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'''
'''
from PyQt5.Qt import pyqtSignal
from PyQt5 import QtCore
import random
class CudaSolver():
'''
'''
progress = pyqtSignal(int, int, name='progress')
done = pyqtSignal(list, name='done')
def __init__(self, calc, series):
'''
Constructor
'''
super().__init__(None)
self.calc = calc
self.series = series
self.generators = []
self.counter = 0
self.total_counter = 0
def add_generator(self, generator):
self.generators.append(generator)
@QtCore.pyqtSlot(dict)
def solve(self, params):
max_signals = 5
max_hold_bars = params.get('max_hold_bars', 1)
#self.executor = Executor(self.series, max_hold_bars)
max_strategies = params.get('num_strategies', 1000)
results = []
min_trades = params.get('min_trades', 0)
min_win_rate = params.get('min_win_rate', 0)
min_sharpe = params.get('min_sharpe', 0)
stop = params.get('stop_loss', None)
tp = params.get('take_profit', None)
is_long = params.get('direction', 'long') == 'long'
while len(results) < max_strategies:
sig_num = random.randint(1, max_signals)
strategy = []
for i in range(0, sig_num):
strategy.append(random.choice(self.generators).generate())
trades = self.executor.execute(strategy, is_long, stop, tp)
if len(trades) >= min_trades:
result = self.evaluate_trades(trades)
if result['win_percentage'] > min_win_rate and result['sharpe'] > min_sharpe:
result['strategy'] = strategy
result['display_name'] = ' && '.join([signal.get_text() for signal in strategy])
result['trades'] = trades
results.append(result)
self.progress.emit(len(results), max_strategies)
self.done.emit([result])
self.counter += 1
self.total_counter += 1
def evaluate_trades(self, trades):
result = {}
profits = [x.pnl() for x in trades]
total_won = len(list(filter(lambda x: x.pnl() > 0, trades)))
if len(trades) > 0:
result['win_percentage'] = total_won / len(trades) * 100
else:
result['win_percentage'] = 0
result['trades_number'] = len(trades)
result['total_pnl'] = sum(profits)
if len(trades) > 0:
result['avg_percentage'] = sum([trade.pnl_percentage() for trade in trades]) / len(trades)
else:
result['avg_percentage'] = 0
gross_profit = sum([max(0, x.pnl()) for x in trades])
gross_loss = sum([min(0, x.pnl()) for x in trades])
if gross_loss != 0:
result['profit_factor'] = gross_profit / (-gross_loss)
else:
result['profit_factor'] = inf
if len(profits) > 0:
mean = numpy.mean(profits)
stddev = numpy.std(profits)
if stddev != 0:
result['sharpe'] = mean / stddev
else:
result['sharpe'] = 0
else:
result['sharpe'] = 0
return result