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main.py
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main.py
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import os
import sys
from pathlib import Path
PROJECT_DIRECTORY = Path(__file__).parent.absolute()
from utils import addFoldersToPath
addFoldersToPath(PROJECT_DIRECTORY)
from poloniex import Poloniex
from db_manager import DBManager
from trade_simulator import TradeSimulator
from test_strategy import TestStrategy
from currency_trailer_strategy import CurrencyTrailerStrategy
from candle_table import CandleTable
from candle_fetcher import CandleFetcher
from point_populator import PointPopulator
from simple_buyer_strategy import SimpleBuyerStrategy
from trend_fetcher import TrendFetcher
from trend_cutter import TrendCutter
from tools import date_to_timestamp
from two_avg_trend_strategy import TwoAvgTrendStrategy
from manual_trend_strategy import ManualTrendStrategy
from manual_attribute_strategy import ManualAttributeStrategy
from scipy_trend_model_strategy import ScipyTrendModelStrategy
from scipy_candle_model_strategy import ScipyCandleModelStrategy
from neural_trend_model import NeuralTrendModel
from neural_candle_model import NeuralCandleModel
from bollinger_strategy import BollingerStrategy
from oscil_strategy import OscilStrategy
from random_strategy import RandomStrategy
from hold_strategy import HoldStrategy
from short_term_strategy import ShortTermStrategy
from signaler import Signaler
from sig import Sig
from parameter_optimizer import ParameterOptimizer
from order_maker import OrderMaker
from order_table import OrderTable
from emailer import Emailer
from snap_fetcher import SnapFetcher
from snap_table import SnapTable
from snap_order_table import SnapOrderTable
from normal_strategy import NormalStrategy
from trader import Trader
from stat_calculator import StatCalculator
import table_names
import time
import threading
import argparse
parser = argparse.ArgumentParser(description='The best Trading Script!')
parser.add_argument('--strategy', help='Pick a strategy to run; i.e. classic', required=True)
args = parser.parse_args()
##TODO: keep testing with more data and keep adjusting parameters
##TODO: buy btc
##TODO: write automated platform
##TODO: setup a aws server
##TODO: keep thinking of possible biases
##TODO: Fix limit on tradesimulator
##lookinto fixing stddev_adjust, possibly sell/buy more first time(after num_past...)
## verify stddev and avg are calculated properly( no off by one errors)
HALF_DAY = 43200
def main():
if args.strategy == "classic":
test = Sig("tn", 1451793600, "BTC", 1.1, 42, "BUY")
CandleFetcher.update_tables(table_names.short_term_tables)
trader = Trader(Trader.CLASSIC)
trader.run()
##DBManager.drop_matching_tables("SIGNAL")
##signaler = Signaler(table_names.short_term_tables)
##signaler.update()
##OrderTable.create_tables()
##om = OrderMaker()
##om.slow_sell("XRP", 1, sell_all = True)
##om = OrderMaker()
##om.slow_sell("ETH", 1, sell_all = True)
##OrderMaker.get_last_trade_rate("USDT_BTC")
##DBManager.drop_matching_tables("SIGNAL")
##CandleFetcher.fetch_candles_after_date("REP", date_to_timestamp("2016-6-1"), 300)
##CandleFetcher.update_tables(table_names.short_term_tables)
##trader = Trader(Trader.CLASSIC)
##trader.run()
##while(True):
##print(time.time())
##CandleFetcher.update_tables_imperative([table_names.BTC_300],[True])
