def __init__(self):
self.cache = cache()
self.cache_input = True
self.cache_results = True
self.cache_chart = True
self.logs = logs()
#configurable variables
self.input_file_name = ""
self.score_only = False #set to true to only calculate what is required for scoring a strategy
#to speed up performance.
self.shares = 0.1 #order size
self.wll = 180 #window length long
self.wls = 2 #window length short
self.buy_wait = 0 #min sample periods between buy orders
self.buy_wait_after_stop_loss = 6 #min sample periods between buy orders
#after a stop loss order
self.markup = 0.01 #order mark up
self.stop_loss = 0.282 #stop loss
self.enable_flash_crash_protection = True #convert a stop loss order into a short term hold position
self.flash_crash_protection_delay = 180 #max_hold in minutes
self.stop_age = 10000 #stop age - dump after n periods
self.atr_depth = 60 * 1 #period depth of the averae true range, used to split input data into quartiles
self.macd_buy_trip = -0.66 #macd buy indicator
self.rsi_enable = 0 #enable/disable the relative strength indicator
self.rsi_length = 1 #RSI length
self.rsi_period_length = 10 #RSI period length
self.rsi_gate = 50 #RSI gate (RSI must be below gate to enable buy orders)
self.min_i_pos = 0 #min periods of increasing price
#before buy order placed
self.min_i_neg = 0 #min periods of declining price
#before sell order placed
self.stbf = 2.02 #short trade biasing factor
#-- increase to favor day trading
#-- decrease to 2 to eliminate bias
self.nlsf = 5.0 #non-linear scoring factor - favor the latest trades
#max factor = exp(self.nlsf) @ the last sample periord
self.commision = 0.006 #mt.gox commision
self.quartile = 1 #define which market detection quartile to trade on (1-4)
self.input_data = []
self.input_data_length = 0
self.market_class = []
self.current_quartile = 0
self.classified_market_data = False
self.max_length = 1000000
self.reset()
return
def __init__(self):
self.cache = cache()
self.cache_input = True
self.cache_results = True
self.cache_chart = True
self.logs = logs()
#configurable variables
self.input_file_name = ""
self.score_only = False #set to true to only calculate what is required for scoring a strategy
#to speed up performance.
self.shares = 0.1 #order size
self.wll = 180 #window length long
self.wls = 2 #window length short
self.buy_wait = 0 #min sample periods between buy orders
self.buy_wait_after_stop_loss = 6 #min sample periods between buy orders
#after a stop loss order
self.markup = 0.01 #order mark up
self.stop_loss = 0.282 #stop loss
self.enable_flash_crash_protection = True #convert a stop loss order into a short term hold position
self.flash_crash_protection_delay = 180 #max_hold in minutes
self.stop_age = 10000 #stop age - dump after n periods
self.atr_depth = 60 * 1 #period depth of the averae true range, used to split input data into quartiles
self.macd_buy_trip = -0.66 #macd buy indicator
self.rsi_enable = 0 #enable/disable the relative strength indicator
self.rsi_length = 1 #RSI length
self.rsi_period_length = 10 #RSI period length
self.rsi_gate = 50 #RSI gate (RSI must be below gate to enable buy orders)
self.min_i_pos = 0 #min periods of increasing price
#before buy order placed
self.min_i_neg = 0 #min periods of declining price
#before sell order placed
self.stbf = 2.02 #short trade biasing factor
#-- increase to favor day trading
#-- decrease to 2 to eliminate bias
self.nlsf = 5.0 #non-linear scoring factor - favor the latest trades
#max factor = exp(self.nlsf) @ the last sample periord
self.commision = 0.006 #mt.gox commision
self.quartile = 1 #define which market detection quartile to trade on (1-4)
self.input_data = []
self.input_data_length = 0
self.market_class = []
self.current_quartile = 0
self.classified_market_data = False
self.max_length = 1000000
self.reset()
return
def __init__(self):
self.cache = cache()
self.cache_input = True
self.cache_results = False
self.cache_chart = False
self.logs = logs()
self.input_file_name = ""
self.text_summary = "" #text summary of the results
self.input_data = []
self.classification = []
self.input_data_length = 0
self.current_quartile = 0
self.period = 0
self.positions = []
self.score_only = False #set to true to only calculate what is required for scoring a strategy
#to speed up performance.
self.max_length = 10000000
self.enable_flash_crash_protection = False
self.flash_crash_protection_delay = False
self.reset()
return
# login = sp.SpreadSheets.offline.AccessDetails.vz_wireless
# scraped_vz_w = scraper.scrape_verizon_wireless(login[0], login[1])
# sp.SpreadSheets.online.BalanceSheet.Verizon().write_wireless(scraped_vz_w)
#
# logs("\n\t\t\t\tVZ DSL\n===================================================")
# logins = sp.SpreadSheets.offline.AccessDetails.verizon
# scraped_vz_d = []
# for login in logins:
# scraped_acc = scraper.scrape_verizon_dsl(username=login[1], password=login[2], line=login[0], secret=login[3])
# scraped_vz_d.append(scraped_acc)
# sp.SpreadSheets.online.BalanceSheet.Verizon().write_dsl(scraped_vz_d)
#
# logs("\n\t\t\tComcast\n===================================================")
# login = sp.SpreadSheets.offline.AccessDetails.comcast
# success, scraped_comc = scraper.scrape_comcast(username=login[0], password=login[1])
# if success:
# sp.SpreadSheets.online.BalanceSheet.Other().write(scraped_comc)
#
# logs("\n\t\t\tATT DSL\n=====================================================")
# logins = sp.SpreadSheets.offline.AccessDetails.att
# scraped_att_d = []
# for login in logins:
# print(login)
# success, scraped_att = scraper.scrape_att(username=login[1], password=login[2], line=login[0])
# scraped_att_d.append(scraped_att)
# sp.SpreadSheets.online.BalanceSheet.ATT().write(scraped_att_d)
# sp.SpreadSheets.online.BalanceSheet.Overview().refresh_last_update_time()
except Exception as ex:
logs(f"Exception in main: {ex}")
