(minutes = 10)).time()
first_temp_df = ts.select_timeframe(Tele2.df, first_start, first_end)
second_temp_df = ts.select_timeframe(Tele2.df, second_start, second_end)
first_time_move_list = []
second_time_move_list = []
for date in date_list:
# print(first_temp_df[(first_temp_df['date'] == date) & (first_temp_df['time'] == first_end)]['TheoPrice'])
# print(ts.select_date_time(first_temp_df, date, first_end)['TheoPrice'][0])
correlatation_list = []
time_index_list = []
#print(Tele2.df['TheoPrice'])
trade_day_list = []
for date in date_list:
noon_theo_price = ts.select_date_time(Tele2.df, date, datetime.time(12,0,0))['TheoPrice'][0]
if (not np.isnan(noon_theo_price)):
trade_day_list.append(date)
column_list = [trade_day_list]
for first_time in tqdm(time_list):
if first_time < (ts.cal_datetime(ETIME) - datetime.timedelta(minutes = TIME_FRAME_LENGTH)).time():
time_index_list.append(first_time)
first_start = first_time;
first_end = (ts.cal_datetime(first_time) + datetime.timedelta(minutes = TIME_FRAME_LENGTH)).time();
first_temp_df = ts.select_timeframe(Tele2.df, first_start, first_end)
#
single_column = []
first_move_theo = ts.select_date_time(first_temp_df, date, first_end)[METRIC][0] - ts.select_date_time(first_temp_df, date, first_start)[METRIC][0]
first_time_move_list = []
for date in trade_day_list:
first_move_theo = ts.select_date_time(first_temp_df, date, first_end)[METRIC][0] - ts.select_date_time(first_temp_df, date, first_start)[METRIC][0]
#print(Tele2.df)
#print(Tele2.grouped)
#print(Tele2.num_of_groups)
#print(Tele2.group_names)
date_list = Tele2.df['date'].unique()
time_list = Tele2.df['time'].unique()
change_direction_match_ratio_list = []
correlatation_list = []
time_index_list = []
#print(Tele2.df['TheoPrice'])
column_list = [time_list[:-(TIME_FRAME_LENGTH + 1)]]
for first_time in tqdm(time_list):
if first_time < (ts.cal_datetime(ETIME) -
datetime.timedelta(minutes=TIME_FRAME_LENGTH)).time():
time_index_list.append(first_time)
first_start = first_time
first_end = (ts.cal_datetime(first_time) +
datetime.timedelta(minutes=TIME_FRAME_LENGTH)).time()
print(first_start)
print(first_end)
first_temp_df = ts.select_timeframe(Tele2.df, first_start, first_end)
single_column = []
for second_time in time_list[:-(TIME_FRAME_LENGTH + 1)]:
if (second_time >= first_time):