##time.sleep(1)
##DBManager.drop_matching_tables("SNAP")
##print threading.get_ident()
##sf = SnapFetcher("SNAP_USDT_BTC_100")
##sf.run()
##SnapTable.delete_rows("SNAP_USDT_BTC_100")
##SnapOrderTable.delete_rows("SNAP_ORDER_USDT_BTC_100")
##OrderMaker.slow_sell("ETC", 60)
##OrderMaker.update_orders()
##OrderMaker.place_buy_order("NXT", 0.01)
##CandleFetcher.fetch_candles_after_date("REP", date_to_timestamp("2016-6-1"), 300)
##e = Emailer()
##e.email_signal(test)
##om = OrderMaker([])
##om.get_top_buy_price("USDT_BTC")
##DBManager.drop_matching_tables("SNAP")
##p = Poloniex()
##signaler = Signaler(to_email = False, to_print = True)
##signaler.run()
##signaler.print_all_signals()
##CandleFetcher.update_all()
##CandleFetcher.fetch_candles_after_date("BTC", date_to_timestamp("2017-1-1"), 7200)
##CandleFetcher.fetch_candles_after_date("NXT", date_to_timestamp("2016-1-1"), 7200)
##CandleFetcher.cut_table(table_names.BTC_300, date_to_timestamp("2017-2-17"))
##CandleFetcher.cut_table(table_names.BTC_300, date_to_timestamp("2017-1-1"), date_to_timestamp("2017-2-1"))
##CandleFetcher.cut_table(table_names.BTC_300, date_to_timestamp("2017-1-1"), date_to_timestamp("2017-2-1"))
##CandleFetcher.cut_table(table_names.BTC_300, date_to_timestamp("2017-2-16"), date_to_timestamp("2017-2-17"))
##strat = ShortTermStrategy(table_names.BTC_300)
##date2 = date1+ 4*HALF_DAY
##tn = CandleFetcher.cut_table(table_names.BTC_300, date1, date2)
##strat = ShortTermStrategy(tn)
##DBManager.drop_table(tn)
##total_balance = 0
##total_percent = 1
##date1 = date_to_timestamp("2016-6-1")
##for i in range(9):
##date2 = date1+ 60*HALF_DAY
##tn = CandleFetcher.cut_table(table_names.BTC_300, date1, date2)
##(balance, percent) = simulate_short_term_strategy([tn])
##DBManager.drop_table(tn)
##date1 = date2
##total_balance += balance
##total_percent *= (percent+1)
##print "Total Balance:", total_balance
##print "Total Percent:", total_percent
##test_short()
##date2 = date1+ HALF_DAY
##tn = CandleFetcher.cut_table(table_names.BTC_300, date1, date2)
##simulate_short_term_strategy([tn])
##populate_exp_avg_points(table_name_ETH_14400)
##grab_trend_all(trend_name_XMR, "XMR")
##cut_trend(table_name_ETH_14400, table_name_ETH)
##strat = ShortTermStrategy(table_names.BTC_2017_02_19_300)
##DBManager.drop_matching_tables("OSCIL")
##simulate_oscil_strategy(table_names.BIG_HALF1_14400)
##simulate_bollinger_strategy(table_names.BIG_HALF2_7200)
##simulate_bollinger_strategy([table_names.NXT_HALF2_7200])
##simulate_bollinger_strategy(table_names.BIG_HALF2_7200)
##simulate_bollinger_strategy([table_names.BTC_HALF2_7200])
##simulate_bollinger_strategy(table_names.BIG_HALF2_14400)
##simulate_hold_strategy(table_names.SMALL_HALF2_14400)
##simulate_bollinger_strategy(tn_HALF_7200)
##simulate_hold_strategy(tn_7200)
##simulate_bollinger_strategy(tn2_HALF_7200)
##simulate_bollinger_strategy(tn1_HALF_7200)
##tn2_HALF_1800 = [table_names.XRP_HALF_1800, table_names.LTC_HALF_1800, table_names.ETC_HALF_1800, table_names.DASH_HALF_1800, table_names.REP_HALF_1800]
##simulate_bollinger_strategy([table_names.BTC_HALF, table_names.ETH_HALF, table_names.XMR_HALF])
##simulate_bollinger_strategy([table_names.BTC_HALF_7200, table_names.ETH_HALF_7200, table_names.XMR_HALF_7200])
##simulate_bollinger_strategy(tn2_7200)
##simulate_bollinger_strategy([table_names.XMR_HALF])
##optimize(table_names.BIG_HALF2_7200)
##simulate_bollinger_strategy([table_names.XRP_HALF, table_names.LTC_HALF, table_names.ETC_HALF, table_names.REP_HALF, table_names.DASH_HALF])
##present_bollinger(table_names.BTC_7200)
##present_bollinger(table_names.DASH_14400)
##simulate(table_name_BTC_14400)
##simulate(table_name_LTC_14400)
def test_short():
total_profit = 1
total_balance = 0
total_balance_bitsec = 0
date1 = date_to_timestamp("2016-6-1")
for i in range(1):
date2 = date1+ 10*30*2*HALF_DAY
tn = CandleFetcher.cut_table(table_names.BTC_300, date1, date2)
strat = ShortTermStrategy(tn, calc_stats = False)
##strat = BollingerStrategy(tn, set_default = True)
(profit, balance, balance_bitsec) = test_against_hold(strat)
total_profit *= 1+profit
total_balance += balance
total_balance_bitsec += balance_bitsec
date1 = date2
##sc = StatCalculator(tn)
##volatility = sc.get_volatility()
##volume = sc.get_volume()
##print ("Volatility:", volatility)
##print ("Volume:", volume)
DBManager.drop_table(tn)
print("total profit:", total_profit)
print("total balance:", total_balance)
print("total balance bitsec:", total_balance_bitsec)
##print(total_profit_bitsec)
def test_against_hold(strat):
print("**************************************NORMAL************************************************")
tn = strat.table_name
trade_sim = TradeSimulator([tn], [strat], limit = -100, to_print_trades = False, to_log = True)
trade_sim.run()
f_bitsec = trade_sim.bit_sec
f_balance = trade_sim.balance
f_profit_percent = trade_sim.profit_percent
f_balance_bitsec = trade_sim.profit_per_bitsec
strat = HoldStrategy(tn, 100)
trade_sim = TradeSimulator([tn], [strat], limit = -100, to_log = True)
trade_sim.run()
s_bitsec = trade_sim.bit_sec
s_balance = trade_sim.balance
s_profit_percent = trade_sim.profit_percent
print()
##print "First: bits, bitsec", f_bits, f_bitsec
##print "Second: bits, bitsec", s_bits, s_bitsec
print(("balance dif:", f_balance-s_balance))
print(("profit dif:", f_profit_percent-s_profit_percent))
print("**************************************NORMAL DONE*******************************************")
##total = 0
##for r in strat.runs:
##total+=r
##print total/len(strat.runs)
##return f_profit_percent
return (f_profit_percent, f_balance, f_balance_bitsec)
## optimize parameters
def optimize(table_name_array):
po = ParameterOptimizer(table_name_array)
po.optimize_bollinger()
def simulate_short_term_strategy(candle_table_name_array):
strat_array = []
for tn in candle_table_name_array:
strat = ShortTermStrategy(tn)
strat_array.append(strat)
trade_sim = TradeSimulator(candle_table_name_array, strat_array, to_log = True)
trade_sim.run()
balance = trade_sim.balance
percent = trade_sim.profit_percent
return (balance, percent)
def simulate_oscil_strategy(candle_table_name_array):
strat_array = []
for tn in candle_table_name_array:
strat = OscilStrategy(tn)
strat_array.append(strat)
trade_sim = TradeSimulator(candle_table_name_array, strat_array, to_log = True)
trade_sim.run()
def simulate_bollinger_strategy(candle_table_name_array):
strat_array = []
for tn in candle_table_name_array:
strat = BollingerStrategy(tn, set_default = True)
strat_array.append(strat)
trade_sim = TradeSimulator(candle_table_name_array, strat_array, to_log = True)
trade_sim.run()
##strat_array[0].print_trade_plans()
def simulate_hold_strategy(candle_table_name_array):
strat_array = []
for tn in candle_table_name_array:
strat = HoldStrategy(tn)
strat_array.append(strat)
trade_sim = TradeSimulator(candle_table_name_array, strat_array, to_log = True)
trade_sim.run()
def present_bollinger(candle_table_name):
strat = BollingerStrategy(candle_table_name)
print(candle_table_name)
print(("# of stddev from mean: ", strat.get_current_bb_score()))
def simulate_scipy_trend_strategy(candle_table_name, trend_table_name, model):
strat = ScipyModelStrategy(candle_table_name, trend_table_name, model)
trade_sim = TradeSimulator(candle_table_name, strat)
trade_sim.run()
def simulate_scipy_candle_strategy(candle_table_name, sim_candles, model):
strat = ScipyCandleModelStrategy(sim_candles, model)
trade_sim = TradeSimulator(candle_table_name, sim_candles, strat)
trade_sim.run()
def simulate_two_trend_strategy(trends_table, candle_table_name):
strat = TwoAvgTrendStrategy(candle_table_name, trends_table)
trade_sim = TradeSimulator(candle_table_name, strat)
trade_sim.run()
def simulate_manual_trend_strategy(candle_table_name):
strat = ManualTrendStrategy(candle_table_name)
trade_sim = TradeSimulator(candle_table_name, strat)
trade_sim.run()
def simulate_manual_attribute_strategy(candle_table_name, attr_name):
sim_candles = CandleTable.get_candle_array(candle_table_name)
strat = ManualAttributeStrategy(sim_candles, attr_name)
trade_sim = TradeSimulator(candle_table_name, sim_candles, strat)
trade_sim.run()
def simulate_test_strategy(table_name):
test_strat = TestStrategy()
trade_sim = TradeSimulator(table_name, test_strat)
trade_sim.run()
def simulate_buyer_strategy(table_name):
strat = SimpleBuyerStrategy()
trade_sim = TradeSimulator(table_name, strat)
trade_sim.run()
def simulate_trailer_strategy(tn_reference, tn_target):
strat = CurrencyTrailerStrategy(tn_reference, mode = CurrencyTrailerStrategy.EXP)
trade_sim = TradeSimulator(tn_target, strat)
trade_sim.run()
def simulate_random_strategy(candle_table_name):
candles = CandleTable.get_candle_array(candle_table_name)
strat = RandomStrategy(candles)
trade_sim = TradeSimulator(candle_table_name, candles, strat)
trade_sim.run()
##drops table that matches the given table name
def drop_table(table_name):
DBManager.drop_table(table_name)
## cut from the trend table to create a new trend table that matches the given candle table
def cut_trend(c_table_name, t_table_name):
tc = TrendCutter(c_table_name, t_table_name)
tc.create_cut_table()
##grabs google trends data
def grab_trend(table_name, keyword, date, num_months):
tf = TrendFetcher(table_name, keyword, date, num_months)
tf.fetch()
##grabs google trends data for multiple months
def grab_trend_all(table_name, keyword):
##grab_trend(table_name, keyword, "/2015", 3)
##time.sleep(3)
##grab_trend(table_name, keyword, "01/2016", 3)
##time.sleep(3)
##grab_trend(table_name, keyword, "04/2016", 3)
##time.sleep(3)
##grab_trend(table_name, keyword, "07/2016", 3)
grab_trend(table_name, keyword, "08/2016", 3)
time.sleep(3)
grab_trend(table_name, keyword, "11/2016", 3)
def populate_sim_avg_points(source_table_name, num_history_points):
pp = PointPopulator(source_table_name)
pp.create_moving_avg_simple(num_history_points)
def populate_exp_avg_points(source_table_name):
pp = PointPopulator(source_table_name)
pp.create_moving_avg_exp()
def populate_sim_roc_points(source_table_name):
pp = PointPopulator(source_table_name)
pp.create_roc()
if __name__ == '__main__':
main()